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Preview: Industrial Informatics, IEEE Transactions on - new TOC

IEEE Transactions on Industrial Informatics - new TOC



TOC Alert for Publication# 9424



 



Table of Contents

Feb. 2018

Presents the table of contents for this issue of this publication.



IEEE Industrial Electronics Society

Feb. 2018

Provides a listing of current staff, committee members and society officers.



Two-Step Interpolation Algorithm for Measurement of Longitudinal Cracks on Pipe Strings Using Circumferential Current Field Testing System

Feb. 2018

Pipe strings are critical facilities for well drilling, production, and transportation in oil and gas industry. Due to the stress corrosion cracking, longitudinal cracks are the most common defects on pipe strings. This paper presents a simple two-step interpolation algorithm based on a circumferential current field testing method for the measurement of longitudinal cracks on pipe strings. The theory and finite element method model of the circumferential current field testing method are analyzed. The two-step interpolation algorithm fitted by characteristic signals obtained from simulations is proposed to size longitudinal cracks. The first-step is to measure and calibrate the crack length by a quadratic polynomial interpolation formula and the second-step is to measure the crack depth by a cubic polynomial interpolation formula. Experiments are conducted to verify the efficacy of the proposed two-step interpolation algorithm based on the circumferential current field testing system. The results suggest that the two-step interpolation algorithm can obtain the length and depth information of longitudinal cracks effectively on pipe strings using the circumferential current field testing system.



Battery-Less Short-Term Smoothing of Photovoltaic Generation Using Sky Camera

Feb. 2018

There is a growing concern over addressing the adverse effects of variations in the output power of distributed generators such as photovoltaic generation (PVG) systems that continue to be widely introduced into power networks. Nowadays, most network operators are requiring these intermittent energy resources to seek compliance with new regulations pertaining to the restriction of their export power fluctuations. This paper aims to investigate the smoothing of the export power fluctuations primarily attributed to clouds passing over the PVG plant, which are traditionally compensated by integrating a battery storage (BS) system. The idea of incorporating short-term solar prediction information into the conventional smoothing approach is examined to indicate how it affects the engagement of BS in the smoothing process. Afterward, an enhanced solar forecasting scheme based on whole-sky imaging is proposed and its performance is demonstrated through several real-time experiments complemented with simulation studies. The results reveal that the proposed PVG smoothing strategy is capable of successfully filtering rapid export power fluctuations to an acceptable extent and the conventional generation reserves will experience a negligible amount of remaining undesired power variation. This clearly bears out the hypothesis of battery-less PVG regulation.



Analysis, Design, and Implementation of Passivity-Based Control for Multilevel Railway Power Conditioner

Feb. 2018

In recent years, railway power conditioner (RPC) has been used to improve the power quality of a traction power system. In engineering application of RPC, mismatches between parameters used in controller and actual values are inevitable, which will increase the difficulty of current controller design and deteriorate compensation performance. In this paper, a passivity-based control (PBC) system is studied for multilevel RPC to enhance its tolerance for mismatches. According to the topology of multilevel RPC, equivalent electrical and mathematical models are developed. To employ PBC, Euler–Lagrange system model of RPC is established by Park (αβ/dq) transformation, and the passivity of RPC and stability of PBC are proved. On this basis, the robustness of PBC is analyzed and criteria for damping selection are derived. Finally, simulation and experiments have been carried out to verify the structure and control method in the paper.



Image Autoregressive Interpolation Model Using GPU-Parallel Optimization

Feb. 2018

With the growth in the consumer electronics industry, it is vital to develop an algorithm for ultrahigh definition products that is more effective and has lower time complexity. Image interpolation, which is based on an autoregressive model, has achieved significant improvements compared with the traditional algorithm with respect to image reconstruction, including a better peak signal-to-noise ratio (PSNR) and improved subjective visual quality of the reconstructed image. However, the time-consuming computation involved has become a bottleneck in those autoregressive algorithms. Because of the high time cost, image autoregressive-based interpolation algorithms are rarely used in industry for actual production. In this study, in order to meet the requirements of real-time reconstruction, we use diverse compute unified device architecture (CUDA) optimization strategies to make full use of the graphics processing unit (GPU) (NVIDIA Tesla K80), including a shared memory and register and multi-GPU optimization. To be more suitable for the GPU-parallel optimization, we modify the training window to obtain a more concise matrix operation. Experimental results show that, while maintaining a high PSNR and subjective visual quality and taking into account the I/O transfer time, our algorithm achieves a high speedup of 147.3 times for a Lena image and 174.8 times for a 720p video, compared to the original single-threaded C CPU code with -O2 compiling optimization.



