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Relation Model of Burden Operation and State Variables of Blast Furnace Based on Low Frequency Feature Extraction*

IFAC-PapersOnLine 2017年第1期50卷 13796-13801页

作者： Zhang, Kexin Wu, Min An, Jianqi Cao, Weihua Liu, Zhentao Ning, Fulong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Faculty of Engineering China University of Geosciences wuhan430074 China Department of Computer Science School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

In the blast furnace ironmaking process, the running state of the blast furnace can be directly adjusted by the burden operation. Therefore, the explicit relation between the burden operation and the running state variables is vital for the blast furnace operators. However, the state variables of the blast furnace are affected by many factors and the burden matrix is complicated. In this paper, a support vector regression(SVR) predicting model based on low frequency feature extraction is proposed to solve these problems. First, the low frequency components of state variables time series which are mostly affected by the burden operation are extracted. Then, the dimensions of the burden matrix are reduced by a devised simple expression method. Finally, the relation model based on SVR is proposed to predict the values of the expected state variables according to the simplified burden matrix and the value of initial state variables. The simulation on real factory data indicates that the model reflects the accurate quantitative relation between the burden operation and the state variables. © 2017

关键词： Blast furnaces Blast furnace practice Extraction Feature extraction Support vector regression Blast furnace iron making Blast furnace operators burden operation Initial state variables Low frequency components Relation models State variables Support vector regression (SVR)

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Crowdsensing game with demand uncertainties: A deep reinforcement learning approach

arXiv

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arXiv 2018年

作者： Zhan, Yufeng Xia, Yuanqing Zhang, Jiang Li, Ting Wang, Yu School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Lab Department of Computer Science University of North Carolina at Charlotte CharlotteNC28223 United States

Currently, explosive increase of smartphones with powerful built-in sensors such as GPS, accelerometers, gyroscopes and cameras has made the design of crowdsensing applications possible, which create a new interface between human beings and life environment. Until now, various mobile crowdsensing applications have been designed, where the crowdsourcers can employ mobile users (MUs) to complete the required sensing tasks. In this paper, emerging learning-based techniques are leveraged to address crowdsensing game with demand uncertainties and private information protection of MUs. Firstly, a novel economic model for mobile crowdsensing is designed, which takes MUs' resources constraints and demand uncertainties into consideration. Secondly, an incentive mechanism based on Stackelberg game is provided, where the sensing-platform (SP) is the leader and the MUs are the followers. Then, the existence and uniqueness of the Stackelberg Equilibrium (SE) is proven and the procedure for computing the SE is given. Furthermore, a dynamic incentive mechanism (DIM) based on deep reinforcement learning (DRL) approach is investigated without knowing the private information of the MUs. It enables the SP to learn the optimal pricing strategy directly from game experience without any prior knowledge about MUs' information. Finally, numerical simulations are implemented to evaluate the performance and theoretical properties of the proposed mechanism and approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Two-Layer Distributed Formation-Containment control of Multiple Euler-Lagrange Systems by Output Feedback

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IEEE Transactions on Cybernetics 2018年第2期49卷 675-687页

作者： Li, Dongyu Zhang, Wei He, Wei Li, Chuanjiang Ge, Shuzhi Sam Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China Shanghai Institute of Satellite Engineering Shanghai Key Laboratory of Deep Space Exploration Technology Shanghai201109 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore

This paper addresses the distributed formation-containment (DFC) problem for multiple Euler-Lagrange systems with model uncertainties via output feedback in both constant and time-varying formation cases. First, a novel definition of the DFC problem is proposed using a two-layer framework. Since only parts of the followers can acquire the states of the dynamic leader, we design a distributed finite-time sliding-mode estimator to obtain accurate estimations of the desired position and velocity for each agent. Next, to deal with the absence of velocity sensors, we propose two DFC control laws combined with the high-gain observer for the leaders and the followers, respectively, while the time-varying formation in the first layer and the leader-based containment in the second layer can be achieved. Further, the adaptive neural networks are applied to deal with the model uncertainties due to their superior approximation capability. The uniform ultimate boundedness of all the state errors can be guaranteed by Lyapunov stability theory. In addition, a unified framework is given which can be transformed to four other basic distributed problems. Finally, simulation examples are presented to illustrate the feasibility of the theoretical results. © 2013 IEEE.

