One of the most important tasks in modern robotics is to determine the motion of the robot and a reconstruction of the environment. Applications can be found in robot navigation, autonomous control and visual perception. There are several different sensors available for this task, such as cameras and Lidar. While current robot navigation methods are already capable of providing highly accurate pose-estimates, they are not optimal in terms of robustness yet. This has inspired lots of interesting research work in recent years, e.g. visual–inertial SLAM, visual perception, Lidar-based localization, robust control, 3D reconstruction, and robot path planning, etc. Moreover, intelligent navigation and control technology has also attracted extensive attentions on applications of exploration robots, medical service robot, fire-fighting robot and other automated robots.
Associate Prof. Fei Xie, Nanjing Normal University, China
Fei Xie is an associate professor at Nanjing Normal University, member of Chinese Association of Automation, Chinese Association for Artificial Intelligence, Chinese Society of Inertial Technology, Jiangsu Association of Automation, Nanjing 3D Printing Association, reviewer of Automatica and ACTA Automatica Sinica. Besides, he presided over projects supported by National Natural Science Foundation of China, Natural Science Foundation of Jiangsu, National Key R&D Program of China, Science and Technology Achievements Transformation Projects in Jiangsu Province, sub-projects of State Grid of China and project of Patent Commercialization. He was selected as leading talent of Scientific and Technological Innovation and Entrepreneurship of Eastern Wu and received Science and Technology Award of Jiangsu Province, Outstanding Paper Award of JSEE, First Award for Outstanding Teaching of Undergraduate Course of Nanjing Normal University. Additionally, his research areas include machine vision and deep learning, intelligent sensing of robot and embedded system. He published over 40 papers indexed by SCI and Ei on journals, and owns more than 20 authorized patents.
Workshop 2: Affordance-guided tools recognition and manipulation for service robot
Title 1: Environment and tools recognition for Home IOT Robot System
It is well known that the rapid development of artificial intelligence has greatly improved the intelligence of robot, and more and more high-intelligent robots in movies are appearing in our daily life. Yet home service robot cannot provide confluent and pleasant service, one of the biggest challenges is that robot cannot recognize household environment and tools widely and deeply. In order to make home service robot recognize environment and manipulate tools like human, we combine IOT and home service robot to build the Home IOT Robot System, and research on 1) home cognition map, 2) room knowledge library and 3) tool knowledge library. We firstly propose the concept of simultaneous localization, calibration and mapping of the IOT robot system, with two kinds of map forms (i.e., two-dimensional vector map and three-dimensional holographic map), and two kinds of IOT robot system forms (i.e., Kinect network robotic system and wireless sensor network robot system). Under the Home IOT Robot System, we research the semantic representation model of household tools affordance, the perspective-independent method for human behavior recognition, and the skill self-learning algorithm for service robots with reinforcement learning and imitation learning.
Prof. Peiliang Wu, College of Information Science and Engineering, Yanshan University, China
Peiliang Wu was born in Hebei Province, China, in 1981. He is Professor and Ph. D. Supervisor of School of Information Science and Technology, Yanshan University, an Academic Visitor of Edinburgh Napier University, and a Postdoctoral Fellow of Institute of Automation, Chinese Academy of Sciences. He received his Ph. D. degree from Yanshan University in 2010. His main research interests include home service robot, robot operation skill learning, computer vision, SLAM, Multi-Agent systems, Intelligent Space, industrial assembly line optimal scheduling, competitive multi-robot Association, etc. He has published over 50 journal and conference research papers and 1 book in these areas. He has held several research grants from the National Natural Science Foundation of China, Natural Science Foundation of Hebei Province of China, China Postdoctoral Science Foundation, National key research and development program of China, as well as from industry, in which the Natural Science Foundation of Hebei Province of China was selected as the outstanding project in 2018.
Workshop 3 : Intelligent Systems and Applications Empowered by Computing, Communication and Control Techniques
Title 1: Intelligent Systems and Applications Empowered by Computing, Communication and Control Techniques
Keywords: Intelligent Systems, Computing, Communication, Control
Intelligent systems incorporate functions of computation, communication, and control in order to describe and analyze a situation, and make smart decisions based on the available data in a predictive or adaptive manner. The “smartness” of the intelligent system can be attributed to autonomous operation based on intelligent computation, closed-loop control, and networking capabilities. Recently, emerging techniques, such as machine learning, edge/cloud computing, pervasive/ubiquitous computing, big data, and Internet of Things (IoT) technologies have considerably contributed to the development of future intelligent systems and applications. The major challenges in current intelligent systems include (i) how to design intelligent systems with control approaches and physical components, (ii) how to optimize the performance indices (e.g. efficiency, cost, security) of intelligent applications with computing techniques. This workshop provides a platform for researchers and scholars to discuss the ongoing progress of intelligent systems and applications empowered by computation, communication and control techniques.
