Load frequency control is critical for maintaining grid stability, particularly in modern power systems with wind power penetration and increasing exposure to denial-of-service attacks. This paper presents a data-driven dynamic event-triggered reinforcement learning framework for constrained H∞load frequency control in multi-area power systems. The control problem is formulated as a min–max optimization task, and a dynamic event-triggered strategy is designed to reduce computational and communication burdens. A neural network-based reinforcement learning framework is developed to approximate the near-optimal event-triggered control strategy without requiring explicit system dynamics. To further counteract the impact of frequency-based denial-of-service attacks, a dedicated attacks compensation mechanism is designed. Theoretical analysis proves input-to-state stability of the closed-loop system and guarantees convergence of the neural network parameters. Extensive simulation studies on multi-area power systems with wind power integration demonstrate that the proposed method ensures stable frequency regulation while effectively alleviating the transmission burdens and mitigating the adverse effects of cyberattacks.

This article deals with fully distributed data-driven bipartite formation control for nonlinear discrete-time multi-input-multi-output multiagent systems (MASs) with unknown dynamics models and quantized information. Initially, a distributed combined measurement error function (DCMEF) is developed for MASs characterized by cooperative and competitive interactions. This function is designed to transform bipartite formation challenges into traditional consensus problems. Subsequently, a distributed compact form dynamic linearization model is established based on the designed DCMEF and input-output data of the MASs, eliminating the need for a strongly connected communication topology. Following this, a logarithmic quantization scheme and a dynamic event-triggered communication mechanism are devised to reduce the communication burden and enhance convergence speed. Finally, a data-driven fully distributed dynamic event-triggered bipartite formation control method is proposed, and its convergence is rigorously proven. Simulation studies and hardware experiments are conducted to validate the effectiveness of the proposed method.

The high heterogeneity of Internet of Things (IoT) devices causes severe imbalance in network traffic data, and the cost of collecting and labeling sufficient intrusion samples is high or impossible, resulting in data scarcity in IoT security. Therefore, this article proposes a domain adaptive IoT intrusion detection algorithm based on causal embedding autoencoder and a graph attention network (AEC–GAT) feature extraction and joint domain adversary, which leverages abundant data resources from traditional network intrusion detection to improve the detection accuracy in IoT environments. First, a feature extraction method combining an AEC–GAT is designed. The AEC uses causal inference to uncover deep semantic links between domains, while GAT captures device interaction patterns to enhance semantic relevance and structure awareness in the features. Second, to address the pronounced class imbalance in IoT datasets, focal loss is introduced to replace the traditional cross-entropy (CE) loss. This formulation dynamically adjusts the sample weight through the scaling factor to guide the algorithm to focus on the minority samples that are difficult to classify. Meanwhile, a class adaptive independent domain discriminator method is proposed, which incorporates a class-level alignment mechanism within a joint adversarial training method. This method dynamically adjusts both the training intensity and the loss weight of each class specific domain discriminator. The experimental results show that the algorithm in this article significantly improves the detection performance of IoT intrusion detection by migrating traditional network intrusion detection domain knowledge, and has superior performance in various indicators compared to existing algorithms.

This article studies a data-driven event-triggered sliding-mode control approach for wind turbines with prescribed performance and quantified information to maximize power generation efficiency. Initially, a partial form of the dynamic linearization model is established for the controlled wind turbine system. A logarithmic quantizer is considered to quantize data before it is transmitted. Then, a data-driven event-triggered sliding-mode control scheme is established, where a smooth function is employed to limit the control error to a prescribed range, and an event-triggered scheme is designed to reduce the communication frequencies of the controlled plant. Finally, the convergence of the formulated approach is rigorously demonstrated, and the simulation results further verify the effectiveness of the developed method.

