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Industrial Informatics, IEEE Transactions on

Issue 4 • Date Nov. 2012

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Displaying Results 1 - 25 of 33
  • Table of contents

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  • IEEE Transactions on Industrial Informatics publication information

    Page(s): C2
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  • Guest Editorial Special Section on Soft Computing in Industrial Informatics

    Page(s): 731 - 732
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  • Minimal Resource Allocating Networks for Discrete Time Sliding Mode Control of Robotic Manipulators

    Page(s): 733 - 745
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3657 KB) |  | HTML iconHTML  

    This paper presents a discrete-time sliding mode control based on neural networks designed for robotic manipulators. Radial basis function neural networks are used to learn about uncertainties affecting the system. The online learning algorithm combines the growing criterion and the pruning strategy of the minimal resource allocating network technique with an adaptive extended Kalman filter to update all the parameters of the networks. A method to improve the run-time performance for the real-time implementation of the learning algorithm has been considered. The analysis of the control stability is given and the controller is evaluated on the ERICC robot arm. Experiments show that the proposed controller produces good trajectory tracking performance and it is robust in the presence of model inaccuracies, disturbances and payload perturbations. View full abstract»

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  • Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics Based on Feedforward and Recurrent Neural Networks

    Page(s): 746 - 756
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3463 KB) |  | HTML iconHTML  

    This paper presents new results on a neural network approach to nonlinear model predictive control. At first, a nonlinear system with unmodeled dynamics is decomposed by means of Jacobian linearization to an affine part and a higher-order unknown term. The unknown higher-order term resulted from the decomposition, together with the unmodeled dynamics of the original plant, are modeled by using a feedforward neural network via supervised learning. The optimization problem for nonlinear model predictive control is then formulated as a quadratic programming problem based on successive Jacobian linearization about varying operating points and iteratively solved by using a recurrent neural network called the simplified dual network. Simulation results are included to substantiate the effectiveness and illustrate the performance of the proposed approach. View full abstract»

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  • An Adaptive Speed Sensorless Induction Motor Drive With Artificial Neural Network for Stability Enhancement

    Page(s): 757 - 766
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2352 KB) |  | HTML iconHTML  

    An artificial neural network (ANN) based adaptive estimator is presented in this paper for the estimation of rotor speed in a sensorless vector-controlled induction motor (IM) drive. The model reference adaptive system (MRAS) is formed with instantaneous and steady state reactive power. Selection of reactive power as the functional candidate in MRAS automatically makes the system immune to the variation of stator resistance. Such adaptive system performs satisfactorily at very low speed. However, it is observed that an unstable region exists in the speed-torque domain during regeneration. In this work, ANN is applied to overcome such stability related problem. The proposed method is validated through computer simulation using MATLAB/SIMULINK. Sample results from a laboratory prototype (using dSPACE-1104) have confirmed the usefulness of the proposed estimator. View full abstract»

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  • Fuzzy Adaptive Internal Model Control Schemes for PMSM Speed-Regulation System

    Page(s): 767 - 779
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    In this paper, the speed regulation problem for permanent magnet synchronous motor (PMSM) system under vector control framework is studied. First, a speed regulation scheme based on standard internal model control (IMC) method is designed. For the speed loop, a standard internal model controller is first designed based on a first-order model of PMSM by analyzing the relationship between reference quadrature axis current and speed. For the two current loops, PI algorithms are employed respectively. Second, considering the disadvantages that the standard IMC method is sensitive to control input saturation and may lead to poor speed tracking and load disturbance rejection performances, a modified IMC scheme is developed based on a two-port IMC method, where a feedback control term is added to form a composite control structure. Third, considering the case of large variations of load inertia, two adaptive IMC schemes with two different adaptive laws are proposed. A method based on disturbance observer is adopted to identify the inertia of PMSM and its load. Then a linear adaptive law is developed by analyzing the relationship between the internal model and identified inertia. Considering the control input saturation in practical applications, a fuzzy adaptive law based IMC scheme is developed based on apriori experimental tests and experiences, where a fuzzy inferencer based supervisor is designed to automatically tune the parameter of speed controller according to the identified inertia. The effectiveness of the proposed methods have been verified by Matlab simulation and TMS320F2808 DSP experimental results. View full abstract»

