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A lensless holographic microscope based on in-line holograms and optical diffraction tomography is an ideal imaging system for label-free 3D biological samples and can achieve large-volume imaging with single-cell resolution in a convenient way. However, due to the phase information loss and the missing cone problem, the imaging quality is significantly degraded by the reconstructed artifacts of twin images and out-of-focus images, which severely hinders the identification and interpretation of the objects. We propose an artifacts-free lensless on-chip tomography certified by three-dimensional deconvolution, which facilitates the extraction of real object morphology through straightforward yet effective computation. Initially, a globally valid systemic point spread function (PSF) is generated by simulating the imaging output of an ideal point light source positioned at the origin of the object space coordinate. Subsequently, an iterative three-dimensional deconvolution process is applied to the primitive imaging outcome of the lensless on-chip tomography using this PSF. Through rapid iterations, the optimized imaging result is swiftly obtained. Both the simulated and experimental results indicate that the artifacts-free lensless on-chip tomography can effectively circumvent the reconstructed artifacts and retrieve the real object morphology, which is critical for detailed observation and further quantitative analysis. In addition, we anticipate that the proposed approach has the potential to be transferred to other 3D imaging systems in systemic artifacts removal after corresponding modifications. [ABSTRACT FROM AUTHOR]

Senile plaque blue autofluorescence was discovered around 40 years ago, however, its impact on Alzheimer's disease (AD) pathology has not been fully examined. We analyzed senile plaques with immunohistochemistry and fluorescence imaging on AD brain sections and also Aβ aggregation in vitro. In DAPI or Hoechst staining, the nuclear blue fluorescence could only be correctly assigned after subtracting the blue plaque autofluorescence. The flower-like structures wrapping dense-core blue fluorescence formed by cathepsin D staining could not be considered central-nucleated neurons with defective lysosomes since there was no nuclear staining in the plaque core when the blue autofluorescence was subtracted. Both Aβ self-oligomers and Aβ/hemoglobin heterocomplexes generated blue autofluorescence. The Aβ amyloid blue autofluorescence not only labels senile plaques but also illustrates red cell aggregation, hemolysis, cerebral amyloid angiopathy, vascular plaques, vascular adhesions, and microaneurysms. In summary, we conclude that Aβ-aggregation-generated blue autofluorescence is an excellent multi-amyloidosis marker in Alzheimer's disease. [ABSTRACT FROM AUTHOR]

In this paper, a distributed nonsingular terminal sliding mode control (NTSMC) is proposed for vehicular platoons subjected to nonlinear uncertainty, external disturbance, and input saturation. Due to the presence of input saturation, the platoon control becomes more complicated. However, only considering the impact of uncertainty on the system, input saturation will usually lead to a decline in the driving performance, even lead to string instability. First, the input saturation is compensated by a single parameter, which is simple and direct. The radial basis function neural network (RBFNN) based on disturbance observer is employed to compensate the nonlinear uncertainty and external disturbance, respectively. The basis function of neural network is only related to the velocity and acceleration of the leader. Therefore, the nonlinearity of the vehicle systems does not need to meet the matching conditions. Then, a distributed NTSMC is designed to realize the internal stability, which weakens the chattering of traditional sliding mode control (SMC) to some extent. In addition, the nonsingular problem in terminal sliding mode control (TSMC) is solved. The string stability is realized by employing a coupled sliding mode control (CSMC). Finally, simulation results demonstrate the effectiveness and feasibility of the proposed strategy. [ABSTRACT FROM AUTHOR]

Due to the energy crisis and environmental deterioration, the emerging hybrid energy ship power system gradually replaced the traditional ship power system to keep environmental friendliness by employing the clean energy. However, the increase of energy storage and photovoltaic generation system brings enormous challenge to the optimization scheduling of hybrid energy ship power system. For this reason, an improved genetic algorithm-based optimal scheduling strategy for the hybrid energy ship power system is developed in this paper. Firstly, a novel hybrid energy ship power system model including the diesel generator, energy storage system, propulsion system, dynamic load and photovoltaic power generation device is constructed under the constraint of energy efficiency and greenhouse gases emissions. Considering the various navigation situations that the ship may encounter, such as photovoltaic power generation limit in extreme weather and diesel generator power change in load shedding, the corresponding scheduling optimization problems for the hybrid energy ship power system are established. Under the cost and gas emission constraints, an improved genetic algorithm-based scheduling optimization algorithm is proposed. By introducing the nonlinear parameter change model in crossover and mutation operator, the performance of improved genetic algorithm can be enhanced, such as convergence speed and global optimization ability. Compared with current works, the proposed scheduling optimization strategy can achieve the lowest cost while reducing environmental impacts. Finally, simulation results under the given navigation cases demonstrate the superiority of the proposed improved genetic algorithm-based scheduling optimization strategy. [ABSTRACT FROM AUTHOR]

Due to the integration of cyber–physical systems, smart grids have faced the new security risks caused by false data injection attacks (FDIAs). FDIAs can bypass the traditional bad data detection techniques by falsifying the process of state estimation. For this reason, this paper studies the detection and isolation problem of FDIAs based on the adaptive Kalman filter bank (AKFB) in smart grids. Taking the covert characteristics of FDIAs into account, a novel detection method is proposed based on the designed AKF. Moreover, the adaptive threshold is proposed to solve the detection delay caused by a priori threshold in the current detection methods. Considering the case of multiple attacked sensor nodes, the AKFB-based isolation method is developed. To reduce the number of isolation iterations, a logical decision matrix scheme is designed. Finally, the effectiveness of the proposed detection and isolation method is demonstrated on an IEEE 22-bus smart grids. [ABSTRACT FROM AUTHOR]