Aiming at the area search task of a multi-robot system in an unknown complex obstacle environment, we propose a cooperative area search algorithm based on a dual improved bio-inspired neural network (DIBNN). First, we improve the BNN model to reduce the interference of the complex obstacle environment on robot decision making. Each robot generally chooses the neuron with the largest sum of surrounding activity values among adjacent neurons as its next movement position. Then, we propose a collaborative search mechanism. When a robot falls into a local deadlock state in the complex obstacle environment, the mechanism will guide the robot to quickly find unsearched areas. Finally, we conduct multi-robot area search simulation experiments under different obstacle environments and compare them with three baseline algorithms in this field. The simulation results verify that the proposed algorithm can efficiently guide the multi-robot to complete the area search task in the complex obstacle environment.Note to Practitioners—The motivation of this article arises from the need to develop fast and effective area search algorithms for practical applications such as UAV swarm reconnaissance and multiple mobile robots area search and rescue. The algorithms based on BNN has been widely used in search tasks under unknown environments due to its good scalability and efficiency. However, the efficiency of area search in complex obstacle environments cannot be guaranteed. In order to achieve efficient area search in unknown complex obstacle environments, the DIBNN algorithm is proposed. It utilizes a cooperative search mechanism and achieves better performance. DIBNN can also be applied to multi-robot systems in different scenarios, demonstrating strong scalability.

Intention tremor is a rhythmic and involuntary limb swing movement that causes significant inconvenience to the daily life of patients. Repetitive control is suitable for functional-electrical-stimulation-based intention tremor suppression because it can significantly attenuate the periodic signals. However, the performance of the repetitive controller may be weakened in tremor suppression due to uncertainties in the dynamics of the musculoskeletal model and the tremor frequency. In this paper, we propose an advanced methodology for tremor suppression by combining$H_{\infty }$control with high-order repetitive control. The proposed controller can not only guarantee the robust stability of the system subjected to model uncertainty, but also effectively suppress tremors with varying frequency. Comparative experiments on the unimpaired subjects and intention tremor patients were carried out to verify the effectiveness of the proposed method. The experimental statistical analysis results show that the proposed$H_{\infty }$high-order repetitive controller can suppress tremors by up to 84.97%, which is about 11% and 31% higher than the single memory loop repetitive controller and the traditional filter-based controller, respectively. Note to Practitioners—Patients afflicted with wrist intention tremors encounter significant challenges while executing routine activities such as eating, writing, and dressing. Functional electrical stimulation for tremor suppression operates by generating electrical pulses that oppose the tremor motion, thereby inducing muscle contractions and diminishing tremor magnitude. Given the periodic nature of tremor signals acting as system disturbances, repetitive control emerges as an effective method for tremor suppression. However, the conventional repetitive controller cannot significantly improve the tremor suppression performance in practice due to the uncertain property of the musculoskeletal dynamics and the variation of tremor frequency. In this paper, a robust controller combining$H_{\infty }$control with high-order repetitive control is proposed to address above issues. The high-order repetitive controller can effectively suppress the periodic tremor signals with varying frequency, and the$H_{\infty }$controller can provide robust stability and the desired tracking performance by properly choosing of the weighting functions. The comparative experimental results, conducted on our self-built wrist tremor suppression experimental platform, involving unimpaired subjects and patients with intention tremors, validate that the proposed control approach enhances tremor suppression efficacy by 11% and 31%, compared to the traditional repetitive controller and the filter-based controller, respectively. The feasibility and effectiveness of the proposed approach is only initially verified by small scale test, and more clinical verifications will be carried out in tremor patients in the future.

