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Deadbeat predictive current control (DPCC) can predict motor behavior based on SPMSM model. However, during the operation of motor system, motor parameters (such as stator inductance and flux linkage) vary frequently according to different working conditions, which may lead to controller parameter mismatch, causing current harmonic content to increase and efficiency to decrease. In order to solve these problems caused by parameter variation, first, this paper proposes a current prediction error model by considering uncertainties of model parameters. Second, stator inductance and flux linkage are decoupled based on current prediction error model, which can reduce the interaction between parameters. Finally, the Kalman Filter (KF) algorithm is presented to filter the decoupled parameters. It is shown that the stator inductance and flux linkage can be identified accurately and the complexity of computation can be simplified. The traditional DPCC method, Extended Kalman Filter (EKF) based DPCC method and the proposed DPCC method are comparatively analyzed in this paper. Simulation and experiment indicate that the proposed parameter decoupling identification method can effectively reduce current harmonic content, current fluctuation and current tracking errors caused by parameter mismatch. [ABSTRACT FROM AUTHOR]

Addressing non-convexity plays a fundamental role in solving the optimal electricity-gas flow models. In this paper, an improved spatial branch-and-bound algorithm is proposed to solve the non-convex problem, which is formulated as a mixed-integer bilinear programming, for its exact solution. The core of the algorithm is to divide the non-convex model into convex and small sub-models by branching on specific continuous variables, so that the non-convex problem can be equivalent to a rooted tree for exploration. The exactness of the algorithm is guaranteed by the same criterion as the classical branch-and-bound algorithm. To alleviate the computational burden, a novel two-stage spatial branching strategy is developed to improve the effectiveness and efficiency of the branching operations. The performance of the proposed algorithm is verified on two integrated electricity-gas systems with different sizes. Numerical results demonstrate that our method achieves a balance among feasibility, optimality, and efficiency. The comparison with another 6 convexification-based methods, 3 state-of-the-art non-convex optimization solvers, and 2 spatial branch-and-bound algorithms with classical branching rules further shows the superiority of our algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose to extend the standard Convolutional Dictionary Learning problem to a tensor representation where the activations are constrained to be “low-rank” through a Canonical Polyadic decomposition. We show that this additional constraint increases the robustness of the CDL with respect to noise and improve the interpretability of the final results. In addition, we discuss in detail the advantages of this representation and introduce two algorithms, based on ADMM or FISTA, that efficiently solve this problem. We show that by exploiting the low rank property of activations, they achieve lower complexity than the main CDL algorithms. Finally, we evaluate our approach on a wide range of experiments, highlighting the modularity and the advantages of this tensorial low-rank formulation. [ABSTRACT FROM AUTHOR]

The integration of the variable renewable energies makes the operation conditions of the power system ever-changeable. Consequently, the power system operational reliability evaluation is increasingly important. This paper introduces the concept of decision-dependent uncertainty (DDU) in the operational reliability evaluation. Unlike the exogenous uncertainties, DDU reveals that the decisions of the system operation could significantly affect the resolution of the uncertainties which influence the reliability metrics. In this paper, the proposed DDU modeling method links the device reliability indices, i.e., the forced outage rate, and the operational-decision variables. The impacts of DDU on operational reliability are analyzed based on a reliability-constrained stochastic unit commitment (UC) model. An adaptive reliability improvement UC (ARIUC) algorithm is proposed to efficiently solve the problem. Case studies underline the necessity of considering DDU in power system operational reliability evaluations. [ABSTRACT FROM AUTHOR]

Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties. An example is an operation during a predicted hurricane with uncertain line out-ages. However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging. This paper evaluates some possible ways that machine learning can be used to reduce this computational burden. First, a set of feasibility studies is conducted. Results suggest that using machine learning as an assistant to the stochastic unit commitment solver is more advantageous than using it as a standalone solver. In particular, the machine learning model is trained to facilitate solving the problem by determining the unnecessary constraints that can be removed from the original problem without affecting the final accuracy. The variables that can be used as input features/predictors or outputs for the machine learning model are determined through feasibility studies. Then, an algorithm to train and utilize a machine learning model is proposed. The method is tested on a 500-bus synthetic South Carolina system. Various test cases show an average reduction in solution time by more than 90% by using the trained machine learning model to assist the stochastic unit commitment solver. [ABSTRACT FROM AUTHOR]

