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9th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2020

作者： Cherednichenko, Aleksey Huang, Xinqi Wang, Yanyan Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Science and Technology on Complex System Control Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9781728198057

This paper presented a p-norm constraint Lorentzian adaptive algorithm to estimate the sparse multi-path channel ina-stable noise environment. We derived the proposed algorithm based on the Lagrange multiplier method and we also evaluated the performance of the developed adaptive algorithm via estimating a sparse multi-path channel. The gotten results on the basis of computer simulations proved that the developed Lorentzian adaptive algorithm is superior to the popular method in the comparisons. © 2020 IEEE.

关键词： Channel estimation

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A Novel Microwave Humidity Sensor Based on a Complementary Interdigital Capacitor Resonator

SSRN

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SSRN 2024年

作者： Wang, Litian Chen, Jie Li, Dan Qian, Lirong Xiong, Yang Miao, Lin Zhang, Rong Li, Cuiping Tianjin Key Laboratory of Film Electronic and Communication Devices School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Resonant for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China

This study presents a novel microwave humidity sensor that utilizes a complementary interdigital capacitor resonator (CIDCR), an advanced design derived from the traditional complementary split ring resonator (CSRR) structure. The CIDCR design enhances the sensor's performance by concentrating the electric field in a more compact region, leading to improved sensitivity and miniaturization. The sensor is coated with a composite film of chitosan (CS) and polyacrylamide-glycerol (PAMG), combining the high moisture sensitivity of PAMG with the stability of CS. The CIDCR-based sensor demonstrates superior performance, including high sensitivity, a broad detection range, rapid response/recovery times, and excellent long-term stability. Density functional theory (DFT) calculations further elucidate the moisture-sensitive mechanisms at the molecular level, highlighting the role of hydrogen bonding in the composite film. Application tests confirm the sensor's potential for use in respiratory monitoring, non-contact sensing, and health monitoring, showcasing the advantages of the CIDCR structure in achieving enhanced humidity sensing capabilities. © 2024, The Authors. All rights reserved.

关键词： Humidity sensors

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High-Performance Nonlinear control for Three-Phase Two-Level Boost PFC Rectifier

High-Performance Nonlinear Control for Three-Phase Two-Level...

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2024 International Conference on Materials and Energy: Energy in Electrical Engineering, ICOME-EE 2024

作者： Sriprang, Songklod Nahid-Mobarakeh, Babak Takorabet, Noureddine Pierfederici, Serge Siangsanoh, Apinya Yodmanee, Kanapot Mungporn, Pongsiri Thounthong, Phatiphat Poonnoy, Nitchamon Bizon, Nicu Suyata, Thong-In Noosawang, Sakrit Université de Lorraine GREEN-ENSEM Laboratory 54505 France Rajamangala University of Technology Rattanakosin Wang Klai Kangwon Campus Petchkasem RD Prachuap Khiri Khan77110 Thailand Université de Lorraine LEMTA Laboratory 54505 France Thailand Thai-French Innovation Institute King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Faculty of Technical Education Dept. Teacher Training in Electrical Engineering Thailand University of Pitesti Faculty of Electronics Communication and Computers Pitesti110040 Romania University of Technology Faculty of Rajamangala Department of Electrical Engineering Thanyaburi 39 Moo 1 Rangsit-Nakhonnayok Road Pathum Thani 12110 Thailand King Mongkut's University of Technology Thonburi Faculty of Engineering Dept. of Control System and Instrumentation Engineering Bangkok10140 Thailand

ISBN: (纸本)9798331531577

This paper presents a high-performance nonlinear control strategy for a three-phase, two-level boost Power Factor Correction (PFC) rectifier. The proposed control method aims to achieve superior performance in terms of power factor correction, output voltage regulation, and dynamic response under various operating conditions. Utilizing advanced nonlinear control techniques, the system ensures optimal tracking of the reference signals, minimizing total harmonic distortion (THD) and improving overall efficiency. The effectiveness of the control strategy is validated through extensive simulations and experimental results, demonstrating significant improvements in the transient and steady-state performance of the rectifier. This approach provides a robust and efficient solution for PFC applications in modern power electronics systems. © 2024 IEEE.

关键词： Nonlinear control systems

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End-to-end Flow Scheduling Optimization for Industrial 5G and TSN Integrated Networks

End-to-end Flow Scheduling Optimization for Industrial 5G an...