This paper addresses a data-driven sliding mode bipartite consensus issue for nonlinear discrete-time multi-agent systems with antagonistic interactions and limited communication resources. Initially, the signed graph theory is employed, and a combined measurement error function is formulated, transforming the bipartite consensus issue into a traditional consensus issue. An enhanced compact form dynamic linearization model is then established based on the input/output data and the formulated combined measurement error function. Moreover, a dynamic event-triggered function and a sliding-mode surface are designed, leading to the development of a fully distributed dynamic event-triggered sliding-mode bipartite consensus (DET-SMBC) approach. The proposed DET-SMBC approach is subsequently extended to a fully distributed dynamic event-triggered robust sliding-mode bipartite consensus (DET-RSMBC) scheme to improve robustness. The convergences of the tracking errors of both methods are rigorously deduced. Finally, simulation studies and hardware experiments are conducted to demonstrate the effectiveness of the proposed methods. Note to Practitioners—In multi-agent systems, the applicability of existing methods can be reduced by some issues, such as uncertain dynamics models, unknown disturbances, and the limitation of communication bandwidth. These issues can influence existing methods’ usefulness and cause instability, so DET-SMBC and DET-RSMBC methods are proposed in this paper. Compared with existing results, identifying a precise dynamics model for each controlled plant is unnecessary, the necessity of high-performance hardware for data transmission is relieved, and the effects of unknown disturbances are reduced. Moreover, the proposed methods are applied to realistic servo motor systems to conduct speed bipartite consensus tasks well. It is noted that most complicated mechanisms are controlled by servo motors, so the proposed methods can be applied to more practical engineering systems.

Load frequency control is essential for maintaining power system stability, especially under uncertainties and input delays. This paper proposes a reinforcement learning-based dual-channel dynamic event-triggered fixed-time load frequency control approach for uncertain multi-area power systems with input delays. A non-singular fast terminal sliding mode technique is employed to guarantee that the tracking error converges within a fixed time. To address system uncertainties and input delays, actor neural networks are designed to estimate the modeling uncertainties and provide compensation, and critic neural networks evaluate execution costs. To further enhance efficiency, a dual-channel event-triggered mechanism is designed, reducing communication overhead through independent dynamic event-triggering strategies for control input and output channels. The stability of the proposed method is rigorously analyzed using the Lyapunov method. Simulation results demonstrate faster convergence, reduced communication costs, and improved frequency stability compared to existing methods.

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches.

Federated Learning (FL) allows data owners to train neural networks together without sharing local data, allowing the Industrial Internet of Things (IIoT) to share a variety of data. However, traditional federated learning frameworks suffer from data heterogeneity and outdated models. To address these issues, this paper proposed a dual-blockchain based multi-layer grouping federated learning architecture (BMFL). BMFL divides the participant groups based on the training tasks, then realizes the model training combining synchronous and asynchronous through the multi-layer grouping structure, and uses the model blockchain to record the characteristic tags of the global model, allowing group-manners to extract the model based on the feature requirements and solving the problem of data heterogeneity. In addition, to protect the privacy of the model gradient parameters and manage the key, the global model is stored in ciphertext, and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash unchanged. Finally, we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault dataset. The experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm, and the key revocation overhead time is reasonable.

Currently, cracks are the most common defect in pavement diseases. Long-term non-maintenance can lead to crack lengthening and expansion, causing serious traffic accidents, as well as shortening the service life of pavement cracks. Therefore, it is of utmost importance to maintain cracks at an early stage. Due to the effect of some challenging factors, such as various shape information of the cracks, complex textured backgrounds, light shadows, similar texture objects, micro cracks and other factors, accurate crack detection still faces a certain challenges. To solve the above problems, a dual-encoding multi-scale fusion network based on the combination of convolutional neural network (CNN) and transformer network is proposed, named DMF-Net. To obtain stronger feature representations, a dual-encoding path is built to acquire global context features and local detail information simultaneously, where global context features are extracted based on the transformer branch, and the local detail features are extracted based on the CNN branch to detect tiny details of the cracks. Meanwhile, an interactive attention learning (IAL) module is introduced to effectively fuse the global features from the transformer branch and the local detail information from the CNN branch, achieving mutual communication and learning of different feature information. In addition, to enrich the feature representation ability, an attention-based feature enhancement (AFE) module is introduced to acquire more global contexts. Furthermore, faced with the crack detection task with class imbalance issue, a triple attention module (TAM) is built to emphasize the micro cracks. Finally, in the segmentation prediction stage, the deep supervision mechanism is also introduced to accelerate the convergence speed of the model, and serve effective multi-scale feature fusion. Compared with the current mainstream segmentation models, excellent performance has been obtained, which could provide a feasible scheme for the early maintenance of pavement cracks. The source code about proposed DMF-Net is available at https://github.com/Bsl1/DMFNet.git.

