New Zeroing Neural Network Models for Solving Nonstationary Sylvester Equation With Verifications on Mobile Manipulators IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-14 Xiaogang Yan; Mei Liu; Long Jin; Shuai Li; Bin Hu; Xin Zhang; Zhiguan Huang
Recurrent neural networks (RNNs) have found a great variety of application areas. As a special type of RNNs, zeroing neural network (ZNN, or termed Zhang neural network) has been reported to have powerful abilities for solving various time-varying problems. In this paper, to overcome drawbacks and improve the performance of existing ZNN models, several modified ZNN models are proposed, allowing nonconvex sets for mapping operations in activation functions as well as possessing accelerated finite-time convergence property. In addition, theoretical analyses show that the proposed ZNN models are of global convergence property and with upper bounds of convergence time estimated. Finally, comparative and illustrative simulation results, including a verification on a mobile manipulator, are presented to illustrate the effectiveness and superiority of proposed ZNN models to existing models for solving nonstationary Sylvester equations.
Data-driven Evaluation for Error States of Standard Electricity Meters on Automatic Verification Assembly Line IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-14 Yang Jiao; Hong-bin Li; Chen Hu; Zhu Zhang; Chuan-ji Zhang
An automatic verification assembly line (AVAL) verifies the reliability and the accuracy of electricity smart meters (SM) using standard meters. As time goes by, the standard meters on an AVAL may experience metrological performance degradation, affecting verification results. Thus, the control over the standard meters' error states is of great significance. Traditionally, their error states can only be acquired at regular intervals and remain unknown during the AVAL's operation. To address this issue, we propose a data-driven method to evaluate standard meters' error states without interrupting the verification task. Instead of using an additional standard meter with a higher accuracy, a statistics method is applied to the verification data collected from an AVAL to conduct this work. The proposed method consists of four phases: creating evaluation parameters, identifying the reference meter, calculating deviations and recognizing error states. A case study on an AVAL verifies the effectiveness of the proposed method.
Robust Faulted Line Identification in Power distribution Networks via Hybrid State Estimator IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-13 Junjun Xu; Zaijun Wu; Xinghuo Yu; Chengzhi Zhu
Distribution networks with high penetration of distributed generation (DG) yield complicated and uncertain power flow, which make most existing faulted line identification methods not adaptable for industrial applications. Driven by this motivation, a novel single-phase-to-ground (SPTG) faulted line identification method is proposed based on hybrid state estimator (HSE). The first step of the method is to present a HSE for power distribution networks using power flow measurements (PFM) mixed phasor measurement units (PMU). Then, a SPTG fault on a power line is treated as an event that suddenly increases one virtual bus in the monitored network, so as to form the extended bus admittance matrix and augmented HSE based on the specific network topology. In this way, the faulted line identification could be obtained by computing parallel estimated results transversally. Robustness and effectiveness of the proposed HSE and the HSE-based SPTG faulted line identification method are validated by means of a cyber-physical system (a co-simulation platform), where two typical three-phase power distribution networks are considered to simulate with its hybrid measurement system.
A Hierarchical Deep Domain Adaptation Approach for Fault Diagnosis of Power Plant Thermal System IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-13 Xiaoxia Wang; Haibo He; Lusi Li
Fault diagnosis of a thermal system under varying operating conditions is of great importance for the safe and reliable operation of a power plant involved in peak shaving. However, it is a difficult task due to the lack of sufficient labeled data under some operating conditions. In practical applications, the model built on labeled data under one operating condition will be extended to such operating conditions. Data distribution discrepancy can be triggered by variation of operating conditions and may degenerate the performance of the model. Considering the fact that data distributions are different but related under different operating conditions, this paper proposes a hierarchical deep domain adaptation (HDDA) approach to transfer a classifier trained on labeled data under one loading condition to identify faults with unlabeled data under another loading condition. In HDDA, a hierarchical structure is developed to reveal effective information for final diagnosis by layer-wisely capturing representative features. HDDA learns domain-invariant and discriminative features with the hierarchical structure by reducing distribution discrepancy and preserving discriminative information hidden in raw process data. For practical applications, the taguchi method is used to obtain the optimized model parameters. Experimental results and comprehensive comparison analysis demonstrate its superiority.
Risk-Constrained Day-Ahead Scheduling for Concentrating Solar Power Plants with Demand Response Using Info-Gap Theory IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-12 Yuxuan Zhao; Zhenzhi Lin; Fushuan Wen; Yi Ding; Jiaxuan Hou; Li Yang
The emerging concentrating solar power plant (CSPP) represents one of the promising technologies for promoting solar power applications. In this work, risk-constrained day-ahead (DA) scheduling strategies for a virtual power plant (VPP) integrating a CSPP with some responsive residential and industrial loads are proposed considering the uncertainties of electricity price, thermal production of the solar field of the CSPP, and participation factor of residential demand response (RDR). The well-established information gap decision theory (IGDT) is utilized to hedge against the risk caused by these uncertainties. Based on IGDT, both a robust scheduling strategy for the risk-aversion (RA) decision maker and an opportunistic scheduling strategy for the opportunity-seeking (OS) decision maker are presented for hedging the profit risk of the VPP against variations of electricity price, thermal production and demand response. Simulation results show that the presented IGDT based method can act as an effective tool for managing risks from uncertainties, and also demonstrate that the RA VPP should focus more on the thermal production of the CSPP so as to guarantee the desired profit, whereas the OS VPP should pay more attention to the market price so as to achieve a windfall profit.
Anonymous Reputation System for IIoT-enabled Retail Marketing atop PoS Blockchain IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-12 Dongxiao Liu; Amal Alahmadi; Jianbing Ni; Xiaodong Lin; xuemin Shen
Industrial Internet of Things (IIoT) is revolutionizing the retail industry for manufacturers, suppliers, and retailers to improve operational efficiency and consumer experience. In IIoT-enabled retail marketing, reputation systems play a critical role to boost mutual trust among industrial entities and build consumer confidence. In this paper, we focus on reputation management in the consumer-retailer channel, where retailers can accumulate reputations from consumer feedbacks. To encourage consumers to post feedbacks without worrying about being tracked or retaliated, we propose an anonymous reputation system that preserves consumer identities and individual review confidentialities. To increase system transparency and reliability, we further exploit the tamper-proof nature and the distributed consensus mechanism of blockchain technology. With system designs based on various cryptographic primitives and a Proof-of-Stake (PoS) consensus protocol, our blockchainbased reputation system is more efficient to offer high levels of privacy guarantees compared with existing ones. Finally, we explore the implementation challenges of the blockchain-based architecture and present a proof-of-concept prototype system by Parity Ethereum. We measure the on/off-chain performance with the scalability discussion to demonstrate the feasibility of the proposed system.
Network Flow Labeling for Extended Target Tracking PHD filters IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-12 Shishan Yang; Florian Teich; Marcus Baum
The Probability Hypotheses Density (PHD) filter is a method for tracking multiple target objects based on unlabeled detections. However, as the PHD filter employs a first-order approximation of random finite sets, it does not provide track labels, i.e., targets of consecutive time steps are not associated with each other. In this work, an intuitive and efficient labeling strategy on top of the extended target PHD filter is proposed. The approach is based on solving a network flow problem and makes use of the Wasserstein metric to account for the spatial extent of the objects. The resulting tracker is evaluated with laser scanner data from two traffic scenarios.