Prof. Heng Li, Central South University, China
Heng Li works as an associate professor at Central South University. He received his bachelor’s and Ph. D. degrees from Central South University in 2011 and 2017 respectively. He worked as a Research Assistant at University of Victoria from November 2015 to November 2017. He joined Central South University in November 2017. His research areas include smart energy systems and smart factories. Dr. Li was a recipient of the Excellent Ph.D. Thesis Award of Central South University and Hunan Province, Hunan Provincial Natural Science Award and China Railway Academy Science and Technology Award, and Best Paper Award of ICIVIS 2021.
Dr. Fei Tong, Southeast University, China
Fei Tongworks as an associate researcher at Southeast University. He was a postdoctoral research fellow at the Department of Control Science and Engineering of Zhejiang University from Dec. 2016 to Dec. 2018. He received his M.S. degree in Computer Engineering at Chonbuk National University in 2011 and his Ph. D. degree in Computer Science at University of Victoria (UVic) in 2016. His research areas include Ad Hoc networks, the Internet of Things, 4G/5G communication systems, etc. He receivedOutstanding Graduate Entrance Award, UVic Fellowship, UVic Graduate Award, Department and University Nomination for the Vanier Canada Graduate Scholarships, EE/CS Networks Leadership Award, Mitacs Globalink Research Award, the third Heidelberg Laureate Forum Invitation to attend (globally 10% acceptance rate), and the 2016 Chinese Government Award for Outstanding Self-financed Students Abroad during pursuing his Ph. D. degree at UVic.
Dr. Si-Zhe Chen, Guangdong University of Technology, China
Si-Zhe Chenworks as an associate professor and head of the Department of Electrical Engineering of Guangdong University of Technology. He received his bachelor's and Ph. D. degrees at South China University of Technology in 2005 and 2010 respectively. He worked as a research assistant at the Department of Electrical Engineering of Hong Kong Polytechnic University from July 2008 to October 2009. He joined School of Automation of Guangdong University of Technology in July 2010. He is a Standing Director of the Energy Storage System and Equipment Subcommittee of the IEEE PES Energy Storage and Stationary Battery Satellite Committee-China. His research areas include control and power electronics technology in renewable energy generation and battery management system. Dr. Chen was a recipient of Excellent Ph. D. Thesis Award of South China University of Technology in 2011 and Jiangxi Provincial Science and Technology Progress Award in 2019.
Dr. Shan Lu, Shenzhen Polytechnic, China
Shan Luworks as an assistant professor and deputy director of the Institute of Intelligence Science and Engineering at Shenzhen Polytechnic. He received his B.S. degree in 2011 and the Ph. D. degree in 2016 at Zhejiang University. His research areas include data-driven optimization, cyber system control and signal processing. He has undertaken national and provincial funds and projects in the field of smart manufacturing and process control system, and works as a principal investigator of Innovation Team of Guangdong Province in China.
Dr. Guanghui Wang, Henan University, China
Guanghui Wangworks as an assistant professor at the School of Software of Henan University (HENU). He is a member of Key Technology of Henan International Joint Laboratory of Intelligent Network Theory. He received his Ph. D. degree in Information Networks at Nanjing University of Posts and Telecommunications (NUPT) in 2019. He was a visiting research student at the Department of Computer Science of University of Victoria (UVic) and his tutor is Prof. Jianping Pan. His research areas include Blockchain, Federated Learning, Privacy Preservation, and Internet of Things.
Dr. Yuanzhi Ni, Jiangnan University, China
Yuanzhi Niworks as an assistant professor at the Department of Automation at Jiangnan University. He received his B.S. degree in automation and Ph.D. degree in control science and engineering at Nanjing University of Science and Technology. He was a jointly supervised Ph. D. student at the Department of Electrical and Computer Engineering of the University of Victoria from 2015 to 2017. His research areas include internet-of-vehicles and networked control.
Dr. Quan Ouyang, Nanjing University of Aeronautics and Astronautics, China
Quan Ouyang works as an assistant professor at Nanjing University of Aeronautics and Astronautics. He received B. S. degree in Automation at Huazhong University of Science and Technology in 2013 and the Ph. D. degree in Control Science and Engineering at Zhejiang University in 2018. His research areas include battery management, modeling and control of fuel cell systems, and nonlinear control.