Pavement crack detection poses a formidable challenge due to the intricate texture structures of cracks and the complex environmental settings in which they are situated. In recent years, the advancement of deep learning techniques has prompted a surge in the utilization of Convolutional Neural Network (CNN)-based methods for pavement crack detection. While CNNs have exhibited remarkable results in crack detection tasks, they primarily excel at capturing local details with limited receptive fields, which can be insufficient for grasping global contextual information. Given the intricate nature of crack textures, it becomes imperative to leverage both global and local features for accurate detection. To address this issue, a transformer-based network with feature complementary fusion, refer to TFCF-Net, is introduced, which amalgamates Transformer and CNN architectures. Proposed TFCF-Net model prioritizes the Transformer branch for feature encoding, considering its strength in extracting global features, while the CNN branch is set as auxiliary encoding branch, which plays a complementary role for local feature extraction. Proposed TFCF-Net operates by utilizing global features as a foundation and iteratively refining them using local features, thus facilitating precise crack detection. This design enables proposed network to comprehensively capture both global and local information while judiciously fusing these two types of information based on the distinctive characteristics of cracks. To ensure effective fusion of global and local information, an Information Complementary Fusion (ICF) module is presented, which could efficiently merge the outputs of both encoding branches. To further optimize the fused information, a multi-dimensional attention (MA) module is proposed to embed into the, which enhances the model’s ability to capture long-range dependencies by optimizing information from multiple dimensions. Additionally, to improve the quality of input features on the decoding side, a multi-dimensional attention feature representation (MAFR) module is proposed, which expands the receptive field of the deepest semantic information, enabling the extraction of multi-scale feature representations. This paper rigorously evaluate proposed TFCF-Net against state-of-the-art (SOTA) models using three publicly available pavement crack datasets. Experimental results unequivocally demonstrate the superior performance of the proposed TFCF-Net.

This paper addresses the challenge of resource-efficient control in vehicle platooning systems, particularly focusing on communication requirements that involve network encryption. First, we construct a virtual output function to establish a virtual dynamic linearization model for these systems. Then, we design an encoding-decoding mechanism based on a logarithmical quantizer to facilitate digitized encryption communication. Furthermore, we investigate a dead-zone-based event-triggered communication strategy, enabling users to balance costs and performances. Subsequently, we formulate an event-triggered model-free adaptive platooning control method relying solely on input and output data from the vehicle platooning systems. Finally, the convergence of the proposed method is rigorously proved, and simulation study results demonstrate the proposed method’s effectiveness.

This article investigates the online learning and energy-efficient control issues for nonlinear discrete-time multiagent systems (MASs) with unknown dynamics models and antagonistic interactions. First, a distributed combined measurement error function is formulated using the signed graph theory to transfer the bipartite formation issue into a consensus issue. Then, an enhanced linearization controller model for the controlled MASs is developed by employing dynamic linearization technology. After that, an online learning adaptive event-triggered (ET) actor-critic neural network (AC-NN) framework for the MASs to implement bipartite formation control tasks is proposed by employing the optimized NNs and designed adaptive ET mechanism. Moreover, the convergence of the designed formation control framework is strictly proved by the constructed Lyapunov functions. Finally, simulation and experimental studies further demonstrate the effectiveness of the proposed algorithm.

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

This article investigates the information congestion problems for nonlinear discrete-time multi-input–multi-output multiagent systems (MASs) with fading channels when executing formation tasks. We first establish a virtual linear data model with a time-varying pseudo-Jacobian matrix variable for the MASs, which is independent of the dynamics model. Then, we formulate an event-triggered control scheme and a predictive compensation method to alleviate the communication burden and information congestion effects, respectively. Moreover, we propose two formation schemes for the MASs, considering limited communication resources, fading channels, and random delays to perform formation control and bipartite formation control tasks. The convergences of these two control protocols are strictly proved. Finally, simulations and hardware tests are conducted to verify the effectiveness of the proposed strategies.