A Real-Time Heterogeneous Emulator of a High-Fidelity Utility-Scale Variable-Speed Variable-Pitch Wind Turbine

Feb. 2018

Wind energy has the highest development rates of renewables. The increasing complexity of wind turbine (WT) systems requires careful analysis and design with thorough testing and certification procedures. Hardware emulators contribute to safe and cost-effective assessment and testing of WT in research and industry. Most of the available emulators concentrate on emulating electrical subsystems with simplified mechanical models. In this paper, a real-time (RT) heterogeneous emulator that combines RT discrete-time step simulation and a high-fidelity linear parameter-varying model of a utility-scale WT system is proposed and implemented on a heterogeneous CPU/GPU platform. The RT emulator is built on an embedded NVIDIA Jetson TK1 board for a National Renewable Energy Laboratory 5-MW WT as a case study. The proposed emulator is capable of further integration of electrical models and control systems of WT.



Extracting and Defining Flexibility of Residential Electrical Vehicle Charging Loads

Feb. 2018

The popularization of electric vehicles raises concerns about their negative impact on the electrical grid. Extracting electric vehicle charging load patterns is a key factor that allows smart grid operators to make intelligent and informed decisions about conserving energy and promoting the stability of the electrical grid. This paper presents an unsupervised algorithm to extract electric vehicle charging load patterns nonintrusively from the smart meter data. Furthermore, a method to define flexibility for the collective electric vehicle charging demand by analyzing the time-variable patterns of the aggregated electric vehicle charging behaviors is presented. Validation results on real residential loads have shown that the proposed approach is a promising solution to extract electric vehicle charging loads and that the approach can effectively mitigate the interference of other appliances that have similar load behaviors as electric vehicles. Furthermore, a case study on real residential data to analyze electric vehicle charging trends and quantify the flexibility achievable from the aggregated electric vehicle load in different time periods is presented.



PPMA: Privacy-Preserving Multisubset Data Aggregation in Smart Grid

Feb. 2018

Privacy-preserving data aggregation has been extensively studied in smart grid. However, almost all existing schemes aggregate the total electricity consumption data of the whole user set, which sometimes cannot meet the fine-grained demands from control center in smart grid. In this paper, we propose a privacy-preserving multisubset data aggregation scheme, named PPMA, in smart grid. PPMA can aggregate users’ electricity consumption data of different ranges, while guaranteeing the privacy of individual users. Detailed security analysis shows that PPMA can protect individual user's electricity consumption privacy against a strong adversary. In addition, extensive experiments results demonstrate that PPMA has less computation overhead and no more extra communication and storage costs.



Critical Links Identification for Selective Outages in Interdependent Power-Communication Networks

Feb. 2018

Critical infrastructure, such as the smart grid, is vulnerable to failures and attacks. The complex nature of these systems embeds hidden vulnerabilities that threaten their functionality when exploited. In this paper, we perform a vulnerability analysis of the smart grid based on the power flow dynamics and in the presence of the essential communication network. Our analysis identifies a small number of power lines and communication links that can trigger a cascading failure and result in a blackout when removed. We quantify the failure effect in the form of fractional loss in the served load. Moreover, we formulate a mathematical model to present both components of the smart grid and their interdependency. A scalable algorithm is introduced to analyze the output of the model. We evaluate the proposed model and algorithm on the IEEE 14, 30, 57, and 300 Bus systems and associated communication networks, and report on the collected results.



Classification and Discrimination Among Winding Mechanical Defects, Internal and External Electrical Faults, and Inrush Current of Transformer

Feb. 2018

In this paper, the mechanical faults of transformers including the winding radial deformation and axial displacement on 1.6 MVA transformer winding are investigated. Then, by estimating the parameters of the detailed model of this transformer winding in MATLAB software and changing these parameters in a manner that is proportional to the mechanical defects in electro-magnetic transients program software, the sampled differential current of the transformer is extracted for each disturbance. Next, the internal and external electrical faults and inrush current of the transformer are simulated. Afterwards, these signals are analyzed using maximal overlap discrete wavelet transform with Daubechies4 wavelet function, and their features are extracted. These extracted features are considered for training the classifiers of Decision Tree and artificial neural network. According to the simulation results, the proposed procedure is capable of classifying and discriminating among winding mechanical defects, internal and external electrical faults, and inrush current with a good accuracy that is the main novelty of this paper in comparison to other published works, which are limited to classifying only some of the mentioned faults.



Multimodal Forecasting Methodology Applied to Industrial Process Monitoring

Feb. 2018

Industrial process modeling represents a key factor to allow the future generation of industrial manufacturing plants. In this regard, accurate models of critical signals need to be designed in order to forecast process deviations. In this work, a novel multimodal forecasting methodology based on adaptive dynamics packaging and codification of the process operation is proposed. First, a target signal is decomposed by means of the empirical mode decomposition in order to identify the characteristic intrinsic mode functions. Second, such dynamics are packaged depending on their significance and modeling complexity. Third, the operating condition of the considered process, reflected by available auxiliary signals, is codified by means of a self-organizing map and presented to the modeling structure. The forecasting structure is supported by a set of ensemble adaptive-neurofuzzy-inference-system-based models, each one being focused on a different set of signal dynamics. The performance and effectiveness of the proposed method are validated experimentally with industrial data from a copper rod manufacturing plant and performance comparison with classical approaches. The proposed method shows improved performance and generalization over classical single-model approaches.