With the proliferation of distributed generators and energy storage systems, traditional passive consumers in power systems have been gradually evolving into the so-called “prosumers,” i.e., proactive consumers, which can both produce and consume power. To encourage energy exchange among prosumers, energy sharing is increasingly adopted, which is usually formulated as a generalized Nash game (GNG). In this paper, a distributed approach is proposed to seek the Generalized Nash equilibrium (GNE) of the energy sharing game. To this end, we first prove the strong monotonicity of the game. Then, the GNG is converted into an equivalent optimization problem. An algorithm based on Nesterov's methods is thereby devised to solve the equivalent problem and consequently find the GNE in a distributed manner. The convergence of the proposed algorithm is proved rigorously based on the nonexpansive operator theory. The performance of the algorithm is validated by experiments with three prosumers, and the scalability is tested by simulations using 1888 prosumers. [ABSTRACT FROM AUTHOR]

To meet the execution requirements of delay-sensitive services in vehicular edge computing (VEC) networks, vehicular services need to be offloaded to edge computing nodes. For complex, large-scale services, the services need to be migrated if the services are not completed before the vehicles leave the coverage of edge computing nodes. Trust and resource matching between areas thus become major problems. This paper studies the decision model of vehicular service offloading and migration. First, software-defined network (SDN) technology is introduced into the traditional network architecture, and a two-layer distributed SDN-controlled VEC network architecture is designed, which is divided into a domain control layer and an area control layer. In this framework, we use the consortium blockchain as a carrier to share network topology information between SDN controllers to prevent information leakage. We then established a service offloading and migration optimization problem model to minimize service execution delay, reduce energy consumption and maximize the throughput of the blockchain system. We describe the problem model as a Markov Decision Process (MDP), introduce a deep reinforcement learning (DRL) algorithm named asynchronous advantage actor-critic (A3C) and design a dynamic service offloading and migration algorithm (DSOMA) based on A3C to solve the problem. Simulation results show that DSOMA can increase the throughput of the blockchain system, and DSOMA is superior to the deep Q-learning (DQN) algorithm and greedy offloading algorithm in reducing service execution delay and system energy consumption. [ABSTRACT FROM AUTHOR]

As an emulous alternative to deep neural networks, Deep Forest emerges with features like low complexity, fewer hyper-parameters, and good robustness, which are predominantly desired in distributed computing applications and ecosystems. Recently, an efficient distributed Deep Forest system, named ForestLayer, was proposed, designing a fine-grained sub-Forest-based task-parallel algorithm to improve the parallel computing efficiency of Deep Forest. However, the sub-Forest splitting of ForestLayer is static and one-off without adaptability to the computing environment, nevertheless, the size of splitting granularity has a significant impact on the system performance. To further improve the computing efficiency and scalability of the distributed Deep Forest, in this paper, we propose a novel distributed Deep Forest algorithm, named BLB-gcForest (Bag of Little Bootstraps-gcForest), which augments the gcForest (multi-Grained Cascade Forest) approach for constructing Deep Forest. BLB-gcForest carries out parallel computation for each tree in sub-Forests at a finer parallel granularity and integrates with the Bag of Little Bootstraps (BLB) mechanism to reduce massive transmitted feature instances for Cascade Forest Layers, utterly improving both computation efficiency and communication efficiency. Moreover, to solve the problem of the forest splitting granularity, we further design an adaptive sub-Forest splitting algorithm to ensure the maximum resource utilization for parallel computation of each sub-Forest. Experimental results on four well-known large-scale datasets, namely YEAST, LETTER, MNIST, CIFAR10, show that the training efficiency of BLB-gcForest achieves up to 20.3x and 1.64x speedups compared with the state-of-the-art gcForest and ForestLayer, respectively while guaranteeing higher accuracy and better robustness [ABSTRACT FROM AUTHOR]