Autonomous robotics working in the uncertain environment have drawn increasing interests from researchers. Here, an issue of online motion optimization under unknown environment is considered while preserving the safety and improving the flexible manoeuvrability of robot–environment interaction. This problem is addressed by improving the conventional dynamic movement primitives (DMPs) framework with force tracking constraints. First, an initial motion is learned through the DMPs. At the stage of skill generalization, a temporal coupling term combining with force constraints scheme which is inspired by the barrier Lyapunov function and finite‐time prescribed performance is deduced and adds to the original DMPs, so as to remain the contacting force staying within a predefined limit while aligning the motion along with surface of unknown environment adaptively. In this way, not only the contacting force can be guaranteed within a safe margin, but the shape of generalizing motion is preserved. Then the convergence and stability of the proposed DMPs are proved which is grounded on Laplace transformation‐based stability analysis to ensure the performance and safety. Finally, the proposed method is instantiated combined with conventional PID controller through the compared simulations to verify its effectiveness. [ABSTRACT FROM AUTHOR]

A distributed sliding mode control based on neural network and disturbance observer is investigated for heterogeneous vehicle systems in this paper. The vehicle systems in this paper consider both parameter uncertainty and disturbance. First, the radial basis function neural network is applied to estimate the parameter uncertainty owing to its universal approximation ability, and the disturbance observer is designed to compensate for the external disturbance. Compared with the existing adaptive methods, the disturbance observer can estimate the external disturbance directly and effectively. Then, constant time headway policy is employed to regulate the inter-vehicle distance and improve the string stability. Unlike most existing sliding mode control strategy for vehicle platoons, the string stability is achieved via employing sufficient conditions on the control parameters rather than employing coupled sliding mode control. Afterward, modified constant time headway policy is designed to reduce the effect of nonzero initial inter-vehicle spacing and improve the traffic density. Finally, the simulation results with constant time headway policy and modified constant time headway policy are provided to demonstrate the effectiveness and advantages of the proposed approaches. [ABSTRACT FROM AUTHOR]

For autonomous vehicles, the acquisition frequency difference between LiDAR (10–20 Hz) and camera (over 100 Hz) makes simultaneous update of two perceptive systems (2D/3D) less efficient. Nowadays, frame interpolation is in urgent need for increasing frame rate of point cloud sequences obtained by LiDAR. However, a major limitation of current full supervised methods is that high frame rate ground truth sequences are hard to access. We propose a novel Self-supervised Point Cloud Frame Interpolation Network (SPINet) accommodating with variable motion situation, retaining geometric consistency, but without the necessity of utilizing G.T. data. Extensive experiments show that our proposed SPINet outperforms the current full supervised methods. [ABSTRACT FROM AUTHOR]

5G communication technologies are expected to provide high rate and low delay services. To meet the requirements, more base stations (BS), including macrocell BS (MacBS) and microcell BS (MicBS), have to be deployed. In this dense multi-tier heterogeneous networks, the user quality of service (QoS) can be significantly improved by shortening communication distance between base stations and users. However, the network energy consumptions of base stations have been growing quickly. How to save energy consumption in these dense layered network has become a problem we have to face. In this paper, we proposed a microcell BS (MicBS) switch algorithm to reduce the network energy consumption. The BS energy consumption is associated with traffic load, which is denoted as the number of users a BS serves. Considering the time-varying traffic load, we proposed a metric named coverage ratio to characterize how many users can enjoy the services. When the coverage ratio exceeds the upper threshold, a switch off algorithm is activated. MicBSs whose energy costs are higher than their economic profit will be switched off one by one. On the contrary, if this metric is below the lower threshold, a switch on algorithm is activated. A group of inactive MicBSs surrounded by multiple unserved users will be switched on simultaneously. After the switching operations, the network coverage ratio is expected to fall between the upper and lower bounds. Simulation results show that the coverage ratio is kept with the desired level. Compared to some existing algorithms, the proposed algorithm shows more flexible switching operation and more effective energy saving. [ABSTRACT FROM AUTHOR]

The new generation of the industrial cyber-physical system (ICPS) supported by the edge computing technology facilitates the deep integration of sensing and control. System observability is the key factor to characterize the internal relationship of them. In most existing works, the observability is regarded as the assumption for subsequent sensing and control. But, in fact, with the gradually expanded network scale, this assumption is more difficult to directly satisfy sensing design. For this problem, we propose the observability guaranteed method (OGM) for edge sensing and control co-design. Specifically, the nonconvex observability condition is transformed into the convex range of key parameters of the sensing strategy based on the graph signal processing (GSP) technology. Then, we establish the relationship between these parameters and control performance. In OGM, except the previous design from sensing to control, we reversely adjust the sensing design for control demands to satisfy observability. Finally, our algorithm is applied into the hot rolling laminar cooling process based on the semiphysical evaluation. The effectiveness is verified by the results. [ABSTRACT FROM AUTHOR]