Multivariate Alarm Systems for Time-Varying Processes Using Bayesian Filters With Applications to Electrical Pumps

Feb. 2018

Alarm systems are critically important for safety and efficiency of industrial plants. However, many alarm variables in contemporary alarm systems are generated in a way being isolated from related process variables, resulting in false and missing alarms. This paper is motivated by abnormality detection for condensate-water electrical pumps in thermal power plants and proposes a method to design multivariate alarm systems for time-varying processes. A novel feature to distinguish normal and abnormal conditions is observed on the variation rates of multiple linear regression model parameters. A model estimator based on Bayesian filters is formulated to track the variations of model parameters in normal conditions, and not to do so in abnormal conditions so that absolute cumulative modeling errors are large enough to raise alarms. The effectiveness of the proposed method is validated by industrial case studies.



Optical and Mechanical Excitation Thermography for Impact Response in Basalt-Carbon Hybrid Fiber-Reinforced Composite Laminates

Feb. 2018

In this paper, optical and mechanical excitation thermography was used to investigate basalt-fiber-reinforced polymer, carbon-fiber-reinforced polymer, and basalt-carbon fiber hybrid specimens subjected to impact loading. Interestingly, two different hybrid structures including sandwich-like and intercalated stacking sequence were used. Pulsed phase thermography, principal component thermography, and partial least-squares thermography (PLST) were used to process the thermographic data. X-ray computed tomography was used for validation. In addition, signal-to-noise ratio analysis was used as a means of quantitatively comparing the thermographic results. Of particular interest, the depth information linked to Loadings in PLST was estimated for the first time. Finally, a reference was provided for taking advantage of different hybrids in view of special industrial applications.



Hierarchical Decentralized Optimization Architecture for Economic Dispatch: A New Approach for Large-Scale Power System

Feb. 2018

In this paper, a new hierarchical decentralized optimization architecture is proposed to solve the economic dispatch problem for a large-scale power system. Conventionally, such a problem is solved in a centralized way, which is usually inflexible and costly in computation. In contrast to centralized algorithms, in this paper we decompose the centralized problem into local problems. Each local generator only solves its own problem iteratively, based on its own cost function and generation constraint. An extra coordinator agent is employed to coordinate all the local generator agents. Besides, it also takes responsibility to handle the global demand supply constraint based on a newly proposed concept named virtual agent. In this way, different from existing distributed algorithms, the global demand supply constraint and local generation constraints are handled separately, which would greatly reduce the computational complexity. In addition, as only local individual estimate is exchanged between the local agent and the coordinator agent, the communication burden is reduced and the information privacy is also protected. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. Several case studies implemented on the IEEE 30-bus and the IEEE 118-bus are discussed and tested to validate the proposed method.



Consensus of Networked Euler–Lagrange Systems Under Time-Varying Sampled-Data Control

Feb. 2018

This paper is concerned with the consensus of multiple Euler–Lagrange systems with time-varying sampled-data control. Different from traditional sampled-data strategies, a time-varying sampled-data strategy is developed to realize the consensus of multiple Euler–lagrange systems, in which a function that can be distinct at different sampling instants is proposed to modulate the sampling interval. In addition, a new definition of average sampling interval, which is parallel to the average dwell time in switching control or average impulsive interval in impulsive control, is proposed to characterize the number of the updating of the sampling controller during some certain interval. The proposed average sampling interval makes our sampled-data strategy more suitable for a wide range of sampling signals. By utilizing the comparison principle, a sufficient criterion is obtained to guarantee the consensus of multiple Euler–Lagrange systems. The sufficient criterion is heavily dependent on the actual control duration time and the communication graph. Finally, a simulation example is presented to verify the applicability of the proposed results.



A Dendritic Cell Immune System Inspired Scheme for Sensor Fault Detection and Isolation of Wind Turbines

Feb. 2018

In this paper, a fault detection and isolation (FDI) methodology based on an immune system (IS) inspired mechanism known as the dendritic cell algorithm (DCA) is developed and implemented. Our proposed DCA-based FDI methodology is then applied to a well-known wind turbine test model. The proposed DCA-based scheme performs both detection as well as isolation of sensor faults given dual sensor redundancy, unlike other works in the literature that only address the fault detection problem and rely on analytical redundancy approach for accomplishing the fault isolation task. A nonparametric statistical comparison test is also performed to compare the performance of the DCA-based FDI scheme with another IS-based scheme known as the negative selection algorithm. Through extensive simulation case study scenarios the capabilities and performance of our proposed methodologies have been fully demonstrated and justified.



