Automated heartbeat classification in Holter monitoring is pivotal for IoT-enabled healthcare, enabling continuous cardiac surveillance. However, this task faces two major challenges: (1) weak pathological electrocardiogram (ECG) features are often obscured by noise, and (2) discriminative ECG patterns vary significantly in their temporal spans, depending on the heartbeat types and contextual rhythms. To address these issues, we propose a novel multi-view multi-range attention (MVMRA) network, which integrates three key modules: First, a multi-view feature extraction module employs 1-D and 2-D convolutional networks to respectively capture intra-beat morphological details and multi-band inter-beat correlations, enriching latent representations while disentangling weak pathological information. Next, a multi-view cross-attention fusion module adopts a lumpedbranch interactive structure, leveraging high-level semantic features to sequentially learn temporal-wise and channel-wise attention masks. This progressively refines multi-view features, enhancing adaptability to diverse ECG patterns. Finally, a multi-range group attention module dynamically aggregates discriminative information across multiple temporal ranges by using compressed and selected attention mechanisms, simulating variable diagnostic scopes and emphasizing pathology-specific contexts. Extensive evaluations on the MIT-BIH arrhythmia database confirm that our model outperforms existing approaches, achieving state-of-the-art accuracies of 97.23% and 98.23% for five-class and three-class tasks, respectively. Moreover, the network delivers real-time inference (< 27 ms) on an ARM64 embedded platform, highlighting its potential for practical IoT-based cardiovascular disease detection.

Wearable single-lead electrocardiogram (ECG) devices exhibit significant advantage for capturing subtle variations in cardiac activity, offering new potential for the continuous assessment and management of chronic pain. An essential task of ECG-based monitoring is the accurate recognition of pain intensity. However, developing an online pain intensity recognition method presents several critical challenges, including high computational complexity, limited robustness to noise, etc. To address these challenges, this article proposes an end-to-end neural network by integrating multiple temporal feature compression and fusion strategies. The proposed algorithm comprises three primary sequential stuctures: 1) feature compression module (FCM); 2) pain-related feature extraction module (PFEM); and 3) hidden feature fusion complementary module (HFFCM). First, FCM utilizes a Resnet compression to exploit the latent information embedded in the autocorrelation of ECG time series. Subsequently, PFEM extracts the hidden pain features through self-attention. Finally, HFFCM dynamically integrates and complements these features through a cross-attention mechanism built upon large-kernel convolutions. Evaluated on the public biovid dataset, the proposed algorithm outperforms state-of-the-art methods, achieving accuracy of 72.41%, in pain tolerance stimulus or no pain state classification. In addition, the proposed algorithm simplifies the attention mechanism through large-kernel convolutional modulation, reducing the model’s computational complexity to only 0.43 GFLOPs, which is 65.6% less than the existing transformer-based approaches. Overall, this article proposes an efficient online method for pain intensity recognition, offering a reliable solution that enhances chronic pain monitoring in the field of Internet of Things (IoT).

Wearable electroencephalogram (EEG) devices have shown great potential in enabling real-time monitoring of subtle changes in brain activity, providing new possibilities for the assessment and management of chronic pain. However, recognizing pain-related biomarkers from EEG data remains a complex, multitask challenge. Most existing research focuses on single-task approaches and rarely addresses this issue within a unified framework. In this article, we propose a novel deep neural network (DNN) model called multiscale frequency domain MetaFormer (MFDFormer), which is designed to simultaneously predict the presence, type, and intensity of chronic pain. The proposed MFDFormer comprises two primary subnetworks: 1) multiscale feature extractor (MFE) and 2) frequency domain MetaFormer (FDFormer) encoder. The MFE extracts diverse EEG features through convolutions with different kernel sizes, while a self-attention mechanism is integrated into MFE to emphasize the importance of interdependency among these features. The FDFormer encoder refines the output MFE features using causal convolutions to capture local patterns and projects them into a higher dimensional representation domain. Additionally, it incorporates spatial and temporal frequency domain learners (TFDLs) in parallel to effectively capture the spatial-temporal information of EEG data. Based on the publicly available brain function in chronic pain (BFCP) dataset, the proposed MFDFormer demonstrates superior performance over state-of-the-art algorithms, achieving accuracies of 97.59%, 96.02%, and 83.48% in pain or nonpain state classification (P/NSC), pain type classification (PTC), and pain intensity classification (PIC) tasks, respectively. This article proposes a unified, end-to-end DNN-based framework for multitask chronic pain recognition, providing a reliable solution with the potential to advance pain diagnosis and management in IoT and smart wearable applications.