In this paper, we consider a computation offloading problem in a three-tier network consisting of multiple mobile users (MUs), multiple edge clouds, and a central cloud. On this basis, we formulate a system Energy Time Cost ($ETC$) minimization problem by jointly optimizing the resource allocation, mobile-edge matching decision, and offloading decision. Due to the difficulty of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Gale-Shapley based Minimum/Sequential Offloading Algorithm (GS-MOA/GS-SOA), to optimize offloading decisions by minimizing the system $ETC$ , in which the mobile-edge matching strategies based on the proposed Gale-Shapley based Mobile-Edge mAtching aLgorithm (GS-MEAL) and resource allocations are optimized iteratively. The simulations show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Weighted multi-view clustering (MVC) aims to combine the complementary information of multi-view data (such as image data with different types of features) in a weighted manner to obtain a consistent clustering result. However, when the cluster-wise weights across views are vastly different, most existing weighted MVC methods may fail to fully utilize the complementary information, because they are based on view-wise weight learning and can not learn the fine-grained cluster-wise weights. Additionally, extra parameters are needed for most of them to control the weight distribution sparsity or smoothness, which are hard to tune without prior knowledge. To address these issues, in this paper we propose a novel and effective Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) clustering algorithm, which can automatically learn the cluster-wise weights to discover the discriminative clusters across multiple views and thus can enhance the clustering performance by properly exploiting the cluster-level complementary information. To learn the cluster-wise weights, we design a new weight learning scheme by exploring the relation between the mutual information of the joint distribution of a specific cluster (containing a group of data samples) and the weight of this cluster. Finally, a novel draw-and-merge method is presented to solve the optimization problem. Experimental results on various multi-view datasets show the superiority and effectiveness of our cluster-wise weighted CURE over several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Estimating set similarity and detecting highly similar sets are fundamental problems in areas such as databases and machine learning. MinHash is a well-known technique for approximating Jaccard similarity of sets and has been successfully used for many applications. Its two compressed versions, $b$ b -bit MinHash and Odd Sketch, can significantly reduce the memory usage of the MinHash, especially for estimating high similarities (i.e., similarities around 1). Although MinHash can be applied to static sets as well as streaming sets, of which elements are given in a streaming fashion, unfortunately, $b$ b -bit MinHash and Odd Sketch fail to deal with streaming data. To solve this problem, we previously designed a memory-efficient sketch method, MaxLogHash, to accurately estimate Jaccard similarities in streaming sets. Compared with MinHash, our method uses smaller sized registers (each register consists of less than 7 bits) to build a compact sketch for each set. In this paper, we further develop a faster method, MaxLogOPH++. Compared with MaxLogHash, MaxLogOPH++ reduces the time complexity for updating each coming element from $O(k)$ O (k) to $O(1)$ O (1) with a small additional memory. We conduct experiments on a variety of datasets, and experimental results demonstrate the efficiency and effectiveness of our methods. [ABSTRACT FROM AUTHOR]

The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system, or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. It is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLP D-OPF model for the same distribution system. [ABSTRACT FROM AUTHOR]

With growth in the number of smart devices and advancements in their hardware, in recent years, data-driven machine learning techniques have drawn significant attention. However, due to privacy and communication issues, it is not possible to collect this data at a centralized location. Federated learning is a machine learning setting where the centralized location trains a learning model over remote devices. Federated learning algorithms cannot be employed in the real world scenarios unless they consider unreliable and resource-constrained nature of the wireless medium. In this paper, we propose a federated learning algorithm that is suitable for cellular wireless networks. We prove its convergence, and provide a sub-optimal scheduling policy that improves the convergence rate. We also study the effect of local computation steps and communication steps on the convergence of the proposed algorithm. We prove, in practice, federated learning algorithms may solve a different problem than the one that they have been employed for if the unreliability of wireless channels is neglected. Finally, through numerous experiments on real and synthetic datasets, we demonstrate the convergence of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Breakthroughs in unsupervised domain adaptation (uDA) can help in adapting models from a label-rich source domain to unlabeled target domains. Despite these advancements, there is a lack of research on how uDA algorithms, particularly those based on adversarial learning, can work in distributed settings. In real-world applications, target domains are often distributed across thousands of devices, and existing adversarial uDA algorithms – which are centralized in nature – cannot be applied in these settings. To solve this important problem, we introduce FRuDA: an end-to-end framework for distributed adversarial uDA. Through a careful analysis of the uDA literature, we identify the design goals for a distributed uDA system and propose two novel algorithms to increase adaptation accuracy and training efficiency of adversarial uDA in distributed settings. Our evaluation of FRuDA with five image and speech datasets show that it can boost target domain accuracy by up to 50% and improve the training efficiency of adversarial uDA by at least $11\times$ 11 × . [ABSTRACT FROM AUTHOR]