Learning-based Demand Response for Privacy-Preserving Users IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : Amir Ghasemkhani; Lei Yang; Junshan Zhang
Demand response (DR), as a vital component of smart grid, plays an important role in shaping the load profiles in order to improve the system reliability and efficiency. Incentivebased DR has been used in many DR programs by incentivizing customers to adapt their loads to supply availability. Note that users' behavior patterns can be easily identified from fine-grained power consumption when interacting with the load serving entity (LSE), giving rise to serious privacy concerns. One common approach to address the privacy threats is to incorporate perturbations in users' load measurements. Although it can protect the users' privacy, the usage data modification would degrade the LSE's performance in achieving an optimal incentive strategy due to unknown characteristics of the augmented perturbations. In this paper, we cast the incentive-based DR problem as a stochastic Stackelberg game. To tackle the challenge induced by users' privacy protection behaviors, we propose a two time-scale reinforcement learning algorithm to learn the optimal incentive strategy under users' perturbed responses. The proposed algorithm computes the expected utility cost to mitigate the impacts of the random characteristics of the augmented perturbations, and then updates the incentive strategy based on the perceived expected utility costs. We derive the conditions under which the proposed incentive scheme converges almost surely to an ε-optimal strategy. The efficacy of the proposed algorithm is demonstrated through extensive numerical simulation using real data.
A Deep Learning Framework for Tactile Recognition of Known as well as Novel Objects IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-08 Zineb Abderrahmane; Gowrishankar Ganesh; Andre Crosnier; Andrea Cherubini
This paper addresses the recognition of daily-life objects by a robot equipped with tactile sensors. The main contribution is a deep learning framework that can recognize objects already touched as well as objects never touched before. To this end, we train a Deconvolutional Neural Network that generates synthetic tactile data for novel classes. Then, we use both these synthetic data and the real data collected by touching objects, to train a Convolutional Neural Network to recognize both known (trained) objects and novel objects. Furthermore, we propose a method for integrating newly encountered data into novel classes. Finally, we evaluate the framework using the largest available dataset of tactile objects descriptions.
Fault Tolerant Consensus for Vehicle State Estimation: A Cyber-physical Approach IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-08 Ehsan Hashemi; Mohammad Pirani; Khajepour Amir; Baris Fidan; Shih-Ken Shen; Bakhtiar Litkouhi
A novel cyber-physical method is proposed and experimentally verified for reliable distributed estimation of vehicle longitudinal velocity, robustly to road friction condition variations. In this method, the vehicle speed estimated at each of the four corners of the vehicle, using a linear parameter-varying observer in the physical layer, and speed data measured by a conventional low-cost GPS are incorporated in a distributed structure (in the cyber layer) to enhance the reliability of the estimate. The method minimizes a cost function quantizing the effect of disturbances on each corner's estimation and adversaries due to occasional GPS signal drops. A fault-tolerant estimation policy is integrated to deal with large deviations in corner estimations, which have unexpectedly high levels of confidence. The main advantages of the proposed method are increased reliability on various road surface conditions and robustness to faults, as confirmed by road tests. Several experimental tests, including lane change and low-excitation maneuvers, with various powertrain configurations on dry and slippery roads demonstrate the efficiency of the algorithm.
Enforcing Position-Based Confidentiality with Machine Learning Paradigm through Mobile Edge Computing in Real-Time Industrial Informatics IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-08 Arun Kumar Sangaiah; Darshan Vishwasrao Medhane; Tao Han; M. Shamim Hossain; Ghulam Muhammad
Position-based services that deliver networked amenities based on roaming users positions have become progressively popular with the propagation of smart mobile devices. Position is one of the important circumstances in Position-based services (PBS). For effective PBS, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques, however only few deliberate the progress of position-based real-time applications considering significant tasks of PBS. In this paper, a method for conserving position confidentiality of roaming PBS users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor, and estimates user destination along with position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm which will assure the timely delivery of position-based services at the network edge. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBS.
Load Balancing for Reliable Self-Organizing Industrial IoT Networks IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-08 M. Carmen Lucas-Estan; Javier Gozalvez
Industry 4.0 will interconnect and digitalize traditional industries to enable smart and adaptable factories that efficiently utilize resources and integrate systems. A key enabler of this paradigm is the communications infrastructure that will support the ubiquitous connectivity of Cyber-Physical Production Systems. The integration of wireless networks will facilitate the dynamic reconfiguration of the factories of the future, and collection and management of large amounts of data. This vision requires reliable and low latency wireless links with the necessary bandwidth to support data intensive applications and spatio-temporal variations of data resulting from the reconfiguration of Industrial IoT systems. To this aim, this paper proposes a load balancing scheme that dynamically manages the wireless links based on their quality and the amount of data to be transmitted by each node. The proposed scheme avoids the saturation of channels, and significantly augments the reliability of industrial wireless networks in comparison with existing solutions.
A Physically Inspired Data-driven Model for Electricity Theft Detection with Smart Meter Data IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-08 Yuanqi Gao; Brandon Foggo; Nanpeng Yu
Electricity theft is the third-largest form of theft in the United States. It not only leads to significant revenue losses, but also creates the risk of fires and fatal electrical shocks. In the past, utilities have fought electricity theft by sending field operation groups to conduct physical inspections of electrical equipment based on suspicious activity reported by the public. However, the recent rapid penetration of advanced metering infrastructure makes it possible to detect electricity theft by analyzing the information gathered from smart meters. In this paper, we develop a physically inspired data driven model to detect electricity theft with smart meter data. The main advantage of the proposed model is that it only leverages the electricity usage and voltage data from smart meters instead of unreliable parameter and topology information of the secondary network. Hence a speedy and widespread adoption of the proposed model is feasible. We show that a modified linear regression model accurately captures the physical relationship between electricity usage and voltage magnitude on the Kronreduced distribution secondaries. Our results show that electricity theft on a distribution secondary will lead to negative and positive residuals from the regression for dishonest and honest customers respectively. The proposed model is validated with real-world smart meter data. The results show that the model is effective in identifying electricty theft cases.
Model Predictive Direct Speed Control with Torque Oscillation Reduction for PMSM Drives IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-07 Ming Liu; Ka Wing Chan; Jiefeng Hu; Wenzheng Xu; Jose Rodriguez
Servo drives require high dynamics and reliability on speed control. Conventional cascade linear controllers suffer from proportional-integral (PI) parameters tuning work and low dynamics. In this paper, an improved model predictive direct speed control (MPDSC) is proposed with rapid speed tracking and very small offset. The new scheme eliminates the cascaded structure by predicting the future speed in discrete steps. The optimal voltage vector is selected according to an evaluation criterion for speed and flux tracking. To reduce the system cost and improve the reliability, a load torque observer is adopted to estimate the actual load torque. Besides, to avoid torque oscillations and overshoots during rapid speed variation, a torque suppression factor is incorporated into the cost function. Furthermore, a myopic prediction correction method is developed to enhance both the dynamic and steady-state responses. Simulation and hardware-in-the-loop (HIL) results are presented to validate the effectiveness of the proposed method.
Copyright protection for holographic video using spatiotemporal consistent embedding strategy IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-07 Xiaowei Li; Ying Wang; Qionghua Wang; Seok-Tae Kim; Xin Zhou
The interesting of 3D holographic display is increasing daily due to it can generate full depth cue without utilizing any special glasses. However, the content of 3D holographic video currently without any protection can be illegally distributed and maliciously manipulated. Therefore, there is an urgent need to protect the copyright of holographic video against malicious copy. Since watermarking is an effective way to protect the ownership of holographic sequence, this paper proposes a spatiotemporal consistent embedding algorithm for the holographic video watermarking. Imperceptibility requirement in holographic video watermarking is more challenging compared with static holograms because of the temporal dimension existing in videos. The embedding algorithm should not only consider spatially embedding strength for each frame of the video, but it should also take the temporal dimension into account in order to guarantee the visual quality of the moving object. Before embedding, to defend the imperceptibility of watermark from the holographic moving object, the embedding parameters are evaluated by the salient object from the inter-frame and intra-frame. Different from previous video watermarking algorithm, in order to ensure the robustness, in our paper, instead of 2D watermark, 3D watermark converted data (QR code) is embedded in the cellular automata (CA) domains using 3D CA filters. Finally, the QR codes can be extracted from the watermarked holographic frames, and the final 3D watermark can be digitally reconstructed with different depths cue using computational integral imaging reconstruction algorithm. The experimental results demonstrate that the proposed method exhibits superior performance compared to several methods in literatures, especially, the robustness to against additive noise and compression attacks.