As a representative technical approach in Global Navigation Satellite System (GNSS) high-precision positioning, Precise Point Positioning (PPP), using a single receiver, can obtain absolute positioning accuracy ranging from decimeter-level to centimeter-level. An accurate stochastic model of observations is crucial for enhancing the PPP positioning quality. Currently, the stochastic model of observation is mainly established through empirical formulas. However, in urban environments, due to the influence of Non-Line-of-Sight (NLOS) errors, empirical formulas cannot reliably characterize the actual error magnitudes of observations, thereby degrading the PPP positioning accuracy. To address this issue, this study develops a resilient stochastic model scheme based on the intelligent identification results of NLOS, aiming to enhance PPP positioning accuracy in complex scenarios. Firstly, a stochastic model based on intelligent recognition result of NLOS signals is developed. This model utilizes the recognition results to calculate the Position Dilution of Precision (PDOP) accurately, and dynamically adjusts the observation weights of LOS and NLOS signals according to the quantity ratio of LOS and NLOS signals and the corresponding satellite geometry. Secondly, a graph neural network (GNN)-based model for NLOS signal recognition is introduced. The model leverages a GNN to extract environmental features from sky satellite images, enabling accurate identification of NLOS signals across different scenarios. Next, by integrating the designed stochastic model method and the intelligent NLOS identification model, an Enhanced Precise Point Positioning (EPPP) algorithm is proposed to improve positioning accuracy in urban environments. Finally, four real-world datasets from the urban forest scenarios and two datasets from the overpass scenarios were selected to verify the effectiveness of the proposed method. The experimental results show that the proposed EPPP model outperforms empirical stochastic models, and the PPP static positioning accuracy is improved by 38.8%-78.44%, 45.31%-69.19%, 20.52%-71.0%, 31.77%-75.7% in the east, north, up and three-dimensional directions, respectively, and the PPP dynamic positioning accuracy is improved by 19.67%-58.02%, 14.43%-57.35%, 6.56%-34.80%, 10.96%-44.87% in the east, north, up and three-dimensional directions, respectively.

The reception of nonline-of-sight (NLOS) signals in urban areas, such as urban canyons and overpasses, can cause severe errors in global navigation satellite system (GNSS) positioning. Machine learning-based NLOS mitigation methods have become increasingly popular. However, existing methods cannot obtain satisfactory NLOS recognition accuracy across multiple locations or scenarios. Furthermore, in scenarios with severe occlusion, directly removing recognized NLOS signals may reduce the number of available satellites for positioning algorithms, resulting in lower positioning precision. To address these issues, this study proposes a deep learning-based NLOS interference mitigation method to improve the precision of GNSS single-point positioning (SPP). First, to improve NLOS signal recognition across multiple locations, we propose the dual self-attention mechanism (DSN) model for NLOS recognition, which utilizes self-attention networks to construct both spatial and temporal channels for modeling spatial environmental characteristics and signal temporal features, respectively. Second, to mitigate the interference from NLOS signals, we design a novel weighting scheme using NLOS recognition results to revise the elevation angle-based scheme. Next, we propose the SPP-DSN algorithm by combining the DSN model and the designed weighting scheme to improve positioning precision in urban areas. Finally, we collected real-world data to conduct experiments to investigate the performance of our proposed method. The experimental results show that our proposed DSN model can effectively improve NLOS recognition accuracy across multiple locations. Compared to the regular SPP algorithm, our proposed SPP-DSN method can enhance positioning precision by over 29% in urban canyons and more than 10% under overpasses.

Long-term and mobile healthcare applications have increased the use of single-channel electroencephalogram (EEG) systems. However, electromyography (EMG) artifacts often disturb EEGs. The lack of spatial correlation, diversity of waveforms, and time-varying overlap make eliminating EMG interference from a single-channel EEG difficult. To overcome these challenges, we create DSATCN, a dual-stream learning model that makes use of multi-level and multi-scale temporal dependencies in different frequency bands to perform robust EEG reconstruction. The first DSATCN stream extracts low-frequency band EEG features with reduced EMG interference. The second stream selectively combines the high-level features of the first stream with its own low-level features to refine the EEG reconstruction across the entire frequency band, lowering the risk of overfitting. Both streams employ a novel attention-based temporal convolution network (ATCN) to adaptively separate the overlapping features of EEGs and EMGs. The ATCN has multiple stages to represent various temporal dependencies at different levels. Each stage consists of multi-scale dilated convolutions and fast Fourier transform modulations, which efficiently enrich the receptive fields and establish global self-attention mechanisms. The stages’ outputs are merged by relaxed attentional feature fusion modules, which bridge semantic gaps between features at various levels. Extensive experimental results on three semi-simulated datasets containing 318,700 samples show that the proposed model significantly outperforms the existing methods in EEG reconstruction accuracy. And its computational cost meets the criteria for real-time processing. Our code is available at https://github.com/BaenRH/DSATCN.

