<![CDATA[ IEEE Transactions on Industrial Informatics - new TOC ]]>
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TOC Alert for Publication# 9424 2019April 18<![CDATA[Table of contents]]>154C11842469<![CDATA[IEEE Industrial Electronics Society]]>154C2C259<![CDATA[High-Speed Scene Flow on Embedded Commercial Off-the-Shelf Systems]]>154184318525406<![CDATA[An Improved Artificial Bee Colony Algorithm With its Application]]>154185318653443<![CDATA[Molecular Evolution Based Dynamic Reconfiguration of Distribution Networks With DGs Considering Three-Phase Balance and Switching Times]]>154186618761902<![CDATA[FPGA Implementation of Passivity-Based Control and Output Load Algebraic Estimation for Transformerless Multilevel Active Rectifier]]>algebraic estimator is devised. Then, a linear controller based on the exact static error dynamics passive output feedback (ESEDPOF) is proposed, where the uncertainty estimation is taken into account. Since the controller estimator is based on the continuous-time plant model, its real-time implementation on a digital platform requires a discretization of the controller under sufficiently fast sampling, such that the properties of the closed-loop nonlinear sampled-data system are preserved. For this reason, the medium-scale field-programmable gate array Spartan-6 XC6SLX16 is used for implementing the ESEDPOF controller, the online algebraic estimator, the enhanced phase-locked loop, and the multilevel pulsewidth modulator. The parallel processing provided by these devices and the capability in the design of custom modules allow optimizing the hardware description and obtaining an update time for the control law of 9.683 $mu$s. Experimental validation shows an excellent dynamical performance and a near-unity power factor.]]>154187718893440<![CDATA[A New Predictive Approach to Wide-Area Out-of-Step Protection]]>154189018982829<![CDATA[An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation]]>154189919102853<![CDATA[DC Marine Power System: Transient Behavior and Fault Management Aspects]]>154191119256272<![CDATA[Optimal Number of Electric Vehicles for Existing Networks Considering Economic and Emission Dispatch]]>154192619351649<![CDATA[Link Quality Estimation in Industrial Temporal Fading Channel With Augmented Kalman Filter]]>154193619462747<![CDATA[Optimal Speed Setting for Cloud Servers With Mixed Applications]]>154194719551003<![CDATA[Adaptive Image-Based Visual Servoing With Temporary Loss of the Visual Signal]]>a priori visual information is proposed to predict all of the missing feature points and to ensure the execution of IBVS. The mixture parameter for the image Jacobian matrix can also affect the control of IBVS. The settings for the mixture parameter are heuristic so there is no a systematic approach for most IBVS applications. An adaptive control approach is proposed to determine the mixture parameter. The proposed method uses a reinforcement learning (RL) method to adaptively adjust the mixture parameter during the robot movement, which allows more efficient control than a constant parameter. A logarithmic interval state-space partition for RL is used to ensure efficient learning. The integrated visual servoing control system is validated by several experiments that involve wheeled mobile robots reaching a target with a desired configuration. The results for simulation and experiment demonstrate that the proposed method has a faster convergence rate than other methods.]]>154195619652090<![CDATA[Recycled and Remarked Counterfeit Integrated Circuit Detection by Image-Processing-Based Package Texture and Indent Analysis]]>154196619742574<![CDATA[New Discrete-Solution Model for Solving Future Different-Level Linear Inequality and Equality With Robot Manipulator Control]]>et al. discretization (ZeaD) formula to discretize the continuous solution model. Meanwhile, theoretical analyses and results are presented to show the excellent properties of the NDS model. Numerical results illustrate the effectiveness and superiority of the NDS model for solving FDLLIE. Furthermore, application experiments for motion planning of robot manipulator are conducted to substantiate the efficacy of the NDS model for FDLLIE solving.]]>154197519841406<![CDATA[Vision-Based Guidance and Switching-Based Sliding Mode Controller for a Mobile Robot in the Cyber Physical Framework]]>154198519973222<![CDATA[A Novel Framework for Gear Safety Factor Prediction]]>154199820071866<![CDATA[Event-Triggered-Based Distributed Cooperative Energy Management for Multienergy Systems]]>154200820226020<![CDATA[Multiperiod Planning of Distribution Networks Under Competitive Electricity Market With Penetration of Several Microgrids Part II: Case Study and Numerical Analysis]]>154202320313117<![CDATA[A Novel Measurement-Based Dynamic Equivalent Model of Grid-Connected Microgrids]]>154203220436028<![CDATA[A Sliding Window Based Dynamic Spatiotemporal Modeling for Distributed Parameter Systems With