A Variable-Gain Finite-Time Convergent Recurrent Neural Network for Time-variant Quadratic Programming with Unknown Noises Endured IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-06 Weibing Li; Zhizhuo Su; Zhiguo Tan
A variable-gain finite-time convergent and noise-enduring zeroing neural network (VGFTNE-ZNN) is for the first time proposed for time-variant convex quadratic programming (QP). Differing from the existing finite-time convergent ZNNs with constant or variable design gains (i.e., CGFT-ZNN and VGFT-ZNN) that have limited noise-handling capabilities, the proposed VGFTNE-ZNN can endure additive noises by dynamically adjusting its design gains in finite time. Design gains of the unpolluted VGFTNE-ZNN are allowed to be constant when the QP problem is solved, while the design gain of the existing unpolluted VGFT-ZNN unrealistically increases to infinity when time evolves to infinity. Unlike existing polluted ZNNs with known noises involved, more practical unknown noises are successfully handled by the VGFTNE-ZNN. The finite-time convergence and noise-endurance properties of the VGFTNE-ZNN are mathematically proved based on Lyapunov theory. Numerical verifications are comparatively performed with the superiorities of the VGFTNE-ZNN substantiated as compared with the existing CGFT-ZNN and VGFT-ZNN.
Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-06 Mengting Liu; Richard Yu; Yinglei Teng; Victor Leung; Mei Song
Recent advances in industrial Internet of things (IIoT) provide plenty of opportunities for various industries. To address the security and efficiency issues of the massive IIoT data, blockchain is widely considered as a promising solution to enable data storing/processing/sharing in a secure and efficient way. To meet the high throughput requirement, this paper proposes a novel deep reinforcement learning (DRL) based performance optimization framework for blockchain-enabled IIoT systems, the goals of which are three-fold: 1) providing a methodology for evaluating the system from the aspects of scalability, decentralization, latency and security; 2) improving the scalability of the underlying blockchain without affecting the system's decentralization, latency and security; 3) designing a modulable blockchain for IIoT systems, where the block producers, consensus algorithm, block size and block interval can be selected/adjusted using the DRL technique. Simulations results show that our proposed framework can effectively improve the performance of blockchain-enabled IIoT systems and well adapt to the dynamics of IIoT.
Data-Driven Energy Management in a Home Microgrid Based on Bayesian Optimal Algorithm IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-03-28 Guangzhong Dong; Zonghai Chen
Microgrid is a key enabling solution to future smart grids by integrating distributed renewable generators and storage systems to efficiently serve the local demand. However, due to the intermittent and uncertainty of distributed renewable energy, the reliability and economic operations of microgrid are facing increasing new challenges. Traditionally, economic dispatch issue is considered as solving an offline or online optimization problem whose objective function is prior known. However, accurate and determined function expression is difficult to formulate, and wrong expression may result in waste of electricity cost and causing security issues. Thus, it is desirable to reformulate the economic dispatch problem, and solve it in a data-driven way. This paper proposes a data-driven energy management solution based on Bayesian optimization algorithm (BOA) for a single grid-connected home microgrid. The proposed solution formulates the optimization problem without a closed-form objective function expression, and solves it using BOA-based data-driven framework. The proposed solution is a kind of black-box function sequential global optimization strategy, and does not require derivative operation on the objective function. Besides, it can also solve the microgrid operation and parameter prediction uncertainty. Simulation results demonstrate the effectiveness of the proposed solution.
Distributed Cooperative Economic Optimization Strategy of a Regional Energy Network based on Energy Cell-tissue Architecture IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-05 Yang Gao; Qian Ai; Xiaoyu Wang; Muhammad Yousif
The conventional centralized control method can effectively solve the influence of system power imbalance on the optimal dispatching of a power network and stabilize the power fluctuation of tie lines when the control precision is relatively high. However, when there are disturbances and uncertainties in the system, the robustness is insufficient. The distributed control method in economic optimization scheduling has the advantages of simple communication and high reliability and is well adapted to the energy dispersion characteristics of a DG within the regional energy network. However, the control accuracy is slightly less than that of the centralized control method, and when the distributed power encounters a random input or exit, the corresponding sparse network matrix needs to be updated every time, complicating the use of this system for economic operation. In addition, when message loss or communication noise occurs in the communication path, the robustness of distributed control is greatly reduced. To address these issues, this paper proposes a novel power allocation framework to solve the economic optimization problem of a regional energy network that requires only local information exchange among neighboring energy cells and tissues. Correspondingly, to meet the supply-demand balance, a distributed cooperative approach based on the equal incremental principle is used to impart decentralized autonomy to the whole system. In addition, the concept of virtual consistency variables is introduced to manage topological change caused by power excursions in the energy cell and to implement plug-and-play capabilities. Relative to the conventional collaborative control method, the case studies demonstrate the scalability, plug-and-play capability, and robustness of decentralized autonomous control strategies in terms of communication delay, communication failure, and message loss, among other aspects.
Efficient Insertion of Multiple Objects Parallel Connected by Passive Compliant Mechanisms in Precision Assembly IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-05 Dengpeng Xing; Lv Yan; Song Liu; De Xu; Fangfang Liu
This paper proposes an efficient strategy to simultaneously insert multiple objects, which are parallel connected by compliant mechanisms, in precision assembly. The distinctions of this task include: each object is held compliantly; multiple objects are parallel connected to a manipulator; not all the peg-in-hole have the same insertion condition; and high accuracy is required for each insertion. This configuration can provide sufficient compliance and improve insertion efficiency for massive precision assembly. We model the relationship between the compliant mechanism's state and force, and analyze the horizontal compliance of parallel compliant mechanisms. Based on the model, using a fitting and optimization method the states of all but one compliant mechanisms are acquired from microscopic views and the states of the remaining one are obtained via the resultant force provided by a force sensor. To efficiently plan the parallel insertion, we propose a strategy to horizontally compensate according to the resultant force and the horizontal compliance, and to vertically insert based on the insertion ratio expectation, the horizontal offsets of each individual insertion, and the horizontal force. Experiments are carried out to demonstrate the validation of the proposed method.
Efficient Fire Detection for Uncertain Surveillance Environment IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2019-02-05 Khan Muhammad; Salman Khan; Mohamed Elhoseny; Syed Hassan Ahmed; Sung Wook Baik
Tactile Internet can combine multiple technologies by enabling intelligence via mobile edge computing and data transmission over a 5G network. Recently, several convolutional neural networks (CNN) based methods via edge intelligence are utilized for fire detection in certain environment with reasonable accuracy and running time. However, these methods fail to detect fire in uncertain IoT environment having smoke, fog, and snow. Furthermore, achieving good accuracy with reduced running time and model size is challenging for resource constrained devices. Therefore, in this paper, we propose an efficient CNN based system for fire detection in videos captured in uncertain surveillance scenarios. Our approach uses light-weight deep neural networks with no dense fully connected layers, making it computationally inexpensive. Experiments are conducted on benchmark fire datasets and the results reveal the better performance of our approach compared to state-of-the-art. Considering the accuracy, false alarms, size, and running time of our system, we believe that it is a suitable candidate for fire detection in uncertain IoT environment for mobile and embedded vision applications during surveillance.
Recent Industrial Applications of Infrared Thermography: A Review IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-12-05 Roque Alfredo Osornio-Rios; Jose Alfonso Antonino-Daviu; Rene de Jesus Romero-Troncoso
Infrared thermography (IRT) is a noninvasive technique that is drawing an increasing attention in industry. The spectacular advancement in the features of the infrared cameras that has come together with their progressive cost reduction has expanded the use of this technique to many industrial applications that were unfeasible just a few years ago. This paper compiles and comments the most recent scientific contributions related to the application of this technique in the industrial context. The paper classifies the analyzed references into three main groups: electrical, mechanical, and other applications. Especial emphasis is made on induction-motor-related applications of the IRT due to the extensive participation of these machines in the industrial context. The paper provides a critical review of most of the analyzed references, emphasizes the way in which the infrared technique is applied to the specific application and presents the limitations and pending issues as well as future challenges regarding the application of the technique.
