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第36届中国控制会议

作者： Jianqi An Yuwan Yang Min Wu Takao Terano School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Computer Science School of ComputingTokyo Institute of Technology

ISBN: (纸本)9781538629185

In the blast furnace,due to the different changing frequency of different operations,and the different reaction time of gas,liquid and solid materials,there exists multi-timescale characteristics in the iron-making ***,not enough attention has been paid to this characteristic,most of the analyses on the blast furnace state before are under a fixed time ***,this paper makes a analysis of influencing factors on carbon monoxide utilization rate of blast furnace based on multi-timescale ***,the factors that affect the carbon monoxide utilization rate are analyzed from different time ***,in the short time scale,the individual influencing time scale of the permeability index,total pressure difference and top temperature is found by the support vector machine（SVM） *** in the long time scale,the influencing time scale of burdening is *** the validity of each model is verified by the field data of the blast furnace.

关键词： Multi-time scales Carbon monoxide utilization Support vector machine Blast furnace

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A novel channel pruning method for deep neural network compression

arXiv

引用

arXiv 2018年

作者： Hu, Yiming Sun, Siyang Li, Jianquan Wang, Xingang Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing101408 China

In recent years, deep neural networks have achieved great success in the field of computer vision. However, it is still a big challenge to deploy these deep models on resource-constrained embedded devices such as mobile robots, smart phones and so on. Therefore, network compression for such platforms is a reasonable solution to reduce memory consumption and computation complexity. In this paper, a novel channel pruning method based on genetic algorithm is proposed to compress very deep Convolution Neural Networks (CNNs). Firstly, a pre-trained CNN model is pruned layer by layer according to the sensitivity of each layer. After that, the pruned model is fine-tuned based on knowledge distillation framework. These two improvements significantly decrease the model redundancy with less accuracy drop. Channel selection is a combinatorial optimization problem that has exponential solution space. In order to accelerate the selection process, the proposed method formulates it as a search problem, which can be solved efficiently by genetic algorithm. Meanwhile, a two-step approximation fitness function is designed to further improve the efficiency of genetic process. The proposed method has been verified on three benchmark datasets with two popular CNN models: VGGNet and ResNet. On the CIFAR-100 and ImageNet datasets, our approach outperforms several state-of-the-art methods. On the CIFAR-10 and SVHN datasets, the pruned VGGNet achieves better performance than the original model with 8× parameters compression and 3× FLOPs reduction. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Robust Iterative Learning control for Alloy Batch Weighing Process in Refining Furnace 36

Robust Iterative Learning Control for Alloy Batch Weighing P...

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第36届中国控制会议

作者： Yibo Huang Jianqi An Yong He Min Wu Takao Terano School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Computer Science School of ComputingTokyo Institute of Technology

ISBN: (纸本)9781538629185

This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties and the frequency of the vibration feeder. First, a two dimensional（2D） weighing model was established based on the analysis of the dynamic characteristics of alloy weighing process. Second, a control scheme is proposed for the 2D model of alloy weighing ***, a robust iterative learning controller is developed and a stability condition of the 2D system is derived through linear matrix inequality（LMI） obtained by a 2D Lyapunov-Krasovskii function. Finally, the simulation results show that the proposed method can sufficiently improve the control precision of the alloy weighing process.

关键词： Iterative learning control 2D weighing model Batch process Alloy weighing process Robust control

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From Parallel Plants to Smart Plants:Intelligent control and Management for Plant Growth

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IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 161-166页

作者： Mengzhen Kang Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences (SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology IEEE State Key Laboratory of Management and Control for Complex Systems (SKL-MCCS) Institute of AutomationChinese Academy of Sciences (CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description U+0028 the crop model U+0029, prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed. © 2017 Chinese Association of automation.

关键词： Agriculture Artificial intelligence Decision support systems Expert systems Sustainable development

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control design of an electro-pneumatic gearbox actuator

Control design of an electro-pneumatic gearbox actuator

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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Adam Szabo Tamas Becsi Peter Gaspar Szilard Aradi Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary Computer and Automation Research Institute Hungarian Academy of Sciences Budapest Hungary

The paper deals with the model-based control of an electro-pneumatic gearbox actuator. Electro-pneumatic systems have many advantages, such as long lifetime and high operational safety, but their nonlinear behavior makes predicting and controlling them difficult. The objective of the research is to design an optimal control of an electro-pneumatic gearbox actuator. In order to control a nonlinear system with linear control methods a multi-state state-space representation of the system is presented. Based on a preliminary comparison an open-loop, a PID, and a linear quadratic controller were chosen for development. The developed controllers were tested in a Model in The Loop environment, and their performances were compared based on previously defined controlling requirements. Both the open-loop and the PID controller were able to meet the given requirements. The open-loop controller has high parameter sensitivity and relative low performances in case of collision speed and shift time, while the PID controller has the highest number of solenoid valve actuations. The linear quadratic controller has good overall performance in case of neutral to gear changes, however it cannot stabilize the system in neutral position within the required time, which can be a consequence of the given simplifications. With respect to the results of the Model in the Loop tests two suggestions were made regarding the applicable controller.

关键词： Solenoids Pistons Valves Actuators Gears Embedded systems

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Seasonal Scheduling of Office Electricity Use via Adaptive Dynamic Programming

Seasonal Scheduling of Office Electricity Use via Adaptive D...

