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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

Aug. 2017

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



IEEE Industrial Electronics Society

Aug. 2017

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.



Comments on “Bridging Service-Oriented Architecture and IEC 61499 for Flexibility and Interoperability”

Aug. 2017

In the paper by W. Dai et al. [2015], a formal mapping between IEC 61499 and service-oriented architecture (SOA) is presented, and an SOA-based execution environment architecture is described. In this comment, we discuss the mapping and the execution environment architecture, as well as the suggested potentials for exploiting those. We present specific arguments and make cases that call the authors' claims into question.



Response to “Comments on Bridging Service-Oriented Architecture and IEC 61499 for Flexibility and Interoperability”

Aug. 2017

Service-oriented architecture is increasingly applied in industrial cyber-physical systems to provide better flexibility and interoperability between various systems and devices. In the previous work ([3]) a service-based execution environment for IEC 61499 is proposed to enhance flexibility and interoperability in distributed automation systems. In this paper, we discuss questions raised by some readers, for example, performance overhead of the proposed method. Also, other clarifications are made in order to address possible confusions expressed in the received comments.



FPGA-Based Reconfigurable Data Acquisition System for Industrial Sensors

Aug. 2017

A sensor data acquisition system is an essential part of an industrial automation control system. However, the variety of sensor producer causes the difficulty of protocol's unity. The mechanism which requires the acquisition boards to be powered off when the number or types or manufacturer of sensors is changed is unreasonable. Meanwhile, traditional sensor reading cycle depends more on the embedded program skills. In this paper, to solve these problems, a new method is proposed to design a reconfigurable data acquisition system for industrial sensors, in which field-programmable gate array (FPGA) is adopted as the core controller. We use both dynamic system reconfiguration and static system reconfiguration in our design. Performance of the proposed system is verified in practical application of an automated production system of poly carboxylic acid water reducing agent.



Decentralized Control Algorithm for Fast Monitoring and Efficient Energy Consumption in Energy Harvesting Wireless Sensor Networks

Aug. 2017

The paper proposes a fully decentralized algorithm to control nodes transmission radii for simultaneously coping with energy-type requirement (e.g., network power consumption) and application-type requirement (e.g., fast monitoring and estimation of variables of interest). The stability of the closed loop network under the proposed control law is proved. Simulation validations show the effectiveness of the proposed approach in nominal scenario as well as in the presence of uncertain power requirements and harvesting system supply, outperforming a well known related approach existing in the literature.



Automatic Inference of Finite-State Plant Models From Traces and Temporal Properties

Aug. 2017

Closed-loop model checking, a formal verification technique for industrial automation systems, increases the richness of specifications to be checked and reduces the state space to be verified compared to the open-loop case. To be applied, it needs the controller and the plant formal models to be coupled. There are approaches for controller synthesis, but little has been done regarding plant model construction. While manual plant modeling is time consuming and error-prone, discretizing a simulation model of the plant leads to state excess. This paper aims to solve the problem of automatic plant model construction from existing specification, which is represented in the form of plant behavior examples, or traces, and temporal properties. The proposed method, which is based on the translation of the problem to the Boolean satisfiability problem, is evaluated and shown to be applicable on several case study plant model synthesis tasks and on randomly generated problem instances.



Novel Calculation Method of Indices to Improve Classification of Transformer Winding Fault Type, Location, and Extent

Aug. 2017

In this paper, disk space variation, radial deformation, short circuit, and axial displacement as four common transformer winding faults, in different locations and with various extents, were practically applied to 20 kV winding of a 1.6 MVA distribution transformer. For classification of the fault type, its location, and extent, a new calculation method for transfer function (TF) comparison indices called windowed calculation was proposed. The whole frequency range of the TF is scanned in this method. It was shown that the presented method increases the visual fault detection ability even for fault-type detection and significantly enhances the accuracy of the classification winding faults type, location, and extent. In this study, Fisher discriminant analysis (FDA) was utilized for dimension reduction and feature extraction. The ability of the FDA for maximizing the between-class separability while minimizing their within-class variability was utilized for this application so as to decrease the dimension and extract appropriate features from a lot of calculated features by the proposed method.



Prediction of Failure in Lubricated Surfaces Using Acoustic Time–Frequency Features and Random Forest Algorithm

Aug. 2017

Scuffing is one of the most problematic failure mechanisms in lubricated mechanical components. It is a sudden and almost not predictable failure that often leads to extensive cost in terms of damages and/or delay in production lines. This study presents a promising solution that can prevent scuffing for the machinery industry in the future. To achieve this goal, a signal processing approach by means of an acoustic emission is introduced for the prediction of scuffing. An acoustic dataset was collected from metallic surfaces reciprocating under a constant load (typical conditions for semi journal bearings). The coefficient of friction values were measured during the entire experiments and were referred to as the ground truth of the momentary surface state. Based on the friction behavior, three friction regimes were defined that are running-in, steady-state, and scuffing. The present approach is based on tracking the changes in acoustic emission by means of three sets of wavelet-derived features. Those features include: 1) energy, 2) entropy, and 3) statistical information about the content of acoustic emission and the response of each feature to the different friction regimes was individually investigated. The applicability of machine learning classification and regression was studied for scuffing prediction. Both approaches were applied separately but can be unified together to increase the prediction time interval of surface failure. For classification, an extra friction regime was introduced designating as pre-scuffing and is defined as a time span of 3 min before the real surface failure. Random forest classifier was used to differentiate the features from the different friction regime. The best performance in classification of features from pre-scuffing regime reached a confidence level as high as 84%. In regression approach, the extracted features sequences were used together with random forest regressor. Our strategy allowed predicting scuffing up to 5 min p- eceding its real occurrence.



Online Inductor Parameters Identification by Small-Signal Injection for Sensorless Predictive Current Controlled Boost Converter

Aug. 2017

In a sensorless predictive current controlled boost converter, parameterizing the inductor plays an important role in controller performance. In this paper, a solution for inductor parameters online identification is investigated. A small-signal injection strategy is proposed to create a transient state, and convergence problem of inductance identification in steady state can be avoided. Then, a charge balance current observer (CBCO), derived from capacitor current charging balance concept, is adopted to estimate the inductor current for inductance identification. Since inductance is not used in CBCO, current estimation is not affected by inductance identification error. Because of rank-deficient problem, instead of identifying inductor parasitic resistance solely, the inductor equivalent parasitic resistance is derived. By applying it into the conventional current observer for current control loop, the accuracy of current estimation can still be guaranteed since more parasitic effects are included. To improve the accuracy of inductance identification, a load identification method is investigated. Furthermore, the effect of the equivalent series resistance of output capacitor on the proposed algorithm is analyzed. Finally, its effectiveness is verified by experimental results.



