检索结果-内蒙古大学图书馆

Parallel Emergency: Social Renormalization via Monads and Computational Social systems

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2020年第2期7卷 286-292页

作者： Wang, Fei-Yue Univ Arizona Tucson AZ USA Robot & Automat Lab Tucson AZ USA Chinese Acad Sci Intelligent Control & Syst Engn Ctr Inst Automat Beijing Peoples R China Chinese Acad Sci Key Lab Complex Syst & Intelligence Sci Beijing Peoples R China Chinese Acad Sci Res Educ & Acad Exchanges Inst Automat Beijing Peoples R China State Key Lab Management & Control Complex Syst Beijing Peoples R China INCOSE San Diego CA USA IFAC New York NY USA ASME New York NY USA AAAS Washington DC USA Chinese Assoc Sci & Technol Beijing Peoples R China ACM China Council Beijing Peoples R China IFAC TC Econ & Social Syst Beijing Peoples R China IEEE SMC Soc Beijing Peoples R China Chinese Assoc Automat Beijing Peoples R China Chinese Assoc Automat Supervis Council Beijing Peoples R China

Welcome to the second issue of IEEE Transactions on Computational Social systems (TCSS) this year. First of all, I would like to report that, as of this March, the CiteScore of TCSS has reached to 5.15, a historic high, especially considering the fact that the numbers of published articles had increased significantly in the last two years. Congratulations and thanks to all of you, for your professional support and dedication.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Parallel Education Based intelligent Tutoring systems Framework

A Parallel Education Based Intelligent Tutoring Systems Fram...

引用

IEEE International Conference on Networking, Sensing and control

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong Wei Cui Joleen Liang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA Squirrel AI Learning Shanghai China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

although online education has improved efficiency of learners' access to high-quality educational resources, real-time interaction between instructors and learners have not yet been achieved in online personalized learning. intelligent Tutoring systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research & development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed.

关键词： Human computer interaction Conferences Education Market research Sensors Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

引用

IEEE International Conference on Networking, Sensing and control

作者： Xiaoyan Gong Ying Tang Xiwei Liu Sifeng Jing Wei Cui Joleen Liang Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA Squirrel AI Learning Shanghai China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

Nowadays, AI education from Kindergarten to 9 th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts.

关键词： Ethics Systematics Education Government Tools Sensors Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

引用

第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Visual Pencil: Design of Portable Human-Computer Interaction Based on 2D Visual Tracking

Visual Pencil: Design of Portable Human-Computer Interaction...

引用

IEEE International Conference on Mechatronics and Automation

作者： Ru Tong Tianzhu Wang Junzhi Yu State Key Lab Management and Control for Complex Systems Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China College of Engineering Peking University Beijing China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Convenient and efficient human-computer interaction has always been the goal pursued by researchers. In this paper, we present a vision-based handwriting interaction device for non-touch screens called Visual Pencil. Unlike the conventional interactive method that captures the user's gestures using a motionless visual sensor, Visual Pencil directly mounts the visual sensor in the device. More specifically, the visual data are processed by the feature detection algorithm, and the movement of the pen tip in a 2D plane is calculated, so as to control the cursor and achieve the purpose of interaction. We further define performance indicators for evaluating the design and select more reasonable parameters through specific experiments to achieve the best interaction performance. The experiments verify that the proposed design is able to fulfill the desired operations. The significance of this design lies in that it integrates the functions of the mouse and smart pen, thus removing the requirement of more auxiliary equipment. This work sheds light on the interaction with non-touch screen computers.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Learning-Based Framework for Error Compensation in 3D Printing

引用

IEEE TRANSACTIONS ON CYBERNETICS 2019年第11期49卷 4042-4050页

作者： Shen, Zhen Shang, Xiuqin Zhao, Meihua Dong, Xisong Xiong, Gang Wang, Fei-Yue Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Intelligent Mfg Ctr Qingdao 266109 Shandong Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan 523808 Peoples R China