In this article, we consider a class of decentralized resource allocation problems over directed networks, where each node only communicates with its in-neighbors and attempts to minimize its own cost when network-wide resource constraints as well as local capacity limits are satisfied. Decentralized optimization to solve this problem has been a significant focus within engineering research due to its advantages in scalability, robustness, and flexibility. Most existing methods are synchronous while few works are devoted to asynchronously solving the problem. The problem becomes even more challenging when the networks are directed. To address the resource allocation problem when the above issues are considered, we propose a novel decentralized asynchronous algorithm based on the gossip-based communication protocol and epigraph strategy. An important feature of the algorithm is that it is implemented in a completely decentralized manner in the case of asynchronous communication and directed networks. We provide theoretical proof to guarantee the convergence of the proposed algorithm, which indicates that it can successfully allocate the optimal resource. When solving the resource allocation problem over time-varying directed networks, we further discuss a related decentralized asynchronous algorithm according to the random sleep protocol. Numerical examples are given to demonstrate the viability and performance of the algorithms. [ABSTRACT FROM AUTHOR]

Despite the success of stochastic variance-reduced gradient (SVRG) algorithms in solving large-scale problems, their stochastic gradient complexity often scales linearly with data size and is expensive for huge data. Accordingly, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly convex problems with linear prediction structure, e.g., least squares and logistic/softmax regression. HSDMPG enjoys improved computational complexity that is data-size-independent for large-scale problems. It iteratively samples an evolving minibatch of individual losses to estimate the original problem, and can efficiently minimize the sampled subproblems. For strongly convex loss of $n$ n components, HSDMPG attains an $\epsilon$ ε -optimization-error within $\mathcal {O} \left(\kappa \log ^{\zeta +1}\left(\frac{1}{\epsilon }\right)\frac{1}{\epsilon }\bigwedge n\log ^{\zeta }\left(\frac{1}{\epsilon }\right)\right)$ O κ log ζ + 1 1 ε 1 ε ⋀ n log ζ 1 ε stochastic gradient evaluations, where $\kappa$ κ is condition number, $\zeta =1$ ζ = 1 for quadratic loss and $\zeta =2$ ζ = 2 for generic loss. For large-scale problems, our complexity outperforms those of SVRG-type algorithms with/without dependence on data size. Particularly, when $\epsilon =\mathcal {O}(1/\sqrt{n})$ ε = O (1 / n) which matches the intrinsic excess error of a learning model and is sufficient for generalization, our complexity for quadratic and generic losses is respectively $\mathcal {O} (n^{0.5}\log ^{2}(n))$ O (n 0. 5 log 2 (n)) and $\mathcal {O} (n^{0.5}\log ^{3}(n))$ O (n 0. 5 log 3 (n)) , which for the first time achieves optimal generalization in less than a single pass over data. Besides, we extend HSDMPG to online strongly convex problems and prove its higher efficiency over the prior algorithms. Numerical results demonstrate the computational advantages of HSDMPG. [ABSTRACT FROM AUTHOR]

In recent years, NAND flash memory has been widely used in mobile devices, laptops, desktops, and data center storage systems due to its low-power consumption, high performance, high density, lightweight, shock resistance, and high-reliability natures. However, as the stacked layers and the storage density increase, flash memory also suffers from a higher raw bit error rate (RBER) and shorter lifetime. Observing that reliable cell states suffer from less data retention errors and program disturbance, we propose a differential evolution coding scheme to increase the probability of storing data in more reliable cell states, thereby reducing the RBER. We conducted the experiments over a development platform of SSD storage device with 3D-TLC charge trap (CT) NAND flash memory. The experimental results showed that the proposed differential evolution algorithm with asymmetric coding scheme could averagely reduce RBER by 48.88%, 65.45%, 52.61%, 61.99%, 80.19%, and 33.18% compared with baseline, asymmetric coding algorithm, asymmetric coding scheme with stripe-pattern elimination algorithm, UAC- $n$ LC, word-line batch score modulation programming, and SCB schemes. [ABSTRACT FROM AUTHOR]

