Multivariate time series forecasting has drawn increasing attention due to its practical importance. Existing approaches typically adopt either channel-mixing (CM) or channel-independence (CI) strategies. CM strategy can capture inter-variable dependencies but fails to discern variable-specific temporal patterns. CI strategy improves this aspect but fails to fully exploit cross-variable dependencies like CM. Hybrid strategies based on feature fusion offer limited generalization and interpretability. To address these issues, we propose C3RL, a novel representation learning framework that jointly models both CM and CI strategies. Motivated by contrastive learning in computer vision, C3RL treats the inputs of the two strategies as transposed views and builds a siamese network architecture: one strategy serves as the backbone, while the other complements it. By jointly optimizing contrastive and prediction losses with adaptive weighting, C3RL balances representation and forecasting performance. Extensive experiments on seven models show that C3RL boosts the best-case performance rate to 81.4% for models based on CI strategy and to 76.3% for models based on CM strategy, demonstrating strong generalization and effectiveness.

Huilin Deng, Hongchen Luo, Yue Zhu, Long Li, Zhuoyue Chen, Xinghao Zhao, Ming LI, Chuyang Zhao, Jihai Zhang, MengChang Wang, Yang Cao, Yu Kang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Shared-control teleoperation systems, which combine human cognitive versatility and robotic precision, are essential for executing complex tasks in inaccessible environments. A critical challenge in such systems is accurately estimating the operator’s intent to ensure the robot’s assistive actions align with the operator’s goals. Traditional trajectory prediction methods estimate the robot’s future path as the operator’s intent. However, without accounting for communication delays, these methods predict the robot’s execution trajectory, which diverges from the operator’s intended trajectory due to the delays. To address this issue, we propose the delay-aware Robot Trajectron (DA-RT), which restores the causal relationship between observed commands and actions by leveraging temporally aligned data and a delay-conditioned attention pooling mechanism. This enables DA-RT to accurately estimate the operator’s intended trajectory under communication delays. Then, we integrate DA-RT with an artificial potential field based controller and a dynamic arbitration mechanism to form a delay-aware shared-control framework, which adjusts the level of assistance based on delay magnitude and the agreement between the operator’s command and the robot’s motion. In human-in-the-loop experiments with fifteen participants, our approach demonstrated significant improvements in both task success rate and efficiency, outperforming traditional methods.

As a typical application of the Internet of Things, mobile robot teleoperation systems play a crucial role in dangerous tasks or special scenarios. The passive takeover mechanism provides effective safety assurance for such systems, relying on a takeover request (TOR) generator to issue warnings and an authority transfer controller to manage control handover. However, variable communication delays can degrade the safety and smoothness of the passive takeover process. To address this issue, we propose a trajectory prediction-based adaptive takeover method that redesigns both the TOR generator and the authority transfer controller. The TOR generator issues early warnings based on the predicted robot trajectory in autonomous control mode. The authority transfer controller uses the control input that tracks the predicted human-intended trajectory as a reconstruction of the delayed human input, and adaptively adjusts the weights between the autonomous input and the reconstructed human input based on the risk indicator and the delay impact level. The effectiveness of the proposed method is validated through the human-in-the-loop simulation. The results show that our method ensures better safety and smoothness in the takeover process compared to other methods.

Zero-shot anomaly detection (ZSAD) enables the inspection of unseen objects by bridging textual prompts and visual features, showing great potential in flexible manufacturing. While existing ZSAD methods rely on predefined prompts and struggle with unseen defects, Multimodal Large Language Models (MLLMs) offer promising solutions through their generative and interpretative capabilities. However, adapting MLLMs to Industrial Anomaly Detection (IAD) remains challenging due to fine-grained anomaly patterns and subtle visual distinctions. We propose VMAD (Visual-enhanced MLLM Anomaly Detection), a framework that enriches MLLM with visual IAD knowledge through two key components: a Defect-Sensitive Structure Learning scheme that transfers patch-similarities for improved discrimination, and a Locality-enhanced Token Compression that leverages multi-level local features for fine-grained detection. We also introduce RIAD, a comprehensive IAD dataset with detailed anomaly annotations. Extensive experiments on MVTec-AD, Visa, WFDD, and RIAD demonstrate VMAD’s superior performance. The dataset and code will be publicly available at https://github.com/denghuilin-cyber/VMAD.

In the above article [1], an earlier draft of Fig. 6 was inadvertently included. The correct Fig. 6 is presented on the next page.Fig. 6.Zero-shot anomaly segmentation on MVTec-AD, WFDD, and ViSA datasets.

This article first proposes a discrete-time sliding flexible performance preset boundary (DT-SFPPB)-based control algorithm for input saturated discrete-time nonlinear systems (IS-DTNSs). Compared to the existing discrete-time prescribed performance control (DT-PPC) algorithms, the PPB of them present a “trumpet” shape, resulting in fundamental conservation of the transient performance, and whenever the initial error is altered, it is essential to recheck whether the new error meets the original constraint condition, if not, a new PPB with a larger measure has to be reselected. By designing a novel DT-SFPPB associated with the initial error, which can always envelope the initial error with an arbitrarily preset initial measure, indicating that the proposed approach can be utilized for IS-DTNSs with arbitrary initial error without compromising the initial transient performance. Furthermore, the coupling effect between performance preset and input saturation is also considered, by designing a novel equilibrium boundary related to saturation, so that the proposed approach can achieve the synergy between performance preset and input security, i.e., the designed DT-SFPPB can flexibly expand when input saturation occurs to avoid vulnerability, and when the control input is within the safe boundary, it rapidly reverts to the original PPB to guarantee the specified performance metrics. The findings demonstrate that the developed approach guarantees that the system output tracks the desired signal with the specified performance metrics, and all of the tracking errors are always enveloped within their corresponding DT-SFPPBs. The devised approach is exemplified by means of simulation examples.

Vision-Language Models (VLMs) like CLIP have advanced image representation through open-vocabulary semantic alignment. Yet, existing few-shot transfer learning methods largely overlook the intrinsic interdependencies between text and image embeddings, limiting their ability to fully transfer CLIP’s pretrained capabilities. To address this gap, we propose Hyperspherical Interpolation Variational Encoding (HIVE), a novel method for few-shot image classification. Our core idea is to shift away from directly training feature extraction capabilities for downstream tasks, and instead focus on exploring the semantic transformation relationships between upstream and downstream tasks. By modeling semantics from coarse to fine granularity, HIVE enables the transfer of original feature extraction and modality alignment capabilities to downstream tasks. Extensive experiments on eight established benchmarks, including CUB and EuroSAT, validate HIVE’s efficacy, achieving up to 46.2% and 80.0% improvements over the original CLIP in 1-shot and 16-shot classification tasks, respectively. Our work underscores the importance of preserving pretrained geometric constraints while exploiting semantic hierarchies for effective few-shot adaptation, providing a principled approach for vision-language model customization.

Humans can naturally learn and adapt to walking patterns in a variety of terrains. To simulate this learning characteristic, this article introduces a neural dynamics-based impedance optimization and trajectory adaptation approach for our designed soft exosuit, with a dual-driven configuration to assist both ankles of individuals. This method adaptively learns the impedance of the human ankle joint using measured interaction forces and dynamically adjusts trajectories to align with real-time human-robot interaction. Additionally, an adaptive control framework integrating neural dynamics-based optimization with several adaptive laws is developed to achieve stable tracking of updated reference trajectories, with Lyapunov stability analysis confirming uniform ultimate boundedness (UUB) of the closed-loop system. The designed controller offers the benefit of concurrently addressing trajectory adaptation, force control, and impedance tuning for soft exosuits. Experimental validation on human subjects across various terrains demonstrates that the proposed method reduces maximum trajectory tracking error to 0.016 rad (lower than PID and ADRC controllers) and enables impedance parameters to converge within 3 gait cycles. The controller concurrently addresses trajectory adaptation, force control, and impedance tuning, offering a lightweight (8 kg) and wearability-optimized solution for walking assistance.

This article first proposes a sliding flexible prescribed performance boundary-guided reinforcement learning (SFPPB-RL) control approach for input-constrained nonlinear systems (ICNSs). By designing a sliding flexible prescribed performance boundary, which not only can adaptively adjust the initial boundary according to the initial error, but also dynamically adjust the constraint relaxation according to the coupling correlation between the input constraint and the performance constraint, a novel prescribed performance control (PPC) approach is proposed. Compared with the existing "horn" shape performance boundary-based PPC methods, the limitation of having to repeatedly debug design parameters or sacrifice initial transient performance to meet different initial error requirements is eliminated. Meanwhile, the coupling effect between the input constraint and the performance constraint is also considered, and the balance between input safety and control performance is achieved by constructing an auxiliary system. Furthermore, combining identifier-critic-actor structure-based RL strategy and backstepping technique, a sliding flexible PPB-guided reinforcement learning (SFPPB-RL) optimal control algorithm is developed, which minimizes the cost function while ensuring input safety and prescribed performance indicators. The validity of the proposed algorithm is demonstrated via simulations.

The integration of electric vehicles (EVs) into power systems via vehicle-to-grid (V2G) technology offers new opportunities for bidirectional energy exchange and resource allocation. However, time delays and adversarial attacks in communication networks can undermine the coordination of EV aggregators and power systems. To address this challenge, this paper presents a hybrid counterfactual learning approach for control of EV aggregators in the multi-area power systems V2G and load frequency control (LFC) framework. The proposed hybrid approach integrates counterfactual multi-agent learning, adversarial training, and monotonic neural network (CMA-HMNN). The multi-agent counterfactual learning marginalizes the impact of individual actions on the overall reward, thereby better coordinating controllable resources and reducing variances in adversarial multi-agent training. By enforcing deviation-command monotonicity constraints within the neural network architecture, the proposed approach can satisfy Lyapunov stability conditions and improve the stability of power systems with integrated EVs. Adversarial training based on the fast gradient sign method (FGSM) is applied to enhance the robustness of the networks against perturbations. Even under concurrent time-varying communication delays and malicious threats from communication networks, the method effectively coordinates multiple generation resources and EV aggregators. Compared with four DRL-based control methods, the superiority of the proposed method is verified on the three-area power system and IEEE 39-bus power system with wide EVs integrations.