Identification of Flux Linkage Map of Permanent Magnet Synchronous Machines Under Uncertain Circuit Resistance and Inverter Nonlinearity

Feb. 2018

This paper proposes a novel scheme for the identification of the whole flux linkage map of permanent magnet synchronous machines, by which the map of dq-axis flux linkages at different load or saturation conditions can be identified by the minimization of a proposed estimation model. The proposed method works on a conventional three-phase inverter based vector control system and the immune clonal based quantum genetic algorithm is employed for the global searching of minimal point. Besides, it is also noteworthy that the influence of uncertain inverter nonlinearity and circuit resistance are cancelled during the modeling process. The proposed method is subsequently tested on two PMSMs and shows quite good performance compared with the finite element prediction results.



ISOMAP-Based Spatiotemporal Modeling for Lithium-Ion Battery Thermal Process

Feb. 2018

The real-time monitoring of temperature distribution in lithium-ion batteries (LIBs) is crucial for their safety and optimal operation in electrical vehicles. An accurate and effective thermal model is needed for online temperature monitoring since limited sensors are available in vehicle application. In this paper, a data-based spatiotemporal modeling method is researched for online estimation of temperature distribution of LIBs. First, Isometric Mapping (ISOMAP) method is used for time/space separation and model reduction. Then, the low-dimensional representation can be obtained in terms of ISOMAP based mapping functions. The unknown temporal dynamics in the low-dimensional space can be approximated using neural network model with parameters trained using extreme learning machine (ELM) algorithm. Finally, the spatiotemporal model of the thermal process can be reconstructed by integrating the neural network model and the mapping functions. The generalization bound of the proposed spatiotemporal model can be analyzed using Rademacher complexity. Simulation results showed that the proposed modeling method can model the LIB thermal process very well.



Optimal Offering of Demand Response Aggregation Company in Price-Based Energy and Reserve Market Participation

Feb. 2018

This paper investigates the combined price-based scheduling/participation of generation company (GENCO) and demand response aggregation company (DRACO) in energy and reserve markets. The temporally coupled customer behavior can be better represented using the load profile attributes, when compared to the traditional approach with random willingness assignment. The proposed cost models for energy and reserve offerings consider the effect of load type, load pattern consumption, and availability/flexibility patterns of each type of load with time of use constraints. The load curtailment (LC) cost model accounts for criticality and willingness of the responsive loads via utilization factor and availability factors, respectively. The proposed cost models present a realistic picture of LC cost by eliminating the random willingness factor of the existing LC cost models. Thereafter, various cases of market participation with different reserve payment policies are formulated for combined participation of GENCO and DRACO. In addition, the sensitivity of participation decision of various entities to the seasonal load variation is examined for summer and winter loading profiles. The proposed cost models and scheduling framework is simulated using GENCO with ten thermal units and DRACO with various load types, profiles distributed across different load sectors comprising of commercial, residential, industrial, municipal, and agricultural loads. The combined participation resulted in improved market surplus with reduced GENCO surplus. Also, the energy and reserve market surplus dependence on seasonal load patterns is observed across different test cases and payment policies.



Image-Based Characterization of Alternative Fuel Combustion With Multifuel Burners

Feb. 2018

Many industrial high-temperature processes such as cement production employ multifuel burners in order to achieve the required energy input with low-cost alternative fuel. So far, a constant operation of multifuel burners with high fractions of alternative fuel (>70%) is not possible due to inherent fluctuating fuel properties. Energy input and product quality are directly affected by varying points of combustion time, different scattering of fuel, and insertion of unburned fuel or chemical substances into the product. We propose an image-processing system based on infrared images that detects the alternative fuel streakline and derives parameters for the characterization of the flight and burning behavior. Using these parameters, an adjustment of the burner settings depending on the fluctuating fuel properties can be carried out. This automatic monitoring and control of the combustion process allows an increased use of alternative fuels in constant operation. Experimental data from a rotary kiln for cement clinker production are used to validate the image-processing system.



Unified System- and Circuit-Level Optimization of RES-Based Power-Supply Systems for the Nodes of Wireless Sensor Networks

Feb. 2018

An extensive utilization of wireless sensor networks has evolved during the last years for monitoring various environmental and artificial processes. When operating in remote locations, the nodes of wireless sensor networks are typically power supplied by an energy production and management system, comprising low-power renewable energy sources, a power electronic converter, and a battery-based energy storage unit. In this paper, a methodology is proposed for optimally designing the energy production and processing system of a wireless sensor network node simultaneously at both the renewable power-supply system level and the power converter circuit level, through a unified design process. The impact of the objective function type on the power-supply design is also investigated in this paper. Design optimization and experimental results are presented, which demonstrate that the optimized power-supply structures derived by applying the proposed optimization technique exhibit lower cost of generated energy compared to partially optimized or totally nonoptimized structures and by that reduce the cost of the overall wireless sensor network node.