This article studies fully distributed data-driven problems for nonlinear discrete-time multiagent systems (MASs) with fixed and switching topologies preventing injection attacks. We first develop an enhanced compact form dynamic linearization model by applying the designed distributed bipartite combined measurement error function of the MASs. Then, a fully distributed event-triggered bipartite consensus (DETBC) framework is designed, where the dynamics information of MASs is no longer needed. Meanwhile, the restriction of the topology of the proposed DETBC method is further relieved. To prevent the MASs from injection attacks, neural network based detection and compensation schemes are developed. Rigorous convergence proof that the bipartite consensus error is ultimately bounded is presented. Finally, the effectiveness of the designed method is verified through simulations and experiments.

Automatic crack detection remains challenging due to factors such as irregular crack shapes and sizes, uneven illumination, complex backgrounds, and image noise. Deep learning has shown promise in computer vision for pixel-wise crack detection, but existing methods still suffer from limitations such as information loss, insufficient feature fusion, and semantic gap issues. To address these challenges, a novel pavement crack segmentation network, called PAF-Net, is proposed, which incorporates progressive and adaptive feature fusion. To mitigate information loss caused by feature downsampling, a progressive context fusion (PCF) block is introduced to capture context information from adjacent scales. To better capture strong features from local regions, a dual attention (DA) block is proposed that leverages both global and local context information, reducing the semantic gap issue. Furthermore, to achieve effective multi-scale feature fusion, a dynamic weight learning (DWL) block is proposed that enables efficient fusion of feature maps from different network layers. Additionally, a multi-scale input unit is incorporated to provide the proposed segmentation network with more contextual information. To evaluate the performance of PAF-Net, we conduct experiments using four common evaluation metrics and compare it with multiple mainstream segmentation models on three public datasets. The proposed PAF-Net demonstrates superior segmentation accuracy for pixel-level crack detection compared to other segmentation models, as evident from qualitative and quantitative experimental results.

Dynamic gesture recognition is a typical human-computer interaction method owing to its great potential in practical applications. Currently, most of research work on gesture recognition has mainly focused on vision-based and surface electromyography (sEMG) methods. Compared to vision-based methods, the sequential sEMG signal can directly depict the muscle activity of different gestures which could lead to higher recognition efficiency. However, the effective feature design and selection of sEMG signal is still complicated since muscle fatigue and small electrode displacement will affect the recognition precision of sEMG signals. In this paper, a novel end-to-end dynamic gesture recognition method is developed. The raw sEMG signals are converted into an image form by using the time-frequency transformation method to obtain more comprehensive information for model training and test. And a recognition model based on Convolutional Neural Network (CNN) model is built for high-precision time-frequency image recognition. Experiments indicate that the proposed method could acquire distinguishing features from the pre-prossed images and the overall recognition accuracy on different gestures can reach up to 98.3%.

The material transportation planning with a mobile robot can be regarded as the ordered clustered traveling salesman problem. To solve such problems with different priorities at stations, an improved adaptive genetic simulated annealing algorithm is proposed. Firstly, the priority matrix is defined according to station priorities. Based on standard genetic algorithm, the generating strategy of the initial population is improved to prevent the emergence of non-feasible solutions, and an improved adaptive operator is introduced to improve the population ability for escaping local optimal solutions and avoid premature phenomena. Moreover, to speed up the convergence of the proposed algorithm, the simulated annealing strategy is utilized in mutation operations. The experimental results indicate that the proposed algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed.

