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3rd International Workshop on intelligent systems, IWIS 2023

作者： Jiang, Ruoyu She, Jinhua 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 Engineering Tokyo University of Technology Tokyo192-0982 Japan

ISBN: (纸本)9798350305043

This paper presents a high-precision control method based on the model-following control (MFC) and equivalent-input-disturbance (EID) approaches. The MFC approach ensures that the output of the plant tracks the output of a desired nominal model. The EID estimates and eliminates an external disturbance. The combination of these two methods effectively achieves high-precision tracking. The validity of this method is demonstrated by a vibration-suppression example through simulations. © 2023 IEEE.

关键词： Equivalent-input-disturbance model-following control vibration suppression

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Bifurcation control and circuit simulation of a fractional-order synthetic gene networks 41

Bifurcation control and circuit simulation of a fractional-o...

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第41届中国控制会议

作者： Feng Liu Feiyue Zhuo Shujiang Sun Zhi-Hong Guan Hua O Wang School of Automation China University of Geosciences Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Artificial Intelligence and Automation Department of Mechanical Engineering Boston University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper presents a ring synthetic gene network model described by fractional differential equations,studies the conditions of Hopf bifurcation in the model,and discusses the limit cycle oscillation phenomenon due to the existence of bifurcation,which causes the system to become unstable,and then We propose a method to control the bifurcation behavior in the system,so that the system can be stable over a large *** addition,the dynamic behavior of the fractional-order model is simulated by triggering a genetic oscillator(a single inhibitory gene) composed of a transistor composed of linear and nonlinear electronic *** circuit simulation results well verify the theoretical analysis conclusion,and the circuit simulation model can well demonstrate the biological characteristics of gene regulatory networks.

关键词： Synthetic gene networks fractional-order bifurcation control circuit simulation

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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Non-fragile Joint State and Fault Estimation for complex Networks with Missing Measurements and Switching Topology 41

Non-fragile Joint State and Fault Estimation for Complex Net...

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第41届中国控制会议

作者： Pingping Gao Jun Hu Chaoqing Jia Zhihui Wu Department of Mathematics Harbin University of Science and Technology School of Automation Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,a non-fragile joint state and fault estimation problem is investigated for time-varying complex networks with switching topology and randomly occurring nonlinearities under the missing *** phenomena of the switching topology,randomly occurring nonlinearities and missing measurements are described by three mutually independent Bernoulli random variables,where the uncertain occurrence probabilities are *** addition,the parameter perturbations of the estimator gain matrix are characterized by a set of zero-mean multiplicative noises.A novel time-varying estimation method is designed and an upper bound of estimation error covariance matrix is obtained by solving two recursive matrix ***,the desired estimator gain matrix is parameterized by minimizing the trace of the obtained upper ***,a sufficient condition is given to ensure the boundedness of the upper bound matrix by using the mathematical induction ***,a simulation study is given to verify the feasibility of the proposed estimation scheme.

关键词： Time-varying complex networks switching topology randomly occurring nonlinearities missing measurements uncertain occurrence probabilities joint estimation

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Temporal Attention Convolution Network for Energy Time Series Forecasting 7

Temporal Attention Convolution Network for Energy Time Serie...

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7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Hu, Min Xu, Haijun Wang, Xiaohua Li, Liang Li, Hongbo Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China MediaTek Hefei230009 China

ISBN: (纸本)9781510656437

Energy time series are characterized by stochasticity, short-term periodicity, and long-term periodicity. Many traditional models such as the autoregressive model, Gaussian process regression are unable to capture the complex time relationships of energy time series. Recurrent neural networks (RNNs), which are widely used for energy time series forecasting, are prone to gradient disappearance or gradient explosion when processing a long sequence data. Thus, RNNs are failed to capture the complete information of the whole long sequence data. To extract complex irregular patterns of energy time series and selectively learn spatio-temporal features, a Temporal Attention Convolution Network (TACN) is proposed in this paper. This structure can extract spatial and temporal features of energy time series, which full filled with our demand. Firstly, to solve the problem that RNNs are prone to information loss when processing the long sequence data, a temporal convolutional network (TCN) is introduced. TCN enables the output to capture the complete information of the whole long sequence data. Then, to reinforce the TCN for extracting effective spatio-temporal features, Squeeze-and-Excitation Network (SENet), a module for computing channel attention, was introduced to this TCN structure. By applying this module, the ability of the acquisition of important channel information has been enhanced. Finally, to further improve the robustness of the model, an autoregressive linear module is constructed to capture the linear relationship among the variables. Experiments on the publicly available datasets Electricity and Solar-Energy show that compare with existing deep learning models, TACN achieves more ideal performance. © 2022 SPIE. All rights reserved.