Second-Order Continuous-Time Algorithm for Optimal Resource Allocation in Power Systems IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-19 Dong Wang; Zhu Wang; Changyun Wen; Wei Wang
In this paper, based on differential inclusions and the saddle point dynamics, a novel second-order continuous-time algorithm is proposed to solve the optimal resource allocation problem in power systems. The considered cost function is the sum of all local cost functions with a set of affine equality demand constraints and an inequality constraint on generating capacity of the generator. In virtue of nonsmooth analysis, geometric graph theory, and Lyapunov stability theory, all generators achieve consensus on the Lagrange multipliers associated with a set of affine equality constraints while the proposed algorithm converges exponentially to the optimal solution of the resource allocation problem starting from any initial states over an undirected and connected graph. Moreover, the obtained results can be further extended to the optimal resource allocation problem in case of switching communication topologies. Finally, two numerical examples involving a smart grid system composed of five generators and the IEEE 30-bus system demonstrate the effectiveness and the performance of the theoretical results.
Online Distributed MPC-Based Optimal Scheduling for EV Charging Stations in Distribution Systems IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-03-06 Yu Zheng; Yue Song; David J. Hill; Ke Meng
The increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an optimal power flow problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with the latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed model predictive control based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests, and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule, which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulations on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.
Symmetric-Strong-Tracking-Extended-Kalman-Filter-Based Sensorless Control of Induction Motor Drives for Modeling Error Reduction IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-28 Zhonggang Yin; Guoyin Li; Yanqing Zhang; Jing Liu
This paper proposes a real-time speed identification method by using a symmetric strong tracking extended Kalman filter (SSTEKF) for induction motor sensorless drive. In SSTEKF, the residual sequences are forced orthogonal to each other, and the gain matrix is tuned in real-time by introducing fading factors into the covariance matrix of the predicted state. The modeling error is reduced, and the mutational state is tracked rapidly based on SSTEKF. Simultaneously, the Cholesky triangular decomposition is used to change the working way of the multiple fading factor matrix in the error covariance matrix. The application of the Cholesky triangular decomposition guarantees that the error covariance matrix is symmetric in the process of iteration, and the stability of the algorithm is enhanced. Therefore, the estimation accuracy, the tracking speed, and the noise suppression of the proposed method are better than the EKF. The correctness and effectiveness of the proposed method are verified by experimental results.
Two-Loop Covert Attacks Against Constant Value Control of Industrial Control Systems IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-03-26 Weize Li; Lun Xie; Zhiliang Wang
In the field of covert data integrity attacks, considerable attention has focused on two important issues. One is the issue of how to change the state of a plant, and the other is how to avoid being detected by anomaly detectors. A two-loop covert attack is presented to provide an integrated solution for these two issues. As an exploratory attempt to establish the feasibility of machine learning-based covert attacks, it applies the least squares support vector machine to constructing covert attacks. The proposed attack consists of an attack loop and a covert loop, which are based on an attack agent and a covert agent, respectively. The attack agent can move the steady state of a target plant to a desired state, and the covert agent can closely imitate the normal steady state of the plant to cover up the attack agent. In particular, the attack is directed to proportional-integral-derivative algorithms. Experiments are carried out to demonstrate the feasibility of the proposed attack and show the applicability of machine learning methods in constructing covert attacks.
Task Allocation Algorithm for Energy Resources Providing Frequency Containment Reserves IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-02 Christian Giovanelli; Olli Kilkki; Seppo Sierla; Ilkka Seilonen; Valeriy Vyatkin
The uncertainty caused by the variability in renewable energy production requires the engagement of consumer-side energy production and consumption to provide sufficient flexibility and reliability for the power grid. This study presents an algorithm for allocating tasks to distributed energy resources allowing consumers to provide flexibility for frequency containment reserves. The task allocation algorithm aims at supporting the plug and play of energy resources, and it avoids the need for hard real-time messages during the coordination of the resources. The algorithm combines a novel control strategy with an information and communication technology architecture. The main decision logic of the algorithm is defined together with the distributed control logic. A prototype implementation of the overall system for frequency control is used to evaluate the performance of the algorithm. The simulation results show that the algorithm achieves the specified objectives, and has advantages compared to the state-of-the-art solution.
Exponential Tracking Control of Robotic Manipulators With Uncertain Dynamics and Kinematics IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-27 Bing Xiao; Shen Yin
This paper addresses a long-standing yet well documented open problem on task-space trajectory tracking control of robotic manipulators subject to both uncertain dynamics and uncertain kinematics. The main contribution is to establish a theoretical framework for designing an observer-based controller to achieve exponential tracking control. Two observers are designed for precisely estimating the uncertain kinematics and dynamics. It is theoretically proved that the entire observer–controller system is proved to be globally exponentially stable. Both the estimation errors and the trajectory tracking error can globally exponentially converge to their stable equilibrium points, respectively. To the best knowledge of the author, this works may be the first result for robot exponential tracking control. The tracking performance is, therefore, more robust to system uncertainties. The settling time of the closed-loop tracking error system can be tuned to be small arbitrarily. Experimental tests are also conducted to validate the effectiveness of the designed control framework.
Multiagent Gathering With Collision Avoidance and a Minimax Distance Criterion—Efficient Algorithms and Hardware Realization IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-09 Bhaskar Vundurthy; K. Sridharan
Multiple autonomous agents working cooperatively have contributed to the development of robust large-scale systems. While substantial work has been done in manufacturing and domestic environments, a key consideration for small hardware agents engaged in collaborative factory automation and welfare support systems is limited area and power on-board. When the agents attempt to meet for performing a task, it is natural for them to encounter obstacles and it is desirable for each agent to optimize its resources during its navigation. In this paper, we develop efficient geometric algorithms to find a point, termed as the gathering point (and denoted by $P_G$ ), for the agents that minimizes the maximum of path lengths . In particular, we present an $O(n \log _2 n)$ time algorithm for calculation of $P_G$ for an environment with two agents and $n$ static polygonal obstacles. We then use the notion of a weighted minimax point to derive an efficient algorithm (with complexity of $O(k^2 + kn \log _2 n)$ ) for computing $P_G$ for an environment with $k$ agents and $n$ obstacles. An enhancement to a dynamic environment is then presented. We also present details of an efficient hardware realization of the algorithms. Each agent, equipped with only an ATmega328P microcontroller and no external memory, executes the algorithms. Experiments with multiple agents navigating amidst static as well as dynamic obstacles are reported.
WSN Design and Verification Using On-Board Executable Specifications IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-05-25 Salvatore Gaglio; Giuseppe Lo Re; Gloria Martorella; Daniele Peri
The gap between informal functional specifications and the resulting implementation in the chosen programming language is notably a source of errors in embedded systems design. In this paper, we discuss a methodology and a software platform aimed at coping with this issue in programming resource-constrained wireless sensor network nodes (WSNs). Whereas the typical development model for the WSNs is based on cross compilation, the proposed approach supports high-level symbolic coding of abstract models and distributed applications, as well as their test and their execution, directly on the target hardware. As a working example, we discuss the application of our methodology to specify the functional behavior of a radio transceiver chip. The resulting executable specifications are augmented with automatically generated runtime verification code. Our approach is also compared to code development for two prominent WSN general-purpose operating systems.