As an emerging communication technology, time-sensitive networking (TSN) promises the real time and deterministic interaction of massive data in Industrial Internet of Things. However, it is challenging to schedule the time-sensitive flows timely and superiorly through the mechanism analysis for current TSN scheduling models, especially in complex industrial scenarios. In this article, we propose an analysis approach of flow sequences based on divisibility theory to characterize the flow conflicts and dependencies, which derives the scheduling flexibility based on flow position diversity (PD) and the equivalent flow judgment conditions for slot occupancy. Integrating the abovementioned derivation, a parallel computing framework with the generalized slot length is established to lower the scheduling complexity. Within each computing unit, an incremental scheduling algorithm with the flow judgment conditions and PD-based search boundary is proposed. It reduces the scheduling complexity further while maintaining load balance for the mixed transmission of periodic and aperiodic flows. To achieve the optimality of runtime and load balance, two PD-based flow sorting strategies are designed, respectively. The evaluation results show that compared with the existing works, the runtime efficiency of scheduling at scale is increased by at least 1500 times in complex traffic scenarios while the load balance on the network links is also improved. [ABSTRACT FROM AUTHOR]

With the advent of Industry 4.0 and the popularization of smart terminal equipment, the interaction between industrial field information systems and production equipment has intensified. To meet the real-time transmission of time-triggered flow and the coordinated transmission of best effort flow, time-sensitive network-related technologies are used to implement flow queue forwarding by strictly following the gate control list. First, response-time analysis method is proposed to predict the upper bound of delay under a scheduling model following IEEE 802.1Qbv. Second, according to response-time analysis, a deadline monotonic scheduling algorithm with temporary priority expansion is proposed to divide the priority into more levels, which is not limited by the queue type, so as to ameliorate the transmission sequence of switch export flow. Finally, an adaptive priority adjustment scheduling algorithm with temporary priority expansion is designed to construct the optimal scheduling method further improving the scheduling success rate and reducing worst-case end-to-end delays. Compared with similar algorithms, the algorithm proposed improves the scheduling success rate by at least 30%, reduces the total delay by at least 21% and the TT flow delay by 11% in high network utilization conditions. [ABSTRACT FROM AUTHOR]

This article studies the joint localization and tracking issue for the autonomous underwater vehicle (AUV), with the constraints of asynchronous time clock in cyberchannels and model uncertainty in physical channels. More specifically, we develop a reinforcement learning (RL)-based asynchronous localization algorithm to localize the position of AUV, where the time clock of AUV is not required to be well synchronized with the real time. Based on the estimated position, a scalable sampling strategy called multivariate probabilistic collocation method with orthogonal fractional factorial design (M-PCM-OFFD) is employed to evaluate the time-varying uncertain model parameters of AUV. After that, an RL-based tracking controller is designed to drive AUV to the desired target point. Besides that, the performance analyses for the integration solution are also presented. Of note, the advantages of our solution are highlighted as: 1) the RL-based localization algorithm can avoid local optimal in traditional least-square methods; 2) the M-PCM-OFFD-based sampling strategy can address the model uncertainty and reduce the computational cost; and 3) the integration design of localization and tracking can reduce the communication energy consumption. Finally, simulation and experiment demonstrate that the proposed localization algorithm can effectively eliminate the impact of asynchronous clock, and more importantly, the integration of M-PCM-OFFD in the RL-based tracking controller can find accurate optimization solutions with limited computational costs. [ABSTRACT FROM AUTHOR]

In this article, the scheduling problem over a delayed network is investigated. Different from the existing literature, we propose a hybrid stochastic model which incorporates both delay and packet loss. Based on this proposed model, an energy-efficient co-design problem is considered under power-limited scenarios. A global optimal offline solution is presented explicitly. Aided by arriving information feedback, we propose an online schedule based on an absolute threshold. The optimality of our proposed schemes and thresholds selection is theoretically analyzed. Meanwhile, we have extended the conclusions of the single-system case to the multisystem case. Optimal strategies from both offline and online perspectives are provided with rigorous proof, respectively. The stability condition is given for the estimator to converge in an infinite-time horizon. The comparisons between offline and online strategies are proved the global superiority of the online strategy to the offline one. Numerical simulations have validated the correctness and superiority of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Human-on-the-loop (HOTL) system is regarded as a promising technology to allow autonomous underwater vehicle (AUV) to track the most adequate target point as soon as possible. However, the unique characteristics of the underwater environment make it challenging to perform the tracking task. This article is concerned with a finite-time tracking control issue for AUV, subjected to unavailable velocity signals in the measurement side and uncertain model parameters in physical side. A HOTL system, including operator, buoys, AUV and sensors, is first provided to construct a cooperative tracking network. For such system, operator in surface control center decides the tracking mission based on all available data. Then, a buoy-assisted localization estimator is utilized by AUV to acquire its position, through which a fast terminal sliding mode observer is developed to estimate the velocity of AUV in finite time. With the estimated velocity information, an adaptive-nonsingular fast terminal sliding mode tracking controller is designed to drive AUV to the target point in finite time. For the proposed velocity observer and tracking controller, the signum and differential functions are employed together to improve the convergence speed and reduce the chattering. Besides that, the proposed solution can not only guarantee finite-time velocity observation, but also achieve finite-time tracking control. Finally, simulation and experimental results are both presented to verify the effectiveness. [ABSTRACT FROM AUTHOR]

Wireless networked control systems (WNCSs) have the potential to revolutionize industrial automation in smart factories. Optimizing closed-loop performance while maintaining stability is a fundamental challenge in WNCS due to limited bandwidth and nondeterministic link quality of wireless networks. In order to bridge the gap between network design and control system performance, we propose an optimal dynamic transmission scheduling strategy that optimizes the performance of multiloop control systems by allocating network resources based on predictions of both link quality and control performance at run time. We formulate the optimal dynamic scheduling problem as a nonlinear integer programming problem, which is relaxed to a linear programming problem. We further extend the optimization problem to balance control performance and communication cost. The proposed optimal dynamic scheduling strategy renders the closed-loop system mean-square stable under mild assumptions. Its efficacy is demonstrated by simulating a four-loop control system over an IEEE 802.15.4 wireless network simulator—TOSSIM. The run-time network reconfiguration protocol tailored for optimal scheduling is designed and implemented on a real wireless network consisting of IEEE 802.15.4 devices. Hybrid simulations integrating a real wireless network and simulated physical plant control are performed. Simulation and experimental results show that the optimal dynamic scheduling can enhance control system performance and adapt to both constant and variable wireless interference and physical disturbance to the plant. [ABSTRACT FROM AUTHOR]

