Speakers -2022 4th International Conference on Information Science, Electrical and Automation Engineering（ISEAE 2022）

Keynote Speakers

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Prof. Fushuan Wen, Zhejiang University, China（IEEE Fellow）

文福拴教授，浙江大学

Title: Localized Energy Transaction and Transactive Energy in Emerging Power Systems

Abstract: The following issues will be addressed in this speech:

1.Introduction to localized energy transaction and transactive energy

2.Localized energy transaction: challenges and solutions

3.Transactive energy among microgrids

4.Intraday energy transactions among distributed energy resources (DERs)

5.Applications of blockchain in localized energy transaction and transactive energy

6.Research problems to be addressed

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Prof. Lide Fang(Dean of the School), Hebei University, China

方立德教授（院长），河北大学质量技术监督学院

Title: Research on remote metering and calibration based on modern communication and network technology

Abstract: There are two problems in the traditional method of quantity value transmission and traceability. One is that it must be carried out offline. The working measuring instruments must be removed regularly and sent to the corresponding technical institutions for verification or calibration under the specified experimental environment, which affects the normal production process. The other is that they are both static or steady-state testing processes, and can only obtain the basic error of the instrument under the reference working conditions, Additional errors in field applications cannot be obtained. The development of instrument Internet of things technology makes people see the hope to solve these two problems. If the remote value transmission and traceability system of measuring instruments can be established by using Internet of things technology, the additional error of instruments can be obtained, which fundamentally ensures the accuracy and reliability of on-site measurement data. This paper investigates the research status of remote value transmission and Traceability Technology in the field of measurement instruments at home and abroad, puts forward three ways to realize remote value transmission in the field of measurement, namely, transmission standard table, standard table on site and transmission value method, and compares the difficulty of these three ways, The research progress of remote value transmission and Traceability Technology Based on Electrical and optical methods is introduced.

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Prof. Lidan Wang, Southwest University, China

王丽丹教授，西南大学

Title: Coming Soon......

Abstract: Coming Soon......

Experience: Dr. Lidan Wang is a Professor, doctoral supervisor in the School of Artificial Intelligence, Southwest University, China, and Secretary General of Chongqing Association for Artificial Intelligence. In 2008, she received her Ph.D. degree in Computer Software and Theory from Chongqing University, China. She visited Imperial College London in the UK from 2010 to 2011 year, University of Windsor in Canada in 2013, Nanyang Technological University in Singapore from 2016 to 2017, Texas A&M University in Qatar in 2017, and University of Tasmania in Australia in 2018, respectively. Her research interests include brain-inspired computing & memristor neuromorphic chips, nonlinear systems & chaotic circuits, artificial neural networks & FPGA technology, and various application fields including big data science, image processing, and biomedical engineering. She was awarded numerous grants from the National Key Research and Development Program of China and the National Natural Science Foundation of China, accumulating over ¥10 million in research grant funds. She has published over 100 academic papers indexed by SCI/SCIE, and more than thirty students have received their master or doctoral degrees under her supervision. In addition, Dr. Wang serves as an Associate Editor for the Artificial Intelligence Review and as a Topic Editor for Electronics. Dr. Wang also serves as Secretary-general of Chongqing Artificial Intelligence Society.

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Prof. Kai Yang, Huazhong University of Science and Technology, China

杨凯教授，华中科技大学

Title: Research on direct-driving PM machine and its control system

Abstract: To reach higher torque density and lower torque ripple, a novel axial-radial flux permanent magnet machine (ARFPMM) and its control system are proposed. T-type SMC core, axial rotor, and radial rotor are applied to support axial-radial flux path and make full use of the space. A dimensionality reduction method is proposed to transform the FEA model from 3D to 2D. The error between the proposed 2D model and the standard 3D model is less than 1%, while the calculation time is reduced more than 90%. A new topology of fractional slot concentrated winding (FSCW) with unbalanced phase belt is proposed, which reduces the harmonic magnetomotive force by 76%. The concentration effect and stratification effect of FSCW is first introduced and the influence on short pitch factor is analyzed. The high frequency harmonic magnetomotive force is reduced by optimizing the width of stator tooth. Asymmetric bidirectional skewing technology is proposed to optimize the cogging torque. The cogging torque is reduced by 86% while the average torque is reduced by 0.3%.

