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Asian control Conference

作者： Lingfeng Mu Hengyu Huang Haitao Song Zhejiaqi Ma Chengda Lu Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China CCTEG Xi 'an Research Institute (Group) Co. Ltd. Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This work investigates the active suppression of stick-slip vibration based on the multi-modal analysis and equivalent-input-disturbance (EID) method. By building a lumped parameter model with a high degree of freedom, the multi-modal properties of the vibration are examined. The multi-modal disturbance signals of the drilling string system are analyzed using the variational modal decomposition method. The EID method is used to design a vibration suppression controller. The parameters in the EID method are adjusted based on the primary modal frequencies to obtain the equivalent disturbance at the appropriate frequency. According to the simulation results, the proposed approach improves the effectiveness of active vibration suppression of the drill string, which is of some significance in guiding the drilling operation.

关键词： Vibrations Drilling Accuracy Perturbation methods Modal analysis Simulation Buildings

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Recommendation with the cold-start problem in evolving user-movie network

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Journal of Computers (Taiwan) 2019年第5期30卷 18-30页

作者： Zhang, Shu-Juan Zhang, Juan Jin, Zhen School of Mechatronic Engineering North University of China Shanxi Taiyuan030051 China School of Data Science and Technology North University of China Shanxi Taiyuan030051 China Complex Systems Research Center Shanxi University Shanxi Taiyuan030006 China Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi University Shanxi Taiyuan030006 China

Faced with the problem of information overload on the Internet, recommender systems have been paid more and more attention. At present, many recommendation algorithms have been proposed. However, there are two problems in these recommendation methods which must be solved. Most of them are studied in a single round of recommendation, but lots of complex systems in reality are evolving with time. Besides, the cold-start problem has always been intractable in the recommender system. Therefore, in this paper, we propose two recommendation methods (i.e., Method I and Method II) to solve the movie cold-start problem for the evolving user-movie network which is comprised of users and movies, and then examine the recommendation performance and long-term effects of these two methods in evolving networks, and evaluate influence factors through the numerical simulation. The experiment results show that the network structure, the movie attributes and the length of the recommendation list affect the recommendation accuracy and its novelty. The effects of Method I and Method II are better than those of other recommendation methods in previous works. Moreover, in Method Iwe should lay more emphasis on the relation between nodes, while in Method II we should focus on node attributes. © 2019 Computer Society of the Republic of China. All rights reserved.

关键词： Recommender systems

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DATA SMOOTHING FILLING METHOD BASED ON SCRNA-SEQ DATA ZERO-VALUE IDENTIFICATION

arXiv

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arXiv 2024年

作者： Jiang, Linfeng Zhu, Yuan School of Automation China University of Geosciences Hongshan District No. 388 Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hongshan District No. 388 Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Hongshan District No. 388 Lumo Road Wuhan430074 China

Single-cell RNA sequencing (scRNA-seq) determines RNA expression at single-cell resolution. It provides a powerful tool for studying immunity, regulation, and other life activities of cells. However, due to the limitations of the sequencing technique, the scRNA-seq data are represented with sparsity, which contains missing gene values, i.e., zero values, called dropout. Therefore, it is necessary to impute missing values before analyzing scRNA-seq data. However, existing imputation computation methods often only focus on the identification of technical zeros or imputing all zeros based on cell similarity. This study proposes a new method (SFAG) to reconstruct the gene expression relationship matrix by using graph regularization technology to preserve the high-dimensional manifold information of the data, and to mine the relationship between genes and cells in the data, and then uses a method of averaging the clustering results to fill in the identified technical zeros. Experimental results show that SFAG can help improve downstream analysis and reconstruct cell trajectory. © 2024, CC BY.

关键词： RNA

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Convolutional ordinal regression forest for image ordinal estimation

arXiv

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arXiv 2020年

作者： Zhu, Haiping Shan, Hongming Zhang, Yuheng Che, Lingfu Xu, Xiaoyang Zhang, Junping Shi, Jianbo Wang, Fei-Yue The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-inspired Technology Shanghai201210 China The GRASP Laboratory University of Pennsylvania PhiladelphiaPA United States The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The University of Chinese Academy of Sciences Beijing100049 China

Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers independently, the proposed method aims at learning an ordinal distribution for ordinal regression by optimizing those binary classifiers simultaneously. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e. facial age estimation and image aesthetic assessment, showing significant improvements and better stability over the state-of-the-art ordinal regression methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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Learning Greenhouse Climate control Policy from Monitored Data

Learning Greenhouse Climate Control Policy from Monitored Da...

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Chinese Automation Congress (CAC)

作者： Xiaoxuan Zhao Haoyu Wang Xiujuan Wang Udom Lewlomphaisarl Dong Li Jing Hua Mengzhen Kang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technologies Beijing China National Electronics and Computer Technology Center Pathum Thani Thailand Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences Hangzhou China Qingdao AgriTech Co. Ltd. Qingdao China

The knowledge of solar greenhouse growers on environment control plays an important role in greenhouse production and management. We proposed a way to extract the control strategies from the monitored data of greenhouses by building a long short-term memory (LSTM) model. The result is verified according to the real monitored data of a solar greenhouse, which shows that the model can learn the control strategy of a ventilator in the solar greenhouse. Through monitored data and models, the knowledge of greenhouse ventilation control can be learned, and automatic control can be achieved in a greenhouse with a similar configuration.

