This paper proposes a robust data-driven distributed control strategy for dynamic energy scheduling in hybrid energy storage systems (HESSs) comprising fuel cells (FCs) and lithium-ion batteries. The proposed framework integrates a robust recurrent deep network (R2DN)-based controller that enables fully distributed coordination among heterogeneous energy sources without relying on centralized communication. By embedding degradation indicators of FCs and virtual state-of-charge (SoC) variables of batteries into the control design, the strategy achieves adaptive power allocation among multiple FCs, SoC equalization of batteries, and fast DC-bus voltage regulation under dynamic operating conditions. The R2DN controller provides intrinsic stability guarantees through contraction and Lipschitz constraints, ensuring robustness against parameter variations and external disturbances. Simulation in MATLAB/SIMULINK verified the performance of proposed control method.

As a key component of a three-phase inverter, the insulated gate bipolar transistor (IGBT) switching device plays a critical role in ensuring system stability, making its state monitoring highly important. However, due to the complex structure of three-phase inverters, many detection methods are unable to monitor the health status of these critical components in real time. To address this issue, this paper proposes an online monitoring method based on a convolutional neural network (CNN) algorithm. This method monitors the status of key components in real time by analyzing output parameters, thereby reducing the number of required sensors and simplifying the monitoring process. The proposed method has been validated through simulation experiments on a three-phase inverter, and the corresponding experimental results are presented.

With the widespread application of audio communication in the internet of things, secure and efficient audio data transmission has become increasingly critical. However, existing chaos-based encryption schemes often face challenges in flexibly generating complex chaotic attractors and achieving a practical balance between security, flexibility, and efficiency. To address these limitations, this paper proposes a novel universal method for generating multi-structure chaotic attractors based on a quaternion rotation matrix transformation. This method enables flexible control over the position, scale, and number of wing units in attractors by configuring quaternion parameters and multi-level pulse function. Building upon this, we design a high-performance chaos-based audio encryption algorithm, and implement an encryption system for wireless transmission using a microcontroller and LoRa module. The system integrates audio recording, encryption, wireless transmission, and decryption. Comprehensive security analyses demonstrate the proposed algorithm achieves robust performance. The successful integration of flexible chaotic attractor design, robust cryptographic security, and hardware realization confirms the practical viability of our scheme for information transmission in resource-constrained environments.

With the popularity of cloud storage, video data, especially videos containing personal information, faces serious security challenges. However, existing video encryption schemes typically adopt full-frame encryption, resulting in high computational overhead, low efficiency, and difficulty balancing privacy protection and lightweight encryption requirements. To this end, a lightweight video encryption scheme based on object detection and adaptive frame multiplexing is designed in this paper. First, the YOLOv8 algorithm is employed to accurately identify multiple human target regions in videos, thereby avoiding the computational resource waste associated with full-frame encryption. Second, combined with SCI-HMC hyperchaotic map, an adaptive frame multiplexing strategy is adopted to realize the dynamic adjustment of chaotic sequences by correlating the encryption process before and after. On this basis, a cube lightweight encryption algorithm based on video frame combination is designed, which is spliced layer by layer in terms of color channels, traversed in terms of 3 layers of data (one video frame), and performs the 3D confusion and 3D mod diffusion sequentially according to the target coordinates, and finally realizes the accurate encryption of the region of interest. The experimental results show that the scheme performs well in terms of practicality and resistance to attacks. The information entropy of the encrypted area is as high as 7.9982, and the encryption speed can be increased to 0.2343 seconds per frame. This deep collaboration framework tightly integrates modern object detection with dynamic encryption processes, providing a balanced security and lightweight solution for cloud video data protection.

As the application of image emotion recognition technology grows increasingly widespread, emotional data faces potential privacy risks during transmission and storage. Images depicting negative emotions are particularly prone to revealing an individual is psychological state and sensitive information. Therefore, effective security protection for such images is of practical importance. This paper design a chaotic system and use CNN-based recognition to select the images requiring enhanced protection. First, a novel second-order memristor is designed and coupled with a neuron model to construct a chaotic system (SOM-Rulkov). Analysis of its phase diagram, bifurcation diagram, and Lyapunov exponent spectrum indicates that SOM-Rulkov exhibits rich dynamical characteristics, which can provide a pseudo-random key stream with good performance for encryption algorithms. Then, using a CNN to recognise emotions in images, employing the identified negative emotion images as encryption images to enhance data security during transmission and storage. During the encryption process, This paper proposes an enhanced diffusion structure with a parallel pool mechanism that enables four-directional diffusion to improve encryption efficiency. Experimental results show that the proposed scheme achieves strong security and high speed for emotional image privacy protection.

Locally active memristor (LAM), which has an ability to amplify fluctuations, is a natural component for constructing artificial neuron circuits. This paper proposes a novel third-order neuron circuit by paralleling two LAMs and a capacitor. Firstly, two parallel LAMs are equivalently modeled as a second-order LAM to facilitate theoretical analysis. Regarding the third-order neuron, the parameter design and operating condition are obtained by calculating its small signal impedance functions poles or Jacobin matrixs eigenvalues. It is demonstrated that the neuron exhibits various neuromorphic behaviors, including periodic spiking, chaos and burst-number adaptation. Due to the two different LAMs, the proposed thirdorder system has multiple equilibrium points, leading to the generation of coexisting attractors. Interestingly, the generated chaotic attractor does not revolve around a single unstable equilibrium point, but is located between two unstable equilibrium points. Furthermore, the emergence of oscillating behaviors is dependent on the distance between the two unstable equilibrium points. Detailed theoretical and simulation analysis are presented to investigate the neuron dynamics and provide an explanation for the observed neuromorphic behaviors. Finally, physical circuit implementation of the neuron is constructed based on the memristor emulator, which also demonstrates the practicability of the proposed neuron model and the correctness of the theoretical analysis.

This paper investigates the asynchronous filtering problem for single-machine infinite bus (SMIB) power systems subject to stochastic transmission line faults. The system is modeled as a discrete-time Markov jump system (MJS) to capture the random switching behavior induced by transmission line faults. To address the asynchrony between the system modes and the filter operation, a hidden Markov model (HMM) is adopted. The filtering problem is reformulated as a regulation problem by introducing a quadratic performance index based on output estimation errors, offering a filtering-based alternative to control strategies. To solve the associated coupled algebraic Riccati equations (CAREs), an inverse reinforcement learning (IRL)–based algorithm is developed, which enables model-free filtering without requiring prior knowledge of the system dynamics or transition probabilities. The convergence of the proposed algorithm is rigorously analyzed, and a numerical example based on an SMIB power system with stochastic faults is provided to validate its effectiveness.

When organisms face complex environments, the cognitive resource limitation is an important mechanism to ensure the quality of perceived information and prevent information overload. Organisms allocate cognitive resources rationally by regulating attention, thus promoting more important cognitive orientations. However, this phenomenon has been scarcely investigated within the realm of memristive biomimetic circuits. The prefrontal cortex (PFC), as the highest central hub for attention control, achieves goal-oriented attentional selection by assigning the basal ganglion to suppress irrelevant information. Based on this biological mechanism, a neuromorphic circuit has been designed in this paper to implement the attentional regulation function of the PFC between bimodal sensory inputs. When organisms face multi-sensory information input, the enhancement and inhibition effects in the supramodal attention effects are considered. In addition, the circuit realizes biological phenomena such as temporal consistency, semantic consistency, emotional attention, and attention fatigue. The attention regulation mechanism is further extended to more senses with variable sensitivities, providing variable strategies for performing tasks in different scenarios. Performance analysis results demonstrate that the circuit exhibits excellent robustness. This work provides guidance for the further development of information processing in brain-inspired intelligence.

As the brain-like intelligence develops rapidly, it is urgent to design a more convenient and efficient control framework to cope with the challenge of processing multisensory signals in parallel. Therefore, a multisensory memristive circuit with dual adaptive, parallel processing, and multilevel reinforcement features is proposed. The circuit is mainly composed of modules for receptors, STM and LTM, attention, environmental monitoring and mutual associative memory. Automatic encoding of different sensorial signals is realised by the receptor modules. Dual adaptive regulation of the internal associative memory and external environmental changes on the circuit is implemented by modules of attention and environmental monitoring. Multilevel reinforcement memory is achieved through the interconnection of multiple dimensional features of the same objects. The process of encoding transformation of stimuli, experience memory, and feedback learning is automatically achieved in the brain-inspired neural network structure, which avoids the problems such as encoding difficulties during the conversion of the operating objects, and enables the realization of more brain-like intelligence. The circuit is applied to gripping and recognizing in robotic arms and the scenario memory of different production lines is simulated, which is promising for application in automated factories.

The symmetry-breaking phenomenon, appearing, under suitable conditions, when identical reaction cells, quiet on their own, are let interact via diffusion processes, is dubbedTuring Instability. Its local form exposes the local destabilization, which allows two multistable cells lose stability at one of its locallyasymptotically-stableoperating points. While the globalTuring Instabilityand its mechanisms have been recently explained in (Ascoli et al., 2022), its local form and an experimental demonstration of these complex effects on a physical memristive medium have not been reported yet. This paper investigates a local form ofTuring Instabilityin a two-cell array, when one of the possiblelocally asymptotically-stableandlocally-activestatic solutions loses stability, when let interact with an identical reaction cell via diffusion processes, resulting in the emergence of two different static solutions after transients fade away. In order to study its mechanisms, this paper first introduces a current-controlled Chua Corsage Memristor (CCM), and demonstrates the operating point destabilization in a single current-controlled CCM-based cell. Adding a dissipative resistor and a capacitor to the current-controlled CCM, preliminarily poised on anedge of chaosoperating point, gives birth to two unstable circuits, inducing a local quiescent bi-stability and a local oscillation, respectively. The mechanisms behind a local form ofTuring Instability, appearing in a current-controlled CCM-based two-cell array, have been elucidated, and the bifurcation, spawning symmetry-breaking effects, locally, across the cellular network, has been identified. Both numerical and experimental results confirm the correctness of the theoretical analysis.

Designing a universal framework for configuring bionic spiking circuits is an attractive topic for spike-based applications. This paper comprehensively considers the electrophysiological properties of a biological neuron and electrical features of mem-elements, then presents a new universal framework for configuring fully mem-element-based bionic spiking circuits. The universal framework contains a current-controlled locally active memristor (LAM), a charge-controlled memcapacitor, and a flux-controlled meminductor to respectively characterize the electrophysiological properties of ion channels, liquid bilayer membrane, and electromagnetic induction of a biological membrane. The universal framework only involves the three mem-elements and a DC current stimulus. Afterward, a case of a mem-element emulator-based bionic spiking circuit is configured employing the universal framework. Numerical explorations and experimental measurements based on analog hardware circuits are performed to validate the effectiveness of the universal framework in generating abundant bifurcation behaviors and spiking activities. Actually, the universal framework is available for employing different mem-element emulators or physical mem-elements to construct bionic spiking circuits. The universal framework is extensible and sheds new light on the configuration of fully mem-element-based bionic spiking circuits.

With the rapid development of the electronic information industry, massive private data are continuously uploaded to the Internet, posing severe challenges to data security. Thus, a high-capacity private data information protection library based on correlation generator is proposed. For private data uploaded by multiple individuals or organizations, the proposed library enables efficient bulk protection. Through the biometric images held by individuals or organizations, correlation values are generated by the correlation generator, which are combined with such initial values of the four dimensional hyperchaotic system (4DHS) to generate private keys and master keys. The chaotic sequences generated by the iteration of the system are combined with the encryption scheme to provide effective protection of private data information. Afterwards, the scheme is tested for simulation and security, which verify the feasibility and security of the proposed scheme.