In dynamic and complex environments, the positioning accuracy of global navigation satellite system (GNSS) will be seriously reduced. Deep reinforcement learning (DRL) has been found to give effective dynamic policy learning for complex GNSS positioning correction tasks. However, catastrophic interference in DRL models caused by the high correlation between successive positioning states, together with instability in gradient backpropagation in deep neural networks (DNNs), produces inaccurate DRL value approximation thereby degrades GNSS positioning performance. In this article, we develop a dictionary learning-based reinforcement learning (RL) algorithm with the nonconvex log regularizer for GNSS positioning correction. To avoid DNN instability problems, a dictionary learning-structured RL model is proposed. It has a feed-forward learning architecture obviating the need for gradient backpropagation. The nonconvex log regularizer for dictionary learning reduces the correlation between states and thereby alleviates interference in RL. This provides sparse representations, which can more effectively capture features and produce representations with lower biases than convex regularizers. Furthermore, the nonconvex optimization is made efficient through a decomposition scheme that generates an explicit closed-form solution using the proximal operator. Finally, based on the proposed dictionary learning-structured RL model, a novel positioning correction method is developed to enhance GNSS positioning accuracy. The experimental results indicate that the proposed method outperforms state-of-the-art sparse coding-based RL methods in benchmark environments. Moreover, the proposed method effectively improves GNSS positioning accuracy relative to the glsms Kalman filter acrlong KF method and the glsms weighted least squares acrlong WLS method.

Global navigation satellite systems (GNSSs) provide efficient positioning services for location-aware Internet of Things (IoT) devices. However, GNSS non-line-of-sight (NLOS) signals can result in severe positioning errors in urban canyon areas. Existing deep-learning-based NLOS signal classification methods cannot appropriately model the spatiotemporal information of NLOS interference, resulting in limited accuracy across multiple locations. This study presents a spatiotemporal information-driven model that can capture environmental characteristics and signal temporal information simultaneously to improve NLOS classification accuracy across multiple locations. First, a visualization analysis of the signal distribution across multiple locations demonstrates the impact of environmental characteristics. In addition, the significance of both the spatial environmental features and the signal temporal features for NLOS classification is clarified by constructing a tree diagram of the data set. Second, we propose an airspace attention mechanism module and a long short-term memory (LSTM)-based temporal feature extraction module to model both types of features, respectively. Third, the learnable sparse regularizer is utilized to reduce feature redundancy and thereby realize a sparse representation, which improves model generalization performance. Finally, the spatiotemporal information-driven cross-attention model is developed to perform NLOS classification, which uses a cross-attention fusion strategy to integrate the two modules. We use real-world data sets collected across multiple urban canyon locations to test our model. Experiments show that the proposed model can achieve 98% classification accuracy across multiple locations. Generalization performance in unknown environments can be improved over 7% compared to several state-of-the-art models.

This paper proposes two online policy-iteration (PI) algorithms for solving linear continuous-time$H_\infty$regulation problems with unknown dynamics. Our results are completely learning-orientated in the sense that prior model knowledge of initial stabilizing control policies arising from solving the Game Algebraic Riccati Equation (GARE) associated with the$H_\infty$regulation problem is now removed, which thereby resolves a long-standing challenge in the existing PI works to achieve model-free learning. To this end, two offline PI algorithms, consisting of the single-looped and the double-looped, are first proposed by nesting a homotopy-based initialization to solve a series of Lyapunov equations associated with the GARE. Then, two online PI algorithms are further proposed by utilizing the system data to avoid the model requirement for online solving the GARE. The single-looped PI algorithm has the feature of simultaneously learning control and disturbance policies, while a double-looped PI updates control policies before carrying out a series of learning disturbance policies. These two online PI algorithms work in a model-free manner and do not require prior knowledge of the system matrices over the whole learning period including the control policy initialization and can lead to the desired control policy with satisfactory system performance. We demonstrate the effectiveness of the proposed learning algorithms with an example of a power systemNote to Practitioners—Solving the$H_\infty$regulation problem for linear continuous-time systems can be achieved by finding the Nash equilibrium of the two-player zero-sum game. However, it is a challenge for control practitioners to design the$H_\infty$regulation controller with completely unknown dynamics due to the fact that it is nontrivial to obtain precise prior knowledge of models/dynamics for many engineering systems. The current methods usually utilized system data to solve the Nash equilibrium solution by offline or online iterative computation, but most of them still needed prior knowledge of the system dynamics for policy seeking such as stabilizing/admissible control or disturbance policies in the initialization. To address such a challenge, this paper develops two homotopy-based online PI algorithms that solve the$H_\infty$regulation problem in a fully model-free manner. It is shown that the developed algorithms can find the Nash equilibrium solution by online measuring the system data, and overcomes the difficulty of finding an initial stabilizing control policy with unknown system dynamics. The validity of the algorithms is illustrated through a simulation study.

