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arXiv 2023年

作者： Dang, Zhuohang Luo, Minnan Jia, Chengyou Dai, Guang Wang, Jihong Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi’an710049 China The SGIT AI Laboratory Xi’an710048 China The State Grid Shaanxi Electric Power Company Ltd. State Grid Corporation of China Xi’an710048 China The School of Information Science and Technology University of Science and Technology of China Hefei230026 China Abu Dhabi United Arab Emirates Baidu Inc Beijing100085 China

Encoding only the task-related information from the raw data, i.e., disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Although significant advances have been made by regularizing the information in representations with information theory, two major challenges remain: 1) the representation compression inevitably leads to performance drop;2) the disentanglement constraints on representations are in complicated optimization. To these issues, we introduce Bayesian networks with transmitted information to formulate the interaction among input and representations during disentanglement. Building upon this framework, we propose DisTIB (Transmitted Information Bottleneck for Disentangled representation learning), a novel objective that navigates the balance between information compression and preservation. We employ variational inference to derive a tractable estimation for DisTIB. This estimation can be simply optimized via standard gradient descent with a reparameterization trick. Moreover, we theoretically prove that DisTIB can achieve optimal disentanglement, underscoring its superior efficacy. To solidify our claims, we conduct extensive experiments on various downstream tasks to demonstrate the appealing efficacy of DisTIB and validate our theoretical analyses. Copyright © 2023, The Authors. All rights reserved.

关键词： Information theory

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Price change prediction of ultra high frequency financial data based on temporal convolutional network

arXiv

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arXiv 2021年

作者： Dai, Wei An, Yuan Long, Wen School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Through in-depth analysis of ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are constructed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market. Copyright © 2021, The Authors. All rights reserved.

关键词： Long short-term memory

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Study of Reduction of Inrush Current on a DC Series Motor with a Low-Cost Soft Start System for Advanced Process Tools 6th

Study of Reduction of Inrush Current on a DC Series Motor wi...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Amesimenu, Governor David Kwabena Chang, Kuo-Chi Sung, Tien-Wen Wang, Hsiao-Chuan Shyirambere, Gilbert Chu, Kai-Chun Hsu, Tsui-Lien School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

The existence of high armature current is the main limitation while starting a DC series motor. This initial current can have dangerous effects on the DC motor such as damage of windings and reduction in the lifetime of the machine. There are two basic methods of starting of DC motor namely the resistor starting method and the soft start using solid-state devices. In this project, to lessen the inrush current, the solid-state method was adopted to start the motor smoothly. The system uses Arduino Atmega328p-PU Microcontroller to send pulses to the motor driver which regulates the running of the motor. By focusing on DC series motor, power electronic converters and microcontrollers, the system was modeled and simulated in Proteus ISIS Software. The results recorded from the soft starter system were compared with a direct online starting system considering the starting current and voltage of the motor with an open loop control system. The soft starter system was able to decrease the direct online current value from 0.996A to 0.449A which represents approximately 28.1% reduction of the starting current to protect the motor. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DC motors

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An Optimizing Multilevel Thresholding for Image Segmentation Based on Hybrid Swarm Computation Optimization 16th

An Optimizing Multilevel Thresholding for Image Segmentation...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Dao, Thi-Kien Wang, Hong-Jiang Yu, Jie Nguyen, Huu-Quynh Ngo, Truong-Giang Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Image segmentation

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Study of the Intelligent Algorithm of Hilbert-Huang Transform in Advanced Power System 6th

Study of the Intelligent Algorithm of Hilbert-Huang Transfor...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Zhang, Cheng Liu, Jia-Jing Chang, Kuo-Chi Wang, Hsiao-Chuan Lin, Yuh-Chung Chu, Kai-Chun Hsu, Tsui-Lien School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

With the rapid increase in population and electricity consumption, power grid has long formed a large-scale interconnection of systems. The power system is a complex multi-dimensional dynamic system, the traditional system parameter processing method has gradually shown its limitations, which affects the stability and reliability analysis of the system. This study introduces a popular time-frequency intelligent application analysis methodology for nonlinear nonstationary signalsHilbert-huang transform (HHT) algorithm, then summaries the applications of HHT method for low frequency oscillation of advanced power system, power quality detection and harmonic analysis with combining the research achievements of domestic and foreign scholars in recent years. However, this study discusses the interpolation function and endpoint effect of HHT method in practice and further research on its application in advanced power system. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hilbert-Huang transform

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Study of Advanced Power Load Management Based on the Low-Cost Internet of Things and Synchronous Photovoltaic Systems 6th

Study of Advanced Power Load Management Based on the Low-Cos...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Turatsinze, Elias Chang, Kuo-Chi Li, Pei-Qiang Chang, Cheng-Kuo Chu, Kai-Chun Zhou, Yu-Wen Omer, Abdalaziz Altayeb Ibrahim School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

The main problem with todays power consumption is to determine which load is consuming much power so that it can be disconnected to reduce high electrical bills. The main concern of todays researches is to find an alternative way of installing a grid-connected PV system or other renewable resources free of harmful gases so that they add up to the electrical grids to provide enough power. This study presents how to synchronize a photovoltaic system with a grid to save money and electrical load management based on IoT technology. However, current sensing is used to sense the current when loads are connected and send the instantaneous value of current to the Arduino Uno which is programmed to find the total power consumed and then ESP 8266 WiFi module uploads the data to the Thingspeak for analysis and monitoring where utility company or users can access the information about the usage of electricity since many PV systems are in remote areas. A simulation of an inverter with PLL circuit for generating the desired signal waveform and frequency is done in MATLAB/Simulink and the objective was successfully achieved which showed that the output voltage is a sinusoidal waveform of the same frequency and same phase angle and a current sensor can be installed at the output of the inverter to sense the power generated for analysis and monitoring of the proposed PV system. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Design of Leakage Current Monitoring System for Low Voltage Power Grid Based on NB-IoT 6th