In biological decision-making, adaptive behavior arises from the interaction between structured task context, expectation, action selection, and feedback-based learning. While most existing studies reproduce reward-driven responses under clear sensory stimuli, decision formation under weak or absent sensory evidence, such as low or 0% contrast conditions, remains insufficiently explored. To address this issue, this work proposes a biologically plausible memristive decision-making framework based on a block-structured task paradigm. The proposed system adopts a closed-loop architecture composed of four functional modules: stimulus, expectation, action, and reward/punishment. Sensory information is encoded when available, while the expectation pathway provides prior-guided modulation when sensory evidence becomes weak or unreliable. Action selection is generated through competitive integration, and reward–punishment feedback dynamically corrects decision bias and reinforces appropriate responses. Through this hierarchical interaction, stable decision behavior can be achieved even in the absence of explicit sensory inputs. PSPICE simulations are conducted to analyze system dynamics and validate the corrective role of the reward–punishment mechanism under weak and ambiguous conditions. In addition, the proposed framework is demonstrated in an industrial autonomous navigation scenario, illustrating its scalability and applicability for adaptive decision-making under uncertainty.

High renewable penetration and reduced system inertia introduce significant challenges for transient stability assessment and control. This article proposes a causality-aware, large language model–enhanced distribution-preserving graph representation learning framework (LLM-DP-GRL) for fast and accurate stability prediction and decision-making. The DP-GRL model captures both structural and distributional properties of network states, whereas large language models provide physics-informed priors that improve data efficiency and generalization under multicontingency and out-of-distribution scenarios. A causal intervention module further quantifies bus-level influence on stability margins, offering interpretable insights consistent with system dynamics. The learned surrogate model is integrated into a cooperative preventive–emergency control strategy, enabling real-time stability margin evaluation and optimization. Tests on the IEEE 39-bus and 118-bus systems show that LLM-DP-GRL achieves higher accuracy, faster convergence, and improved robustness compared with conventional machine learning, LSTM, and GNN-based methods. The proposed approach reduces online control computation from over 35 min (TDS-based) to 39 s while maintaining inference latency below 30 ms. These results demonstrate that combining graph learning, LLM-guided priors, and causal analysis provides an effective and scalable solution for stability assessment and emergency control in low-inertia, high-renewable power systems.

This paper proposes a new chaotic system, and experimental analysis shows that its Lyapunov exponent can reach up to 6.85, demonstrating excellent chaotic performance. It is highly suitable as a pseudorandom number generator for use in secure communications. Based on this, we designed a medical image encryption algorithm. The average values of Pixels Change Rate (NPCR) and the Unified Average Changing Intensity (UACI) for encrypted images can reach 99.6200% and 33.4699, respectively. The pixel value distribution is uniform, and the correlation between adjacent elements is between-0.01 and 0.01, demonstrating excellent security. Additionally, we adopted a distributed cloud storage strategy for efficient and secure image sharing, and we used Cyclic Redundancy Check (CRC) to ensure end-to-end data correctness. With these technologies at its core, we designed a medical image sharing system to securely and efficiently enable cross-domain sharing of medical images. A series of simulation experiments demonstrated that the system offers excellent security and stability.

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%.

This article introduces a memristor-based neural network with bidirectional cyclic constructed from square wave pulse functions. Additionally, we design an image encryption and compression network framework incorporating a self-attention mechanism based on this memristor neural network. Specifically, this study introduces a novel model of a high-dimensional multiscroll memristor neural network (MMNN) that incorporates three neurons and a synapse made from memristive elements. Its complex dynamic behavior is analyzed in depth. Theoretical analysis and numerical simulations demonstrate that the proposed MMNN can generate an unlimited number of hyperchaotic multiscroll attractors and various other types of dynamics, which can be fine-tuned by modifying the system’s parameters or initial conditions. Additionally, a simulated equivalent circuit for MMNN proves the validity and practicality of the multiscroll attractor behavior. Finally, an image encryption and compression network framework based on end-to-end and MMNN and the self-attention mechanism is proposed, called SAECNet. Extensive performance evaluation highlights the system’s high accuracy in compression and reconstruction, strong encryption capabilities, and solid defense against potential attacks.

To ensure stringent security strategies for image information involving personal privacy during conveyance and storage, we propose an innovative multiface privacy protection scheme based on chaos theory. Compared to single-face encryption algorithms, the proposed scheme has broader potential applications in fields, such as smart cities and smart transportation. Specifically, a new spatiotemporal chaotic system named the sine-cosine coupled mapping lattice system (SCCML) is designed. It features a larger parameter domain, complexity, and profound unpredictability, yet maintains a simpler construction aimed at providing potential benefits and implementations in the field of information security. In the proposed multiface privacy protection scheme, multiple faces within an image are rapidly and accurately identified and then encrypted using the proposed SCCML-based digital separation loop encryption algorithm. The encryption algorithm exhibits a synchronous scrambling diffusion mechanism. Additionally, the introduction of mixed multibase cascade diffusion offers multiple layers of security for facial data, prevents diffusion singularity, and enhances diversity, making it significantly more challenging to crack. Experimental verification on a real multiface image dataset shows that the algorithm is superior, practical, safe, and efficient.

An autonomous mobile robot with chaotic navigation, named chaotic mobile robot (CMR), has shown significant potential applications for dangerous or special duties in Internet of Things. As the control source of CMR, the application of novel chaotic dynamical systems with good design flexibility, topological transitivity, and sensitivity to initial conditions, will favor analyzing the whole workplace. To this purpose, a novel dual-route fractal process (DRFP) is proposed based on Julia and advanced Julia functions, which implements the automatic design of multifolded chaotic prototype models for given seed chaotic system. In comparison with the traditional Julia fractal process, the DRFP shows significant advantage in filling in the chaotic trajectory in empty inner space, which is beneficial to design CMR with high performance. The simulation and experiment are presented to validate the design feasibility of the DFRP. Finally, a two-wheeled differential drive system coupled with the chaotic model is designed to observe its navigation performances in either open region or bounded region with obstacles.

The demand of information security drives the core application of pseudo-random generator in cryptography. Furthermore, chaos theory offers novel methodologies for the Internet of Things (IoT) to address nonlinear and high-dimensional dynamic challenges, demonstrating substantial potential in enhancing security, energy efficiency, and predictive accuracy. Compared with dissipative chaotic systems, the phase space conservation, symmetric Lyapunov exponential spectrum and non-reconfiguration of conservative chaotic systems significantly improve the attack resistance and dynamic stability, and the pseudo-random number generator designed by conservative chaotic systems has obvious advantages. In this paper, we explore a novel method for designing a new class of Hamiltonian chaotic systems with simple structural and complex behaviors. A five-dimensional (5D) system is presented and analyzed in detail. Using bifurcation diagram, Lyapunov exponents (LEs), complexity, phase portraits, and other indicators. Compared to existing systems, the proposed system exhibits better characteristics such as structural simplicity and heightened complexity. Additionally, the National Institute of Standards and Technology (NIST) has verified that the proposed system exhibits good randomness and has been deployed on a hardware platform for digital signal processing.

The chaotic system as a source of randomness is very important for chaos-based secure communication. Different from other chaotic maps, this work explores an n-dimensional (n-D) nondegenerate discrete hyperchaotic map with any desired Lyapunov exponents (LEs). First, theoretical analysis proves that the proposed map is chaotic. Based on this proof, it is designed for any desired LEs. To illustrate the effectiveness of the n-dimensional (n-D) new map, we use some chaotic and nonchaotic maps as examples. The simulation results demonstrated that the proposed maps exhibit stronger chaotic properties and more complex dynamic behaviors compared to some previous results. Moreover, the chaotic signals generated by our maps passed NIST and TestU01 tests, which show that our map has better randomness. Furthermore, a chaotic system with higher LEs do not necessarily have higher complexity degree. Next, the proposed map is implemented by hardware digital signal processing platform, indicating the feasibility for industrial applications. More importantly, compared with other maps, the proposed map has simple structure with more complex behaviors and fewer parameters, and it is easy to set the desired LEs. Finally, we design a novel image encryption algorithm based on the proposed chaotic system and dynamic S-boxes. The experimental results show that the proposed chaotic system can be effectively applied in the field of data encryption.

Memristors have been widely integrated into neurons as the bridge for introducing external magnetic induction currents. The complex oscillation induced by the external magnetic stimulation is a hot topic in neuron dynamics. When a memristor is introduced into the Hindmarsh-Rose (HR) neuron to simulate the external magnetic field, a novel memristive neuromorphic hyperchaotic oscillator is constructed. The memristor weight can trigger complex neuronal firing dynamics, including the rare hyperchaotic bursting. Furthermore, when the technology of offset boosting-oriented attractor doubling is employed, a double-scroll hyperchaotic attractor can be generated, which could split into three independent coexisting attractors under some specific offsets. More interesting, two symmetric periodic attractors and two symmetric hyperchaotic attractors can coexist under certain conditions. In this work, a neuron with coexisting hyperchaotic attractors is constructed and exhaustively explored, which provides a good candidate for constituting large-scale brain-like neuromorphic oscillator. A PCB-based hardware circuit produces the oscillations validating the numerical simulations and theoretical analyses.

The context and the state of mind are important retrieval cues for long-term memory, which helps information to be retrieved quickly. However, most memristive circuits focus on the process of information memory, few studies consider the process of information retrieval. In this work, a brain-inspired circuit with context-dependent and state-dependent memory is proposed based on the three-level processing model of memory information, which integrates the processes of information memory and information retrieval. The circuit includes sensory memory module, short-term memory module, long-term memory module, information retrieval module, status module, and context module. In the circuit, information, contexts, and states are eventually transferred to long-term memory module for storage and retrieval. Meanwhile, the factors influencing information retrieval are considered, such as the degree of information memory, the time interval between information memory and retrieval, the context, and the state. And the proposed circuit has scalability, which realizes the memory of information in multiple contexts. Finally, based on the characteristics of memristors, the proposed circuit is extended for detecting damage to the machining accuracy of the mobile CNC lathe. Combining brain-inspired circuits with human memory mechanism, this work provides further reference for the research of brain-like intelligence.

Edge computing devices based on the von Neumann architecture can’t fulfill the demand for computational resources in Generative Adversarial Networks. This paper proposes a memristive circuit design for a light-weight and efficient Deep Convolutional Generative Adversarial Networks (DCGAN), which can be integrated into edge computing devices for image generation. The DCGAN scheme can perform convolution operations, deconvolution operations, and various activation functions in a fast and low-power way. Moreover, a high-precision segmental approximate linear weight mapping method based on the 2-Memristor crossbar array structure is proposed to improve the precision of memristive neural networks on edge computing devices. Finally, the results show that the DCGAN scheme significantly reduces the power consumption, time consumption, and input ports while keeping the area overhead unchanged. In the Oxford 17 image generation task, the DCGAN scheme achieves faster speed and lower power consumption compared to the traditional structure. The DCGAN scheme based on memristive circuits provides some references for implementing more intelligent applications on edge devices.

Knowledge distillation is widely used as an effective model compression technique to improve the performance of small models. Most of the current researches on knowledge distillation focus on the algorithmic level and ignore the potential benefits of hardware implementation. In this paper, a multi-loss knowledge distillation online training circuit based on memristor crossbar array is designed, which can improve the inference efficiency and reduce the power consumption of deep learning models on edge devices. The circuit is able to process data in real time, and it can be used to handle stuck-at-faults (SAF) caused by factors such as manufacturing defects in the memristor. Moreover, a fault detection scheme with low time cost is proposed in order to address the low efficiency of stuck-at-fault detection in memristor crossbar arrays. The scheme is combined with a self-compensating pruning method and knowledge distillation online training mechanism, which significantly improves the model training and inference capability of the circuit under fault conditions. Experimental results show that the multi-loss knowledge distillation online training improves the accuracy by 4.15% and 63.48% respectively in two models compared with traditional training schemes. The fault-tolerance scheme reduces the power consumption of the memristor crossbar arrays by 41.2% and 72.6% respectively on the two models, demonstrating its potential and advantages in edge computing.