Photovoltaics face the threat of many potential faults in daily operation, which calls for accurate fault diagnosis to avoid huge economical losses. This paper investigates practical and troublesome scenarios, where the photovoltaics station has a large number of unlabeled samples and only a few are labelled. In the labelled set, the sample sizes of different types are unbalanced. To this end, a new fault sample rebalancing framework based on semi‐supervised learning (FSR‐SSL) is proposed. Specifically, a dual‐threshold selection mechanism is proposed to choose trusted pseudo‐label samples from unlabeled data to expand the training set. Moreover, a fault sample rebalancing strategy is designed to further filter the obtained trusted pseudo‐label samples, thereby flexibly adding different amounts of pseudo‐label data to various types. As the training rounds increase, the fault samples are gradually rebalanced and the model learning bias caused by type imbalance is well overcome. The extensive numerical experiments show that the proposed FSR‐SSL method reaches 99% accuracy. Compared with existing methods, the accuracy is increased by up to 33%. [ABSTRACT FROM AUTHOR]

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at: https://github.com/TongkunGuan/RFN. [ABSTRACT FROM AUTHOR]

The consensus problem based on full state feedback has been extensively studied, but rarely for leaderless distributed finite-time consensus of networked Euler-Lagrange systems based on output-feedback. In this work, the finite-time consensus problem of networked Euler-Lagrange systems subject to unknown velocity is studied. A finite-time consensus protocol is first proposed based on the relative positions and velocities. Then, the more challenging situation of networked Euler-Lagrange systems subject to unmeasurable velocity is considered. A model-independent observer based on the super-twisting algorithm (STA) is developed for velocity error estimation, based upon which a finite-time output feedback control protocol is developed for consensus of the networked Euler-Lagrange systems. Stability analysis shows that the networked Euler-Lagrange systems can achieve consensus in finite time. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed control algorithms. [ABSTRACT FROM AUTHOR]

Information freshness is essential to industrial wireless networks (IWNs) and can be quantified by the age-of-information (AoI) metric. This paper addresses an AoI-aware spectrum sharing (AgeS) problem in IWNs, where multiple device-to-device (D2D) links opportunistically access the spectrum to satisfy their AoI constraints while maximizing primal links’ throughput. Particularly, we orchestrate the access of D2D links in a distributed manner. Since distributed scheduling results in incomplete observation, D2D links share the spectrum with uncertainty on the transmission environment. Therefore, we propose a distributed scheduling scheme, called D-age, to deal with the transmission uncertainty in the AgeS problem, where an adaptation of actor-critic method is adopted with AoI constraints tackled in the dual domain. To address the non-stationary environment and multi-agent credit assignment issue, cooperative multi-agent reinforcement learning (MARL) approach is developed, where multiple local actors are designed to guide D2D links to make real-time decisions via distributed scheduling policies, which are evaluated by an edge-assisted global critic with action-aware advantage functions. Integrated with graph attention networks (GATs), the critic selectively learns contextual information by assigning different importances to neighboring links, which enables the evaluation of scheduling policies in a scalable and computation-efficient manner. Theoretical guarantee of the time-averaged AoI constraints is provided and the effectiveness of D-age in terms of both AoI violation ratio and the capacity of primal links is demonstrated by simulation. [ABSTRACT FROM AUTHOR]

In wireless communication networks, traffics of base stations (BS) can be effectively offloaded by sharing the cached content between mobile devices through wireless device-to-device (D2D) communication. To encourage the content caching of D2D users, a blockchain incentive scheme is proposed; the game theory is introduced to optimize the benefits for both the edge computing server (ECS) and D2D users in the blockchain network. In Stackelberg game, D2D users act as followers. After the successful implementation of the cache strategy, D2D users are rewarded by the system, which is a new block mining process. In this process, the required computing power is provided by the ECS. Therefore, D2D cache users need to pay the cost to purchase computing power from the ECS. As the game leader, the ECS prices the computing resources, and D2D users adjust their caching strategies based on the price. In this paper, we propose four algorithms based on different pricing methods and different cache reward methods. Simulations are performed to compare the cache quality and cache content dispersion of the four algorithms. The results show that the uniform pricing scheme with a linear reward relationship is more suitable for the scenarios that require higher cache quality, and the discriminatory pricing scheme with a nonlinear reward relationship is more suitable for the scenarios that require more evenly cache content distribution. [ABSTRACT FROM AUTHOR]

Accurate and real‐time diagnosis of the inverter is crucial for the reliability, safety and generation efficiency of the photovoltaic (PV) system. Recently, deep learning (DL) is widely used for accurate diagnosis, which automatically extracts useful features instead of relying on experts. For real‐time diagnosis, in an emerging edge computing paradigm, massive data generated by end‐device are processed in nearby edge nodes, which reduces energy consumption and latency. However, large amounts of data are required for model training in the cloud, which leads to tremendous burdens of transmission and computation. To tackle these issues, a data‐driven diagnosis method based on compressed sensing (CS) and convolutional neural network (CNN) is proposed for open‐circuit faults of PV inverters. Based on CS, raw signals are compressed in edge nodes with the optimal compression ratio determined by experiments. The proposed CS‐CNN is evaluated on an edge‐cloud semi‐physical experiment platform. Compared with common compression methods, the accuracy of CS is improved by more than 3%, and the time consumption is only one‐fifth. Compared with the CNN of the same structure, the transmission and computation time is largely reduced by an order of magnitude. The developed CS‐CNN compresses 85% of data with a test accuracy of 99.18%. [ABSTRACT FROM AUTHOR]

