检索结果-内蒙古大学图书馆

Generative Adversarial Networks:Introduction and Outlook

IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 588-598页

作者： Kunfeng Wang Chao Gou Yanjie Duan Yilun Lin Xinhu Zheng Fei-Yue Wang IEEE State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Department of Computer Science and Engineering University of Minnesota Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology

Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning *** goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that *** their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application ***, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

关键词： ACP approach adversarial learning generative adversarial networks(GANs) generative models parallel intelligence zero-sum game

来源：评论

学校读者我要写书评

暂无评论

Adaptive Neural Admittance control for Collision Avoidance in Human-Robot Collaborative Tasks

Adaptive Neural Admittance Control for Collision Avoidance i...

引用

2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Xinbo Yu Wei He Chengqian Xue Bin Li Long Cheng Chenguang Yang School of Automation and Electrical Engineering Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Bristol Robotics Laboratory University of the West of England Bristol U.K

This paper proposed an adaptive neural admittance control strategy for collision avoidance in human-robot collaborative tasks. In order to ensure that the robot end-effector can avoid collisions with surroundings, robot should be operated compliantly by human within a constrained task space. An impedance model and a soft saturation function are employed to generate a differentiable reference trajectory. Then, adaptive neural network control with position constraint, based on integral barrier Lyapunov function (IBLF), is designed to achieve precise tracking while guaranteeing constrained satisfaction. Utilizing Lyapunov stability principles, we prove that semi-globally uniformly bounded stability is guaranteed for all states of the closed-loop system. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experimental platform. Collisions with surroundings can be avoided in human-robot collaborative tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Broad learning system based on maximum correntropy criterion

arXiv

引用

arXiv 2019年

作者： Zheng, Yunfei Chen, Badong Wang, Shiyuan Wang, Weiqun Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China College of Electronic and Information Engineering Southwest University Chongqing400715 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

As an effective and efficient discriminative learning method, Broad Learning System (BLS) has received increasing attention due to its outstanding performance in various regression and classification problems. However, the standard BLS is derived under the minimum mean square error (MMSE) criterion, which is, of course, not always a good choice due to its sensitivity to outliers. To enhance the robustness of BLS, we propose in this work to adopt the maximum correntropy criterion (MCC) to train the output weights, obtaining a correntropy based broad learning system (C-BLS). Thanks to the inherent superiorities of MCC, the proposed C-BLS is expected to achieve excellent robustness to outliers while maintaining the original performance of the standard BLS in Gaussian or noise-free environment. In addition, three alternative incremental learning algorithms, derived from a weighted regularized least-squares solution rather than pseudoinverse formula, for C-BLS are developed. With the incremental learning algorithms, the system can be updated quickly without the entire retraining process from the beginning, when some new samples arrive or the network deems to be expanded. Experiments on various regression and classification datasets are reported to demonstrate the desirable performance of the new methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

From Parallel Plants to Smart Plants:Intelligent control and management for Plant Growth

引用

IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 161-166页

作者： Mengzhen Kang Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences (SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Beijing Engineering Research Center of Intelligent Systems and Technology IEEE State Key Laboratory of Management and Control for Complex Systems (SKL-MCCS) Institute of AutomationChinese Academy of Sciences (CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description U+0028 the crop model U+0029, prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed. © 2017 Chinese Association of Automation.

关键词： Agriculture Artificial intelligence Decision support systems Expert systems Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Review on Development of Smart Education

Review on Development of Smart Education

引用

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

作者： Wanruo Shi Xiwei Liu Xiaoyan Gong Xiaojie Niu Xinzhu Wang Sifeng Jing Hao Lu Nan Zhang Jie Luo Faculty of Education Beijing Normal University Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Chinese Association of Automation Beijing China The State Key Laboratory of Software Development Environment Beihang University Beijing China

Smart education is leading the development direction of Chinese education informatization and becoming a main theme of education development in the era of which technology changes education. There are seven main branches of smart education, namely Intelligent Tutoring System (ITS), smart campus, Big Data in Education (BDE), knowledge graph, educational robots, virtual teachers, and personalized education. Based on literature survey and market research, this paper gives a comprehensive overview of the research and application of smart education from above seven aspects and proposes its development based on the status of smart education development.

关键词： Education Big Data Data mining Market research Analytical models Learning (artificial intelligence) Data models

来源：评论

学校读者我要写书评

暂无评论

Comparison of control Methods Based on Imitation Learning for Autonomous Driving

Comparison of Control Methods Based on Imitation Learning fo...

引用

International Conference on Intelligent control and Information Processing (ICICIP)

作者： Yinfeng Gao Yuqi Liu Qichao Zhang Yu Wang Dongbin Zhao Dawei Ding Zhonghua Pang Yueming Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China The state Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China China Automotive Technology & Research Center Co. Ltd Tianjin China Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology China

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Recently, some learning-based methods such as reinforcement learning and imitation learning have been used to address the control problem for autonomous driving. Note that reinforcement learning has strong reliance on the simulation environment and requires a handcraft design of the reward function. Considering different factors in autonomous driving, a general evaluation method is still being explored. The purpose of imitation learning is to learn the control policy through human demonstrations. It is meaningful to compare the control performances of current main imitation learning methods based on the provided dataset. In this paper, we compare three typical imitation learning algorithms: Behavior cloning, Dataset Aggregation (DAgger) and Information maximizing Generative Adversarial Imitation Learning (InfoGAIL) in the The Open Racing Car Simulator (TORCS) and Car Learning to Act (CARLA) simulators, respectively. The performance of algorithms is evaluated on lane-keeping task in racing and urban environment. The experiment results show DAgger performs best in simple lane keeping problem, and InfoGAIL has the unique advantage of distinguishing different driving styles from expert demonstrations.