Chua corsage memristor (CCM) is characterized by its local activity and can be used to construct neuron circuits. Edge of chaos is a subset of the locally active domain, which is responsible for the emergence of complexity and neuromorphic behaviors. When two identical resting “dead” CCM neurons poised on the edge of chaos are coupled through a linear passive resistor, these two neurons can be activated and a couple of oscillations appear. This phenomenon is referred to as the Smale paradox, which has not been observed from hardware circuits. The present paper addresses this issue by proposing the stability criterion of the two-port coupled system using the small-signal analysis method and then derives an emergence condition of the Smale paradox based on two coupled “dead” CCM neurons in terms of the parameter value ranges. Simulation results demonstrate the correctness of the theoretical analysis. Interestingly, anti-phase synchronization is observed after two identical neurons are coupled with a linear resistor, which is different from the traditional in-phase synchronization between resistively coupled oscillators. The resistively coupled memristive neurons are implemented by hardware based on the poor man’s circuit. The experimental results confirm the reproduction of the Smale paradox and reveal the effect of the coupling resistance on the dynamics of the system.

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system’s potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme with the 3D-MCM for image encryption. Experimental results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks.

Traditional video encryption methods protect video content by encrypting each frame individually. However, in resource-constrained environments, this approach consumes significant computational resources. To overcome this challenge, this paper proposes a novel method called “Encrypt a story (EAS)”, which aims to enhance encryption efficiency by focusing on encrypting specific segments of the video rather than encrypting each frame. The EAS refers to selecting segments in the time dimension of the video that contain important information or key events for encryption. This method leverages video segmentation techniques to focus encryption efforts on continuous key frames, significantly reducing the consumption of computational resources. To address the need for a large number of key streams during the encryption process, this paper proposes a discrete sinusoidal memristive Rulkov neuron map (DSM-RNM). Through attractor analysis, complexity comparison, Lyapunov exponent, and NIST tests, we validated its ability to generate high-performance pseudorandom sequences, which significantly enhances the security of the encryption algorithm. Notably, the DSM-RNM is shown to exhibit a phenomenon of infinitely coexisting attractors. Furthermore, by constructing a digital circuit to capture the attractors of the DSM-RNM, its potential for industrial applications is demonstrated. Evaluation results show that the EAS saves approximately 90% of the time while ensuring security, exhibiting strong practicality and efficiency

Solid oxide fuel cells (SOFCs) offer a promising solution for enhancing reliability and sustainability in microgrid power supply with the growing penetration of renewable energy sources. The proposed method addresses key challenges in existing SOFC control approaches, including model dependence, usage of nonoptimal control policy, reliance on an offline-trained neural network (NN), and complex design. Compared with model-based methods, this method uses NN and policy iteration technology to learn system dynamics and approximate optimal control policy, thereby eliminating model dependence. Compared with offline learning-based methods, this method achieves online policy evaluation and NN updating to eliminate tedious offline training and data acquisition processes. Compared with the online learning-based SOFC control approaches, this method employs a fixed-weight recurrent NN to avoid slow or even no convergence caused by recursive least squares-based NN weights updating process, reducing design complexity without sacrificing control performance. The superiority of the proposed method is validated through hardware-in-the-loop tests.

In this paper, we propose a novel inductor-free chaotic circuit by using two locally active memristors (LAMs), a capacitor, and a DC bias. These two LAMs are the current-controlled and voltage-controlled type, respectively, exhibiting S-type and N-type DC V-I (voltage-current) characteristics. Only when the two LAMs are both operated in the negative differential resistance (NDR) regions, may the periodical and chaotic dynamic behaviors appear in the proposed circuit. This may be because the local activity contributes to the generation of complexity. With different initial conditions, the coexisting attractors are observed in the circuit, which is analyzed through the equilibrium points and phase portraits. Finally, physical circuit realizations of the inductor-free chaotic circuit are presented, including the S-type and N-type memristor emulators. Both simulation and experimental results demonstrate the feasibility of the proposed chaotic circuit.

Currently, discrete memristors are a focal point in the study of chaotic maps. Similar to memristors, memcapacitors-another type of memory circuit component-have not received widespread attention in the design of chaotic maps. In this article, we propose a 4-D memcapacitive hyperchaotic logistic map (4D-MHLM) by integrating memcapacitors with the logistic map. The dynamical behavior of the 4D-MHLM is analyzed using Lyapunov exponent analysis, and the impact of different parameters on system performance is discussed. The complexity of generating pseudo-random sequences with the 4D-MHLM is investigated through complexity analysis, including spectral entropy complexity and C0 complexity. Notably, attractor analysis reveals a unique phenomenon of infinite coexisting attractors within the 4D-MHLM. Finally, the chaotic attractor generated by the 4D-MHLM is successfully implemented on a hardware platform. Theoretical analysis and digital circuit implementation results indicate that the 4D-MHLM exhibits rich dynamical behavior and higher complexity, offering significant value for practical applications.

Chaos systems find extensive applications in cryptography and pseudorandom number generation due to their ability to generate pseudo-random signals. This paper focuses on enhancing the complexity of chaotic systems by introducing the memristor, a nonlinear component. We propose a novel map called the 2D memristive Cubic map (2D-MCM), which integrates the memristor with the Cubic map to create a discrete mapping. The 2D-MCM exhibits rich dynamical behavior and a broad parameter space. Notably, the 2D-MCM displays boosting bifurcation behavior. As the control parameters increase, the 2D-MCM demonstrates an expanded range of values, indicating its ability to generate a larger number of pseudo-random sequences. To validate its performance, we establish a hardware platform to physically capture the attractors of the 2D-MCM. To verify the performance of the 2D-MCM in generating pseudorandom sequences, we designed a video encryption algorithm based on the 2D-MCM. This algorithm selectively encrypts specific areas within the video, with correlation coefficients of the encrypted video in the horizontal, vertical, and diagonal directions being 0.0002, -0.0005, and 0.0004, respectively. Through simulation experiments and security analysis, we demonstrate that the 2D-MCM performs well in video encryption tasks.

To provide robust security measures for audio data during transmission, this article has developed a novel dual-channel audio encryption scheme based on chaos theory. Specifically, a new 2-D chaotic system called 2-D infinite collapse with logistic map (2-D-ICLM) is designed in this article. Compared to traditional 2-D chaotic systems, the 2-D-ICLM exhibits a larger parameter space, complexity, and richness, along with high unpredictability and randomness. These characteristics provide potential advantages and applications in the field of encryption. In the proposed encryption scheme, audio information serves as input to a hash function, which generates the initial values and parameters for the 2-D-ICLM, producing the keystream for the cryptographic system. Considering the correlation between the two channels of audio information, the information from the left and right channels is fused to create a new audio signal for encryption. Scrambling and diffusion processes are performed synchronously in the encryption algorithm, with the ciphertext information from the left channel utilized in the encryption of the right channel audio. The experimental results prove the effectiveness of the suggested audio encryption technique, effectively countering various conventional attack methods and showcasing its robust security features. The correlation of adjacent elements of ciphertext audio is 0.0013, the NSCR and UACI is around 0.9960 and 0.3345, and the efficiency is 0.0003 s/KB.

Memtranstor (MT) is proposed as a complement to the concept of memory element, which is conceived based on a direct correlation between magnetic flux φ and charge q. The electrical characteristics of MT is different from other memory elements, which enables MT to have more application scenarios. Thus, we design a floating MT emulator containing a constitutive DC control voltage vs to mimic a hardware MT. In this emulator, the inverse memtranstance (MT-1) can be directly measured and divided into two different states, namely, state 1: MT-1 is always positive or always negative, state 2: MT-1 is either positive or negative. We propose a control method of changing the direction and magnitude of the electric field of vs to achieve these two different states. For the proposed MT emulator, we also analyze typical nonlinear characteristics of pinched hysteresis loops (PHLs) and the variation of MT-1 under different vs and sinusoidal excitation frequencies. The correctness and feasibility of the emulator have been systematically validated through theoretical analysis, PSpice simulation, and hardware experiments. To further demonstrate the potential application of MT, we verify artificial synaptic plasticity of the MT emulator by experiments. The experiments results show that MT emulator can resemble long-term potentiation and long-term depression. The proposed MT emulator has a certain reference value for future research of MT-based nonlinear dynamics and can facilitate MT-based application research.

Most memristive bionic circuits focus on how to realize bionic functions, few studies consider the biomimetic of the circuit structure and operation rules, so it is difficult to learn, memorize, and make decisions as biological neural networks. In this work, a multifunctional neuromorphic circuit inspired by tunable neural circuitry motifs is proposed. The circuit is more closely with biological characteristics in both structure and functions, which is designed based on neural circuit architectures. By connecting different neural circuitry motifs, the circuit realizes operant conditioning functions such as random exploration, behavioral frequency modulation, and decision-making. Also, the circuit integrated classical conditioning and operant conditioning in order to mimic the decision-making process, which was driven by the association of secondary and primary stimuli. In addition, the factors influencing decision-making are researched, such as the rates of learning and forgetting, and the conversion of short-term to long-term memory. The operational results of the proposed circuits in LTspice show that they can mimic the aforementioned functions, which have advantages in bionicity and scalability. This work can be applied in intelligent robotic platforms to achieve exploration and rescue in complex environments.

At present, design of discrete memristor based chaotic maps starts to attract the attention of the scientists, but it is still in its incipient stage. In this paper, spatiotemporal chaos in the Sine map lattice with discrete memristor coupling is investigated. Firstly, the$3\times m$higher dimensional chaotic map is proposed, where there are$m$discrete memristors and$m$state variable difference items as the inputs of the discrete memristors. Since it is a spatiotemporal chaotic system, thus it can generate massive chaotic sequences according to the system dimension. Secondly, dynamical characteristics of the system is carried out theoretically and numerically. It shows that there are$m$positive Lyapunov exponents with high complexity. The two examples with one memristor and two memristors are analyzed, and it indicates that the system has rich dynamics including hyperchaos and multistability. Finally, analogue circuit and DSP digital circuit of the two illustrative examples and an Knowm memristor based example are designed to verify the physical realizability of the proposed discrete memristor chaotic maps.

The generation of complicated grid hyperchaotic hidden attractors is an interesting but challenging work. Up to now, no grid hyperchaotic system with stable equilibria has been reported and implemented yet. This paper presents a two-step design scheme for constructing novel grid hyperchaotic system with only stable equilibria. First, the simple variable boosting is applied to a single-stable-equilibrium hyperchaotic system to generate offset boosted hyperchaotic hidden attractors in two directions. At this point, it is associated with the increase of the number of equilibria in corresponding directions. To solve this problem, the configuration of two specific sawtooth waveform functions implements the offset switching in two directions but only extends the number of the equilibria in a single direction. Meanwhile, all these equilibria have the same stability conditions that will not change with the increase of the grid. Consequently, various grid hyperchaotic hidden attractors are generated. Numerical simulations are presented to intuitively show the phase portraits and cross sections. Finally, hardware experiments using analog circuit implementation are presented to verify the feasibility of the grid hyperchaotic system.

Balanced ternary digital logic circuits based on memristors and MOSFET devices are introduced. First, balanced ternary minimum gate TMIN, maximum gate TMAX and ternary inverters are designed and verified by simulation. Next, logic circuits such as ternary encoders, decoders and multiplexers are designed using these three basic gates. For further validation, a ternary 3–1 encoder was hardware-implemented successfully using in-house fabricated memristors and MOS transistors. Two different design approaches, namely the decoder-based method and the multiplexer-based method are introduced and applied to realize combinational logic circuits such as balanced ternary half-adder, multiplier, and numerical comparator. We simulate the circuits using 50nm CMOS technology parameters and BSIM models and present comparisons and analyses of the two design methods in view of the power consumption and component device counts, which can guide subsequent research and development of integrated multi-valued logic circuits. The decoder-based method has advantages both in terms of component numbers and power consumption, but the multiplexer-based method has the advantages of being based on a simple operating principle and ease of implementation.