Service restoration implemented by multiple distributed energy resources (DERs) is a resilience-enhancing paradigm for modern distribution systems. To address the challenges of complex system modeling and the problem of cooperative control over heterogeneous multiple agents, this article proposes a graph reinforcement learning (G-RL) method based on heterogeneous multiagent systems (MASs). The method leverages graph-structured data to enhance the representation of distribution system states and employs graph attention networks (GATs) to deeply explore the power flow features and spatial characteristics of nodes in the restoration process. Additionally, a multihead self-attention (MHSA) is incorporated to strengthen collaboration among heterogeneous agents, enabling them to focus on relevant information from multiple perspectives during training. Finally, a joint simulation test platform is developed using Python and OpenDSS, and case studies on a 123-bus distribution system are conducted. Experimental results demonstrate that the proposed approach achieves efficient and autonomous service restoration by enhancing spatial feature extraction and improving collaborative decision-making among agents.

Defect inspection faces the challenge of insufficient data. Although existing defect generation methods can produce high-quality defect images, the time-consuming generation process hinders the online availability. To solve it, we propose FastAno, a four-step sampling model for rapid defect generation. Specifically, we first introduce the Adaptive Defect-specific Loss, which calculates region-weighted feature loss to enhance shortcut mapping of defect distribution. Secondly, we propose the Dynamic Attention Optimization Strategy, which enhances the attention activation of the anomaly semantics to improve the generation of defects, while adaptively suppressing the activation of normal semantics to mitigate the degradation of non-defect regions. Extensive experiments on MVTec AD dataset demonstrate that our method achieves significantly faster generation speed while maintaining high generation quality.

Industrial video anomaly detection aims to perform real-time analysis of video streams from industrial production lines and provide anomaly alerts. Conventional video anomaly detection methods focus more on the overall image, as they aim to identify anomalies among multiple normal samples appearing simultaneously. However, industrial scenarios, where the primary focus is on a single type of product, require attention to local areas to capture fine-grained details and specific patterns. Directly applying conventional methods to industrial scenarios can result in an inability to focus on products moving along fixed trajectories, ineffective utilization of their equidistant periodicity, and greater susceptibility to lighting variations. To address these issues, we propose FreqNet, an encoder-decoder framework that learns frequency-domain features from videos to capture periodic and dynamic characteristics, enhancing the model's robustness. Specifically, a trajectory filter is proposed that takes advantage of the significant difference between moving objects and static backgrounds in the frequency domain by assigning higher weights to fixed moving trajectories. Moreover, a multi-feature fusion module is proposed, in which the frequency domain features of the video are first extracted to leverage the unique equidistant periodicity information of videos from industrial production lines. The extracted frequency domain features are subsequently fused with spatio-temporal features and contextual information is further integrated from the fused representation, effectively mitigating the impact of lighting variations on production lines. Extensive experiments on the benchmark IPAD dataset demonstrate the superiority of our proposed method over the state-of-the-art.

In-Context Reinforcement Learning (ICRL) enables agents to learn automatically and on-the-fly from their interactive experiences. However, a major challenge in scaling up ICRL is the lack of scalable task collections. To address this, we propose the procedurally generated tabular Markov Decision Processes, named AnyMDP. Through a carefully designed randomization process, AnyMDP is capable of generating high-quality tasks on a large scale while maintaining relatively low structural biases. To facilitate efficient meta-training at scale, we further introduce decoupled policy distillation and induce prior information in the ICRL framework. Our results demonstrate that, with a sufficiently large scale of AnyMDP tasks, the proposed model can generalize to tasks that were not considered in the training set through versatile in-context learning paradigms. The scalable task set provided by AnyMDP also enables a more thorough empirical investigation of the relationship between data distribution and ICRL performance. We further show that the generalization of ICRL potentially comes at the cost of increased task diversity and longer adaptation periods. This finding carries critical implications for scaling robust ICRL capabilities, highlighting the necessity of diverse and extensive task design, and prioritizing asymptotic performance over few-shot adaptation.

Recently, deep learning based city flow prediction has been extensively used in the establishment of smart cities. These methods are data-hungry, making them unscalable to areas lacking data. Although transfer learning can use data-rich source domains to assist target domain cities in city flow prediction, the performance of existing methods cannot meet the needs of actual use, because the long-distance road network connectivity is ignored. To solve this problem, we propose a transfer learning method based on spatiotemporal graph convolution, in which we construct a co-occurrence space between the source and target domains, and then align the mapping of the source and target domains’ data in this space, to achieve the transfer learning of the source city flow prediction model on the target domain. Specifically, a dynamic spatiotemporal graph convolution module along with a temporal encoder is devised to simultaneously capture the concurrent spatiotemporal features, which implies the inherent relationship among the road network structures, human travel habits, and city bike flow. Then, these concurrent features are leveraged as cross-city invariant representations and nonlinearly spanned to a co-occurrence space. The target domain features are thereby aligned with the source domain features in the co-occurrence space by using a Mahalanobis distance loss, to achieve cross-city bike flow prediction. The proposed method is evaluated on the public bike flow datasets in Chicago, New York, and Washington in 2015, and significantly outperforms state-of-the-art techniques.

Predicting remaining useful life (RUL) of bearings under scarce labeled data is significant for intelligent manufacturing. Current approaches typically encounter the challenge that different degradation stages have similar behaviors in multisensor scenarios. Given that cross-sensor similarity improves the discrimination of degradation features, we propose a multisensor contrast method for RUL prediction under scarce RUL-labeled data, in which we use cross-sensor similarity to mine multisensor similar representations that indicate machine health condition from rich unlabeled sensor data in a co-occurrence space. Specifically, we use ResNet18 to span the features of different sensors into the co-occurrence space. We then obtain multisensor similar representations of abundant unlabeled data through alternate contrast based on cross-sensor similarity in the co-occurrence space. The multisensor similar representations indicate the machine degradation stage. Finally, we focus on finetuning these similar representations to achieve RUL prediction with limited labeled sensor data. The proposed method is evaluated on a publicly available bearing dataset, and the results show that the mean absolute percentage error is reduced by at least 0.058, and the score is improved by at least 0.122 compared with those of state-of-the-art methods.

Abstract During the production of printed circuit board (PCB), there will be defects due to inappropriate operations, which will affect the use of electronic products. Majority defect detection methods cost a large number of annotated samples to train detection models. However, PCB defect samples are difficult to collect. Moreover, existing few-shot object detection methods tend to extracting low-level features from support and query images via the shared backbone such as ResNet-50. However, it is not sufficient to obtain fine-grained prior guidance. To address the above issues, we propose a few-shot PCB defect detection model with double-branch attention. Specifically, the joint attention enhancement (JAE) module is proposed to fully mine effective information of query PCB images in multiple dimensions to enhance the representation of latent defects. Then, the multi-scale guidance (MSG) module is proposed to integrate prior knowledge within support PCB images into vectors to reweight query PCB images. Experiments on the PCB defect dataset demonstrate that AP of FSPDD outperforms state-of-the-art methods under different shot settings (k=1,2,3,5,10,30) and our proposed FSPDD has a good generalization ability, in which AP reachs 0.273 when $$k=30$$ k = 30 and is 5.28% higher than SOTA methods.

With the development of intelligent automation technology and advancement of modernization, the degree of interconnection between power systems is increasing. With the main purpose of involving hybrid energy storage systems (HESS) in optimizing system frequency, this work proposes a learning-based tube model predictive control (MPC) for the multi-area interconnected power systems with wind power and HESS. The suggested method has strong adaptability due to the introduction of a new robust constraint handled by a learning mechanism. By identifying the uncertainty set of coupling strength of online data in the learning stage, the optimal MPC problem is calculated in the adaptive stage, which effectively reduces the adverse effects of disturbances and noises in multi-area interconnected power systems. Moreover, an input to state stability criterion is provided to ensure the robust stability of the system with uncertain disturbances and noises. With simulations on a four-area interconnected power system with wind power and HESS, the effectiveness of proposed method is discussed on an improved IEEE 39-bus system.

A memory output sliding mode load frequency control (MOSMLFC) strategy is proposed for multi-area interconnected power systems under historical-frequency-triggered denial-of-service (DoS) attacks. Due to the use of the open network, the multi-area power system is prone to cyber-attacks. Different types of attack models have been built to describe the actual attack behavior, so that effective strategies can be quickly formulated in the event of an attack. Therefore, a historical-frequency-triggered DoS attacks model is presented from the perspective of attackers, with the aim of destroying the stable state of the multi-area power system. It is assumed that attackers determine the timing of DoS attacks by monitoring the operational status of multi-area power systems and designing the triggering condition with historical frequency. A MOSMLFC strategy is investigated to ensure the security performance of multi-area power systems under historical-frequency-triggered DoS attacks, which applies the memory output information of the power system to realize the controller design. The security condition of multi-area power systems under historical-frequency-triggered DoS attacks is obtained by Lyapunov’s theorem and linear matrix inequality (LMI). Numerical examples are tested over the IEEE 10-generator 39-bus system and the results prove the usefulness and superiority of the proposed method. Note to Practitioners—Load frequency control is widely applied in multi-area power systems to achieve a balance between the load demand and generation. Frequent cyber-attacks are a threat to the normal operation of the power system. It is therefore necessary to develop appropriate strategies to defend against cyber-attacks. So far, there have been many different forms of cyber-attacks. This has prompted defenders to build different types of attack models to describe the actual attack behavior in order to preemptively formulate appropriate defensive strategies. Smart attacker may notice that certain characteristics of the target system are important, such as the power system frequency. This motivates us to propose a historical frequency-triggered DoS attack model that contributes to a deep understanding of the impact of cyber-attacks on the power system. We propose a unique sliding mode control approach to ensure the stable performance of power system state and output simultaneously.

Visual impairments significantly affect individuals’ ability to perform essential tasks such as communication, object search, and navigation. Traditional wearable augmented vision devices rely on modular designs that separate functions like perception and path planning, leading to cumulative errors and inefficiencies in real-world applications. To address these challenges, we propose an end-to-end multimodal large model framework, UniANS, specifically designed for wearable augmented vision devices. UniANS integrates visual perception, speech interaction, and path planning into a unified framework. Such integration improves task coordination, reduces error propagation, and enhances overall performance. We also propose a prompt design strategy with a mixture of cluster-conditional low-rank adaptation experts architectures and dual-branch encoders, combined with advanced preprocessing techniques for visual and speech modules. The framework has been validated through ablation studies, showing superior performance in accuracy and task effectiveness compared to existing methods. We further showcase its capabilities in addressing challenges related to communication, object search, and indoor navigation tasks. The design of UniANS enhances mobility and quality of life for visually impaired individuals.