Intelligent transportation is the trend of development in the future. There are still a lot of difficulties in intelligent transportation, such as traffic object identification and statistics, traffic collaborative scheduling model, real-time traffic flow prediction, traffic guidance and so on. With the application of multi-sensor technology and v2x communication technology in intelligent transportation, how to use MEC, big data analysis, deep learning and other methods to establish better models, quickly and accurately carry out real-time traffic flow statistics and prediction, and improve traffic efficiency and safety is the current research hotspot. For example, the traffic flow prediction and monitoring method are based on the intersection of main roads. Make full use of the data statistics of traffic flow, research and design the traffic flow prediction and implementation monitoring method based on the main road Association intersection. Such as the main road green belt control model and algorithm. Combined with the timing ratio of road signal, the green wave control model and algorithm of main road are studied and designed to solve the problem of traffic efficiency of main road.
This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of Intelligent Transportation and understand how governance strategy can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshop title”.
Prof. Jianchun Jiang, Chongqing University of Posts and Telecommunications, China
Jianchun Jiang received his Ph. D. degree in Instrument Science and Technology at Chongqing University. He is a visiting scholar at Tampere University of Technology. His research interests include V2X Communication Vehicle Networking, Embedded System, Intelligent Transportation, Vehicle & Road Coordination. He participated in the National Natural Science Foundation, National Key Projects, National Key Research and Development Project, Natural Science Foundation of Chongqing Key Projects. Based on these projects, he published academic papers and owns patents.
Embedded system is a special computer system that can flexibly cut software and hardware modules. It has been widely used in communication, automobile, medical, industrial control and other fields. Compared with the general computer system, the embedded system has the advantages of high reliability and high real-time. However, since the embedded system often pays attention to the characteristics of portability, the configuration of its computing resources and storage resources is often low, and the system computing capacity and data storage capacity are limited. Intelligent system has been widely concerned by academia and industry at present. System intelligence is usually based on high computing power and high storage capacity. How to realize the intelligent embedded system and improve the intelligence of the system as much as possible in the environment of limited computing power and storage capacity has become a hot issue in the field of embedded system and the future development direction of embedded system.
This workshop aims to bring together the research accomplishments provided by researchers from academia and industry. Another goal is to show the latest research results in the fields of embedded systems and intelligent systems. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshop title”.
Assoc. Prof. Quan Zhou, Huazhong University of Science and Technology, China
Quan Zhou received a Ph. D. degree in Computer Science and Technology at Huazhong University of Science and Technology. He worked as an associate professor at School of Computer Science and Technology, Huazhong University of Science and Technology. His research interests include Real-time Embedded Systems and Machine Learning. He presided over or participated in the National Natural Science Foundation, National Key Projects, National Key Research and Development Project, etc. Based on these projects, he published more than 10 papers on the journals and conferences classified as CCF A and B as the first author.
Workshop 6 : Computer Vision and Intelligent Perception
Title 1: Computer Vision for Intelligent Scene Perception
Keywords: Computer Vision, Multi-modality Representation Learning, Intelligent Scene Perception and Application
Visual understanding and multi-modality representation fusion are essential to intelligent scene perception. With the rapid progress in machine learning technologies, there are tons of remarkable advances in intelligent scene understanding, whose performance and application fields are extended greatly. However, the complexity of scene could be a challenge for efficient perception. For some application as automatic drive, pedestrian reidentification and robot tracking, the performance and efficiency are typically affected by disturbances in the natural scene. How to efficiently combine information from visual and other modalities to enhance the robustness of perception systems under accidental perturbation and complexity issues is crucial and meaningful.
This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of intelligent scene perception. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Prof. Bin Jiang, Hunan University, China
Bin Jiang received a Ph. D. degree in Computational Intelligence and System Science at Tokyo Institute of Technology. He worked as a professor at College of Computer Science and Electronic Engineering of Hunan University. His research areas include Artificial Intelligence, Machine Vision, Machine Learning, and Big Data Technology. He participated in National Natural Science Foundation, National Key Projects, National Key Research and Development Project and Natural Science Foundation of Hunan Province.