Asset-Based Dynamic Impact Assessment of Cyberattacks for Risk Analysis in Industrial Control Systems

Feb. 2018

With the evolution of information, communications, and technologies, modern industrial control systems (ICSs) face more and more cybersecurity issues. This leads to increasingly severe risks in critical infrastructure and assets. Therefore, risk analysis becomes a significant yet not well investigated topic for prevention of cyberattack risks in ICSs. To tackle this problem, a dynamic impact assessment approach is presented in this paper for risk analysis in ICSs. The approach predicts the trend of impact of cybersecurity dynamically from full recognition of asset knowledge. More specifically, an asset is abstracted with properties of construction, function, performance, location, and business. From the function and performance properties of the asset, object-oriented asset models incorporating with the mechanism of common cyberattacks are established at both component and system levels. Characterizing the evolution of behaviors for single asset and system, the models are used to analyze the impact propagation of cyberattacks. Then, from various possible impact consequences, the overall impact is quantified based on the location and business properties of the asset. A special application of the approach is to rank critical system parameters and prioritize key assets according to impact assessment. The effectiveness of the presented approach is demonstrated through simulation studies for a chemical control system.



A New Fault Classifier in Transmission Lines Using Intrinsic Time Decomposition

Feb. 2018

As nonstationarity exists in fault signals of transmission lines, their classification and quantification remain a challenging issue. This paper presents a new scheme for feature extraction in an attempt to achieve high fault classification accuracy. The proposed scheme consists of three steps: first, the proper rotation components (PRCs) matrix of current signals captured from one end of the protected line is constructed using the intrinsic time decomposition, a fast time-domain signal processing tool with no need for sensitive tuning parameters. Second, the singular value decomposition and nonnegative matrix factorization are employed to decompose the PRCs into its significant components. Finally, eight new normalized features extracted from the output of the data processing techniques are fed into the probabilistic neural network classifier. The data processing techniques employed for classification substantially improve the overall quality of the input patterns classified and increase the generalization capability of the trained classifiers. The proposed scheme is evaluated through two simulated sample systems in the PSCAD/EMTDC software and field fault data. Moreover, the effects of the current transformer saturation, decaying dc component, and noisy conditions are evaluated. The comparison results and discussion regarding the different aspects of the problem confirm the efficacy of the proposed scheme.



A Stochastic Home Energy Management System Considering Satisfaction Cost and Response Fatigue

Feb. 2018

Home energy management (HEM) systems enable residential consumers to participate in demand response programs (DRPs) more actively. However, HEM systems confront some practical difficulties due to the uncertainty related to renewable energies as well as the uncertainty of consumers’ behavior. Moreover, the consumers aim for the highest level of comfort and satisfaction in operating their electrical appliances. In addition, technical limits of the appliances must be considered. Furthermore, DR providers aim at keeping the participation of consumers in DRPs and minimize the “response fatigue” phenomenon in the long-term period. In this paper, a stochastic model of an HEM system is proposed by considering uncertainties of electric vehicles availability and small-scale renewable energy generation. The model optimizes the customer's cost in different DRPs, while guarantees the inhabitants’ satisfaction by introducing a response fatigue index. Different case studies indicate that the implementation of the proposed stochastic HEM system can considerably decrease both the customers’ cost and response fatigue.



Adaptive Signal Selection of Wide-Area Damping Controllers Under Various Operating Conditions

Feb. 2018

Since operating conditions of power systems always change, the input and output signals of wide-area damping controller (WADC), which are selected at an operating point, may not be able to guarantee the damping effect at other operating points. This paper focuses on a new adaptive signal selection for WADC against several operating conditions, such as various load demands, control signal failure, line and generator outages, and effect of communication latency. The joint controllability and observability is used to determine the best input and output pairs of WADC at any operating points. Small-signal and transient stabilities study in the IEEE 50-machine system including renewable sources, i.e., wind and solar photovoltaic generators are conducted to evaluate the effect of the proposed method. Study result demonstrates that the WADC with the adaptive signal selection yields superior damping effect to the WADC with the fixed signal selection over wide range operations.



A Prediction Backed Model for Quality Assessment of Screen Content and 3-D Synthesized Images

Feb. 2018

In this paper, we address problems associated with free-energy-principle-based image quality assessment (IQA) algorithms for objectively assessing the quality of Screen Content (SC) and three-dimensional (3-D) synthesized images and also propose a very fast and efficient IQA algorithm to address these issues. These algorithms separate an image into predicted and disorder residual parts and assume disorder residual part does not contribute much to the overall perceptual quality. These algorithms fail for quality estimation of SC images as information of textual regions in SC images are largely separated into the disorder residual part and less information in the predicted part and subsequently, given a negligible emphasis. However, this is in contrast with the characteristics of human vision. Since our eyes are well trained to detect text in daily life. So, our human vision has prior information about text regions and can sense small distortions in these regions. In this paper, we proposed a new reduced-reference IQA algorithm for SC images based upon a more perceptually relevant prediction model and distortion categorization, which overcomes problems with existing free-energy-principle-based predictors. From experiments, it is validated that the proposed model has a better capability of efficiently estimating the quality of SC images as compared to the recently developed reduced-reference IQA algorithms. We also applied the proposed algorithm to judge the quality of 3-D synthesized images and observed that it even achieves better performance than the full-reference IQA metrics specifically designed for the 3-D synthesized views.