As a classic paradigm of chaos generation, Chua’s circuit has been widely studied and applied in different fields. Various Chua’s circuits employing voltage-controlled Chua’s diodes have been designed and implemented, but rare such circuits employing current-controlled Chua’s diodes have been reported to date. Following the technical steps which Chua used to design the famous Chua’s circuit, this paper presents a systematic design procedure of the chaotic circuits employing current-controlled nonlinear resistors. Two simple circuit topologies are presented as paradigms, which respectively are dual to the classical Chua’s circuit and the canonical Chua’s circuit. In specific, we obtain two simple implementations of the dual Chua’s circuits consisting of two inductors, one capacitor, one passive/active linear resistor, and one piecewise-linear current-controlled Chua’s diode. The numerical simulations and the previously unreported physical implementations confirmed the feasibility of the design. Consequently, it enriches the genealogy of the Chua’s circuit family.

As a paradigm bridging the mathematical chaos to physical one, Chua’s circuit and its dimensionless Chua’s system have been widely studied and applied. From the completeness point of view, it is an interesting issue under investigation that how many potential Chua’s circuit variants exist within the framework of a single Chua’s system. Follow the duality principle in electrical circuits, this paper presents a Chua’s circuit employing ideal charge-controlled memristive model, which complements the circuit genealogy of the classical Chua’s system to four, i.e., Chua’s circuit, dual Chua’s circuit, memristive Chua’s circuit, and dual memristive Chua’s circuit (DMCC). To study the initial condition-related dynamical behaviors of the DMCC, we further present the flux-charge analysis. Some complex coexisting dynamics that cannot be typically observed in the voltage-current-domain have been investigated in the flux-charge-domain. Based on the dimensionless equations of the DMCC, an equivalent circuit is implemented for verification.

Bionic firing circuits are beneficial for bionic hardware applications. It is an attractive but challenging task to design a simple bionic firing circuit to generate spiking and bursting behaviors. To hit this aim, this paper newly proposes a universal configuration framework for mem-element-emulator-based bionic firing circuit, which deploys the charge-controlled memcapacitor and voltage-controlled locally active memristor (LAM) to characterize the electrophysiological behaviors of liquid bilayer and ion channels of a neuronal membrane, respectively. The configuration framework only contains one memcapacitor, LAM, DC voltage, and current stimulus. Then, a charge-controlled memcapacitor and an N-type LAM are employed as a case to verify the effectiveness of the configuration framework. Numerical simulations, PSpice circuit simulations, and discrete component-based hardware experiments are performed, which display that the bionic firing circuit can generate abundant spiking and bursting behaviors. One key feature is that the configuration framework can employ different charge-controlled memcapacitors and voltage-controlled LAMs to build more potential bionic firing circuits. To the authors’ knowledge, this configuration framework is extendable and sheds new light on the design of bionic firing circuits.

Memristor possesses synapse-like properties that can mimic excitation and inhibition between neurons. This article introduces the Sigmoid functions to the memristor and constructs a new memristive Hopfield neural network (HNN). Its most distinctive feature is the simple topology, which contains only unidirectional connections in neurons. The equilibrium points analysis reveals the mechanism of its multiscroll attractors generation. Homogeneous and heterogeneous coexisting attractors are observed with the variation of the network parameters. Note that the state equation of memristor can affect the number of coexisting attractors. A hardware implementation is designed for it, and the multiscroll attractors are captured in the oscilloscope. Finally, it is also applied to developing an image encryption algorithm with excellent performance.

The systematic control of chaotic phase orbits in desired location and orientation favors of the generation of multiwing/scroll attractors for potential industrial applications, which is an attractive but challenging work. In this article, we present a universal matrix transformation method for generating multistructure chaotic attractors. First, several basic transformation matrices or their composite forms are chosen to implement multiplex control of a seed. On this foundation, applying it in a general 3-D system and further obtaining the universal transformed system. Taking the Lorenz system, Chua's system, and Rössler system as the seeds, we construct various multistructure attractors with custom-shaped distributions via stringing together multiple separate units. The novelties are that the method is universal, has wide applicability, and does not introduce any extra variables for state-extension. Numerical phase orbits and hardware implementations verified the feasibility of the method. A pseudorandom number generator with qualified performance is implemented to support its potential applications.

Proton exchange membrane fuel cells (PEMFCs) have been widely used as clean energy storage devices in electrified transportation system. The key challenges in the existing control approaches of PEMFCs include model dependence, the usage of non-optimal control policy and the reliance on offline-trained neural networks. To address these challenges, this paper proposes a novel machine learning-based optimal control strategy for the PEMFC in electrified transportation system. Furthermore, the proposed method employs a recurrent neural network (RNN) to successfully avoid the problem of slow or even no convergence that may be caused in recursive least square-based neural network weights updating process. It offers excellent control performance with guaranteed convergence and stability. The superiority of the proposed method is validated through Hardware-in-the-Loop (HIL) tests.

This article proposes a new compact model and presents a circuit-theoretical analysis for a third-order memristive neuromorphic element fabricated by Kumar et al. The proposed model mainly consists of a box-shaped resistor model and a simplified piecewise-linear memristor model. Since the dynamic behavior of the box-shaped hysteresis in the quasistatic current–voltage curve is mostly unexplored, we first extract the box-shaped resistor model and construct its oscillators. The coordinate system shifting method and dynamic route analysis method are used to reveal the operating mechanism of boxshaped resistor-based oscillators. Both the theoretical analysis and simulation verification indicate that the box-shaped hysteresis characteristic facilitates the generation of neuromorphic action potentials. The proposed new compact model not only captures quasi-static characteristics but also includes dynamic behaviors, such as action potential, periodic spiking, and periodic bursting. The influences of the model parameters are further investigated to reveal the mechanism of the neuromorphic behaviors in the box-shaped hysteresis and positive differential resistance regions. Simulation results manifest the feasibility of the proposed model and the correctness of the presented analysis methods, which pave the way to the optimized design of memristive devices and the research of neuromorphic dynamics.

Due to the synapse-like nonlinearity and memory characteristics, memristor is often used to construct memristive neural networks with complex dynamical behaviors. However, memristive neural networks with multistructure chaotic attractors have not been found yet. In this article, a novel method for designing multistructure chaotic attractors in memristive neural networks is proposed. By utilizing a multipiecewise memristive synapse control in a Hopfield neural network (HNN), various complex multistructure chaotic attractors can be produced. Theoretical analysis and numerical simulation demonstrate that multiple multistructure chaotic attractors with different topologies can be generated by conducting the memristive synapse-control in different synaptic coupling positions. Differing from traditional multiscroll attractors, the generated multistructure attractors contain multiple irregular shapes instead of simple scrolls. Meanwhile, the number of structures can be easily controlled with the memristor control parameters. Furthermore, we design a module-based analog memristive neural network circuit and the arbitrary number of multistructure attractors can be obtained by selecting corresponding control voltages. Finally, based on the memristive HNNs, a novel image encryption cryptosystem with a permutation-diffusion structure is designed and evaluated, exhibiting its excellent encryption performances, especially the extremely high key sensitivity.

Analog circuit implementation of neuron model is an essential category of neuromorphic circuit since it can reproduce neuron firing patterns and assist in exploring neuron-based applications. The neuron models built by electrophysiological ion transport mechanism can effectively mimic the neuron firing patterns. However, they are rather difficult to implement on analog level because these neuron models involve complex exponential nonlinearities for characterizing the ion channels. Thanks to the superiorities of locally active memristor in constructing artificial neuron circuit, two locally active memristors are employed to characterize the sodium and potassium ion channels and then a memristive neuromorphic circuit is successfully built in this paper. Numerical simulations demonstrate that the memristive neuromorphic circuit can generate abundant firing patterns with respect to memristor- and stimulus-related parameters. Moreover, quasi-periodic bifurcation behavior and coexisting firing patterns are triggered by different memristor initial conditions. Employing off-the-shelf discrete circuit components, a PCB-based analog circuit is manually constructed and hardware experiments are executed. Experimental results captured from hardware measurements satisfactorily verify the numerically simulated ones and effectively show the feasibility of the memristive neuromorphic circuit in generating neuron firing patterns.

Locally-active memristor (LAM) is one of the promising candidates of artificial neurons, indicating it has potential applications in neuromorphic computing. Quantitative theoretical analysis on LAMs can provide benefits for designing related oscillator circuits and systems. This study begins with the aim of assessing the importance of DCV-Icharacteristic in the performance of LAMs by using small-signal analysis method. The DCV-Icurve of the LAM is specified by two parameters involving resistance (conductance) and differential resistance (differential conductance). In addition to these two static parameters, we extract a crucial dynamic parameter to describe the behavior of the LAM. Theoretical analysis demonstrates that the performance of generic current-controlled and voltage-controlled LAMs is closely associated with three crucial parameters, i.e., the above two static and one dynamic parameters. Hence, only based on these three parameters, can one derive the small-signal equivalent circuit of LAMs and determine the oscillation frequency range and condition for simple LAM-based oscillators. By applying the presented universal dynamics analysis results, we further propose a modified mathematical model with higher accuracy to mimic the quasi-static and oscillating behaviors of a real Nb2O5device, and provide some fundamental guidance for the design of LAM-based high-frequency oscillators.

This paper presents a series of multi-stage hybrid memristor-CMOS ternary combinational logic stages that are optimized for reducing silicon area occupation. Prior demonstrations of memristive logic are typically constrained to single-stage logic due to the variety of challenges that affect device performance. Noise accumulation across subsequent stages can be amortized by integrating ternary logic gates, thus enabling higher density data transmission, where more complex computation can take place within a smaller number of stages when compared to single-bit computation. We present the design of a ternary half adder, a ternary full adder, a ternary multiplier, and a ternary magnitude comparator. These designs are simulated in SPICE using the broadly accessible Knowm memristor model, and we perform experimental validation of individual stages using an in-house fabricated Si-doped HfOxmemristor which exhibits low cycle-to-cycle variation, and thus contributes to robust long-term performance. We ultimately show an improvement in data density in each logic block of between$5.2\times - 17.3\times $, which also accounts for intermediate voltage buffering to alleviate the memristive loading problem.

The search for a compatible application of memristor-CMOS logic gates has remained elusive, as the data density benefits are offset by slow switching speeds and resistive dissipation. Active microdisplays typically prioritize pixel density (and therefore resolution) over that of speed, where the most widely used refresh rates fall between 25–240 Hz. Therefore, memristor-CMOS logic is a promising fit for peripheral I/O logic in active matrix displays. In this paper, we design and implement a ternary 1–3 line decoder and a ternary 2–9 line decoder which are used to program a seven segment LED display. SPICE simulations are conducted in a 50-nm process, and the decoders are synthesized on an Altera Cyclone IV field-programmable gate array (FPGA) development board which implements a ternary memristor model designed in Quartus II. Our approach to logic synthesis demonstrates a potential way forward for simulating large-scale memristor-CMOS circuits without embedded RRAM for functional verification, and our SPICE results show an improvement in data density of a variety of decoders by a factor between 3.6-8.5. While the switching speed of memristors are one of several bottlenecks to using them in combinational logic, the comparatively slow refresh rates of typical microdisplays indicate this to be a tolerable trade-off, which promotes data density over speed.

Eight-switch three-phase post-fault inverter (ESTPI) is widely used as the reconfigured topology of three-phase three-level neutral point clamped DC/AC inverter when open-circuit fault occurs. A key technical challenge for the ESTPI is the post-fault controller design. The existing post-fault control approaches for the ESTPI suffer from the difficulty in trading off the current tracking performance, neutral point voltage (NPV) balancing and common mode voltage (CMV) levels. To solve this problem, this paper proposes an improved predictive current control algorithm. The proposed algorithm contributes to achieve accurate output current tracking while greatly reducing the CMV with a balanced NPV. Simulation and experimental results are presented to validate the effectiveness of the proposed algorithm.

