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An intelligent Optimization Strategy Based on Prediction Model for Carbon Efficiency in Sintering Process

An Intelligent Optimization Strategy Based on Prediction Mod...

引用

第37届中国控制会议

作者： Jie Hu Min Wu Jinhua She School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

In order to improve the utilization of carbon in a sintering process, it is necessary to predict and optimize carbon efficiency. In the paper, first, comprehensive coke ratio（CCR） is taken to be a metric of the carbon efficiency. By analyzing the sintering mechanism, sintering parameters affecting the CCR are determined. Next, the fuzzy C-means clustering algorithm is used to identify different operating conditions. Then, least-squares support vector machine（LS-SVM） sub-models are established for the different operating conditions, and a CCR prediction model is established by incorporating the sub-models using the TS fuzzy intelligent fusion method. Finally, based on the CCR prediction model, a differential evolution algorithm is used to optimize the CCR by adjusting the operating parameters. Simulations using actual run data show that the prediction accuracy of the CCR prediction model is higher than that of a back-propagation neural network model and a single LS-SVM model, and the carbon efficiency optimization strategy reduced the CCR by 1.97 kg/t on average. Thus, the method provides us a guidance for an actual sintering process.

关键词： Sintering process Carbon efficiency Fuzzy C-means clustering Differential evolution algorithm Least square-support vector machine model

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Further Result on the Stability Analysis of Time-Delay System Based on a Novel Inequality

Further Result on the Stability Analysis of Time-Delay Syste...

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Chinese control Conference (CCC)

作者： Yibo Huang Jianqi An Yong He Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

This paper investigates the issue for stability of linear system with time-varying delay. First, by combining the Free-matrix technique and the B-L inequality, a new inequality named Free-matrix Legendre inequality is proposed. Then, based on this inequality, two sets of stability criteria are developed which are less conservative than the existing ones due to the fact that more freedom are introduced. Finally, a numerical example is given to illustrate the advantage of our method.

关键词： Symmetric matrices Linear matrix inequalities Time-varying systems Stability criteria Delays Delay systems

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Analysis and Fractional PD control Bifurcation of a Fractional-Order Genetic Regulatory Networks with Delays

Analysis and Fractional PD Control Bifurcation of a Fraction...

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第37届中国控制会议

作者： Feng Liu Ting Dong Qicheng Mei Shiqi Zheng Guang Ling Longsheng Wei School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Science Wuhan University of Technology

In this paper, a fractional-order gene regulatory network model with two delays is investigated. The existence condition of Hopf bifurcation is deduced by analyzing its characteristic equation, where the total delay is taken as a bifurcation parameter. These bifurcation behaviors may cause the network system heavy oscillation and instability. An fractional PD control method is proposed to control bifurcations in the system. Finally, numerical simulations are provided to verify the theoretical analysis. Simulation results show that the nonlinear behavior of the system can be controlled by this method.

关键词： Hopf bifurcation fractional PD control genetic regulatory networks time delays

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Iterative Learning control for Batch Weighing and Feeding Process

Iterative Learning Control for Batch Weighing and Feeding Pr...

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第37届中国控制会议

作者： Fayang You Jianqi An School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

Due to the fact that material properties may change, weighing and feeding process is difficult to achieve high accuracy with conventional control methods. This paper discusses application of iterative learning control in weighing and feeding process,which takes advantage of previous control information. First, a continual state-space model is established based on dynamic analysis of weighing and feeding process. Next, this model is discretized into a two-dimensional state space model, which takes into account more state information about the process. Then, an iterative learning controller is presented, which improves the tracking performance with the increase of feeding times. A multi-objective simulated annealing is used to find the controller parameters with better control performance under the stability condition. Finally, simulation results are given, which show that a faster feed rate and more accurate feed accuracy is obtained after several feeding batches.

关键词： Weighing and Feeding process Iterative learning control Batch Process Multi-object optimization 2D state space model Linear matrix inequality

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Design of High-Gain-Observer-Based State Feedback controller for Positive Time-Delay System

Design of High-Gain-Observer-Based State Feedback Controller...

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第37届中国控制会议

作者： Mingyuan Xie Jinhua She Zhentao Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

This paper investigates the stabinzability of positive time-deray system,The nonnegative constraint makes the design of a control law different from a general ***,a method to calculate the L gain of a positive time-delay system is ***,the design of a state feedback controller and a high-gain observer is presented by taking full advantage of the characteristics of the positiveness of the ***,the observer and feedback controller are combined to stabilize the system and improve dynamic performance.A numerical example illustrates the provided method is effective.

关键词： Positive system Delay L1 gain Asymptotically stable High-Gain Observer Observer-based feedback controller

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Burden Distribution Decision for Blast Furnace Based on Multiple Condition State

Burden Distribution Decision for Blast Furnace Based on Mult...

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第37届中国控制会议

作者： Huicong Chen Jianqi An School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

The relationship of state parameters and burden distribution is uncertain in blast furnaces. In the present industry,burden operation mainly relies on the experiences of the workers. So it is difficult to control burden operation. To solve these problems, this paper presents a model to adjust the burden distribution using fuzzy C-means（FCM） and wavelet analysis. First,multiple condition states of blast furnaces are analysed through data processing, the state parameters are clustered based on the similarity, and then this paper searches for the corresponding burden parameters from the history data. Finally, for different state clusters, best burden parameters are selected to adjust the conditions. Simulation results show that the burden distribution adjustments based on the state parameters clustering are efficient.