Packet Size Optimization for Lifetime Maximization in Underwater Acoustic Sensor Networks IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-05-29 Huseyin Ugur Yildiz; Vehbi Cagri Gungor; Bulent Tavli
Recently, underwater acoustic sensor networks (UASNs) have been proposed to explore underwater environments for scientific, commercial, and military purposes. However, long propagation delays, high transmission losses, packet drops, and limited bandwidth in underwater propagation environments make realization of reliable and energy-efficient communication a challenging task for UASNs. To prolong the lifetime of battery-limited UASNs, two critical factors ( i.e. , packet size and transmission power) play vital roles. At one hand, larger packets are vulnerable to packet errors, while smaller packets are more resilient to such errors. In general, using smaller packets to avoid bit errors might be a good option. However, when small packets are used, more frames should be transmitted due to the packet fragmentation, and hence, network overhead and energy consumption increases. On the other hand, increasing transmission power reduces frame errors, but this would result in unnecessary energy consumption in the network. To this end, the packet size and transmission power should be jointly considered to improve the network lifetime. In this study, an optimization framework via an integer linear programming (ILP) has been proposed to maximize the network lifetime by joint optimization of the transmission power and packet size. In addition, a realistic link-layer energy consumption model is designed by employing the physical layer characteristics of UASNs. Extensive numerical analysis through the optimization model has been also performed to investigate the tradeoffs caused by the transmission power and packet size quantitatively.
Dual-Setting Directional Overcurrent Relays for Protecting Automated Distribution Networks IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-03-30 Amin Yazdaninejadi; Sajjad Golshannavaz; Daryoush Nazarpour; Saeed Teimourzadeh; Farrokh Aminifar
This paper elaborates a new protection scheme based on dual-setting DOCRs for automated distribution networks. In this course, the reduction in relays operation time saturates as the number of these relays increases. Thus, optimal deployment of dual-setting DOCRs should be specified in an efficient manner. To do so, a multiobjective optimization approach is established, which compromises the reduction of total operation time and the number of dual-setting DOCRs. Coordination constraints are accommodated and nonstandard inverse-time characteristics are established to intensify flexibility of the proposed strategy. The proposed model lies within a nonlinear programming fashion, which is tackled by particle swarm optimization. Moreover, the augmented $\varepsilon $ -constraint approach is utilized to reach the Pareto-optimal solutions following which the fuzzy decision making process determines the best compromised solution. Detailed simulation studies are carried out to interrogate performance of the proposed approach.
The Extension of Semantic Formalization of Service Workflow Specification Language IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-19 Wattana Viriyasitavat; Li Da Xu; Zhuming Bi
Service-Oriented Computing (SOC) is changing the way modern information systems that are designed, operated, and evolved. SOC makes possible to aggregate distributed resources at the phases of decision-making support and system operations. When myriads of resources with similar functionalities are available, effective methodologies are demanded to select services and compose them as service workflows for the specified goals. The computation for workflow composition is very complex since it depends on the numbers of services and their dynamic characteristics. Therefore, composing optimized workflows in a timely manner poses a great challenge. We are highly motivated to reduce the complexity of service selection and composition. The formalized semantics in SWSpec is extended so that unqualified or inferior services can be eliminated directly from the scope of the design solution space. In this paper, a brief review of the proposed SWSpec language is given and the focus is on the sematic formalization. A new compositional proof-system is developed with a set of inference rules and the proven system properties. The proposed semantic formalization has its great significance in reducing the complexity of composing workflows and developing efficient algorithms for compliance checking.
Neural Network Control of a Two-Link Flexible Robotic Manipulator Using Assumed Mode Method IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-03-22 Hejia Gao; Wei He; Chen Zhou; Changyin Sun
In this paper, the n-dimensional discretized model of the two-link flexible manipulator is developed by the assumed mode method (AMM). Subsequently, based on the discretized dynamic model, both full-state feedback control and output feedback control are investigated to achieve the trajectory tracking and vibration suppression. In order to guarantee the stability strictly, uniform ultimate boundedness (UUB) of the closed-loop system is realized by the Lyapunov's stability. Furthermore, through appropriately choosing control parameters, the states of the system will converge to zero within a small neighborhood. Eventually, extensive simulations and experiments on the Quanser platform for a two-link robotic manipulator are carried out to demonstrate the feasibility of the proposed neural network controller.
Constrained Sampled-Data ARC for a Class of Cascaded Nonlinear Systems With Applications to Motor-Servo Systems IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-02 Weichao Sun; Yanbin Liu; Huijun Gao
In this paper, sampled-data adaptive robust control is proposed for a class of uncertain cascaded nonlinear system with states and inputs constraints. The systematic design procedure can be divided into two steps: i) design a sampled-data adaptive robust controller for the plant to not only stabilize the closed-loop system but also track the desired command although there are a variety of uncertainties and disturbances in the system; ii) design a reference governor for the control system to avoid the states and inputs violating their limits. Finally, the proposed method is employed in Motor-servo system to demonstrate the effectiveness.
Deep Multiview Heartwave Authentication IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-10-08 Chin Leng Peter Lim; Wai Lok Woo; Satnam S. Dlay; Di Wu; Bin Gao
This paper presents a heartwave based authentication method that utilizes an ensemble of deep belief networks (DBNs) under different parameters to increase the reliability of feature extraction. The multiview outputs are further embedded into a single view before inputting into a stacked DBN for classification. The result of the proposed novel architecture achieved a classification rate of 98.3% with 30% training data. Importantly, it is able to perform user classification using heartwave signals acquired under intense physical exercise where heart rate ranges from 50 bpm to as high as 180 bpm. Under extreme physical duress, the heartwave from an individual experiences extreme morphological variations that render conventional classification approaches nonapplicable.
Supervisory Control Approach and its Symbolic Computation for Power-Aware RT Scheduling IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-09 Rajesh Devaraj; Arnab Sarkar; Santosh Biswas
Safety-critical systems implemented on multicore platforms need to satisfy stringent power dissipation constraints such as thermal design power (TDP) thresholds used by chip manufacturers. Power dissipation beyond TDP may trigger dynamic thermal management (DTM) in order to ensure thermal stability of the system. However, the application of DTM makes the system susceptible to higher unpredictability and performance degradations for real-time tasks. This paper proposes a formal scheduler synthesis framework that guarantees adherence to a system level peak power constraint while allowing optimal resource utilization in multicores. Our proposed framework makes use of supervisory control of timed discrete event systems as the underlying formalism. All steps starting from individual models to construction of the scheduler have been implemented through binary decision diagram based symbolic computation, so that the state-space complexity associated with the framework may be controlled. Furthermore, the synthesis framework has been extended to handle tasks with phased execution behavior. Conducted experiments have shown promising results and indicate to the practical efficacy of our approach.
Automatic Selection of Optimal Parameters Based on Simple Soft-Computing Methods: A Case Study of Micromilling Processes IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-03-26 Iván la Fé-Perdomo; Gerardo Beruvides; Ramon Quiza; Rodolfo Haber; Marcelino Rivas
Nowadays, the application of novel soft-computing methods to new industrial processes is often limited by the actual capacity of the industry to assimilate state-of-the-art computational methods. The selection of optimal parameters for efficient operation is very challenging in microscale manufacturing processes, because of intrinsic nonlinear behavior and reduced dimensions. In this paper, a decision-making system for selecting optimal parameters in micromilling operations is designed and implemented using simple and efficient soft-computing techniques. The procedure primarily consists of four steps: an experimental characterization; the modeling of cutting force and surface roughness by means of a multilayer perceptron; multiobjective optimization using the cross-entropy method, taking into account productivity and surface quality; and a decision-making procedure for selecting the most appropriate parameters using a fuzzy inference system. Finally, two different alloys for micromilling processes are considered, in order to evaluate the proposed system: a titanium-based alloy and a tungsten-copper alloy. The experimental study demonstrated the effectiveness of the proposed solution for automated decision-making, based on simple soft-computing methods, and its successful application to a real-life industrial challenge.