关键词： Gallium nitride Trajectory Data models Entropy Task analysis Linear programming Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Review on Teacher's Online Practice in Community

Review on Teacher's Online Practice in Community

引用

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

作者： Xinzhu Wang Xiaoyan Gong Xiwei Liu Yan Yan Nan Zhang Jie Luo Faculty of Education Beijing Normal University Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Chinese Association of Automation Beijing China The State Key Laboratory of Software Development Environment Beihang University Beijing China

Teacher's Online Practice in Community (TOPIC) is an informal and practical online learning community that improves teachers' professional development. Domestic researches on TOPIC have begun to take shape. By sorting out relevant literature retrieved from China National Knowledge Infrastructure (CNKI), this article summarizes research topics such as knowledge management, teacher professional development, and community management and summarizes qualitative and quantitative research methods commonly used by domestic researches. This article finally puts forward some limitations of current researches in this field and forecasts the possible future research directions.

关键词： Knowledge transfer Training Reflection Organizations Focusing

来源：评论

学校读者我要写书评

暂无评论

Raunet: Residual attention u-net for semantic segmentation of cataract surgical instruments

arXiv

引用

arXiv 2019年

作者： Ni, Zhen-Liang Bian, Gui-Bin Zhou, Xiao-Hu Hou, Zeng-Guang Xie, Xiao-Liang Wang, Chen Zhou, Yan-Jie Li, Rui-Qi Li, Zhen University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Cas Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China

Semantic segmentation of surgical instruments plays a cru-cial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge due to specular reec-tion and class imbalance issues. In this paper, an attention-guided net-work is proposed to segment the cataract surgical instrument. A new at-tention module is designed to learn discriminative features and address the specular reection issue. It captures global context and encodes se-mantic dependencies to emphasize key semantic features, boosting the feature representation. This attention module has very few parameters, which helps to save memory. Thus, it can be exibly plugged into other networks. Besides, a hybrid loss is introduced to train our network for addressing the class imbalance issue, which merges cross entropy and logarithms of Dice loss. A new dataset named Cata7 is constructed to evaluate our network. To the best of our knowledge, this is the first cataract surgical instrument dataset for semantic segmentation. Based on this dataset, RAUNet achieves state-of-the-art performance 97.71% mean Dice and 95.62% mean IOU. Copyright © 2019, The Authors. All rights reserved.

关键词： Surgical equipment

来源：评论

学校读者我要写书评

暂无评论

Study and Generalization on an End-to-End Spatial-temporal Driving Model

Study and Generalization on an End-to-End Spatial-temporal D...

引用

Chinese Automation Congress (CAC)

作者： Tingting Yao Xin Chen Sheng Yuan Huaying Wang Lili Guo Bin Tian Yunfeng Ai Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Artificial Intelligence Department Beijing Automotive Technology Center Co. Ltd. China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Autonomous driving has attracted increasing attentions from both academia and industry, among which end-to-end driving model is a nascent research topic. In this work a deep learning based model is studied and generalized, which directly maps raw videos to steering angles without any manual feature extraction procedure. The model is learned and evaluated from human driving videos from both public dataset and self-recorded dataset under real environment, which is more practical and application oriented. Besides, the model combines both the spatial and temporal information by applying 3D convolutional neural network and recurrent unit. For the dataset studied, ablation experiment is performed to further verify the unity of recurrent block. Finally, transfer learning is applied to transfer the domain knowledge learned from the large-scale public dataset to two different small-scale dataset in unseen scenes. Comprehensive quantitative and qualitative evaluations demonstrate that the RMSE on pretrained model is 0.12603, while the transfer learning for the two unseen scenes achieve 0.13079 and 0.13198, respectively, which shows the effectiveness and generalization ability of the model even under different scenarios and abrupt turns.

关键词： 5G mobile communication Integrated circuits

来源：评论

学校读者我要写书评

暂无评论

Flexible Robotic Grasping Strategy with Constrained Region in Environment

引用

International Journal of Automation and computing 2017年第5期14卷 552-563页

作者： Chao Ma Hong Qiao Rui Li Xiao-Qing Li School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment, which is inspired by the grasping operations of the human beings. More precisely, constrained region in environment is formed by the environment, which integrates a bio-inspired co-sensing framework. By utilizing the concept of constrained region in environment, the grasping by robots can be effectively accomplished with relatively low-precision sensors. For the grasping of dexterous robotic hands, the attractive region in environment is first established by model primitives in the configuration space to generate offline grasping planning. Then, online dynamic adjustment is implemented by integrating the visual sensory and force sensory information, such that the uncertainty can be further eliminated and certain compliance can be obtained. In the end, an experimental example of BarrettHand is provided to show the effectiveness of our proposed grasping strategy based on constrained region in environment.

关键词： Grasping strategy compliant grasping dexterous robotic hands attractive region in environment constrained region inenvironment.

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：