Title 1: Challenges and Developments for Person re-identification
Keywords: Person re-identification, Deep Learning, Artificial Intelligence, Intelligent Video Surveillance System
As the important and interesting research field, person re-identification (Re-ID) has been presented and made great progress in recent years. Re-ID has been studied as a specific person retrieval problem across non-overlapping cameras. Given a query person-of-interest, the goal of Re-ID is to determine whether this person appears in another place at a distinct time captured by a different camera. The query person can be represented by an image or a video sequence. Due to the urgent demand for public safety and increasing number of surveillance cameras in university campuses, theme parks, streets, etc., person Re-ID is imperative in intelligent video surveillance system designs. Given its research impact and practical importance, person Re-ID is a fast-growing vision community.
Person Re-ID is a challenging task due to the presence of different viewpoints, varying low-image resolutions, illumination changes, unconstrained poses, occlusions, heterogeneous modalities, etc. With the advancement of deep learning, person Re-ID has achieved inspiring performance on the widely used benchmarks. However, there is still a large gap between the research-oriented scenarios and practical applications.
This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of person Re-ID. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical schemes. Please name the title of the submission email with “workshop: Challenges and development for person re-identification”.
Assoc. Prof. Zhigang Liu, Northeast Petroleum University, China
Zhigang Liu received a Ph. D. degree in Computer Resources and Information Engineering at Northeast Petroleum University and was a visiting scholar at the Department of Electrical & Computer Engineering of National University of Singapore from 2018 to 2019. He is member of IEEE and CCF, director of Department of Computer Science and Engineering of Northeast Petroleum University, a reviewer of IEEE Transactions on Neural Networks and Learning Systems, Journal of Photogrammetry and Remote Sensing, IET Intelligent Transport Systems, etc. His research areas include machine learning, computer vision, especially, data/label- and computation-efficient deep learning for visual recognition. He participated in National Natural Science Foundation, Natural Science Foundation of Heilongjiang Province, Scientific and Technological Projects of Petro-China, and Youth Science Foundation of Northeast Petroleum University. Based on these projects, he published many academic papers.
Workshop 8 : Cooperative Control of Multi-Robot Systems and Application
Title 1: Cooperative Control of Multi-Agent Systems and its Application to Fire Fighting Robot
Cooperative control of multi-robot systems is a popular research topic in recent years, it has attracted lots of researchers’ attention, this is mainly due to its extensive applications in many fields, such as formation control, wireless sensor network systems, fire fighting robot, smart grid. As one of the fundamental problems, consensus of multi-agent systems has been widely studied, multi-agent systems consist of a large group of spatially distributed and networked intelligent agents, each agent represents a strategic entity capable of perceiving and computing scientific data from a physical world, and all agents communicate with each other via shared network. Since the communication link among agents is usually wireless, some physical limitations related issues, such as network induced limited communication resources, packet losses, cyber attacks, communication delays, should be resolved, however, these issues are not fully exploited in the existing results.
In this workshop, we aim to present some recent developments and advances of multi-robot systems and investigate how to apply theoretical results to some applications. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Prof. Guanglei Zhao, Yanshan University, China
Guanglei Zhao received an M. S. degree at Huazhong University of Science and Technology in 2009 and a Ph. D. degree at Shanghai Jiao Tong University in 2014. He works as a professor at Institute of Electrical Engineering of Yanshan University. He was a visiting scholar at The University of Melbourne from 2012 to 2013. He was the finalist of Zhang Si-Ying (CCDC) Outstanding Youth Paper Award in 2019 and authored over 50 papers in technical journals and conferences. Additionally, he presided over projects supported by National Natural Science Foundation of China, Natural Science Foundation of Hebei Province, China Postdoctoral Science Foundation, and National Key Research and Development Program of China, in which the project supported by Natural Science Foundation of Hebei Province was selected as the outstanding project in 2020. Moreover, his research areas include networked control systems, multi-agent systems, hybrid systems and robust control.
Workshop 9 : Machine Learning Applied in Image Processing
Title 1: Machine Learning applied in Image Processing
Keywords: Images Processing, Machine Learning, Deep Learning, Information Extraction, Feature Extraction
Nowadays there are many ways to obtain images including remote sensing images, mobile phone images, camera images, and so on. The image quality is becoming higher and higher, which provides more detailed information. However, as the details of images become increasingly rich, it becomes more difficult to extract information from images, the main reason is that traditional image technology is difficult to apply rich details. With the development of deep learning technology, how to apply machine learning technology to image processing has become a hot issue in the field of image processing.
This workshop aims to bring together the research accomplishments provided by researchers from academia and the applied area. The other goal is to show the latest research results in the field of image processing.We encourage authors to submit related research papers on the subject of both: theoretical, approaches, application and reviews.