关键词： Lagrange multipliers

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Maintaining Diversity in an SVM integrated Case Based GA for Solar Flare Prediction

Maintaining Diversity in an SVM integrated Case Based GA for...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Yoshio Taniguchi Yoshihiko Kubota Setsuo Tsuruta Yoshiyuki Mizuno Takayuki Muranushi Yuko Hada Muranushi Yoshitaka Sakurai Rainer Knauf Andrea Kutics Hitachi Industry & Control Solutions Ltd. Yokohama Japan School of System Design and Technology Tokyo Denki University Tokyo Japan Faculty for the Study of Contemporary Society Kyoto Women’s University Kyoto Japan RIKEN Advanced Institute of Computational Science Kobe Japan Kwasan and Hida Observatories Kyoto University Kyoto Japan School of Interdisciplinary Sciences Meiji University Tokyo Japan Dept. of Computer Science and Automation Technische Universität Ilmenau Ilmenau Germany Dept. of Natural Sciences International Christian University Tokyo Japan

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Unusually intense solar flares may cause serious calamities such as damages of electric/nuclear power plants. It is thereupon highly demanded, but is quite difficult, to predict intense solar flares due to the imbalanced character of the available data. To cope with this problem, we have heretofore developed and applied a Case Based Genetic Algorithm (CBGALO) that contains a Local Optimizer, which is a Support Vector Machine (SVM). However, the prediction performance significantly depends on input data for learning. Hereupon, CBGALO is further extended by a Case Based automatically restartable Good combination searching GA for both learning features and input data (CBRsGcmbGA). Even the powerful but computationally expensive Deep Learning cannot automatically (evolutionarily, in our approach) search the learning data. Our approach solved this problem a little better by the case-based approach. However, it became obvious that even this work suffers from the typical GA effect in falling into local optima. To improve the results, we hence developed newly a diversity maintenance approach that inserts good individuals with large Hamming distance into the case base as elite individuals in GA's population. In 2 out of 3 classes of solar flares, the performance of our new approach became as high as the best ones among the conventional world top records. Namely, even in those ≥ C class solar flares, our approach applying the Hamming distance to increase diversity had as high a performance 0.662 as compared with the conventional world top record 0.650.

关键词： Genetic algorithms Support vector machines Evolutionary computation Deep learning

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Construction of Barrier in a Fishing Game with Point Capture

引用

IEEE Transactions on Cybernetics 2017年第6期47卷 1409-1422页

作者： Zha, Wenzhong Chen, Jie Peng, Zhihong Gu, Dongbing School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

This paper addresses a particular pursuit-evasion game, called as 'fishing game' where a faster evader attempts to pass the gap between two pursuers. We are concerned with the conditions under which the evader or pursuers can win the game. This is a game of kind in which an essential aspect, barrier, separates the state space into disjoint parts associated with each player's winning region. We present a method of explicit policy to construct the barrier. This method divides the fishing game into two subgames related to the included angle and the relative distances between the evader and the pursuers, respectively, and then analyzes the possibility of capture or escape for each subgame to ascertain the analytical forms of the barrier. Furthermore, we fuse the games of kind and degree by solving the optimal control strategies in the minimum time for each player when the initial state lies in their winning regions. Along with the optimal strategies, the trajectories of the players are delineated and the upper bounds of their winning times are also derived. © 2013 IEEE.

关键词： Optimal systems

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Distributed Least Squares Solver for Network Linear Equations

arXiv

引用

arXiv 2018年

作者： Yang, Tao George, Jemin Qin, Jiahu Yi, Xinlei Wu, Junfeng State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China U.S. Army Research Laboratory AdelphiMD20783 United States Department of Automation University of Science and Technology of China Hefei230027 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In this paper, we study the problem of finding the least square solutions of over-determined linear algebraic equations over networks in a distributed manner. Each node has access to one of the linear equations and holds a dynamic state. We first propose a distributed least square solver over connected undirected interaction graphs and establish a necessary and sufficient on the step-size under which the algorithm exponentially converges to the least square solution. Next, we develop a distributed least square solver over strongly connected directed graphs and show that the proposed algorithm exponentially converges to the least square solution provided the step-size is sufficiently small. Moreover, we develop a finite-time least square solver by equipping the proposed algorithms with a finite-time decentralized computation mechanism. The theoretical findings are validated and illustrated by numerical simulation examples. Copyright © 2018, The Authors. All rights reserved.

关键词： Linear equations

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Cooperative differential evolution with dynamical population for short-term traffic flow prediction problem

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International Journal of Performability Engineering 2018年第4期14卷 785-794页

作者： Wang, Danping Hu, Kunyuan He, Maowei Chen, Hanning Department of Information Service and Intelligent Control Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100039 China Shenyang University Shenyang110044 China School of Computer Science and Software Tianjin Polytechnic University Tianjin300387 China

Differential Evolution (DE) is a heuristic stochastic search algorithm based on population differences, which has advantages of simple parameters and fast convergence rate. However, it has weak robustness, especially for multimodal problems. Therefore, this paper proposes a Cooperative Differential Evolution with Dynamical population (DynCDE). In the proposed algorithm, the K-means method is employed to partition the whole population. For the high convergence rate of DE/current-to-best/ 1/bin, the neighbor-based mutation strategy is applied and the dynamic population size method based on aging mechanism and lifecycle mechanism is designed to keep the balance between exploration and exploitation. This modified DE has the potential to improve prediction accuracy of neural networks. Finally, this DynCDE-based neural network model is applied to solving the short-term traffic flow prediction problem, which offers very excellent results. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Neural network models

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Scientific-practical results of monitoring anthropogenic influence upon environment of mining territories

Scientific-practical results of monitoring anthropogenic inf...

