CNCC2023 will be held in Shenyang from October 26 to 28, during which 129 technical forums will be held, covering more than 30 directions such as artificial intelligence, security, computing +, software engineering, education, network, chip, cloud computing, etc. This article introduces the "Edge Computing Enables Industrial Digital Transformation and Upgrading" Technology Forum to be held on October 28.
As a bridge connecting the physical and digital worlds, edge computing has become an indispensable key element of industrial digital transformation to meet the needs of industrial digitalization in terms of agile connection, real-time business, data optimization, and application intelligence. This forum focuses on inviting well-known scholars and industry experts in the field to discuss the development trend of international frontier technology and application scenarios in the industrial field of edge computing, in order to promote in-depth exchanges and cooperation in the academic, technical and industrial aspects of edge computing in China.
To register and learn more about the technical forum, please identify the QR code below to enter the official website of CNCC2023.
Edge intelligence is a new paradigm of edge computing to empower ubiquitous distributed artificial intelligence, and is one of the key supporting technologies for realizing the vision of intelligent connection of all things, which has been widely used in industrial systems, equipment independent optimization decision-making, fault diagnosis, navigation and autonomous driving, and has become an indispensable key element of industrial digital transformation.
At present, edge computing also faces challenges such as the access and massive real-time data generated by a large number of heterogeneous devices on the edge side to the system operation efficiency and security, and how to ensure the balance of data privacy, scene heterogeneity and learning efficiency while training models.
In view of the above challenges, this forum discusses issues such as massive heterogeneous device access, data privacy and security, heterogeneity and learning of scenarios, coordination and balance of training, and improving system operation efficiency, and explores application scenarios and fields such as autonomous optimization decision-making on the industrial edge side, edge gesture recognition, and cloud-edge collaboration for mobile perception.
Forum arrangement
| order | topic | Keynote speaker | unit |
| 1 | Human-machine collaborative control and cross-domain real-time optimization based on edge computing | Zeng Peng | Shenyang Institute of Automation, Chinese Academy of Sciences |
| 2 | Intelligent collaborative computing at the edge | Chen Xu | Sun Yat-sen University |
| 3 | Multimodal awareness of cloud-edge collaboration | He Shibo | Zhejiang University |
| 4 | Federated learning approach for heterogeneous scenarios | Chengnan | Xidian University |
Chairman of the Forum
Wang Jun
Professor/PhD supervisor, Dean of School of Computer Science and Technology, Shenyang University of Chemical Technology
Director of Liaoning Provincial Key Laboratory of Intelligent Technology of Chemical Process Industry
Visiting scholar at Demontfort University, visiting scholar of Sakura Science and Technology Program in Japan, postdoctoral fellow of the Chinese Academy of Sciences; National Excellent Innovation and Entrepreneurship Mentor, Provincial Famous Teacher, Provincial College Innovation Talent, Provincial Excellent Science and Technology Worker, Provincial Excellent Teacher, Shenyang Leading Talent, National Engineering Education Certification Expert of the Ministry of Education, National Science and Technology Expert of the Ministry of Science and Technology; Expert in the review of master's and doctoral theses of the Ministry of Education, specially invited expert on the establishment of provincial scientific research integrity, member of the advisory committee, and member of the provincial discipline review group.
Co-Chairs
Liu Yiyang
Researcher of Shenyang Institute of Automation, Chinese Academy of Sciences
Chairman of CCF YOCSEF Shenyang Sub-forum, researcher of Shenyang Institute of Automation, Chinese Academy of Sciences, graduate tutor. In recent years, he has been mainly engaged in the research of industrial digital twin modeling, simulation and optimization, intelligent optimization control and other fields. He is currently a member of the National Automation System and Integration Standardization Technical Committee (TC159), an expert in the field of advanced manufacturing technology of "National Technology Forecasting" of the Ministry of Science and Technology, and was selected into the "Xingliao Talent Program". As the project leader, he has presided over the key special project of the National Key R&D Program "Industrial Software", the National Natural Science Foundation of China, the Industrial Internet Innovation and Development Project, and more than 30 projects entrusted by enterprises.
Forum speaker
Zeng Peng
Deputy Director, Researcher/PhD Supervisor, Shenyang Institute of Automation, Chinese Academy of Sciences
Executive Deputy Director of Liaoning Liaohe Laboratory. Chairman of the Edge Computing Special Committee of the Chinese Society of Automation, Vice Chairman of the Information Domain Physical System Control and Decision Committee of the Chinese Society of Automation, and expert of the National Key R&D Program "Industrial Software" Key Special Expert Group. He has won one first prize for national standard innovation contribution, one first prize for technological invention in Liaoning Province, one first prize for scientific and technological progress in Liaoning Province, and one first prize for academic achievements in natural sciences in Liaoning Province.
Human-machine collaborative control and cross-domain real-time optimization based on edge computing
The introduction of industrial Internet and edge computing technology will greatly change the industrial production operation mode, will form a new mode of human-machine collaborative precision operation for special processes, and propose a new architecture based on edge computing to build a multi-level closed-loop of human-machine collaboration in view of the challenging problems faced by the new model, such as multi-source random error perception and estimation, human-machine collaborative high transparency and high-precision control, and cross-domain tolerance optimization of design and manufacturing cooperation. New theory and method of human-machine transparent collaborative control based on real-time perceptual feedback.