In this article, an optimal output synchronization solution to the$H_{\infty}$optimization of linear discrete-time (DT) multiagent systems is investigated. Compared with current approaches, the issue of designated convergence rate is handled with system optimality, while less computation cost is required. Specifically, the internal model principle is employed to derive a cooperative regulation problem of DT systems, wherein no explicit solution to output regulation equations is needed for learning. Then, we introduce a convergence rate parameter to construct a group of auxiliary cooperative systems, based on which the zero-sum game in$H_{\infty}$optimization is formulated. The data-efficient off-policy reinforcement learning and output-feedback technique are applied to solve the enhanced Bellman equations with a designated convergence rate. This results in an online optimal synchronization solution learning from only the input–output data along the system trajectories. It is shown that the proposed optimal synchronization protocol achieves asymptotic synchronization for the original systems with the consensus error converging to zero at a designated rate. The effectiveness of the proposed approach is verified by the simulation results.

This paper studies optimal day-ahead scheduling of a battery swapping and charging system (BSCS) for electric vehicles (EVs) from a new perspective of multiple decision makers. It is considered that the BSCS locally incorporates the battery swapping and charging processes, and the two processes are managed by two operators, called a battery swapping operator (BSO) and a battery charging operator (BCO), respectively. Our main contribution is to propose a bilevel model where the BSO acts as the leader to receive and serve the battery swapping requests from EV users, and the BCO acts as the follower to interact with the grid and control battery charging and discharging power. We reformulate the bilevel optimization problem into an equivalent single-level problem that is a nonconvex mixed-integer nonlinear program (MINLP), and its size can easily become very large. To solve the problem efficiently, we develop a new heuristic composed of two parts, i.e., an estimation of the integer solution and an algorithm based on the alternating direction method (ADM). The results show that the proposed heuristic performs well in solving large-scale problems, providing close-to-optimal solutions quickly. In addition, compared to a social welfare maximization model that follows most existing related works, the proposed bilevel model can increase the number of swapped-out batteries by 35% and the batteries’ average energy state by 6%, improving the quality of battery swapping services.

This article analyzes the problem of the sliding-mode control (SMC) design for discrete-time piecewise nonhomogeneous Markov jump nonlinear systems (MJNSs) subject to an external disturbance with time-varying transition probabilities (TPs). A discrete-time asynchronous integral sliding surface is constructed, which yields matched-nonlinearity-free sliding-mode dynamics (SMDs). Then, by using the mode-dependent Lyapunov function technique, a sufficient condition is established for ensuring the stochastic stability of SMD with extended dissipation. The solution to designing controller gains is obtained. Moreover, an SMC law and an adaptive law are, respectively, derived for driving the system trajectories to move into a predetermined sliding-mode region with specified precision. Finally, the feasibility and effectiveness of the new design are verified and demonstrated by a simulation example.

Abstract Unmanned aerial vehicles as an important role for 5G and beyond networks are becoming more and more popular and have been equipped with various sensors to enable diverse emerging applications, e.g. locating sound‐emitting targets. Multi‐channel blind source separation algorithm has been applied into the unmanned aerial vehicles and micro aerial vehicles, where underdetermined mixture blind source separation is a challenging problem, i.e. the number of sources is more than the number of microphones. An optimization underdetermined blind source separation algorithm to separate the multi‐channel audio mixture signals recorded by an unmanned aerial vehicle is proposed. In the algorithm, firstly a hierarchical clustering to estimate channel as the mixing matrix initialization is employed, while using direction of arrival permutation algorithm to deal with the permutation alignment and update the mixing matrix using multiplication update method. Then the model parameters are estimated using improved expectation‐maximization update rules for the fast convergence. Finally, the frequency‐domain sources are estimated through Wiener filtering and time‐domain sources are obtained via inverse short‐time Fourier transform. Experimental results covering synthetic and real‐recorded speech source mixtures show that the proposed algorithm achieves better separation results than the state‐of‐the‐art methods.