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  • Flame Image-Based Burning State Recognition for Sintering Process of Rotary Kiln Using Heterogeneous Features and Fuzzy Integral

    Page(s): 780 - 790
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    Accurate and robust recognition of burning state for sintering process of rotary kiln plays an important role in the design of image-based intelligent control systems. Existing approaches such as consensus-based methods, temperature-based methods and image segmentation-based methods could not achieve satisfactory performance. This paper presents a flame image-based burning state recognition system using a set of heterogeneous features and fusion techniques. These features, i.e., the color feature, the global and local configuration features, are able to characterize different aspects of flame images, and they can be extracted from pixel values directly without segmentation efforts. In this study, ensemble learner models with four types of base classifiers and five fusion operators are examined with comprehensive comparisons. A total of 482 typical flame images, including 86 over-burning state images, 193 under-burning state images, and 203 normal-burning state images, were used in our experiments. These images were collected from the No. 3 rotary kiln at the Shanxi Aluminum Corporation in China, and labeled by the rotary kiln operational experts. Results demonstrate that our proposed image-based burning state recognition systems outperform other methods in terms of both recognition accuracy and robustness against the disturbance from smoke and dust inside the kiln. View full abstract»

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  • Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems

    Page(s): 791 - 800
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    This paper presents a novel adaptive Gravitational Search Algorithm (GSA) for the optimal tuning of fuzzy controlled servo systems characterized by second-order models with an integral component and variable parameters. The objective functions consist of the output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The proposed adaptive GSA solves the optimization problems resulting in a new generation of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs) with a reduced time constant sensitivity. A design method for T-S PI-FCs is then proposed and experimentally validated in the representative case study of the optimal tuning of T-S PI-FCs for the position control system of a servo system. View full abstract»

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  • Identification and Learning Control of Ocean Surface Ship Using Neural Networks

    Page(s): 801 - 810
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2948 KB) |  | HTML iconHTML  

    This paper presents the problems of accurate identification and learning control of ocean surface ship in uncertain dynamical environments. Thanks to the universal approximation capabilities, radial basis function neural networks (NNs) are employed to approximate the unknown ocean surface ship dynamics. A stable adaptive NN tracking controller is first designed using backstepping and Lyapunov synthesis. Partial persistent excitation (PE) condition of some internal signals in the closed-loop system is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition, the proposed adaptive NN controller is shown to be capable of accurate identification/learning of the uncertain ship dynamics in the stable control process. Subsequently, a novel NN learning control method which effectively utilizes the learned knowledge without re-adapting to the unknown ship dynamics is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed method. View full abstract»

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  • Hybrid Incremental Modeling Based on Least Squares and Fuzzy K -NN for Monitoring Tool Wear in Turning Processes

    Page(s): 811 - 818
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1474 KB) |  | HTML iconHTML  

    There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model. View full abstract»

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  • Design a Wind Speed Prediction Model Using Probabilistic Fuzzy System

    Page(s): 819 - 827
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1617 KB) |  | HTML iconHTML  

    Generation of wind is a very complicated process and influenced by large numbers of unknown factors. A probabilistic fuzzy system based prediction model is designed for the short-term wind speed prediction. By introducing the third probability dimension, the proposed prediction model can capture both stochastic and the deterministic uncertainties, and guarantee a better prediction in complex stochastic environment. The effectiveness of this intelligent wind speed prediction model is demonstrated by the simulations on a group of wind speed data. The robust modeling performance further discloses its potential in the practical prediction of wind speed under complex circumstance. View full abstract»

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  • Evolutionary Pinning Control and Its Application in UAV Coordination