Time-Dependent Boundary Conditions]]>154204420531819<![CDATA[A Model-Based Recurrent Neural Network With Randomness for Efficient Control With Applications]]>154205420632514<![CDATA[3-D Human Pose Estimation Using Cascade of Multiple Neural Networks]]>et al. method with a small number of basis shapes and 2) make this initial shape more alike to the original shape by using the CMNN. In comparing to existing works, the proposed method shows a significant outperformance in both accuracy and processing time. This paper also introduces a new system called Human3D that can estimate the 3-D pose of all people in a single RGB image. This system comprises two part: convolution pose machine (CPM) for estimating 2-D poses of all people in an RGB image and CMNN for reconstructing 3-D poses of them from outputs of the CPM.]]>154206420721273<![CDATA[Nonlinear Output Feedback Finite-Time Control for Vehicle Active Suspension Systems]]>154207320823880<![CDATA[Optimizing Configuration of Cyber Network Considering Graph Theory Structure and Teaching–Learning-Based Optimization (GT-TLBO)]]>nth elements of the cyber network that observe the cyber protocols and choosing the best one has not been done before. Choosing the best configuration for any nth elements cyber network that can be connected to a bulk power network needs a robust and adequate computer algorithm considering cyber protocols and decision-making goals. In this paper, a novel method is proposed in order to introduce the best configuration for a cyber-network system to accommodate cyber protocols and have a remarkable effect on decreasing expected energy not supplied (EENS) compared with those previously studied. To this end, two mathematical concepts are proposed; a graph theory to consider cyber protocols and teaching–learning-based optimization to select the best cyber configuration with minimum EENS. During the first time, choosing the best configuration with each specific goal for nth devices of a cyber-network system is applicable by converting it into an $n times n$ adjacency matrix and using the proposed graph theory mixed with teaching–learning-based optimization algorithm. Moreover, Monte Carlo simulation was used in this paper as one of the most precise probabilistic methods. The test results indicate that the proposed method selected for identifying the best configuration of a cyber-network system has significantly improved reliability indices c-
mpared to those in previous papers and it could be useful for every wide power–cyber network. Additionally, different types of cyber network are studied for validating the proposed method. This method is applied to the realistic feeder of the Hormozgan Regional Electric Company as a smart pilot system.]]>154208320901620<![CDATA[Hierarchical Monitoring and Root-Cause Diagnosis Framework for Key Performance Indicator-Related Multiple Faults in Process Industries]]>154209121004967<![CDATA[Event-Triggered Adaptive Neural Network Controller in a Cyber–Physical Framework]]>${text{41}}%$ and ${text{64}}%$ in the two case studies, respectively. The experimental results also prove the designed controller to be efficient when compared with event-triggered incremental PID controller using the same data transmission framework.]]>154210121111734<![CDATA[An Online-Calibrated Time Series Based Model for Day-Ahead Natural Gas Demand Forecasting]]>154211221231683<![CDATA[A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning]]>154212421351452<![CDATA[Data-Driven-Based Optimization for Power System Var-Voltage Sequential Control]]>154213621453272<![CDATA[Application of Bayesian Networks in Reliability Evaluation]]>15421462157642<![CDATA[Nonlinear Robust Fault-Tolerant Control of the Tilt Trirotor UAV Under Rear Servo's Stuck Fault: Theory and Experiments]]>154215821661838<![CDATA[New Recurrent Neural Network for Online Solution of Time-Dependent Underdetermined Linear System With Bound Constraint]]>154216721761024<![CDATA[Automatic Laser Control System for Selective Laser Sintering]]>154217721852336<![CDATA[Current Sharing Control for Parallel DC–DC Buck Converters Based on Finite-Time Control Technique]]>$n$ parallel dc–dc buck converters.]]>154218621983838<![CDATA[An Iterative Multilayer Unsupervised Learning Approach for Sensory Data Reliability Evaluation]]>154219922091135<![CDATA[Operational Control of Mineral Grinding Processes Using Adaptive Dynamic Programming and Reference Governor]]>ad hoc optimization guarantees that the input constraints are not violated, with the priority of regulating the grinding product particle size if regulation of both indices is not feasible. Since the dynamic model of the controlled plant is complicated because of the strongly nonlinear and intricately coupled nature of ball mills and hydrocyclones, a novel policy iteration algorithm is proposed for optimal regulator design without system modeling. Simulation results comparing performances of a mineral grinding process with and without the reference governor show the effectiveness of the proposed method.]]