• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-21
Junzhen Zhu; Qingxu Min; Jianbo Wu; Gui Yun Tian

Eddy current pulsed thermography (ECPT) as one of the emerging NDT&E nondestructive testing and evaluation techniques has been used for the evaluation of the integrity of rail tracks, especially for rolling contact fatigue (RCF) detection and crack sizing. This paper proposes a probability of detection (POD) analysis framework for the ECPT system. Specifically, three different features, i.e. max thermal response, first-order different imaging and its ratio mapping, were used to quantify the length of the angular slot by linear fitting. Based on the fitting relation between these features and the slot length, POD curves for linear-coil based ECPT system of angular defect detection were calculated and compared. Results show that max thermal response feature has the highest repeatability and detectability for shorter slot detection. First-order different imaging and ratio mapping features are more convincing for longer slot detection.

更新日期：2018-08-22
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-21
Xuan Li; Chunjie Zhou; Yu-Chu Tian; Yuanqing Qin

Industrial control systems (ICSs) are facing more and more cybersecurity issues, leading to increasingly severe risks in critical infrastructure. To mitigate risks, developing an appropriate security strategy is of paramount importance. However, existing efforts on decision making in ICSs inherit some limitations, such as the lack of consideration of the strategy for securing both cyber and physical domains and a trade-off between security and system requirements. To overcome these limitations, a decision-making approach is presented in this paper for intrusion response in ICSs. Aiming to determine the optimal security strategy against attacks promptly, it tries to secure the most “dangerous” attack paths and respond to functional failures. In this approach, measures that cover both cyber and physical domains are designed with in-depth analysis of attack propagation. They ensure the completeness of candidate security strategy space. A number of Pareto optimal solutions are determined from the strategy space through multi-objective optimization. The objective is to maximize the objective vector composed of security benefit, system benefit and state benefit. Then, these solutions are prioritized by using a distance-based evaluation method, which pursues the optimal protection ability by making the objective vector of the selected strategy closest to the ideal one. The effectiveness of the proposed approach is demonstrated with a case study on a simulated process control system.

更新日期：2018-08-22
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-21
Xian-Bing Meng; Han-Xiong Li; Hai-Dong Yang

The temperature monitoring is indispensable to the optimal and safe operation of the Lithium-ion battery. In this paper, a spatiotemporal learning model designed by evolutionary algorithm is proposed to predict the thermal distribution. To formulate the multi-characteristic spatial dynamics, the chicken swarm optimization based fusion of different dimensionality reduction methods is proposed for learning spatial basis functions. Through integration with the time/space separation based approach and equivalent circuit model based thermal model, the reduced-order model is derived. The related parameters of the reduced-order model are identified by integrating chicken swarm optimization with time/space separation based approach. A Bayesian regularized neural network based compensation model is developed to compensate for the model errors caused by the spatiotemporal coupled dynamics. Based on the Rademacher complexity, the generalization bound of the proposed model is analyzed. Simulations and comparisons demonstrate the superiority of the proposed model.

更新日期：2018-08-22
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-21
Huihui Pan; Weichao Sun

In this paper, an output feedback finite-time control method is investigated for stabilizing the perturbed vehicle active suspension system to improve the suspension performance. Since the physical suspension systems always exist the phenomenon of uncertainty or external disturbance, a novel disturbance compensator with finite-time convergence performance is proposed for efficiently compensating the unknown external disturbance. Moreover, the presented compensator is advantageous over the existing ones since it is continuous and can completely remove the matched disturbance. And from the viewpoint of practical implementation, continuous control law will not lead to chattering, which is desirable for electrical and mechanical systems. For the nominal suspension system without disturbance, a homogeneous controller with a simple filter is constructed to achieve a finite-time convergence property, where the filter is applied to obtain the unknown velocity signal. Thus, the nominal controller combines a disturbance compensator into an overall continuous control law, which provides two independent parts with a separate design unite and a highly flexibility for selecting the control gains. According to the geometric homogeneity and finite-time separation principle, it can be shown that the active suspension is finite-time stabilized. A designed example is given to illustrate the effectiveness of the presented controller for improving the vehicle ride performance.

更新日期：2018-08-22
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-21
Xiaofei Zhang; Yunze He; Tomasz Chady; Gui Yun Tian; Jingwei Gao; Hongjin Wang; Sheng Chen

Impact damage, caused by low-energy impact, is inevitable during the whole life time of carbon fiber reinforced plastic (CFRP) material. However, the barely visible impact damage (BVID) is difficult to be detected by visual methods. Ultrasonic thermography (UT) is an emerging non-destructive testing technique that visualizes damage in thermal images captured by an infrared (IR) camera when the material is stimulated by ultrasound. However, noise and blurry edges around the high temperature areas may cause confusion and lead to unreliable results in the thermal images of UT test. In this work, an impact damage inspection method is proposed based on manifold learning for CFRP material. Low-power ultrasonic excitation is used for this UT. The IR image sequences are processed as data sets in high-dimensional space. These data sets are reduced to lower dimensions by manifold learning to find the intrinsic structure in the two-dimensional manifold. Each dimension of the embedding manifold correlates highly with one degree of freedom underlying the original pixel: steady and random components. The steady component, which reflects the temperature rise caused by damage, is used for VID and BVID detection. The experimental system was set up, and CFRP plate specimens with different impact damage were tested. All the impact damage could be detected and shown in reconstructed static image with little noise. The proposed method using image sequences could provide a visualized, reliable and effective impact damage inspection and localization means for CFRP material during manufacturing and in service.

更新日期：2018-08-22
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date :
Amit Joshi; Laya Das; BalaSubramanium Natarajan; Babji Srinivasan

The two way communication of information between agents in the smart grid, while making way for better monitoring and control, comes at the cost of elevated communication traffic. Compressive sensing is a technique that exploits sparsity of power consumption data (in the Haar basis) and achieves sub-Nyquist compression. Household power consumption data however has varying sparseness due to for example multi-state appliances. Compressing this data with a fixed ratio can lead to non-optimal results (less compression or large reconstruction error). In this regard, a dynamic compression scheme that estimates a signal's sparsity and decides the amount of compression is desirable. We demonstrate that this approach, when applied with existing estimators of sparsity has its limitations in overemphasizing one objective compared to the other. We propose a new measure derived from coefficient of variation and demonstrate that it achieves a better trade off between reconstruction performance and compression ratio. In addition, we employ a dynamic spatial compression scheme to account for spatial correlation between data of neighboring nodes and present a framework that incorporates dynamic temporal and spatial compression. We present the results on three publicly available data sets at different sampling rates and outline key findings of the study.