As a typical cyber-physical system, 3D printing has developed very fast in recent years. There is a strong demand for mass customization, such as printing dental crowns. However, the accuracy of the 3D printed objects is low compared with traditional methods. The main reason is that the model to be printed is arbitrary and usually the quantity is small. The deformation is affected by the shape of the object and there is a lack of a universal method for the error compensation. It is neither easy nor economical to perform the compensation manually. In this paper, we present a framework for the automatic error compensation. We obtain the shape by technologies such as 3D scanning. And we use the "3D deep learning" method to train a deep neural network. For a specific task, such as dental crown printing, the network can learn the function of deformation when a large amount of data is used for training. To the best of our knowledge, this is the first application of the deep neural network to the error compensation in 3D printing. And we propose the "inverse function network" to compensate for the error. We use four types of deformations of the dental crowns to verify the performance of the neural network: 1) translation;2) scaling up;3) scaling down;and 4) rotation. The convolutional AutoEncoder structure is employed for the end-to-end learning. The experiments show that the network can predict and compensate for the error well. By introducing the new method, we can improve the accuracy with little need for increasing the hardware cost.

关键词： 3D printing additive manufacturing cyber-physical system (CPS) deep learning error compensation

来源：评论

学校读者我要写书评

暂无评论

Dynamic horizon value estimation for model-based reinforcement learning

arXiv

引用

arXiv 2020年

作者： Wang, Junjie Zhang, Qichao Zhao, Dongbin Zhao, Mengchen Hao, Jianye State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Huawei Noah’s Ark Lab Beijing China

Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, the fixed rollout with an inaccurate model has a potential to harm the learning process. In this paper, we investigate the idea of using the model knowledge for value expansion adaptively. We propose a novel method called Dynamic-horizon Model-based Value Expansion (DMVE) to adjust the world model usage with different rollout horizons. Inspired by reconstruction-based techniques that can be applied for visual data novelty detection, we utilize a world model with a reconstruction module for image feature extraction, in order to acquire more precise value estimation. The raw and the reconstructed images are both used to determine the appropriate horizon for adaptive value expansion. On several benchmark visual control tasks, experimental results show that DMVE outperforms all baselines in sample efficiency and final performance, indicating that DMVE can achieve more effective and accurate value estimation than state-of-the-art model-based methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework

引用

IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2019年第6期20卷 2395-2400页

作者： Lin, Yilun Dai, Xingyuan Li, Li Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

Traffic flow prediction is one of the most popular topics in the field of the intelligent transportation system due to its importance. Powered by advanced machine learning techniques, especially the deep learning method, prediction accuracy noticeably increases in recent years. However, most existing methods applied a data-driven paradigm and tend to ignore the outliers, which result in poor performance while handling burst phenomena in the traffic system. To overcome this problem, the prediction model needs to recognise different patterns and handle them in different ways. In this paper. we propose a new prediction model (called pattern sensitive network) that can handle different traffic patterns automatically. By using adversarial training. our model can make more accurate predictions in unusual states without compromising its performance in usual states. Experiments demonstrate that our method can work well in both usual traffic states and unusual traffic states.

关键词： Traflic flow prediction deep learning generative adversarial network

来源：评论

学校读者我要写书评

暂无评论

Target Location and Gait Planning for Humanoid Robot Climbing Stairs 4

Target Location and Gait Planning for Humanoid Robot Climbin...

引用

4th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Zhang, Huayan Zhang, Lei Yuan, Fei Bian, Gui-Bin Xin, Shan Beijing Univ Civil Engn & Architecture Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing Key Lab Robot Bion & Funct Res Beijing 10016 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781728100647

Climbing stairs is an important ability for humanoid robots. This paper proposes a target location and gait planning method for humanoid robot climbing stairs. Target location enables the humanoid robot to accurately locate the stair position, and the gait planning algorithm makes the humanoid robot to climb the stairs. In order to enable the humanoid robot to climb multiple stairs, this paper proposes control strategy of motion for humanoid robot. The effectiveness of our proposed method was confirmed by experiments. The experimental results show that this method improves the accuracy of target location, and enables the humanoid robot Nao to climb a staircase with 2.928 cm steps.

关键词： Humanoid robots Foot Robot kinematics Trajectory Legged locomotion Planning

来源：评论

学校读者我要写书评

暂无评论

Detecting Traffic Information From Social Media Texts With Deep Learning Approaches

引用

IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2019年第8期20卷 3049-3058页

作者： Chen, Yuanyuan Lv, Yisheng Wang, Xiao Li, Lingxi Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Shandong Peoples R China Indiana Univ Purdue Univ Dept Elect & Comp Engn Indianapolis IN 46202 USA

Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-ofword model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches.

关键词： Deep learning social transportation traffic information detection social media text mining

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：