A Kernel Direct Decomposition-Based Monitoring Approach for Nonlinear Quality-Related Fault Detection

Aug. 2017

This article considers the issue of quality-related process monitoring. A novel kernel direct decomposition (KDD) algorithm is proposed and a KDD-based nonlinear quality-related fault detection approach is designed. The proposed KDD algorithm first maps original process variables into feature space to deal with the nonlinearities among these variables. Feature matrix is then directly decomposed into two orthogonal parts according to its full correlation with output matrix without building any regression model. Compared with conventional nonlinear methods, the KDD-based approach has the following advantages: 1) it is simpler in design as it omits the steps of constructing a regression model like kernel partial least squares (KPLS); 2) its performance is more stable because it extracts the full correlation information of feature matrix unlike KPLS-based methods which only use the partial correlation information of several selected latent variables; and 3) it has a simpler diagnosis logic since it only uses two statistics to determine the type of fault while most existing methods need four. Simulations on a literature example and a simulated industrial process are used to demonstrate the advantages of the new method.



On the Use of Energy Storage Systems and Linear Feedback Optimal Control for Transient Stability

Aug. 2017

In this paper, we study a distributed control strategy that harnesses the highly granular data available in future power systems in order to improve system resilience to disturbances. Specifically, we investigate the role of external energy storage systems (ESSs) in stabilizing the dynamics of power systems during periods of disruption. We consider an information-rich multiagent framework and focus on ESS output control via linear feedback optimal (LFO) control to achieve transient stability. The LFO control scheme relies on receiving timely state information to actuate distributed ESSs in order to drive the synchronous generators to stability. We evaluate the performance of the LFO control on the 39-bus 10-generator New England test power system in the presence of ideal and nonideal conditions including communication latency, finite sampling rate, and sensor noise. The LFO controller is found to have a simple structure, be tunable, and to have fast response to achieving transient stability while being sensitive to information latency and data rate.



Analytical Rule-Based Approach to Online Optimal Control of Smart Residential Energy System

Aug. 2017

Online optimal control of a multicarrier home energy system, including a fuel cell (FC) with combined heat and power functionality, a furnace, and a battery as an energy storage system, is presented in this paper. An appropriately defined objective function (OF) is formulated to address the operation costs of the home energy system by considering the electrical time-of-use price and time-varying electrical and thermal demand. To determine the optimal control strategy of the FC, the defined OF is solved analytically to reach a closed-form solution for the optimal operation of the FC, which results in the global optimum compared with the near-optimal point in previous works of FC scheduling. To attain the whole system optimal operation, a rule-based solution for the optimal operation of the battery is proposed according to its characteristics. The proposed method is applied to a realistic case study including real load demand and experimentally verified data of the FC. The results are compared with previous approaches, in which optimization techniques have been used to achieve near-optimal scheduling of FC with/without a battery. It is shown that the proposed control strategy results in more exact scheduling and can be applied successfully in real applications.



Diagnosis of Series DC Arc Faults—A Machine Learning Approach

Aug. 2017

Increasing prevalence of dc sources and loads has resulted in dc distribution being reconsidered at a microgrid level. However, in comparison to ac systems, the lack of a natural zero crossing has traditionally meant that protecting dc systems is inherently more difficult-this protection issue is compounded when attempting to diagnose and isolate fault conditions. One such condition is the series arc fault, which poses significant protection issues as their presence negates the logic of overcurrent protection philosophies. This paper proposes the IntelArc system to accurately diagnose series arc faults in dc systems. IntelArc combines time-frequency and time-domain extracted features with hidden Markov models (HMMs) to discriminate between nominal transient behavior and arc fault behavior across a variety of operating conditions. Preliminary testing of the system is outlined with results showing that the system has the potential for accurate, generalized diagnosis of series arc faults in dc systems.



Enhanced Robustness of State Estimator to Bad Data Processing Through Multi-innovation Analysis

Aug. 2017

To enhance the robustness of a power system state estimator to topology errors, bad critical measurements, multiple non-interacting, or interacting bad data (BD), this paper presents a new robust detection method by exploiting the temporal correlation and the statistical consistency of measurements. Particularly, we propose three innovation matrices to capture the measurement correlation and statistical consistency by processing the forecasted states/measurements and the interpolated reliable information from phasor measurement units. The latter is achieved by using a robust generalized maximum-likelihood estimator. We then propose to apply the projection statistics (PS) to the proposed innovation matrices for BD detection. Extensive Monte Carlo simulations and QQ-plots are carried out to obtain an analytical threshold of the statistical test of the PS. Because of the robustness of PS and the enhanced measurement redundancy by the innovations, the proposed method is able to handle various types of BD in both PMU observable and PMU partially observable power systems. Moreover, the proposed method is suitable for parallel implementation, and can be integrated with online applications. Comparison results with existing methods under different BD conditions on IEEE 14-bus, 118-bus, and Polish 2383-bus test systems demonstrate the effectiveness and robustness of the proposed method.



A Fast Density and Grid Based Clustering Method for Data With Arbitrary Shapes and Noise

Aug. 2017

This paper presents a density- and grid- based (DGB) clustering method for categorizing data with arbitrary shapes and noise. As most of the conventional clustering approaches work only with round-shaped clusters, other methods are needed to be explored to proceed classification of clusters with arbitrary shapes. Clustering approach by fast search and find of density peaks and density-based spatial clustering of applications with noise, and so many other methods are reported to be capable of completing this task but are limited by their computation time of mutual distances between points or patterns. Without the calculation of mutual distances, this paper presents an alternative method to fulfill clustering of data with any shape and noise even faster and with more efficiency. It was successfully verified in clustering industrial data (e.g., DNA microarray data) and several benchmark datasets with different kinds of noise. It turned out that the proposed DGB clustering method is more efficient and faster in clustering datasets with any shape than the conventional methods.



Resource Consumption Cost Minimization of Reliable Parallel Applications on Heterogeneous Embedded Systems

Aug. 2017

Heterogeneous processors are increasingly being used in embedded systems where parallel applications with precedence-constrained tasks widely exist. Reliability is an important functional safety requirement and reliability goal should be satisfied for safety-critical parallel applications; meanwhile, resource is limited in embedded systems and it should be minimized. This study solves the problem of resource consumption cost minimization of a reliable parallel application on heterogeneous embedded systems without using fault tolerance. The problem is decomposed into two subproblems, namely, satisfying reliability goal and minimizing resource consumption cost. The first subproblem is solved by transferring the reliability goal of the application to that of each task, and the second subproblem is solved by heuristically assigning each task to the processor with the minimum resource consumption cost while satisfying its reliability goal. Experiments with real parallel applications verify that the proposed algorithm obtains minimum resource consumption costs compared with the state-of-the-art algorithms.



Covert Attacks in Cyber-Physical Control Systems

Aug. 2017

The advantages of using communication networks to interconnect controllers and physical plants motivate the increasing number of networked control systems in industrial and critical infrastructure facilities. However, this integration also exposes such control systems to new threats, typical of the cyber domain. In this context, studies have been conducted, aiming to explore vulnerabilities and propose security solutions for cyber-physical systems. In this paper, a covert attack for service degradation is proposed, which is planned based on the intelligence gathered by another attack, herein proposed, referred as system identification attack. The simulation results demonstrate that the joint operation of the two attacks is capable to affect, in a covert and accurate way, the physical behavior of a system.