更新日期：2018-08-20
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date :
Jongyeop Kim; Kyung-Joon Park; Wonhong Jeon; Jihwan Choi

As various wireless devices share the same frequencies in the unlicensed 2.4 GHz Industrial Scientific Medical (ISM) band, frequency sharing has become a challenging issue in the heterogeneous network. Many Wi-Fi applications increase network traffic and lead to the significant performance loss of other protocol devices including ZigBee for critical missions (e.g., medical devices) in the same band. In this paper, we propose a coexistence solution of the Guide Busy Tone (GBT), providing reliable communications to the ZigBee network under Wi-Fi interference, and present fairness criteria in the tradeoff relation between Wi-Fi and ZigBee with GBT. The proposed GBT design, consisting of a GBT signaler and a busy tone canceller, reserves a channel for ZigBee through the full-duplex technique under heavy Wi-Fi traffic. Our experimental evaluation shows that the packet delivery ratio of the ZigBee network can be improved up to nearly 100% under the saturated Wi-Fi traffic by using GBT, which is scalable for the multi-node case as well.

更新日期：2018-08-20
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date :
Chunhua Peng; Lu Xu; Xun Gong; Huijuan Sun; Lei Pan

As the problem of three-phase unbalance in distribution network with distributed generations (DG) is prominent, a multi-objective dynamic reconfiguration method for three-phase balance is proposed to make reconfiguration more reasonable and effective. Based on three-phase power flow calculation and reconfiguration process analysis of distribution networks with DGs, a new reconfiguration model with comprehensive optimization objectives of minimizing three-phase unbalance factor and number of switching times is established. A network connectivity discrimination method based on the algebraic connectivity of graph theory is adopted to quickly remove infeasible solutions, and a new multi-objective molecular differential evolution (MOMDE) algorithm is designed to improve optimization depth and avoid prematurity in the optimization process, which is based on the principle of closer molecules with greater inter-molecular repulsion. The multi-objective optimal dynamic reconfiguration of a modified IEEE 34-bus test feeder with DGs is carried out and the simulation results demonstrate the effectiveness and superiority of the proposed method.

更新日期：2018-08-20
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-17
Younes Seyedi; Houshang Karimi; Santiago Grijalva

The output power of distributed energy resources (DERs) may experience irregular fluctuations due to variations of renewable sources, which need to be monitored in order to reliably control the grid. This paper proposes a novel approach for centralized detection of such irregularities based on the time-series analysis of the data reported by phasor measurement units (PMUs). In this approach, a network controller constructs datasets of time-aligned real/reactive powers for different zones. The datasets are transformed into sequences of short-time local outlier probability (ST-LOP) which are analyzed to identify the DER events. The network controller estimates features such as the average duration and the similarity degree which is a measure of spatio-temporal correlation between the DER events. As a use case, event-triggered control of solar photovoltaic (PV) systems with energy storage devices is investigated. The simulation results for the IEEE 123-bus network corroborate the effectiveness of the developed analytics for detection and mitigation of ramp-rate solar power fluctuations. Smart microgrids and active distribution networks can employ the developed analytics to improve a range of diagnostic and control functionalities.

更新日期：2018-08-18
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-17
Ningchen Wang; Qinghan Yu; Hai Wan; Xiaoyu Song; Xibin Zhao

The execution time of conventional incremental off-line schedule approaches for time-triggered networks increases rapidly when networks become larger. When the traffic in a network changes, they need to reschedule all influenced flows once again. Traffic changes at the cluster level involve many data flows. An incremental scheduler cannot react quickly to such changes. We propose an algorithm based on mixed integer linear programming and counterexample guided methodology. Our algorithm can generate adaptive schedule for cluster-level changes of the system. The adaptive schedule can react quickly to the changes during runtime. Our algorithm enhances the incremental schedulers. It allows schedulers to react to changing at both the flow level and the cluster level. Experiments show that our approach is effective. In the scenarios of coupling train consists, our algorithm can generate the schedule table of the train network within a few seconds.

更新日期：2018-08-18
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-16
Michael Pertl; Francesco Carducci; Michaelangelo D. Tabone; Mattia Marinelli; Sila Kiliccote; Emre Can Kara

The demand for vehicle charging will require large investments in power distribution, transmission, and generation. However this demand is often also flexible in time, and can be actively managed to reduce the needed investments, and to better integrate renewable electricity. Harnessing this flexibility requires forecasting and controlling electric vehicle charging at thousands of stations. This paper addresses the problem of forecasting and management of the aggregate flexible demand from tens to thousands of electric vehicle supply equipment (EVSEs). First, it presents an equivalent time-variant storage model for flexible demand at an aggregation of EVSEs. The proposed model is generalizable to different markets, and also to different flexible loads. Model parameters representing multiple EVSEs can be easily aggregated by summation, and forecasted using autoregressive models. The forecastability of uncontrolled demand and storage parameters is evaluated using data from 1341 non-residential EVSEs located in Northern California. The median coefficient of variation (CV) is as low as 24% for the forecast of uncontrolled demand at the highest aggregation and 10-15% for the storage parameters.The benefits of aggregation and forecastability are demonstrated using an energy arbitrage scenario. Purchasing energy day ahead is less expensive than in the real-time market, but relies on a uncertain forecast of charging availability. The results show that the forecastability significantly improves for larger aggregations. This helps the aggregator make a better forecast, and decreases the cost of charging in comparison to an uncontrolled case by 60% with respect to an oracle scenario.

更新日期：2018-08-17
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-15
Tuan Tran Nguyen; Jens Spehr; Sebastian Zug; Rudolf Kruse

For highly available automated driving, a robust road estimation is indispensable. In order to tackle the challenges of this task, many works employ a fusion of multiple sources, e.g., visually-detected lane markings, leading vehicle, digital maps, etc. However, each source has certain advantages and drawbacks depending on the operational scenarios. Hence, the assumption made by many existing approaches that the sources always are equally reliable for the fusion process is inappropriate. Therefore, this work proposes a novel concept by incorporating reliabilities into the multi-source fusion so that the road estimation task can alternately select only the most reliable sources. Thereby, the reliability for each source is estimated online using classifiers trained with the sensor measurements, the past performance and the context. Using real data recordings, experimental results show that the presented reliability-aware fusion increases the availability of automated driving up to 7 percentage points compared to the average fusion.

更新日期：2018-08-17
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-14
Feng Xu; Zexin Li; Zhuoyun Nie; Hui Shao; Dongsheng Guo

Recurrent neural network (RNN) has recently been viewed as a significant alternative to online mathematical problem solving. This paper offers important improvements by proposing the first RNN model to solve the time-dependent underdetermined linear system with bound constraint. In particular, by introducing a time-dependent non-negative vector, the bound-constrained underdetermined linear system is initially transformed into a time-dependent system that comprises linear and nonlinear equations. The newly constructed RNN model can thus zero in on the time-dependent equations. Then, the model is theoretically proven to have convergence properties, and the simulation results further substantiate the efficacy of the proposed RNN model to solve the time-dependent underdetermined linear system with bound constraint. Finally, the proposed RNN model is applied to physically-constrained redundant robot manipulators, thereby indicating the applicability of the proposed model.