Volitional control of prostheses is most commonly achieved by myoelectric signalling. The electromyograph (EMG) is detected and processed by a controller, that decodes and relates the signal to the corresponding position of the prosthetic. Myoelectric signalling is limited in users by two factors: lack of nerve endings corresponding to the position of the amputation, and neurological damage resulting in poor signal control. Improved prosthesis control has been demonstrated by the addition of feedback sensors based on computer vision and inertial measurement units. Computer vision requires a significant level of processing, resulting in a high latency and high power usage. In this paper, we propose a means of overcoming this limitation by use of in-vivo retinal signalling to complement EMG for improved control. This is demonstrated using a real-time conductance- based simulator as the sole method of control for an upper-limb prosthesis. Input image streams are received by a camera and used to activate the combined rod and cone photoreceptor cell responses. This in turn generates a spike train which is counted and averaged over time, and passed to an Arduino-based control system which modulates the behavior of the prosthesis. We seek to use this system to lower the experimental barriers of in-vivo ganglion electrical signalling by presenting a way to use retina emulation. A link to the simulator is provided.

In the modern society, physical infrastructure and information technology are inseparable. The concept of cyber-physical systems (CPSs) is then proposed and has been widely concerned by researchers in recent years. Various models have been introduced to meet the actual needs. In one type of those models, the physical part and the cyber part are coupled and influence each other. The cyber part monitors and controls the physical part, but at the same time it may also bring certain harm; the fault in one part may also be transmitted to the other and affect the performance of the counterpart. Here, this paper introduces an asymmetric interdependent model and studies how the coupling pattern of CPSs affects the system robustness. In addition, the coupling pattern of CPSs is optimized by using the simulated annealing (SA) algorithm to reduce the performance loss. The results of this paper may find applications in the future CPS planning.

Dynamic vision sensors are able to operate at high temporal resolutions within resource constrained environments, though at the expense of capturing static content. The sparse nature of event streams enables efficient downstream processing tasks as they are suited for power-efficient spiking neural networks. One of the challenges associated with neuromorphic vision is the lack of interpretability of event streams. While most application use-cases do not intend for the event stream to be visually interpreted by anything other than a classification network, there is a lost opportunity to integrating these sensors in spaces that conventional high-speed CMOS sensors cannot go. For example, biologically invasive sensors such as endoscopes must fit within stringent power budgets, which do not allow MHz-speeds of image integration. While dynamic vision sensing can fill this void, the interpretation challenge remains and will degrade confidence in clinical diagnostics. The use of generative adversarial networks presents a possible solution to overcoming and compensating for a vision chip's poor spatial resolution and lack of interpretability. In this paper, we methodically apply the Pix2Pix network to naturalize the event stream from spike-converted CIFAR-10 and Linnaeus 5 datasets. The quality of the network is benchmarked by performing image classification of naturalized event streams, which converges to within 2.81% of equivalent raw images, and an associated improvement over unprocessed event streams by 13.19% for the CIFAR-10 and Linnaeus 5 datasets.

Neuromorphic computing can solve computationally hard problems with energy efficiencies unattainable for von Neumann architectures. A locally-active memristor, which possesses the capability to amplify infinitesimal fluctuations in energy and can be used to generate neuromorphic behaviors, is a natural candidate for constructing an electronic equivalent of biological neurons. This paper identifies some unknown neuromorphic dynamics of the Chua corsage memristor (CCM), and shows that the CCM, when biased at the edge of chaos domain, can exhibit rich dynamics of biological neurons. Using Chua’s theories of local activity and edge of chaos, we demonstrate that under the destabilizing of the input voltage and the circuit parameters (inductance or capacitance), two CCM-based circuits can produce thirteen types of neuromorphic behaviors either on, or near the edge of chaos domain via supercritical or subcritical Hopf bifurcation. In addition, we give the conditions to test the edge of chaos of the CCM and the CCM-based circuit only by using the poles and the zero of their admittance functions.

This study employs the Caputo-Fabrizio fractional derivative to determine the model of fractional-order RC circuits with arbitrary voltage input which can be widely used in a variety of electrical systems. Analog circuit implementation of fractional-order RC circuits defined by Caputo-Fabrizio fractional derivative is presented and verified by comparing with the model proposed in this work. For the purpose of judging whether the fractional-order model defined by the Caputo-Fabrizio derivative is practical, the comparison experiments are carried out. By using Laplace transform, the analytical solutions of fractional-order RC circuits based on the Caputo-Fabrizio derivatives with constant and periodic voltage sources are deduced. Fractional-order model of RC circuits with arbitrary input are also calculated using the convolution formula. The correctness of the derivation of the model using the Caputo-Fabrizio derivative is verified. Through discussing the impedance model of capacitor in frequency domain, the analog realization of fractional capacitor based on the Caputo-Fabrizio derivative is derived. The fractional-orders of the RC circuits models defined by the Caputo and Caputo-Fabrizio fractional derivatives are fitted respectively through repeated charging and discharging experiment data. The fractional-order models based on the Caputo and Caputo-Fabrizio derivatives, and the integer-order model are all compared with the experiment data.

This paper presents the first experimental demonstration of a ternary memristor-CMOS logic family. We systematically design, simulate and experimentally verify the primitive logic functions: the ternary AND, OR and NOT gates. These are then used to build combinational ternary NAND, NOR, XOR and XNOR gates, as well as data handling ternary MAX and MIN gates. Our simulations are performed using a 50-nm process which are verified with in-house fabricated indium-tin-oxide memristors, optimized for fast switching, high transconductance, and low current leakage. We obtain close to an order of magnitude improvement in data density over conventional CMOS logic, and a reduction of switching speed by a factor of 13 over prior state-of-the-art ternary memristor results. We anticipate extensions of this work can realize practical implementation where high data density is of critical importance.

As a nonlinear similarity measure defined in the kernel space, the correntropic loss (C-Loss) can address the stability issues of second-order similarity measures thanks to its ability to extract high-order statistics of data. However, the kernel adaptive filter (KAF) based on the C-Loss uses the stochastic gradient descent (SGD) method to update its weights and, thus, suffers from poor performance and a slow convergence rate. To address these issues, the conjugate gradient (CG)-based correntropy algorithm is developed by solving the combination of half-quadratic (HQ) optimization and weighted least-squares (LS) problems, generating a novel robust kernel correntropy CG (KCCG) algorithm. The proposed KCCG with less computational complexity achieves comparable performance to the kernel recursive maximum correntropy (KRMC) algorithm. To further curb the growth of the network in KCCG, the random Fourier features KCCG (RFFKCCG) algorithm is proposed by transforming the original input data into a fixed-dimensional random Fourier features space (RFFS). Since only one current error information is used in the loss function of RFFKCCG, it can provide a more efficient filter structure than the other KAFs with sparsification. The Monte Carlo simulations conducted in the prediction of synthetic and real-world chaotic time series and the regression for large-scale datasets validate the superiorities of the proposed algorithms in terms of robustness, filtering accuracy, and complexity.

The hyperbolic cosine function with high-order errors can be utilized to improve the accuracy of adaptive filters. However, when initial weight errors are large, the hyperbolic cosine-based adaptive filter (HCAF) may be unstable. In this paper, a novel normalization based on the logarithmic hyperbolic cosine function is proposed to achieve the stabilization for the case of large initial weight errors, which generates a logarithmic HCAF (LHCAF). Actually, the cost function of LHCAF is the logarithmic hyperbolic cosine function that is robust to large errors and smooth to small errors. The transient and steady-state analyses of LHCAF in terms of the mean-square deviation (MSD) are performed for a stationary white input with an even probability density function in a stationary zero-mean white noise. The convergence and stability of LHCAF can be therefore guaranteed as long as the filtering parameters satisfy certain conditions. The theoretical results based on the MSD are supported by the simulations. In addition, a variable scaling factor and step-size LHCAF (VSS-LHCAF) is proposed to improve the filtering accuracy of LHCAF further. The proposed LHCAF and VSS-LHCAF are superior to HCAF and other robust adaptive filters in terms of filtering accuracy and stability.

Switched inductor (SL) step-up dc-dc converters can be used for high voltage gain applications such as in PV systems. In this paper, a study of a N-cell high voltage gain boost dc-dc converter performing maximum power point tracking from a PV source is presented. First, the time domain dynamic model is derived. Then, the linearized s- domain model is first obtained. It is obtained that contrarily to the conventional canonical boost converter, the N-cell switched inductor converter presents a stable zero in the duty-cycle-to-PV-voltage transfer function which can be considered as an advantage to design a fast voltage-based MPPT control having the same response speed that corresponds to current mode control. Using the resulting control-to-output transfer function, a fast voltage-based MPPT controller is designed. Finally, numerical simulation are used to evaluate the performances of the converter when used in PV applications under different weather conditions.

The conventional design and optimization of passive compensation network (PCN) for inductive power transfer (IPT) system is based on specific topologies. The demerits of this design method are: i) The topology is mostly chosen by experience; ii) The design parameters are not multi-objective optimal. Aiming at these issues, this paper proposes an optimal PCN design scheme based on evolutionary algorithm (EA) to synchronously optimize the topology and parameters of PCN for IPT system. The mathematical modeling of the PCN is presented and derived by transfer matrix. Then based on this mathematical modeling the multi-objective functions (output fluctuation and efficiency) for the PCN are established, as well as the constraints (load range and coupling range). The corresponding multi-objective optimal design algorithm based on EA is further constructed. Finally, one optimized PCN case that has minimum output current fluctuation and high-efficiency is chosen to validate the effectiveness of the proposed design scheme.

Reliable communication is of great significance for the intelligentization of DC microgrids. A new signal and power composite modulation (SPCM) method is proposed to achieve Power Line Communication (PLC) for Photovoltaic panels and Switched Reluctance Generator (SRG) based DC microgrids using Current Chopping Control (CCC) method. By tuning the reference value of chopping current, voltage ripples with different frequencies can be brought out on the power bus for transmitting data. At the receiver side, Fast Fourier Transform (FFT) method is employed for analyzing the voltage ripples to demodulate the carrier frequency and extract the transmitted data. The simulation results are presented for verifying the feasibility of the proposed SPCM strategy.

Memristors have great application in many fields such as neural networks, non-volatile memory and nonlinear circuits by virtue of the nanoscale and non-volatile characteristics. Multi-valued devices possess significant meaning in digital logic circuit, chaos control and synapse networks. In this paper, the concept of multi-valued memristors is proposed, and the ternary flux-controlled memristor is taken as an example investigated concretely. Moreover, the specific ternary mathematical model is given and a series of numerical analyses have been studied. After that, a ternary flux-controlled memristor emulator is realized by off-the-shelf circuit components, both Multisim simulations and hardware experiments are performed to verify its effectiveness and the results shows that the theoretical analysis based on the mathematical model is in good agreement with the simulation and experimental results, which laid the theoretical foundation for the construction of multi-valued digital logic and other multi-valued applications.

Additive manufacturing technologies have been widely applied in both household and industrial applications. The fabrication time of a three-dimensional (3-D) printed object can be shortened by optimizing its segments printing order. The computational times required by existing nozzle path planning algorithms can increase rapidly with the number of printing segments. In this article, the nozzle path planning problem is formulated as an undirected rural postman problem and a computationally efficient heuristic search algorithm is proposed to find fast routes and mitigate overheads in printing processes. Both simulation and experimental results concur that the proposed algorithm can significantly speed up printing processes and outperform its counterparts in 3-D printing applications.

The generalized lock scheduling problem (GLSP) is a mixed integer optimization problem which consists of a ship placement (SP) and a lockage operation scheduling (LOS) sub-problem. In previous research, the GLSP is solved by different exact and heuristic methods, which are confirmed inferior with respect to computation time and solution quality. Consequently, none of those methods is efficient for handling practical large-scale GLSP. For the first time, we show that high-quality solutions of GLSP can be efficiently obtained by using an innovative approach proposed in this paper. Specifically, an ingenious solution structure of GLSP is designed, by which the GLSP is converted to a combinatorial optimization problem. Furthermore, an adaptive large neighborhood search (ALNS) heuristic based on the principle of destruction and reconstruction of solutions is proposed for solving the GLSP. Test results using a large number of instances reported in the literature are compared with those obtained by two exact methods, the mixed integer linear programming (MILP) and combinatorial Benders' decomposition (CBD) method. The results show that our ALNS achieves optimal solutions within less time in terms of most of the small-scale instances. Much better solutions are obtained by the ALNS within a few minutes for those large-scale instances that cannot be solved to optimality by exact methods within 2 h. Especially, the advantage of the proposed method is more remarkable when there is no specific chronological rules forced, which indicates that the proposed method is capable of handling the GLSP in a broader scope of situations.

