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SSRN 2022年

作者： Sova, Oleg Shyshatskyi, Andrii Nalapko, Oleksii Marchenko, Halyna Trotsko, Oleksandr Symonenko, Oleksandr Merkotan, Dmytro Lyashenko, Anna Havryliuk, Oksana Velychko, Vira Department of Automated Control Systems Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty Kyiv Ukraine Research Department of Electronic Warfare Development Central Scientific Research Institute of Armament and Military Equipment of the Armed Forces of Ukraine Kyiv Ukraine Research Laboratory of Research Automation Central Scientific Research Institute of Armament and Military Equipment of the Armed Forces of Ukraine Kyiv Ukraine Department of Finance and Banking Poltava University of Economics and Trade Poltava Ukraine Scientific Center Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty Kyiv Ukraine

The object of research is decision-making support systems. Local wars and armed conflicts of recent decades are characterized by high dynamics of operations (combat operations) and a significant amount of diverse information circulating in information systems. These features determine the search for new approaches to increase the efficiency of decision-making support systems, given their reliability. This article solves the problem of developing a method of multicriteria evaluation in conditions of *** the course of the research, the authors used the main provisions of the theory of artificial intelligence, automation theory, theory of complex technical systems and general scientific methods of cognition, namely analysis and synthesis. The proposed methodology was developed taking into account the practical experience of the authors of this work during the military conflicts of the last decade. The method of multicriteria evaluation is universal and can be used to assess the state of the objects of analysis of any architecture. The results of the research will be useful in:– development of new control algorithms in decision-making support systems;– substantiation of recommendations for improving the efficiency of operational management;– analysis of analysis (monitoring) objects in the course of hostilities (operations);– creation of perspective technologies to increase the efficiency of operational management;– assessment of the adequacy, reliability, sensitivity of the scientific and methodological apparatus of operational management in decision-making support systems;– development of new and improvement of existing management *** of further research will be aimed at developing a methodology for intelligent management in special-purpose decision-making support systems. © 2022, The Authors. All rights reserved.

关键词： Efficiency

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Effective Throughput Optimization of SAG Milling Process Based on BPNN and Genetic Algorithm

Effective Throughput Optimization of SAG Milling Process Bas...

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Industrial Cyber-Physical systems (ICPS)

作者： Zhenhong Liao Ce Xu Wen Chen Qifu Chen Feng Wang Jinhua She 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 Department of Mineral Resources Development and Utilization Changsha Research Institute of Mining and Metallurgy CO. LTD. Chagnsha China Hunan Valin Lianyuan Iron And Steel Co. Ltd. Loudi China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

Grinding is an energy-consuming process in mineral processing industry. Improving grinding processing capacity per unit power consumption is an effective means to reduce grinding production cost. In this paper, a new index for evaluating the effective processing throughput of SAG milling is proposed. The production process model is established by BP neural network (BPNN). Through combining the process mechanism and production constraints, the genetic algorithm is adopted to optimize the operating parameters of the SAG milling process to maximize the effective throughput, thus improving the grinding efficiency. The experimental results showed that through optimization of effective throughput function proposed in this paper, the SAG mill processing capacity has been increased by 4% and the operating power drawn reduced by 1.12%. It has important guiding significance for the actual production process.

关键词： Industries Power demand Neural networks Milling Throughput Minerals Materials processing

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Optimization of Robot End-Effector Load Gravity Compensation for Unmanned Live Work in Electric Power Distribution Networks Based on Improved PSO Algorithm

Optimization of Robot End-Effector Load Gravity Compensation...