Energy-Efficient Sensor Data Collection Approach for Industrial Process Monitoring

Feb. 2018

The use of wireless sensor network for industrial applications has attracted much attention from both academic and industrial sectors. It enables a continuous monitoring, controlling, and analyzing of the industrial processes, and contributes significantly to finding the best performance of operations. Sensors are typically deployed to gather data from the industrial environment and to transmit it periodically to the end user. Since the sensors are resource constrained, effective energy management should include new data collection techniques for an efficient utilization of the sensors. In this paper, we propose adaptive data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption. We provide and compare three different data collection techniques. The first one uses the analysis of data variances via statistical tests to adapt the sampling rate, whereas the second one is based on the set-similarity functions, and the third one on the distance functions. Both simulation and real experimentations on telosB motes were performed in order to evaluate the performance of our techniques. The obtained results proved that our proposed adaptive data collection methods can reduce the number of acquired samples up to 80% with respect to a traditional fixed-rate technique. Furthermore, our experimental results showed significant energy savings and high accurate data collection compared to existing approaches.



Image Encryption Based on Interleaved Computer-Generated Holograms

Feb. 2018

An encryption method based on interleaved computer-generated holograms (CGHs) displayed by a spatial light modulator (SLM) is demonstrated. Arbitrary decrypted complex optical wave fields are reconstructed in the rear focal plane of two phase-only holograms, generated from original image using a vector decomposition algorithm. Two CGHs are encoded into one hologram by interleaving the column of pixels, which optically combines the optical wave fields of two neighboring phase-only modulated pixels. The designed image encryption system may avoid the inherent silhouette problem and alleviate the precise alignment requirements of interference encryption. Video encryption and real-time dynamic decryption is demonstrated using one SLM.



Fully Distributed Hierarchical Control of Parallel Grid-Supporting Inverters in Islanded AC Microgrids

Feb. 2018

In this paper, a fully distributed hierarchical control strategy is proposed for operating networked grid-supporting inverters (GSIs) in islanded ac microgrids (MGs). The primary control level implements frequency and voltage control of an ac MG through a cascaded structure, consisting of a droop control loop, a virtual impedance control loop, a mixed ${H_2}/{H_infty }$-based voltage control loop, and a sliding-mode-control-based current loop. Compared to conventional proportional-plus-integral-based cascaded control, the proposed cascaded control does not require a precise model for the GSI system. The proposed secondary control level implements distributed-consensus-based economic automatic generation control and distributed automatic voltage control, which integrates the conventional secondary control and tertiary control into a single control level by bridging a gap between traditional secondary control and tertiary control. Simulation results demonstrate the effectiveness of the proposed hierarchical control strategy.



Stability Analysis of LCL-Type Grid-Connected Inverter Under Single-Loop Inverter-Side Current Control With Capacitor Voltage Feedforward

Feb. 2018

When single-loop inverter-side current control is used in the LCL-type inverters, there may be more than one stable region with regard to computation delay in control path. The system is stable if computation delay is small enough, but it, named as the first stable region, may be too narrow to finish the control codes in high-frequency system. On the other hand, the second stable region is highly sensitive to the grid impedance variation and is hardly applicable in weak grids. To deal with the situation, this paper investigates the influence of the capacitor voltage feedforward on the system stability considering flexible computation delay in feedforward path. It is found that the capacitor voltage feedforward is able to suppress the resonance of the LCL filter. However, a new resonance may arise if computation delay is not carefully handled. The influence of computation delay in both forward path and feedforward path on system stability is systematically analyzed. The analytical results show that after applying feedforward control with optimized delay, the system stability is greatly improved and is not sensitive to the grid impedance variation. The simulation and experiment results verify the analytical results.



Hierarchical Control Design for a Shipboard Power System With DC Distribution and Energy Storage Aboard Future More-Electric Ships

Feb. 2018

DC distribution is now becoming the major trend of future mobile power systems, such as more-electric aircrafts and ships. As dc distribution has different nature to the conventional ac system, a new design of well-structured control and management methods will be mandatory. In this paper, a shipboard power system with dc distribution and energy storage system (ESS) is picked as the study case. To meet the requirement of control and management of such a large-scale mobile power system, a hierarchical control design is proposed in this paper. In order to fully exploit the benefit of the ESS, as well as to overcome the limitation in controllability, a novel inverse-droop control method is proposed, in which the power sharing is according to the source characteristic, instead of their power rating. A frequency-division method is also proposed as an extension to the inverse-droop method for enabling a hybrid ESS and its autonomous operation. On the basis of the proposed methods, the control methods for management and voltage restoration levels are also proposed to establish a comprehensive control solution. Real-time simulations are carried out to validate the performance of the proposed control design under different operating conditions. When compared to more conventional droop-based approaches, the new proposal shows enhancement in efficiency.