This article investigates the multitarget tracking problem for multiple Lagrangian plants (MLPs) in the presence of sampled-data interactions, uncertain dynamic terms, and input-to-output redundancy. Two classes of impulsive estimator-based control (IEC) algorithms, including the first- and higher-order IEC algorithms, are newly designed to observe the dynamic uncertain terms, estimate the states of the multiple targets, and finally solve the above-mentioned problem. Based on the properties of the small-value norms, Lyapunov stability theory, Schur stability theory, and Hurwitz criterion, some sufficient conditions and the convergence radius are derived for guaranteeing the convergence of these IEC algorithms. Finally, numerical simulations are performed on networked heterogeneous manipulators to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This article is dedicated to solving the coordination problem using a reset algorithm that consists of linear impulsive dynamics, over which antagonistic reciprocity among interacting individuals is inevitable. It shows that reciprocal agents are convergent (including consensus and clusters) whenever the scaling parameters of an agent, corresponded to continuous/discrete dynamics, enjoy the same proportion value that matches to all agents. Otherwise, all participating agents share nothing eventually, that is, they achieve stability. This immediately gives rise to that the final aggregated values of agents are entirely determined by the underlying parameters, on condition that the connection property of communication topologies is preserved. Therefore, the proposed setup features a unified perspective on consensus, clusters (including bipartite consensus), and stability that are separately studied in most of the existing literature. The developed method is well supported via numerical examples. [ABSTRACT FROM AUTHOR]

X-ray-induced acoustic computed tomography (XACT) provides X-ray absorption-based contrast with acoustic detection. For its clinical translation, XACT imaging often has a limited field of view. This can result in image artifacts and overall loss of quantification accuracy. In this article, we aim to demonstrate model-based XACT image reconstruction to address these problems. An efficient matrix-free implementation of the regularized LSQR (MF-LSQR)-based minimization scheme and a noniterative model back-projection (MBP) scheme for computing XACT reconstructions have been demonstrated in this article. The proposed algorithms have been numerically validated and then used to perform reconstructions from experimental measurements obtained from an XACT setup. While the commonly used back-projection (BP) algorithm produces limited-view and noisy artifacts in the region of interest (ROI), model-based LSQR minimization overcomes these issues. The model-based algorithms also reduce the ring artifacts caused due to the nonuniformity response of the multichannel data acquisition. Using the model-based reconstruction algorithms, we are able to obtain reasonable XACT reconstructions for acoustic measurements of up to 120° view. Although the MBP is more efficient than the model-based LSQR algorithm, it provides only the structural information of the ROI. Overall, it has been demonstrated that the model-based image reconstruction yields better image quality for XACT than the standard BP. Moreover, the combination of model-based image reconstruction with different regularization methods can solve the limited-view problem for XACT imaging (in many realistic cases where the full-view dataset is unavailable), and hence pave the way for future clinical translation. [ABSTRACT FROM AUTHOR]

The square-wave voltage signal injection method with the proportional-integral (PI) observer is commonly adopted to estimate the rotor position of interior permanent magnet synchronous motor (IPMSM). However, the saliency estimation method with PI observer is a continuous injection method and the polarity identification method is a discrete one, which causes extra transition time and complexity. To solve this problem, a fast unipolar sequence-pulse injection method is proposed to unify the process of saliency and polarity identification and the complexity of the algorithm as well as the identification time are reduced. At the same time, a four-stage search algorithm is proposed to ensure the fast convergence and improve the accuracy by reducing the number of the pulse voltage signals. The experiment on motor drive platform verifies the effectiveness of the proposed method to IPMSMs with different saliency ratios. [ABSTRACT FROM AUTHOR]

The minimum dominating set (MDS) of the network is a node subset of smallest size that every node in the network is either in this subset or is adjacent to one or more nodes of this subset. MDS has found wide applications, ranging from network monitoring, routing, to epidemic control, and text processing. However, the majority of existing studies on MDS problem are confined to single networks. In real world, more and more complex systems consist of a set of elements linked up by different types of connections, which are best modeled as multiplex networks with interacting network layers. Though vastly important, the MDS of the multiplex networks has not yet been formally defined and its application and identification remain open issues. In this article, we present the definition of the MDS of the multiplex network and show some of its possible applications. For solving the MDS problem of the multiplex network, we built a spin-glass model and solve it through the belief-propagation (BP) equations under the replica symmetry mean-field theory. As a consequence, we can predict the relative size of the MDS of the multiplex network theoretically and we can propose a BP-guided decimation algorithm to construct an approximate optimal dominating set in practice. Then the algorithm is improved in both accuracy and efficiency by embedding a novel multiplex network-oriented leaf-removal strategy. The effectiveness of the proposed algorithms is finally verified by comparing with other methods on a number of the multiplex network examples. [ABSTRACT FROM AUTHOR]