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Chinese automation Congress (CAC)

作者： Jinge Chen Xin Chen Chenyu Hu Guangxian Huang Shu Sun Xu Jian 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 Distribution Department State Grid Hubei Electric Power Co. Ltd. Xianning Power Supply Company Xianning China Equipment Technology Center State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan China

In the field of electric power distribution network operation, most tasks involve contact operations. A key technology to enable the flexible operation of robots in live distribution network tasks is the installation of six-dimensional force sensors on the robot's end flange. These sensors allow the robot to perceive external environmental contact forces. However, the presence of the end load interferes with the accurate perception of external forces. Aiming at the gravity compensation of robot end load, an optimization algorithm of gravity compensation based on particle swarm optimization is proposed. In this method, the optimal solution model is established with the objective of minimum sum of squared errors, and the particle swarm optimization algorithm is used to estimate the mounting declination angle of the force sensor and improve the precision of gravity compensation. The experiments show that the proposed method can effectively reduce the maximum gravity compensation error and the average gravity compensation error in each direction after optimization, in which the maximum gravity compensation error of the resultant force is reduced by 9.11% and the average gravity compensation error of the resultant force is reduced by 12.34%.

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Avian influenza viruses suppress innate immunity by inducingtrans-transcriptional readthrough via SSU72

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Cellular & Molecular Immunology 2022年第6期19卷 702-714页

作者： Yan Zhao Fengming Huang Zhen Zou Yuhai Bi Yang Yang Cong Zhang Qiang Liu Daozhen Shang Yiwu Yan Xiangwu Ju Song Mei Peng Xie Xiao Li Mingyao Tian Shuguang Tan Huijun Lu Zongsheng Han Kangtai Liu Yuqing Zhang Junbo Liang Zhu Liang Qingchao Zhang Jiahui Chang William JLiu Cong Feng Tanshi Li Michael Q.Zhang Xiaoyue Wang George FGao Yingxia Liu Ningyi Jin Chengyu Jiang State Key Laboratory of Medical Molecular Biology Institute of Basic Medical SciencesChinese Academy of Medical SciencesDepartment of BiochemistryPeking Union Medical CollegeBeijing 100005China Shenzhen Key Laboratory of Pathogen and Immunity State Key Discipline of Infectious DiseaseSecond Hospital Affiliated with Southern University of Science and TechnologyShenzhen Third People’s HospitalShenzhenGuangdong 518112China CAS Key Laboratory of Pathogenic Microbiology and Immunology Institute of MicrobiologyCenter for Influenza Research and Early-warning(CASCIRE)CAS-TWAS Center of Excellence for Emerging Infectious Diseases(CEEID)Chinese Academy of SciencesBeijing 100101China School of Life Science University of Science and Technology of ChinaHefeiAnhui 230027China Department of Automation Tsinghua UniversityBeijing 100084China Genetic Engineering Laboratory Institute of Military VeterinaryAcademy of Military Medical SciencesChangchunJilin 130062China State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy West China HospitalSichuan UniversityChengduSichuan 610000China State Key Laboratory of Medical Molecular Biology Department of Biochemistry and Center for BioinformaticsInstitute of Basic Medical SciencesChinese Academy of Medical SciencesSchool of Basic MedicinePeking Union Medical CollegeBeijing 100005China National Institute for Viral Disease Control and Prevention Chinese Centers for Disease Control and PreventionBeijing 102206China Department of Emergency General Hospital of the PLAHaidian DistrictBeijing 100853China Department of Biological Sciences and Center for Systems Biology The University of Texas at DallasRichardsonTXUSA

Innate immunity plays critical antiviral roles. The highly virulent avian influenza viruses (AIVs) H5N1, H7N9, and H5N6 can betterescape host innate immune responses than the less virulent seasonal H1N1 virus. Here, we report a mechanism by whichtranscriptional readthrough (TRT)-mediated suppression of innate immunity occurs post AIV infection. By using cell lines, mouselungs, and patient PBMCs, we showed that genes on the complementary strand (“trans” genes) influenced by TRT were involved inthe disruption of host antiviral responses during AIV infection. The trans-TRT enhanced viral lethality, and TRT abolishmentincreased cell viability and STAT1/2 expression. The viral NS1 protein directly bound to SSU72, and degradation of SSU72 inducedTRT. SSU72 overexpression reduced TRT and alleviated mouse lung injury. Our results suggest that AIVs infection induce TRT byreducing SSU72 expression, thereby impairing host immune responses, a molecular mechanism acting through the NS1-SSU72-trans-TRT-STAT1/2 axis. Thus, restoration of SSU72 expression might be a potential strategy for preventing AIV pandemics.