    Page(s): 828 - 838
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2155 KB) |  | HTML iconHTML  

    Maximizing the controllability of complex networks by selecting appropriate nodes and designing suitable control gains is an effective way to control distributed complex networks. In this paper, some novel particle swarm optimization (PSO) approaches are developed to enhance the controllability of distributed networks. The proposed PSO algorithm is combined with a global search scheme and a modified simulated binary crossover (MSBX). In addition, the node importance-based method is introduced to study the controllability of distributed complex networks. A set of experiments show that the PSO with the global search and the MSBX (PSO-GSBX) can outperform some well-known evolutionary algorithms and pinning schemes. Following the PSO-GSBX approach, some interesting findings about pinned nodes, coupling strengths and the eigenvalues for enhancing the controllability of distributed networks are revealed. The obtained results and methods are applied in unmanned aerial vehicle (UAV) coordination to show their effectiveness. These findings will help to understand controllability of complex networks and can be applied in control science and industrial system. View full abstract»

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  • A Fuzzy-Based Sensor Validation Strategy for AC Motor Drives

    Page(s): 839 - 848
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2002 KB) |  | HTML iconHTML  

    Measurements validation is a critical feature in monitoring systems required by most industry applications to achieve higher level reliability. This paper presents the use of the measurement thresholds generated from the propagation of parametric uncertainty using polynomial chaos theory (PCT) to validate the sensor measurements of an AC motor drive by means of fuzzy techniques. If measurements fail the validation check, they are replaced by reconstructed data to maintain the operation. Reconstruction is performed with a PCT observer, which also supports the evaluation of the thresholds. The algorithms proposed here have been implemented and tested both in simulation and in real time experiments on a field oriented controlled induction machine. View full abstract»

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  • Knowledge-Based Global Operation of Mineral Processing Under Uncertainty

    Page(s): 849 - 859
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2055 KB) |  | HTML iconHTML  

    In this paper, a novel knowledge-based global operation approach is proposed to minimize the effect on the production performance caused by unexpected variations in the operation of a mineral processing plant subjected to uncertainties. For this purpose, a feedback compensation and adaptation signal discovered from process operational data is employed to construct a closed-loop dynamic operation strategy. It uses the signal to regulate the outputs of the existing open-loop and steady-state based system so as to compensate the uncertainty in the steady-state operation at the plant-wide level. The utilization mechanism of operational data through constructing increment association rules is firstly described. Then, a rough set based rule extraction approach is developed to generate the compensation rules. This includes two steps, namely the determination of the variables to be compensated based on the significance of attributes in the rough set theory and the extraction of the compensation rules from process data. Based upon the operational data of the mineral processing plant, relevant rules are obtained. Both simulation and industrial experiments are carried out for the proposed global operation, where the effectiveness of the proposed approach has been clearly justified. View full abstract»

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  • A Multiobjective Optimization Based Fuzzy Control for Nonlinear Spatially Distributed Processes With Application to a Catalytic Rod

    Page(s): 860 - 868
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3424 KB) |  | HTML iconHTML  

    This paper considers the problem of multiobjective fuzzy control design for a class of nonlinear spatially distributed processes (SDPs) described by parabolic partial differential equations (PDEs), which arise naturally in the modeling of diffusion-convection-reaction processes in finite spatial domains. Initially, the modal decomposition technique is applied to the SDP to formulate it as an infinite-dimensional singular perturbation model of ordinary differential equations (ODEs). An approximate nonlinear ODE system that captures the slow dynamics of the SDP is thus derived by singular perturbations. Subsequently, the Takagi-Sugeno fuzzy model is employed to represent the finite-dimensional slow system, which is used as the basis for the control design. A linear matrix inequality (LMI) approach is then developed for the design of multiobjective fuzzy controllers such that the closed-loop SDP is exponentially stable, and an L2 performance bound is provided under a prescribed H constraint of disturbance attenuation for the slow system. Furthermore, using the existing LMI optimization technique, a suboptimal fuzzy controller can be obtained in the sense of minimizing the L2 performance bound. Finally, the proposed method is applied to the control of the temperature profile of a catalytic rod. View full abstract»

