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Chinese Automation Congress (CAC)

作者： Guoyu Zhao Jianqi An Yunpeng Guo Chenglin Yang Yong He School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Automation China University of Geosciences Wuhan China

Blast furnace operating parameters regulate the gas utilization rate (GUR), and different operating parameters affect the GUR on different time scales. However, the existing methods only analyze and model the prediction on a single-time scale, and the accuracy of the model needs to be improved. In this paper, a multi-time-scale prediction method of blast furnace gas utilization rate based on causality is proposed. Firstly, according to the production characteristics of the blast furnace, burden and blast supply are the operation means to regulate the blast furnace on different time scales. Then, this paper presents a blast furnace operation parameter extraction method fusing correlation coefficient and information entropy, and selects two most informative parameters to characterize the operation. On this basis, the causal analysis method is used to prove that the selected operation parameters affect the development trend of GUR. Finally, the long and short time scale prediction models of GUR are established, which are verified by the data of the actual blast furnace production process, improving the prediction accuracy of the GUR efficiently.

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Design of a curled hyper-redundant manipulator and the motion control with tip-following algorithm

Design of a curled hyper-redundant manipulator and the motio...

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第35届中国控制与决策会议

作者： Aoshun Zhang En Li Feng Zhang Rui Guo Mingrui Luo Yuwei Zhang School of Artificial Intelligence University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Engineering Laboratory for Intelligent Industrial Vision CAS State Grid Intelligent Technology Co.Ltd. State Grid Shandong Electric Power Company Jinan Power Supply Company

In order to meet the operational needs in some narrow environments,the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degrees of *** present,the research of hyper-redundant manipulator mainly focuses on the straight push *** the limitation of working space,this paper designed a hyper-redundant manipulator delivered by a curled *** to the characteristics of turntable delivery,a tip-following algorithm based on curve fitting and a tip-following strategy based on terminal posture record are ***,with the algorithm applied to the physical robot we made,we successfully realized the tipfollowing motion control of the hyper-redundant manipulator.

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Causality Analysis and Modeling of Fuel Ratio Influencing Factors in Blast Furnace Ironmaking Process

Causality Analysis and Modeling of Fuel Ratio Influencing Fa...

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Asian control Conference

作者： Binjie Qian Weihua Cao Qifu Chen Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Blast furnace ironmaking is a key process in iron and steel production, and the fuel ratio has an important impact on the smelting efficiency, cost and environmental impact of the blast furnace. This study aims to provide new ideas and method-ological support for the optimized management and control of the blast furnace ironmaking process by exploring the effects of different factors on the fuel ratio. We firstly sorted out the factors affecting the fuel ratio based on the blast furnace process mechanism, and then deeply analyzed the causal relationship of each parameter on the fuel ratio based on the transfer entropy value, and screened out the parameters that did not have a significant effect on the fuel ratio. Taking the remaining key parameters as inputs, the LSTM and GRU models, which can capture time series information, are used to predict the mean and variance of the fuel ratio, respectively, and the two are combined to compute the final fuel ratio, which overcomes the problem of poor model generalization brought by small fluctuations in the training data of fuel ratio. The experimental results show that the convergence speed of the model is significantly improved after parameter screening, and the fuel ratio can be effectively predicted within a certain error range.

关键词： Computational modeling Time series analysis Smelting Process control Training data Predictive models Blast furnaces

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Prediction of gas utilization ratio based on singular spectrum analysis, improved PSO and KELM

Prediction of gas utilization ratio based on singular spectr...