Prof. Chengming Zhang, Shandong Agricultural University, China
Chengming Zhang received a Ph. D. degree in photogrammetry and remote sensing at Shandong University of Science and Technology. He works as a professor at the College of Information Science and Engineering of Shandong Agricultural University. Moreover, his main research areas are remote sensing and geographic information system in land use monitoring and evaluation. He presided over a number of agricultural remote sensing projects supported by Ministry of Science and Technology and Shandong Province. He is mainly engaged in the research of remote sensing technology in agriculture and environment at present.
Workshop 10 : Intelligent Cooperative Robot: Modelling, Control and Interaction
Title 1: Intelligent Systems and Applications of Cooperative Robot
Keywords: Intelligent Cooperative, Robot Mechanics, Sense Perception, Cooperation Control, Interaction
Intelligent Cooperative Robot (ICR) can be used to perform complex tasks in an unknown environment, partial information, computation and distributed control which cannot be performed effectively by single robot. ICR can be efficiently used in many applications such as spacecraft, mobile robots, unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs) and other applications of robot. Thus, ICR has attracted attention over the past decade. Although a lot of development has been done in ICR, there arestill many challenging issues. These issues include robot mechanics, cooperation control, sense perception, path planning, collision avoidance, task allocation, interaction and communication among robots, coordination, navigation, etc.
Prof. Guohua Cui, Shanghai University of Engineering Science, China
Guohua Cui received the Master's and Ph. D. degrees at School of Mechanical Science and Engineering of Jilin University. He is the dean of School of Mechanical and Automotive Engineering of Shanghai University of Engineering Science. He was the candidate of 333 Talent Project of Hebei Province and 100 Outstanding, Innovative Talents Support Program of Colleges and Universities in
Hebei Province and the 5th “Top-notch Talents Training Plan” of Songjiang District in Shanghai. Additionally, he is the member of Co-integration Robot Committee of Chinese Association of Automation and Shanghai Robot Industry Association. Moreover, his research interests include robot mechanics, multi-robot cooperative control, and robot fault diagnosis and health assessment.
Workshop 11 : Remote Sensing for Smart Farming
Title 1: AI in Precision Agriculture
Keywords: Smart Farming, Remote Sensing Image Analysis, Machine Learning, Control System, Data Assimilation
Smart agriculture has gained increasing attention recently due to the rising population and high food demand. Traditional approaches of information and knowledge collection for the monitoring of crop fields are laborious, time-consuming, and less robust. The advancement of sensing technology makes it possible to acquire data efficiently with unprecedented resolutions for timely non-destructive monitoring. In addition, state-of-the-art machine learning (e.g. deep learning) and high-performance computing can benefit from these opportunities addressing the new food production challenges related to cropping system optimization for improving productivity and reducing environmental impacts. Moreover, robotics and automation technologies make precision and automated site-specific agriculture management possible in the future.
In this workshop, we aim at disseminating the latest research findings in exploiting remote sensing technologies and image analysis algorithms for smart farming. It includes, but is not limited to, land cover classification, disease detection, data assimilation, advanced control system, and execution of management interventions. We encourage prospective authors to submit related papers on both theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshop title”.
Assis. Prof. Tianxiang Zhang, University of Science and Technology Beijing, China
Tianxiang Zhang received the B. Eng. degree in flight vehicles design and engineering at Beijing Institute of Technology in 2015, the M. Sc. degree in aerospace engineering at University of Manchester in 2016 and Ph. D. degree at Loughborough University in 2020. He is now an assistant professor (postdoctoral researcher) at School of Automation and Electrical Engineering of University of Science and Technology Beijing. He is a reviewer of journals indexed by SCI/EI such as IEEE Transactions on Industrial Informatics, ISPRS Journal of Photogrammetry and Remote Sensing, Agricultural Water Management, Agronomy, Unmanned Systems, ect. His research areas include remote sensing image analysis, machine learning, data assimilation method, and agriculture and industrial intelligence.
Title 1: Intelligent Control and Fault Diagnosis based on Artificial Intelligence
Keywords: Intelligent Control, Fault Diagnosis, Support Vector Machine, Optimization Algorithm
Intelligent control and fault diagnosis of industrial equipment have always been the focus of scholars. Because of the rapid development of industrial technology and traditional equipment with low robustness and accuracy, it has been unable to meet the current industrial requirements for intelligence. According to the characteristics of industrial equipment, it is very important to design an excellent intelligent control and fault diagnosis method. The research of intelligent control and fault diagnosis methods based on artificial intelligence algorithm has attracted extensive attention in recent years such as artificial neural network, support vector machine, fuzzy theory and deep learning. These methods can be applied to the vast majority of industrial equipment and achieved excellent results, but there are still many areas worthy of improvement and innovation, e. g. the use of optimization algorithm to improve the artificial intelligence algorithm or combine with the mechanism of equipment targeted research.
