检索结果-内蒙古大学图书馆

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Learner Model Integrating Cognitive and Metacognitive And Its Application on Scratch Programming Projects

引用

IFAC-PapersOnLine 2020年第5期53卷 644-649页

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao 266044 China Department of Electrical and Computer Engineering Rowan University Glassboro NJ 08028 USA

learner’s cognitive and metacognitive are key personal profile for individualized teaching. To evaluate learner’s comprehensive characteristics, existing learner model were reviewed. Two challenges of constructing an accurate and comprehensive learner model integrating cognitive and metacognitive were summarized. A plan of constructing a comprehensive learner model was made based on analysis of existing massive online learning environment, sensor information technology and educational data-mining. As a case study, a method of how to map learning data onto learners’ cognitive and metacognitive was proposed based on an analysis of a number of pupils’ Scratch projects. Three mapping table were established. Pupil’s cognitive skill could be evaluated from technology shown from Scratch project, namely, data structure, algorithm, computational practices and overall evaluation. Content shown from Scratch project were used to infer pupil’s cognitive style. Meta-cognitive ability can be measured from computational practices and behavior in programming process.

关键词： learner model cognitive state metacognitive ability individualized teaching

来源：评论

学校读者我要写书评

暂无评论

Training and testing object detectors with virtual images

arXiv

引用

arXiv 2017年

作者： Tian, Yonglin Li, Xuan Wang, Kunfeng Wang, Fei-Yue Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors. Copyright © 2017, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

An IoT Edge Computing system Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

引用

IEEE International Conference on Networking, Sensing and control

作者： Shichao Chen Qijie Li Hua Zhang Fenghua Zhu Gang Xiong Ying Tang Macau University of Science and Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Macau China Beijing University of Technology The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Engineering Research Center of Intelligent Systems and Technology Institute of Automation and Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Glassboro NJ USA Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT.

关键词： Energy consumption Processor scheduling Computer architecture Internet of Things Optimization Monitoring Edge computing

来源：评论

学校读者我要写书评

暂无评论

Semi-autonomous greenhouse environment control by combining expert knowledge and machine learning

Semi-autonomous greenhouse environment control by combining ...

引用

Chinese Automation Congress (CAC)

作者： Mengzhen Kang Yuchen Weng Haitong Pang Liangde Li Xing-Rong Fan Jing Hua Fangle Chang Xiujuan Wang Longhua Ma The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Ningbo Institute of Technology Zhejiang University Ningbo China The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao AgriTech Co. Ltd Qingdao Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The concept of "autonomous greenhouse" hints the automatic setting of control target, and giving orders on facilities to reach these targets (set point). Information and communication technologies are widely used, but how to integrate common sense or knowledge in decision-making is still challenging. Semi-autonomous greenhouse control is to set rules with joint advantages of experienced grower and powerful machinery, with the support of knowledge graph and semantic analysis. The contribution of each side is dependent on the intelligence level of computers, the availability of data, etc. In this work, the hybrid system between human and machinery is shown with the temperature control in greenhouse. The target is set mainly by expert knowledge, while the temperature controlled by vent opening is decided by an algorithm. Result shows that the target is better reached compared to that of the experience-based approach. This example shows the feasibility of human-machine hybrid intelligence system for greenhouse environment control. Although this approach is tested for temperature control, it can be extended to control multiple factors simultaneously (temperature, light intensity, humidity, etc.) by driving multiple facilities (heating, lightening, venting, etc.).

关键词： Green products Temperature measurement Monitoring Temperature sensors Vents Agriculture Temperature distribution

来源：评论

学校读者我要写书评

暂无评论

Open-/Closed-loop Active Learning for Data-driven Predictive control

arXiv

引用

arXiv 2024年

作者： Feng, Shilun Shi, Dawei Shi, Yang Zheng, Kaikai The State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing100081 China The Department of Mechanical Engineering Faculty of Engineering University of Victoria VictoriaBCV8N 3P6 Canada

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, an closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

来源：评论

学校读者我要写书评

暂无评论

Enhanced Data Augmentation for Bearing Fault Diagnosis by Using a Spectral Flow Model

Enhanced Data Augmentation for Bearing Fault Diagnosis by Us...