关键词： AIV infection TRT SSU72 NS1 Immune escape

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An Adaptive Robust Event-Triggered Variational Bayesian Filtering Method with Heavy-Tailed Noise

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Sensors 2025年第10期25卷 3130-3130页

作者： Deng, Di Yi, Peng Xiong, Junlin Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Automation University of Science and Technology of China Hefei230026 China

Event-triggered state estimation has attracted significant attention due to the advantage of efficiently utilizing communication resources in wireless sensor networks. In this paper, an adaptive robust event-triggered variational Bayesian filtering method is designed for heavy-tailed noise with inaccurate nominal covariance matrices. The one-step state prediction probability density function and the measurement likelihood function are modeled as Student’s t-distributions. By choosing inverse Wishart priors, the system state, the prediction error covariance, and the measurement noise covariance are jointly estimated based on the variational Bayesian inference and the fixed-point iteration. In the proposed filtering algorithm, the system states and the unknown covariances are adaptively updated by taking advantage of the event-triggered probabilistic information and the transmitted measurement data in the cases of non-transmission and transmission, respectively. The tracking simulations show that the proposed filtering method achieves good and robust estimation performance with low communication overhead. © 2025 by the authors.

关键词： Gaussian distribution

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Optical Imaging of Epigenetic Modifications in Cancer:A Systematic Review

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Phenomics 2022年第2期2卷 88-101页

作者： Yang Du Pei Zhang Wei Liu Jie Tian CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China The University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Department of Supportive Care Center and Day Oncology UnitPeking University Cancer Hospital and InstituteBeijing 100142China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China School of Life Science and Technology Xidian UniversityXi’an 710071ShaanxiChina

Increasing evidence has demonstrated that abnormal epigenetic modifications are strongly related to cancer ***,sensitive and specific detection of epigenetic modifications could markedly improve biological investigations and cancer precision medicine.A rapid development of molecular imaging approaches for the diagnosis and prognosis of cancer has been observed during the past few *** biomarkers unique to epigenetic modifications and targeted imaging probes have been characterized and used to discriminate cancer from healthy tissues,as well as evaluate therapeutic *** this study,we summarize the latest studies associated with optical molecular imaging of epigenetic modification targets,such as those involving DNA methylation,histone modification,noncoding RNA regulation,and chromosome remodeling,and further review their clinical application on cancer diagnosis and ***,we further propose the future direc-tions for precision imaging of epigenetic modification in *** by promising clinical and preclinical studies associated with optical molecular imaging technology and epigenetic drugs,the central role of epigenetics in cancer should be increasingly recognized and accepted.

关键词： Epigenetic modification Optical imaging Cancer Imaging probe

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Topologically assisted optimization for rotor design

arXiv

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arXiv 2023年

作者： Wang, Tianyu Yang, Yannian Chen, Xuanwu Li, Pengyu Iollo, Angelo Maceda, Guy Cornejo Y. Noack, Bernd R. School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Institute of Aero Engine Tsinghua University Beijing10084 China Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Institut de Mathématiques de Bordeaux Université de Bordeaux and Memphis Team Centre Inria de l’Université de Bordeaux Talence33400 France