Despite remarkable progress in robotic exoskeletons, exoskeletons are still far from matching human-level guidance and locomotion performance in gait training or movement enhancement. A desirable exoskeleton would first provide a standard gait profile by learning from human operators while requiring body weight support with active human-following to govern dynamic locomotion synchronization. To address these issues, in this article, we propose a human operator-involved dynamic locomotion synchronization control framework for the lower limb exoskeleton actively following gait training with gravity-supporting. First, we designed a human motion capture system based on a five-link model for the locomotion of a human operator. To reproduce human-level motor skills, we use whole-body teleoperation to leverage human control intelligence to command the locomotion of a robotic exoskeleton system. Specifically, using the linear inverted pendulum (LIP) model, the human operator's divergent component of motion (DCM) is obtained by the human motion capture system. The dynamic similarity is used to generate a reference DCM for the robotic exoskeleton to synchronize the human operator's movement. Finally, a fuzzy-based adaptive controller is designed to track the synchronous trajectory for the exoskeleton in the presence of robotic dynamics uncertainties with input saturation. Experiments on the human subject are carried out to demonstrate the effectiveness of the proposed method.

This article first presents a dual flexible prescribed performance control (DFPPC) approach of input saturated high-order nonlinear systems (IS-HONSs). Compared to the existing PPC approaches of IS-HONSs, under which the performance constraint boundaries (PCBs) are usually fixed and bounded, resulting in a restriction of the initial error in the algorithm implementation; in addition, the coupling relationship between performance constraints and input saturation is usually ignored, resulting in the methods are very fragile when input saturation occurs. By designing the novel tensile model-based PCBs that depend on output and input constraints, the proposed DFPPC method provides sufficient resilience for both the initial conditions and the input saturation, so that the proposed DFPPC method can not only be suitable for multiple types of initial errors by adjusting the parameters, including , , and , where , and denote the initial PCBs; but also can achieve a good balance between input saturation and performance constraints, i.e., when the control input reaches or exceeds the saturation threshold, the PCBs can adaptively extend to avoid the singularity, and when the control input returns to the saturation threshold range, the PCBs are then adaptively restored to the original PCBs. The results show that the proposed DFPPC algorithm guarantees semi-global boundedness for all closed-loop signals, while ensuring that the system output accurately tracks the desired signal, and it consistently maintains the tracking error within the PCBs. The developed algorithm is illustrated by means of simulation instances.

The issue of sliding flexible prescribed performance control (SFPPC) of input saturated nonlinear systems (ISNSs) is first studied in this article. Compared to the traditional PPC and the finite-time PPC algorithms for ISNSs, under which the performance constraint boundaries (PCBs) present the symmetrical or asymmetric “horn” shape, which leads to a large jitter in the tracking error before the system reaches steady state; and once the parameters are selected, the PCBs are fixed, when the initial state (or reference signal) changes, it is necessary to reverify whether the initial error still satisfies the initial constraint condition. By designing a new pair of sliding flexible PCBs (SFPCBs) associated with the initial error, a novel SFPPC algorithm is presented in this article, which presents two main advantages: 1) the SFPCBs can slide adaptively with the initial tracking error without increasing the measure of the initial PCBs, implying that the proposed SFPPC algorithm can be applied to ISNSs with arbitrary initial errors without sacrificing the initial control performance; 2) the proposed SFPPC algorithm achieves a tradeoff between performance constraint and input saturation, i.e., the SFPCBs can adaptively increase when the control input exceeds the maximum allowable threshold, effectively avoiding singularity, and when the control input is within the saturation threshold range, the SFPCBs can adaptively revert back to the original PCBs. The results demonstrate that the proposed SFPPC approach can guarantee that the system output tracks the desired signal, and the tracking error always kept within the SFPCBs that depend on initial error, input, and output constraints. The developed algorithm is exemplified by means of simulation instances.

Solder paste position offset is a critical stencil printing quality indicator, the prediction of which using available data is important for printing quality improvement. However, existing data-driven works on solder paste position offset prediction often suffer from their poor adaptation to changing printing stages and small training samples. To address the above problems, we propose a novel multi-scale convolutional attention network based on meta-transfer strategy for solder paste position offset prediction in surface mount technology assembly lines. First, to better capture the fluctuating trends of printing sequence in different cleaning cycles, we propose a multi-scale convolutional attention network, in which a multi-head attention with position encoding is designed at each level to adaptively capture the changing trend of solder paste position offset at different stages. Then, to improve the precision of prediction model under insufficient samples, we introduce a meta-transfer strategy. Specifically, the parameters of the model are updated in the meta-training process through the bi-level optimization and the parameter fine-tuning method is used in the meta-testing process to improve the prediction performance of proposed model under complex working conditions. The proposed method is verified over practical dataset and compared with other advanced methods. The results show that the proposed method can accurately and robustly achieve solder paste position offset prediction, especially in small sample dataset.

Distributed Acoustic Sensing (DAS) can capture spatio-temporal vibration images of vehicles on expressways, which can be utilized for traffic monitoring. Compared to ubiquitously deployed cameras, DAS traffic monitoring offers advantages such as full coverage, resistance to environmental interference, low computational requirements, and cost-effectiveness. However, real-world complexities result in challenges for DAS traffic images, including low signal-to-noise ratio, signal missing, and uneven intensity. As DAS traffic applications are still in their early stages, effective solutions to these challenges are yet to be developed. This paper proposes a new deep learning method named DAS High Speed Traffic Trajectory (DAS-HTT) network, which contributes threefold: (i) Multi-Scale Context Extraction Module (MSCE) effectively enlarges the receptive field to capture long-range contextual information comprehensively; (ii) Stripe Convolution Decoder (SCD) acquires remote information along four directions, preventing irrelevant region interference in feature learning; (iii) Hierarchically Hough Transform Fusion Decoder (HHTFD) introduces the structural information of trajectory linearity, reducing the reliance on label data while enhancing trajectory continuity. We conducted experiments on an operating expressway, demonstrating that DAS-HTT outperforms existing methods across seven metrics, providing trajectories that are more consistent with ground truthes.

This brief studies the discounted stochastic linear quadratic regulator (LQR) problem for systems suffering from additive noise of unknown mean. A completely model-free (MF) value iteration (VI) algorithm is developed to learn the optimal control policy using off-line system trajectories. The generated control policies are proven to converge to a small neighborhood of the optimal ones with high probability. In addition, an MF algorithm is proposed to learn a feasible discount factor. The proposed MF algorithms are illustrated through several examples.

The comprehensive regulatory oversight of traffic emissions frequently encounters the missing not-at-random (MNAR) pattern, characterized by the long-term block missing in adjacent road segments, arising from insufficient monitoring points and nonuniform spatiotemporal distribution. The spatiotemporal block missing simultaneously disrupts the spatiotemporal correlation, introducing significant biases in spatiotemporal modeling for incomplete data. The emerging diffusion model recovers the information of the missing regions in a self-supervised manner and focuses on the generation process of the missing regions to address biases. However, the dynamics and spatiotemporal heterogeneity of traffic emissions limit its applicability in unknown spatiotemporal missing. To address this issue, this article proposes a novel progressive Diffusion Model-based framework for SpatioTemporal Imputation of traffic emissions (STI-dm). Specifically, a self-supervised masked training strategy is first devised to construct the nonlocal similarity prior of traffic emission data, explicitly introducing the MNAR missing mechanism for the diffusion process. Furthermore, an enhanced approach of noise injection and supervised denoising is adopted to rectify misconceptions of nonlocal alignment, decreasing modeling biases associated with incomplete data in the generation process. The imputation and prior modeling processes are progressively performed until obtaining stable results, and each of the preceding modeling processes benefits from the gradual improvement results in the other. Experimental evidence indicates that STI-dm surpasses the current state-of-the-art algorithms in scenarios with intricate spatiotemporal patterns and varying rates of missing data.

Abstract The swift advancement of convolutional neural networks (CNNs) in numerous real-world utilizations urges an elevation in computational cost along with the size of the model. In this context, many researchers steered their focus to eradicate these specific issues by compressing the original CNN models by pruning weights and filters, respectively. As filter pruning has an upper hand over the weight pruning method because filter pruning methods don’t impact sparse connectivity patterns. In this work, we suggested a Bayesian Convolutional Neural Network (BayesCNN) with Variational Inference, which prefaces probability distribution over weights. For the pruning task of Bayesian CNN, we utilized a combined version of L1-norm with capped L1-norm to help epitomize the amount of information that can be extracted through filter and control regularization. In this formation, we pruned unimportant filters directly without any test accuracy loss and achieved a slimmer model with comparative accuracy. The whole process of pruning is iterative and to validate the performance of our proposed work, we utilized several different CNN architectures on the standard classification dataset available. We have compared our results with non-Bayesian CNN models particularly, datasets such as CIFAR-10 on VGG-16, and pruned 75.8% parameters with float-point-operations (FLOPs) reduction of 51.3% without loss of accuracy and has achieved advancement in state-of-art.

Anomaly inspection aims at identifying various defects in real time on modern industrial production lines. However, due to insufficient anomaly data, existing detectors cannot effectively accomplish the classification of defects, thereby failing to provide guidance for subsequent production. To address it, we propose TF2, a few-shot text-free training-free defect image generation method, which jointly models the image distribution of class-agnostic defects and backgrounds, achieving efficient semantic enhancement. Firstly, we propose the Response Alignment Strategy, which merges the reversed latent space of both defect-free and defective samples, generating new defect images not limited to textual descriptions yet with consistent content. Moreover, we introduce the Defect Moving Strategy and the Regional Average Loss to merge the reversed latent space of the moving areas and enhance the variability of detail features, increasing both the location and content diversity of defects. Extensive experiments demonstrate the superiority of our model over the state-of-the-art competitors. The metrics indicate that our generated anomaly data focuses on balancing both image quality and diversity, effectively improving the performance of downstream anomaly inspection tasks.