On the other hand, the torque ripple of the machine is also relevant to the harmonic components of the current. Therefore, the control strategy of such kind of machine is also worth researching. To suppress current harmonics of the machine, a novel ADRC controller combining a complex coefficient extended state observer (CCF-ESO) is proposed. CCF-ESO takes advantage of the orthogonality of d-axis current harmonics and q-axis current harmonics to accurately extract AC disturbance. Then, the current loop ADRC controller based on the CCF-ESO is able to achieve a desired AC disturbance rejection performance. Experimental results shows that the proporsed method can reduce the 6th order harmonics of d- and q-axis by 42%.

ISEAE 2021

Prof. Xiaoqiang Yan, University of Science & Technology Beijing

闫晓强教授，北京科技大学

Title: Vibration control technology and industrial application of modern large rolling mill
Abstract: Rolling mill vibration is an urgent technical problem in the field of rolling in the world in the past 60 years. When rolling high strength thin strip steel, the rolling mill often has strong vibration phenomenon, which leads to defects such as vibration marks on the surface of strip and roll. It seriously affects the surface quality of strip steel and can not meet the needs of high-end users. At the same time, it also threatens the safety production of rolling mill and reduces the service life of parts, which has become the bottleneck of high-quality thin strip production in iron and steel enterprises.
After 13 years of research on the vibration of 23 rolling mills in China, the research group has expand from the traditional research methods to the vibration energy, from the single parameter research to the multi parameter coupling effect research, from the influence factors of rolling mill itself to the whole flow field of casting and rolling, and found the "ghost" of inducing rolling mill vibration. The theory system of multi state coupling vibration in electromechanical and hydraulic field of rolling mill is established, the heredity of vibration gene in casting and rolling process is revealed, the coupling vibration decoupling vibration suppressor of rolling mill is invented, and the cooperative control technology of continuous casting and rolling mill is developed, so that the rolling mill vibration is significantly suppressed and the enterprise obtains significant economic benefits.
In 2018, the appraisal expert group of China metal society and China Heavy Machinery Industry Association in 2020 agreed that the achievement has reached the international leading level.
In recent three years, rolling mill vibration research has won three first prizes of provincial and ministerial level scientific and technological progress, and has become a key promotion project of China Society of metals.

Prof. Jie Zhu, Shanghai Jiao Tong University
朱杰教授，上海交通大学

Title: The Efficient and Scalable Artificial Intelligence in Healthcare

Abstract: As the pace of life and work accelerates, it is important to monitor the health of individuals on a daily basis. However, few of us can get access to such treatment because of the expensive cost and the lack of time. Now, artificial intelligence enables people to make use of the professional treatments which could only be provided by specialists in the past. We have done a lot of works in the healthcare fields. For example, we established an emotional disorder diagnosis system to learn and judge people's mental state through people's voice information by a deep learning based method. For autism spectrum disorder (ASD), we built an intelligent diagnosis system based on distinguishing small differences in fine-grained action understanding and recognition in videos. Clinical trials have proven that the proposed diagnosis systems can obtain a high-confidence diagnosis results, which have certain advantages among similar researches. Having said that, the superior performance comes from the deeper and wider neural networks, which leads to high computational cost and high memory footprint. As a result, such neural networks are hard to be deployed on people’ s daily life, i.e., wearable devices. In order to alleviate this issue, we have put forward to several structured network pruning methods and knowledge distillation based methods. Experimental results show that the proposed methods can greatly compress the neural networks with negligible accuracy loss.

Prof. Feng Zhao, University of Science and Technology of China
赵峰教授，中国科学技术大学
Title: Machine Learning Using Biometrics – An Introduction

Abstract: Machine learning techniques have been widely used in many fields, such as fingerprint recognition, face recognition, speech recognition, natural language processing, and so on. In this talk, I will give an introduction to biometrics-based machine learning, including the concepts, seven key steps, applications and benefits.