更新日期：2018-08-17
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-14
Jose Antonio Juarez Juarez Abad; Arturo Pablo Sandoval-Garcia; Jesus Linares-Flores; Jose Fermi Guerrero-Castellanos; Pedro Banuelos-Sanchez; Marco Antonio Contreras-Ordaz

This paper presents the design and embedded implementation of a robust controller for the transformerless multilevel active monophase rectifier. In order to reduce the effects caused by the uncertainty originated by the output load, an 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 in 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 FPGA Spartan-6 XC6SLX16 is used for implementing the ESEDPOF controller, the online algebraic estimator, the enhanced phase locked loop (EPLL), and the multilevel PWM. 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 micro seconds. Experimental validation shows an excellent dynamical performance and a nearunit power factor.

更新日期：2018-08-17
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-14
Sara Gallardo-Saavedra; Luis Hermandez; Oscar Duque-Perez

Photovoltaic energy is the renewable energy with the greatest growth and use. The tendency of the last years is directed towards the formation of increasingly larger plants, which implies that optimizing their maintenance is becoming extremely important. Aerial thermography has become a convenient quality assessment tool for photovoltaic power plants, being reliable, cost-effective and time-saving. However, it is essential to be aware of its strengths and limitations in order to apply and interpret the results correctly. This paper presents a study about the influence of Spatial Resolution of thermographic images on the severity of failures, evaluating the results obtained in a set of experimental aerial and manual inspections performed in a 3 MW PV plant in Spain. The research analyzes how aerial thermography should be arranged as a function of the thermographic camera and lens used with the aim of satisfying the resolution requirements. Indications about the correct procedure to perform aerial thermographic inspections are also provided.

更新日期：2018-08-17
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-14
Ma Hongjun; Yanli Liu; Tianbo Li; Guang-Hong Yang

This paper studies the diagnosis and compensation for sensors and actuators faults in a quadrotor unmanned aerial vehicle. Without adding sensors or actuators for increased hardware redundancy, an observer-based adaptive controller is proposed to estimate and compensate for the faults. First, by a feedback linearization technique, an inner controller is designed to transform the form of considered quadrotor unmanned aerial vehicle with faults into a nonlinear system with Lipschitz-like nonlinearities and parameteric faulty models. Second, the estimations for unmeasurable state and actuators faults are performed in an output-feedback outer controller to compensate for the actuators faults. Third, a nonlinear high-gain observer is designed to provide the information of state and faults to the outer controller, with the compensations for sensors faults. A Lyapunov-based analysis shows that appropriate choices of the controller parameters can guarantee the exponential converge of errors in estimation and trajectory tracking under uncertainties and faults. The robustness to the external disturbances is also discussed. Simulations are given to verify the effectiveness of the presented scheme. The proposed approach is also implemented on a quadrotor unmanned aerial vehicle, to show its feasibility in a realtime application.

更新日期：2018-08-17
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date :
Mingjun Li; Haotian Cao; Xiaolin Song; Yanjun Huang; Jianqiang Wang; Zhi Huang

This paper presents a shared control driver assistance system based on the driving intention identification and situation assessment to avoid obstacles. A constrained linear-time-varying model predictive controller (LTV-MPC) is designed to follow the obstacle-avoidance path which is obtained by artificial potential method in real time. A human driver's driving intention and the desired maneuver are recognized by the inductive multi-label classification with unlabeled data (iMLCU) approach that is trained based on the lateral offset and lateral velocity to the road center line. In addition, the situation assessment of collision risk is represented by the time to collision (TTC) and the performance evaluation is designed according to lateral deviation. All of them are employed for the design of the shared control fuzzy controller. The cooperative coefficient, denoting the control authority between the controller and a human driver, is determined by three fuzzy controllers in different conditions, which are the consistent, the advanced inconsistent, and the lagged inconsistent fuzzy controller, respectively. More importantly, there are two scenarios studies are provided to verify the proposed system. The results prove that the shared control driver assistance system can successfully help drivers to avoid obstacles and obtains great vehicle stability performance in different scenarios.

更新日期：2018-08-13
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date :
Haobin Shi; Gang Sun; Yuanpeng Wang; Kao-Shing Hwang

Image-based visual servoing (IBVS) can reach a desired position for a relatively stationary target using continuous visual feedback. Proper feature extraction and appropriate servoing control laws are essential to performance for IBVS. IBVS control can be interrupted or interfered abruptly if no features are extracted when the observed object is occluded. To address the problem of missing feature points in current images during a visual navigation task, a homography method that uses 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.

更新日期：2018-08-13
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-10
Siyu Shao; Stephen McAleer; Ruqiang Yan; Pierre Baldi

We develop a novel deep learning framework to achieve highly-accurate machine fault diagnosis using transfer learning to enable and accelerate the training of deep neural network. Compared with existing methods, the proposed method is faster to train and more accurate. First, original sensor data are converted to images by conducting a Wavelet transformation to obtain time-frequency distributions. Next, a pre-trained network is used to extract lower level features. The labeled time-frequency images are then used to fine-tune the higher-levels of the neural network architecture. This paper creates a machine fault diagnosis pipeline and experiments are carried out to verify the effectiveness and generalization of the pipeline on three main mechanical datasets including induction motors, gearboxes, and bearings with sizes of 6,000, 9,000, and 5,000 time series samples, respectively. We achieve state-of-the-art results on each dataset, with most datasets showing test accuracy near 100%, and in the gearbox dataset, we achieve significant improvement from 94.8% to 99.64%. We created a repository including these datasets located at mlmechanics.ics.uci.edu.

更新日期：2018-08-11
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-10
Feng Tao; Johnathan Votion; Yongcan Cao

This paper investigates the problem of extracting actionable patterns/models from unlabeled and erroneous datasets in an unsupervised way. We propose a novel iterative multi-layer micro-macro method (IM3) that defines data reliability, learns micro-macro models, and refines learned models iteratively. The IM3 method includes a general data reliability definition, a micro-macro model complexity determination, and an iterative data reliability and model complexity update. In particular, we propose a consistency index based approach to address underfitting and overfitting in an unsupervised way. The refinement of the learned models is enabled via dropping the most unreliable data until the data reliability is above a given threshold. The sensitivity of the proposed IM3 method with respect to the reliability threshold selection is further quantified via false alarm and miss detection. Evaluation of the proposed method and quantitative analysis of its sensitivity are provided on a polynomial regression problem via Monte Carlo simulations.

更新日期：2018-08-11
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-10
Haibo Du; Congrang Jiang; Guanghui Wen; Wenwu Zhu; Yingying Cheng

The problem of current sharing control for parallel DC-DC buck converter system is investigated in this paper. Specifically, to achieve the goal of sharing control and improve the system dynamic performance, a new kind of finite-time current sharing control algorithm is designed and employed. Under the proposed control algorithm, rigorous proofs show that not only the output voltage of the converter system can reach the desired reference voltage in a finite time but also the objective of current sharing can be achieved within almost the same time. In addition, when the external load is time-varying and unknown, a finite-time load estimator is given to handle the load variation and an adaptive finite-time current sharing control algorithm is subsequently developed. Experimental results are presented to verify the effectiveness of the proposed method, and to show its advantages over some traditional control algorithms. It is shown that a faster convergence rate and a better load disturbance rejection performance can be yielded by the present algorithm. Finally,it is shown the main results can be extended to the case of n parallel DC-DC buck converters.