SRE: Semantic Rules Engine for the Industrial Internet-Of-Things Gateways

Feb. 2018

The advent of the Internet-of-Things (IoT) paradigm has brought opportunities to solve many real-world problems. Energy management, for example, has attracted huge interest from academia, industries, governments, and regulatory bodies. It involves collecting energy usage data, analyzing it, and optimizing the energy consumption by applying control strategies. However, in industrial environments, performing such optimization is not trivial. The changes in business rules, process control, and customer requirements make it much more challenging. In this paper, a semantic rules engine (SRE) for industrial gateways is presented that allows implementing dynamic and flexible rule-based control strategies. It is simple, expressive, and allows managing rules on-the-fly without causing any service interruption. Additionally, it can handle semantic queries and provide results by inferring additional knowledge from previously defined concepts in ontologies. SRE has been validated and tested on different hardware platforms and in commercial products. Performance evaluations are also presented to validate its conformance to the customer requirements.



Fast Smoke Detection for Video Surveillance Using CUDA

Feb. 2018

Smoke detection is a key component of disaster and accident detection. Despite the wide variety of smoke detection methods and sensors that have been proposed, none has been able to maintain a high frame rate while improving detection performance. In this paper, a smoke detection method for surveillance cameras is presented that relies on shape features of smoke regions as well as color information. The method takes advantage of the use of a stationary camera by using a background subtraction method to detect changes in the scene. The color of the smoke is used to assess the probability that pixels in the scene belong to a smoke region. Due to the variable density of the smoke, not all pixels of the actual smoke area appear in the foreground mask. These separate pixels are united by morphological operations and connected-component labeling methods. The existence of a smoke region is confirmed by analyzing the roughness of its boundary. The final step of the algorithm is to check the density of edge pixels within a region. Comparison of objects in the current and previous frames is conducted to distinguish fluid smoke regions from rigid moving objects. Some parts of the algorithm were boosted by means of parallel processing using compute unified device architecture graphics processing unit, thereby enabling fast processing of both low-resolution and high-definition videos. The algorithm was tested on multiple video sequences and demonstrated appropriate processing time for a realistic range of frame sizes.



Reservoir Computing Meets Smart Grids: Attack Detection Using Delayed Feedback Networks

Feb. 2018

A new method for attack detection of smart grids with wind power generators using reservoir computing (RC) is introduced in this paper. RC is an energy-efficient computing paradigm within the field of neuromorphic computing and the delayed feedback networks (DFNs) implementation of RC has shown superior performance in many classification tasks. The combination of temporal encoding, DFN, and a multilayer perceptron (MLP) as the output readout layer is shown to yield performance improvement over existing attack detection methods such as MLPs, support vector machines (SVM), and conventional state vector estimation (SVE) in terms of attack detection in smart grids. The proposed algorithms are shown to be more robust than MLP and SVE in dealing with different variables such as the amplitude of the attack, attack types, and the number of compromised measurements in smart grids. The attack detection rate for the proposed RC-based system is higher than 99%, based on the accuracy metric for the average of 10 000 simulations.



Guest Editorial Special Section on Engineering Industrial Big Data Analytics Platforms for Internet of Things

Feb. 2018

Over the last few years, a large number of Internet of Things (IoT) solutions have come to the IoT marketplace. Typically, each of these IoT solutions are designed to perform a single or minimal number of tasks (primary usage). We believe a significant amount of knowledge and insights are hidden in these data silos that can be used to improve our lives; such data include our behaviors, habits, preferences, life patterns, and resource consumption. To discover such knowledge, we need to acquire and analyze this data together in a large scale. To discover useful information and deriving conclusions toward supporting efficient and effective decision making, industrial IoT platform needs to support variety of different data analytics processes such as inspecting, cleaning, transforming, and modeling data, especially in big data context. IoT middleware platforms have been developed in both academic and industrial settings in order to facilitate IoT data management tasks including data analytics. However, engineering these general-purpose industrial-grade big data analytics platforms need to address many challenges. We have accepted six manuscripts out of 24 submissions for this special section (25% acceptance rate) after the strict peerreview processes. Each manuscript has been blindly reviewed by at least three external reviewers before the decisions were made. The papers are briefly summarized.



Bilateral LSTM: A Two-Dimensional Long Short-Term Memory Model With Multiply Memory Units for Short-Term Cycle Time Forecasting in Re-entrant Manufacturing Systems

Feb. 2018

Forecasting short-term cycle time (CT) of wafer lots is crucial for production planning and control in the wafer manufacturing. A novel recurrent neural network called “bilateral long short-term memory (bilateral LSTM)” is proposed to model a short-term cycle time forecasting (CTF) of each re-entrant period of a wafer lot. First, a two-dimensional (2-D) architecture is designed to transmit the wafer and layer correlations by using wafer and layer connections. Subsequently, aiming to store various error signals caused by the diverse CT data, a multiply memory structure is presented to extend the capacity of constant error carousel (CEC) in the LSTM model. The experiment results indicate that the proposed model outperforms conventional models in the accuracy and stability for the short-term CTF. Further comparative experiments reveal that the 2-D architecture can enhance the prediction accuracy and the multi-CEC structure can improve the forecasting stability for the short-term CTF of wafer lots.