Enhancing Power System Operational Flexibility With Flexible Ramping Products: A Review

Aug. 2017

With the increased variability and uncertainty of net load induced from high penetrations of renewable energy resources and more flexible interchange schedules, power systems are facing great operational challenges in maintaining balance. Among these, the scarcity of ramp capability is an important culprit of power balance violations and high scarcity prices. To address this issue, market-based flexible ramping products (FRPs) have been proposed in the industry to improve the availability of ramp capacity. This paper presents an in-depth review of the modeling and implementation of FRPs. The major motivation is that although FRPs are widely discussed in the literature, it is still unclear to many that how they can be incorporated into a co-optimization framework that includes energy and ancillary services. The concept and a definition of power system operational flexibility as well as the needs for FRPs are introduced. The industrial practices of implementing FRPs under different market structures are presented. Market operation issues and future research topics are also discussed. This paper can provide researchers and power engineers with further insights into the state of the art, technical barriers, and potential directions for FRPs.



Scheduling Algorithms of Flat Semi-Dormant Multicontrollers for a Cyber-Physical System

Aug. 2017

Recently, the modeling and design of distributed controllers in cyber-physical systems (CPSs), which suffer from messages lost, delay variation, and jitter, has gained lots of research attentions. A special CPS, arbitrated networked control system (ANCS), has been designed for scheduling or arbitrating networks in a control system. In this paper, we propose a novel ANCS with dual communication channels. The proposed ANCS uses a hierarchical flexible time-division multiple access (TDMA)/fixed priority scheduling policy that is based on the event trigger protocol. A flat semi-dormant multicontrollers (FSDMC) model is developed for the proposed ANCS. We then model the FSDMC as an N/(d,c)-M/M/c/K/SMWV queue, and obtain various performance indices. Based on the model, a multiobjective optimization problem is then formulated to minimize the nonlinear energy consumption function and the nominal delay function presented in this study. To resolve the multiobjective optimization problem, a scheduling algorithm based on the multiobjective particle swarm optimization algorithm is proposed to generate the Pareto front and the corresponding nondominated vector sets. An optimal stopping algorithm is also designed to obtain the optimal value of the number of semi-dormant controllers. The optimal values of various parameters of the control system are obtained by using the above nondominated vector sets, and are applied to the proposed ANCS. Extensive numerical results are provided to illustrate the usefulness of the proposed algorithms and the effects of the control system parameters on the optimal policy.



Diagnosis of Stator Faults Severity in Induction Motors Using Two Intelligent Approaches

Aug. 2017

Three-phase induction motors are the primary means of transformation of electrical energy into mechanical energy in industry, since they are robust and present low cost. However, despite being robust, these machines are subject to electrical or mechanical faults. Thus, identifying a defect in a running motor may decrease the risk of possible damage. This paper proposes an alternative approach to identify defects in the stator of these motors, by analyzing current signals in the time domain. In addition, it presents the determination of the consequent fault severity by means of two proposals: 1) a multiagent system with a classifier behavior; and 2) a neural estimator. The faults observed are related to short circuits between turns in the stator coil of 1% to 10%. Experimental results are observed with motors of different powers, under various adverse situations of electrical feed and a wide range of load conditions on the machine shaft.



Auxiliary Hybrid PSO-BPNN-Based Transmission System Loss Estimation in Generation Scheduling

Aug. 2017

The conventional transmission loss estimation methods used by power system utilities in scheduling problems rely on the exactness of the network model. However, the transmission network model in the system operator database is erroneous and not updated periodically. Therefore, the transmission losses calculated based on the erroneous network model is also erroneous. In this context, this paper proposes an auxiliary hybrid model using a back propagation neural network (BPNN) and a particle swarm optimization (PSO) technique to estimate transmission losses, while solving power system scheduling problems. Here, the historical information of the power system is processed by the BPNN and its control parameters are optimized using PSO. In the proposed PSO-BPNN loss estimator, power system variables such as real power generation levels, reactive power injection values, and ambient temperature are used as the input variables. The proposed loss estimator is validated using IEEE 30 bus system and Ontario power system.



Robust Security Constrained-Optimal Power Flow Using Multiple Microgrids for Corrective Control of Power Systems Under Uncertainty

Aug. 2017

This paper proposes a new robust security-constrained optimal power flow (SCOPF) method to balance the economy. and security requirements under uncertainties associated with renewable generation and load demand. Given the significant growth in microgrid (MG) deployments over the world, this paper explores the potential of using multiple MGs in supporting main grid's security control. Corrective control is employed to relieve postcontingency overflows by effectively coordinating system generators and multiple MGs. An incentive-based mechanism is designed to encourage the MGs to actively cooperate with the main grid for postcontingency recovery, which makes the proposed method to distinguish from the previous models using a traditional centralized control method, such as direct load control. A scenario-decomposition-based approach is then developed to solve the proposed robust SCOPF problem. Numerical simulations on IEEE 14- and IEEE 118-bus systems demonstrate the effectiveness and efficiency of the proposed method.



A Hybrid Diesel-Wind­PV-Based Energy Generation System With Brushless Generators

Aug. 2017

This paper presents an experimental implementation of a standalone microgrid topology based on a single voltage source converter (VSC) and brushless generators. The microgrid system is energised with different renewable energy sources namely wind and solar PV array. However, a diesel generator (DG) set and a battery energy storage system (BESS) are also used to maintain the reliability of the system. The proposed topology has the advantage of reduced switching devices and simple control. The implemented topology has DG set as an ac source. The wind generator and the solar PV array are dc sources which are connected to the dc link of the VSC. The BESS is also used at the dc link to facilitate the instantaneous power balance under dynamic conditions. Along with the system integration, the VSC also has the capability to mitigate the power quality problems such as harmonic currents, load balancing, and voltage regulation. A wide variety of test results are presented to demonstrate all the features of the proposed system.



Probabilistic Weighted Support Vector Machine for Robust Modeling With Application to Hydraulic Actuator

Aug. 2017

An effective model of the hydraulic actuator is crucial for high-accuracy driving control. However, modeling this driving process is difficult due to strong nonlinearities in the hydraulic system and load as well as unknown influence from sources of noise including sampling, modeling, measurement, and operation errors in the process. In this paper, a probabilistic weighted least square support vector machine (LS-SVM) is proposed to model this kind of processes under noise. The proposed method can increase robustness and accuracy even with outliers or non-Gaussian noise. First, a distribution construction method is developed to extract the probabilistic distribution of the distributed LS-SVM models caused by outliers and/or noise samples. Then, a new objective function is constructed using this distribution information to negate outlier or noise influence. Since this method considers the probabilistic information of outliers and/or noise samples, it has greatly improved the LS-SVM modeling capabilities in a noisy environment. Successful application of the proposed method to artificial cases and practical hydraulic actuators as well as comparison to several common modeling methods further highlight its superiority in modeling the nonlinear processes with noise.



Dynamic Partial Reconfiguration of Concurrent Control Systems Implemented in FPGA Devices

Aug. 2017

A novel prototyping technique for concurrent control systems implemented in field programmable gate array (FPGA) devices is proposed in the paper. The method allows for dynamic modification of the implemented system. It means that the functionality of a part of the controller can be changed, while the rest of the system is still running. The approach applies to unified modeling language state machine diagrams as a specification of the system. Contrary to other methods, the presented concept requires neither major changes to the design, nor the application of external, specialized tools. The proposed idea has been experimentally verified with the use of Xilinx FPGAs.