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  • Enhancement of Speech Recognitions for Control Automation Using an Intelligent Particle Swarm Optimization

    Page(s): 869 - 879
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2268 KB) |  | HTML iconHTML  

    For over two decades, speech control mechanisms have been widely applied in manufacturing systems such as factory automation, warehouse automation, and industrial robotic control for over two decades. To implement speech controls, a commercial speech recognizer is used as the interface between users and the automation system. However, users' commands are often contaminated by environmental noise which degrades the performance of speech recognition for controlling automation systems. This paper presents a multichannel signal enhancement methodology to improve the performance of commercial speech recognizers. The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization. It overcomes the limitation of the existing signal enhancement approaches whereby the parameters inside commercial speech recognizers are required to be tuned, which is impossible in a real-world situation. Also, it overcomes the limitation of the existing optimization algorithm including gradient descent methods, genetic algorithms and classical particle swarm optimization that are unlikely to develop optimal beamformers for maximizing speech recognition accuracy. The performance of the proposed methodology was evaluated by developing beamformers for a commercial speech recognizer, which was implemented on warehouse automation. Results indicate a significant improvement regarding speech recognition accuracy. View full abstract»

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  • Quantum-Inspired Particle Swarm Optimization for Power System Operations Considering Wind Power Uncertainty and Carbon Tax in Australia

    Page(s): 880 - 888
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    In this paper, a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model is developed. The probability of stochastic wind power based on nonlinear wind power curve and Weibull distribution is included in the model. In order to solve the revised dispatch strategy, quantum-inspired particle swarm optimization (QPSO) is also adopted, which shows stronger search ability and quicker convergence speed. The dispatch model is tested on a modified IEEE benchmark system involving six thermal units and two wind farms using the real wind speed data obtained from two meteorological stations in Australia. View full abstract»

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  • Optimal Dispatch of Electric Vehicles and Wind Power Using Enhanced Particle Swarm Optimization

    Page(s): 889 - 899
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    In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system. View full abstract»

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  • Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination

    Page(s): 900 - 912
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1987 KB) |  | HTML iconHTML  

    The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms. View full abstract»

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  • Energy-Efficient Thrust Allocation for Semi-Submersible Oil Rig Platforms Using Improved Harmony Search Algorithm

    Page(s): 913 - 924
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    In this paper, the thrust allocation problem for semi-submersible oil rig platform is formulated as an optimization problem, with an objective to minimize the power consumption. The electrical power consumed by the oil rig platform depends on the thrust generated by the thrusters and the efficiency of the electrical propulsion system. A detailed mathematical model to compute the efficiency of the electrical propulsion system is developed and the numerical results obtained are compared with experimental test results. The formulated energy-efficient thrust allocation problem is solved using Improved Harmony Search (IHS) algorithm. The percentage savings in total power consumption for the oil rig platform as compared to the Mincon method for Genetic Algorithm (GA), Harmony Search (HS), and IHS methods are 48.76%, 51.13%, and 53.90%, respectively. In addition, the total power consumption for energy-efficient thrust allocation approach is lesser as compared to conventional thrust allocation approach for all the considered algorithms. View full abstract»

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  • Optimal Switch Placement by Alliance Algorithm for Improving Microgrids Reliability

    Page(s): 925 - 934
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1306 KB) |  | HTML iconHTML  

    A method for optimal switches placement in distribution systems with distributed generation is presented in this paper. According to both technical and economical issues, the method allows minimizing the unsupplied loads in case of permanent faults, while limiting the number of installed switches. The problem is formulated as a mixed integer non linear programming problem (MINLP) and the solution is obtained by a new metaheuristic algorithm, i.e., the Alliance Algorithm. The method is based on self-microgrids forming and allows improving the continuity of the service as confirmed by simulation results on both an IEEE standard and a real test network. View full abstract»