Assoc. Prof. Xianbo Sun, Hubei Minzu University, China
Xianbo Sun received a Ph. D. degree in traffic information engineering and control at Wuhan University of technology. He has been engaged in the teaching and research of control theory and control engineering, industrial automation, Internet of things and other disciplines for a long time. He published more than 20 papers in academic journals, including 4 papers indexed by SCI, 2 papers indexed by Ei and 1 monograph. He presided over and completed 1 project supported by National Natural Science Foundation of China, 2 provincial projects, 4 Enterprise scientific research projects. He owns more than 10 national invention patents, utility model patents and software copyrights.
Title 1: Information Utilization of Intelligent Systems
Keywords: Pattern Recognition, Data Mining, Robotic Learning, Navigation, Planning
Utilization of information behind raw data helps intelligent systems to understand how the environment acts on them and to perform better in their tasks. Taking navigation as an example, recognizing landmarks or estimating pose of targets accurately help agents to construct map and plan their routes. Researchers have made efforts on extensive techniques to achieve various levels of information utilization. This includes data mining, pattern recognition, machine learning and deep learning, achieving from data to feature, feature to model and a further higher level of information utilization, which has applied in robotics, manufacture, social media and so on. While different systems require a different level of information utilization in various tasks, how to acquire information while trading off between utility and cost is still an interesting but challenging topic. The research on this issue will benefit a large range of applications on robotic, industrial and transportation systems, etc.
Assis. Prof. Meibao Yao, Jilin University, China
Meibao Yao received the B. S. and Ph. D. degrees at School of Astronautics of Harbin Institute of Technology in 2014 and 2019 respectively. She works as an assistant professor at the School of Artificial Intelligence of Jilin University. Additionally, she published more than 10 articles in journals indexed by SCI and conferences indexed by Ei including the International Journal of Robotics Research (IJRR) and International Conference on Robotics and Automation (ICRA). She was the area chair of the 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) and won the Best Presentation in Session Award. Moreover, her research interests include pattern recognition, machine learning, reinforcement learning and their applications in robotics.
Workshop 14 : Intelligent Robotic Manipulation and Grasping: Design, Control and Application
Intelligent robotic manipulation and grasping are vital technologies for many applications and operations in advancing manufacturing automation. They offer the possibility of widening robotic application fields by allowing the correct interaction of robots with artificial and natural environments, humans and other robots, extend the automation effort towards sectors where delicate, soft and limp materials have to be handled, improve the level of hygiene and cleanliness of products and services to match regulations and real needs, and open the chance to work at the micro and nano scale where human hands cannot be used, typically for assembly micro-systems or for pharmaceutical and medical applications. The development of qualified and successful manipulation and grasping tools and control algorithms requires multi and interdisciplinary works where mechanical creativity, surface interaction, physics knowledge, intelligent control competences, communication and information-sharing experience, sensors and actuation components miniaturization skills, cooperation and coordination strategies are harmonically joined.
The objective of this workshop is to provide researchers from academia or industry with a communication platform to share their latest achievements in the fields of intelligent manipulation and grasping. The scope of the workshop may include, but don’t limit to the following research topics: stable grasping strategy, obstacle-avoiding motion planning, object recognition, contact force sensing, grasping force control, dexterous gripper design, under-actuated mechanism and control, and grasping applications in industry, farming and service areas. We sincerely welcome prospective authors to submit related research papers about both: theoretical approaches and practical case reviews. Please name the title of the submission email with 'paper title/workshop title'.
Prof. Wenjie Chen, Anhui University, China
Wenjie Chen received his B. Eng. degree and M. Eng. degree in mechanical engineering at Beihang University in 1985 and 1988 respectively, and a Ph.D. degree in mechatronics at Nanyang Technological University in 1999. He works as a professor and doctoral supervisor at the School of Electrical Engineering and Automation of Anhui University. Additionally, he is the expert of 100 Talents Plan of Anhui Province, the senior member of IEEE, and the executive deputy director of Anhui Province Engineering Laboratory of Human-Machine Integration Systems and Intelligent Equipment. He is now leading a team to engage in the research of flexible assembly and intelligent robotic manipulation and grasping. Prof. Chen has more than 30 years’ experience in the research, design and development of industrial automation equipment, with particular expertise in the application of Mechanical Intelligence Technologies to solve adaptive problems in precision assembly and robot control. He presided or participated in over 5 projects funded by National Natural Science Foundation of China and dozens of robotic operation and assembly projects funded by Singapore government and/or industrial partners. His research achievements were successfully applied in the production line or assembly process of famous international enterprises such as Rolls-Royce, Philips, and PSA Singapore.