更新日期：2018-08-11
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-10
Van-Thanh Hoang; Kang-Hyun Jo

Estimating 3D human poses from given 2D shape is still an inherently ill-posed problem in computer vision. This paper proposes a method called Cascade of Multiple Neural Networks (CMNN) to solve this problem in two steps: (i) Create the initial estimated 3D shape using Zhou et al. method with a small number of basis shapes. (ii) Make this initial shape more alike to the original shape by using 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 3D pose of all people in a single RGB image. This system comprises two part: Convolution Pose Machine (CPM) for estimating 2D poses of all people in an RGB image and CMNN for reconstructing 3D poses of them from outputs of CPM.

更新日期：2018-08-11
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-09
Aamir Mahmood; Emiliano Ge Sisinni; Lakshmikanth Guntupalli; Raul Rondon; Syed Ali Hassan; Mikael Gidlund

Low-power wide-area network (LPWAN) technologies are gaining momentum for internet-of-things (IoT) applications since they promise wide coverage to a massive number of battery-operated devices using grant-free medium access. LoRaWAN, with its physical (PHY) layer design and regulatory efforts, has emerged as the widely adopted LPWAN solution. By using chirp spread spectrum modulation with qausi-orthogonal spreading factors (SFs), LoRa PHY offers coverage to wide-area applications while supporting high-density of devices. However, thus far its scalability performance has been inadequately modeled and the effect of interference resulting from the imperfect orthogonality of the SFs has not been considered. In this paper, we present an analytical model of a single-cell LoRa system that accounts for the impact of interference among transmissions over the same SF (co-SF) as well as different SFs (inter-SF). By modeling the interference field as Poisson point process under duty-cycled ALOHA, we derive the signal-to-interference ratio (SIR) distributions for several interference conditions. Results show that, for a duty cycle as low as 0.33%, the network performance under co-SF interference alone is considerably optimistic as the inclusion of inter-SF interference unveils a further drop in the success probability and the coverage probability of approximately 10% and 15%, respectively for 1500 devices in a LoRa channel. Finally, we illustrate how our analysis can characterize the critical device density with respect to cell size for a given reliability target.

更新日期：2018-08-10
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-09
Kuntal Satpathi; Abhisek Ukil; Soumya Shubhra Nag; Josep Pou; Michael Adam Zagrodnik

DC marine vessels with medium voltage compact dc power systems are dominated by a significant amount of active loads and a finite number of generation sources. In such scenarios, the network configuration of the dc power system is expected to get dynamically altered to fulfil the required generation and load demands for the desired marine mission. Such varying network configurations make the transient responses significantly different from the conventional ac grids and the prospective dc grids. In this regard, this paper performs systematic transient studies to devise fault management strategies for the dc marine vessels. Platform supply vessel (PSV) is taken as an example of the marine vessel, due to its complex operating scenarios and wider applicability in the marine industry. Pole-to-pole short-circuit faults are considered owing to its severity. A novel directional protection for the dc PSV is proposed based on the directional zonal interlocking and short-time Fourier transform. The efficacy of the proposed method is substantiated by confirming against a range of fault impedances initiated at the generator terminals, load terminals, lines and buses of the dc PSV. All the analysis are conducted in the real-time simulation model of the dc PSV.

更新日期：2018-08-10
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-07

This paper presents a new approach to wide-area out-of-step (OOS) protection through predicting the power and rotor angle curves by the auto-regressive moving-average (ARMA) method. To reduce the computation burden of ARMA, its parameters are estimated using an extended Kalman filter. The predictive feature of the proposed approach results in saving hundreds of milliseconds before the actual instability occurs, which can be served to activate appropriate countermeasures against wide-spread system instability. The OOS condition is detected using the extended equal area criterion developed for multi-machine power systems, in which the single-machine infinite-bus model of the system is built using wide-area measurements. The proposed approach is adapted with the communication requirements of IEEE Std. C37.118, and thus, it can be used as a practical wide-area OOS protection scheme. The IEEE 39-bus and 118-bus systems are used to evaluate the efficiency of the proposed approach. The obtained results demonstrate effectiveness of the proposed scheme in predicting OOS phenomena.

更新日期：2018-08-08
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-08-07
Long Chen; Mingyue Cui; Feihu Zhang; Biao Hu; Kai Huang

Scene flow is an essential part of stereo-based perception system for autonomous driving and mobile robotics. As in most of these platforms, the computing resource is limited but the computing requirement is high, embedded and parallelized algorithms are of vital importance for real-time tasks. This paper develops a cross-platform embedded scene flow algorithm by using an OpenCL (Open Computing Language) programming. Meanwhile, we propose a method to achieve a good performance by using a novel coarse-grained software pipeline for the embedded stream application. Experimental results show that the proposed algorithm can boost the average processing speed to 50 fps for different commercial-off-the-shelf (COTS) hardware, including desktop GPUs, FPGAs and mobile phone platforms. For certain GPUs the peak frame rates can also reach 1000 fps. By comparing the efficiency among the serial platform, we illustrate that with the help of OpenCL programming, COTS platforms can provide enough computing resources for the stereobased perception algorithm.

更新日期：2018-08-08
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-06
Bonu Ramesh Naidu; Gayadhar Panda; Pierluigi Siano

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-08
Payam Mahmoudi Nasr; Ali Yazdian-Varjani

One of the most dangerous insider threats in a supervisory control and data acquisition (SCADA) system is deontological threat. The concept of deontological threat has been introduced to underline the operator performance when he/she does not perform his/her duties perfectly or decides to abuse the privileges in order to perform malicious operations in remote substations. In this paper, a new alarm-and-trust-based access management system (ATAMS) has been proposed that is able to reinforce the security of the SCADA system against the deontological threats. In the proposed ATAMS, the accessibility of a remote substation will be determined based on the operator trust and the integrity level of the substation. The value of operator trust is calculated using the performance of the operator, periodically or when an anomaly is detected. One of the opportunities of the ATAMS is its ability in detecting the anomalies, which is rooted in the deontological threats.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-06
Nuwan Ganganath; Jing V. Wang; Xinzhi Xu; Chi-Tsun Cheng; Chi K. Tse

After a blackout, it is essential to restore the blackout area rapidly to minimize possible losses. In parallel restoration, the blackout area is first partitioned into several subsystems, which will then be restored in parallel to accelerate the restoration process. In order to ensure restoration reliability, each subsystem should have enough generation power and satisfy a set of constraints before triggering the parallel restoration process. This paper models this as a constrained optimization problem and proposes a partitioning strategy to solve it in three steps. In the first step, some existing methods and expert knowledge are used for initialization of the partitioning process. The second step ensures the satisfaction of modeled constraints. The third step operates greedily to find suitable partitions for parallel restoration. The proposed strategy is implemented and evaluated on IEEE 39- and 118-bus power systems. Evaluation results show that it provides adequate subsystems for parallel restoration. Unlike some existing partitioning strategies, the proposed strategy can be used to partition a power system into multiple subsystems in a single execution.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-21
Priyank Shah; Ikhlaq Hussain; Bhim Singh