Objective assessment of gait is important in the treatment and rehabilitation of patients with different diseases. In this paper, we propose a gait evaluation system using the Procrustes and Euclidean distance matrix analysis. We design and develop an android app to collect real time synchronous accelerometer and gyroscope data from two inertial measurement unit sensors through Bluetooth connectivity. The data is collected from 12 young (ten for modeling and two for validation) and 20 older subjects. We analyze the data collected from real world for stride, step, stance, and swing gait features. We validate our method with the measurements of gait features. The generalized Procrustes analysis is used to estimate a standard normal mean gait shape (NMGS) for ten young subjects. Each gait feature of both young and older subjects is then converted to find the best match with the NMGS using the ordinary Procrustes analysis. The shape distance between the NMGS and each gait shape is estimated using Riemannian shape distance, Riemannian size-and-shape distance, Procrustes size-and-shape distance, and root-mean-square deviation. A t-test is performed to provide statistical evidence of gait shape differences between young and older gaits. A mean form, which is considered as a standard normal mean gait form (NMGF), and inter-feature distances are estimated from the set of ten young subjects. The form difference is estimated between the NMGF and individual gaits of young and older. The degree of abnormality is then estimated for individual features and the result is plotted to visualize the feature in a gait. Experimental results demonstrate the performance of the proposed method.

This paper studies the distributed adaptive fuzzy containment tracking control for a class of high-order stochastic pure-feedback nonlinear multiagent systems with multiple dynamic leaders and performance constraint requirement. The control inputs are quantized by hysteresis quantizers. Mean value theorems are used to transfer the nonaffine systems into affine forms and a nonlinear decomposition is employed to solve the quantized input control problem. With a novel structure barrier Lyapunov function, the distributed control strategy is developed. It is strictly proved that the outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders, the containment tracking errors satisfy the performance constraint requirement and the resulting leader-following multiagent system is stable in probability based on Lyapunov stability theory. At last, simulation is provided to show the validity and the advantages of the proposed techniques.

Vibro-driven robotic (VDR) systems use stick-slip motions for locomotion. Due to the underactuated nature of the system, efficient design and control are still open problems. We present a new energy preserving design based on a spring-augmented pendulum. We indirectly control the friction-induced stick-slip motions by exploiting the passive dynamics in order to achieve an improvement in overall travelling distance and energy efficiency. Both collocated and non-collocated constraint conditions are elaborately analysed and considered to obtain a desired trajectory generation profile. For tracking control, we develop a partial feedback controller for the driving pendulum which counteracts the dynamic contributions from the platform. Comparative simulation studies show the effectiveness and intriguing performance of the proposed approach, while its feasibility is experimentally verified through a physical robot. Our robot is to the best of our knowledge the first nonlinear-motion prototype in literature towards the VDR systems.

Industry 4.0 is characterized by smart manufacturing, implementation of Cyber Physical Systems (CPS) for production, i.e., embedded actuators and sensors, networks of microcomputers, and linking the machines to the value chain. It further considers the digital enhancement and reengineering of products. It is also characterized by highly differentiated customized products, and well-coordinated combination of products and services, and also the value added services with the actual product or service, and efficient supply chain. All these challenges require continuous innovation and learning, which is dependent on people and enterprise's capabilities. Appropriate management approaches can play a vital role in the development of dynamic capabilities, and effective learning and innovation climate. This paper aims at offering a viewpoint on best suitable management practices which can promote the climate of innovation and learning in the organization, and hence facilitate the business to match the pace of industry 4.0. This paper is one of the initial attempts to draw the attention towards the important role of management practices in industry 4.0, as most of the recent studies are discussing the technological aspect. This paper also suggests empirical and quantitative investigation on these management approaches in the context of industry 4.0.