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IEEE Conference on Global communications (GLOBECOM)

作者： Houling Liu Mingyan Li Fuqiang Gu Qihao Li Weiting Zhang Songtao Guo College of Computer Science Chongqing University P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China P. R. China College of Communication Engineering Jilin University Changchun P. R. China School of Electronic and Information Engineering Beijing Jiaotong University Beijing P. R. China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The integrated of the fifth generation (5G) and time-sensitive networking (TSN) is a promising approach to meet the requirements of deterministic forwarding with extremely low latency and high flexibility for the Industrial Internet of Things (IIoT). However, due to the large dissimilarity of protocol stacks, the 5G system normally serves as a logical bridge for TSN, performing hold-and-forward operations in Base Stations (BSs) under the current 5G-TSN frameworks. To provide ubiquitous and seamless connectivity for IIoT devices, this paper focuses on the integrated enhancement of 5G and TSN by optimizing the scheduling of time-sensitive flows with end-to-end latency of 5G-TSN transmission taken into consideration. Specifically, we propose a novel architecture named GF-CQF, which combines 5G Grant-free (GF) access with TSN Cyclic Queuing and Forwarding (CQF). Subsequently, the distributed flow scheduling problem based on GF-CQF architecture is established due to the high timeliness demands. To alleviate the impact of the uncertainty inherent in 5G channels on end-to-end deterministic transmission, a feature-aware decentralized real-time scheduling (FDRS) policy based on Multi-agent Reinforcement Learning is proposed. FDRS allows each agent at BS to adaptively allocate TSN injection slots for flows mainly based on dynamic 5G transmission performance and TSN network resource state so that TSN queue overflow can be avoided and flow delay constraints can be guaranteed. Simulations show that FDRS offers superior scheduling capabilities under the limited TSN queue resources.

关键词： Wireless communication Training Job shop scheduling Uncertainty Protocols 5G mobile communication Reinforcement learning Real-time systems Resource management Industrial Internet of Things

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Energy Consumption Prediction for Manufacturing in Industrial IoT Based on Heterogeneous GNN

Energy Consumption Prediction for Manufacturing in Industria...

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IEEE International Conference on communications in China (ICCC)

作者： Xuan Su Mingyan Li Qihao Li Chao Chen Songtao Guo Xu Wang College of Mechanical and Vehicle Engineering Chongqing University Chongqing P. R. China College of Computer Science Chongqing University Chongqing P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China College of Communication Engineering Jilin University Changchun P. R. China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Energy consumption prediction plays a crucial role in green manufacturing for industry, providing a theoretical foundation for energy-saving planning, workshop scheduling, and process optimization. However, owing to the processing complexity of mechanical products and the multiplicity of production information, previous methods can only achieve prediction for individual devices, which are quite restrictive to collaborative manufacturing planning. To deal with product-level energy management, this paper first proposes a heterogeneous manufacturing association graph (HetMG) which contains various manufacturing elements including product projects, decomposed orders and processing devices. Then, with HetMG, an energy consumption prediction method, HetEP, is introduced based on heterogeneous graph neural network (GNN), which incorporates Relational Graph Convolutional Networks (R-GCNs), and Long Short-Term Memory (LSTM) to mine the spatial-temporal relation of heterogeneous elements during the manufacturing process. Moreover, Graph Attention Network (GAT) is utilized in HetEP to adaptively learn the graph-structured data so that informative representations of each element can be obtained. The experimental results based on a manufacturing dataset from a generator factory demonstrate that the proposed HetEP yields superior prediction performance compared to the device-oriented prediction methods in terms of prediction accuracy.

关键词： Representation learning Performance evaluation Energy consumption Green manufacturing Job shop scheduling Prediction methods Production facilities

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Symplectic Wigner Distribution in the Linear Canonical Transform Domain: Theory and Application

arXiv

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arXiv 2025年

作者： He, Yangfan Zhang, Zhichao School of Communication and Artificial Intelligence School of Integrated Circuits Nanjing Institute of Technology Nanjing211167 China School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Computational Science Application of Hainan Province Hainan Normal University Haikou571158 China Key Laboratory of Numerical Simulation of Sichuan Provincial Universities School of Mathematics and Information Sciences Neijiang Normal University Neijiang641000 China

This paper devotes to combine the chirp basis function transformation and symplectic coordinates transformation to yield a novel Wigner distribution (WD) associated with the linear canonical transform (LCT), named as the symplectic WD in the LCT domain (SWDL). It incorporates the merits of the symplectic WD (SWD) and the WD in the LCT domain (WDL), achieving stronger capability in the linear frequency-modulated (LFM) signal frequency rate feature extraction while maintaining the same level of computational complexity. Some essential properties of the SWDL are derived, including marginal distributions, energy conservations, unique reconstruction, Moyal formula, complex conjugate symmetry, time reversal symmetry, scaling property, time translation property, frequency modulation property, and time translation and frequency modulation property. Heisenberg’s uncertainty principles of the SWDL are formulated, giving rise to three kinds of lower bounds attainable respectively by Gaussian enveloped complex exponential signal, Gaussian signal and Gaussian enveloped chirp signal. The optimal symplectic matrices corresponding to the highest time-frequency resolution are generated by solving the lower bound optimization (minimization) problem. The time-frequency resolution of the SWDL is compared with those of the SWD and WDL to demonstrate its superiority in LFM signals time-frequency energy concentration. A synthesis example is also carried out to verify the feasibility and reliability of the theoretical analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Wigner-Ville distribution