This article proposes distributed demand response (DR) approaches for a multienergy residential community, which is equipped with various energy conversion and storage devices to serve multiple residential loads (e.g., electricity, natural gas, and heating loads). In the proposed DR approaches, each of the energy devices and loads is an individual decision-maker and also a node in a randomly connected communication network. The DR approaches are tolerant to incomplete information which is caused by random inaction of nodes and links in the network. At first, in order to coordinate nodes' behaviors in distributed DR, different information transmission mechanisms among nodes are employed. Particularly, Steiner tree broadcast, in which nodes are networked according to their energy types, is proposed to lower the nodes' computational complexity and the network's communication overhead. Based on the information transmission mechanisms, the initial DR problem is transformed into network problems that are solvable in a random network. Then, based on the randomized alternating direction method of multipliers, distributed algorithms are designed to optimally solve the network problems in the presence of incomplete information. In simulation, real-world datasets of multiple energy loads and prices are used, and three proposed DR approaches are compared in terms of convergence performance and communication overhead.

This paper proposes fast convergent distributed algorithms for weighted average consensus of input data. For acyclic graphs, we give an algorithm that converges to the exact weighted average consensus in a finite number of iterations, equal to the graph diameter. For loopy (cyclic) graphs, we offer two remedies. In the first one, we give another distributed algorithm to enable our average consensus algorithm applicable to a loopy graph by converting it into a spanning tree. In the second one, we consider a slightly modified average consensus problem whose optimal solution approximates the consensus solution with arbitrary precision, and give a modified average consensus algorithm with guaranteed exponential convergence to the optimal solution. The proposed average consensus algorithms enjoy low complexities, robustness to transmission adversaries, and asynchronous implementation. Our algorithms are conceptually different from the popular graph Laplacian approach, and converge much faster than the latter approach.

The fifth‐generation‐enabled Internet of Things (5G‐enabled IoT) has been considered as a key enabler for the automation of almost all industries. In 5G‐enabled IoT, resource‐limited passive devices are expected to join the IoT using the WiFi backscatter communication (WiFi‐BSC) technology. However, WiFi‐BSC deployment is currently limited due to high equipment cost and short transmission range. To address these two drawbacks, in this paper, we propose a low‐cost and long‐range node‐assisted WiFi backscatter communication scheme. In our scheme, a WiFi node can receive backscatter signals using two cheap regular half‐duplex antennas (instead of using expensive full‐duplex technique or collaborating with multiple other nodes), thereby reducing the equipment cost. Besides, WiFi nodes can help relay backscatter signals to remote 5G infrastructure, greatly extending the backscatter’s transmission range. We then develop a theoretical model to analyze the throughput of WiFi‐BSC. Extensive simulations verify the effectiveness of our scheme and the accuracy of our model.

Dynamic uncertainty is a potential factor destabilizing the closed-loop system. This paper aims at constructing an adaptive fuzzy output-feedback control scheme for a class of switched nonlinear systems interconnected with unmodeled dynamics. In the investigated model, the x-system is interconnected with the unmodeled z-dynamics. Two types of unmodeled dynamics (i.e., all modes are stable and some modes are unstable) are considered in this paper. Separate switched state observer and adaptive fuzzy controller are designed for each mode of the x-system. In our control scheme, only two adaptive parameters are required to update online. With the help of multiple Lyapunov function, two lemmas are proposed to guide us how to determine the input-to-output gains of the x-system and the unmodeled dynamics when the influence of switching is considered. By using the small-gain approach, the closed-loop switched nonlinear system is guaranteed to be of input-to-state practically stability (ISpS). With the concept of ISpS, we prove that the closed-loop system's output is convergent to a small neighborhood of zero, and all the signals in the closed-loop system are bounded. Finally, three simulations are also given to illustrate the effectiveness of our main result.

Subspace learning based transfer learning methods commonly find a common subspace where the discrepancy of the source and target domains is reduced. The final classification is also performed in such subspace. However, the minimum discrepancy does not guarantee the best classification performance and thus the common subspace may be not the best discriminative. In this paper, we propose a latent elastic-net transfer learning (LET) method by simultaneously learning a latent subspace and a discriminative subspace. Specifically, the data from different domains can be well interlaced in the latent subspace by minimizing Maximum Mean Discrepancy (MMD). Since the latent subspace decouples inputs and outputs and, thus a more compact data representation is obtained for discriminative subspace learning. Based on the latent subspace, we further propose a low-rank constraint based matrix elastic-net regression to learn another subspace in which the intrinsic intra-class structure correlations of data from different domains is well captured. In doing so, a better discriminative alignment is guaranteed and thus LET finally learns another discriminative subspace for classification. Experiments on visual domains adaptation tasks show the superiority of the proposed LET method.