A Customized Real-Time Compilation for Motion Control in Embedded PLCs IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-25 Huifeng Wu; Yi Yan; Danfeng Sun; Rene Simon
General programmable logic controllers (PLCs) are difficult to adapt to changeable applications, especially for those with special motion control; this has seriously affected the development efficiency. This paper presents the concept of the embedded PLC (ePLC), whose hardware structure can be customized according to actual requirements. We proposed a three-layer architecture, and its customizable application layer could be compiled in real time. Correspondingly, the PLC program was divided into an engine program, control program, and customizing program. The description and compilation method of the customizing program was provided. We presented a customized winding machine language based on the proposed ePLC software structure. This was implemented in an automatic winding machine, which was easier to use compared with some widely used languages (e.g., G-Code).
Switching State-Space Degradation Model With Recursive Filter/Smoother for Prognostics of Remaining Useful Life IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-02-28 Yizhen Peng; Yu Wang; Yanyang Zi
Remaining useful life (RUL) is a critical metric in prognostics and health management (PHM) because it reflects the future health status and fault progression of products. Most RUL estimation methods are based on degradation data. In practice, due to changing degradation mechanisms during products’ whole life cycle, the degradation data may consist of two or more distinct phases, and the time points of these mechanisms switching are usually nondeterministic. This property makes RUL estimation a difficult task. To solve this problem, this paper proposes a switchable state-space degradation model to characterize degradation paths with nondeterministic switching manner dynamically. To update the model parameters by newly available data, a novel statistical procedure based on Rao-Blackwellized filter/smoother and an expectation maximization algorithm is derived. To improve the robustness and efficiency of the RUL prediction, a semianalytic prediction model is developed, which can avoid significant fluctuation in RUL estimation. The developed methodologies can automatically track different degradation phases and adaptively update parameters related to prior distributions. Two real products degradation cases are used to verify our methodologies.
Deviation Contribution Plots of Multivariate Statistics IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-06-13 Ruomu Tan; Yi Cao
As data analytic techniques evolve and the accessibility of process measurements improves, data-driven process monitoring has enjoyed a quick development in both theoretical and application perspectives recently. Although abundant process measurements will facilitate data-driven process monitoring and lead to better monitoring indexes, it becomes difficult to identify the underlying variables that are responsible for a fault directly with the monitoring indexes as the scope of measured variables is getting broader. To restrain the scope and identify the source of fault, contribution plots are commonly used in fault diagnosis in order to quantify the influence of process variables in presence of fault. Nevertheless, as sophisticated monitoring techniques become more and more complicated, deriving corresponding contribution plots is challenging. The concept of deviation contribution plots is proposed to address this issue. By extending the original definition of contribution for linear processes, the deviation contribution is defined to quantify the contribution of deviations in originally measured variables to the deviation of monitoring indexes. The ability of the proposed deviation contribution plots to identify influential variables in monitoring algorithms based on nonlinear feature extractions is verified by both numerical simulation and the Tennessee Eastman process benchmark case study.
Reliable Communication in Transmission Grids based on Nondisjoint Path Aggregation Using Software-Defined Networking IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-18 Boyang Zhou; Qiang Yang; Yansong Wang; Chunming Wu
In electrical transmission grids, the redundant communication paths nondisjointly overlapping at links can be established between certain substations and the control center to guarantee reliable packet delivery under link failures. However, the generation of nondisjoint paths with multiplicative and concave constraints is with the NP-complete complexity and the failovers can lead to out-of-order packets. This paper presents an OpenFlow-based nondisjoint path aggregation mechanism to heuristically compute the constrained nondisjoint paths in a centralized fashion and reorganize the out-of-order packets at edge switches. The solution is evaluated through simulations of the IEEE 30-bus network scenario under 2% link failure rate and the result confirms its effectiveness: the packet delivery success rate is significantly improved in comparison with the current nondisjoint and disjoint path algorithms. TCP throughput is improved by 121.62% with the packet reordering. Also, the memory usage for the packet reordering buffer is reduced by 76.563% compared with the distributed cognitive packet network.
Agent-Based Aggregated Behavior Modeling for Electric Vehicle Charging Load IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-05 Kalpesh Chaudhari; Nandha Kumar Kandasamy; Ashok Krishnan; Abhisek Ukil; H. B. Gooi
Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand–supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors, such as driver behavior, location of charging stations, electricity pricing, etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors that influence the charging demand of EVs. Several studies have modeled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This paper aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using NetLogo is employed in this paper to closely mimic the human aggregate behavior and its influence on the load demand due to charging of EVs.
Multiobjective Predictability-Based Optimal Placement and Parameters Setting of UPFC in Wind Power Included Power Systems IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-03-23 Sadjad Galvani; Mehrdad Tarafdar Hagh; Mohammad Bagher Bannae Sharifian; Behnam Mohammadi-Ivatloo
Uncertainty management is a challenging task in decision making of the operators of the power systems. Prediction of the system state is vital for the operation of a system with stochastic behavior especially in a power system with a significant amount of renewable energies such as wind power. Predictable power systems are in more interest of operators, of course. This paper proposes a multiobjective framework for optimal placement and parameters setting of a unified power flow controller (UPFC) considering system predictability. The well-known multiobjective nondominated sorting genetic algorithm is implemented to handle various objective functions such as active power losses and predictability of system in the presence of operational constraints and uncertainties. The point estimate method is used for modeling probabilistic nature of the wind power. Using the proposed method, statistical information of voltage magnitude and apparent power of converters of UPFCs can be obtained, which are very useful in making decision on the sizing of UPFCs. Comprehensive discussions are provided using the simulations on the IEEE 57-bus test system. Also, in order to validate the obtained results, a multiobjective particle swarm optimization algorithm is implemented and the results of two algorithms are compared with each other.
Power System Real-Time Emulation: A Practical Virtual Instrumentation to Complete Electric Power System Modeling IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-05-16 Ali Parizad; Sobhan Mohamadian; Mohamad Esmaeil Iranian; Josep M. Guerrero
Hardware-in-the-loop (HIL) simulation is a technique that is being used increasingly in the development and test of complex systems. Real-world testing of an intricate system in a field-like power plant can be challenging, time-consuming, expensive, and hazardous. HIL emulators allow engineers to test devices thoroughly and efficiently in a virtual environment with high reliability and minimum risk of defect. In this paper, the complete electric power system (including generator, turbine-governor, excitation system, transmission lines, transformer, external grid and related loads) is implemented in a MATLAB/Simulink environment. Different virtual instrument pages are modeled in the graphical programming language of LabVIEW which enable fast and reliable measurement functions such as data acquisition, archiving, real-time graphical display and processing. The interaction between MATLAB and LabVIEW is accomplished by generating a Pharlap ETS Targets * .dll file which enables the two software to exchange real-time data. Also, a real 1518-kW excitation system is considered as a test case for the introduced HIL system. This equipment is connected to LabVIEW software through a National Instrument PXI technology. Different scenarios (electrical frequency/active power change, voltage step response, etc.) are simulated in the designed power system emulator (PSE). The validity of the implemented model for the excitation system is verified by finding good matching between MATLAB and HIL simulation results.
Fragmentation-Based Distributed Control System for Software-Defined Wireless Sensor Networks IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-02 Hlabishi Isaac Kobo; Adnan M. Abu-Mahfouz; Gerhard Petrus Hancke
Software-defined wireless sensor networks (WSNs) are a new and emerging network paradigm that seeks to address the impending issues in WSNs. It is formed by applying software-defined networking to WSNs whose basic tenet is the centralization of control intelligence of the network. The centralization of the controller rouses many challenges such as security, reliability, scalability, and performance. A distributed control system is proposed in this paper to address issues arising from and pertaining to the centralized controller. Fragmentation is proposed as a method of distribution, which entails a two-level control structure consisting of local controllers closer to the infrastructure elements and a global controller, which has a global view of the entire network. A distributed controller system brings several advantages and the experiments carried out show that it performs better than a central controller. Furthermore, the results also show that fragmentation improves the performance and thus have a potential to have major impact in the Internet of things.