We develop and apply a novel shape optimization exemplified for a two-blade rotor with respect to the figure of merit (FM). This topologically assisted optimization (TAO) contains two steps. First a global evolutionary optimization is performed for the shape parameters and then a topological analysis reveals the local and global extrema of the objective function directly from the data. This non-dimensional objective function compares the achieved thrust with the required torque. Rotor blades have a decisive contribution to the performance of quadcopters. A two-blade rotor with pre-defined chord length distribution is chosen as the baseline model. The simulation is performed in a moving reference frame with a k−ω turbulence model for the hovering condition. The rotor shape is parameterized by the twist angle distribution. The optimization of this distribution employs a genetic algorithm. The local maxima are distilled from the data using a novel topological analysis inspired by discrete scalar-field topology. We identify one global maximum to be located in the interior of the data and five further local maxima related to errors from non-converged simulations. The interior location of the global optimum suggests that small improvements can be gained from further optimization. The local maxima have a small persistence, i.e., disappear under a small Ε perturbation of the figure of merit values. In other words, the data may be approximated by a smooth mono-modal surrogate model. Thus, the topological data analysis provides valuable insights for optimization and surrogate modeling. Copyright © 2023, The Authors. All rights reserved.

关键词： Turbulence models

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AGV Semantic Attack Detection Based on Hidden Markov Model

AGV Semantic Attack Detection Based on Hidden Markov Model

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IEEE International Conference on Industrial Technology (ICIT)

作者： Sichao Zhang Wei Liang Yinlong Zhang Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Department of Automation Technology ABB Corporate Research Sweden Vasteras Sweden

ISBN: (纸本)9781728119496

Automatic Guided Vehicle (AGV) semantic attack is now becoming an important research topic in digital workshops. Unfortunately, the conventional attack detections could trigger the wrong attack detection alarms, because of the unexpected wireless radio frequency interference. This paper investigates a new method for AGV semantic attack detection based on Hidden Markov Model (HMM). Our method is able to discriminate the typical wireless retransmission in presence of environment interference or in the context of AGV semantic attack by comparing the hidden state transition probability with the predefined threshold. Our model has been evaluated on our developed platform. The extensive experimental results and analysis prove the effectiveness of our method.

关键词： Wireless communication Conferences Semantics Hidden Markov models Interference Markov processes Radiofrequency interference

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Correction to: Optimization of the control paper production system of using the extremum seeking control method

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SN Applied Sciences 2023年第3期5卷 1-1页

作者： Ermilina, Olga Chemodanov, Vladimir Department “Automation and control” Penza State Technological University Penza Russian Federation Department “Control systems informatics and power engineering” Moscow Aviation Institute (National Research University) Moscow Russian Federation

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On Nash-Stackelberg-Nash Games under Decision-Dependent Uncertainties: Model and Equilibrium

arXiv

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arXiv 2022年

作者： Zhang, Yunfan Liu, Feng Wang, Zhaojian Chen, Yue Feng, Shuanglei Wu, Qiuwei Hou, Yunhe State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing China MOE Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Renewable Energy Research Center China Electric Power Research Institute Beijing China Center for Electric Power and Energy Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby Denmark Shenzhen Institute of Research and Innovation The University of Hong Kong Hong Kong

In this paper, we discuss a class of two-stage hierarchical games with multiple leaders and followers, which is called Nash-Stackelberg-Nash (N-S-N) games. Particularly, we consider N-S-N games under decision-dependent uncertainties (DDUs). DDUs refer to the uncertainties that are affected by the strategies of decision-makers and have been rarely addressed in game equilibrium analysis. In this paper, we first formulate the N-S-N games with DDUs of complete ignorance, where the interactions between the players and DDUs are characterized by uncertainty sets that depend parametrically on the players’ strategies. Then, a rigorous definition for the equilibrium of the game is established by consolidating generalized Nash equilibrium and Pareto-Nash equilibrium. Afterward, we prove the existence of the equilibrium of N-S-N games under DDUs by applying Kakutani’s fixed-point theorem. Finally, an illustrative example is provided to show the impact of DDUs on the equilibrium of N-S-N games. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

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