We study the problem of visually monitoring a set of points on a 2.5D terrain using an unmanned aerial vehicle (UAV) with a downward-facing camera. The goal is to find a tour of minimum length for the UAV offline to visually inspect all points of interest. Varying terrain and limited field of view of the camera restrict the visibility of the UAV and can create obstacles in the flight path, making the problem challenging. The problem is NP-hard and generalizes the traveling salesperson problem (TSP). We present several algorithms to solve this problem. Our main theoretical contribution is a constant-factor approximation algorithm (assuming fixed parameters for the terrain). We also present a practical algorithm that uses the solution to a generalized TSP (GTSP) subinstance. We benchmark the GTSP-based algorithm using a branch-and-cut integer linear programming formulation and find that the proposed algorithm scales to much larger instances and is computationally fast. We also show proof-of-concept using field deployment of a UAV to visually monitor points of interest in the environment. [ABSTRACT FROM AUTHOR]

A unified distributed reinforcement learning (RL) solution is offered for both static and dynamic economic dispatch problems (EDPs). Each agent is assigned with a fixed, discrete, virtual action set, and a projection method generates the feasible, actual actions to satisfy the constraints. A distributed algorithm, based on singularly perturbed system, solves the projection problem. A distributed form of Hysteretic Q-learning achieves coordination among agents. Therein, the Q-values are developed based on the virtual actions, while the rewards are produced by the projected actual actions. The proposed algorithm deals with continuous action space and power loads without using function approximations. Theoretical analysis and comparative simulation studies verify algorithm's convergence and optimality. [ABSTRACT FROM AUTHOR]

Multi-target multi-camera tracking (MTMCT) targets to generate trajectories of the object that appeared under multiple cameras automatically. MTMCT can be treated as a combination of intra-camera tracking and cross-camera tracking. The existing work only employs the global description to perform the tracklet generating. However, the global description cannot model the local similarity between targets, leading to existing methods not to be robust to occlusion and fast motion. To handle the mentioned problem, we propose an online Optical-based Pose Association (OPA) for multi-target multi-camera tracking. The proposed method utilizes local pose matching to solve the occlusion problem, and applies optical flow to reduce the distance caused by fast motion. For optical-based pose association, we firstly employ OpenPose to generate human pose for each proposal. Then, we utilize the optical flow generated by PWC-Net to adjust the estimated pose for the previous frame. Finally, the modified Object Keypoint Similarity is used to compute the similarity between the pose of the current frame and adjusted pose in the prior frame. Once obtaining the optical-based pose similarity, we combine it with the visual and bounding box spatial similarities to generate the final similarity matrix, and apply the Kuhn-Munkras algorithm for data association. The experiments on the MTMCT and MOT datasets verify the rationality of using human pose information and prove the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Most of today's smart grids are highly vulnerable to cascading failure attacks in which the failure of one or more critical components may trigger the sequential failure of other components, resulting in the eventual breakdown of the whole system. Existing works design different ranking methods for critical node or link identifications that fail to identify potential cascading failure attacks. In this work, we first consider the system from the attacker's point of view with a limited attack budget to study the smart grid vulnerability, referred to as Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. We propose an efficient algorithm by considering the interdependency property of the system, called Greedy Based Partition Algorithm (GBPA) to solve the MICLLB problem. In addition, we design an algorithm, namely Homogeneous-Equality Based Defense Algorithm (HEBDA) to help reduce damages in case the system is suffering from the cascading failure attacks. Through rigorous theoretical analysis and experimentation, we demonstrate that the investigated problem is NP-complete problem and our proposed methods perform well within reasonable bounds of computational complexity. [ABSTRACT FROM AUTHOR]

Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams. To solve this problem, reliable association of the same person across distant viewpoints and temporal instances is essential. We present a self-supervised framework to adapt a generic person appearance descriptor to the unlabeled videos by exploiting motion tracking, mutual exclusion constraints, and multi-view geometry. The adapted discriminative descriptor is used in a tracking-by-clustering formulation. We validate the effectiveness of our descriptor learning on WILDTRACK T. Chavdarova et al., “WILDTRACK: A multi-camera HD dataset for dense unscripted pedestrian detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2018, pp. 5030–5039. and three new complex social scenes captured by multiple cameras with up to 60 people “in the wild”. We report significant improvement in association accuracy (up to 18 percent) and stable and coherent 3D human skeleton tracking (5 to 10 times) over the baseline. Using the reconstructed 3D skeletons, we cut the input videos into a multi-angle video where the image of a specified person is shown from the best visible front-facing camera. Our algorithm detects inter-human occlusion to determine the camera switching moment while still maintaining the flow of the action well. Website: http://www.cs.cmu.edu/~ILIM/projects/IM/Association4Tracking. [ABSTRACT FROM AUTHOR]