Workshop 15 : Intelligent Robot & Nonlinear Systems and Control & Modeling and Intelligent Control & Adaptive Control
Title 1: Intelligent Control, Robot Attitude Control, Path Planning, Control Algorithm, Anti-disturbance Control
Keywords:Intelligent Control, Robot Attitude Control, Path Planning, Nonlinear Systems Control Algorithm, Anti-disturbance Control
The anti- disturbance control of robot has been a research hot light in recent years. Robots are widely used in resource exploration, agricultural plant protection, industrial manufacturing, etc. To work in harsh environments, it is necessary to establish the nonlinear mathematical model of robots and design robust controllers to meet the requirements of various performance such as good attitude control stability, good dynamic response and high accuracy. And choosing an appropriate control algorithm such as adaptive control, robust control, anti- disturbance control, etc is the key.
In this workshop, our purposes include presenting recent developments and showing advances of anti-disturbance control in robot, path planning and intelligent control. Then we apply theoretical results to applications. Moreover, anti-disturbance control in robot has also attracted extensive attention on multi-joint robot tracking control, small unmanned aerial vehicle, service robot, adventure robot.
Prof. Xumei Lin, Qingdao University of Technology, China
Xumei Lin is a professor and Master Supervisor of School of Information and Control Engineering of Qingdao University of Technology, Academic leader in Control Science of Qingdao University of Technology. She received a Ph. D. degree at University of Science and Technology of China in 2006. Her main research interests include robot intelligent control, computer vision, path planning. She published over 30 research papers on journal and conference and 1 book in these areas. She participated in projects supported by Natural Science Foundation of Shandong Province of China and industrial companies.
Workshop 16 : Data Mining, Recommendation System, Decision Making
Title 1: Data Mining and Intelligent Recommendation
Keywords:Data Mining, Intelligent Recommendation, Statistical Analysis, Data Visualization, Business Intelligence
Data mining is the extraordinary process of revealing implicit, previously unknown and potentially valuable information from a large amount of data in a database, and is a hot issue in the field of artificial intelligence and database research. Data mining is also a decision support process, which is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, database, visualization and other technologies to analyze data in a highly automated manner, make inductive reasoning, and extract potential patterns from them to help decision makers adjust market strategies, reduce risks and make correct decisions. The business and industrial communities need to convert the huge amount of data generated into useful information and knowledge, which in turn can be used for a wide range of applications, including business management, production control, market analysis, engineering design, and scientific exploration.
This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of data mining and intelligent recommendation. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshop title”.
Assoc. Prof. Xuewei Li, Wuhan Sports University, China
Xuewei Li received a Ph. D. degree in Management Science and Engineering at Wuhan University. She works as an associate professor at the College of Sports Engineering and Information Technology of Wuhan Sports University. She has an interdisciplinary background and research experience in data analysis and data mining, business intelligence, and personalized recommendation research. She published more than 10 papers in domestic and international journals. She presided over and completed 3 provincial and ministerial level research projects, and participated in the research of the National Natural Science Foundation of China and the Humanities and Social Sciences Project of the Ministry of Education. In recent years, She has conducted research on data mining integrated intelligent decision-making problem, intelligent recommendation optimization, intelligent sports, intelligent education, etc.
Workshop 17 : Knowledge Graph and Semantic Computing
Knowledge graph (KG) provides effective resources for many important artificial intelligence tasks and was widely applied in all walks of life. Many industrial organizations regard KG as part of the core business. (e.g. Googles Knowledge Vault realizes the leap from keyword search to entity semantic search via KG.), many science communities use KG to aid scientific discovery (e.g. Know Life integrates diseases, symptoms, causes, risk factors, drugs, side effects, and more life science information to understanding complex diseases and supporting decisions.) and many not-for-profit projects release large-scale open KGs (e.g. DBPedia, Word Net, Freebase, etc) for natural language question answering, knowledge inference, prediction, completion, and so on. This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of knowledge graphs. Topics relevant to this workshop include, but are not limited to: (1)Knowledge extraction and knowledge graph construction; (2) Knowledge graph representation, completion, link prediction and reasoning; (3) Linked data, knowledge integration and knowledge graph storage management; (4) Knowledge graph application (visualization, semantic search, question and answer, intelligent recommendation, etc.).