This paper presents a fuzzy-logic-based fourth-order generalized integrator (FOGI) frequency locked loop (FLL) based control for optimal operation of solar energy conversion system (SECS) having distribution static compensator (DSTATCOM) capabilities along with supplying active power to the distribution network. The proposed SECS is multifunctional having capabilities of power factor correction, load balancing, and harmonics mitigation in a three-phase distribution system. The FOGI-FLL has higher order filtering capabilities compared to conventional algorithms. The frequency tracking capabilities of proposed control technique exhibit better performance as compared to a conventional algorithm. The comparative performance of harmonics filtering and frequency tracking capabilities is demonstrated between FOGI-FLL and various conventional algorithms. Simulation results demonstrate the behavior of the system at various conditions. To validate the proposed algorithm, a prototype is developed and test results demonstrate the reliable performance of the system at various conditions, such as load unbalancing, variable insolation, solar photovoltaic to DSTATCOM mode at abnormal grid conditions, such as distorted grid voltages, unbalanced grid voltages, voltage sag, and swell. The total harmonics distortions of grid voltages and currents are found well within limit of the IEEE 519 standard.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-21
Xiaoping Ma; Honghui Dong; Xiang Liu; Limin Jia; Guo Xie; Zheyong Bian

A wireless monitoring network is an effective way to monitor and transmit information about railway infrastructure conditions. Its lifetime is significantly affected by the energy usage among all sensors. This paper proposes a novel cluster-based valid lifetime maximization protocol (CVLMP) to extend the lifetime of the network. In the CVLMP, the cluster heads (CHs) are selected and rotated with the selection probability and energy information. Then, the clusters are determined around the CHs based on the multi-objective optimization model, which minimizes the total energy consumption and balances the consumption among all CHs. Finally, the multi-objective model is solved by an improved nondominated sorting genetic algorithm II. The simulation results show that, compared with two other strategies in the prior literature, our proposed CVLMP can effectively extend the valid lifetime of the network as well as increase the inspected data packets received at the sink node.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-11-27
Alaaeddine Yousfi; Marcin Hewelt; Christine Bauer; Mathias Weske

Ubiquitous business processes are the new generation of processes that pervade the physical space and interact with their environments using a minimum of human involvement. Although they are now widely deployed in the industry, their deployment is still ad hoc . They are implemented after an arbitrary modeling phase or no modeling phase at all. The absence of a solid modeling phase backing up the implementation generates many loopholes that are stressed in the literature. Here, we tackle the issue of modeling ubiquitous business processes. We propose patterns to represent the recent ubiquitous computing features. These patterns are the outcome of an analysis we conducted in the field of human–computer interaction to examine how the features are actually deployed. The patterns’ understandability, ease-of-use, usefulness, and completeness are examined via a user experiment. The results indicate that these four indexes are on the positive track. Hence, the patterns may be the backbone of ubiquitous business process modeling in industrial applications.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-12
Md. Masud Rana; Li Li; Steven W. Su; Wei Xiang

The distribution power subsystems are usually interconnected to each other, so the design of the interconnected optimal filtering algorithm for distributed state estimation is a challenging task. Driven by this motivation, this paper proposes a novel consensus filter based dynamic state estimation algorithm with its convergence analysis for modern power systems. The novelty of the scheme is that the algorithm is designed based on the mean squared error and semidefinite programming approaches. Specifically, the optimal local gain is computed after minimizing the mean squared error between the true and estimated states. The consensus gain is determined by a convex optimization process with a given suboptimal local gain. Furthermore, the convergence of the proposed scheme is analyzed after stacking all the estimation error dynamics. The Laplacian operator is used to represent the interconnected filter structure as a compact error dynamic for deriving the convergence condition of the algorithm. The developed approach is verified by using the renewable microgrid. It shows that the distributed scheme being explored is effective as it takes only 0.00004 seconds to properly estimate the system states and does not need to transmit the remote sensing signals to the central estimator.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-04
Shuo Jiang; Bo Lv; Weichao Guo; Chao Zhang; Haitao Wang; Xinjun Sheng; Peter B. Shull

While most wearable gesture recognition approaches focus on the forearm or fingers, the wrist may be a more suitable location for practical use. We present the design and validation of a real-time gesture recognition wristband based on surface electromyography and inertial measurement unit sensing fusion, which can recognize 8 air gestures and 4 surface gestures with 2 distinct force levels. Ten healthy subjects performed an initial gesture recognition experiment, followed by a second experiment 1 h later and a third experiment 1 day later. Classification accuracies for the initial experiment were 92.6% and 88.8% for air and surface gestures, respectively, and there were no changes in accuracy results during testing 1 h. and 1 day later ( $p$ $>$ 0.05). These results demonstrate the feasibility of wrist-based gesture recognition paving the way for potential future integration in to a smart watch or other wrist-worn wearable for intuitive human computer interaction.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-15
Tapiwa Moses Chiwewe; Gerhard Petrus Hancke

Cognitive radio and dynamic spectrum access can reform the way that radiofrequency spectrum is accessed. Problems of spectrum scarcity, coexistence, and unreliable wireless communication that affect industrial wireless networks can be addressed. In this paper, a game theoretic dynamic spectrum access algorithm that improves upon on a hedonic coalition formation algorithm for spectrum sensing and access is presented. The modified algorithm is tailored for faster convergence and scalability and makes use of a novel simultaneous multichannel sensing and access technique. Results to demonstrate the performance improvements of the adapted algorithm are presented and the use of different decision rules are investigated revealing that a conservative decision rule for exploiting spectrum opportunities performs better than an aggressive decision rule in most scenarios. The algorithm that was developed could be a key enabler for future cognitive radio networks.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-11-24

In this paper, a novel distributed dynamic state estimation (DSE) method for real-time monitoring of power systems is implemented. In modern large-scale power grids, the number of deployed meters and the frequency of collecting data have remarkably increased. Such a growth in the spatiotemporal size of collected data overwhelms the existing monitoring system with a centralized star structure. Streaming of data from all meters of the network to the central control center also increases communication latency. To overcome these challenges, the power system is partitioned into subsystems with local estimators, and the DSE process is distributed among local estimators. The distributed DSE is hosted at regional control centers and utilizes distributed extended Kalman filtering based on internodal transformation theory to estimate the dynamic states of power systems. The local estimators only require data within their own subsystem, and information is exchanged only between neighboring subsystems. The proposed distributed DSE is implemented on a 68-bus test system, and its accuracy is demonstrated.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-07
Jun Yi; Junren Bai; Wei Zhou; Haibo He; Lizhong Yao