关键词： Ventilators Green buildings Buildings Production Ventilation Data models Data mining

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Public service management system of urban placenames for Sanya case

Public service management system of urban placenames for San...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Wei Liu Fenghua Zhu Xisong Dong Gang Xiong Yongqiang Zhu Huazhou Wu Taozhong Liu Yuexiang Hu The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Instituteof Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Dongguan Research Institute of CASIA Chinese Academy of Sciences Dongguan China Xiamen Kingtopinfo Technology Co. Ltd Xiamen China Joint Laboratory of Parallel Management & Control and Business Intelligence Hainan Zhongke Flower Ocean Cloud Commerce Technology Co. Ltd Haikou China

ISBN: (纸本)9781509029280

The rapidly growing amount of social interaction makes geographical names an important and indispensable medium for people in social and economic exchanges. Especially in today's information age, the frequency of usage of name information has increased greatly. In this paper, a method for the construction of a cities' names database is proposed. This will include the processing of the outcome data from the names pilot survey, the consolidation into the database, the development of the cloud service and management system based on the geographical names and zoning database, and the construction of a relatively complete set of the public service system of geographical names based on a website and touch screen interface. The method has been applied in SanYa, China successfully, and very good results have been obtained. The platform can provide not only accurate and convenient services of geographical names, but also promote its economic development and social progress.

关键词： Urban areas Standards Cloud computing Economics Spatial databases Geospatial analysis

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： complex networks

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Blockchain based Consensus Checking in Cloud Storage

Blockchain based Consensus Checking in Cloud Storage

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International Symposium on Autonomous Decentralized systems (ISADAS)

作者： Guanqiu Qi Zhiqin Zhu Matthew Haner Jaesung Sim Jian Sun Yi Chai Yinong Chen Li Yongfu Mathematics and Computer Information Science Mansfield University of Pennsylvania Mansfield PA 16933 USA College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Electronic and Information Engineering Southwest University Chongqing 400715 China Ministry of Education Key Laboratory of Complex System Safety and Control (Chongqing University) Chongqing 400044 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287 USA

ISBN: (数字)9781728116723

ISBN: (纸本)9781728116730

In cloud computing, data is duplicated to prevent data loss. One way to achieve data consistency in such a distributed computing systems is to use a blockchain. Based on practical Byzantine fault tolerance (PBFT), a specific type of blockchain, this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains data consistency, but also has much higher efficiency than other general blockchain algorithms. We provide experimental results that demonstrate the algorithm's data consistency, efficiency, and reliability.

关键词： Cloud computing Fault tolerance Fault tolerant systems Peer-to-peer computing

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Analysis and circuit implementation of a new fourth-order multi-wing chaotic system based on memristors

Analysis and circuit implementation of a new fourth-order mu...

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Chinese Automation Congress (CAC)

作者： Lei Jia Xiaowei Jiang Xianhe Zhang Ming Chi Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Ministry of Education School of Automation China University of Geosciences Wuhan P. R. China School of Electrical Engineering and Automation Hubei Normal University Huangshi P. R. China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan P. R. China

ISBN: (纸本)9781665426480

In this paper, a new fourth-orders memristor chaotic system is obtained by introducing a smooth memristor model as the feedback term based on the modified three-orders chaotic system. The system can generate double-wing, four-wing chaos or hyperchaos for different parameters and initial conditions. Through the analysis of Lyapunov exponent and other dynamics, we can see that the system has complex chaotic, hyperchaotic, and continuous chaotic. Other dynamical behaviors such as multi-stability and extreme multi-stability are found by comparing phase and bifurcation diagrams, and equilibrium point calculations. Finally, the chaotic behavior is successfully verified by circuit simulation.

关键词： Chaos Automation Circuit simulation Buildings Memristors Bifurcation Encryption

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First-principles study of bulk and two-dimensional structures of the AMnBi family of materials (A=K,Rb,Cs)

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Physical Review B 2020年第3期102卷 035444-035444页

作者： Ziming Zhu Chunyan Liao Si Li Xiaoming Zhang Weikang Wu Zhi-Ming Yu Rui Yu Wei Zhang Shengyuan A. Yang Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China Research Laboratory for Quantum Materials Singapore University of Technology and Design Singapore 487372 Singapore School of Materials Science and Engineering Hebei University of Technology Tianjin 300130 China Key Lab of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems and School of Physics Beijing Institute of Technology Beijing 100081 China School of Physics and Technology Wuhan University Wuhan 430072 China Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials College of Physics and Energy Fujian Normal University Fuzhou 350117 China School of Physics and Technology Center for Quantum Transport and Thermal Energy Science Nanjing Normal University Nanjing 210023 China

Magnetic materials with high mobilities are intriguing subjects of research from both fundamental and application perspectives. Based on first-principle calculations, we investigate the physical properties of the already synthesized AMnBi (A=K, Rb, Cs) family materials. We show that these materials are antiferromagnetic (AFM), with Néel temperatures above 300 K. They contain AFM ordered Mn layers, while the interlayer coupling changes from ferromagnetic (FM) for KMnBi to AFM for RbMnBi and CsMnBi. We find that these materials are narrow gap semiconductors. Owing to the small effective mass, the electron carrier mobility can be very high, reaching up to 105cm2/(Vs) for KMnBi. In contrast, the hole mobility is much suppressed, typically lower by two orders of magnitude. We further study their two-dimensional (2D) single layer structures, which are found to be AFM with fairly high mobility ∼103cm2/(Vs). Their Néel temperatures can still reach room temperature. Interesting, we find that the magnetic phase transition is also accompanied by a metal-insulator phase transition, with the paramagnetic metal phase possessing a pair of nonsymmorphic-symmetry-protected 2D spin-orbit Dirac points. Furthermore, the magnetism can be effectively controlled by the applied strain. When the magnetic ordering is turned into FM, the system can become a quantum anomalous Hall insulator with gapless chiral edge states.

关键词： Edge states First-principles calculations Magnetic phase transitions Spin-orbit coupling Topological materials Transport phenomena

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