Safety as a fundamental requirement for human-swarm interaction has attracted a lot of attention in recent years. Most existing approaches solve a constrained optimization problem at each time step, which has a high real-time requirement. To deal with this challenge, this article formulates the safe human-swarm interaction problem as a Stackerberg-Nash game, in which the optimization is performed over the entire time domain. The leader robot is supposed to be in a dominant position, interacting directly with the human operator to realize trajectory tracking and responsible for guiding the swarm to avoid obstacles. The follower robots always take their best responses to leader's behavior with the purpose of achieving the desired formation. Following the bottom-up principle, we first design the best-response controllers, that is, Nash equilibrium strategies, for the followers. Then, a Lyapunov-like control barrier function-based safety controller and a learning-based formation tracking controller for the leader are designed to realize safe and robust cooperation. We show that the designed controllers can make the robotic swarms move in a desired geometric formation following the human command and modify their motion trajectories autonomously when the human command is unsafe. The effectiveness of the proposed approach is verified through simulation and experiments. The experiment results further show that safety can still be guaranteed even when there exists a dynamic obstacle.

This article presents a unified adaptive fuzzy control approach for high-order nonlinear systems (HONSs) with multitype state constraints. Existing methods always require the upper and lower constraint boundaries are strictly positive and negative functions (or constants), respectively, which is often inconsistent with the actual constraints. In this article, "multitype state constraint" means that the upper and lower constraint boundaries include multiple types, such as both being strictly positive (or negative), sometime be positive or negative, and so on (cases ①-⑥). By designing a unified mapping function (UMF), the multitype state constraints are processed under removal the feasibility conditions (FCs). Furthermore, a technical design makes the proposed method also applicable to unconstrained HONSs without changing the control structure. By means of a fuzzy-logic system (FLS) and fixed-time stability theory (FTST), the proposed algorithm can ensure that the tracking error converges to a zero-centered neighborhood within a fixed time, and the singularity which often appears in the existing fixed-time control (FTC) methods of HONSs is effectively avoided. Simulation results demonstrate the scheme developed.

This article presents a prescribed-time output feedback control (PTOFC) algorithm for cyber-physical systems (CPSs) under output constraint occurring in any finite time interval (OC-AFT) and malicious attacks. The OC-AFT meaning that the output constraint only occurs during a finite number of time periods while being absent in others, which is more general and complex than traditional infinite-time/deferred output constraints. A stretch model-based nonlinear mapping function is constructed to handle the OC-AFT, and a salient advantage is that the proposed algorithm is also suit for CPSs with infinite-time/deferred output (or funnel) constraints, as well as those that are constraint-free, without necessitating changes to the control structure. The uncertain terms (including system model uncertainties, malicious attacks, and external disturbances) are compensated by fuzzy logic systems. Furthermore, a novel practical prescribed-time stability criterion is proposed, under which a novel PTOFC scheme is given. The results demonstrate that the proposed scheme can ensure that both tracking error and observation error converge to a neighborhood centered on zero within a prescribed time, while accommodating the OC-AFT and malicious attacks. Additionally, the settling time remains unaffected by control parameters and initial states, and the limitations of excessive initial control inputs and singularity problems in existing prescribed-time control algorithms are eliminated. The developed algorithm is exemplified through simulation instances.

A flexible prescribed performance control (FPPC) approach for input saturated nonlinear systems (ISNSs) with unmeasurable states is first presented in this article. Compared to the standard prescribed performance control (SPPC) or funnel control methods for ISNSs, the “flexibility” of the proposed FPPC algorithm is reflected in two aspects: 1) the proposed FPPC algorithm simultaneously considers multiple key indicators (including the steady state accuracy, convergence time, and overshoot), which are widely demanded in industrial production; 2) the proposed FPPC algorithm achieves a tradeoff between performance constraint and input saturation, i.e., the performance boundary can adaptively increase when the control input exceeds the saturation threshold, effectively avoiding singularity; conversely, when the control input is within the saturation threshold range, the performance constraint boundary can adaptively revert back to the original performance boundary. In addition, the unmeasured states are observed by the state observer, and the unknown nonlinear functions are approximated by fuzzy logic systems. The results demonstrate that the proposed output feedback control algorithm can ensure that all closed-loop signals are semiglobally bounded, the system output can track the desired signal within a prescribed time, and the tracking error is consistently maintained within flexible performance boundaries that depend on input and output constraints. The developed algorithm is exemplified through simulation instances.

Event cameras, such as dynamic vision sensors (DVS), are biologically inspired vision sensors that have advanced over conventional cameras in high dynamic range, low latency and low power consumption, showing great application potential in many fields. Event cameras are more sensitive to junction leakage current and photocurrent as they output differential signals, losing the smoothing function of the integral imaging process in the RGB camera. The logarithmic conversion further amplifies noise, especially in low-contrast conditions. Recently, researchers proposed a series of datasets and evaluation metrics but limitations remain: 1) the existing datasets are small in scale and insufficient in noise diversity, which cannot reflect the authentic working environments of event cameras; and 2) the existing denoising evaluation metrics are mostly referenced evaluation metrics, relying on APS information or manual annotation. To address the above issues, we construct a large-scale event denoising dataset (multilevel benchmark for event denoising, E-MLB) for the first time, which consists of 100 scenes, each with four noise levels, that is 12 times larger than the largest existing denoising dataset. We also propose the first nonreference event denoising metric, the event structural ratio (ESR), which measures the structural intensity of given events. ESR is inspired by the contrast metric, but is independent of the number of events and projection direction. Based on the proposed benchmark and ESR, we evaluate the most representative denoising algorithms, including classic and SOTA, and provide denoising baselines under various scenes and noise levels. The corresponding results and codes are available athttps://github.com/KugaMaxx/cuke-emlb.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

A novel practical prescribed-time control (PPTC) approach for high-order nonlinear systems (HONSs) subject to state constraints is studied in this article. Different from the existing methods which always require the constraint boundaries to be continuous functions, the state constraints considered in this article are discontinuous (i.e., the state constraints occur only in some time periods and not in others), which can be found in many practical systems. By designing a novel stretch model-based nonlinear mapping function (NMF), the state constraints are dealt with directly, and the limitations that the virtual control function depends upon the feasibility condition (FC) and the tracking error depends upon the constraint boundaries in the conventional schemes are removed. Meanwhile, the proposed method is a unified one, which is also effective for HONSs with conventional continuous state constraints/ deferred state constraints/ funnel constraints or constraints-free without altering the control structure. Furthermore, by designing a newly time-varying scaling transformation function (STF), a more relaxed criterion for practical prescribed-time stable (PPTS) is given, based on which a newly PPTC algorithm is designed. The result shows that the proposed algorithm can preset the upper bound of the settling time, which does not depend upon the initial state of the system and control parameters, the limitations of singularity problem and excessive initial control input in existing methods are removed. Simulation examples verify the algorithm developed.

Heavy-duty diesel vehicles are important sources of urban nitrogen oxides (NOx) in actual applications for environmental compliance, emitting more than 80% of NOx and more than 90% of particulate matter (PM) in total vehicle emissions. The detection and control of heavy-duty diesel emissions are critical for protecting public health. Currently, vehicles on the road must be regularly tested, every six months or once a year, to filter out high-emission mobile sources at vehicle inspection stations. However, it is difficult to effectively screen high-emission vehicles in time with a long interval between annual inspections, and the fixed threshold cannot adapt to the dynamic changes of vehicle driving conditions. An on-board diagnostic device (OBD) is installed inside the vehicle and can record the vehicle’s emission data in real time. In this paper, we propose a temporal optimization long short-term memory (LSTM) and adaptive dynamic threshold approach to identify heavy-duty high-emitters by using OBD data, which can continuously track and record the emission status in real time. First, a temporal optimization LSTM emission prediction model is established to solve the attention bias discrepancy problem on time steps that is caused by the large number of OBD data streams in practice. Then, the concentration prediction error sequence is detected and distinguished from the anomalous emission contexts using flexible criteria, calculated by an adaptive dynamic threshold with changing driving conditions. Finally, a similarity metric strategy for the time series is introduced to correct some pseudo anomalous results. Experiments on three real OBD time-series emission datasets demonstrate that our method can achieve high accuracy anomalous emission identification.

This article investigates the event-triggered distributed model predictive control (DMPC) for perturbed coupled nonlinear systems subject to state and control input constraints. A novel compound event-triggered DMPC strategy, including a compound triggering condition and a new constraint tightening approach, is developed. In this event-triggered strategy, two stability-related conditions are checked in a parallel manner, which relaxes the requirement of the decrease of the Lyapunov function. An open-loop prediction scheme to avoid periodic transmission is designed for the states in the terminal set. As a result, the number of triggering and transmission instants can be reduced significantly. Furthermore, the proposed constraint tightening approach solves the problem of the state constraint satisfaction, which is quite challenging due to the external disturbances and the mutual influences caused by dynamical coupling. Simulations are conducted at last to validate the effectiveness of the proposed algorithm.

Open-set domain adaptation (OSDA) aims to achieve knowledge transfer in the presence of both domain shift and label shift, which assumes that there exist additional unknown target classes not presented in the source domain. To solve the OSDA problem, most existing methods introduce an additional unknown class to the source classifier and represent the unknown target instances as a whole. However, it is unreasonable to treat all unknown target instances as a group since these unknown instances typically consist of distinct categories and distributions. It is challenging to identify all unknown instances with only one additional class. In addition, most existing methods directly introduce marginal distribution alignment to alleviate distribution shift between the source and target domains, failing to learn discriminative class boundaries in the target domain since they ignore categorical discriminative information in the adaptation. To address these problems, in this article, we propose a novel prototype-based shared-dummy classifier (PSDC) model for the OSDA. Specifically, our PSDC introduces an auxiliary dummy classifier to calibrate the source classifier and simultaneously develops a weighted adaptation procedure to align class-wise prototypes for adaptation. We further design a pseudo-unknown learning algorithm to reduce the open-set risk. Extensive experiments on Office-31, Office-Home, and VisDA datasets show that the proposed PSDC can outperform existing methods and achieve the new state-of-the-art performance. The code will be made public.

In this article, two novel distributed variational Bayesian (VB) algorithms for a general class of conjugate-exponential models are proposed over synchronous and asynchronous sensor networks. First, we design a penalty-based distributed VB (PB-DVB) algorithm for synchronous networks, where a penalty function based on the Kullback-Leibler (KL) divergence is introduced to penalize the difference of posterior distributions between nodes. Then, a token-passing-based distributed VB (TPB-DVB) algorithm is developed for asynchronous networks by borrowing the token-passing approach and the stochastic variational inference. Finally, applications of the proposed algorithm on the Gaussian mixture model (GMM) are exhibited. Simulation results show that the PB-DVB algorithm has good performance in the aspects of estimation/inference ability, robustness against initialization, and convergence speed, and the TPB-DVB algorithm is superior to existing token-passing-based distributed clustering algorithms.