This paper concentrates on the adaptive state-feedback quantized control problem for a class of multiple-input-multiple-output (MIMO) switched nonlinear systems with unknown asymmetric actuator dead-zone. In this study, we employ different quantizers for different subsystem inputs. The main challenge of this study is to deal with the coupling between the quantizers and the dead-zone nonlinearities. To solve this problem, a novel approximation model for the coupling between quantizer and dead-zone is proposed. Then, the corresponding robust adaptive law is designed to eliminate this nonlinear term asymptotically. A direct neural control scheme is employed to reduce the number of adaptive laws significantly. The backstepping-based adaptive control scheme is also presented to guarantee the system performance. Finally, two simulation examples are presented to show the effectiveness of our control scheme.

Blind source separation (BSS) is a typical unsupervised learning method that extracts latent components from their observations. In the meanwhile, convolutive BSS (CBSS) is particularly challenging as the observations are the mixtures of latent components as well as their delayed versions. CBSS is usually solved in frequency domain since convolutive mixtures in time domain is just instantaneous mixtures in frequency domain, which allows to recover source frequency components independently of each frequency bin by running ordinary BSS, and then concatenate them to form the Fourier transformation of source signals. Because BSS has inherent permutation ambiguity, this category of CBSS methods suffers from a common drawback: it is very difficult to choose the frequency components belonging to a specific source as they are estimated from different frequency bins using BSS. This paper presents a tensor framework that can completely eliminate the permutation ambiguity. By combining each frequency bin with an anchor frequency bin that is chosen arbitrarily in advance, we establish a new virtual BSS model where the corresponding correlation matrices comply with a block tensor decomposition (BTD) model. The essential uniqueness of BTD and the sparse structure of coupled mixing parameters allow the estimation of the mixing matrices free of permutation ambiguity. Extensive simulation results confirmed that the proposed algorithm could achieve higher separation accuracy compared with the state-of-the-art methods.

This paper studies a new model of energy storage (ES) sharing in a residential community in which some homes have physical ESs (PESs) but some do not. The non-PES homes can buy ES capacity from PES homes, creating virtual ESs (VESs). Based on the transaction results between PESs and VESs, an online algorithm is developed for real-time energy management of ES sharing among the homes. During online control, non-negative long-term utilities of homes and practical charging/discharging constraints of PESs and VESs are considered. The advantage of the proposed algorithm is that system state forecasting, such as home load, renewable generation, and grid price, is not required. The algorithm only needs current system states to make a control decision. Theoretic analysis shows that the worst-case system cost under the algorithm is upper bounded, guaranteeing the online solution is near-optimal. In the simulation, real-time data of grid price and home power use is employed, and the proposed algorithm is benchmarked against a greedy algorithm and a theoretic lower bound.

Subspace learning has been widely utilized to extract discriminative features for classification task, such as face recognition, even when facial images are occluded or corrupted. However, the performance of most existing methods would be degraded significantly in the scenario of that data being contaminated with severe noise, especially when the magnitude of the gross corruption can be arbitrarily large. To this end, in this paper, a novel discriminative subspace learning method is proposed based on the well-known low-rank representation (LRR). Specifically, a discriminant low-rank representation and the projecting subspace are learned simultaneously, in a supervised way. To avoid the deviation from the original solution by using some relaxation, we adopt the Schatten [Formula: see text]-norm and [Formula: see text]-norm, instead of the nuclear norm and [Formula: see text]-norm, respectively. Experimental results on two famous databases, i.e. PIE and ORL, demonstrate that the proposed method achieves better classification scores than the state-of-the-art approaches.

This paper investigates a problem of designing an adaptive asymptotic cooperative control scheme for nonlinear time-varying multiagent systems, which can simultaneously tolerate unknown actuator failures and unknown control directions. To address such the problem, we propose a conditional inequality, which allows multiple piecewise Nussbaum functions to acquire the control robustness. Benefiting from this robustness, a part of failure uncertainties and system errors are compensated for, while the remaining parts are handled by adaptive control technique. Moreover, structural properties of the proposed adaptive laws are utilized so that Barbalat's lemma is applicable to make all the followers asymptotically converge to the leader based on the neighborhood information.

In green cities, one of the most promising energy system designs is the multi-energy system, which is capable of integrating different energy resources to supply stable energy for users. To schedule diverse energy efficiently, the energy trading among different energy entities is a big issue in multi-energy systems. This paper proposes auction mechanisms for energy trading in a smart multi-energy district, in which the district manager sells electricity, natural gas, and heating energy to users and meanwhile trades with outer energy networks. Two auction mechanisms are designed under the day-ahead and real-time markets, respectively. For each auction, energy allocation is optimized by solving a social welfare maximization problem, which is strictly subject to constraints of physical multi-energy system models. It is theoretically proven that both auctions are able to guarantee the properties of economic efficiency, truthfulness, and individual rationality. With these properties, users are incentivized to participate into the auctions with fairness. Finally, real data are adopted to evaluate the performance of the proposed mechanisms. The theoretic analysis of the properties is verified as well.