This paper studies modelling and dynamic analysis of underactuated capsule systems exhibiting friction-induced hysteresis. The motion mechanism is novel in utilizing internal centripetal torques generated by a vibration micro-motor mounted on the platform. Up to now, most investigations in frictional interactions towards capsule systems were confined into static or quasi-dynamic circumstance, where it is difficult to facilitate online use and control. It is the first time the dynamic frictional characteristics (non-reversible drooping and hysteresis) are studied towards these systems. An analytical study is primarily conducted to reveal the non-reversible characteristic for the static friction, the pre-sliding regime as well as the pure sliding regime, and the frictional limit boundaries are identified. Subsequently, the studies are mainly focused on dynamic analysis, including friction-driven vibrational responses and qualitative changes induced by control parameter (mass ratio) in capsule dynamics. It is found that the models predict periodic responses for the parameters considered and the average capsule velocity can be controlled through proper tuning of the control parameter around identified control points. The results demonstrate good captions of experimentally observed frictional characteristics, quenching of friction-induced vibrations and satisfaction of energy requirements.

Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study investigates the use and contribution of wrist-worn multisensors for activity recognition. We found that accelerometers are the most important sensors and heart rate data can be used to boost classification of activities with diverse heart rates. We propose a genetic algorithm-based fusion weight selection (GAFW) approach which utilizes GA to find fusion weights. For all possible classifier combinations and fusion methods, the study shows that 98% of times GAFW can achieve equal or higher accuracy than the best classifier within the group.

The radio frequency identification (RFID) technology is expected to play a vital role in identifying objects and serving to prevent counterfeiting and fraud. The use of RFID tags may cause privacy violation of users carrying an RFID tag. Owing to the unique identification number of the RFID tag, the possible privacy threats are information leakage of a tag, traceability of the consumer, denial of service attack and impersonation of a tag. Owing to the large number of tags in any application individual secret value for each tag requires a large number of complex hash computations in the database side. To reduce the computation overhead many protocols use a group secret value for tags in the database side. This study proposes a new authentication protocol which provides privacy and security in a more efficient manner using individual secret values for each tag and also avoids many complex hash operations in the database side. The evaluation indicates that the proposed protocol requires a low storage, computation and communication cost but offers larger ranges of privacy and security protection.

In this paper, a novel four-step acceleration profile is proposed for the inner mass (moving part) of a capsubot (capsule robot) system that works on the principle of the internal force-static friction. By using the acceleration profile of the inner mass the capsubot system can move in a given desired direction. Optimum selection of the different parameters of the acceleration profile is investigated considering the design constraints. Comparison of the simulation results with previous proposed profiles demonstrates its superiority. A novel encapsulated capsubot has been developed utilizing the acceleration profile in a self-contained format.

This paper proposes a 6-DOF (Degree of Freedom) heavy-load parallel manipulator with a redundant actuation and fault-tolerant actuator (RFTA). The novel RFTA model of the proposed manipulator is developed and its working principle is described. In order to achieve the given motion, the mathematic models of the proposed manipulator with the RFTA are derived. As a prototype of an earthquake simulator, two experiments are performed. The experimental results demonstrate that the RFTA is able to supply the required double driving force and appropriate used as an actuator of a low frequency earthquake simulator. The results of the fault-tolerant experiment show the earthquake simulator with the RFTA is capable of tolerating some local faults. The proposed parallel manipulator can also be applied under other heavy-load environments.

An optimal training strategy is devised for the linearly decoded orthogonal space–time block coded (OSTBC) wireless systems in quasi-static fading channel, based on the performance analysis using pairwise error probability (PEP) and symbol error probability (SEP). The PEP/SEP analyses allow us to find a generic expression for the performance improvement due to optimal training compared to the conventional case for OSTBC system equipped with any number of transmit and receive antennas and any linear modulation scheme. It is observed that the linear processing in the receiver, the most attractive feature of OSTBC, although destroys the orthogonality in the presence of channel estimation error, does not reduce diversity, but causes performance penalty as a loss of signal-to-noise ratio (LoSNR) due to training. This loss is quantified analytically and minimised by optimal allocation of power between training and data symbols. The performance of optimal power allocation improves with the higher number of space–time blocks in a frame. Furthermore, the LoSNR depends only on the OSTBC and is independent of any modulation scheme and the full rate Alamouti and other high rate OSTBCs suffer more in terms of performance due to training compared to the lower rate OSTBC.