关键词： Time series

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An Actor-Critic Framework Deep Reinforcement Learning Approach to High-speed Train Timetable Rescheduling Problem 4

An Actor-Critic Framework Deep Reinforcement Learning Approa...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Tian, Zhilong Li, Runmei Xiong, Gang Zhu, Fenghua Beijing Jiaotong University School of Automation and Intelligence Beijing China Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan China Chinese Academy of Sciences State Key Laboratory for Multimodal Artificial Intelligence Systems Beijing China

ISBN: (纸本)9798350349252

High-speed trains are inevitably affected by emergencies in their daily operation, which may cause the trains to fail to run according to the original planned timetable. Therefore, how to adjust the timetable of subsequent trains in emergencies is crucial for railway operations. This paper introduces a deep reinforcement learning algorithm based on an actor-critic network, which minimizes the total delay time by defining the departure order as an action. Numerical experiments are performed on the timetable of Beijing-Shanghai high-speed railway line. © 2024 IEEE.

关键词： Deep reinforcement learning

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ARTIFACT FEATURE PURIFICATION FOR CROSS-DOMAIN DETECTION OF AI-GENERATED IMAGES

arXiv

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arXiv 2024年

作者： Meng, Zheling Peng, Bo Dong, Jing Tan, Tieniu Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China

In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, bring potential security risks to our society. Existing generated image detection methods suffer from performance drop when faced with out-of-domain generators and image scenes. To relieve this problem, we propose Artifact Purification Network (APN) to facilitate the artifact extraction from generated images through the explicit and implicit purification processes. For the explicit one, a suspicious frequency-band proposal method and a spatial feature decomposition method are proposed to extract artifact-related features. For the implicit one, a training strategy based on mutual information estimation is proposed to further purify the artifact-related features. Experiments show that for cross-generator detection, the average accuracy of APN is 5.6% ∼ 16.4% higher than the previous 10 methods on GenImage dataset and 1.7% ∼ 50.1% on DiffusionForensics dataset. For cross-scene detection, APN maintains its high performance. Via visualization analysis, we find that the proposed method extracts flexible forgery patterns and condenses the forgery information diluted in irrelevant features. We also find that the artifact features APN focuses on across generators and scenes are global and diverse. The code will be available on GitHub. Copyright © 2024, The Authors. All rights reserved.

关键词： Purification

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A Multi-view Non-negative Matrix Factorization Single-cell Clustering Algorithm for Missing Data

A Multi-view Non-negative Matrix Factorization Single-cell C...

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2022 International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2022

作者： Yu, Ailian Zhu, Yuan School of Automation China University of Geosciences Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

A significant study focus on the field of bioinformatics has been on how to quickly and accurately accomplish the cell clustering task using computational approaches for single-cell RNA sequencing (scRNA-seq) data. Technical issues with scRNA-seq data include missing values, high noise levels, and a large batch impact. While the single-cell data recovery method based on data filling can enhance data quality, it will subjectively alter the distribution of data. To address the above-mentioned issues, we propose a non-negative matrix factorization clustering algorithm (scMVNMF) based on multi-view data of scRNA-seq. Under the assumption of essentially unchanging original data distribution, it can not only identify the typical low-dimensional representation patterns of multi-view data but also produce final explicable clustering findings. The results of the experiments demonstrate that the suggested algorithm has some advantages over the filling approach in terms of clustering tasks and can maintain a certain level of stability when the quality of single-cell data varies. © 2022 IEEE.

关键词： Non-negative matrix factorization

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Text Semantic Analysis and intelligent Interaction in Parallel Dispatching systems for Technical Station 4

Text Semantic Analysis and Intelligent Interaction in Parall...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Jiao, Yuantao Wang, Jian Li, Runmei Xiong, Gang Chen, Shichao Beijing Jiaotong University School of Automation and Intelligence Beijing China Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China Cloud Computing Center Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Dongguan China Chinese Academy of Sciences State Key Laboratory for Multimodal Artificial Intelligence Systems Beijing China

ISBN: (纸本)9798350349252

Technical station dispatching system plays an important role in cargo operation, but due to the large number of dispatching systems and complex operation, dispatchers rely on manual experience to complete the task, and many scenarios cannot be applied in the real system. Therefore, this paper utilizes the data-driven form to establish a parallel dispatching system for technical stations, semantically analyzes text data rich in dispatcher experience, completes the Computational Experiments on the textual semantic data in the artificial dispatching system, and synchronizes the Parallel Execution of data interaction with the real dispatching system. © 2024 IEEE.

关键词： parallel intelligence railway dispatching system technical station

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Graph Neural Network with Heterogeneous Attributes for Node Classification

Graph Neural Network with Heterogeneous Attributes for Node ...

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Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Bohou Jiang Jiahui Wang Kun Yue School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

Node classification on heterogeneous graphs with multiple node types and relationships has been widely used in commodity classification, friend recommendation, and so on. Most of the existing models need to define the meta paths manually with incomplete attribute information, leading to the low accuracy of classification. To improve the accuracy of node classification task, we propose a novel graph neural network with heterogeneous attributes (GNNHA) by introducing the heterogeneous attributes of nodes and edges without defining meta paths. First, we design a spatial mapping for heterogeneous edges by mapping and fusing the source and target nodes to a hidden layer space. Then, we make a unique linear projection for each node to obtain the embeddings containing the attributes of nodes and edges by aggregating the information of neighbor nodes. Finally, we provide an improved loss function by leveraging the information loss of mapping and node class imbalance. Our model can capture more complete heterogeneous graph attributes and obtain more objective and accurate classification results. Experimental results show that our proposed model outperforms state-of-the-art ones.

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