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PEPT: Expert Finding Meets Personalized Pre-training

arXiv

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arXiv 2023年

作者： Peng, Qiyao Xu, Hongyan Wang, Yinghui Liu, Hongtao Huo, Cuiying Wang, Wenjun School of New Media and Communication Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology Beijing China Du Xiaoman Technology Beijing China Yazhou Bay Innovation Institute Hainan Tropical Ocean University Hainan China

Finding experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on their historical answered questions, and accurately matching them with target questions. Recently, the application of Pre-trained Language Models (PLMs) have gained significant attraction due to their impressive capability to comprehend textual data, and are widespread used across various domains. There have been some preliminary works exploring the usability of PLMs in expert finding, such as pre-training expert or question representations. However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. Specifically, in our pre-training framework, we integrate historical answered questions of one expert with one target question, and regard it as a candidate aware expert-level input unit. Then, we fuse expert IDs into the pre-training for guiding the model to model personalized expert representations, which can help capture the unique characteristics and expertise of each individual expert. Additionally, in our pre-training task, we design: 1) a question-level masked language model task to learn the relatedness between histories, enabling the modeling of question-level expert interest;2) a vote-oriented task to capture question-level expert expertise by predicting the vote score the expert would receive. Through our pre-training framework and tasks, our approach could holistically learn expert representations including interests and expertise. Our method has been extensively evaluated on six real-world CQA datasets, and the experimental results consistently demonstrate the superio

关键词： Question answering

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AI-aided power electronic converters automatic online real-time efficiency optimization method

引用

Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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A Hybrid Model for Building-level Day Ahead Power Load Forecasting Based on Gated Recurrent Unit Networks

A Hybrid Model for Building-level Day Ahead Power Load Forec...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Zhang, Hualu Deng, Boya Shi, Jie Northeast Electric Power University Jilin Key Laboratory of Modern Power System Simulation Control and Green Power New Technology Ministry of Education Jilin China Nanjing Nanrui Information Communication Technology Co. Ltd. Nari Group Corporation State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665414395

Accurate forecasting of day-ahead electrical load is a key to optimize the operation of the integrated energy system (IES) and a fundamental function of the cyber-physical system (CPS) of IES. Due to the characteristics of different features, data collected by CPS often have different sampling frequencies, resulting in mining data trends inadequately when making a load forecast. This paper proposes an GRU-based model to combine historical data with different sampling frequencies in forecasting day-ahead building-level load. First, day-level features are clustered by K-means. Hour-levels features are then partitioned according to clustering results and inputted separately into a gated recurrent unit (GRU) based neural network to capture relationships between external features and load fluctuations. The temporal dependencies of minute-level data are captured by the GRU network directly without any external features. Finally, the least square method is used to combine forecasting results at each hour of two models. The simulation results present that the proposed model performs better than a single model when applying to real-world data. © 2021 IEEE.

关键词： Forecasting

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Study on subjective perception of S3D image with regular-texture 9

Study on subjective perception of S3D image with regular-tex...

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Su, Zhibin Liu, Bing Li, Weiwei Ren, Hui Huang, Rui Communication University of China School of Information and Communication Engineering Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism CITVC KEHUA CO.LTD. Technology Service Branch Beijing China

With the rapid development of binocular stereo vision technology, S3D images and videos have been widely used in many aspects. However, there are few researches on the detail perception of comfortable experience and stereo sense based on image content with certain disparity. Considering that texture is an important visual information contained in an image, in this paper, we designed the computer-generated S3D images containing two kinds of parallax based on three types of texture distribution and six regular textures for subjective evaluation. The influence of texture features on visual comfort, depth perception and the region of interest were studied through the related data analysis. For comparison with the content of disparities, the 2D images with same texture were designed and rated for assessment. Experimental results have shown that the texture was an important factor affecting the comfort perception. However, only when the subject and the background had texture at the same time, the texture was an important factor affecting the depth perception. The different texture distribution between the main subject and background would cause the change of area of interest and evaluation result. Overall, the larger the area of the texture, the lower the comfort evaluation results, and the higher the depth perception. Our studies will not only confirm the law of perception on S3D images with certain features, but also provide the guide for multidimensional content production and display, which have practical value for the development of S3D image and video technology. © 2020 IEEE.

关键词： Depth perception

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