In this paper, we propose an adaptive neural-network-based asymptotic control algorithm for a class of nonlinear systems subject to unknown actuator quantization. To this end, we exploit the sector property of the quantization nonlinearity and transform actuator quantization control problem into analyzing its upper bounds, which are then handled by a dynamic loop gain function-based approach. In our adaptive control scheme, there is only one parameter required to be estimated online for updating weights of neural networks. Within the framework of Lyapunov theory, it is shown that the proposed algorithm ensures that all the signals in the closed-loop system are ultimately bounded. Moreover, an asymptotic tracking error is obtained by means of introducing Barbalat's lemma to the proposed adaptive law.

One of the major advantages of smart grid is to allow a large number of electric vehicles (EVs) to participate in energy dispatch as elastic energy storage devices via vehicle-to-grid (V2G) technology. As mechanism design for V2G energy trading can stimulate energy interaction between EVs and grids, it is really significant to V2G systems. This paper focuses on efficient mechanism design for energy trading in a two-layer V2G architecture, which includes a grid-aggregator layer and aggregator-EV layer. We propose two auction mechanisms for the two layers, respectively, and discuss three essential economic properties of the mechanisms, i.e., truthfulness, individual rationality, and efficiency. Then, based on these two mechanisms, we illustrate the detailed operation procedure of the two-layer V2G energy trading architecture. Performance evaluation shows that the proposed auction mechanisms greatly reduce social costs, i.e., enhance efficiency, while guaranteeing truthfulness and individual rationality.

With the development of the detection technology using multispectra sensors, spectral decomposition (SD) attracts more and more attention in the biomedical signal processing and image processing. In this paper, a local smoothness constrained nonnegative matrix factorization (NMF) with nonlinear convergence rate (NMF-NCR) is proposed to solve SD problem and our contributions are as follows. First, it proves that the gradients of the cost function with respect to each variable matrix are Lipschitz continuous. Then, a proximal function is constructed for optimizing the cost function. As a result, our method can achieve an NCR much faster than the traditional methods. Simulations show the advantage in solving SD of our algorithm over the compared methods.

Most existing control results for pure-feedback stochastic systems are limited to a condition that tracking errors are bounded in probability. Departing from such bounded results, this paper proposes an asymptotic fuzzy neural network control for pure-feedback stochastic systems. The control goal is realized by proposing a novel semi-Nussbaum function-based technique and employing it in adaptive backstepping controller design. The proposed Nussbaum function is integrated with adaptive control technique to guarantee that the tracking error is asymptotically stable in probability.

This paper proposes an adaptive fuzzy asymptotic control method for multiple input multiple output (MIMO) nonlinear systems with unknown input coefficients, with a focus on handling unknown input nonlinearities and control directions. For all the existing Nussbaum gain-based approaches, it is difficult to investigate unknown input coefficients problem since multiple time-varying coefficients and disturbances coexist and should be simultaneously tackled in the stability analysis. To overcome the above difficulty, we propose a robust Nussbaum gain-based approach for the adaptive fuzzy asymptotic control of MIMO nonlinear systems. Benefiting from the proposed Nussbaum gain-based approach, bounded disturbances including unmodeled system dynamics and universal approximation errors are handled. Furthermore, the proposed approach helps extend the bounded fuzzy control result to the asymptotic convergence. Hence, both the control robustness and control accuracy are prompted within the frame of the developed Nussbaum gain approach. Finally, a simulation example is carried out to illustrate the effectiveness of the proposed control method.

Focal underdetermined system solver (FOCUSS) is a powerful method for basis selection and sparse representation, where it employs the [Formula: see text]-norm with p ∈ (0,2) to measure the sparsity of solutions. In this paper, we give a systematical analysis on the rate of convergence of the FOCUSS algorithm with respect to p ∈ (0,2) . We prove that the FOCUSS algorithm converges superlinearly for and linearly for usually, but may superlinearly in some very special scenarios. In addition, we verify its rates of convergence with respect to p by numerical experiments.

This paper addresses the problem of state estimation for a class of discrete-time stochastic complex networks with a constrained and randomly varying coupling and uncertain measurements. The randomly varying coupling is governed by a Markov chain, and the capacity constraint is handled by introducing a logarithmic quantizer. The uncertainty of measurements is modeled by a multiplicative noise. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the coupling information, and an augmented estimation error system is obtained using the Kronecker product. Sufficient conditions are established, which guarantee that the estimation error system is stochastically stable and achieves the strict (Q, S, R)-γ-dissipativity. Then, the estimator gains are derived using the linear matrix inequality method. Finally, a numerical example is provided to illustrate the effectiveness of the proposed new design techniques.