关键词： Multiple Condition Burden Distribution Fuzzy C-Means Blast Furnace

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Model Reduction for Switched Genetic Regulatory Networks with Time-Varying Delays 36

Model Reduction for Switched Genetic Regulatory Networks wit...

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第36届中国控制会议

作者： Mengqi Xue Yang Tang Ligang Wu Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and Technology Research Institute of Intelligent Control and Systems Harbin Institute of Technology

ISBN: (纸本)9781538629185

The model reduction problem is studied in this work for the switched genetic regulatory networks（GRNs） with timevarying delays. The attention is focused on constructing a reduced-order model to approximate the considered high-order GRNs under that the switching signal is subject to some certain constraints, such that the error system between the original system and the reduced-order one is exponentially stable with a weighted H∞ performance. By utilizing the bounding technique as well as the dwell time method, the stability conditions and the weighted H performance are established for the error system. Then, the solvability conditions for the reduced-order models for the GRNs are also established by using the projection method. Finally,numerical simulation is presented to illustrate the effectiveness of the proposed method.

关键词： Switched System Genetic Regulatory Networks Model Reduction Time-Varying Delays

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Empirical Research on a Fuzzy Model of Music Emotion Classification based on Pleasure-Arousal Model

Empirical Research on a Fuzzy Model of Music Emotion Classif...

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第37届中国控制会议

作者： Siqi Huang Li Zhou Zhentao Liu Shan Ni Jingxian He School of Arts and Communication China University of Geosciences School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Affective computing plays a key role in music artificial intelligence, in which a music emotion classification model is indispensable. Both discrete classification model and continuous dimensional model are commonly used for music emotion classification. However, these models are not designed in views of composers, which is insufficient for the perception of music emotion. In this paper, a fuzzy music emotion classification model is proposed by extracting the music expression marks considering the composers emotion. Experiments on subjective evaluation in the feeling of pleasure and arousal according to the change of three selected features（i.e., tempo, register, and dynamic） are conducted by listeners from different subjects. The experimental results show that the correlation between the proposal fuzzy model and the the results from questionnaires reaches 80% on average, which demonstrate the validity of the proposed fuzzy music emotion classification model. The proposal could be applied to music emotion generation conveyed from either composers or players, and to emotion recognition of music as for audiences.

关键词： Music emotion Classification Affective computing Pleasure-Arousal model Fuzzy logic

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To Improve Drilling Efficiency by Multi-objective Optimization of Operational Drilling Parameters in the Complex Geological Drilling Process

To Improve Drilling Efficiency by Multi-objective Optimizati...

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第37届中国控制会议

作者： Chao Gan Weihua Cao Kangzhi Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Electrical and Electronic Engineering Chiba University

Operational drilling parameters optimization is necessary for increase drilling efficiency in complex geological drilling process. There are four key objectives to evaluate the drilling efficiency, including drilling cost, rate of penetration（ROP）, drill bit life, and drill bit’s specific energy. In this paper, we proposed a multi-objective optimization method to improve drilling efficiency in the complex geological drilling process considering all the four key objectives. Firstly, the characteristics of complex geological drilling process and optimization problems are analyzed to find the vital process parameters and problems for drilling efficiency optimization. Then, a multi-objective optimization model which combines the four key objectives is ***, an improved nondominated sorting genetic algorithm（improved NSGA-II） is used to optimize the operational drilling parameters include weight on bit（WOB） and rotational speed（RS） to make drilling efficiency improved. The real case results demonstrate that our method increases the drilling efficiency in four key objectives and saves the simulation time. The proposed method provides the foundation for intelligent optimization control in complex geological drilling process.

关键词： Operational Drilling Parameters Multi-objective Optimization Complex Geological Drilling Process Drilling Efficiency Improved NSGA-II

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Deep Forest-Based Classification of Hyperspectral Images

Deep Forest-Based Classification of Hyperspectral Images

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第37届中国控制会议

作者： Xu Yin Ruilin Wang Xiaobo Liu Yaoming Cai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Computer Science China University of Geosciences

The classification of hyperspectral images(HSIs) is a hot topic in the field of remote sensing technology. In recent years, convolutional neural network(CNN) has achieved great success for HSI classification. However, CNN has to do a great effort in parameters tuning which is time-consuming. Furthermore, a large number of samples are required to train CNN,nevertheless, it is expensive to obtain enough training samples from HSIs. In this paper, we propose a novel classification approach based on deep forest. To reduce the dimension of hyperspectral data, principal component analysis(PCA) is performed during the pre-processing. In contrast to the CNN, our method has fewer hyper-parameters and faster training speed. To the best of our knowledge, this is among the first deep forest-based hyperspectral spectral information classification. Extensive experiments are conducted on two real-world HSI datasets to show the proposed method is significantly superior to the state-ofthe-art methods.

关键词： Deep Forest Hyperspectral Image（HSI） Deep Neural Network（DNN） Principal Component Analysis（PCA）

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