Reliable Filter Design for Sensor Networks Using Type-2 Fuzzy Framework

Aug. 2017

This paper studies the problem of reliable filter problem for a category of sensor networks in the framework of interval type-2 fuzzy model. In the filter design, the random link failures, which are caused possibly by missing measurements as well as by probabilistic communication failures, are considered to illustrate more realistic dynamical behaviors of sensor networks. In order to tackle the uncertainties existing in systems, interval type-2 (IT2) fuzzy approach is utilized to establish the model, wherein upper and lower membership functions together with weighting coefficients are employed to express the uncertainties. An distributed IT2 fuzzy filter model is constructed to estimate system states. Using the Lyapunov theory, sufficient conditions have been given to ensure that the filtering error system is mean-square asymptotically stable and satisfies the predefined average $ \mathcal {H}_{\infty }$ performance level. Moreover, the criteria to design the filter parameters are developed through using cone complementary linearization approach. Finally, a practical example is given to validate the proposed method.



Online Spatiotemporal Extreme Learning Machine for Complex Time-Varying Distributed Parameter Systems

Aug. 2017

Many industrial processes are complex nonlinear distributed parameter systems (DPSs) with time-varying spatiotemporal dynamics. However, time-varying spatiotemporal dynamics and the nonlinear relationships between spatial points are currently not given much consideration in the existing data-driven modeling methods. Thus, accurately modeling a nonlinear DPS with time-varying spatiotemporal dynamics using these current methods is challenging. Here, we propose a spatiotemporal extreme learning machine (ELM) to accurately model time-varying and nonlinear DPSs. First, we develop the nonlinear spatial activation function to describe the nonlinear relationships between spatial points. As a result, in contrast to the traditional ELM method which is only used to model the temporal dynamics, the spatiotemporal ELM inherently takes the spatial information into consideration. Next, an online time coefficient model is developed, which accounts for the time-varying temporal dynamics of the DPS. After the integration of the spatial activation function with the time coefficient model, this modeling method is able to adapt to real-time spatiotemporal variation. Unlike the existing data-driven DPS modeling approaches, the proposed method has the capability to accurately represent the nonlinear relationships between spatial points and has the adaptive ability for modeling time-varying dynamics. Finally, through application on practical curing experiments, the proposed method can improve the modeling precision for an unknown, time-varying, and nonlinear DPS due to smaller modeling error as compared to the several commonly used DPS modeling methods.



Reconstruction of Function Block Logic Using Metaheuristic Algorithm

Aug. 2017

An approach for automatic reconstruction of automation logic from execution scenarios using a metaheuristic algorithm is proposed. IEC 61499 basic function blocks are chosen as implementation language and reconstruction of Execution Control Charts for basic function blocks is addressed. The synthesis method is based on a metaheuristic algorithm that combines ideas from ant colony optimization and evolutionary computation. Execution scenarios can be recorded from testing legacy software solutions. At this stage results are only limited to generation of basic function blocks having only Boolean input/output variables.



Behavior Modeling and Auction Architecture of Networked Microgrids for Frequency Support

Aug. 2017

As intermittent generation profiles introduce more stress due to the sudden imbalance in supply and demand, power systems are becoming more dependent on online aggregated support of networked utility-independent private microgrids. During the aggregation process, microgrid operators' preferences and bidding behaviors are priority. New aggregation approaches are required considering the individual behaviors of the networked and geographically dispersed independent microgrids. In this paper, we present a novel bidding behavior modeling and an auction architecture consisting of a central aggregator and networked microgrid agents. The bidding behavioral states of the microgrid agents are formalized as partially observable Markov processes for the belief updates and short-term policy determination in order to maximize the individual profit. A reverse auction model is adopted to enable competitive negotiations between the central aggregator and networked microgrid agents. The auction and aggregation processes were implemented in a power system control area along with an automatic generation control (AGC) scheme to contribute frequency control. The proposed AGC and auction mechanism were verified with an industrial multi-agent framework in a laboratory-based real-time application.



Self-Repairable Smart Grids Via Online Coordination of Smart Transformers

Aug. 2017

The introduction of active devices in Smart Grids, such as smart transformers, powered by intelligent software and networking capabilities, brings paramount opportunities for online automated control and regulation. However, online mitigation of disruptive events, such as cascading failures, is challenging. Local intelligence by itself cannot tackle such complex collective phenomena with domino effects. Collective intelligence coordinating rapid mitigation actions is required. This paper introduces analytical results from which two optimization strategies for self-repairable Smart Grids are derived. These strategies build a coordination mechanism for smart transformers that runs in three healing modes and performs collective decision-making of the phase angles in the lines of a transmission system to improve reliability under disruptive events, i.e., line failures causing cascading failures. Experimental evaluation using self-repairability envelopes in different case networks, ac power flows, and varying number of smart transformers confirms that the higher the number of smart transformers participating in the coordination, the higher the reliability and the capability of a network to self-repair.



Application of DIP to Detect Power Transformers Axial Displacement and Disk Space Variation Using FRA Polar Plot Signature

Aug. 2017

While frequency response analysis (FRA) technique has been successfully used to assess the mechanical integrity of active parts within power transformers, it still exhibits some drawbacks including its inability to detect incipient and minor winding deformations and the requirement for an expert to analyze the results. Although several papers have investigated the impact of various faults on the transformer FRA signature, no attempt was made to automate and improve the fault detection accuracy of the current technique. The main contribution of this paper is the presentation of a new approach for the FRA technique through incorporating the magnitude and phase angle plots that can be measured using any commercial frequency response analyzer into one polar plot. Contrary to the current industry practice that only relies on the magnitude of the measured FRA signature for fault identification and quantification, the proposed polar plot that comprises more features than the magnitude plot will facilitate the use of digital image processing (DIP) techniques to improve the detection accuracy, standardize, and automate the FRA interpretation process. In this regard, three-dimensional models for two three-phase power transformers of different ratings, sizes, and windings structures are modeled using a finite element analysis technique to simulate various levels of axial displacement (AD) and disk space variation (DSV) at different locations of the transformer windings. Impact of minor fault levels on the proposed polar plot signature is investigated through the application of various DIP techniques. Simulation results are validated through practical measurements on a scaled-down transformer. Results show that the proposed polar plot along with the DIP technique is able to detect minor fault levels of AD and DSV with high accuracy.



Design and Cosimulation of Hierarchical Architecture for Demand Response Control and Coordination

Aug. 2017

Demand response (DR) plays a key role for optimum asset utilization and to avoid or delay the need of new infrastructure investment. However, coordinated execution of multiple DRs is desired to maximize the DR benefits. In this study, we propose a hierarchical DR architecture (HDRA) to control and coordinate the performance of various DR categories such that the operation of every DR category is backed-up by time delayed action of the others. A reliable, cost-effective communication infrastructure based on ZigBee, WiMAX, and fibers is designed to facilitate the HDRA execution. The performance of the proposed HDRA is demonstrated from the power system and communication perspectives in a cosimulation environment applied to a 0.4 kV/400 kVA real distribution network considering electric vehicles as a potential DR resource (DRR). The power simulation is performed employing a real time digital simulator whereas the communication simulation is performed using OMNeT++. The HDRA performance demonstrated the maximum utilization of available DR potential by facilitating simultaneous execution of multiple DRs and enabling participation of single DRR for multiple grid applications.