Many previous studies of soft exosuits improved human locomotion performance. However, there is no example to control a soft exosuit using human ankle impedance adaption in assistance tasks compliantly. In this article, the human–environment interaction information is exploited into the exosuit control. A novel fuzzy-based optimization and control method of soft exosuit is proposed to provide plantarflexion assistance for human walking by changing the human–robot interaction. In particular, a fuzzy neurodynamics optimization is developed to learn the unknown human ankle impedance parameters automatically. A fuzzy approximation technique is applied to improve the control performance of the exosuit when a human is walking with unknown human–robot interaction model parameters. This control scheme guarantees that the human–robot dynamics follows a target human ankle impedance model to obtain the compliant interaction performance. Experiments on different participants verify the effectiveness of the control scheme. Results show that a compliant human–robot interaction is achieved by learning the human–environment interaction parameters, i.e., the human ankle parameters. It indicates that our proposed method can facilitate exosuit control to achieve compliant robot–human–environment interaction.

It is of great significance to develop a coordinated battery swapping station (BSS) recommendation method to reduce the cost of electric vehicles (EVs) and optimize the operation of BSS system. In this paper, the BSS recommendation problem is studied by comprehensively considering the battery swapping cost, diversity of BSS capacities, and differentiated demands of EVs, so as to be as close to the actual situation as possible. To describe the interactions among EVs, we propose a game theory-based approach to recommend appropriate BSSs for EVs to minimize the total cost (namely the sum of travel cost and battery swapping cost) of each EV. Under the game framework, a price function is designed to regulate the swapping price of each BSS, which acts as a coordination signal to induce EVs to join the game and also to alleviate congestion of BSSs. Then, an iterative algorithm is devised to seek the Nash equilibrium, through which a suitable BSS is determined for each EV. Compared to the shortest distance approach, the case studies indicate that the proposed approach can effectively reduce the average cost of EVs, improve the success rate of battery swapping, and balance the utilization ratio of BSSs.

The large-scale grouping of the battery system leads to the inconsistency of the battery pack. Aiming at tacking this issue, an inconsistency evaluation method is deployed for the battery pack based on an improved Gaussian mixture model (GMM) and feature fusion approach. Specifically, the proposed adaptive forgetting factor recursive least squares (AFFRLS) algorithm allows the open-circuit voltage and other parameters to be jointly identified without the open circuit voltage-state of charge (OCV-SOC) test. An online capacity estimation approach with the extended Kalman particle filter (EPF) is put forward for capacity estimation. Further, an improved GMM is proposed to visualize battery pack inconsistency, using the K-means++ algorithm to initialize category centers. The standard deviation coefficient approach quantifies the inconsistency. Finally, the real-life vehicle data are performed to validate the effectiveness of the proposed method. The experimental results show that the proposed method can evaluate the battery parameters accurately. With the increase in service time, the inconsistency of the battery pack is gradually deteriorating.

Due to its great efficiency, scalability, and inclusivity, distributed cooperative learning control has gotten a lot of attention. For complex uncertain multiagent systems, it is challenging to model the uncertainties and exploit the cooperative learning ability of the systems. To address these issues, we proposed a novel convex temporal convolutional network-based distributed cooperative learning control for uncertain discrete-time nonlinear multiagent systems. A new concept of using a convex temporal convolutional network (CTCNet) is proposed for estimating the uncertain agent dynamics in a cooperative way. Unlike previous methods that require adjustment of network weights for different control tasks, the proposed CTCNet can map the high-dimensional input-output space into a deep space spanned by basis features that represent the inherent properties of the system, so it has good robustness for different tasks. Consequently, to improve the control performance, a CTCNet-based distributed cooperative learning control method that shares learned knowledge through the communication topology among adaptive laws of CTCNet is proposed. Furthermore, the asymptotic convergence of system tracking errors to an arbitrarily small neighborhood of the origin is strictly proved. Finally, the simulation results are given to illustrate that our suggested method has higher control accuracy, stronger robustness, and anti-interference ability than the existing methods.

Leader-following cluster consensus is investigated for multiagent systems with weighted cooperative–competitive networks and measurement noise. A stochastic approximation protocol is proposed for interactively balanced and sub-balanced networks, and pinning control is introduced to deal with the divergence phenomenon in interactively unbalanced networks. With these protocols, sufficient conditions for reaching a strong mean-square leader-following cluster consensus are established for all the three types of networks, which are also extended to the cases without measurement noise. Numerical examples illustrate the effectiveness of the proposed protocols and theoretical analysis.

Cross-domain lithology identification (CDLI) is a common case in lithology identification, which aims to train a machine learning model using the logging data of an interpreted well to predict the lithology of another uninterpreted well. Compared with the general lithology identification problem, the CDLI problem is more challenging for two reasons: the data distribution shift between the wells, and the expensive label acquisition on the uninterpreted well. To tackle these issues, we propose a novel framework that embeds active learning (AL) and domain adaptation into lithology identification. The proposed framework is composed of two components: an AL algorithm that selects the most uncertain and diverse target samples to query their real labels, and a source reweighting method that leverages the target labels to reduce data distribution discrepancy. Experimental results on two real-world data sets demonstrate that the proposed method can more effectively suppress the performance degradation caused by the data distribution shift than the baselines, with fewer target label queries.

The recent years have witnessed a great success of artificial intelligence applications in geological prospecting, so that the traditional manual work, which is time-consuming and labor-intensive, could be accomplished automatically or at least in a human–machine cooperation way. This letter presents a first attempt in proposing an automatic way to predict the cross-well sandstone that plays a crucial role in formation characterization and reservoir exploration. Such a two-stage framework is composed of i) a convolution neural network (CNN)-based coarse prediction module and ii) a geological experience-based error correction (EC) module. Experiments demonstrate that our proposed module can achieve comparable accuracy as experts.

Lithology identification plays an essential role in formation characterization and reservoir exploration. As an emerging technology, intelligent logging lithology identification has received great attention recently, which aims to infer the lithology type through the well-logging curves using machine-learning methods. However, the model trained on the interpreted logging data is not effective in predicting new exploration well due to the data distribution discrepancy. In this article, we aim to train a lithology identification model for the target well using a large amount of source-labeled logging data and a small amount of target-labeled data. The challenges of this task lie in three aspects: 1) the distribution misalignment; 2) the data divergence; and 3) the cost limitation. To solve these challenges, we propose a novel active adaptation for logging lithology identification (AALLI) framework that combines active learning (AL) and domain adaptation (DA). The contributions of this article are three-fold: 1) the domain-discrepancy problem in intelligent logging lithology identification is first investigated in this article, and a novel framework that incorporates AL and DA into lithology identification is proposed to handle the problem; 2) we design a discrepancy-based AL and pseudolabeling (PL) module and an instance importance weighting module to query the most uncertain target information and retain the most confident source information, which solves the challenges of cost limitation and distribution misalignment; and 3) we develop a reliability detecting module to improve the reliability of target pseudolabels, which, together with the discrepancy-based AL and PL module, solves the challenge of data divergence. Extensive experiments on three real-world well-logging datasets demonstrate the effectiveness of the proposed method compared to the baselines.

In this article, an adaptive neural safe tracking control scheme is studied for a class of uncertain nonlinear systems with output constraints and unknown external disturbances. To allow the output to stay in the desired output constraints, a boundary protection approach is developed and utilized in the output constrained problem. Since the generated output constraint trajectory is piecewise differentiable, a dynamic surface method is utilized to handle it. For the purpose of approximating the system uncertainties, a radial basis function neural network (RBFNN) is adopted. Under the output of the RBFNN, the disturbance observer technology is employed to estimate the unknown compound disturbances of the system. Finally, the Lyapunov function method is utilized to analyze the convergence of the tracking error. Taking a two-link manipulator system, as an example, the simulation results are presented to illustrate the feasibility of the proposed control scheme.

The scheduling and control of wireless cloud control systems involving multiple independent control systems and a centralized cloud computing platform are investigated. For such systems, the scheduling of the data transmission as well as some particular design of the controller can be equally important. From this observation, we propose a dual channel-aware scheduling strategy under the packet-based model predictive control framework, which integrates a decentralized channel-aware access strategy for each sensor, a centralized access strategy for the controllers, and a packet-based predictive controller to stabilize each control system. First, the decentralized scheduling strategy for each sensor is set in a noncooperative game framework and is then designed with asymptotical convergence. Then, the central scheduler for the controllers takes advantage of a prioritized threshold strategy, which outperforms a random one neglecting the information of the channel gains. Finally, we prove the stability for each system by constructing a new Lyapunov function, and further reveal the dependence of the control system stability on the prediction horizon and successful access probabilities of each sensor and controller. These theoretical results are successfully verified by numerical simulation.

In this article, the consensus problem of linear systems is revisited from a novel geometric perspective. The interaction network of these systems is assumed to be piecewise fixed. Moreover, it is allowed to be disconnected at any time but holds a quite mild joint connectivity property. The system matrix is marginally stable and the input matrix is not of full-row rank. By directly examining the subspace determined by the network, we first establish convergence by resorting to an observability condition. Then, according to joint connectivity, we are able to extend this convergence uniformly to the entire orthogonal complement of the consensus manifold. In this way, we work out the necessary and sufficient condition for exponential consensus. It turns out that, with a suitably designed feedback matrix, exponential consensus can be realized globally and uniformly if and only if a jointly (δ,T) -connected condition and an observability condition relying only on the system and input matrices are satisfied. We also characterize the lower bound of the convergence rate. Simple yet effective examples are presented to illustrate the findings.

This article studies the containment control problem for a group of linear systems, consisting of more than one leader, over switching topologies. The input matrices of these linear systems are not required to have full-row rank and the switching can be arbitrary, making the problem quite general and challenging. We propose a novel analysis framework from the viewpoint of a state transition matrix. Specifically, according to the inherent linearity, we successfully establish a connection between state transition matrices of the above multileader system and a virtual leader-following system obtained by combining those leaders. This enlightening result relates the containment problem to a consensus one. Then, by analyzing the property of the state transition matrix, we uncover that each component of any follower's state converges to the convex hull spanned by the corresponding components of the leaders', provided some mild conditions are satisfied. These conditions are derived in terms of the concept of a positive linear system. A special case of the second-order linear system is further discussed to illustrate these conditions. Moreover, two different design methods of the feedback gain matrix are provided, which additionally require that the network topology contains a united spanning tree all the time.