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Chinese control and Decision Conference, CCDC

作者： Zhuang Li Jianqi An Yunpeng Guo Guoyu Zhao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The gas utilization ratio (GUR) in a blast furnace is directly linked to the efficiency, cost-effectiveness and environmental impact on the blast furnace ironmaking process. However, The stability of the published GUR time series prediction models need to be improved. This paper presents an improved particle swarm optimization (PSO) incorporating linearly decreasing inertia weights (LDIW) to optimize the kernel-based extreme learning machine (KELM) for single-step prediction. This paper uses singular spectrum analysis (SSA) to preprocess the data and extract the key components from the GUR time series to solve the problem of high volatility of the GUR time series. In addition, this paper introduces LDIW to improve the optimization ability of particle swarm optimization algorithm, which enhances the stability of a single-step prediction model. Then this paper uses the improved PSO algorithm to extract the optimal parameters of KELM, and establishes a single-step GUR prediction model based on the improved PSO-KELM. Finally, this paper uses the actual production process data of blast furnace to verify the prediction model. The results show that the prediction accuracy of GUR and the overall stability of the model are significantly improved, providing important guidance for the blast furnace ironmaking process.

关键词： Extreme learning machines Time series analysis Production Predictive models Blast furnaces Prediction algorithms Stability analysis

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An Online Education Effect Assessing Method Based on YOLOv8 and Vision Transformer

An Online Education Effect Assessing Method Based on YOLOv8 ...

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Chinese Automation Congress (CAC)

作者： Bo-Yang Zhang Zhen-Tao Liu Xiang-Yu Zeng School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Automation China University of Geosciences Wuhan China

Due to the rapid growth of online education worldwide, assessing the learning effectiveness of students during online classes has become increasingly challenging for teachers. In this paper, a method of assessing online education effect based on YOLOv8 and Vision Transformer is proposed. Firstly, the drowsiness state of students in an online teaching unconstraint environment is estimated by using YOLOv8, and then the gaze direction of awake students is estimated by using Vision Transformer to assess the students' attention levels during online education. The results from YOLOv8 are determined based on the calculated probabilities of drowsiness or wakefulness. And then gaze estimation method proposed in this paper was compared with state-of-the-art methods on the MPIIFaceGaze and Gaze360 datasets in which the angular errors of gaze estimation are 4.58° and 12.27°, respectively. We conducted experiments and analysis on a self-made dataset, from which the results demonstrate the feasibility of our method in an unconstrained environment.

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Nonlinear Underwater Robot System Environmental Disturbance Suppression Based on Feedback Linearization and EID Method

Nonlinear Underwater Robot System Environmental Disturbance ...

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Asian control Conference

作者： Chengyu Li Xin Chen Yang Zhou Wei Tan Wenpeng He School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

A disturbance suppression approach combining feedback linearization and equivalent input disturbance (EID) method is proposed to control nonlinear underwater robots' position. Firstly, the complex nonlinear model of the underwater robot is transformed into a linear model by feedback linearization. Then, to enhance the system's ability to reject unknown disturbances in the marine environment, the EID method is utilized for estimation and compensation. This helps improve the system's ability to handle disruptions. Additionally, a linear quadratic regulator (LQR) is employed to ensure system stability and achieve rapid convergence. Finally, the proposed approach is validated by demonstrating its effectiveness through simulation results.

关键词： Autonomous underwater vehicles Regulators Simulation Position control Mathematical models Stability analysis Feedback linearization

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Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning

arXiv

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arXiv 2024年

作者： Zhu, Yuanyang Wang, Zhi Zhu, Yuanheng Chen, Chunlin Zhao, Dongbin The Department of Control Science and Intelligent Engineering School of Management and Engineering Nanjing University Nanjing210093 China The Research Center for Novel Technology of Intelligent Equipment Nanjing University Nanjing210093 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between the discrete atomic actions, the explosion in the number of discrete actions can possess undesired properties and induce a higher variance for the policy gradient estimator. In this paper, we introduce a straightforward architecture that addresses this issue by constraining the discrete policy to be unimodal using Poisson probability distributions. This unimodal architecture can better leverage the continuity in the underlying continuous action space using explicit unimodal probability distributions. We conduct extensive experiments to show that the discrete policy with the unimodal probability distribution provides significantly faster convergence and higher performance for on-policy reinforcement learning algorithms in challenging control tasks, especially in highly complex tasks such as Humanoid. We provide theoretical analysis on the variance of the policy gradient estimator, which suggests that our attentively designed unimodal discrete policy can retain a lower variance and yield a stable learning process. © 2024, CC BY.