Improvements in the production and energy consumption of the aluminum electrolysis process (AEP) directly depend on the operating parameters of the electrolytic cell. To balance the conflicting goals of efficiency and productivity with reduced energy consumption and emissions, AEP operating parameter optimization is formulated as a constrained multiobjective optimization problem with competing objectives of current efficiency and cell voltage. Then, the improved multiobjective quantum-behaved particle swarm optimization (IMQPSO) algorithm is proposed. The application of an adaptive opposition-based learning strategy and a piecewise Gauss mutation operator can increase the diversity of the population and enhance the global search ability of the IMQPSO. To expand the creativity of the particles, two iterative methods of the mean best position with weighting and the attractor position are redesigned. Experimental analyses are conducted for the benchmark problems and a real case to verify the effectiveness of the proposed method.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-07
Jian Li; Yunong Zhang; Shuai Li; Mingzhi Mao

Improvement of the real-time performance of tracking control is increasingly desirable. It is a routine for most conventional algorithms that the control input at current time instant is to track the current desired output. However, lagging errors resulting from computational time and the fluctuation of the desired output exist for the tracking control. Different from conventional algorithms, a look-ahead scheme of zeroing dynamics (ZD) is established in this paper to achieve the real-time tracking control of both serial and parallel manipulators. With the exploitation of data at current time and that in history, the control inputs generated by the proposed ZD algorithms never lead to lagging errors with the source from the inevitable computational time. To tackle prediction errors for ZD algorithms, a new high-precision discretization formula, as an essential part of ZD algorithms, is presented to confine the prediction error in an ignorable range in comparison with lagging errors.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-11-16
Xiao-Heng Chang; Jun Xiong; Zhi-Min Li; Ju H. Park

This paper investigates the problem of output feedback control for discrete-time systems with two quantized signals in measurement output and control input. Since the measurement output and control input are quantized by general quantizers before they are passed to the controller and the system, the closed-loop system will include the quantization error terms, which might lead to that the performance of the closed-loop system is not guaranteed. For this purpose, this paper proposes a novel quantized output control strategy such that the closed-loop system is asymptotically stable or satisfies the prescribed $\mathcal {H}_\infty$ performance. The corresponding design conditions for the output feedback controllers and the quantizers’ dynamic parameters are presented in terms of solutions to a set of linear matrix inequalities. Finally, a simulation example is given to prove the effectiveness of the proposed design method.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-11-24
Chen Lv; Yang Xing; Junzhi Zhang; Xiaoxiang Na; Yutong Li; Teng Liu; Dongpu Cao; Fei-Yue Wang

As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg–Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-08
Nilotpal Chakraborty; Arijit Mondal; Samrat Mondal

Existing electrical grid systems have a limited amount of real-time monitoring and controlling capabilities of energy generation and consumption facilities, which trigger various technical issues including voltage overloading, demand–supply mismatch, peak load consumption, etc. Some of the primary reasons for these key issues have been identified to be the inefficient utilization of energy infrastructure and uncoordinated power consumption pattern among the consumers. In this paper, we propose a coordinated load scheduling and controlling algorithm to schedule controllable appliances with the objective to minimize peak load consumption. For this purpose, we model the problem into the strip packing problem, a well-known NP-hard problem, and discuss the applicability of existing heuristics in our problem setup. We then discuss a new offline heuristic solution, named MinPeak, specifically designed for load scheduling problem. We have conducted comprehensive simulation studies using available benchmark data sets and have performed extensive comparative analyses of the proposed algorithm with some of the well-known heuristics for strip packing problem. Furthermore, experiments have been carried out using practical electricity consumption data to evaluate the performance of the algorithm in real life. The results obtained are very encouraging in terms of reducing peak load consumption and overall efficiency of the system.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-01-12
Yuanzheng Li; Tianyang Zhao; Ping Wang; Hoay Beng Gooi; Lei Wu; Yun Liu; Jian Ye

Microgrid (MG) represents one of the major drives of adopting Internet of Things for smart cities, as it effectively integrates various distributed energy resources. Indeed, MGs can be connected with each other and presented as a system of multimicrogrid (MMG). This paper proposes the optimal operation of MMGs by a cooperative energy and reserve scheduling model, in which energy and reserve can be cooperatively utilized among MMGs. In addition, values of Shapely are introduced to allocate economic benefits of the cooperative operation. Finally, a case study based on a system of MMGs is conducted, and simulation results verify the effectiveness of the proposed cooperative scheduling model.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-06
Nemanja D. Popović; Dragan S. Popović; Ivan Seskar

This paper proposes a novel advanced distribution management system (ADMS) solution based on a common operation technology platform implemented on a cloud infrastructure. It introduces a virtualization methodology that is used to transition and deploy a traditional, monolithic ADMS in a cloud environment. First, typical ADMS functional blocks (FBs) are identified and their performance is evaluated with respect to four major metrics: processor, memory, network, and storage utilization. Next, a cloud infrastructure-based ADMS solution is presented, based on a collection of FBs mapped to a set of virtual machines. Finally, the proposed architecture is validated via deployment on a physical hardware platform with two representatively sized distribution networks and emulated workloads. Obtained results show that an ADMS, as a mission-critical system, can be deployed on a cloud infrastructure, providing many benefits of virtualized solutions, without negative impact on system performance.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-25
Michael D. O’Toole; Noushin Karimian; Anthony J. Peyton

Recycling automotive, electronic, and other end-of-life waste liberates large quantities of metals, which can be returned to the supply chain. Sorting the nonferrous metals, however, is not straightforward. Common methods range from laborious hand-sorting to expensive and environmentally deleterious wet processes. The goal is to move toward dry processes, such as induction sensors and vision systems, which can identify and sort nonferrous scrap efficiently and economically. In this paper, we present a new classification method using magnetic induction spectroscopy (MIS) to sort three high-value metals that make up the majority of the nonferrous fraction—copper, aluminum, and brass. Two approaches are investigated: the first uses MIS with a set of geometric features returned by a vision system, where metal fragments are matched to known test pieces from a training set. The second approach uses MIS only . A surprisingly effective classifier can be constructed by combining the MIS frequency components in a manner determined by how eddy currents circulate in the metal fragment. An average precision and recall (purity and recovery rate) of around 92% was shown. This has significant industrial relevance, as the MIS-only classifier is simple, scalable, and straightforward to implement on existing commercial sorting lines.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-04
José Luis Gutiérrez-Rivas; José López-Jiménez; Eduardo Ros; Javier Díaz

New smart grid and industrial-Internet-of-things (IIoT) directions require accurate synchronization features to capture, trigger, and manage events in industrial networks with the best possible precision and accuracy. The implementation of redundancy features for time and data distribution to increase fault tolerance, together with an enhancement of the availability of the network services such as generic object-oriented substation events, become mandatory due to their critical nature. For these reasons, the authors of this paper presented a new challenging approach covering the industrial standards requirements regarding timing and availability through the development of mechanisms that make possible the avoidance of single point of failure in ring topologies. This approach uses the White Rabbit (WR) as the main timing distribution technology, able to synchronize devices below 1 ns. Moreover, the development of a mechanism able to change from a primary to a backup time reference in approximately zero time with an observed maximum phase shift of 170 ps implies an important qualitative leap with respect to IIoT and the smart grid (e.g., phasor measurement units). This paper addresses the development and results of the high-availability seamless redundancy protocol for the WR technology to conform a leading-edge deterministic and reliable ultraaccurate timing system with high-availability data features for industrial facilities in accordance with IEC 61850 and IEC 62439-3.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-11-28
Peng Li; Rong-Xi Li; Yuan Cao; Dan-Yong Li; Guo Xie

