检索结果-内蒙古大学图书馆

6th International Conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Miao, Weiwei Sun, Yong Zeng, Zeng Hong, Tao Xiao, Maoran State Grid Jiangsu Electric Power Co. Ltd. Information & Telecommunication Branch Nanjing China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China

ISBN: (纸本)9798350353174

The built-in elastic autoscaling strategy of Kubernetes, a container cloud orchestration and management system, balances the relationship between application service quality and cluster resource usage by regularly monitoring a single resource and comparing it with a set threshold to determine whether to scale up or down. However, the built-in strategy has the problems of incomplete evaluation of a single resource index, response delay and scaling jitter. To address the above problems, this paper uses an adaptive weighting algorithm to obtain a comprehensive prediction index based on long-range time multi-resource prediction sequences. This index is not only more comprehensive than a single index, but also measures the resource usage in the future time period, so that the adaptive comprehensive prediction index has the ability to more comprehensively measure the changes in application workload in the future time period. The experimental results show that the autoscaling strategy based on adaptive integrated resource forecast can effectively solve the problems of regulation lag and scaling jitter. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Simulation of Radiation Non-Destructive Testing Operations Based on Virtual Reality and the NDE 4.0 Paradigm

Simulation of Radiation Non-Destructive Testing Operations B...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Vladimir Kuvshinnikov Evgeny Kovshov Research Laboratory of Digital Computer Systems and Automation JSC “Research and Development Institute of Construction Technology - Atomstroy” Moscow Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The implementation of digital industrial technologies marks a pivotal step towards maximizing automation in production processes, aligning with the principles of Industry 4.0 and the NDE 4.0 paradigms. One key area of focus is the modeling of radiation non-destructive evaluation operations within a virtual reality framework. This innovative approach allows for the creation and software application of simulators specifically designed for non-destructive testing of various materials and products. In this context, a complex multi-component digital twin of radiographic image is proposed. The digital twin serves as a comprehensive virtual model that mirrors the characteristics and behaviors of its physical counterpart. The images are generated using discrete modeling techniques that utilize both fundamental physical principles and empirical data provided by manufacturers. This dual approach ensures that the resulting radiographic images closely resemble those produced during actual X-ray inspections. One of the spatial factors that affects image sharpness and quality of inspection is also included in the model. By utilizing virtual reality and digital twin technologies, radiation non-destructive testing training becomes more efficient and better aligned with the automation goals of contemporary industrial practices.

关键词： Training Radiography Solid modeling Virtual reality Production Organizations Inspection Digital twins X-ray imaging Testing

来源：评论

学校读者我要写书评

暂无评论

Model Predictive control for Nonlinear Bilateral Teleoperation systems With Time Delay 41

Model Predictive Control for Nonlinear Bilateral Teleoperati...

引用

第41届中国控制会议

作者： Yu Liu Chuanzhi Zang Yuqi Liu Peng Zeng School of Artificial Intelligence Shenyang University of Technology Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Bilateral teleoperation systems have received extensive attention as a substitute for the human to perform tasks in remote and hazardous *** paper proposes a model predictive control strategy for nonlinear bilateral teleoperation systems based on Long-Short Term Memory(LSTM) ***,model predictive control is used to achieve good tracking performance in the presence of input constraints in the *** the same time,considering the influence of different proficiency operators operating the master manipulator on the system performance,the error between the actual master manipulator trajectory and the reference trajectory is introduced into a linear feedback term to compensate for the decrease in system control accuracy caused by the error of ***,the LSTM network is used to predict the trajectory of the master manipulator in the future time equivalent to the network transmission *** predictions are used to control the remote slave manipulator,thereby eliminating the impact of communication delay on the ***,in slave sides,the trajectory creators are applied to generate the desired trajectories to achieve good force feedback *** experiments are carried out to verify the proposed control strategy,which can guarantee good performance with both position tracking and force feedback under time delay.