This article is concerned with the fixed-time prescribed tracking control problem for the uncertain stochastic nonlinear systems subject to input quantization and unknown measurement sensitivity. Different from existing results, the sensitivity on the sensor for measuring the system state is considered as an unknown parameter instead of the known one. Due to unknown measurement sensitivity on the sensor, the real system state cannot be obtained by measurement; hence, we put forward a new feedback control algorithm by the use of the unreal measured value of the system state. Moreover, the fixed-time prescribed performance on the output tracking error is investigated by developing a novel performance function. By means of the backstepping method, an adaptive quantized controller is designed for the system. Based on the Lyapunov stability theory, it is proved that the controller can render the output tracking error that satisfies the fixed-time prescribed performance and all signals of the resulting closed-loop system are bounded in probability. Finally, simulation results are provided to illustrate the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

In this article, a coupled sliding mode control (CSMC) is developed for vehicular systems with nonlinear uncertainties by using the disturbance observer (DO) and multi power reaching law. The DO is designed to estimate the nonlinear uncertainties. It is worth mentioning for the DO that the uncertainties include not only parameter uncertainty but also external disturbance, and the bounds of the uncertainties are not required to be known. In addition, the multi power reaching law is constructed to avoid the chattering problem of the traditional sliding mode control (SMC) and to improve the convergence speed effectively. Firstly, the constant time headway policy (CTHP) based on multi power reaching law and SMC is proposed to achieve the string stability for vehicle platoons. Compared with constant spacing policy (CSP), CTHP is more feasible in practice, because the desired spacing between adjacent vehicles is dependent on vehicle speed. Then, a modified constant time headway policy (MCTHP) is proposed for the vehicular systems to decrease the intervehicle spacing and increase the traffic density effectively. Finally, the numerical simulation and experiment are performed to demonstrate the effectiveness and advantage of the developed strategy. [ABSTRACT FROM AUTHOR]

In this article, we investigate the observer-based event-triggered consensus problem of multiagent systems with disturbances. A reset observer consisting of a linear observer and reset element is proposed, the reset element endows the reset observer the ability to improve transient estimation performance compared with traditional linear observers. A hybrid dynamic event-triggering mechanism (ETM) is proposed, in which an internal timer variable is introduced to enforce a lower bound for the triggering intervals such that Zeno-free triggering can be guaranteed even in the presence of disturbances. Then, in order to describe the closed-loop system with both flow dynamics and jump dynamics, a hybrid model is constructed, based on which the Lyapunov-based consensus analysis and dynamic ETM design results are presented. In contrast with linear observer-based consensus protocols and the existing dynamic ETMs, the system performance can be improved and continuous communication between neighboring agents is not needed. Finally, a simulation example is provided to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The number of people in a specific region can provide valuable information in many aspects including Heating Ventilating and Air Conditioning (HVAC) systems adjustment, firefighting rescue, consumer service updating and personnel management, etc. Most of the traditional studies for people counting utilize all the collected data for analyzing, which results in the heavy burden of data processing and extra sensing devices. In this paper, we propose a CSI-based sensor-free crowd counting scheme utilizing only existing WiFi infrastructure in a non-intrusive, low-cost and precise manner. Different from other systems, the best performed data in the received end are chosen to be analyzed for the purpose of obtaining the optimal results. The framework is proposed based on an intuition that different numbers of people wandering in the environment cause distinct impacts on WiFi signals. To begin with, the best performed data are processed and denoised with a wavelet-based denoising algorithm. Then, four features that can depict the relationship between the number of people and data fluctuation are extracted and analyzed. Eventually, the selected features are labeled and taken as inputs of several machine learning algorithms. And the complicated people counting problem is transformed into a classifying issue through extracting and training the features. The experimental results validate the effectiveness of the proposed easy and low-cost crowd counting framework. [ABSTRACT FROM AUTHOR]

Developments in deep learning have resulted in computer-aided diagnosis for many types of cancer. Previously, pathologists manually performed the labeling work in the analysis of colon tissues, which is both time-consuming and labor-intensive. Results are easily affected by subjective conditions. Therefore, it is beneficial to identify the cancerous regions of colon cancer with the assistance of computer-aided technology. Pathological images are often difficult to process due to their irregularity, similarity between cancerous and non-cancerous tissues and large size. We propose a multi-scale perceptual field fusion structure based on a dilated convolutional network. Using this model, a structure of dilated convolution kernels with different aspect ratios is inserted, which can process cancerous regions of different sizes and generate larger receptive fields. Thus, the model can fuse detailed information at different scales for better semantic segmentation. Two different attention mechanisms are adopted to highlight the cancerous areas. A large, open-source dataset was used to verify improved efficacy when compared to previously disclosed methods. [ABSTRACT FROM AUTHOR]