Tool-path optimization has been applied in many industrial applications, including subtractive manufacturing likes drilling and additive manufacturing likes 3D printing. The optimization process involves finding a time-efficient route for tools to visit all the required sites, which is often computationally intensive. In practice, heuristics and meta-heuristics are used to generate sub-optimal results within reasonable durations. The aim of this work is to use artificial neural networks to yield better tool-paths.

Semi-flocking algorithms have been demonstrated to be efficient in maneuvering MSNs in multi-target tracking tasks. In many real-world applications, targets can be assigned with different priorities according to their importance of being tracked. However, existing semi-flocking algorithms normally assume the importance of all targets to be identical, which may not allocate resources in an efficient manner. In this paper, we propose a target evaluation method that incorporates priorities of the targets in the assessment process. Based on the evaluation results, mobile agents decide to track a target or continue to scan the terrain via a probabilistic task switching mechanism. Simulation results indicate a higher effectiveness of the proposed method in target tracking and area coverage when compared with two existing semi-flocking algorithms.

The tensor product model transformation (TPMT) is an emerging numerical framework of the Takagi-Sugeno (T-S) fuzzy (or polytopic) system modeling for a linear matrix inequality based system control design. A nested TPMT (NTPMT) is proposed in this paper, which merges the dimensions of the tensors and performs the TPMT iteratively. The resultant fuzzy model is in a multilevel nested tensor product (TP) structure. The vertex tensor obtained by NTPMT has fewer dimension results than the original TPMT so the number of vertices or fuzzy rules, which have been the main bottleneck for further application of the TPMT in higher dimensional systems, is expected to decrease significantly. It is also proven that the NTPMT contains the hierarchical fuzzy logic, meaning that the NTPMT is capable of conducting hierarchical fuzzy modeling and reduction. Furthermore, because the inclusion of multiple TPMTs is prone to augment the conservativeness of the resultant fuzzy model, a suboptimal convex hull rectification algorithm for the TPMT is developed based on a newly defined tightness measure, and then extended to render the NTPMT as less conservative as possible. Finally, numerical simulations on two real physical systems (two- and four-parameter dimension) are verified to demonstrate the performance of the methods.

Layered additive manufacturing, also known as three-dimensional (3-D) printing, has revolutionized transitional manufacturing processes. Fabrication of 3-D models with complex structures is now feasible with 3-D printing technologies. By performing careful tool-path optimization, the printing process can be speeded up, while the visual quality of printed objects can be improved simultaneously. The optimization process can be perceived as an undirected rural postman problem (URPP) with multiple constraints. In this paper, a tool-path optimizer is proposed, which further optimizes solutions generated from a slicer software to alleviate visual artifacts in 3-D printing and shortens print time. The proposed optimizer is based on a modified ant colony optimization (ACO), which exploits unique properties in 3-D printing. Experiment results verify that the proposed optimizer can deliver significant improvements in computational time, print time, and visual quality of printed objects over optimizers based on conventional URPP and ACO solvers.

This paper proposes a load-oriented control parameters optimization strategy for Doubly Fed Induction Generator (DFIG) to enhance Low-Frequency Oscillation Damping (LFOD) and improve stability of a power system. Enabled by the smart grid measuring technologies, frequency deviations of generators of interest are obtained and employed as the input signals of the designed Supplementary Damping Controller (SDC) of DFIG. In order to acquire the optimal load-oriented control parameters, an hour-ahead load-forecasting scheme is devised, using Artificial Neural Network (ANN) learning techniques. The ANN is trained by a set of data over a 4-year period, and then the control parameters are optimized using Particle Swarm Optimization (PSO) technique for the purpose of minimizing the Critical Damping Index (CDI) of the power system. Numerical results demonstrate that the low-frequency oscillations (LFOs) of the power system can be effectively mitigated using the proposed controller in smart grids integrated with wind power generators.

Ant colony optimization (ACO) algorithms have been widely adopted in solving combinatorial problems, like the traveling salesman problem (TSP). Nevertheless, with a proper transformation to TSP, ACO is capable of solving undirected rural postman problems (URPP) as well. In fact, nozzle path planning problems in 3D printing can be represented as URPP. Therefore, in this work, ACO is utilized as a URPP solver to accelerate the printing process in fused deposition modeling applications. Furthermore, mechanisms which exploit unique properties in 3D models are proposed to further extend the ACO in the above optimization process. These mechanisms are capable of accelerating ACO by adaptively adjusting its number of iterations on-the-fly. Simulation results using real-life 3D models show that the proposed extensions can accelerate ACO without affecting the quality of its solutions significantly.

Recently proposed distributed anti-flocking algorithms have enabled mobile sensor networks (MSNs) to deliver impressive area coverage performances. However, due to lack of information about each other's traverse history, mobile sensor nodes tend to travel extra distances to achieve 100% cumulative area coverage. Inspired by the territorial marking behaviour of solitary animals, this paper proposes a new information map and map updating methods for anti-flocking controlled MSNs. The proposed territorial marking anti-flocking control enables MSNs to achieve improved area coverage performances by encouraging nodes to remain in a part of the terrain. According to the results provided in this paper, the proposed algorithm can be more energy efficient for MSNs in continues monitoring applications.

In this paper, the Pulse Train (PT) control strategy is tentatively applied to the Parallel-connected Buck Converters (PBC) with current-sharing ability. However, in Continuous Conduction Mode, undesirable low-frequency voltage oscillation will be brought about by the PT control. Therefore, the Capacitor-Current-Feedback based PT (CCF-PT) control is newly proposed to eliminate the low-frequency voltage oscillation. The experimental results indicate that, the proposed CCF-PT control method can successfully achieve effective suppression of low-frequency oscillation by properly configuring the capacitor current feedback coefficient.

Modeling and analysis of cascading failures draws wide attention recently due to frequent occurrences of large blackouts all over the world. Models based on complex network theory contribute significantly on analyzing the robustness of power systems and assessing the risk probability, while they fall short of producing the propagation of the cascading failure in exact time points. This paper presents an improved topological model taking timescale into consideration, as well as the relay setting which reveals its operation more accurately according to industrial standards. The paper then validates the model using UIUC 150-bus and IEEE 39-bus test system. In order to assess the vulnerability of a power network, the multi-step attack strategy has been provided. The results demonstrate that timescales and relay settings have a critical impact during the cascading failure and the proposed attack strategy may lead to larger blackouts than normal strategies.

With the rapid intelligentzation of smart grid, power converters with higher performances and lower cost are necessarily required. Pulse Train (PT) controlled power converters have the advantages of fast response and simple structure, but are also suffered from undesired low frequency oscillation as operated in continuous conduction mode (CCM). In this paper, a PT controlled buck converter with dual output interfaces is newly proposed by introducing the coupled inductors. By adjusting the coupling coefficient and the second side voltage of the coupled inductor, the low-frequency oscillation of output voltage could be effectively suppressed. Experimental results are presented to validate the practicability of this new PT controlled converter.

Existing computational models of the retina often compromise between the biophysical accuracy and a hardware-adaptable methodology of implementation. When compared to the current modes of vision restoration, algorithmic models often contain a greater correlation between stimuli and the affected neural network, but lack physical hardware practicality. Thus, if the present processing methods are adapted to complement very-large-scale circuit design techniques, it is anticipated that it will engender a more feasible approach to the physical construction of the artificial retina. The computational model presented in this research serves to provide a fast and accurate predictive model of the retina, a deeper understanding of neural responses to visual stimulation, and an architecture that can realistically be transformed into a hardware device. Traditionally, implicit (or semi-implicit) ordinary differential equations (OES) have been used for optimal speed and accuracy. We present a novel approach that requires the effective integration of different dynamical time scales within a unified framework of neural responses, where the rod, cone, amacrine, bipolar, and ganglion cells correspond to the implemented pathways. Furthermore, we show that adopting numerical integration can both accelerate retinal pathway simulations by more than 50% when compared with traditional ODE solvers in some cases, and prove to be a more realizable solution for the hardware implementation of predictive retinal models.

Nowadays, enabled by current smart grid technology, electricity consumers can play an active role in providing ancillary service (AS) as a type of demand response. Participating AS can assist stabilizing the power grid by following the frequency regulation signal, or dynamic regulation signal (RegD) in this study while receiving economic benefits. Industrial loads are an indispensable component as a demand-side regulating resource of ancillary service due to their intensive electricity consumption. In this paper, we use grid-connected solar photovoltaics panels combined with the energy storage system (ESS) to produce continuous electricity consumption signals in order to follow the RegD signal. The participation of solar energy in real-time regulation provision process is emphasized, which is modeled based on a variety of operation modes in accordance to Australian Standard. Through a particular case study, with the integration of solar energy, the proposed method poses cost-effectiveness in industrial plant scheduling and a favorable load following capability, helping ensure the frequency stability of the electric grid. The proposed methodology is more economically advantageous compared to identical industrial loads only equipped with on-site ESS, and requires less switchings on machines compared to industrial plants with passive use of solar energy.

This paper proposes a load-oriented control parameter optimization strategy for static synchronous compensator (STATCOM) to enhance low-frequency oscillation damping (LFOD) and improve stability of overall complex power systems. Frequency deviations of generators of interest are employed as the input signals of the designed supplementary damping controller of STATCOM. In order to obtain the optimal load-oriented control parameters, a day-ahead load-forecasting scheme is devised, using artificial neural network (ANN) learning techniques. The ANN is trained by a set of data over a 4-year period, and then the control parameters are optimized using Particle Swarm Optimization technique by minimizing the critical damping index. The proposed control strategy is implemented in the IEEE standard complex power system, and the numerical results demonstrate that the low-frequency oscillations (LFOs) of the power system can be effectively mitigated using the proposed controller. Compared to conventional robust controller with universal parameters, this novel load-oriented optimal control strategy shows its superiority in alleviating LFOs and enhancing the overall stability of the power system. Since the proposed control scheme aims to adaptively adjust the controller parameters in correspondence to load variations, this study is envisaged to have practical utilizations in industrial applications.

Finding a shortest feasible path between two given locations is a common problem in many real-world applications. Previous studies have shown that mobile platforms would consume excessive energy when moving along shortest paths on uneven terrains, which often consist of rapid elevation changes. Mobile platforms powered by portable energy sources may fail to follow such paths due to the limited energy available. This paper proposes a new heuristic search algorithm called constraints satisfying A* (CSA*) to find solutions to such resource constrained shortest path problems. When CSA* is guided by admissible heuristics, it guarantees to find a globally optimal solution to a given constrained search problem if such a solution exists. When CSA* is guided by consistent heuristics, it is optimally efficient over a class of equally informed admissible constrained search algorithms with respect to the set of paths expanded. Test results obtained using real terrain data verify the applicability of the proposed algorithm in shortest path planning for energy-constrained mobile platforms on uneven terrains.

The development of a bioinspired image sensor, which can match the functionality of the vertebrate retina, has provided new opportunities for vision systems and processing through the realization of new architectures. Research in both retinal cellular systems and nanodriven memristive technology has made a challenging arena more accessible to emulate features of the retina that are closer to biological systems. This paper synthesizes the signal flow path of photocurrent throughout a retina in a scalable 180-nm CMOS technology, which initiates at a 128 × 128 active pixel image sensor, and converges to a 16 × 16 array, where each node emits a spike train synonymous to the function of the retinal ganglionic output cell. This signal can be sent to the visual cortex for image interpretation as part of an artificial vision system. Layers of memristive networks are used to emulate the functions of horizontal and amacrine cells in the retina, which average and converge signals. The resulting image matches biologically verified results within an error margin of 6% and exhibits the following features of the retina: lateral inhibition, asynchronous adaptation, and a low-dynamic-range integration active pixel sensor to perceive a high-dynamic-range scene.