关键词： Model predictive control bilateral teleoperation long-short term memory network communication delay

来源：评论

学校读者我要写书评

暂无评论

GeneCompass:deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model

引用

细胞研究（英文版） 2024年第12期34卷 830-845页

作者： Xiaodong Yang Guole Liu Guihai Feng Dechao Bu Pengfei Wang Jie Jiang Shubai Chen Qinmeng Yang Hefan Miao Yiyang Zhang Zhenpeng Man Zhongming Liang Zichen Wang Yaning Li Zheng Li Yana Liu Yao Tian Wenhao Liu Cong Li Ao Li Jingxi Dong Zhilong Hu Chen Fang Lina Cui Zixu Deng Haiping Jiang Wentao Cui Jiahao Zhang Zhaohui Yang Handong Li Xingjian He Liqun Zhong Jiaheng Zhou Zijian Wang Qingqing Long Ping Xu The X-Compass Consortiudm Hongmei Wang Zhen Meng Xuezhi Wang Yangang Wang Yong Wang Shihua Zhang Jingtao Guo Yi Zhao Yuanchun Zhou Fei Li Jing Liu Yiqiang Chen Ge Yang Xin Li Baoyang Hu Wei Li Fei Gao Leqian Yu Qi Gu Weiwei Zhai Zhengting Zou Jingqi Yu Wenhui Wu Xinxin Lin Yu Zou Yongshun Ren Fan Li Yixiao Zhao Yike Xin Longfei Han Shuyang Jiang Kai Ma Qicheng Chen Haoyuan Wang Huanhuan Wu Chaofan He Yilong Hu Shuyu Guo Yiyun Li Zaitian Wang Huimin He Shan Zong Jiajia Wang Yan Chen Chunyang Zhang Chengrui Wang Ran Zhang Meng Xiao Yining Wang Xin Qin Jiaxin Qin Chenhao Li Zhufeng Xu Zeyuan Zhang Xiaoning Qi Wuliang Huang Yaoru Luo Qinxuan Luo Ziwen Liu Teng Wang Yiming Huang Shirui Li Kangning Dong Qunlun Shen State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina Institute for Stem Cell and Regenerative Medicine Chinese Academy of SciencesBeijingChina Beijing Institute for Stem Cell and Regenerative Medicine BeijingChina Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Research Center for Ubiquitous Computing Systems Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Computer Network Information Center Chinese Academy of SciencesBeijingChina Institute of Automation Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing TechnologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina Computer Network Information Center Chinese Academy of SciencesBeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina University of Chinese Academy of Sciences BeijingChina CEMS NCMISHCMSMDISRCSDSAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology

Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical ***,the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across *** breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this *** this study,we built an extensive dataset of over 120 million human and mouse single-cell *** data preprocessing,we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model,named *** pre-training,GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised *** fine-tuning for multiple downstream tasks,GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological *** also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal ***,GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.

关键词： knowledge model with cross-species deciphering foundation genecompass informed mechanisms regulatory

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Transformer-Based Framework for Solar Irradiance Forecasting under Incomplete Data Scenarios

引用

IEEE Transactions on Industrial Informatics 2024年第6期20卷 8605-8615页

作者： Zhang, Hanjin Li, Bin Su, Shun-Feng Yang, Wankou Xie, Liping Southeast University School of Economics and Management Nanjing210096 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise forecasting, resulting in additional uncertainties and instability. To address these issues, this study proposes a novel irradiance forecasting model that integrates a Mask-Transformer data imputation module and a prediction module centered around the typical patterns representation mechanism. The Mask-Transformer leverages a mask modeling mechanism to model the context of missing data, facilitating accurate estimation of missing values and reducing noise and uncertainty in the input data. The typical patterns representation mechanism comprises a series decomposition module and a feature fusion module, providing the module with the capability to mitigate nonlinearity and nonstationarity in solar irradiance data. This enhancement leads to improved short-term forecasting performance while maintaining long-term forecasting capabilities. Experimental results on two datasets demonstrate that the proposed model exhibits sufficient robustness and accuracy, making it effective in scenarios with incomplete data. © 2005-2012 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

引用

Journal of Modern Power systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

来源：评论

学校读者我要写书评

暂无评论

Predictive Battery Cooling in Heavy-Duty Fuel Cell Electric Vehicles 10th

Predictive Battery Cooling in Heavy-Duty Fuel Cell Electric ...