引用

Prognostics and system Health management Conference (PHM)

作者： Xiaoyun Gong Mengxuan Hao Chuan Li Wenliao Du Yixiang Huang Henan International Joint Laboratory of Complex Mechanical Equipment Intelligent Monitoring and Control Zhengzhou University of Light Industry Zhengzhou China Research Center for System Health Maintenance Chongqing Technology and Business University Chongqing China The State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

While existing flow-based models have the potential for data enhancement and intelligent fault diagnosis of bearings, these methods are mainly based on the time domain to learn features. In general, time-domain and frequency-domain features are important for recognizing faults. Especially, the spectral features in the frequency domain can reveal highly distinct patterns associated with fault types. Therefore, learning spectral feature helps to obtain comprehensive fault information and achieve high fault diagnosis accuracy. In this work, a spectral flow model framework is developed to learn and generate realistic data from mechanical sensor signals. The performance of fault diagnosis under unbalanced data is improved by learning the information contained in the spectral features, which are utilized to generate insufficient fault data. Experimental results show that the present method can generate a large number of samples with higher similarity and better diversity on the bearing dataset. The generated samples may be used to expand the limited dataset and significantly improve the accuracy of the bearing fault diagnosis task.

关键词： Fault diagnosis Mechanical sensors Vibrations Training Accuracy Frequency-domain analysis Training data

来源：评论

学校读者我要写书评

暂无评论

GPU based Non-dominated Sorting Genetic Algorithm-II for Multi-objective Traffic Light Signaling Optimization with Agent Based Modeling

GPU based Non-dominated Sorting Genetic Algorithm-II for Mul...

引用

International IEEE Conference on intelligent Transportation systems

作者： Z. Shen K. Wang F. Y. Wang State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Center for Military Computational Experiments and Automation the National University of Defense Technology (NUDT)

ISBN: (纸本)9781479929153

Micro-simulation becomes more and more important in the intelligent Transportation systems (ITS) research, because it can provide detailed descriptions of the system. For a multi-agent systems (MAS) modeling of an ITS, the computation burden is large, as it involves the computation of the state changing of all the agents. And, there are many multi-objective optimization problems in the ITS research. In this paper, we solve the traffic light signaling optimization problem and we take the average delay time and the average stop times as two objectives. We use a famous method of Non-dominated Sorting Genetic Algorithm II (NSGA-II). As NSGA-II can be viewed as an intelligent way of running a number of micro-simulations, usually the computation burden is huge. Graphics Processing Units (GPUs) have been a popular tool for parallel computing. The real transportation system runs in parallel and we think that a parallel tool is more suitable for the simulation and optimization of the system. We test GPU based NSGA-II method on a 4 intersection lattice road network, and on the 18 intersection road network of the Zhongguancun area of Beijing. Compared with the CPU version, the GPU version implementation achieves a speedup factor of 21.46 and 27.64 respectively.

关键词： Traffic lights intelligent transportation systems Graphics Processing Unit GRAPPER PICK UP speedup factor Agents state Change grade intelligent Tutoring systems Genetic Algorithm (GA) road network transport system arithmetic Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Path Planning of Mobile Robot Based on an Improved Genetic Algorithm

Path Planning of Mobile Robot Based on an Improved Genetic A...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Zenghua Chen Gang Xiong Sheng Liu Zhen Shen Yue Li State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Automobile Chang'an University Xi'an China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

In order to solve the problem of premature convergence of the basic genetic algorithm when planning the robot running path, the basic genetic algorithm is improved and optimized. Different population initialization methods are used to initialize multiple populations randomly, so as to improve the diversity of populations; Improve the adaptive strategy and elite strategy of crossover and mutation operators to improve the convergence speed of the algorithm; Add the path tortuosity as the planning index in the fitness function to make the planned path smoother, and add constraints to the model to avoid obstacles; Finally, through the transformation of the coding paradigm of the above improved genetic algorithm, it can run on Flink distributed cluster to obtain faster solution speed, so as to meet the efficiency requirements of path planning in large-scale robot cluster system. The optimized algorithm is compared with the basic genetic algorithm. The simulation results show that the improved algorithm is efficient in robot path planning.

关键词： Sociology Clustering algorithms Path planning Planning Trajectory Statistics Collision avoidance

来源：评论

学校读者我要写书评

暂无评论

Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

引用

Frontiers of Information Technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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：