The large-scale application of electric taxis (ETs) in various regions has aroused great interest in developing high-efficiency charging solutions. ETs and charging stations (CSs) can send information to the information management center for real-time information sharing with the technical support of the vehicle-to-grid network. This paper studies the charging problem of intelligent transportation system composed of CSs and networked ETs. Each taxi selects the CS with the lowest charging cost based on the charging price and regional hotspot information. The CS obtains the strategy of taxi and adjusts the charging price to maximize its revenues. We use a multi-leader and multi-follower Stackelberg game to model the interaction between the CSs and ETs. Finally, the hotspot thematic map is generated by using the taxi information of the global positioning system in one day. Simulation results show that the model can effectively reduce the operating cost of taxis and guarantee the revenue of CSs considering the regional hotspot information and reliability coefficient. [ABSTRACT FROM AUTHOR]

Target approaching is an essential ability in marine applications, such as underwater rescue. The recent advances in autonomous underwater vehicles (AUVs) make them ideal options to deliver the target approaching in anchor-free environments, where no device with a known position (anchor) is pre-deployed. To approach a moving target, the trajectory planning of AUVs is an indispensable technical guarantee. However, due to the lack of anchors and the error accumulation during the navigation, the position errors of AUVs grow without bound and degrade trajectory planning performance. Thus, an AUV localization procedure is required and should be used in combination with the trajectory planning. In this paper, we research the multiple-AUV trajectory planning based on AUV cooperative localization and propose a framework named multiple-AUV cooperative localization based trajectory planning (MCLTP). Under MCLTP, scalable cooperative localization for multiple AUVs (SCLMA) and trajectory planning for multiple AUVs (TPMA) algorithms are derived. The design of SCLMA focuses on improving the localization accuracy by alleviating the accuracy degradation caused by the accumulated errors in the inertial measurements. TPMA formulates the trajectory planning into a convex optimization problem with balanced target detection probability and tracking accuracy. Furthermore, the considerations of the practical constraints, such as the kinematic and communication limitations of AUVs, collision and obstacle avoidance, make TPMA reliable in the complex marine environments. Simulation results reveal the advantages of the proposed methods by comparisons with the state-of-the-art alternative algorithms. [ABSTRACT FROM AUTHOR]

In this letter, we present a novel intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communication system, where both the IRS and ground users (GUs) can achieve self-sustainable operations by exploiting the energy harvesting (EH) technique. We consider a practical scenario that the direct links between the UAV and GUs always suffer from blockages, typically in complex urban scene. To mitigate this adverse effect, the IRS can be properly deployed to create a clear propagation environment. We maximize the sum-rate for the proposed system by jointly optimizing the IRS’s phase-shift, transmit power and time allocation of GUs, and trajectory of the UAV. To handle the non-convexity caused by coupled variables, the original problem is decomposed into three subproblems and solved by the block coordinate descent (BCD) method. Through numerical results, the proposed algorithm can achieve significant performance gains over the benchmarks. [ABSTRACT FROM AUTHOR]

The main method of modern ironmaking is blast furnace ironmaking, which is a very complex nonlinear dynamic process with complex physical-chemical coupling. The hot metal is the final product of blast furnace, and its silicon content not only reflects the quality of hot metal but also characterizes the operation status of the blast furnace, so its accurate prediction is very important for the operation of the blast furnace. Given the bottleneck problem in the application of the existing prediction model of hot metal silicon content in the blast furnace, this article proposed a novel data-driven modeling method. First, a nonlinear Takagi–Sugeno (T–S) fuzzy model is constructed for the hot metal silicon content to completely capture the nonlinear dynamics of the blast furnace process. Then, considering the doubts of blast furnace operators about the predicted results of the model, the Bayesian method is used to identify the consequent parameters of the fuzzy model to obtain the probability output, to present the credibility of the predicted results. Furthermore, to improve the robustness of the fuzzy model to the initial fuzzy rules, the sparse priori is adopted to construct a compact fuzzy model with strong generalization performance, in which the key fuzzy rules were screened out. In addition, two optimization methods are derived for each of the above models. Finally, the validity of the proposed methods is verified by the test of actual blast furnace data. [ABSTRACT FROM AUTHOR]

This article considers the distributed output-feedback consensus control problem for nonlinear multiagent systems subject to input delays. Different from the existing related works, the input delay of each agent is described as an unknown time-varying function and is different from each other in this article. To deal with this problem, for each follower, we first construct a novel distributed observer based on the relative output information to asymptotically estimate the state information of the leader, then we introduce a classical observer to asymptotically estimate the state information of the follower based on its output information. By means of two observers, the leader-following consensus problem is transformed into the stability problem of the nonlinear system with unknown input delays. Subsequently, the distributed controller independent of delays is proposed for each follower by the use of the truncated prediction method under some conditions. Based on the Lyapunov stability theory, it is strictly proved that the distributed controller can render all agents achieving consensus. Finally, the effectiveness of the theoretical results is illustrated on the basis of numerical simulations on a group of single-link manipulators. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication with short communication distance is an efficient way to improve spectrum efficiency and mitigate interference. To realize the optimal resource configuration including wireless channel matching and power allocation, a distributed resource matching scheme is proposed based on deep reinforcement learning(DRL). The reward is defined as the difference of achieve rate of D2D users and the consumed power, which is limited by the Signal to Interference plus Noise Ratio (SINR) of the other cellular users on the current channel. The proposed algorithm maximizes the D2D throughput and energy efficiency in a distributed manner, without online coordination and message exchange between users. The considered resource allocation problem is formulated as a random non-cooperative game with multiple players (D2D pairs), where each player is a learning agent, whose task is to learn its best strategy based on locally observed information, multi-user communication resource matching algorithm is proposed based on a Double Deep Q-network (DDQN), where the total cellular throughput and user energy efficiency could converge to the Nash equilibrium (NE) under the mixed strategy. Simulation results show that the proposed algorithm can improve the communication rate and energy efficiency of each user by selecting the optimal strategy, and has better convergence performance compared with existing schemes. [ABSTRACT FROM AUTHOR]