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第37届中国控制会议

作者： Guang Shi Bo Zhao Chao Li Xin Jin Derong Liu National Computer Network Emergency Response Technical Team/Coordination Center of China The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Automation Guangdong University of Technology

In this paper, an adaptive dynamic programming（ADP） based optimization method is proposed to schedule the electricity use of an office, where a battery is considered as the control variable, while solar and wind energies are included as additional energy supplies besides the grid. The electricity demand of an office generally contains socket, lighting and airconditioning demands. Based on the periodic models of electricity price, electricity demand, and solar and wind energies, the optimal control strategies of the battery are determined by the proposed ADP based optimization method, so that the electricity cost from the grid can be saved. Simulation analysis demonstrates that the proposed method can achieve optimal real-time scheduling of office electricity use in different seasons of a year.

关键词： Smart grid electricity scheduling optimal control adaptive dynamic programming

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The learning of fuzzy cognitive maps with noisy data: A rapid and robust learning method with maximum entropy

arXiv

引用

arXiv 2019年

作者： Feng, Guoliang Lu, Wei Pedrycz, Witold Yang, Jianhua Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116023 China School of Automation Engineering Northeast Electric Power University Jilin132012 China School of Control Science and Engineering Dalian University of Technology Dalian116023 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Fuzzy rules

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Results of a pilot experiment on monitoring the condition of buildings and structures using unmanned aerial vehicles

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IOP Conference Series: Earth and Environmental Science 2021年第1期939卷

作者： M Yu Narkevich O S Logunova P I Kalandarov A N Kalitaev G V Tokmazov P Yu Romanov O Alimov Department of Design of Buildings and Building Structures Magnitogorsk State Technical University named after G.I. Nosov Magnitogorsk Russia Department of Computer Engineering and Programming Magnitogorsk State Technical University named after G.I. Nosov Magnitogorsk Russia Department of Automation and Control of Technological Process and Production Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent Uzbekistan Department of Higher Mathematics Admiral F.F. Ushakov State Maritime University Novorossiysk Russia Department of Applied Mathematics and Computer Science Magnitogorsk State Technical University named after G.I. Nosov Magnitogorsk Russia Jizzakh Polytechnic Institute Jizzakh Uzbekistan

The purpose of the experimental study presented in the paper is to generate new knowledge about the possibility of using unmanned aerial vehicles to survey buildings and structures for subsequent extraction of information about their condition in hard-to-reach or remote places. As objects for the pilot experiment, the following were selected: a residential building in an urban environment that has been destroyed during operation; an object of unfinished construction; a roadway on the carriageway. An unmanned aerial vehicle of the model: DJI Mavic Air was used to survey the objects. The results of the study of objects are recorded in the form of photo and video streams for each object. Comparison of the results of the inspection of objects by experts and unmanned aerial vehicles showed the possibility and necessity of using new means to collect information about the condition of buildings and structures. The primary processing of the obtained images revealed their features: the effect of illumination on the color characteristics of the object of study; the presence of a textural component that complicates the process of separating the defect from the background; the presence of objects in the image that are not related to the elements of surface destruction; the change in the initial size of the object over time. The detected features of the images allow the use of standard algorithms and processing and require the synthesis of unique trajectories of their application.

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Advances in Vision-Based Lane Detection：Algorithms,Integration,Assessment,and Perspectives on ACP-Based Parallel Vision

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IEEE/CAA Journal of Automatica Sinica 2018年第3期5卷 645-661页

作者： Yang Xing Chen Lv Long Chen Huaji Wang Hong Wang Dongpu Cao Efstathios Velenis Fei-Yue Wang Advanced Vehicle Engineering Centre Cranfield University Bedford MK43 0AL U.K Vehicle Intelligence Pioneers Ltd Qingdao 266000 China School of Data and Computer Science Sun Yat-Sen University Guangzhou 510275 China Mechanical and Mechatronics Engineering with the University of Waterloo 200 University Avenue West Waterloo ON N2L 3G1 Canada State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China ieee

Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.

关键词： Advanced driver assistance systems(ADASs) ACP theory benchmark lane detection parallel vision performance evaluation

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Semantic Labeling in Very High Resolution Images via A Self-Cascaded Convolutional Neural Network

arXiv

引用

arXiv 2018年

作者： Liu, Yongcheng Fan, Bin Wang, Lingfeng Bai, Jun Xiang, Shiming Pan, Chunhong National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Research Center for Brain-inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing China

Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade objects and intricate fine-structured objects make it very difficult to obtain both coherent and accurate labeling results. For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet). Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation. Technically, multi-scale contexts are captured on the output of a CNN encoder, and then they are successively aggregated in a self-cascaded manner. Meanwhile, for fine-structured objects, ScasNet boosts the labeling accuracy with a coarse-to-fine refinement strategy. It progressively refines the target objects using the low-level features learned by CNN’s shallow layers. In addition, to correct the latent fitting residual caused by multi-feature fusion inside ScasNet, a dedicated residual correction scheme is proposed. It greatly improves the effectiveness of ScasNet. Extensive experimental results on three public datasets, including two challenging benchmarks, show that ScasNet achieves the state-of-the-art performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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