Plume Front Tracking in Unknown Environments by Estimation and Control IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-30 Xiangyuan Jiang; Shuai Li
Oil spill at the Gulf of Mexico a few years ago sets great hazardous to environments. The runtime monitoring and tracking of plume pose challenges to research community. Robot-based approaches have been proposed in previous work to solve this problem in environments with known physical parameters and measurable currents. However, practical implementation of this type of control laws may require additional sensors and the sensing error may significantly impact the convergence. This paper extends previous work by overcoming the challenge to establish a new control law by integrating interactive estimation and control in a unified loop. With a deliberate design, the parameters can be estimated online and the control can be achieved at the same time with provable convergence of the overall system, despite of the interplay of the two parts. An auxiliary system is constructed for efficient estimation of unknown parameters and set projection is incorporated to further improve the transient performance, making the system work well in both slow and fast time-varying environments. Theories of convergence, stability, and transient state are presented to guarantee the performance of plume front tracking. Validation experiments verify the theoretical results and substantiate the efficacy of the proposed scheme for plume tracking in unknown environments.
Fault Prediction via Symptom Pattern Extraction Using the Discretized State Vectors of Multisensor Signals IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-20 Sujeong Baek; Duck-Young Kim
Fault prediction and early degradation detection have received considerable attention in many engineering disciplines. Fault symptoms can be identified by abnormal values or unusual trends in the monitored sensor signals over a certain period prior to fault occurrence. However, how to extract abnormal pattern, particularly those with conditional relations among multiple sensor signals, remains unclear. Pattern extraction is further difficult particularly when there is no gradient relationship between measurements and operational states due to highly scattered data and unclear boundaries for distinguishing operational states. Additionally, defining the time period for symptom periods is challenging. To resolve these issues, we define the terms symptom pattern and symptom period, and then present a symptom pattern extraction method that collects all evidence of potential fault occurrence from multiple sensor signals. We postulate that, given time markers of fault occurrences, a symptom period precedes the occurrence of a fault. Symptom patterns are defined as either only found in the symptom periods or not found in the given time series, but similar to fault patterns. We further discuss an iterative search procedure for determining the length of symptom periods and propose a severity assessment method for symptom patterns. Finally, we apply the symptom pattern extraction and severity assessment methods to an online fault prediction procedure. By assessing the total severity of patterns in the monitoring window, early warning decision can be made. The procedure is tested in the early detection of abnormal cylinder temperature in a marine diesel engine and automotive gasoline engine knocking.
A Distributed Model Predictive Control Strategy for the Bullwhip Reducing Inventory Management Policy IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-12 Dongfei Fu; Hai-Tao Zhang; Ying Yu; Clara Mihaela Ionescu; El-Houssaine Aghezzaf; Robin De Keyser
Given the input/output constraints and cross couplings of supply chain (SC) nodes, model predictive control (MPC) is efficient to seek the optimal solutions to the problems posed by interacting nodes to satisfy customer demands. In supply chain applications, due to the growing spatial distribution and interactions between the supply network elements, the information flow management becomes a challenging yet significant task. To reduce numerical complexity while maintaining implementability, a distributed MPC strategy is proposed. The scheme aims at finding the Nash equilibrium where the controller of each subsystem communicates with other ones in the presence of noncooperative interaction and strong coupled inputs due to the ordering decisions. Extensive numerical simulations verify that the strategy outperforms conventional policies in terms of substantially reduced SC operating cost.
Energy- and Labor-Aware Production Scheduling for Industrial Demand Response Using Adaptive Multiobjective Memetic Algorithm IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-05-22 Xu Gong; Ying Liu; Niels Lohse; Toon De Pessemier; Luc Martens; Wout Joseph
Price-based demand response stimulates factories to adapt their power consumption patterns to time-sensitive electricity prices, so that a rise in energy cost is prevented without affecting production on the shop floor. This paper introduces a multiobjective optimization (MOO) model that jointly schedules job processing, machine idle modes, and human workers under real-time electricity pricing. Beyond existing models, labor is considered due to a common tradeoff between energy cost and labor cost. An adaptive multiobjective memetic algorithm (AMOMA) is proposed to fast converge toward the Pareto front without loss in diversity. It leverages feedback of cross-dominance and stagnation in a search and a prioritized grouping strategy. In this way, adaptive balance remains between exploration of the nondominated sorting genetic algorithm II and exploitation of two mutually complementary local search operators. A case study of an extrusion blow molding process in a plastic bottle manufacturer and benchmarks demonstrate the MOO effectiveness and efficiency of AMOMA. The impacts of production-prohibited periods and relative portion of energy and labor costs on MOO are further analyzed, respectively. The generalization of this method was further demonstrated in a multimachine experiment. The common tradeoff relations between the energy and labor costs as well as between the makespan and the sum of the two cost parts were quantitatively revealed.
Device-Free Activity Recognition Based on Coherence Histogram IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-05-02 Qinghua Gao; Jie Wang; Liming Zhang; Hao Yue; Bin Lin; Hongyu Wang
Device-free activity recognition (DFAR) is a promising technique that detects the activity of a target by analyzing the influence of its existence on surrounding wireless links. It realizes target sensing without the participation or even awareness of the target. The key question of DFAR is how to characterize the influence of the target on wireless links. Existing works mostly utilize statistical features, such as mean and variance in time-domain, and energy as well as entropy in frequency-domain, to characterize the influenced signals. However, statistical features provide only partial information. This paper explores the method on how to characterize the distribution of the signal as a whole. Specifically, we present a novel coherence histogram, which leverages the spatial structural characteristics to better characterize the distribution of the wireless signal. The coherence histogram captures not only the occurrence probability of received signal strength (RSS) measurements, but also the spatial relationship between adjacent RSS measurements as well. Experimental results show that our coherence histogram-based DFAR system could achieve an accuracy of more than 96%, which significantly outperforms other state-of-the-art DFAR systems remarkably.
Smart Wristwatches Employing Finger-Conducted Voice Transmission System IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-05-22 Kwangsub Song; Joon-Hyuk Chang
The principal aim of this paper is to present a novel speech transmission system that conveys speech between an actuator in a wearable wristwatch and the ear bone of a user through a finger. If an individual wears a smart watch equipped with an actuator that can play speech sent via communication lines, speech vibrations propagate from the actuator to fingertips through the human tissue and bone. When an individual places his or her finger into their ear, speech conducted through the finger can be registered and heard. While listening to finger-conducted speech, sounds are muffled, significantly degrading the intelligibility of speech. To mitigate this problem, a formant enhancement filter is applied to the speech prior to being fed into the actuator. With this method, the impulse response of human tissue and bones between the fingertips and wrist on which the watch is worn is first estimated to account for speaker-dependent distortion. Based on the estimated impulse response, a gain filter is used to boost the sound spectra, especially within the formant regions, to compensate for frequency distortion prior to speech transmission. On the other hand, since the impulse responses of humans are quite different for each individual, we propose the novel idea of a personalized algorithm that guides users to select an appropriate gain filter, using the $k$ -medoids clustering algorithm. Also, when an individual uses the proposed system, speech quality is degraded due to the ambient noise and acoustic echo between the microphone and actuator in the watch. Thus, to reduce background noise and acoustic echo, an integrated acoustic echo and background noise suppression algorithm is employed. Extensive simulations of the proposed system were performed by creating a novel phantom, which mimics the human hand with an aid of an ear simulator. We demonstrate that the proposed system has improved speech quality, when transmitting speech from the wearable wristwatch to a human perceptual organ through the finger.