The growing popularity of intelligent manufacturing is driven by deterministic transmission demands of applications in industrial cyber-physical systems (ICPS). However, the ossified shortages of industrial wireless communication such as diverse quality of service (QoS) and complex signaling processes incur a severe long tail of transmission delay distribution. As a solution, the 5th generation (5G) wireless communication technology provides ultra-reliable and low-latency communication (URLLC) for industry scenarios. Moreover, the newly proposed time-sensitive networking (TSN) standards guarantee the transmission determinacy by gate mechanism. In this paper, we propose a heterogeneous time-sensitive network (HTSN) co-designed by 5G and TSN. We first develop a predictive multi-priority wireless scheduling mechanism based on semi-persistent scheduling (SPS) to reduce signaling delay by reserving resources in advance. Then we propose an adaptive data injection mechanism for TSN based on per-stream filtering and policing (PSFP), which dynamically adjusts the priority of data for queue injection in TSN. To further reduce the long tail of delay, we employ a risk-sensitive learning method to improve the worst-case delay. Simulations on a hot rolling production scenario demonstrate that the proposed mechanisms under HTSN achieve great performance in terms of integrated delay and resource utilization. [ABSTRACT FROM AUTHOR]

This article investigates the stabilization control and stabilizing data-rate condition problems for networked control systems, which transmit signals from the sensor to the controller over the communication network with denial-of-service (DoS) attacks. Considering a class of DoS attacks that only constrain its frequency and duration, we aim to explore the constraint condition for stabilization and minimum stabilizing data rate of the networked control systems. The framework consists of two main parts. The first part considers the stabilizing control by the state-feedback approach under ideal bandwidth capacity. While the second part characterizes the average stabilizing data rate in terms of the eigenvalues of system matrix and DoS constraint functions to explicitly reveal the relationship between the attacks and the network bandwidth capacity. The stabilizing result is novel in the sense that the DoS-attack intensity, which is characterized by its frequency and duration, can vary for different time intervals. With this feature, the minimum average data-rate condition can vary for different time intervals according to the intensity of DoS attacks. [ABSTRACT FROM AUTHOR]

A new event-triggered iterative learning control method is proposed for handling the distributed containment control problem of model-free multiagent systems under a fixed directed graph. The designed controller merely uses the input and output signals, controlled model information is not required. At first, the unknown dynamic is transformed into the linearization model upon the base of pseudo partial derivative. Secondly, the novel distributed containment controller is proposed for each follower by use of iterative learning algorithm. Moreover, a new trigger mechanism is designed to save energy of the systems, such that the updating number of the proposed controller can be reduced greatly. Mathematical deduction shows that the controller can render the outputs of the followers converge to a convex hull formed by the outputs of leaders. Finally, simulation examples are given for verifying the significance of proposed method. [ABSTRACT FROM AUTHOR]

This article studies the finite-time observer-based leader-following consensus problem for a class of nonlinear multiagent systems with nonuniform time-varying input delays. Existing works usually assume that input delays are the same constants and the input of the leader is available for each follower. In this article, we propose a new distributed consensus algorithm to relax the conservative condition. A novel finite-time distributed observer is designed for each follower, which can accurately estimate the state information of the leader in a setting time. By means of the observer, the distributed controller is proposed for each follower, and it depends only on the state information of the follower and the estimated-state information of its neighbor agents. Based on the Lyapunov stability theory, it is strictly proved that all agents can achieve a consensus. Finally, the effectiveness of the theoretical results is verified by numerical simulation on a group of single-link manipulators. [ABSTRACT FROM AUTHOR]

In this article, the connectivity‐maintained platoon control problem for networked vehicles with external disturbance is considered. By modeling each vehicle as a nonlinear triple integral system, a specified‐time sliding model disturbance observer and a specified‐time controller are presented to ensure the convergence of the platoon system. The predetermined performance method is applied to maintain the connectivity of vehicles in the platoon, which enables flexibility in the communication connectivity between vehicles. The specified time sliding mode disturbance observer is proposed to estimate the external disturbance within the predefined time and it is proved to be uniformly ultimate boundedness stable by using the time transformation method. A dynamic gain controller with the time constraint is proposed to achieve the stable platoon with connectivity maintained for each vehicle. The proposed approach can simultaneously guarantee the specified‐time convergence and connectivity maintenance without depending on any system parameters. The effective of the proposed controller is proved by the Lyapunov function. Finally, comparative simulation results are obtained to verify the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

In the existing literature, reset control has been shown to have great potential to improve transient performance of linear systems, but reset-induced jump dynamics bring difficulties for stability analysis, especially under the network environment. In this article, we apply reset control to the consensus of multiagent systems (MASs) with event-triggered communication, and a novel reset mechanism (RM) and a hybrid event-triggering mechanism (ETM) are proposed with guaranteed Zeno-freeness. The state space is decomposed into multiple flow sets and jump sets according to the RM and ETM, and a hybrid model of the MASs is constructed such that the reset induced and event-trigger induced jump dynamics can be handled in a unified framework. Based on the hybrid model, a novel hybrid systems framework is presented for consensus analysis and co-design of RM and ETM. Finally, the proposed design is verified with a simulation example. [ABSTRACT FROM AUTHOR]