Wireless networked control systems (WNCS) over mobile ad hoc network (MANET) is a new area of research and has many potential applications, for instance, military or rescue missions, exploring hazardous environments, and so on. For performance evaluation, researchers mostly rely on computer simulations as WNCS experiments are expensive to execute. It will generate a significant benefit to conduct performance analysis of WNCS over MANET using co-simulation that utilises SIMULINK and OPtimised Network Engineering Tool (OPNET) to simulate plant/controller behaviour and the MANET, respectively. Previous conference papers by the authors reported the initial SIMULINK–OPNET co-simulation for only one network size. Here the authors present an extention of their previous work, and presents the SIMULINK–OPNET co-simulation, methodology and comprehensive simulation results that have not been reported previously. It also considers the impact of five network sizes with stationary and mobile nodes. The proposed SIMULINK–OPNET co-simulation is applied to WNCS over MANET using a realistic wireless communication model. It investigates the impact of network data rates, node mobility, the packet delay, packet drop on the system stability and performance.

In this paper, we use an example - a pendulum-driven cart-pole system studied in Li et al. (2005) to investigate the tracking control of underactuated dynamic systems. In this paper, we propose a six-step motion strategy of the pendulum driven cart-pole system. We design a desired profile of the pendulum joint velocity based on the proposed six-step motion strategy. Based on the desired joint velocity profile, we can compute the desired joint position and acceleration. The desired joint trajectories will be used in the proposed control approach. We also propose a closed-loop control approach using the partial feedback linearization technique. Extensive simulation studies are conducted to demonstrate the proposed approaches.

This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments.

The combination of Petri nets (PNs) as an analysis tool for discrete-event dynamic systems and artificial intelligence heuristic search has been shown to be a promising way to solve flexible manufacturing systems (FMS) scheduling problems. However, the NP hard nature of the problem obscures the PN capability of reasoning about the behavior of the system. In this paper, two techniques to alleviate this drawback are presented: a systematic method to avoid the generation of futile paths within the search graph and a novel hybrid stage-search algorithm. The new algorithm is based on the application of A* guided by a PN-based heuristic within a limited local search frame. An optimization policy is applied to maintain, under evaluation, only the most promising paths. For each system state, the algorithm is able to decide whether an enabled operation should be applied and to maintain this decision until new information forces reconsideration. This eliminates permutation paths and useless scheduling sequences. Experimental results show that the algorithm's cost does not grow exponentially with the size of the problem. Comparison with previous work is given to show the superiority of our approach and the potential of PN-based heuristic search.

Addresses a hybrid scheduling methodology for flexible manufacturing systems (FMS) that uses Petri nets (PNs) as a modeling tool and several successfully employed scheduling methods: conflict-solving based on heuristic dispatching algorithms, artificial intelligence (AI) heuristic search, problem decomposition and evolutionary approximation algorithms as search tools. PNs have been traditionally employed in scheduling approaches based on discrete event simulation and more recently, the combination of PNs and AI heuristic search has produced interesting results. PNs also allow easy structural analysis towards a decomposition of the problem. In this paper PNs are employed as a representation paradigm and a decomposition-construction scheduling method is based on them. A PN-based AI systematic heuristic search is used to solve sub-problems which are progressively joined by an evolutionary building procedure. Experimental results based on a preliminary implementation of the method are presented.

The combination of Petri net (PN) and AI to solve flexible manufacturing systems (FMS) scheduling problems has been proven to be a promising approach. However, the NP-hard nature of the problem prevents the PN capability of reasoning about the behavior of a practical system. To overcome this drawback, we propose two techniques: a systematic method to avoid the generation of unpromising paths within the search graph, and a stage-search based algorithm. The algorithm developed is based on the application of the A* algorithm and the PN-based heuristics. The search is performed within a limited local search window where an optimization policy is applied to evaluate the most promising paths. For each state, the algorithm is able to decide whether an enabled operation is applied, and to maintain the decision until new system information makes the reconsideration meaningful. Comparison with previous work is presented to show the superiority of the proposed approach.