Identifying borehole lithofacies through geop- hysical loggings is a fundamental task in petroleum exploration industry. Recent interdisciplinary studies have demonstrated the feasibility of applying machine learning to lithofacies identification. Most of these studies establish a mapping from the logging values at one depth point to the lithofacies type. However, due to the intrinsic properties of geophysical loggings, the logging shape should be taken into consideration, apart from the absolute values. In this article, we present the attempt to predict the lithofacies by feeding logging segments, and for the first time model the logging lithofacies identification problem as 1-D semantic segmentation. Such a logging segmentation task is challenging due to two reasons, strong spatial heterogeneity of lithofacies subsurface distribution and the explicit physical significance of geophysical loggings. To solve these challenges, we propose a novel geophysical logging segmentation network entitled SegLog. Specifically, we develop a global statistics pooling subnetwork and a statistics fusion subnetwork to generate statistical embeddings of geophysical loggings. Based on these statistical embeddings, we design a pixel-enhanced convolutional subnetwork to learn the microdetailed features, indicated by pixel-level logging values. These features are fused with the macrosemantic features extracted by a backbone U-Net to constitute the representations that can simultaneously describe the logging spatial correlation and pixel specificity. Experimental results on two logging datasets from the Jiyang Depression verify the effectiveness of our modeling strategy and its state-of-the-art performance on the lithofacies identification problem.

Optimal energy management of smart grids requires the information exchange between devices, which may disclose private information to the adversaries and further lead to great losses. To this end, this article considers the privacy-preserving optimal energy management problem for smart grids, which integrates both the power allocation of distributed energy resources on the supply side and the demand response of distributed load demands on the demand side. We first propose a cloud-edge computing structure of the smart grid and model the optimal energy management problem as the maximization problem of social welfare including the supply-side net benefit and the demand-side net utility, while maintaining the supply–demand balance and satisfying the operating constraints. A privacy-preserving average consensus algorithm is then developed, where each node sends the projected states to their neighbors to protect the privacy of the initial state. By applying the privacy-preserving average consensus algorithm, we propose a distributed privacy-preserving optimal energy management algorithm based on the generalized alternating direction method of multipliers. Finally, simulation examples are provided to validate the effectiveness of the proposed algorithms.

Driving cycles construction, which aims to generate various vehicle driving profiles corresponding to typical traffic conditions, plays an important role in the evaluation of vehicle emissions, economy and mileage. Existing methods usually represent the speed-time distributions of driving data in the space spanned by hand-crafted features, and select typical sequences to combine driving cycle curves. However, since the driving data is treated as static, the inherent dynamic characteristics and temporal dependency tend to be ignored, resulting in low accuracy and insufficient robustness. To address this issue, this paper proposes a dynamic driving cycle construction framework, in which feature extraction and sequence clusters are achieved in an unsupervised joint learning manner. Specifically, the driving data are firstly encoded by a Bi-directional Long Short-Term Memory (BiLSTM) branch to capture the temporal correlation property of driving sequences. Then, a temporal clustering branch is presented to achieve soft distribution clustering of feature sequences by introducing a relative-entropy-based regularization term into the coding unit. The two branches are iteratively updated until stable feature learning and clustering results are obtained. Consequently, each branch benefits from the additional improvement over the previous branch during the iteration process. Finally, typical driving sequences are selected according to the intra-class/extra-class distance and class proportion, and then assembled to generate driving cycles profiles. To verify the performance of our proposed method, evaluations are performed on the on-road driving data of light vehicles in Fuzhou, in which the constructed driving cycle from our methods is substituted into COPERT model to estimate and visualize the road emissions, and the experimental results demonstrate that our proposed methods can greatly improve the accuracy and robustness of the constructed driving cycle.

The development of electric vehicle (EV) industry is facing a series of issues, among which the efficient charging of multiple EVs needs solving desperately. This paper investigates the coordinated charging of multiple EVs with the aim of reducing the charging cost, ensuring a high battery state of charge (SoC), and avoiding the transformer overload. To this end, we first formulate the EV coordinated charging problem with the above multiple objectives as a Markov Decision Process (MDP) and then propose a multi-agent deep reinforcement learning (DRL)-based algorithm. In the proposed algorithm, a novel interaction model, i.e., communication neural network (CommNet) model, is adopted to realize the distributed computation of global information (namely the electricity price, the transformer load, and the total charging cost of multiple EVs). Moreover, different from the most existing works which make specific constraints on the size, the location, or the topology of the distribution network, what we need in the proposed method is only the transformer load. Besides, due to the use of long and short-term memory (LSTM) for price prediction, the proposed algorithm can flexibly deal with various uncertain price mechanisms. Finally, simulations are presented to verify the effectiveness and practicability of the proposed algorithm in a residential charging area.

Inspired by the collective decision making in biological systems, such as honeybee swarm searching for a new colony, we study a dynamic collective choice problem for large-population systems with the purpose of realizing certain advantageous features observed in biology. This problem focuses on the situation where a large number of heterogeneous agents subject to adversarial disturbances move from initial positions toward one of the destinations in a finite time while trying to remain close to the average trajectory of all agents. To overcome the complexity of this problem resulting from the large population and the heterogeneity of agents, and also to enforce some specific choices by individuals, we formulate the problem under consideration as a robust mean-field game with non-convex and non-smooth cost functions. Through Nash equivalence principle, we first deal with a single-player$H_{\infty }$tracking problem by taking the population behavior as a fixed trajectory, and then establish a mean-field system to estimate the population behavior. Optimal control strategies and worst disturbances, independent of the population size, are designed, which give a way to realize the collective decision-making behavior emerged in biological systems. We further prove that the designed strategies constitute$\epsilon _{N}$-Nash equilibrium, where$\epsilon _{N}$goes toward zero as the number of agents increases to infinity. The effectiveness of the proposed results are illustrated through two simulation examples.

This article investigates a bounded cluster synchronization problem of dynamical systems, which can be of the generic linear type or Lipschitz nonlinear type, over directed switching network. Each cluster is equipped with a virtual leader which produces the desired trajectory for the agents to track. It is required only a fraction of systems is influenced by the leader. The interaction topology, which describes the information exchange among the dynamical systems as well as the virtual leaders, is allowed to be time varying with a well-defined average over an infinite horizon. That is, each augmented cluster, consisting of the agents as well as the corresponding virtual leader, in the time-average network topology is required to have a directed spanning tree. We then transform the cluster synchronization problem into a stability problem via the averaging method. It is proved that the convergence property for both types of dynamical systems is exclusively determined by the averaging system if the network topology switches sufficiently fast compared to original systems. Finally, it is concluded that if the intracluster coupling strength of the time-average topology is stronger than a threshold, then bounded cluster synchronization can be realized for a fast switching linear or nonlinear systems. Two examples are provided to verify our results.

In this letter, considering the lack of core and drilling cuttings, an interpretable semisupervised classification method (ISSCM) under multiple smoothness assumptions is proposed and applied to lithology identification. The contribution is threefold. First, the novel semisupervised learning algorithm is developed based on the decision tree, the interpretability of which is highly beneficial to solve risk-aware problems. Second, both smoothness in the feature space and depth is utilized to generate pseudo-labels for the unlabelled data by using label propagation. Third, an algorithm to approximate the optimal affinity matrix is added to avoid degradation rendered by inappropriate manual settings under multiple smoothness assumptions. All these contributions could yield a classification model that is interpretable, accurate, and insusceptible to imprecise empirical settings. In the experiment, the proposed method is applied to lithology identification and verified by real-world data.

Singularity issue has long been a concern of the task-space control design for Euler-Lagrange systems. In classical task-space controls, robots are often assumed to operate in the task space, where singularities do not exist. Such an assumption limits their potential applications in various workspaces. To address the potential singularity issue associated with Euler-Lagrange systems, this article proposes an adaptive fuzzy-region-based control for Euler-Lagrange systems with kinematically singular configurations. Singular regions are described by the potential energy function. The proposed controller includes a joint-space control, which is active when the system approaches singular regions, and a task-space control, which is used to track the desired trajectory. Therefore, the system can smoothly transit from singular regions to nonsingular regions or can achieve singularity avoidance during the tracking task. In order to achieve singularity avoidance while reducing control effort, the coefficients of the potential energy function are adjusted dynamically based on the designed fuzzy system. Rigorous analysis shows that singularity issues can be properly handled, and the asymptotic stability of the system is ensured. Experiments are conducted to demonstrate the effectiveness of the proposed controller.

Considering the fact that in the real world, a certain agent may have some sort of advantage to act before others, a novel hierarchical optimal synchronization problem for linear systems, composed of one major agent and multiple minor agents, is formulated and studied in this article from a Stackelberg-Nash game perspective. The major agent herein makes its decision prior to others, and then, all the minor agents determine their actions simultaneously. To seek the optimal controllers, the Hamilton-Jacobi-Bellman (HJB) equations in coupled forms are established, whose solutions are further proven to be stable and constitute the Stackelberg-Nash equilibrium. Due to the introduction of the asymmetric roles for agents, the established HJB equations are more strongly coupled and more difficult to solve than that given in most existing works. Therefore, we propose a new reinforcement learning (RL) algorithm, i.e., a two-level value iteration (VI) algorithm, which does not rely on complete system matrices. Furthermore, the proposed algorithm is shown to be convergent, and the converged values are exactly the optimal ones. To implement this VI algorithm, neural networks (NNs) are employed to approximate the value functions, and the gradient descent method is used to update the weights of NNs. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.