The Mathematical Model and Novel Final Test System for Wafer-Level Packaging

Aug. 2017

To develop integrated circuit (IC) test of wafer-level packaging, the electromechanical model of microprobe testing process and the IC final test system of wafer-level packaging based on microprobe arrays are first proposed. An electromechanical model of the process of microprobe testing is derived, which is based on the analysis of the collected real-time force and electrical data using designed force sensing system, voltage measuring circuit and loading system. It is found that the contact resistance is a quartic function with respect to the loading force, and the loading force has nonlinear hysteretic damping characteristics with respect to the displacement and speed of the microprobe. The real-time contact resistance is approximately an exponential function of the damping force. Finally, the effectiveness of the proposed electromechanical model on wafer-level packaging testing using our designed new microprobe arrays testing system is confirmed. It will provide models and methods for developing IC final test of wafer-level packaging.



A Parameter-Independent Clustering Framework

Aug. 2017

The existing clustering algorithms are usually dependent on one or more input parameters, which are not easy to determine in many cases. In this paper, DSets-histeq is presented as a parameter-independent framework for data clustering. By histogram equalization transformation of pairwise data similarity matrices, the dominant sets algorithm is used to generate parameter-independent initial clusters, which are typically relatively large subsets of real clusters. This enables one to expand the initial clusters to the final ones with a cluster-growing algorithm and determine the involved parameters adaptively by making use of the information in the initial clusters. A simple yet effective method is proposed to utilize the information captured in the initial clusters. Experiments on various datasets and comparison with state-of-the-art clustering algorithms are used to illustrate the potential of the proposed framework.



Rapid Self-Compensation Method of Commutation Phase Error for Low- Inductance BLDC Motor

Aug. 2017

Existing methods of compensation of commutation instants are generally developed based on the assumption that the back electromotive force (EMF) is trapezoidal wave and the three-phase windings are symmetrical. But this is not the fact. The actual back EMF waveform and commutation ripple would make the conventional methods disabled. According to the actual back EMF waveforms, the relationship between commutation point shift and the difference of dc-link current, which is sampled at the points before and after commutation, is analyzed. Based on this relationship, a novel self-compensation method of commutation error is proposed, which can be used both in sensorless motor and in hall sensor-based motor. As the shift phase is obtained through analytical method, fast convergence speed can be achieved. Furthermore, the influences caused by the freewheeling of turn-off phase inductance and the unbalanced three-phase windings are analyzed and removed. At last, the validity of the proposed self-compensation method is verified through experimental results.



Multiperiod Risk-Limiting Dispatch in Power Systems With Renewables Integration

Aug. 2017

In this paper, an improved multiperiod risk-limiting dispatch (IMRLD) is proposed as an operational method in power systems with high percentage renewables integration. The basic risk-limiting dispatch (BRLD) is chosen as an operational paradigm to address the uncertainty of renewables in this paper due to its three good features. In this paper, the BRLD is extended to the IMRLD so that it satisfies the fundamental operational requirements in the power industry. In order to solve the IMRLD problem, the convexity of the IMRLD is verified. A theorem is stated and proved to transform the IMRLD into a piece-wise linear optimization problem that can be efficiently solved. In addition, the locational marginal price of the IMRLD is derived to analyze the effect of renewables integration on the marginal operational cost. Finally, two numerical tests are conducted to validate the IMRLD.



Accurate and Efficient Inspection of Speckle and Scratch Defects on Surfaces of Planar Products

Aug. 2017

We propose a unified framework for detecting defects in planar industrial products or planar surfaces of nonplanar products based on a template-matching strategy. The framework includes three parts: an automatic selection of template image for a given test one, a robust geometric alignment between template and test images based on an approximate maximum clique approach, and an illumination invariant image comparison method for defect detection in the aligned images. Experimental results on challenging image datasets demonstrate the excellent performance of the proposed framework.



Unsupervised Recognition and Characterization of the Reflected Laser Lines for Robotic Gas Metal Arc Welding

Aug. 2017

Unsupervised recognition of the reflected laser lines from the arc-light-modified background is prerequisite for the subsequent measurement and characterization of the weld pool shape, which is of great importance for the modeling and control of robotic arc welding. To facilitate the unsupervised recognition, the reflected laser lines need to be segmented as accurate as possible, which requires the segmented laser lines to be as continuous as possible to decrease the adverse effect of the noise blobs. In this paper, the intensity distribution caused by the arc light in the captured image is modeled. Based on the model, an efficient and robust approach is proposed, and it comprises six parts: reduction of the uneven image background by a difference operation, spline enhancement to remove the fuzziness, a gradient detection filter to eliminate the uneven background further, segmentation by an effective threshold selection method, removal of the noise blobs adaptively, and clustering based on the online computed slope of the laser line. After the laser line is clustered, a second-order polynomial is fitted to it. Finally, the weld pool is characterized by the parameters of the clustered laser line and its fitted polynomial. Experimental results verified that the proposed approach for unsupervised reflected laser line recognition is significantly superior to the state-of-the-art approach in terms of recognition accuracy.



Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data

Aug. 2017

In order to deal with the modeling and monitoring issue of large-scale industrial processes with big data, a distributed and parallel designed principal component analysis approach is proposed. To handle the high-dimensional process variables, the large-scale process is first decomposed into distributed blocks with a priori process knowledge. Afterward, in order to solve the modeling issue with large-scale data chunks in each block, a distributed and parallel data processing strategy is proposed based on the framework of MapReduce and then principal components are further extracted for each distributed block. With all these steps, statistical modeling of large-scale processes with big data can be established. Finally, a systematic fault detection and isolation scheme is designed so that the whole large-scale process can be hierarchically monitored from the plant-wide level, unit block level, and variable level. The effectiveness of the proposed method is evaluated through the Tennessee Eastman benchmark process.



Guest Editorial Special Section on Recent Advances in Network Big Data Analysis

Aug. 2017

The papers in this special section examine recent advancements in the area of network big data analysis.



Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics

Aug. 2017

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.



Data-Centered Runtime Verification of Wireless Medical Cyber-Physical System

Aug. 2017

Wireless medical cyber-physical systems are widely adopted in the daily practices of medicine, where huge amounts of data are sampled by the wireless medical devices and sensors, and is passed to the decision support systems (DSSs). Many text-based guidelines have been encoded for work-flow simulation of DSS to automate health care based on those collected data. But for some complex and life-critical diseases, it is highly desirable to automatically rigorously verify some complex temporal properties encoded in those data, which brings new challenges to current simulation-based DSS with limited support of automatical formal verification and real-time data analysis. In this paper, we conduct the first study on applying runtime verification to cooperate with current DSS based on real-time data. Within the proposed technique, a user-friendly domain specific language, named DRTV, is designed to specify vital real-time data sampled by medical devices and temporal properties originated from clinical guidelines. Some interfaces are developed for data acquisition and communication. Then, for medical practice scenarios described in DRTV model, we will automatically generate event sequences and runtime property verifier automata. If a temporal property violates, real-time warnings will be produced by the formal verifier and passed to medical DSS. We have used DRTV to specify different kinds of medical care scenarios and have applied the proposed technique to assist existing wireless medical cyber-physical system. As presented in experiment results, in terms of warning detection, it outperforms the only use of DSS or human inspection, and improves the quality of clinical health care of hospital.