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  • A New Dimensionality Reduction Algorithm for Hyperspectral Image Using Evolutionary Strategy

    Page(s): 935 - 943
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1700 KB) |  | HTML iconHTML  

    Reducing the redundancy of spectral information is an important technique in classification of hyperspectral image. The existing methods are classified into two categories: feature extraction and band selection. Compared with the feature extraction, the band selection method preserves most of the characteristics of the original data without losing valuable details. However, the choice of the effective band remains challenging, especially when considering the computational burden, which makes many enumerative methods infeasible. Recently, immune clonal strategy (ICS) has been applied to solve complex computation problems. The major advantages of algorithms based on ICS are that they are highly paralleled, distributed, adaptive, and self-organizing. Therefore, in this paper, we convert the band selection problem into an optimization issue and propose a new algorithm, ICS-based effective band selection (ICS-EBS), to select effective band combinations. Then, the selected bands are used in classification of hyperspectral image. We evaluated the proposed algorithm by using two data sets collected from the Washington DC Mall and Northwest Tippecanoe County. ICS-EBS was compared against one latest proposed band selection algorithm, interclass separability index Algorithm (ICSIA). We also compared the results with those achieved by other stochastic algorithms such as genetic algorithm (GA) and ant colony optimization (ACO). The experimental results indicate that our proposed algorithm outperforms ICSIA, GA-EBS, and ACO-EBS for hyperspectral image classification. View full abstract»

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  • Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow

    Page(s): 944 - 952
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    This paper proposes an Energy Management System for the optimal operation of Smart Grids and Microgrids, using Fully Connected Neuron Networks combined with Optimal Power Flow. An adaptive training algorithm based on Genetic Algorithms, Fuzzy Clustering and Neuron-by-Neuron Algorithms is used for generating new clusters and new neural networks. The proposed approach, integrating Demand Side Management and Active Management Schemes, allows significant enhancements in energy saving, customers' active participation in the open market and exploitation of renewable energy resources. The effectiveness of the proposed Energy Management System and adaptive training algorithm is verified on a 23-bus 11 kV microgrid. View full abstract»

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  • Effective Noise Estimation-Based Online Prediction for Byproduct Gas System in Steel Industry

    Page(s): 953 - 963
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2110 KB) |  | HTML iconHTML  

    A rapid and accurate prediction of byproduct gas flow in steel industry can help not only to become aware of the operational situations of gas system, but it also provides the energy scheduling workers with sound decision-making mechanisms. In this study, a least square support vector machine (LS-SVM) model based on online hyperparameters optimization is proposed, where the variance of effective noise of the sample is estimated, while a conjugate gradient algorithm is developed to optimize the width of Gaussian kernels and the regularization factor. To assess the quality of the proposed method, we experiment with a test function affected by additive noise and an industrial gas flow data from Shanghai Baosteel Company Ltd. A series of comparative experiments are reported as well. The results demonstrate that the proposed method shows the shortest computing time while ensuring the prediction accuracy. These two features make the approach applicable to real-time prediction of gas flow in steel industry. View full abstract»

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Aims & Scope

Knowledge in the IST (Information Society Technologies) field envisions a technology bifurcation in the field of intelligent automation systems and real-time middle-ware technologies in the next 5-10 years. The scope of the journal considers the industry’s transition towards more knowledge-based production and systems organization and considers production from a more holistic perspective, encompassing not only hardware and software, but also people and the way in which they learn and share knowledge. The journal focuses on the following main topics: Flexible, collaborative factory automation, Distributed industrial control and computing paradigms, Internet-based monitoring and control systems, Real-time control software for industrial processes, Java and Jini in industrial environments, Control of wireless sensors and actuators, Systems interoperability and human machine interface.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Kim F. Man
City University of Hong Kong