This paper presents a novel method for locating two faults, instead of a single fault, in three-terminal transmission lines. It is assumed two faults occur in two different sections of a three-terminal transmission line and the faults may occur simultaneously or one after another before the first fault is cleared. It is also assumed types of faults are unknown and no measurement equipment is available at the junction point where three lines are joined and connected to each other. An objective function is developed and the Nelder-Mead method and multi start points techniques are employed to minimise the objective function. The proposed method is independent of prefault current, type of fault and prefault conditions. Numerical results for a large number fault locations and different scenarios are reported.

With increasing penetration of high performance power electronic devices such as the flexible DC transmission and renewable energy power generation system, the Thevenin equivalent of the power grid at fundamental frequency fails to reflect the actual grid impedance change completely. Considering the practical situation when a large-capacity rectifier is connected to a distribution grid, we derive the expression of its Thevenin equivalent parameters, including the grid voltage and impedance are given at different frequencies, by adopting the multi-time section method. A simple IEEE 9-Bus system with an uncontrolled rectifier is analyzed in this paper. It is found there is a nonlinear relationship between the equivalent impedance and the frequency. The analysis results are proved to be correct through maximum power transmission simulation based on the impedance module margin method.

It is essential to rapidly restore a power system after a blackout to minimize the economic losses and negative social impact. The most common approach of accelerating the restoration process is by restoring the complete network as several subsystems in parallel. Even though a parallel restoration process has obvious advantages over its sequential alternatives, sizes of the subsystems play key roles in controlling the overall restoration time. Existing network partitioning strategies for parallel restoration do not put control on the individual subsystem size. In this paper, we proposed a partitioning strategy that helps to accelerate the restoration process by minimizing the subsystem size differences. Case study results given in this paper illustrate the effectiveness of the proposed partitioning strategy in parallel restoration of the power systems.

Three-phase inverter is a very important interface in microgrid. Droop control and virtual impedance are widely used to improve the power sharing capability and stability of such system, which however becomes ineffective under unbalanced conditions and may even cause potential stability problems. This paper proposes a fully self-adjusted virtual impedance design to guarantee the decoupling of active and reactive power. The arbitrary unbalanced information is acquired through the pseudo-inverse impedance matrix modeled by full-dq technique. Based on the unbalanced impedance identified, the adaptive droop control is achieved for not only accurate power sharing but also reliable voltage support with autonomous unbalanced voltage compensation. Simulations results verify the accurate identification of unbalanced impedance and effectiveness of the proposed method on the unbalanced compensation.

In this paper, we investigate the stability of Takagi-Sugeno fuzzy-model-based (FMB) functional observer-control system. When system states are not measurable for state-feedback control, a fuzzy functional observer is designed to directly estimate the control input instead of the system states. Although the fuzzy functional observer can reduce the order of the observer, it leads to a number of observer gains to be determined. Therefore, a new form of fuzzy functional observer is proposed to facilitate the stability analysis such that the observer gains can be numerically obtained and the stability can be guaranteed simultaneously. The proposed form is also in favor of applying separation principle to separately design the fuzzy controller and the fuzzy functional observer. To design the fuzzy controller with the consideration of system stability, higher order derivatives of Lyapunov function (HODLF) are employed to reduce the conservativeness of stability conditions. The HODLF generalizes the commonly used first-order derivative. By exploiting the properties of membership functions and the dynamics of the FMB control system, convex and relaxed stability conditions can be derived. Simulation examples are provided to show the relaxation of the proposed stability conditions and the feasibility of designed fuzzy functional observer-controller.

This paper enumerates three commonly used filtering algorithms and shows the detailed steps of their incorporation with general nonlinear systems for dynamic state estimation. The mathematical model of a stand-alone solid oxide fuel cell (SOFC) is briefly discussed and derived, which is then mathematically connected to a multiarea, diverse-generator, interconnected complex test system. The mathematical representation of the entire power system is tailored into a certain compact form to provide suitability for the implementation of filtering algorithms for the design of dynamic state estimators. With the utilization of phasor measurement units, the state estimators are able to work in a decentralized manner with the mere knowledge of local noisy voltage and current measurements. Successfully estimating the internal dynamic states of SOFC connected to complex power systems offers a novel methodology for the acquisition of the internal unmeasurable states of SOFC, which will facilitate future controller designs that may require the otherwise inaccessible states.

With power networks undergoing an unprecedented transition from traditional power systems to modern electric grids integrated with renewable energy sources, maintaining frequency stability of generators in modern power systems has become one of the major concerns. Targeting this issue, in this paper, we propose a novel frequency restoration strategy in photovoltaics (PV)-connected power systems using decentralized dynamic state estimation technique and PV power plant as a contingency power source. When a sudden increase in load demand occurs, the output power of PV panels is increased in order to compensate for the shortage of real power capacity of the generator, in order to restore the frequency of a certain generator bus bar. An unscented Kalman filter-based decentralized dynamic estimation is utilized in this study to estimate the frequency of a selected generator bus bar with local noisy voltage and current measurement data acquired by using phasor measurement units. Solar luminous intensity may vary over a period of time in different seasons, weather conditions, etc., which causes the variations in the output power of PV power plants. This irradiance uncertainty is also considered in this study. The proposed control strategy not only incorporates the frequency deviations of a generator bus-bar, but also takes into account the tie-line power deviations under disturbances. Simulation results demonstrate the capacity of proposed control schemes in restoring the frequency of generator bus-bars and also maintaining the tie-line power flowing between adjoining areas at it scheduled value.

This paper explores the dynamic behavior of dual flux coupled memristor circuits in order to further ascertain fundamental theory of memristor circuits. Different cases of flux coupling are mathematically modelled where two memristors are connected in both series and parallel, with consideration given to the polarity of each device. The dynamic behavior is characterized based on the constitutive relations, with a variation of memductance represented in terms of flux, charge, voltage and current. The agreement between theoretical and simulation analyses affirm the memristor closure theorem with coupled memristor circuits behaving as a different type of memristor with higher complexity.

Tremendous efforts have been put into dissecting the inherent characteristics and potential applications of Memcapacitor (MC), which possesses unique abilities of storing both information and energy [1]. Recently, coupling is disclosed as the third relation beyond series and parallel connections of memristive circuits in [2], of which the mechanical dynamic coupling of MCs is taken into account for illustration purpose. Coupled MCs could provide us more opportunities for developing new electronic devices with unique functions. However, very few works currently focus on the practical implementation of coupled MC emulators and its possible application in electronic circuits. In this letter, a practical emulator of coupled MC is newly proposed and then used for structuring Relaxation Oscillators (ROs), of which the period and duty cycle of output oscillating signal can be purposefully controlled in virtue of the coupling action.

This paper presents a novel unscented transform (UT)-based quasi-decentralized load frequency control scheme for power systems. The designed load frequency controllers are decoupled from each other, and can cope with noisy and discrete phasor measurement unit data. The proposed UT-based scheme is applied to a complex nonlinear power system. Furthermore, the design and analysis of the proposed controllers are based on considering the entire network topology.

This paper uses three popular stochastic filtering techniques to acquire the unmeasurable internal states of the doubly fed induction generator (DFIG) in order to realize the widely adopted control scheme, which involves the inaccessible state variable-stator flux. Filtering methods to be discussed in this paper include particle filter, unscented Kalman filter, and extended Kalman filter, where their mathematical algorithms are presented, their implementations in the DFIG wind farm connected to complex power systems are studied, and their performances are compared. The whole power system network topology is taken into consideration for the state estimation, but only local phasor measurement unit measurement data are required. The purpose of using different stochastic filtering techniques to estimate dynamic states of DFIG in power systems is to resolve the long-lasting issue of the unavailability of DFIG internal states used in the DFIG controller design.

In this paper a stability condition is obtained for predicting the boundary of subharmonic oscillation in dc-ac H-bridge inverters under double edge modulation. This condition is analytically derived and expressed in terms of the system state-space model matrices. The availability of such analytical expression reveals the effect of all the parameters of the inverter on its dynamical behavior. The derived theoretical condition is validated by numerical simulations using a system-level switched model obtaining a good matching between the results. This work provides a convenient means of predicting subharmonic oscillation boundary in the parameter space hence facilitating the design of dc-ac inverters free from this kind of instability.

This paper proposes a dynamic slope compensation (DSC) scheme to suppress the fast-scale instabilities in a peak current mode (PCM) controlled boost PFC converter. With the proposed DSC scheme the fast-instabilities can be eliminated and the system power factor is improved as well in comparison to the cases without compensation and with the traditional slope compensation. Simulation and experimental results are given to validate the theoretical analysis and the feasibility of the proposed DSC scheme.

The main purpose of this paper is to explore the oscillating characteristics of an astable multivibrator based on memristor and 555 timer. An analog circuit is utilized to simulate magnetic flux controlled memristor and then applied to implement the astable multivibrator. The voltage across the memristor emulator is analytically calculated. Simulation is carried out to confirm the theoretical analysis. The agreement between simulation and analytical calculation validates the feasibility of using memritor to achieve astable multivibrator.

This paper proposes a design of a single-switch non-isolated three-port converter for a standalone photovoltaic (PV) power system with energy storage. The three-port converter is obtained by combining the switches of two conventional cascaded DC-DC converters. Pulse-width modulation (PWM) and pulse-frequency modulation (PFM) are utilized to regulate the two converters respectively. The proposed design reduces the components and the size of the converter and maximizes the number of control variables. Then, the topology of the proposed converter is analyzed with its operation modes. Finally, simulation and experimental results are given to verify the proposed design.

In this paper, an intelligent economic dispatch (ED) model integrating wind energy, carbon tax, and battery energy storage system (BESS) is developed. BESS is incorporated with wind generation to reduce fluctuation of wind energy output. To verify the suitable storage size for the Australian power grid, a sensitivity analysis is performed with different levels of BESS. Carbon tax is also considered to reduce carbon emissions in the proposed ED scheme. A hybrid computational framework based on quantum-inspired particle swarm optimization (QPSO) is proposed to achieve faster and better optimization performance, and its viability demonstrated on a simplified 14-generator model of the Australian power system using a set of case studies. The proposed dispatch model can minimize the generating cost and enhance renewable power consumption capacity.

This paper proposes a hybrid computational framework based on Sequential Quadratic Programming (SQP) and Particle Swarm Optimization (PSO) to address the Combined Unit Commitment and Emission (CUCE) problem. By considering a model which includes both thermal generators and wind farms, the proposed hybrid computational framework can minimize the scheduling cost and greenhouse gases emission cost. The viability of the proposed hybrid technique is demonstrated using a set of numerical case studies. Furthermore, comparisons are performed with other optimization algorithms.

The memcapacitor has the potential to be the most useful new component of recent years due to its functional and dynamic similarities to the memristor but with much lower power requirements. Although research interest is growing, there are very few memcapacitor based circuits. Here a memcapacitor based chaotic circuit is described with a novel method to emulate a charge controlled memcapacitor.

In this paper, we show a simple circuit setup for experimentally plotting the v - i non-transversal pinched-hysteresis Lissajous fingerprint of a physical memristor - the common fluorescent gas discharge tube. The setup helped us investigate the effects of physical parasitics (inductors and capacitors) on the memristor v - i.

Power switching converters have exhibited a wide range of nonlinear behaviors, such as bifurcation and chaos. In this paper, complex bifurcation behaviors in higher order converters with an inductive impedance load are analyzed by studying the Floquet multipliers and the switching modes of the system. A single inner current loop controlled Čuk converter is used as an example to account. The period-1 orbit loses its stability by period-doubling and subcritical Neimark-Sacker bifurcations. We find that border collision behavior occurs between period-2 orbits. And then the period-2 orbit enters an unstable torus orbit via Neimark-Sacker bifurcation. The dynamical behaviors of the system are clearly studied under certain conditions. The mechanism of the coexisting phenomenon from subcritical Neimark-Sacker bifurcation is explained. Finally, the chaotic phenomenon from torus breakdown is firstly detected in this higher order switching converter with an inductive impedance load, its characteristics about phase shift to different initial conditions are investigated as well.