Certificateless Searchable Public Key Encryption Scheme for Industrial Internet of Things

Feb. 2018

With the widespread adoption of Internet of Things and cloud computing in different industry sectors, an increasing number of individuals or organizations are outsourcing their Industrial Internet of Things (IIoT) data in the cloud server to achieve cost saving and collaboration (e.g., data sharing). However, in this environment, preserving the privacy of data remains a key challenge and inhibiting factor to an even wider adoption of IIoT in the cloud environment. To mitigate these issues, in this paper, we design a new secure channel-free certificateless searchable public key encryption with multiple keywords scheme for IIoT deployment. We then demonstrate the security of the scheme in the random oracle model against two types of adversaries, where one adversary is given the power to choose a random public key instead of any user's public key and another adversary is allowed to learn the system master key. In the presence of these types of adversaries, we evaluate the performance of the proposed scheme and demonstrate that it achieves (computational) efficiency with low communication cost.



Social Big-Data-Based Content Dissemination in Internet of Vehicles

Feb. 2018

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.



Optimal Decision Making for Big Data Processing at Edge-Cloud Environment: An SDN Perspective

Feb. 2018

With the evolution of Internet and extensive usage of smart devices for computing and storage, cloud computing has become popular. It provides seamless services such as e-commerce, e-health, e-banking, etc., to the end users. These services are hosted on massive geodistributed data centers (DCs), which may be managed by different service providers. For faster response time, such a data explosion creates the need to expand DCs. So, to ease the load on DCs, some of the applications may be executed on the edge devices near to the proximity of the end users. However, such a multiedge-cloud environment involves huge data migrations across the underlying network infrastructure, which may generate long migration delay and cost. Hence, in this paper, an efficient workload slicing scheme is proposed for handling data-intensive applications in multiedge-cloud environment using software-defined networks (SDN). To handle the inter-DC migrations efficiently, an SDN-based control scheme is presented, which provides energy-aware network traffic flow scheduling. Finally, a multileader multifollower Stackelberg game is proposed to provide cost-effective inter-DC migrations. The efficacy of the proposed scheme is evaluated on Google workload traces using various parameters. The results obtained show the effectiveness of the proposed scheme.



Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things

Feb. 2018

Currently, a large number of industrial data, usually referred to big data, are collected from Internet of Things (IoT). Big data are typically heterogeneous, i.e., each object in big datasets is multimodal, posing a challenging issue on the convolutional neural network (CNN) that is one of the most representative deep learning models. In this paper, a deep convolutional computation model (DCCM) is proposed to learn hierarchical features of big data by using the tensor representation model to extend the CNN from the vector space to the tensor space. To make full use of the local features and topologies contained in the big data, a tensor convolution operation is defined to prevent overfitting and improve the training efficiency. Furthermore, a high-order backpropagation algorithm is proposed to train the parameters of the deep convolutional computational model in the high-order space. Finally, experiments on three datasets, i.e., CUAVE, SNAE2, and STL-10 are carried out to verify the performance of the DCCM. Experimental results show that the deep convolutional computation model can give higher classification accuracy than the deep computation model or the multimodal model for big data in IoT.



Leveraging Analysis of User Behavior to Identify Malicious Activities in Large-Scale Social Networks

Feb. 2018

With the enormous growth and volume of online social networks and their features, along with the vast number of socially connected users, it has become difficult to explain the true semantic value of published content for the detection of user behaviors. Without understanding the contextual background, it is impractical to differentiate among various groups in terms of their relevance and mutual relations, or to identify the most significant representatives from the community at large. In this paper, we propose an integrated social media content analysis platform that leverages three levels of features, i.e., user-generated content, social graph connections, and user profile activities, to analyze and detect anomalous behaviors that deviate significantly from the norm in large-scale social networks. Several types of analyses have been conducted for a better understanding of the different user behaviors in the detection of highly adaptive malicious users. We attempted a novel approach regarding the process of data extraction and classification to contextualize large-scale networks in a proper manner. We also collected a significant number of user profiles from Twitter and YouTube, along with around 13 million channel activities. Extensive evaluations were conducted on real-world datasets of user activities for both social networks. The evaluation results show the effectiveness and utility of the proposed approach.



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Feb. 2018

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Feb. 2018

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Feb. 2018

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IEEE Industrial Electronics Society

Feb. 2018

Provides a listing of current committee members and society officers.



Information for Authors

Feb. 2018

Provides instructions and guidelines to prospective authors who wish to submit manuscripts.