引用

10th IFAC Symposium on Advances in Automotive control, AAC 2022

作者： Büyüker, Banu Ç. Ferrara, Alessandro Hametner, Christoph Institute of Mechanics and Mechatronics Division of Process Control and Automation TU Wien Vienna Austria Christian Doppler Laboratory for Innovative Control and Monitoring of Automotive Powertrain Systems TU Wien Vienna Austria

In electric vehicles, it is essential to prevent battery overheating due to excessive ohmic losses or inadequate cooling. Indeed, the temperature of battery systems significantly impacts their performance, lifetime, and safety. This paper proposes a predictive cooling optimization method for the battery thermal management system of heavy-duty fuel cell electric vehicles. The predictive cooling strategy is based on a model predictive control (MPC) formulation to maintain the battery temperature in its optimal range (to increase efficiency) and avoid high-temperature peaks (to increase lifetime and safety). The predictive thermal management relies on the ohmic losses forecast provided by a predictive energy management system. Simulations of a real-world driving cycle validate the proposed MPC and assess the impact of the predictive horizon length, which is critical for thermal management performance. The comparison against a simple hysteresis control strategy highlights the significant benefits of the proposed MPC for higher battery efficiency and lifetime. Copyright © 2022 The Authors.

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

Finite-Time Synchronization of Flux-controlled Complex-Valued Memristive Neural Networks

Finite-Time Synchronization of Flux-Controlled Complex-Value...

引用

2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Wang, Yonghuan Wang, Leimin Wu, Genping 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 School of Automation Wuhan430074 China Wuhan Second Ship Design and Research Institute Wuhan430064 China

ISBN: (纸本)9798350316889

In this paper, by using the flux-controlled memristor model, the finite-time synchronization problem of delayed complex-valued memristive neural networks (MCNNs) is studied. Firstly, according to the proposed memristor model, we model the MCNNs as continuous systems on voltage-flux-time (wp, varpi, t) domain. Then, we design a class of approaches to realize state synchronization between the drive and response systems, and the corresponding synchronization conditions are achieved. Finally, the effectiveness of results is illustrated with the simulations. © 2023 IEEE.

关键词： Memristors

来源：评论

学校读者我要写书评

暂无评论

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

引用

Journal of Bionic Engineering 2022年第1期19卷 224-239页

作者： Sen Tian Jin Zhang Xuanyu Shu Lingyu Chen Xin Niu You Wang School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310058China Science and Technology on Parallel and Distributed Laboratory College of ComputerNational University of Defense TechnologyChangsha410199China Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhou310027China

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and ***,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive ***,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the ***,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed *** analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in ***,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic ***,the network model is quantitatively analyzed by evaluation indexes from the perspective of *** experimental results show that the DBN network,LeNet5 network,and BP network have synchronous *** the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural *** KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic *** with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

关键词： Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos Synchronous

来源：评论

学校读者我要写书评

暂无评论

Multi-scale discriminative regions attention network for fine-grained vehicle classification

Journal of Network Intelligence

引用

Journal of Network Intelligence 2021年第2期6卷 164-177页

作者： Rong, Wen-Zhong Han, Jin Cai, Ying-Hao Liu, Gen College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Fine-grained vehicle classification is a challenging task in computer vision due to the low intra-class variance. Some methods have been developed to improve the accuracy of fine-grained vehicle classification by improving the ability of discriminative features extraction, but there is still room for further improvement in the localization accuracy of vehicle discriminative regions. Based on deep convolutional neural networks, we focus on finding a more efficient structure that pays more attention to the discriminative image regions to enhance the ability of fine-grained vehicle classification. We propose a novel Multi-Scale Discriminative Regions Attention Network (MS-DRAN), which extracts feature maps through ResNet-50 backbone network and generates multi-scale feature maps by a Feature Pyramid Network (FPN). Then, MS-DRAN generates discriminative regions attention maps on the multi-scale feature maps. The attention maps from shallow layer perform pixel-level multiplication with the feature maps from deeper layer. By this way, the network gradually extracts a more discriminating feature maps for classification. We also design a multi-task loss function to associate the classification results for each scale and optimize the network parameter in training. Lastly, we validate the MS-DRAN on Stanford Cars-196 dataset and CompCars dataset and achieves 94.3% and 98.1% in accuracy, respectively. ©2021 ISSN 2414-8105 (Online).

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：