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2018 Ukrainian School of Mining Engineering, USME 2018

作者： Kachurin, Nikolay Komaschenko, Vitaliy Golik, Vladimir Morkun, Volodymyr Tula State University Department of Geotechnologies and Underground Constructions 92 Lenina Ave. Tula300012 Russia Belgorod State National Research University Trade-union St. Belgorod117485 Russia North Caucasus Mining Metallurgical Institute 85 Pobedy St. Vladikavkaz308015 Russia Kryvyi Rih National University Computer Science Automation and Control Systems Department 11 Vitalii Matusevych St. Kryvyi Rih50027 Ukraine

The objective of this paper is to improve the ecological monitoring system and to take effective technological measures by way of wastes neutralization. The scientific-practical results of monitoring the anthropogenic influence upon the environment of mining-industrial territories were discussed. The global environmental problems of modernity are connected only with the anthropogenic pollution of the atmosphere. Pollution by hazardous substances at various enterprises harms health and disrupts the dynamic balance of natural systems. It is especially typical for mining-industrial region territories. The solution of the problem of environmentally optimal distributing resources for industrial enterprises of the mining-industrial region will allow making maximal profit from each enterprise by assumption of reducing anthropogenic influence upon environment up to the maximum permissible value, which is specified by normative documents. The patterns of formation of mining enterprises influence consequences on the atmosphere have been improved with the use of experimental and theoretical studies and in order to optimize the resources distribution for preventive measures, which can ensure the atmospheric safety of the mining-industrial region. The proposed methodological principles for a comprehensive assessment of the atmosphere of the mining-industrial region allow to implement an integrated approach to forecasting the intensity of atmospheric pollution, the economic efficiency of production and atmospheric air condition control, using the basic requirements of the environmental imperative for the territory under consideration. © 2018 The Authors, published by EDP sciences.

关键词： Accident prevention

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ESAS: An efficient semantic and authorized search scheme over encrypted outsourced data

arXiv

引用

arXiv 2018年

作者： Liu, Xueyan Guan, Zhitao Du, Xiaojiang Zhu, Liehuang Yu, Zhengtao Ma, Yinglong School of Control and Computer Engineering North China Electric Power University Beijing China Department Zhitao Guan1 of Computer and Information Sciences Temple University PhiladelphiaPA United States School of Computer Beijing Institute of Technology Beijing China School of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan China

Nowadays, a large amount of user privacysensitive data is outsourced to the cloud server in ciphertext, which is provided by the data owners and can be accessed by authorized data users. When accessing data, the user should be assigned with the access permission according to his identities or attributes. In addition, the search capabilities in encrypted outsourced data is expected to be enhanced, i.e., the search results can better present user's intentions. To address the above issues, ESAS, an Efficient Semantic and Authorized Search scheme over encrypted outsourced data, is proposed. In ESAS, by integrating PRSCG (the privacy-preserving ranked search based on conceptual graph) and CP-ABE (ciphertext policy attribute-based encryption), semantic search with file-level fine-grained access authorization can be realized. In addition, search authorization can be done in an offline manner, which can improve search efficiency and reduce the response time. The security analysis indicate that the proposed ESAS meets security requirement. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantic Web

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A prediction model of edge gas flow development state based on BP neural network 5

A prediction model of edge gas flow development state based ...

引用

5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017

作者： Zhang, Jialiang An, Jianqi Wu, Min Cao, Weihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Department of Computer Science School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

The development and distribution states of the blast furnace (BF) gas flow determine whether the reaction in the furnace can proceed normally and decide the utilization efficiency of the carbon in the furnace and the stable smooth operation of the furnace. The internal environment of BF is closed with high temperature and high pressure, which causes that the relationship between the development situation of edge gas flow of the BF and the BF operation is uncertain. In this paper, a prediction model of edge gas flow development state based on BP neural network (BPNN) is proposed, clarifying the relationship between the development state of edge gas flow and the edge burden parameters. Firstly, this paper gets the relative operation factors of effecting the development situation of edge gas flow through analyzing the process mechanism of the BF ironmaking, and proposes the development index of edge gas flow and the heat load of cooling stave to measure the development state of the edge gas flow. Secondly, the intrinsic correlation between the data is verified, and the data are analyzed by Spearman's rank correlation coefficient (SRCC) analysis. Finally, according to the analysis results, combined with the burden mechanism, the edge gas flow prediction model is established to realize the prediction of the development state of the edge gas flow. The calculation of the data collected in the metallurgy scene of the BF suggested that this prediction model of the edge gas flow development state of the BF could describe the relationship between BF operation parameters and the developing state of the edge gas flow.

关键词： Flow of gases

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