Nonnegative matrix factorization (NMF) is an emerging tool for meaningful low-rank matrix representation. In NMF, explicit constraints are usually required, such that NMF generates desired products (or factorizations), especially when the products have significant sparseness features. It is known that the ability of NMF in learning sparse representation can be improved by embedding a smoothness factor between the products. Motivated by this result, we propose an adaptive nonsmooth NMF (Ans-NMF) method in this paper. In our method, the embedded factor is obtained by using a data-related approach, so it matches well with the underlying products, implying a superior faithfulness of the representations. Besides, due to the usage of an adaptive selection scheme to this factor, the sparseness of the products can be separately constrained, leading to wider applicability and interpretability. Furthermore, since the adaptive selection scheme is processed through solving a series of typical linear programming problems, it can be easily implemented. Simulations using computer-generated data and real-world data show the advantages of the proposed Ans-NMF method over the state-of-the-art methods.

Research on the smart grid is being given enormous supports worldwide due to its great significance in solving environmental and energy crises. Electric vehicles (EVs), which are powered by clean energy, are adopted increasingly year by year. It is predictable that the huge charge load caused by high EV penetration will have a considerable impact on the reliability of the smart grid. Therefore, fair energy scheduling for EV charge and discharge is proposed in this paper. By using the vehicle-to-grid technology, the scheduler controls the electricity loads of EVs considering fairness in the residential distribution network. We propose contribution-based fairness, in which EVs with high contributions have high priorities to obtain charge energy. The contribution value is defined by both the charge/discharge energy and the timing of the action. EVs can achieve higher contribution values when discharging during the load peak hours. However, charging during this time will decrease the contribution values seriously. We formulate the fair energy scheduling problem as an infinite-horizon Markov decision process. The methodology of adaptive dynamic programming is employed to maximize the long-term fairness by processing online network training. The numerical results illustrate that the proposed EV energy scheduling is able to mitigate and flatten the peak load in the distribution network. Furthermore, contribution-based fairness achieves a fast recovery of EV batteries that have deeply discharged and guarantee fairness in the full charge time of all EVs.

Focal Underdetermined System Solver (FOCUSS) is a powerful and easy to implement tool for basis selection and inverse problems. One of the fundamental problems regarding this method is its convergence, which remains unsolved until now. We investigate the convergence of the FOCUSS algorithm in this paper. We first give a rigorous derivation for the FOCUSS algorithm by exploiting the auxiliary function. Following this, we further prove its convergence by stability analysis.

This paper presents a convex geometry (CG)-based method for blind separation of nonnegative sources. First, the unaccessible source matrix is normalized to be column-sum-to-one by mapping the available observation matrix. Then, its zero-samples are found by searching the facets of the convex hull spanned by the mapped observations. Considering these zero-samples, a quadratic cost function with respect to each row of the unmixing matrix, together with a linear constraint in relation to the involved variables, is proposed. Upon which, an algorithm is presented to estimate the unmixing matrix by solving a classical convex optimization problem. Unlike the traditional blind source separation (BSS) methods, the CG-based method does not require the independence assumption, nor the uncorrelation assumption. Compared with the BSS methods that are specifically designed to distinguish between nonnegative sources, the proposed method requires a weaker sparsity condition. Provided simulation results illustrate the performance of our method.

The nonnegative convex polyhedral cone (NCPC) learning is discussed in this paper. By exploiting the multiplicative update nonnegative quadratic programming, a multiplicative update algorithm is developed for NCPC learning. The proposed algorithm is promising for nonnegative matrix factorization (NMF) and we verify this by numerical experiments.

The famous common spatial patterns (CSP) algorithm has shown to be useful for event-related desynchronization (ERD) feature extraction of multi-channel electroencephalogram (EEG) signals. Actually, CSP only extracts the linear correlation between each pair of channels. The performance of CSP severely depends on the preprocessing. Moreover, CSP and the subsequent classifier are not optimized by the same criteria. In this paper, we investigated the nonlinear correlation between channels with kernel technique, and proposed a unified prediction framework based on linear ridge regression model. This prediction framework integrates preprocessing, feature extraction and classification, can automatically select the time windows, frequency bands and regularization parameter by minimizing leave-one-out cross-validation error through gradient descent. Experimental results on the dataset IV, BCI competition II show the effectiveness of our approach.