In this paper, we consider the specified-time platoon control for the networked vehicles modelled by the third-order dynamics with uncertain external bounded disturbance. The vehicles are subjected to disturbance and parameter uncertainties. To ensure the platoon can converge within a predefined time, a novel disturbance observer with a time-varying dynamic gain is proposed. By the time transformation approach, the disturbance observer is proved to be uniformly ultimate bounded (UUB) stable in a specified time. The time-varying dynamic gain is also applied to design the novel specified-time controller for the vehicle platoon and rigorous proof is presented based on Lyapunov stability. Under the proposed disturbance observer and controller, the vehicle platoon can be stabilised within the predefined time without depending on any system parameters and initial conditions. Finally, some comparison simulation results are given to verify the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

Orthogonal chirp division multiplexing (OCDM) is a recently proposed multiplexing scheme and has drawn increasing attention in underwater communications. In an OCDM system, a series of orthogonal chirp signals carry the data information. However, the time variation feature of underwater acoustic (UWA) channels is rarely considered in the existing OCDM systems. In this paper, a particular transmission structure is proposed for the OCDM system to process the signal block-by-block at the receiver. Moreover, a low complexity channel estimation algorithm and the corresponding equalizers are derived. The proposed structure has a better ability to track time-varying channels. The simulation results show that the proposed method achieves promising performance under time-varying channels. [ABSTRACT FROM AUTHOR]

This paper studies event-triggered based adaptive neural network (NN) tracking control of a robotic manipulator with output constraints and disturbance. First, a novel asymmetric tan -type barrier Lyapunov function (BLF) is developed to satisfy the requirement of time-varying output constraints. Then, a fixed threshold event triggering is proposed to reduce the energy consumption, which avoids the happening of Zeno behaviour after analysis. Further, a disturbance observer (DO) and an adaptive neural network are devised to estimate the bounded disturbance and the unknown dynamics of the robotic manipulator. The proposed controller can achieve uniform boundness of the solution and adjustment of transient performance. Finally, the effectiveness of the presented methods is verified by related simulation results. [ABSTRACT FROM AUTHOR]

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments. [ABSTRACT FROM AUTHOR]

Industrial wireless networks (IWNs) have attracted significant attention for providing time-critical delivery services, which can benefit from device-to-device (D2D) communication for low transmission delay. In this article, a distributed scheduling problem is investigated for D2D-enabled IWNs, where D2D links have various age-of-information (AoI) constraints for information freshness. This problem is formulated as a constrained optimization problem to optimize D2D packet delivery over limited spectrum resources, which is intractable since D2D users have no prior knowledge of the operating environment. To tackle this problem, in this article, an AoI-aware scheduling scheme is proposed based on primal-dual optimization and actor--critic reinforcement learning. In specific, multiple local actors for D2D devices learn AoI-aware scheduling policies to make on-site decisions with their stochastic AoI constraints addressed in the dual domain. An edge-based critic estimates the performance of all actors’ decision-making policies from a global view, which can effectively address the nonstationary environment caused by concurrent learning of multiple local actors. Theoretical analysis on the convergence of learning is provided and simulation results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article investigates the consensus problem of multiagent systems (MASs) with the reset control approach. Reset control has been shown to possess great potential to improve transient system performance, but reset-induced state jump dynamics bring difficulties for consensus analysis, especially under the network environment. In this article, a novel reset element is developed, and time regularization is utilized to exclude Zeno behavior. The case with continuous communication is first considered, a distributed proportional integral + reset consensus protocol is proposed, which consists of the proportional-integral controller and the reset mechanism. The results are further generalized to the case with asynchronous sampling, which relaxes the requirement for continuous communication and clock synchronization. A hybrid model of the MAS is constructed to describe the system dynamics, and a hybrid systems approach is proposed to handle the reset- and sampling-induced jump dynamics in a unified framework. Moreover, a novel Lyapunov function is proposed and LMI-based stability conditions are given. Finally, an example is provided to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Security issues of cyber-physical systems have become a timely and hot topic in recent years. In this brief, we investigate the power-constrained optimal denial-of-service (DoS) attack strategy with multiple power levels on state estimation over infinite-time horizon. We start from the two-level case and explicitly derive out the basic form of optimal attack strategy. In order to handle the intractability brought about by the larger number of levels, we develop an equivalent property for general multi-level cases that the multi-level scheduling can be viewed as a feasible combination of several sub-schedules with a designed stochastic-triggered manner, which provides a necessary condition of optimal solution. Moreover, based on the practical communication model, we give the condition under which the optimal schedule can be uniquely and explicitly determined for the two-pick strategy. Numerical simulations are accomplished to verify the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

Underwater sensor networks (USNs) are envisioned to enable a large variety of marine applications. Such applications require accurate position information of sensor nodes. However, the openness and inhomogeneity characteristics of underwater medium make it much more challenging to solve the localization issue. This paper is concerned with a privacy-preserving localization issue for USNs in inhomogeneous underwater medium. An honest-but-curious model is considered to develop a privacy-preserving localization protocol. Based on this, a localization problem is constructed for sensor nodes to minimize the sum of all measurement errors, where a ray compensation strategy is incorporated to remove the localization bias from assuming the straight-line transmission. To make the above problem tractable, we consider the unsupervised, supervised and semisupervised scenarios, through which deep reinforcement learning (DRL) based localization estimators are utilized to estimate the positions of sensor nodes. It is noted that, the proposed localization solution in this paper can hide the private position information of USNs, and more importantly, it is robust to local optimum for nonconvex and nonsmooth localization problem in inhomogeneous underwater medium. Finally, simulation studies are given to show the position privacy can be preserved, while the localization accuracy can be enhanced as compared with the other existing works. [ABSTRACT FROM AUTHOR]