关键词： Contrastive Learning

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Cloud Computing Platform and Big Data Service for Incubator Cluster

Cloud Computing Platform and Big Data Service for Incubator ...

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2016年IEEE/INFORMS服务运筹、物流与信息化国际会议

作者： Gang Xiong Chi Zhang Tongkai Ji Banji Guan Aobing Sun Yuexiang Hu Timo R.Nyberg Dongguan Research Institute of CASIA Cloud Computing CenterChinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Economic and Management Beijing University of Chemical Technology Department of Industrial Engineering and Management School of Sciences Aalto University

Hundreds of incubators get birthed worldwide every day, and many new ICT technologies developed continuously, like cloud computing and big data. Therefore, it's possible and necessary to develop a cloud computing platform that gathering a variety of data and resources information needed by thousands of incubators and millions of incubated enterprises. In this paper, after comparing three different countries incubator's management procedure, we mainly talk about the way to design a cloud-based incubator platform including its architecture, main functions, and the interface. We assume that the platform can gather and share a variety of information and resource, like money, human resource, equipment, office place and so on. So the platform is beneficial for the incubated enterprises, incubators, and the government. For example, the platform can provide individual services through big data technology. The platform can analysis the situation of incubators and warn the companies in dangerous condition with the history big data. Finally, this paper describes the use case of cloud-based incubator platform for Guangdong Province.

关键词： business incubator cluster open innovation cloud computing platform crowdsourcing big data service

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Isochronous Temporal Metric for Neighbourhood Analysis in Classification Tasks

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SN Computer Science 2023年第6期4卷 1-12页

作者： Kumar, Amit Krishan Mai, Nguyễn Ngọc Tian, Ke Xia, Yuanqing Institute of Research and Development Duy Tan University Da Nang Vietnam Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Faculty of Chinese Language School of Foreign Language Duy Tan University Da Nang Vietnam State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Machine learning, classification, and clustering techniques use the distance functions to evaluate the proximity between data entries and deduce the best neighbouring element and the closest matching entry. The best neighbour is not only the closest neighbour but a neighbour that is quick to respond. In view of that, a time-based isochronous metric is introduced to evaluate the best neighbours and form linkages by grouping similar entities. The proposed method uses parametric equations of the fastest descent and solves the time variables for attributes localised in curved space–time. The time metric is compared with commonly used distance metrics for accuracy in classification and clustering using benchmark and commonly used datasets. The nearest-neighbour technique is used for evaluating classification accuracy, and an adjusted random index (ARI) is used to evaluate clustering. The proposed method shows better accuracy and ARI in comparison to distance functions. It also assigns better weights to attributes of the dataset and easily identifies repeated patterns in noisy time series data.

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Efficient Dense Spatial Pyramid Network for Lane Detection

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Yinggang Li Xiaobo Lu School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing 210096 China

Lane detection separating the roads and positioning each lane is a significant task in Autonomous-driving field. Most of existing methods take lane detection as a semantic segmentation problem, and the methods based on encoder-decoder networks achieve robust performances than traditional methods with the disadvantages of large amounts of parameters and high computational cost. To improve efficiency and better adapt to adverse environments, we propose an EDSPnet by compiling the efficient dense module of depthwise dilated separable convolution (EDD module) and dense spatial pyramid (DSP) module. The two proposed modules enable our network to make full use of the contextual information from different feature maps when compared to a chain of layers. Different from symmetrical segmentation networks, we concentrate on the encoder block and a simple decoder is designed to match the input resolution. The decoder is complemented by two methods of upsampling: bilinear interpolation and deconvolution, which guarantee the accuracy and efficiency simultaneously. Our method is evaluated on the TuSimple dataset, and the experimental results show that our network has less parameters and is still efficient even compared with the state-of-the-art semantic segmentation networks.

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