Design of Leakage Current Monitoring System for Low Voltage ...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Xu, GuanWei Zheng, RongJin Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Institute of Engineering Smart Grid Simulation Analysis and Integrated Control of Fujian University Engineering Research Center Fuzhou Fujian China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030697167

Leakage current of low-voltage power grid lines will cause major safety hazards such as personal electric shock and electrical fire. Traditional leakage current protectors can not play their due protection role due to their quality problems and aging, and other reasons. When leakage current accidents occur, traditional manual troubleshooting is inefficient. Aiming at the safety of leakage current in a low-voltage power grid, a new leakage current monitoring system is designed based on NB-IoT technology. The system includes the detection and transmission of leakage current and the construction of the cloud platform, which can realize the real-time monitoring of leakage current in low-voltage power grid and prevent and solve the leakage current safety problems. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Leakage currents

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A brief overview of kernel methods with prior information

引用

Procedia Computer Science 2022年 199卷 269-275页

作者： Saiji Fu Shiding Sun Xinfeng Wang Dalian Liu School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China Institute of Mathematics and Physics Beijing Union University Beijing 100101 China Institute of Fundamental and Interdisciplinary Sciences Beijing Union University Beijing 100101 China

With the progress of human society and the development of computer technology, more and more experience has been accu¬mulated, which can serve as prior information in model training. With the assistance of prior information, the performance of some machine learning algorithms has been significantly improved. In this article, we mainly discussed four types of prior information, namely knowledge sets, fuzzy sets, universum data, and privilege information. Specifically, (1) the knowledge sets are constraints composed of domain experience; (2) the fuzzy sets provide the importance of each instance through the fuzzy membership function; (3) the universum data belongs to none of the target classes, but reflects the domain information related to the field of interest; (4) the privileged information can provide more relevant information about training examples, but is not available during the testing phase. In order to illustrate how the prior information is integrated into the kernel methods, the cor¬responding representative models are reviewed in detail, including knowledge-based support vector machine(KSVM), fuzzy support vector machine(FSVM), universum support vector machine(USVM) and privileged support vector machine(SVM+). In addition, this article briefly introduced the most related and advanced research, including the deep learning extensions with prior information. Finally, the current challenge and the future trends of incorporating prior information into machine learning are discussed.

关键词： prior information kernel methods support vector machine deep learning

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A Switch Architecture for Time-Triggered Transmission with Best-Effort Delivery

arXiv

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arXiv 2023年

作者： Li, Zonghui Zhu, Wenlin Shin, Kang G. Wan, Hai Song, Xiaoyu Yang, Dong Ai, Bo The Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2121 United States The Software School Tsinghua University Beijing100084 China The Department of Electrical and Computer Engineering Portland State University PortlandOR United States The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

In Time-Triggered (TT) or time-sensitive networks, the transmission of a TT frame is required to be scheduled at a precise time instant for industrial distributed real-time control systems. Other (or best-effort (BE)) frames are forwarded in a BE manner. Under this scheduling strategy, the transmission of a TT frame must wait until its scheduled instant even if it could have been transmitted sooner. On the other hand, BE frames are transmitted whenever possible but may miss deadlines or may even be dropped due to congestion. As a result, TT transmission and BE delivery are incompatible with each other. To remedy this incompatibility, we propose a synergistic switch architecture (SWA) for TT transmission with BE delivery to dynamically improve the end-to-end (e2e) latency of TT frames by opportunistically exploiting BE delivery. Given a TT frame, the SWA generates and transmits a cloned copy with BE delivery. The first frame arriving at the receiver device is delivered with a configured jitter and the other copy ignored. So, the SWA achieves shorter latency and controllable jitter, the best of both worlds. We have implemented SWA using FPGAs in an industry-strength TT switches and used four test scenarios to demonstrate SWA's improvements of e2e latency and controllable jitter over the state-of-the-art TT transmission scheme. Copyright © 2023, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Simplification of 3D City Models Based on K-Means Clustering 6th

Simplification of 3D City Models Based on K-Means Clustering

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6th International Conference on data Science, ICDS 2019

作者： Cheng, Hui Li, Bingchan Mao, Bo Jiangsu Key Laboratory of Modern Logistics Jiangsu Key Laboratory of Grain Big Data Mining and Application College of Information Engineering Nanjing University of Finance and Economics NanjingJiangsu210023 China College of Electrical Engineering and Automation Jiangsu Maritime Institute No. 309 Gezhi Road NanjingJiangsu China

ISBN: (纸本)9789811528095

With the development of smart cities, 3D city models have expanded from simple visualization to more applications. However, the data volume of 3D city models is also increasing at the same time, which brings great pressure to data storage and visualization. Therefore, it is necessary to simplify 3D models. In this paper, a three-step simplification method is proposed. Firstly, the geometric features of the building are used to extract the walls and roof of the building separately, and then the ground plan and the single-layer roof are extracted by the K-Means clustering algorithm. Finally, the ground plan is raised to intersect with the roof polygon to form a simplified three-dimensional city model. In this paper, experiments are carried out on a certain number of 3D city models of CityGML format. The compression ratio of model data is 92.08%, the simplification result shows better than others. © Springer Nature Singapore Pte Ltd 2020.

关键词： Roofs

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