Lecturer Zhijuan Du, Inner Mongolia University, China
Zhijuan Du was born in Inner Mongolia Autonomous Region, China in 1985. She received her Ph. D. degree at School of Information Renmin University of China in 2018. She works as a lecturer at College of Computer Science of Inner Mongolia University. She is the reviewer of journals ''Computer Science'' and conferences ''CAAIBDSC2021'', etc. Her research interests include knowledge graphs, recommendation systems, deep learning, and social big data mining. She published many articles on conferences and journals such as DASFAA, PAKDD, Journal of Computer Research and Development, Chinese Science, etc. She presided over projects supported by National Natural Science Foundation of China and provincial or ministry level projects and participated in National Key R&D Program of China and the State Key Program of National Natural Science Foundation of China.
Workshop 18 : Deep Representation Learning & Big Data Analytics
Title 1: Deep Representation Learning for Big Data Analytics
Keywords:Big Data Analytics, Deep Representation Learning, Long-tail Distribution, Domain Adaptation, Semantic Reasoning
In this era of big data, we have witnessed a dramatic growth in the volume, variety and complexity of data, including textual, imaging, video and time sequence datasets. These large scale and heterogeneous data arise from multiple sources and applications. So significant challenges arise for the design of effective, scalable algorithms and generalized frameworks to meet the multiple requirements of real-world tasks, e.g. understanding, recognition and control. Deep representation learning is a key component of various intelligent data analytic algorithms, with its capacity to discover the intrinsic structure of data. Consequently, it has become critical to explore advanced deep representation learning techniques for large-scale and heterogeneous data analytics.
This workshop aims to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of deep representation learning technology and understand how big data users can influence it. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with ''paper title_workshop title''.
Associate Prof. Guangyi Xiao, Hunan University, China
Guangyi Xiao received the B. Econ. degree at Hunan University in 2006, the M.Sc. and Ph.D. degrees at the University of Macau in 2009 and 2015, respectively in software engineering. He works as an associate professor at Colleague of Computer Science and Electronic Engineering of Hunan University. His research interests include partial-and-imbalanced transfer learning, computer vision, semantic representation, semantic integration, semantic interoperation, and collaboration systems, mainly applied to the fields of food-computing, law computing, e-commerce, and e-marketplace. He participated in National Natural Science Foundation, National Key Projects. He published tens of high-level research papers on TKDE, ACM MM, TII, JPDC, etc.
Bio-inspired robots have increasingly become common, diverse and ever-evolving recently due to their versatility, adaptability, and high motion performance. With the concepts from nature and biological systems, bio-inspired robotics is about simulating the structure, sensing system, drive system, and computational system of creatures to design the mechanisms that may solve problems in the engineering field. Developing a robot which is simplified, task-specific, multifunctional, and more effective than the system observed in nature is a major challenge for roboticists. In the last years, different types of bio-inspired robots have appeared like soft robots, flying robots, jumping robots, underwater robots, legged robots, multi-finger hands, etc. Bio-inspired robots for different tasks pose several new scientific issues to be solved related to modeling and control, which hinder the pace of scientific progress.
This workshop aims to bring together researchers working in several distinct communities to discuss the lessons learned, open issues, and future directions of bio-inspired robots and applications. Our goal is to show the latest research accomplishments investigate the new issues arising from the development of new types of bio-inspired robots. We encourage prospective authors to submit related distinguished research papers on this subject.
Prof. Yaguang Zhu, Chang'an University, China
Yaguang Zhu serves as a professor at School of Construction Machinery of Chang'an University. He is the head of the Department of Construction Machinery and deputy director of the Institute of Advanced Control and Robotics of Chang'an University. The main research directions are robots in engineering, bionic control, and electromechanical system collaboration technology. He presided over and participated in more than 30 projects and successively published more than 40 academic papers and 36 authorized patents. He also served as a reviewer for famous international journals such as Bioinspiration & Biomimetics, Robotics and Autonomous Systems, Journal of Bionic Engineering, SCIENCE CHINA Information Sciences. He is the editor of Frontiers in Neurorobotics, editorial board member of the International Journal of Robotic Engineering. He served as section chairman, member of organizing committee, and member of technical committee of ICARM 2020, RCAE 2019, ICCSSE 2020, ICCAR 2021, and other international conferences.