Fog Computing Based Face Identification and Resolution Scheme in Internet of Things

Aug. 2017

The identification and resolution technology are the prerequisite for realizing identity consistency of physical-cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in identification applications. With the increase of face identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based face identification and resolution scheme. Face identifier is first generated by the identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of face identification and resolution.



Covert Channels in Personal Cloud Storage Services: The Case of Dropbox

Aug. 2017

Personal storage services are one of the most popular applications based on the cloud computing paradigm. Therefore, the analysis of possible privacy and security issues has been a relevant part of the research agenda. However, threats arising from the adoption of information hiding techniques have been mainly neglected. In this perspective, the paper investigates how personal cloud storage services can be used for building covert channels for stealthy exchange of information through the Internet. To have a realistic use case, we consider the Dropbox application and we present the performance evaluation of two different covert communication methods. To understand the stealthiness of our approach and propose countermeasures, we also investigate some behaviors of Dropbox in a production quality deployment.



Localization Based on Social Big Data Analysis in the Vehicular Networks

Aug. 2017

Location-based services, especially for vehicular localization, are an indispensable component of most technologies and applications related to the vehicular networks. However, because of the randomness of the vehicle movement and the complexity of a driving environment, attempts to develop an effective localization solution face certain difficulties. In this paper, an overlapping and hierarchical social clustering model (OHSC) is first designed to classify the vehicles into different social clusters by exploring the social relationship between them. By using the results of the OHSC model, we propose a social-based localization algorithm (SBL) that use location prediction to assist in global localization in the vehicular networks. The experiment results validate the performance of the OHSC model and show that the presented SBL algorithm demonstrates superior localization performance compared with the existing methods.



Hierarchy-Cutting Model Based Association Semantic for Analyzing Domain Topic on the Web

Aug. 2017

Association link network (ALN) can organize massive Web information to provide many intelligent services in our big data society. Effective semantic layered technologies not only can provide theoretical support for knowledge discovery in Web resources, but also can improve the searching efficiency of related information systems such as Web information system and industrial information system. How to realize the layer division of association semantic by the hierarchy analysis of ALN is an important research topic. To solve this problem, this paper proposes a hierarchy-cutting model of association semantic. First, experiments of four types of keywords with different linking roles are conducted to discover the possible distribution law. Experimental results show that these keywords with association role reveal previous power-law distribution. Then, based on the discovered power-law distribution, up-cutting and down-cutting points are presented to divide the association semantic into three layers. At the same time, theories of the hierarchy-cutting model are presented. Finally, examples of current core topic and permanent topics belonging to a domain are given. The experiments show that hierarchy-cutting points have high accuracy. The multilayer theory of association semantic can provide a theoretical support for knowledge recommendation with different particle sizes on ALNs.



Dynamic Adaptive Replacement Policy in Shared Last-Level Cache of DRAM/PCM Hybrid Memory for Big Data Storage

Aug. 2017

The increasing demand on the main memory capacity is one of the main big data challenges. Dynamic random access memory (DRAM) does not represent the best choice for a main memory, due to high power consumption and low density. However, the nonvolatile memory, such as the phase-change memory (PCM), represents an additional choice because of the low power consumption and high-density characteristic. Nevertheless, the high access latency and limited write endurance have disabled the PCM to replace the DRAM currently. Therefore, a hybrid memory, which combines both the DRAM and the PCM, has become a good alternative to the traditional DRAM memory. Both DRAM and PCM disadvantages are challenges for the hybrid memory. In this paper, a dynamic adaptive replacement policy (DARP) in the shared last-level cache for the DRAM/PCM hybrid main memory is proposed. The DARP distinguishes the cache data into the PCM data and the DRAM data, then, the algorithm adopts different replacement policies for each data type. Specifically, for the PCM data, the least recently used (LRU) replacement policy is adopted, and for the DRAM data, the DARP is employed according to the process behavior. Experimental results have shown that the DARP improved the memory access efficiency by 25.4%.



Can Sensors Collect Big Data? An Energy-Efficient Big Data Gathering Algorithm for a WSN

Aug. 2017

Recently, incredible growth in communication technology has given rise to the hot topic, big data. Distributed wireless sensor networks (WSNs) are the key provider of big data and can generate a significant amount of data. Various technical challenges exist in gathering the real-time data. Energy-efficient routing algorithms can overcome these challenges. The signal transmission features have been obtained by analyzing the experiments. According to these experiments, an energy-efficient big data algorithm (big data efficient gathering, BDEG) for a WSN is proposed for real-time data collection. Clustering communication is established on the basis of a received signal strength indicator and residual energy of sensor nodes. Experimental simulations show that BDEG is stable in terms of the network lifetime and the data transmission time because of the load-balancing scheme. The effectiveness of the proposed scheme is verified through numerical results obtained in MATLAB.



Big Data Analytics for System Stability Evaluation Strategy in the Energy Internet

Aug. 2017

With the significant improvements in the Energy Internet, we have witnessed the explosion of multisource energy big data, whose characteristics of vast volume, fast velocity, and diverse variety not only formulate an essential infrastructure of the Energy Internet, but also bring threats to the system's stability. In this paper, we concern with the system-level stability issues in the Energy Internet and study how to maintain a stable and healthy energy network environment. To this end, we propose a system-level stability evaluation model in the Energy Internet based on a critical energy function to explore small disturbance stability region (SDSR), where SDSR can be acquired via estimating the operational data threshold of distributed generations. The threshold is estimated based on energy consumption rather than equilibrium nodes, which applies the energy function theory and reduces the computation complexity. Moreover, in our proposed model, we add the big data approximate analytics algorithm into hyperplane fitting to optimize and analyze the SDSR. Simulation results on SDSR in a single dominant oscillation mode and multiple dominant oscillation mode have demonstrated the advantages and superiority of our proposed method over the prior schemes.



Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data

Aug. 2017

The improved differential evolutionary algorithm (EA) discussed in this paper is used to solve high-dimensional big data. Specifically, the algorithm improves population diversity by expanding the searching scope of the population, prevents premature deaths of the population through wider and more specific searches, and aims to solve the high-dimensional issue. To achieve this improvement goal, the paper suggests a multilayer hierarchical architecture on the basis of the above-mentioned heuristic mechanism. In each layer of the hierarchical architecture in the dynamic subpopulation, individuals who are more suitable for isolated evolution can better coexist with the original main population. We propose a new multiobjective optimization algorithm based on nondominated sorting and bidirectional local search (NSBLS). The algorithm takes the local beam search as the main body. NSBLS outputs the nondominated solution set through a continuous iterative search when the iteration termination condition is satisfied. It is worthy to note that the iteration of NSBLS is similar to the generation of the EA; therefore, this paper uses generation to represent the iterations. An algorithm introduces a new distribution maintaining strategy based on the sampling theory to combine with the fast nondominated sorting algorithm in order to select a new population into the next iteration. NSBLS will compare with three classical algorithms: NSGA-II, MOEA/D-DE, and MODEA through a series of bi-objective test problems. The proposed nondominated sorting and local search is able to find a better spread of solutions and better convergence to the true Pareto-optimal front compared to the other four algorithms. The outstanding performance of the proposed technology was proven in well-known benchmark problems.