This article addresses a problem of seabed terrain following control (STFC) for an underwater vehicle-manipulator system (UVMS). The motivation is to perform a visual search of marine products closely to seabed in unknown environment. In terms of this issue, we propose a novel and robust STFC framework for our UVMS to maintain an appropriate height to seabed. A nonlinear model predictive control (NMPC) method is formulated to solve the STFC problem. To relieve online computational burden and system noisy influence, Ohtsuka’s continuation/generalized minimal residual (C/GMRES) algorithm incorporated with a tracking differentiator (TD) is investigated. In order to improve the following accuracy, the system state prediction part of the NMPC and a long short-term memory (LSTM) network are elaborated to predict future seabed terrain using a depth gauge and an altimeter, respectively. Finally, the three different physical scenarios for STFC problem are established using ROS to demonstrate the robustness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To solve the problem of the direction of arrival (DOA) maneuvering in acoustic vector sensor (AVS) array, we propose an interactive multi-model Multi-target Multi-Bernoulli (IMM-MeMBer) two-dimensional (2-D) DOA tracking algorithm. The idea of the IMM algorithm is employed to interactively estimate the predicted sampled particles at the expense of calculating the likelihood function of the predicted particles by pseudo-spectrum of multi-signal classification (MUSIC). The proposed method fully considers various possibilities of target motion, which makes the target state estimation more effective. Moreover, the de-noising and exponential weighting of MUSIC pseudo-spectrum improve the likelihood function of particles, thus enhancing the weight of particles in high likelihood region and making these particles more effective in the subsequent resampling process. Simulation results show that the IMM-MeMBer algorithm has better performance than projection approximation subspace tracking of deflation MUSIC (PASTD-MUSIC), MeMBer, particle filter (PF), and random finite set PF (RFS-PF) in the DOA tracking of maneuvering sources. [ABSTRACT FROM AUTHOR]

Online Ride-Hailing(ORH) service enables riders to enjoy on-demand transportation service with mobile devices. Despite the convenience of the service, privacy leakage could be caused because riders need to submit their locations during ride matching. To protect users’ privacy, much effort has been done to construct a privacy-enhanced ORH system in recent years. However, almost all of current privacy-preserving schemes match the nearest driver to the requesting rider, it is not the optimal matching strategy and unnecessary waste of travel distance may be caused in this way. To solve the problem, we propose a privacy-preserving ORH scheme named pRide, which minimizes the overall travel distance by matching the best driver in global perspective instead of the nearest driver in the local region. To find the best driver, we utilize a deep learning model to predict emergence of ride requests in various regions, and enables the ORH server to perform ride matching leveraging prediction results. Based on the framework, we also propose optimizations to enhance pRide. Moreover, to further defend inference attacks from riders, we propose a private comparing algorithm at the cost of a little communication overhead. Through theoretical analysis and experiments, we prove that pRide is able to decrease the overall travel distance securely and efficiently in ORH matching scenarios. [ABSTRACT FROM AUTHOR]

Covert transmission is studied for an intelligent reflecting surface (IRS) aided communication system, where Alice aims to transmit messages to Bob without being detected by the warden Willie. Specifically, an IRS is used to increase the data rate at Bob under a covert constraint. For the considered model, when Alice is equipped with a single antenna, the transmission power at Alice and phase shift at the IRS are jointly optimized to maximize the covert transmission rate with either instantaneous or partial channel state information (CSI) of Willie’s link. In addition, when multiple antennas are deployed at Alice, we formulate a joint transmit beamforming and IRS phase shift optimization problem to maximize the covert transmission rate. One local optimal algorithm and two low-complexity suboptimal algorithms are proposed to solve the problem. Furthermore, for the case of imperfect CSI of Willie’s link, the optimization problem is reformulated by using the triangle and Cauchy-Schwarz inequalities. The reformulated optimization problems are solved using an alternative algorithm, semidefinite relaxation (SDR) and Gaussian randomization techniques. Finally, simulations are performed to verify our analysis. The numerical results show that an IRS can degrade the covert transmission rate when Willie is closer to the IRS than Bob. [ABSTRACT FROM AUTHOR]