Urban vehicle emission prediction can help the regulation of vehicle pollution and traffic control. However, it is hard to predict the spatiotemporal variation of vehicle emission because of the spatial interactions and temporal correlations between different road segments as well as the high nonlinearity and complexity of vehicle emission variation. The existing methods solve the problem by splitting the region into standard segments or grids based on conventional deep learning methods, without considering that urban vehicle emission varies by graph-structured traffic road network and depends on many complex external environment factors. To address these issues, a spatiotemporal graph convolution multifusion network (ST-MFGCN) is proposed to leverage the graph structural properties as the inherent connectivity of road network for urban vehicle emission prediction, which can capture the vehicle emission spatiotemporal variation patterns and learn the effects of complex environmental factors. The proposed model consists of three parts: 1) a spatiotemporal graph convolution module to capture spatiotemporal dependencies by merging closeness, period, and trend sequences with temporal convolution as well as graph convolution is introduced to model the spatial dependencies; 2) an external factor component to divide multisource external factors into global and individual external features; and 3) a general fusion component to merge the spatiotemporal patterns and the external features as well as fit the mutation of emission measurement data by multifusion strategy. Finally, the proposed model is evaluated on the practical monitoring data of vehicle emission data in Hefei, and the results demonstrate that our proposed model can predict regional vehicle emissions effectively.

This article investigates the predictive control scheme and related stability issue for a class of discrete-time perturbed nonlinear system with state and input constraints. First, we propose a novel control framework, i.e., networked dual-mode adaptive horizon model predictive control (MPC), which consists of a local controller, a remote controller that is subject to packet losses, and a judger coordinating the switchings between them. The optimization procedure of MPC with variable prediction horizon is implemented in the remote controller while a simple state-feedback control law is in the local one. Second, to establish the stability condition, we propose a new Lyapunov function. By specifying the relation between the Lyapunov function and the optimal MPC value function, the input-to-state practical stability is established. Finally, simulation results show the effectiveness of our proposed control scheme.

Pansharpening aims to generate the high spatial resolution multispectral (HRMS) images by fusing the spatial and spectral information from the low resolution multispectral (LRMS) images and high resolution panchromatic (PAN) images. Although existing pansharpening methods excel at achieving visual pleasing HRMS, they are limited in providing discriminability for visual tasks. To address this problem, this paper proposes a mask prior guided network (MaskPan) for pansharpening, which incorporates high-level semantic features with low-level detail information to improve the visual discrimination and quality of pansharpened images simultaneously. To make full use of the mask prior, the spatial and spectral features in conjunction with the semantic features are firstly fused in feature domain, and then promoted by an attention mechanism. In addition, the semantic segmentation task is introduced as a new metric to evaluate the visual discrimination of pansharpened images. Experimental results show that the proposed MaskPan can effectively enhance image quality and visual discrimination, thereby improving the pansharpening performance.

Exoskeleton robots can assist humans to perform activities of daily living with little effort. In this paper, a hierarchical control scheme is presented which enables an exoskeleton robot to achieve cooperative manipulation with humans. The control scheme consists of two layers. In low-level control of the upper limb exoskeleton robot, an admittance control scheme with an asymmetric barrier Lyapunov function-based adaptive neural network controller is proposed to enable the robot to be back drivable. In order to achieve high-level interaction, a strategy for learning human skills from demonstration is proposed by utilizing Gaussian mixture models, which consists of the learning and reproduction phase. During the learning phase, the robot observes and learns how a demonstrator performs a specific impedance-based task successfully, and in the reproduction phase, the robot can provide the subjects with just enough assistance by extracting human skills from demonstrations to prevent the motion of the robot end-effector deviating far from desired ones, due to variation in the interaction force caused by environmental disturbances. Experimental results of two different tasks show that the proposed control scheme can provide human subjects with assistance as needed during cooperative manipulation.

This article considers the resilient consensus problems of discrete-time complex cyber-physical networks under F-local deception attacks. A resilient consensus algorithm, where extreme values received are removed by each node, is first introduced. By utilizing the presented algorithm, a necessary and sufficient condition to ensure resilient consensus in the absence of trusted edges is then provided by means of network robustness. We further generalize the notion of network robustness and present the necessary and sufficient condition for the achievement of resilient consensus in the presence of trusted edges. In addition, we show that through appropriately assigning the trusted edges, the resilient consensus can be reached under arbitrary communication network. Finally, the validity of the theoretical findings is demonstrated by simulation examples.

Three‐dimensional (3D) [red, green and blue (RGB) + depth] saliency modelling can help with popular 3D multimedia applications. However, depth images produced from existing 3D devices are often with low quality, e.g. containing noises and holes. In this study, rather than relying on features or predictions directly derived from single depth images, the authors propose to encode deep layout features to facilitate the spatial‐aware saliency prediction. Specifically, they first generate coarse depth‐induced saliency cues which are careless of depth details. Then, to leverage the information of the high‐quality RGB image, they embed both low‐level and high‐level RGB deep features to refine the final prediction. In this way, they take both bottom‐up and top‐down cues together with spatial layout into account and achieve better saliency modelling results. Experiments on five public datasets show the superiority of the proposed method.

Identifying code has been widely used in man-machine verification to maintain network security. The challenge in engaging man-machine verification involves the correct classification of man and machine tracks. In this study, we propose a random forest (RF) model for man-machine verification based on the mouse movement trajectory dataset. We also compare the RF model with the baseline models (logistic regression and support vector machine) based on performance metrics such as precision, recall, false positive rates, false negative rates, F -measure, and weighted accuracy. The performance metrics of the RF model exceed those of the baseline models.

In this paper, we investigate the output containment control problem for a network of heterogeneous linear multiagent systems. The control target is to drive the outputs of the followers into the convex hull spanned by the leaders. To this end, we first derive a necessary condition imposed on both system dynamics and network topology from the viewpoint of internal model principle. Then, based on the necessary condition, we utilize a dynamic controller to drive the outputs of the leaders and followers to track the reference trajectories to achieve containment exponentially. We consider a general network topology which only contains a united spanning tree. Both fixed and dynamic network topologies are taken into consideration. Then, the optimal control problem for containment is further studied. An optimal control law is constructed from an algebraic Riccati equation, which is proved to be a stabilizing one as well. Finally, a reinforcement learning algorithm is introduced to solve the optimal control problem on line without the knowledge the system dynamics. Simulations are given at last to validate our theoretical findings.

This paper investigates the consensus tracking problem of second-order nonlinear multiagent systems (MAS) with disturbance and actuator fault by the sliding mode control method. The communication topology of the MAS is directed and only part of the followers have access to the leader's information. First, a discontinuous sliding mode tracking protocol is studied for consensus tracking of the MAS. Second, to address the shortcoming of chattering and difficulty of setting the control gain in the discontinuous protocol, a continuous sliding mode tracking protocol with an adaptive mechanism is developed. The adaptive mechanism will adjust the gain of the control automatically and enable the tracking protocol to work well without prior knowledge of the MAS. Third, the performance of the adaptive sliding mode protocol for consensus tracking of the MAS in the presence of actuator faults of biased fault and partial loss of effectiveness fault is further investigated. Finally, numerical simulations are performed to illustrate the efficiency of the theoretical results.

An intuitionistic fuzzy cognitive map (IFCM) is an extension of a fuzzy cognitive map (FCM) that forms a graph-oriented fuzzy map describing both causal relationships between pairs of concepts and the states of concepts via intuitionistic fuzzy sets (IFSs). In contrast with an FCM, an IFCM provides much more flexibility in system modeling. However, IFCMs may lead to confusing or unreasonable results in system modeling since they do not fully consider the negative influence from conventional operations on IFSs, the activation process of concepts, and the problem of aggregating knowledge with different importance levels. To solve the challenges of IFCMs, we propose a high-order IFCM based on evidential reasoning (ER) (IFCMR) theory in this study. First, we introduce an evidential intuitionistic fuzzy aggregation (EIFA) operator and a multiplication operation on IFSs using an ER theory. Second, we establish the theory of IFCMR based on the EIFA operator and the newly introduced multiplication operation on IFSs. Third, we propose a scheme of aggregating IFCMRs with different importance levels using the EIFA operator, which can also be utilized to aggregate conflict knowledge and to determine objective connections in terms of an evidential cognitive map (ECM). Finally, several numerical and practical examples are employed to test and verify the feasibility and validity of IFCMRs in comparison with both IFCMs and ECMs.

Helicopters are complex high-order and time-varying nonlinear systems, strongly coupling with aerodynamic forces, engine dynamics, and other phenomena. Therefore, it is a great challenge to investigate system identification for dynamic modeling and adaptive control for helicopters. In this paper, we address the system identification problem as dynamic regression and propose to represent the uncertainties and the hidden states in the system dynamic model with a deep convolutional neural network. Particularly, the parameters of the network are directly learned from the real flight data of aerobatic helicopter. Since the deep convolutional model has a good performance for describing the dynamic behavior of the hidden states and uncertainties in the flight process, the proposed identifier manifests strong robustness and high accuracy, even for untrained aerobatic maneuvers. The effectiveness of the proposed method is verified by various experiments with the real-world flight data from the Stanford Autonomous Helicopter Project. Consequently, an adaptive flight control scheme including a deep convolutional identifier and a backstepping-based controller is presented. The stability of the flight control scheme is rigorously proved by the Lyapunov theory. It reveals that the tracking errors for both the position and attitude of unmanned helicopter asymptotic converge to a small neighborhood of the origin.

In this paper, a closed-loop control has been developed for the exoskeleton robot system based on brain-machine interface (BMI). Adaptive controllers in joint space, a redundancy resolution method at the velocity level, and commands that generated from BMI in task space have been integrated effectively to make the robot perform manipulation tasks controlled by human operator's electroencephalogram. By extracting the features from neural activity, the proposed intention decoding algorithm can generate the commands to control the exoskeleton robot. To achieve optimal motion, a redundancy resolution at the velocity level has been implemented through neural dynamics optimization. Considering human-robot interaction force as well as coupled dynamics during the exoskeleton operation, an adaptive controller with redundancy resolution has been designed to drive the exoskeleton tracking the planned trajectory in human brain and to offer a convenient method of dynamics compensation with minimal knowledge of the dynamics parameters of the exoskeleton robot. Extensive experiments which employed a few subjects have been carried out. In the experiments, subjects successfully fulfilled the given manipulation tasks with convergence of tracking errors, which verified that the proposed brain-controlled exoskeleton robot system is effective.