>154221022211703<![CDATA[Irregularity Detection in Output Power of Distributed Energy Resources Using PMU Data Analytics in Smart Grids]]>154222222321905<![CDATA[A Framework for Efficient Information Aggregation in Smart Grid]]>154223322432335<![CDATA[A Wheel Slip Control Approach Integrated With Electronic Stability Control for Decentralized Drive Electric Vehicles]]>154224422524164<![CDATA[Digital Implementation via FPGA of Controllers for Active Control of Ground Vehicles]]>154225322642010<![CDATA[Disturbance Rejection for Biped Walking Using Zero-Moment Point Variation Based on Body Acceleration]]>154226522763382<![CDATA[An ACO-Based Tool-Path Optimizer for 3-D Printing Applications]]>154227722872132<![CDATA[Optimal Data Caching and Forwarding in Industrial IoT With Diverse Connectivity]]>15422882296771<![CDATA[An Attribute Credential Based Public Key Scheme for Fog Computing in Digital Manufacturing]]>154229723071423<![CDATA[Network Detection of Radiation Sources Using Localization-Based Approaches]]>154230823203733<![CDATA[Family-Based Big Medical-Level Data Acquisition System]]>154232123293907<![CDATA[An Adaptive Dropout Deep Computation Model for Industrial IoT Big Data Learning With Crowdsourcing to Cloud Computing]]>15423302337504<![CDATA[Fuzzy-Folded Bloom Filter-as-a-Service for Big Data Storage in the Cloud]]>154233823481563<![CDATA[SIGMM: A Novel Machine Learning Algorithm for Spammer Identification in Industrial Mobile Cloud Computing]]>154234923593238<![CDATA[Big Data Transmission in Industrial IoT Systems With Small Capacitor Supplying Energy]]>$1+alpha$ approximation offline algorithm when all the information of the packets and the energy receiving periods is known in advance, and a $max lbrace 2,beta rbrace$ competitive ratio online algorithm where the information is not known in advance. For the second problem, we study three cases and give a $6+lceil frac{h}{b/R} rceil$ approximation offline algorithm for the general situation. We also prove that there does not exit a constant competitive ratio online algorithm.]]>154236023711132<![CDATA[Privacy-Preserving Tensor-Based Multiple Clusterings on Cloud for Industrial IoT]]>15423722381810<![CDATA[Guest Editorial Special Section on Big Data Analytics in Intelligent Manufacturing]]>15423822385490<![CDATA[Induction Motor Stator Current AM-FM Model and Demodulation Analysis for Planetary Gearbox Fault Diagnosis]]>154238623941880<![CDATA[Multitask Policy Adversarial Learning for Human-Level Control With Large State Spaces]]>154239524042542<![CDATA[Digital Twin in Industry: State-of-the-Art]]>154240524151301<![CDATA[Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing]]>154241624251921<![CDATA[Fv-SVM-Based Wall-Thickness Error Decomposition for Adaptive Machining of Large Skin Parts]]>154242624343584<![CDATA[A Data-Driven Monitoring Scheme for Rotating Machinery Via Self-Comparison Approach]]>154243524452949<![CDATA[Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning]]>154244624552558<![CDATA[Modeling and Planning for Dual-Objective Selective Disassembly Using <sc>and</sc>/<sc>or</sc> Graph and Discrete Artificial Bee Colony]]>AND/OR graphs (AOGs) have been applied to describe practical disassembly problems by using “AND” and “OR” nodes. An AOG-based disassembly sequence planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While precedence and “AND” relationship issues can be addressed, OR (exclusive OR) relations are not well addressed by the existing heuristic methods. Thus, an ineffective result may be obtained in practice. A conflict matrix is introduced to cope with the exclusive OR relation in an AOG graph. By using it together with precedence and succession matrices in the existing work, this work proposes an effective triple-phase adjustment method to produce feasible disassembly sequences based on an AOG graph. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with the traditional economical criterion leads to a novel dual-objective optimization model such that disassembly profit is maximized and disassembly energy consumption is minimized. An improved artificial bee colony algorithm is developed to effectively generate a set of Pareto solutions for this dual-objective disassembly optimization problem. This methodology is employed to practical disassembly processes of two products to verify its feasibility and effectiveness. The results show that it is capable of rapidly generating satisfactory Pareto results and outperforms a well-known genetic algorithm.]]>154245624681879<![CDATA[LSTM and Edge Computing for Big Data Feature Recognition of Industrial Electrical Equipment]]>154246924772686<![CDATA[Introducing IEEE Collabratec]]>154247824781857<![CDATA[IEEE Open Access Publishing]]>154247924791120<![CDATA[Expand your network, get rewarded]]>154248024801041<![CDATA[IEEE Industrial Electronics Society]]>154C3C353<![CDATA[Information for authors]]>154C4C447