Traffic and Computation Co-Offloading With Reinforcement Learning in Fog Computing for Industrial Applications IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-29 Yixuan Wang; Kun Wang; Huawei Huang; Toshiaki Miyazaki; Song Guo
In the past decade, network data communication has experienced a rapid growth, which has led to explosive congestion in heterogeneous networks. Moreover, the emerging industrial applications, such as automatic driving put forward higher requirements on both networks and devices. On the contrary, running computation-intensive industrial applications locally are constrained by the limited resources of devices. Correspondingly, fog computing has recently emerged to reduce the congestion of content-centric networks. It has proven to be a good way in industry and traffic for reducing network delay and processing time. In addition, device-to-device offloading is viewed as a promising paradigm to transmit network data in mobile environment, especially for autodriving vehicles. In this paper, jointly taking both the network traffic and computation workload of industrial traffic into consideration, we explore a fundamental tradeoff between energy consumption and service delay when provisioning mobile services in vehicular networks. In particular, when the available resource in mobile vehicles becomes a bottleneck, we propose a novel model to depict the users’ willingness of contributing their resources to the public. We then formulate a cost minimization problem by exploiting the framework of Markov decision progress (MDP) and propose the dynamic reinforcement learning scheduling algorithm and the deep dynamic scheduling algorithm to solve the offloading decision problem. By adopting different mobile trajectory traces, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results show that our proposed algorithms outperform other benchmark schemes in the mobile edge networks.
Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-22 Yiwei Cheng; Haiping Zhu; Jun Wu; Xinyu Shao
Machine health monitoring is of great importance in industrial informatics field. Recently, deep learning methods applied to machine health monitoring have been proven effective. However, the existing methods face enormous difficulties in extracting heterogeneous features indicating the variation until failure and revealing the inherent high-dimensional features of massive signals, which affect the accuracy and efficiency of machine health monitoring. In this paper, a novel data-driven machine health monitoring method is proposed using adaptive kernel spectral clustering (AKSC) and deep long short-term memory recurrent neural networks (LSTM-RNN). This method include three steps: First, features in the time domain, frequency domain, and time-frequency domain are, respectively, extracted from massive measured signals. And, an Euclidean distance based algorithm is designed to select degradation features. Second, the AKSC algorithm is introduced to adaptively identify machine anomaly behaviors from multiple degradation features. Third, a new deep learning model (LSTM-RNN) is constructed to update and predict the failure time of the machine. The effectiveness of the proposed method is validated using a set of test-to-failure experimental data. The results show that the performance of the proposed method is competitive with other existing methods.
Programming by Demonstration Using the Teleimpedance Control Scheme: Verification by an sEMG-Controlled Ball-Trapping Robot IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-10-18 Seongsik Park; Woongyong Lee; Wan Kyun Chung; Keehoon Kim
Impedance control allows robots to manipulate physical interactions delicately. However, issues associated with path planning and impedance remain unresolved. Herein we propose a path and impedance planning method for impedance control in a robot based on programming by demonstration through telemanipulation using a surface electromyogram. We considered a task that requires quick and precise adjustment of path and impedance, that is, ball trapping. We implemented a teleoperated robot that can deliver an operator's impedance as well as position during the ball trapping task to the slave side. The operators were asked to perform demonstrations of ball-trapping tasks using the implemented teleoperated robot, where the slave side is a vertical robot with one degree of freedom. The path and impedance were recorded and programmed as control input profiles from a set of successful demonstrations using Gaussian mixture regression. The result showed that using the human demonstration, the robot could learn how to catch a dropped ball without rebounding.
Smart Appliances and RAMI 4.0: Management and Servitization of Ice Cream Machines IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-29 Antonio Corradi; Luca Foschini; Carlo Giannelli; Roberto Lazzarini; Cesare Stefanelli; Mauro Tortonesi; Giovanni Virgilli
The widespread adoption of information and communication technologies (ICT) is profoundly changing manufacturing. Several Internet-of-Things (IoT) and industry 4.0 solutions deployed in production environments have pushed for standardization efforts, most notably reference architecture model industrie 4.0 (RAMI 4.0), typically focusing on smart factory environments. However, the ICT evolution is also enabling novel smart appliance scenarios, where relatively cheap machines, connected and integrated, are deployed outside the typical industrial environment with a wide range of stakeholders involved. The paper reports about a real world use case composed of more than 12000 ice cream machines connected worldwide and shows how, anticipating the state of the art, the underlying design of the ICT platform presents many interesting similarities with RAMI 4.0. The integration of appliances in a smart value chain enables to develop novel services for different stakeholders, ranging from ice cream manufacturer and maintenance technicians to ice cream shop owners and final consumers. The important synergies with RAMI 4.0 and the extensive on-the-field validation make the proposed solution a compelling reference application, from which to draw useful and generally applicable guidelines for the development of future Industry 4.0 smart appliance platforms.
Secure Real-Time Control Through Fog Computation IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-12 Kazuhiro Sato; Shun-ichi Azuma
We consider an asymptotic stabilization problem with a specified confidential level for an Internet of Things system composed of the Cloud, Fog, and a controlled device. A sequence of concealed states, that is, a sequence of the sum of the true state of a controlled device and artificial noise, termed the security input, is transferred to the Fog. As the method for concealing the true state is relatively simple, it enables us to achieve rapid real-time communication between a controlled device and the Fog. The level of confidentiality was measured using the mutual information between the true and concealed states. As a solution to our problem, we obtained Gaussian-type security inputs and a convex optimization problem for calculating feedback gains. Moreover, we proved that the main solution becomes all solutions for any scalar cases. Finally, we demonstrated the feasibility of our proposed method by solving the problem of tracking the reference signal of storage batteries in smart grids.
Automatic Fruit Classification Using Deep Learning for Industrial Applications IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-10-10 M. Shamim Hossain; Muneer Al-Hammadi; Ghulam Muhammad
Fruit classification is an important task in many industrial applications. A fruit classification system may be used to help a supermarket cashier identify the fruit species and prices. It may also be used to help people decide whether specific fruit species meet their dietary requirements. In this paper, we propose an efficient framework for fruit classification using deep learning. More specifically, the framework is based on two different deep learning architectures. The first is a proposed light model of six convolutional neural network layers, whereas the second is a fine-tuned visual geometry group-16 pretrained deep learning model. Two color image datasets, one of which is publicly available, are used to evaluate the proposed framework. The first dataset (dataset 1) consists of clear fruit images, whereas the second dataset (dataset 2) contains fruit images that are challenging to classify. Classification accuracies of 99.49% and 99.75% were achieved on dataset 1 for the first and second models, respectively. On dataset 2, the first and second models obtained accuracies of 85.43% and 96.75%, respectively.
Recent Advances and Trends in On-Board Embedded and Networked Automotive Systems IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-11-05 Lucia Lo Bello; Riccardo Mariani; Saad Mubeen; Sergio Saponara
Modern cars consist of a number of complex embedded and networked systems with steadily increasing requirements in terms of processing and communication resources. Novel automotive applications, such as automated driving, rise new needs and novel design challenges that cover a broad range of hardware/software engineering aspects. In this context, this paper provides an overview of the current technological challenges in on-board and networked automotive systems. This paper encompasses both the state-of-the-art design strategies and the upcoming hardware/software solutions for the next generation of automotive systems, with a special focus on embedded and networked technologies. In particular, this paper surveys current solutions and future trends on models and languages for automotive software development, on-board computational platforms, in-car network architectures and communication protocols, and novel design strategies for cybersecurity and functional safety.
OptDynLim: An Optimal Algorithm for the One-Dimensional RSU Deployment Problem With Nonuniform Profit Density IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-05-28 Zhenguo Gao; Danjie Chen; Shaobin Cai; Hsiao-Chun Wu
Proper deployment of roadside units (RSUs) is of crucial importance to vehicular ad hoc networks (VANETs). However, our understandings to the simple one-dimensional RSU Deployment (D1RD) problem with nonuniform profit density is still seriously limited. In this paper, we analyze the D1RD problem and try to design optimal algorithms for it. We first analyze the properties of the optimal solutions of the D1RD problem involving a single RSU, and then extend to multiple RSUs. Next, we propose an efficient technique named Dynamic Limiting (DynLim), which reduces the solution search space size considerably by adjusting search space limits dynamically. Finally, an optimal algorithm named OptDynLim is proposed based on the DynLim technique, and its optimality is proved. Numerical simulations validate the correctness of our analyzes and show that DynLim can usually reduce solution search space size by more than 99%.
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