In network systems, a group of nodes may evolve into several subgroups and coordinate with each other in the same subgroup, i.e., reach cluster synchronization, to cope with the unanticipated situations. To this end, the leader-following practical cluster synchronization problem of networks of generic linear systems is studied in this paper. An event-based control algorithm that can largely reduce the amount of communication is first proposed over directed communication topologies. In the proposed algorithm, each node decides itself when to transmit its current state to its neighbors and how to update its controller according to the estimations of the states of it and its neighbors. Then, the Lyapunov method is utilized to perform the convergence analysis. It shows that the practical cluster synchronization can be ensured by choosing appropriate parameters no matter what kind of estimation for the state is applied. Furthermore, the Zeno behavior is also excluded for each node under some mild assumptions. Besides, three kinds of common estimations for the states including zero-order hold model, first-order approximate model, and high-order model-based estimations are, respectively, analyzed from the perspective of the exclusion of Zeno behavior. Finally, the validity of the proposed algorithm is demonstrated, the effects of the concerned parameters are simply presented, and the effects of the three estimations are also compared through several simulations.

In this paper, the consensus problem is investigated for a class of nonaffine nonlinear multiagent systems (MASs) with actuator faults of partial loss of effectiveness fault and biased fault. To deal with the control difficulty caused by the nonaffine dynamics, a neural network (NN)-based adaptive consensus protocol is developed based on the Lyapunov analysis. The neuron input of the NN uses both the state information and the consensus error information. In addition, the negative feedback term of the NN weight update law is multiplied by an absolute value of the consensus error, which is helpful in improving the consensus accuracy. With the developed adaptive NN consensus protocol, semiglobal consensus with a bounded residual consensus error of the MAS is achieved, and the bounded NN weight matrix is guaranteed. Finally, simulation results show that the developed adaptive NN consensus protocol has advantages of fast convergence rate and good consensus accuracy and has the capability of rapid response with respect to the actuator faults.

This paper presents a novel indoor localization method for skid-steering mobile robot by fusing the readings from encoder, gyroscope, and magnetometer which can be read as an enhanced dead-reckoning localization method. Compared with the traditional dead-reckoning localization method implemented by encoder only, the accuracy and reliability can be improved significantly in spite of the price of slightly higher cost in digital devices. The proposed strategy consists mainly of an orientation algorithm and a localization algorithm. First, realizing that gyroscope is barely affected by magnetic field and magnetometer-based orientation has no cumulative error, a novel orientation algorithm, based on the self-tuning Kalman filter coupled with a gross error recognizer, is developed. This orientation algorithm can be applied to determine the robot heading angle in the situation with abundant ferromagnetic materials. Second, based on the orientation algorithm we have proposed, a novel localization algorithm is designed by decomposing the robot motion into uniform linear motion and uniform circular motion. The effectiveness of the proposed indoor localization method is verified via the real-world experiment using a tracked mobile robot developed in our laboratory.

The cluster synchronization problem is investigated using intermittent pinning control for the interacting clusters of nonidentical nodes that may represent either general linear systems or nonlinear oscillators. These nodes communicate over general network topology, and the nodes from different clusters are governed by different self-dynamics. A unified convergence analysis is provided to analyze the synchronization via intermittent pinning controllers. It is observed that the nodes in different clusters synchronize to the given patterns if a directed spanning tree exists in the underlying topology of every extended cluster (which consists of the original cluster of nodes as well as their pinning node) and one algebraic condition holds. Structural conditions are then derived to guarantee such an algebraic condition. That is: 1) if the intracluster couplings are with sufficiently strong strength and the pinning controller is with sufficiently long execution time in every period, then the algebraic condition for general linear systems is warranted and 2) if every cluster is with the sufficiently strong intracluster coupling strength, then the pinning controller for nonlinear oscillators can have its execution time to be arbitrarily short. The lower bounds are explicitly derived both for these coupling strengths and the execution time of the pinning controller in every period. In addition, in regard to the above-mentioned structural conditions for nonlinear systems, an adaptive law is further introduced to adapt the intracluster coupling strength, such that the cluster synchronization for nonlinear systems is achieved.

The vehicle emission remote sensing system is one promising solution to monitor the emissions of on-road vehicles that contribute to the air pollution in urban areas. To implement such a system an effective location strategy to place the monitors is yet to be designed. To this purpose we formulate a novel location problem where the minimum subset of roads on which traffic emission monitors are located is to be found only using the topological structure and some other available information of the traffic network. We solve this problem by transforming it into a graph-theoretic problem and considering more characteristics such as the traffic regulations and limits. After modeling the real-world traffic network as a digraph, a two-step algorithm is developed. The first step is to find all directed circuits to establish hypergraph-based set of directed circuits using the depth first searching strategy. In the second step, an approximation algorithm is designed to find the greedy transversal which is a subset of roads to place vehicle emission monitors in order to cover all the traffic circuits. The performance of the location strategy is validated by both theoretical developments and illustrative examples.

In this paper, we address a haze removal problem from a single nighttime image, even in the presence of varicolored and non-uniform illumination. The core idea lies in a novel maximum reflectance prior. We first introduce the nighttime hazy imaging model, which includes a local ambient illumination item in both direct attenuation term and scattering term. Then, we propose a simple but effective image prior, maximum reflectance prior, to estimate the varying ambient illumination. The maximum reflectance prior is based on a key observation: for most daytime haze-free image patches, each color channel has very high intensity at some pixels. For the nighttime haze image, the local maximum intensities at each color channel are mainly contributed by the ambient illumination. Therefore, we can directly estimate the ambient illumination and transmission map, and consequently restore a high quality haze-free image. Experimental results on various nighttime hazy images demonstrate the effectiveness of the proposed approach. In particular, our approach has the advantage of computational efficiency, which is 10-100 times faster than state-of-the-art methods.

In this paper, the non-fragile finite-time tracking control problem is addressed for a class of uncertain linear systems with controller multiplicative coefficient variations. An adaptive control strategy is constructed to ensure that the system tracks a time-varying target orbit. The relationship of the bound of tracking errors and the size of uncertainties and controller multiplicative coefficient variations is deeply investigated. On the basis of Lyapunov stability theory, it shows that the bounded tracking of resulting adaptive system can be reached within a finite time, and the tracking errors of the system can be reduced as small as desired by adjusting controller parameters. The effectiveness of the proposed design is illustrated via a decoupled longitudinal model of F-18 aircraft.

This paper investigates group synchronization for multiple interacting clusters of nonidentical systems that are linearly or nonlinearly coupled. By observing the structure of the coupling topology, a Lyapunov function-based approach is proposed to deal with the case of linear systems which are linearly coupled in the framework of directed topology. Such an analysis is then further extended to tackle the case of nonlinear systems in a similar framework. Moreover, the case of nonlinear systems which are nonlinearly coupled is also addressed, however, in the framework of undirected coupling topology. For all these cases, a consistent conclusion is made that group synchronization can be achieved if the coupling topology for each cluster satisfies certain connectivity condition and further, the intra-cluster coupling strengths are sufficiently strong. Both the lower bound for the intra-cluster coupling strength as well as the convergence rate are explicitly specified.

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

In order to mitigate the air pollution caused by traffic, the monitoring of on-road vehicle emission is really an urgent issue. The Vehicle Emission Remote Sensing System (VERSS) is a promising technology to solve this problem. But there is scarcely any available location strategy for traffic emission monitors yet to our knowledge, which restraints the use of monitors on a large scale of traffic network. In this paper, we make some efforts to solve a novel location problem in the transportation domain, that is, we look for the minimum subset of roads on which traffic emission monitors should be located, thus we can detect as many on-road vehicles as possible. We explicate how to transform the location problem to some graph problems and give the problem formulation mathematically. Then a two-step algorithm is designed to find the set of roads to locate monitors. The simulation test verify its availability. And in the last section some problems that should be studied further are presented at the end of the paper.

The real-time mapping of street atmospheric pollution concentration does play an important role because its knowledge is crucial for strategy-makers to make more effective control strategies to decrease urban atmospheric pollution and improving urban atmospheric environment. Combining the conventional methods (e.g. the dispersion model prediction and neural network prediction) and mobile measurement technology (e.g. the GMAP vehicle) which their characteristics are complementary, a linear model is proposed and then a fusion approach called weighting filter derived from the concept of Kalman filter. Moreover, a self-tuning regulator is introduced to adjust the parameters of filter for the changing noise statistical characteristics over time which mainly caused by season switch. The performances of asymptotic stability and asymptotic optimality are both mathematically proven. Finally a simulation test is conducted to verify this approach.

Some applications of the interconnected embedded systems such as sensor networks rely on all nodes in the network to execute certain tasks simultaneously. To meet this demand for simultaneity, a multi-timer model based fully distributed task synchronization algorithm is proposed in this paper. In this multi-timer model, each node containing an embedded system is characterized by a timer. The microcontrollers (MCUs) within the interconnected embedded systems are switched to the assigned tasks by timer interrupts. Each timer decides when to trigger interrupts by only using the information from its neighbors. Task synchronization is realized by using the proposed synchronization algorithm. Some simulation examples are presented in the end to verify the effectiveness of the proposed synchronization algorithm.

In this paper, the contraction theory is used to analyze the synchronization for a collection of partial-state linearly coupled linear systems. First, the synchronization problem of the linear systems is transformed by defining proper error variables such that a stability problem of error systems is to be investigated. Then, the contraction analysis is performed with respect to the error system dynamics. It turns out that the error system dynamics is contracting, which in turn proves that the original systems reach synchronization exponentially fast. In addition, a brief comparison between Lyapunov method and contraction analysis is also provided. Finally, two examples are presented in order to illustrate the effectiveness of the theoretical result.

This paper studies the fault-tolerant consensus problem for a group of double-integrator agents with actuator faults and strongly connected topology. The proposed fault-tolerant consensus protocol is an active fault-tolerant control strategy which consists of a nominal control and an estimation of fault severity. To solve the fault-tolerant consensus problem, a Lyapuov method is employed based on the algebraic connectivity of strongly connected digraph. The results show that the consensus will be achieved if the nominal control is designed properly and the estimation of actuator fault is within a certain accuracy. Finally, a simulation example is given to demonstrate the validity of the theoretical results.

An extended asynchronous switching model is investigated for a class of switched stochastic nonlinear retarded systems in the presence of both detection delay and false alarm, where the extended asynchronous switching is described by two independent and exponentially distributed stochastic processes, and further simplified as Markovian. Based on the Razumikhin-type theorem incorporated with average dwell-time approach, the sufficient criteria for global asymptotic stability in probability and stochastic input-to-state stability are given, whose importance and effectiveness are finally verified by numerical examples.