Supporting Web Analytics by Aggregating User Interaction Data From Heterogeneous Devices Using Viewport-DOM-Based Heat Maps

Aug. 2017

The players of the digital industry look at network Big Data as an incredible source of revenues, which can allow them to design products, services, and market strategies ever more tailored to users' interests and needs. This is the case of data collected by Web analytics tools, which describe the way users interact with Web contents and where their attention focuses onto during navigation. Given the complexity of information to analyze, existing tools often make use of visualization strategies to represent data aggregated throughout separate sessions and multiple users. In particular, heat maps are often adopted to study the distribution of mouse activity and identify page regions that are more frequently reached during interaction. Unfortunately, since Web contents are accessed via ever more heterogeneous devices, region-based heat maps cannot be exploited anymore to aggregate data concerning user's attention, since the same Web content may move to another page location or exhibit a different aspect depending on the access device used or the user agent setup. This paper presents the design of a visual analytics framework capable to deal with the above limitation by adopting a data collection approach that combines information about regions displayed with information about page structure. This way, the well-known heat map-based visualization can be produced, where interactions can be aggregated on a per-element basis independently of the specific access configuration. Experimental results showed that the framework succeeds in accurately quantifying user's attention and replicating results obtained by manual processing.



Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network

Aug. 2017

This paper proposes an optimized prediction algorithm of radial basis function neural network based on an improved artificial bee colony (ABC) algorithm in the big data environment. The algorithm first uses crossover and mutation operators of the differential evolution algorithm to replace the search strategy of employed bees in the ABC algorithm, then improves the search strategy of onlookers in the ABC algorithm to produce an optimal candidate food source near the population. The algorithm can better balance local and global searching capability. To verify the efficiency of this algorithm in the big data environment, apply it to Lozi and Tent chaotic time series and measured traffic flow time series, and then compare it with K-nearest neighbor model, radial basis function (RBF) neural network model, improved back propagation neural network model, and RBF neural network based on a cloud genetic algorithm model. The experimental results indicate that the accuracy of prediction for Lozi and Tent chaotic time series and the measured traffic flow improves greatly in the big data environment using the proposed algorithm, which proves the effectiveness of the proposed algorithm in predicting traffic flow time series.



Computation Partitioning for Mobile Cloud Computing in a Big Data Environment

Aug. 2017

The growth of mobile cloud computing (MCC) is challenged by the need to adapt to the resources and environment that are available to mobile clients while addressing the dynamic changes in network bandwidth. Big data can be handled via MCC. In this paper, we propose a model of computation partitioning for stateful data in the dynamic environment that will improve the performance. First, we constructed a model of stateful data streaming and investigated the method of computation partitioning in a dynamic environment. We developed a definition of direction and calculation of the segmentation scheme, including single-frame data flow, task scheduling, and executing efficiency. We also defined the problem for a multiframe data flow calculation segmentation decision that is optimized for dynamic conditions and provided an analysis. Second, we proposed a computation partitioning method for single-frame data flow. We determined the data parameters of the application model, the computation partitioning scheme, and the task and work order data stream model. We followed the scheduling method to provide the optimal calculation for data frame execution time after computation partitioning and the best computation partitioning method. Third, we explored a calculation segmentation method for single-frame data flow based on multiframe data using multiframe data optimization adjustment and prediction of future changes in network bandwidth. We were able to demonstrate that the calculation method for multiframe data in a changing network bandwidth environment is more efficient than the calculation method with the limitation of calculations for single-frame data. Finally, our research verified the effectiveness of single-frame data in the application of the data stream and analyzed the performance of the method to optimize the adjustment of multiframe data. We used a MCC platform prototype system for face recognition to verify the effectiveness of the method.



A Hierarchical Data Transmission Framework for Industrial Wireless Sensor and Actuator Networks

Aug. 2017

A smart factory generates vast amounts of data that require transmission via large-scale wireless networks. Thus, the reliability and real-time performance of large-scale wireless networks are essential for industrial production. A distributed data transmission scheme is suitable for large-scale networks, but is incapable of optimizing performance. By contrast, a centralized scheme relies on knowledge of global information and is hindered by scalability issues. To overcome these limitations, a hybrid scheme is needed. We propose a hierarchical data transmission framework that integrates the advantages of these schemes and makes a tradeoff among real-time performance, reliability, and scalability. The top level performs coarse-grained management to improve scalability and reliability by coordinating communication resources among subnetworks. The bottom level performs fine-grained management in each subnetwork, for which we propose an intrasubnetwork centralized scheduling algorithm to schedule periodic and aperiodic flows. We conduct both extensive simulations and realistic testbed experiments. The results indicate that our method has better schedulability and reduces packet loss by up to $22\%$ relative to existing methods.



A Distributed Parallel Cooperative Coevolutionary Multiobjective Evolutionary Algorithm for Large-Scale Optimization

Aug. 2017

A considerable amount of research has been devoted to multiobjective optimization problems. However, few studies have aimed at multiobjective large-scale optimization problems (MOLSOPs). To address MOLSOPs, which may involve big data, this paper proposes a message passing interface MPI -based distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm (DPCCMOEA). DPCCMOEA tackles MOLSOPs based on decomposition. First, based on a modified variable analysis method, we separate decision variables into several groups, each of which is optimized by a subpopulation (species). Then, the individuals in each subpopulation are further separated to several sets. DPCCMOEA is implemented with MPI distributed parallelism and a two-layer parallel structure is constructed. We examine the proposed algorithm using the multiobjective test suites Deb-Thiele-Laumanns-Zitzler and Walking-Fish-Group. In comparison with cooperative coevolutionary generalized differential evolution 3 and multiobjective evolutionary algorithm based on decision variable analyses, which are state-of-the-art cooperative coevolutionary multiobjective evolutionary algorithms, experimental results show that the novel algorithm has better performance in both optimization results and time consumption.



A Manufacturing Big Data Solution for Active Preventive Maintenance

Aug. 2017

Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.



Real-Time Big Data Delivery in Wireless Networks: A Case Study on Video Delivery

Aug. 2017

The huge volume of contents generated from mobile ends has dramatically contributed to big data. Unfortunately, current packet scheduling policies in wireless networks for the underlying big data delivery greatly hinder the utilization of the contents. Specifically, the delivery of real-time big data requires running-time interactions with users, as well as variable bandwidth consumptions to maintain fine user experience. There is no previous work designed for this kind of traffic. To mitigate this gap, a thorough-designed scheduling policy to assign and organize detailed packet transmissions for real-time big data is desired. This paper takes video delivery as a case study, which dominates the current real-time traffic and can be easily extended to other scenarios. We propose a novel scheduling policy, which assigns a proper number of video requests to servers and allocates bandwidth to these requests in a relatively small time scale. It helps with serving more users without compromising user experience of the current ones. We also prove that the scheduling policy has a guaranteed performance on the total number of served requests. Finally, the simulation results demonstrate that our scheduling policy outperforms other state-of-art methods significantly.



Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network

Aug. 2017

The next generation wireless networks are expected to operate in fully automated fashion to meet the burgeoning capacity demand and to serve users with superior quality of experience. Mobile wireless networks can leverage spatio-temporal information about user and network condition to embed the system with end-to-end visibility and intelligence. Big data analytics has emerged as a promising approach to unearth meaningful insights and to build artificially intelligent models with assistance of machine learning tools. Utilizing aforementioned tools and techniques, this paper contributes in two ways. First, we utilize mobile network data (Big Data)-call detail record-to analyze anomalous behavior of mobile wireless network. For anomaly detection purposes, we use unsupervised clustering techniques namely k-means clustering and hierarchical clustering. We compare the detected anomalies with ground truth information to verify their correctness. From the comparative analysis, we observe that when the network experiences abruptly high (unusual) traffic demand at any location and time, it identifies that as anomaly. This helps in identifying regions of interest in the network for special action such as resource allocation, fault avoidance solution, etc. Second, we train a neural-network-based prediction model with anomalous and anomaly-free data to highlight the effect of anomalies in data while training/building intelligent models. In this phase, we transform our anomalous data to anomaly-free and we observe that the error in prediction, while training the model with anomaly-free data has largely decreased as compared to the case when the model was trained with anomalous data.



Mutual Privacy Preserving $k$ -Means Clustering in Social Participatory Sensing

Aug. 2017

In this paper, we consider the problem of mutual privacy protection in social participatory sensing in which individuals contribute their private information to build a (virtual) community. Particularly, we propose a mutual privacy preserving k-means clustering scheme that neither discloses an individual's private information nor leaks the community's characteristic data (clusters). Our scheme contains two privacy-preserving algorithms called at each iteration of the k-means clustering. The first one is employed by each participant to find the nearest cluster while the cluster centers are kept secret to the participants; and the second one computes the cluster centers without leaking any cluster center information to the participants while preventing each participant from figuring out other members in the same cluster. An extensive performance analysis is carried out to show that our approach is effective for k-means clustering, can resist collusion attacks, and can provide mutual privacy protection even when the data analyst colludes with all except one participant.



Public Interest Analysis Based on Implicit Feedback of IPTV Users

Aug. 2017

Modern information systems make it increasingly easy to gain more insight into the public interest, which is becoming more and more important in diverse public and corporate activities and processes. The disadvantage of existing research that focuses on mining the information from social networks and online communities is that it does not uniformly represent all population groups and that the content can be subjected to self-censoring or curation. In this paper, we propose and describe a framework and a method for estimating public interest from the implicit negative feedback collected from the Internet protocol television (IPTV) audience. Our research focuses primarily on the channel change events and their match with the content information obtained from closed captions. The presented framework is based on concept modeling, viewership profiling, and combines the implicit viewer reactions (channel changes) into an interest score. The proposed framework addresses both above-mentioned disadvantages or concerns. It is able to cover a much broader population, and it can detect even minor variations in user behavior. We demonstrate our approach on a large pseudonymized real-world IPTV dataset provided by an ISP, and show how the results correlate with different trending topics and with parallel classical long-term population surveys.



SafeDrive: Online Driving Anomaly Detection From Large-Scale Vehicle Data

Aug. 2017

Identifying driving anomalies is of great significance for improving driving safety. The development of the Internet-of-Vehicle (IoV) technology has made it feasible to acquire big data from multiple vehicle sensors, and such big data play a fundamental role in identifying driving anomalies. Existing approaches are mainly based on either rules or supervised learning. However, such approaches often require labeled data, which are typically not available in big data scenarios. In addition, because driving behaviors differ under vehicle statuses (e.g., speed and gear position), to precisely model driving behaviors needs to fuse multiple sources of sensor data. To address these issues, in this paper, we propose SafeDrive, an online and status-aware approach, which does not require labeled data. From a historical dataset, SafeDrive statistically offline derives a state graph (SG) as a behavior model. Then, SafeDrive splits the online data stream into segments and compares each segment with the SG. SafeDrive identifies a segment that significantly deviates from the SG as an anomaly. We evaluate SafeDrive on a cloud-based IoV platform with over 29 000 real connected vehicles. The evaluation results demonstrate that SafeDrive is capable of identifying a variety of driving anomalies effectively from a large-scale vehicle data stream with an overall accuracy of 93%; such identified driving anomalies can be used to timely alert drivers to correct their driving behaviors.



A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data

Aug. 2017

With the increase of social networking websites and the interaction frequency among users, the prediction of information diffusion is required to support effective generalization and efficient inference in the context of social big data era. However, the existing models either rely on expensive probabilistic modeling of information diffusion based on partially known network structures, or discover the implicit structures of diffusion from users' behaviors without considering the impacts of different diffused contents. To address the issues, in this paper, we propose a novel information-dependent embedding-based diffusion prediction (IEDP) model to map the users in observed diffusion process into a latent embedding space, then the temporal order of users with the timestamps in the cascade can be preserved by the embedding distance of users. Our proposed model further learns the propagation probability of information in the cascade as a function of the relative positions of information-specific user embeddings in the information-dependent subspace. Then, the problem of temporal propagation prediction can be converted into the task of spatial probability learning in the embedding space. Moreover, we present an efficient margin-based optimization algorithm with a fast computation to make the inference of the information diffusion in the latent embedding space. When applying our proposed method to several social network datasets, the experimental results show the effectiveness of our proposed approach for the information diffusion prediction and the efficiency with respect to the inference speed compared with the state-of-the-art methods.



Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks

Aug. 2017

Bogies are an important component of high-speed trains. The level of mechanical performance of bogies has a major influence on the safety and reliability of high-speed train. Therefore, conducting fault diagnoses on bogies with big data is very important. Fault mechanisms of bogies are very complex, and feature signals are nonobvious. For these reasons, fault information of bogies cannot be effectively extracted using the traditional signal processing method. Therefore, this paper adopted the deep neural network to recognize faults in bogies. The deep neural network offers numerous benefits in this context. Using deep neural networks, fault information in a signal spectrum can be extracted in a selfadaptive method. This technique is free of dependence on extensive signal processing knowledge and diagnostic experience. Compared with the traditional intelligent diagnosis method, the deep neural network can obtain a higher diagnostic accuracy. Additionally, the deep neural network does not depend on the sample size, and it can obtain high diagnostic accuracy even when the sample size is relatively small. It also achieves very high diagnostic accuracy applied to high-speed trains with different speeds and different faults, which shows that the method is extensively applicable. Furthermore, the recognition accuracy rate of the deep neural network under normal conditions can reach 100%. This method provides a new paradigm for fault diagnosis of the high-speed train with big data and plays an important role in this field.



IEEE Industrial Electronics Society

Aug. 2017

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Aug. 2017

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