Optimization of island microgrids should configure the module type and size in such a way that multiple objectives can be balanced. This paper presents a bioinspired optimization approach of microgrid sizing, with two salient features. First, the multiple objectives are categorized into four types: reliability, economy, renewable technology, and pollution. We present a triangular aggregation model, which is straightforward and cost effective to compute the fitness. Second, a bioinspired algorithm named Levy-Harmony is developed. We embed the Levy flight into the Harmony vector updating to enhance the global searching ability and, meantime, adopt a bias factor to avoid unnecessary exploration. The searching speed and accuracy are well balanced and improved. The real datasets are used for comparative studies, demonstrating the superiority of the proposed scheme against typical existing approaches.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-11-13
Artzai Picon; Asier Vicente; Sergio Rodriguez-Vaamonde; Jorge Armentia; Jose Antonio Arteche; Iñaki Macaya

In steelmaking process, close control of slag evolution is as important as control of steel composition. However, to date, there are no industrially consolidated techniques that allow us fast and in-situ analysis of the chemical composition of the slag, as in the case of steel with optical emission spectrometer spectrometers. In this work, a method to analyze spectral reflectance of ladle furnace slag samples to estimate their composition is proposed. This method does not require sample preprocessing and is based on a regression algorithm that mathematically maps the spectral reflectance of the slag with its actual composition with errors lower than 10%. Specifically designed normalization and calibration steps have been proposed to allow us a global model training with data from different locations. This allows us real-time monitoring of the thermo-dynamical state of the steel process by feeding a thermodynamic equilibrium optimization model. The optimizer minimizes the cost to reach the target steel quality with lower energy and additive costs. The system has been validated on several ArcelorMittal locations achieving process savings of 0.71 € per liquid steel tons.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-27
Dong Yang; Jian Ma; Youzhi Xu; Mikael Gidlund

Industrial wireless sensor networks (IWSNs) have mainly been used to monitor applications, but recently an interest in control and safety applications has emerged. Functional safety and communication in open transmission systems have been laid down in the IEC 61784-3-3 standard. The standard is based on a cyclic polling mechanism, which consumes a considerable amount of bandwidth; since existing IWSNs are very resource-constrained, this becomes a major challenge. To overcome this problem, this paper proposes a novel framework that uses an event-triggered failsafe mechanism based on synchronous wired polling and wireless time-slotted time division multiple access. We analytically derive the minimum and maximum bound for the most important metric for safety-critical applications, safety function response time (SFRT). A new metric, normal state interrupt time (NSIT), is proposed in this paper. Furthermore, we also implement the proposed framework by using the WirelessHART standard. The results are compared to the classical time-triggered approach used in the IEC 61784-3-3 standard. The obtained results show that the proposed framework can reduce the bandwidth usage by 90% and support safety-critical applications that require a SFRT less or equal to 150 ms.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-10
Hang Li; Andrey V. Savkin

In this paper, we propose a wireless sensor network based safe navigation algorithm for micro flying robots in the industrial Internet of things (IIoT). A micro flying robot cannot be equipped with heavy obstacle detection sensors for local navigation. Therefore, in our method, a wireless sensor network consisting of three-dimensional range finder is used to detect the static and dynamic obstacles in an indoor industrial environment and navigate the micro flying robots to avoid any collisions with the obstacles. Only a path tracking controller is required for the micro flying robot and there is not any complex computation on the micro flying robot. It is an economical and efficient solution for multiple micro flying robots’ navigation and management in the IIoT. The computer simulations confirm the expected performance of the proposed algorithm in static and dynamic environment with multiple micro flying robots.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-09
Yun Luo; Ying Duan; Wenfeng Li; Pasquale Pace; Giancarlo Fortino

In a smart factory environment, a much larger amount of data are transmitted in the workshop networks bringing big challenges to data transfer capability and energy usage efficiency. In the workshop, two main networks, i.e., wired/wireless fieldbus networks and wireless sensor networks, are usually used to collect and transmit data separately; thus, this paper proposes a mobile and hierarchical data transmission architecture to integrate these two networks also taking advantages from the existing mobile intelligence in smart factories, such as automatic guided vehicles (AGVs), to implement a novel data and materials delivery scheme well suited for modern industrial wireless sensor networks (IWSNs). Simulation experiments demonstrated how the proposed approach, running within the IWSN, significantly increases data delivery efficiency along with achieving better energy usage, by 4 times, with respect to the separated networks without any mobile intelligence support.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-04-24
Zhezhuang Xu; Rongkai Wang; Xi Yue; Ting Liu; Cailian Chen; Shih-Hau Fang

In the mobile industrial human–machine interaction (HMI), to establish the data connection, the engineer has to manually select the target machine from a long list, which may lead to wrong connection and waste of time. We observe that the engineer should face to the machine during the interaction to ensure that the machine works accurately, and this characteristic makes the proximity estimation algorithm suitable to simplify the data connection. However, due to the densely deployed machines, the existing algorithms cannot provide sufficient accuracy with limited latency. In this paper, we implement a testbed to evaluate the performance in the mobile industrial HMI. Based on the experimental results, we propose the definition of received signal strength indicator (RSSI) difference and then use it to design the face-to-machine proximity estimation (FaceME) algorithm. The experimental results prove that FaceME can provide guaranteed estimation accuracy and low-time complexity.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-03-26
Michael J. Herrmann; Geoffrey G. Messier

This paper studies how to maximize the lifetime of ISA100.11a and Wireless highway addressable remote transducer (WiHART) compatible sensor networks for a petroleum refinery scenario. When accounting for the energy consumption of a typical refinery process sensor and the node microprocessor, only a relatively small percentage of battery energy on average is spent on wireless communication. However, this paper will demonstrate that optimizing network operation can still considerably extend network lifetime. The longest lifetimes are achieved using a new network optimization approach that accounts for the frame structure of ISA100.11a/WiHART. Results are generated using a full network protocol stack simulation that incorporates three different network optimization approaches and includes the energy consumption of the wireless transceiver, sensor, and microprocessor.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2018-05-28
Kim-Kwang Raymond Choo; Stefanos Gritzalis; Jong Hyuk Park

Industrial Internet of Things (IIoT) is an emerging trend, including in nontraditional technological sector (e.g., oil and gas industry). There are, however, a number of research challenges such using cryptography and other techniques to ensure security and privacy in IIoT applications and services. In this special issue, we present existing state-of-the-art advances reported by the 21 accepted papers. We then conclude the special issue with a number of potential research agenda.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-08-11
Tie Qiu; Yushuang Zhang; Daji Qiao; Xiaoyun Zhang; Mathew L. Wymore; Arun Kumar Sangaiah