Chen Xu
Professor, School of Computer Science, Sun Yat-sen University
He served as the director of the Institute of Advanced Network and Computing Systems and the deputy director of the National and Local Joint Engineering Laboratory, and was selected as a German Humboldt Scholar, a national young talent program and a high-level talent program in Guangdong Province. He has undertaken projects including the National Natural Science Foundation of China Joint Key Project, the National Natural Science Foundation of China Big Data Center Project, Guangdong Innovation Team and other projects. He has won IEEE Distinguished Lecturer, Hong Kong Young Scientist Award, IEEE Computer Society Annual Best Paper Award runner-up, IEEE INFOCOM/IWQoS/ICC Best Paper Award and other academic honors. At present, he serves as the editorial board member of the internationally renowned journals IEEE JSAC Series, TWC, TVT, and the Journal of the Information Institute of the Chinese Academy of Engineering.
Intelligent collaborative computing at the edge
Edge intelligence is a new paradigm of edge computing to empower ubiquitous distributed artificial intelligence, and is one of the key supporting technologies to realize the vision of intelligent connection of everything. This report will introduce the latest research progress of cloud-edge-device collaborative edge intelligence technology in distributed heterogeneous edge computing environment, including high-performance edge collaborative inference for graph deep neural networks, cloud-edge-end multi-level federated learning for data privacy protection, and edge collaborative intelligent robot applications.
He Shibo
Professor at Zhejiang University
He is a long-term professor at the School of Control Science and Engineering of Zhejiang University, the director of the Collaborative Innovation Center for Industrial Cyber-Physical Fusion Systems, and the deputy director of the Institute of Industrial Control. In 2014, he was selected into the National Young Talents Program and the 100 Talents Program of Zhejiang University. His research interests include the Internet of Things and big data analytics. He has published (including received) more than 160 academic papers, including internationally renowned journals PNAS, Nature Communications, Nature Genetics, IEEE ToN, IEEE JSAC, IEEE TMC, IEEE TTC, IEEE TWC, etc., as well as flagship conferences ACM CCS, ACM MobiHoc, IEEE INFOCOM, IEEE RTSS, etc. He has edited 2 textbooks and 2 academic works. The research results have been cited more than 7,000 times by his peers Google Scholar, including more than 4,000 citations in the Web of Science core collection. He has won many honors such as the IEEE Scalable Technology Committee Mid-career Research Achievement Award, the Ministry of Education Youth Science Award and the First Prize of Natural Science, the IEEE Communications Society Asia-Pacific Outstanding Young Research Scholar Award, and the IEEE Globecom Best Paper Award in 6 international conferences. He has served as the editorial board member of 5 journals and the chairman/thematic chair of the program committee of IEEE GlobeCom, i-SPAN, IEEE ICC, ScalCom and other international conferences.
Multimodal awareness of cloud-edge collaboration
In recent years, millimeter-wave radar has achieved a wide range of applications in autonomous driving, smart home systems, and smart healthcare due to its low power consumption, high spatial resolution, and resilience to temperature and light conditions. This report will first introduce the group's recent research in millimeter-wave radar perception: by integrating user recognition at the lowest cost, the existing gesture recognition system is enhanced, so as to achieve more personalized gesture interaction at the edge end and improve user interaction experience. At the same time, the continuous progress of edge intelligence has also triggered the emergence of cloud-edge collaboration as an innovative paradigm for mobile sensing and application. Finally, the recent work on cloud-edge collaborative sensing will be introduced, using multimodal data on the cloud to collaborate with edge-end perception model inference and application.
Chengnan
Professor/PhD supervisor, School of Communication Engineering, Xidian University
He was selected into the National High-level Young Talents Program, Shaanxi Province High-level Young Talents, and IEEE Communication Society Asia-Pacific Young Talents. He has published more than 110 journal papers in IEEE Transactions and other top journals. He serves on the editorial boards of IEEE Internet of Things, IEEE Transactions on Vehicular Technology, IEEE Open Journal of the Communications Society, and Peer-to-Peer Networking and Applications. His current research focuses on B5G/6G, AI-driven future networks, and space-space-ground integrated networks.
Federated learning approach for heterogeneous scenarios
Federated learning has attracted much attention because it can perform collaborative training while protecting data privacy, but the heterogeneity of scenarios brings great challenges to federated learning. Facing the challenge of heterogeneity, this report explores some proven solutions. On the one hand, traditional methods are difficult to adapt to client heterogeneity, resulting in reduced training efficiency and privacy leakage risks. Therefore, we propose a new type of distributed learning scheme, RingSFL. By combining federated learning with model segmentation techniques in the ring topology, the adaptability of the system to heterogeneous clients is improved and data privacy is enhanced. On the other hand, considering that hyperparameters in federated learning are critical to model performance, weak devices are expensive to participate in training, and the same hyperparameter group is difficult to adapt to the heterogeneous data of each client. Therefore, starting from the policy gradient, we propose a solution for personalized adjustment of hyperparameters to better adapt to heterogeneous or dynamic scenarios and improve model performance.
This year marks CNCC's 20th anniversary. Over the past two decades, CNCC has gradually developed to cover 129 technical forums in dozens of directions, with more than 700 domestic and foreign speakers actively participating and more than 13,000 people registering as an annual event in the field of computing. Twenty years of continuous surpassing, as an annual event with many participants, large scale and high level in the field of domestic computing, CCF will carefully plan to bring participants a cutting-edge collision and look forward to the future technology feast, so that every participant can enhance their professional value and gain momentum in the super large professional platform of CNCC! Wait for you to come, act now, welcome to participate and register!