This work presents a comparative study of two new chaotic systems obtained from a LCM (inductor-capacitor-memristor) chaotic circuit. We use a fourth order polynomial and piecewise linear nonlinearities for the memristance functions. These systems have only one equilibrium point and use only three fundamental circuit elements, nevertheless, they still generate 2-scroll and 4-scroll attractors. Chaotic behavior is illustrated using phase portraits, bifurcation diagrams and Lyapunov exponent spectra, revealing several chaotic attractors and notably similar dynamical behavior in both systems.

The main switching block is presented to distinguish the switching behaviors and the degree of the converter's complexity during one switching period. By adopting the coarse-grained symbolic sequence method, the secondary switching block and secondary symbolic sequence are established in terms of main switching block too. Hence the duplicate symbolic sequence is put forward to detect bifurcation types in DC-DC switching converters. A voltage-mode-controlled flyback converter is used as an example to illustrate the applications of the duplicate symbolic sequence.

The main purpose of this paper is to design a circuit possessing meminductive property by making use of a new floating memristive emulator. A concise analog circuit is proposed to simulate a flux controlled memristor and this analog memristive emulator is then used to design a meminductive circuit. To confirm the design effectiveness, PSpice is hence introduced and all the simulated waveforms provide conclusive evidences to validate the correctness of this new memristor emulator and meminductive circuit.

A new all-digital current control technique, called Digital Ramptime current control, is presented in this paper. The control technique, based on Ramptime current control technique, uses multisampling as the sampling strategy that results in a high accuracy of control. In an active power filter experiment, compared with the original Ramptime current control, the performance of the Digital Ramptime current control is found to be satisfactory.

This paper presents a novel power control strategy that decouples the active and reactive power for a synchronous generator connected to a power network. The proposed control paradigm considers the capacitance of the transmission line along with its resistance and reactance as-well. Moreover the proposed controller takes into account all cases of R-X relationships, thus allowing it to function in Virtual Power Plant (VPP) structures which operate at both medium voltage (MV) and low voltage (LV) levels. The independent control of active and reactive power is achieved through rotational transformations of the terminal voltages and currents at the synchronous generator's output. This paper details the control technique by first presenting the mathematical and electrical network analysis of the methodology and then successfully implementing the control using MATLAB-SIMULINK simulation.

In this paper, a memristor analog model based on a light dependent resistor (LDR) is presented. This model can be simplified into two parts: a control circuit and a variable resistor. It can be used to easily verify theoretical presumptions about the properties of memristors. This LDR based memristor model can also be used in both simulations and experiments for future research into memristor applications. Mathematical models that describe the behaviors are derived. Multisim simulations and experimental results are given as well.

In this paper, a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model is developed. The probability of stochastic wind power based on nonlinear wind power curve and Weibull distribution is included in the model. In order to solve the revised dispatch strategy, quantum-inspired particle swarm optimization (QPSO) is also adopted, which shows stronger search ability and quicker convergence speed. The dispatch model is tested on a modified IEEE benchmark system involving six thermal units and two wind farms using the real wind speed data obtained from two meteorological stations in Australia.

After the successful solid state implementation of memristors, a lot of attention has been drawn to the study of memristor based chaotic circuits. In this paper, a Twin-T notch filter feedback controller is designed and employed to control the chaotic behavior in a memristor based chaotic circuit. Both simulation and experiment results validate the proposed control method.

This paper presents passivity-preserving frequency-weighted model order reduction (MOR) techniques for general large-scale RLC systems. Three techniques (Enns', Wang's and Lin and Chiu's) which preserve only stability and not passivity are generalized to include passivity in this paper. Conditions under which the passivity is preserved are also derived. Simulation results are also presented to show the effectiveness of the proposed generalization.

The purpose of this paper is the synthesis from the frequency domain standpoint of a controller for switching power converters with the aim to eliminate bifurcation and chaotic behavior. Firstly the paper analyzes the frequency response of previous delay-based chaos controllers unveiling that they are based in comb-filtering at multiples of the sub-harmonic half of the switching frequency. Secondly, chaos control is explored by using both a single notch filter and a bandstop filter at half of the switching frequency. It is demonstrated that the latter achieves chaos rejection while being an implementation-aware simplification of delay-based methods.

In this paper, an invariant set of the weight of the perceptron trained by the perceptron training algorithm is defined and characterized. The dynamic range of the steady-state values of the weight of the perceptron can be evaluated by finding the dynamic range of the weight of the perceptron inside the largest invariant set. In addition, the necessary and sufficient condition for the forward dynamics of the weight of the perceptron to be injective, as well as the condition for the invariant set of the weight of the perceptron to be attractive, is derived.

Unlike existing single-stage AC/DC converters with uncontrolled intermediate bus voltage, a new single-stage AC/DC converter achieving power factor correction (PFC), intermediate bus voltage output regulation and output voltage regulation is proposed. The single power stage circuit is formed by integrating a boost PFC converter with a two-switch-clamped forward converter. The current stress of the main power switches is reduced due to separated conduction period of the two source currents flowing through the power switch. A dual-loop peak current mode controller is proposed to achieve PFC, and ensure independent bus voltage and output voltage regulations. Experimental results on a 24V/100 W hardware prototype are given to confirm the theoretical analysis and performance of the proposed converter. The converter ranges 86%-92% of conversion efficiency at full load condition.

In this paper, the dynamics of weights of perceptrons are investigated based on the perceptron training algorithm. In particular, the condition that the system map is not injective is derived. Based on the derived condition, an invariant set that results to a bijective invariant map is characterized. Also, it is shown that some weights outside the invariant set will be moved to the invariant set. Hence, the invariant set is attracting. Computer numerical simulation results on various perceptrons with exhibiting various behaviors, such as fixed point behaviors, limit cycle behaviors and chaotic behaviors, are illustrated.

An hybrid Particle Swarm Optimization PSO-based wavelet neural network for modelling the development of fluid dispensing for electronic packaging is presented in this paper. In modelling the fluid dispensing process, it is important to understand the process behaviour as well as determine optimum operating conditions of the process for a high-yield, low cost and robust operation. Modelling the fluid dispensing process is a complex non-linear problem. This kind of problem is suitable to be solved by neural network. Among different kinds of neural networks, the wavelet neural network is a good choice to solve the problem. In the proposed wavelet neural network, the translation parameters are variables depending on the network inputs. Thanks to the variable translation parameters, the network becomes an adaptive one. Thus, the proposed network provides better performance and increased learning ability than conventional wavelet neural networks. An improved hybrid PSO [1] is applied to train the parameters of the proposed wavelet neural network. A case study of modelling the fluid dispensing process on electronic packaging is employed to demonstrate the effectiveness of the proposed method.

This paper describes the fast-scale bifurcation phenomenon of a voltage-mode controlled full-bridge inverter which is widely used in AC power supply applications. Main results are illustrated by exact cycle-by-cycle circuit simulations. It is shown that the fast-scale bifurcation phenomenon is a type of local instability which manifests itself as period-doubling for some intervals within the line cycle. It is also observed that such fast-scale instability always takes place around the middle of every half line cycle, where the reference output voltage reaches the maximum or the minimum. Moreover, an improved discrete-time model is used to theoretically analyze the fast-scale period-doubling bifurcation. Our work presents the discrete-time approach in modeling switching converters, and provides an convenient means of predicting stability boundaries so as to facilitate the design of inverter.

This paper presents a method on how to estimate main effects of gene representation. This estimate can be used not only to understand the domination of genes in the representation but also to design the mutation rate in genetic algorithms (GAs). A new approach of dynamic mutation rate is proposed by integrating the information of the main effects into the genes. By introducing the proposed method in GAs, both solution quality and solution stability can be improved in solving a set of parametrical test functions. The algorithm was applied to two illustrative applications to evaluate the performance of the proposed method, where the first application is on solving uncapacitated facility location problems and the next is on optimal power flow problems, which are employed. Results indicate that the proposed method yields significantly better results than the existing methods.

A new hybrid particle swarm optimization (PSO) that incorporates a wavelet-theory-based mutation operation is proposed. It applies the wavelet theory to enhance the PSO in exploring the solution space more effectively for a better solution. A suite of benchmark test functions and three industrial applications (solving the load flow problems, modeling the development of fluid dispensing for electronic packaging, and designing a neural-network-based controller) are employed to evaluate the performance and the applicability of the proposed method. Experimental results empirically show that the proposed method significantly outperforms the existing methods in terms of convergence speed, solution quality, and solution stability.

In this paper, an improved genetic algorithm has been proposed for solving multi-contingency transient stability constrained optimal power flow (MC-TSCOPF) problems. The MC-TSCOPF problem is formulated as an extended optimal power flow (OPF) with additional generator rotor angle constraints and is converted into an unconstrained optimization problem, which is suitable for genetic algorithms to deal with, using a penalty function. The improved genetic algorithm is proposed by incorporating an orthogonal design in exploring solution spaces. A case study indicates that the improved genetic algorithm outperforms the existing genetic algorithm-based method in terms of robustness of solutions and the convergence speed while the solution quality can be kept.

Determination of the initial process parameters for fluid dispensing process is a highly skilled task and is usually based on skilled engineers’ intuitive sense acquired through long-term experience rather than on a knowledge-based approach. In the face of global competition, the current trial-and -error practice is inadequate. In this paper, a rule-based system is developed to aid the determination of initial process parameters for fluid dispensing process by the genetic algorithm. Based on the rule based system, a set of ranges of process parameters can be recommended with a pre-defined quality requirement of microchip encapsulation. The preliminary validation test of the rule-based system has indicated that it can determine a set of ranges of initial process parameters for fluid dispensing process effectively, from which quality requirement can be achieved without totally relying on engineers’ experience.

This paper presents the control of nonlinear systems with a neural network. In the proposed neural network, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. By using a genetic algorithm with arithmetic crossover and non-uniform mutation, the parameters of the proposed neural network can be tuned. Application examples are given to illustrate the merits of the proposed neural network.

An improved hybrid particle swarm optimization (PSO) that incorporates a wavelet-based mutation operation is proposed It applies wavelet theory to enhance PSO in exploring solution spaces more effectively for better solutions. A suite of benchmark test functions and an application example on tuning an associative-memory neural network are employed to evaluate the performance of the proposed method. It is shown empirically that the proposed method outperforms significantly the existing methods in terms of convergence speed, solution quality and solution stability.

This paper studies a system of parallel-connected dc/dc converters under master-slave current sharing and proportional-integral (PI) PWMcontrol. Two distinct types of bifurcations can be identified. Depending on the value of the integral time constant, the system exhibits either a slow-scale bifurcation (Neimark-Sacker bifurcation) or a fast-scale bifurcation (period-doubling). Extensive simulations are used to capture the behaviour. Trajectories before and after these bifurcations are shown. The boundaries between these two types of bifurcations are located. Parameter spaces of the feedback controller for stable and unstable operation are presented.

A hysteretic current-controlled SEPIC converter, which uses the sum of two inductor currents as the control variable, is discussed. The operation states of the converter are studied based on the theory of sliding mode control. The equivalent control and relative differential equations on the sliding surface are derived, based on which, the stability of equilibrium point is analysed with the calculation of eigenvalues. With numerical calculation and computer simulation, it is shown that the equilibrium point will lose the stability via a Hopf bifurcation when the current reference increases. Subsequently the converter will exhibit complex dynamical behavior including limit cycle, double limit cycle, quasi-periodicity, and chaos by increasing the current reference furthermore.

This paper describes the bifurcation phenomena of a system of parallel-connected boost dc/dc converters. The results provide useful information for the design of stable current sharing in a master-slave configuration. Computer simulations are performed to capture the effects of variation of some chosen parameters on the qualitative behaviour of the system. It is found that variation of some parameters leads to Neimark-Sacker bifurcation. Analysis is presented to establish the possibility of the bifurcation phenomena.