Energy-efficient and robust-time synchronization is crucial for industrial Internet of things (IIoT). Some energy-efficient time synchronization schemes that achieve high accuracy have been proposed recently. However, some unsynchronized nodes namely isolated nodes exist in the schemes. To deal with the problem, this paper presents R-Sync, a robust time synchronization scheme for IIoT. We use a pulling timer to pull isolated nodes into synchronized networks whose initial value is set according to level of spanning tree. Then, another timer is set up to select backbone node and its initial value is related to the distance to parent node. Moreover, we do experiments based on simulation tool NS-2 and testbed based on wireless hardware nodes. The experimental results show that our approach makes all the nodes get synchronized and gets the better performance in terms of accuracy and energy consumption, compared with three existing time synchronization algorithms TPSN, GPA, STETS.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-10-26
Chin-Feng Lai; Shih-Yeh Chen; Ren-Hung Hwang

In order to monitor the stability of industrial systems, engineers installed diversified sensors in systems, and used communication devices to transfer the sensed data to the cloud platform for real-time monitoring and event detection. Furthermore, as industry demand for power grows, the scale and quantity of power systems gradually increase, and the original network data transmission architecture cannot bear such large-scale communication, especially the communication bandwidth tolerance isn’t allowed for trusted industrial Internet of things. Therefore, this trusted transmission problem will be one of challenges of the industrial Internet of things. In the application of device load recognition, how to create power fingerprinting recognition sample data, reduce the cloud platform computation complexity and the transmission quantity of sensed data without losing detection accuracy are the subjects of this study. Therefore, this study proposes a resilient section selection mechanism of power fingerprinting applied to device load recognition, in order to determine the transmission time and select the power fingerprinting section to be resiliently transferred, and replace the cycle-fixed full power fingerprinting data transfer for trusted industrial Internet of things. According to the experimental results, in the case of multi-load, the power fingerprinting of the first 25% section have the maximum recognition of 87.5%.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-12-07
Keke Gai; Meikang Qiu

Recent booming growth of networking-based solutions have brought numerous challenges to security and privacy from both perspectives of insider and outsider threats. The encrypted data are relatively considered a safe storage status; however, the process of encrypting data is still facing adversarial actions and data process generally is inapplicable over ciphertexts. As a type of the encryption approach allowing computations over ciphertexts, a fully homomorphic encryption (FHE) can concurrently deal with the adversarial hazards and support computations on ciphertexts. This paper focuses on the issue of blend arithmetic operations over real numbers and proposes a novel tensor-based FHE solution. The proposed approach is called a FHE for blend operations model that uses tensor laws to carry the computations of blend arithmetic operations over real numbers. In our paper, we provide both theoretical proof and experimental evaluations in order to evince the adoptability of the proposed approach.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-11-15
Xiong Li; Jianwei Niu; Md Zakirul Alam Bhuiyan; Fan Wu; Marimuthu Karuppiah; Saru Kumari

Wireless sensor networks (WSNs) play an important role in the industrial Internet of Things (IIoT) and have been widely used in many industrial fields to gather data of monitoring area. However, due to the open nature of wireless channel and resource-constrained feature of sensor nodes, how to guarantee that the sensitive sensor data can only be accessed by a valid user becomes a key challenge in IIoT environment. Some user authentication protocols for WSNs have been proposed to address this issue. However, previous works more or less have their own weaknesses, such as not providing user anonymity and other ideal functions or being vulnerable to some attacks. To provide secure communication for IIoT, a user authentication protocol scheme with privacy protection for IIoT has been proposed. The security of the proposed scheme is proved under a random oracle model, and other security discussions show that the proposed protocol is robust to various attacks. Furthermore, the comparison results with other related protocols and the simulation by NS-3 show that the proposed protocol is secure and efficient for IIoT.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-09-13
Yang Yang; Ximeng Liu; Robert H. Deng

Healthcare Internet-of-things (IoT) has been proposed as a promising means to greatly improve the efficiency and quality of patient care. Medical devices in healthcare IoT measure patients’ vital signs and aggregate these data into medical files which are uploaded to the cloud for storage and accessed by healthcare workers. To protect patients’ privacy, encryption is normally used to enforce access control of medical files by authorized parties while preventing unauthorized access. In healthcare, it is crucial to enable timely access of patient files in emergency situations. In this paper, we propose a lightweight break-glass access control (LiBAC) system that supports two ways for accessing encrypted medical files: attribute-based access and break-glass access. In normal situations, a medical worker with an attribute set satisfying the access policy of a medical file can decrypt and access the data. In emergent situations, the break-glass access mechanism bypasses the access policy of the medical file to allow timely access to the data by emergency medical care or rescue workers. LiBAC is lightweight since very few calculations are executed by devices in the healthcare IoT network, and the storage and transmission overheads are low. LiBAC is formally proved secure in the standard model and extensive experiments are conducted to demonstrate its efficiency.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-11-09
Debiao He; Mimi Ma; Sherali Zeadally; Neeraj Kumar; Kaitai Liang

Industrial Internet of Things (IIoT) integrates various types of intelligent terminals, mobile devices, and communication technologies to enable the upgrade of traditional industries to intelligent industries. IIoT relies on the powerful data processing capabilities of cloud computing to reduce the cost of various on-demand services as per the requirements of users. However, the privacy and confidentiality of the outsourced data should be protected in this environment because the data are typically “handled” by a third-party service provider. An encryption technique can guarantee the confidentiality of the data but it limits data retrieval due to its innate “all-or-nothing” decryption feature. To apply encryption to privacy-preserving data retrieval, many public key encryption techniques with keyword search systems have been proposed in the literature. However, most of the existing schemes are vulnerable to inside keyword guessing attack (IKGA), which is caused by a small keyword space. To address this problem, we propose a certificateless public key authenticated encryption with keyword search scheme, which is provably secure against IKGA. A performance analysis of the proposed scheme demonstrates that it is more secure and effective compared with other certificateless public key encryption with keyword search schemes.

更新日期：2018-08-07
• IEEE Trans. Ind. Inform. (IF 5.43) Pub Date : 2017-11-15
Chunyong Yin; Jinwen Xi; Ruxia Sun; Jin Wang

In the research of location privacy protection, the existing methods are mostly based on the traditional anonymization, fuzzy and cryptography technology, and little success in the big data environment, for example, the sensor networks contain sensitive information, which is compulsory to be appropriately protected. Current trends, such as “Industrie 4.0” and Internet of Things (IoT), generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, we propose a location privacy protection method that satisfies differential privacy constraint to protect location data privacy and maximizes the utility of data and algorithm in Industrial IoT. In view of the high value and low density of location data, we combine the utility with the privacy and build a multilevel location information tree model. Furthermore, the index mechanism of differential privacy is used to select data according to the tree node accessing frequency. Finally, the Laplace scheme is used to add noises to accessing frequency of the selecting data. As is shown in the theoretical analysis and the experimental results, the proposed strategy can achieve significant improvements in terms of security, privacy, and applicability.

更新日期：2018-08-07
Some contents have been Reproduced with permission of the American Chemical Society.
Some contents have been Reproduced by permission of The Royal Society of Chemistry.