检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Fan, Hui-Chen Wang, Chen Xu, Yi-Hong Fan, Teng-Min Huang, Pao-Hsun Ruan, Yu-Jiao Wu, Tingzhu Kuo, Hao-Chung Wuu, Dong-Sing Lai, Feng-Min Lien, Shui-Yang Xiamen Key Laboratory of Development and Application for Advanced Semiconductor Coating Technology School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China School of Ocean Information Engineering Jimei University Jimei District Fujian Xiamen361021 China National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen361024 China National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen361005 China Department of Photonics Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan Department of Biomedical Engineering Da-Yeh University Changhua51591 Taiwan

In this study, plasma enhanced atomic layer deposition was used to prepare ZnGaO thin films with different ZnO cycle ratios from 0% to 50%, which were employed to fabricate the MSM-type and TFT-type solar-blind photodetector. The properties of the films and the performance of the solar blind photodetector are greatly affected by the ZnO cycle ratio. Appropriate Zn doping content decreases the ratio of oxygen vacancies and Zn interstitials in Ga2O3, while excessive Zn doping degrades the quality of the film. Compared to MSM-type photodetector, an obviously improved performance of ZnGaO TFT-type photodetector has been observed due to its self-amplified photocurrent and the reduced dark current controlled by gate voltage. At a ZnO cycle ratio of 40%, a superior performance of TFT-type solar-blind photodetector with an ultra-low dark current ~4.5×10-13 A, an excellent light switching ratio ~2.2×107, and an outstandingly high sensitivity ~34.31 A/W has been obtained. These results demonstrate that the incorporation of Zn dopants can effectively modulate the performance of Ga2O3 TFT-type photodetector providing a great perspective for cost-effective and high-performance solar-blind PDs. © 2024, The Authors. All rights reserved.

关键词： Zinc oxide

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IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks

arXiv

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arXiv 2022年

作者： Xue, Ping Lu, Yang Chang, Jingfei Wei, Xing Wei, Zhen The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Mine IOT and Security Monitoring Technology Key Laboratory Hefei230088 China The Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei University of Technology Hefei230009 China The Intelligent Manufacturing Institute of HeFei University of Technology Hefei230009 China

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors, and suffer from the trade-off between accuracy and computational complexity. In contrast, considering the limited learning ability and information loss caused by the limited representational capability of BNNs, we propose IR2Net to stimulate the potential of BNNs and improve the network accuracy by restricting the input information and recovering the feature information, including: 1) information restriction: for a BNN, by evaluating the learning ability on the input information, discarding some of the information it cannot focus on, and limiting the amount of input information to match its learning ability;2) information recovery: due to the information loss in forward propagation, the output feature information of the network is not enough to support accurate classification. By selecting some shallow feature maps with richer information, and fusing them with the final feature maps to recover the feature information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better trade-off between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with ∼10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://***/pingxue-hfut/IR2Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Recovery

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La Doping of Cofe Prussian Blue Analogs for Improved Catalytic Activity Toward Norfloxacin Degradation

SSRN

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SSRN 2024年

作者： Yi, Huiyu Liang, Chenge Li, Yungui Zhang, Suorong Fang, Qile Sichuan Provincial Sci-Tech Cooperation Base of Low-cost Wastewater Treatment Technology Department of Environmental Engineering Southwest University of Science and Technology Mianyang621010 China Research and Development Center for Watershed Environmental Eco-Engineering Beijing Normal University Zhuhai Campus Zhuhai519087 China NHC Key Laboratory of Nuclear Technology Medical Transformation Mianyang Central Hospital School of Medicine University of Electronic Science and Technology of China Sichuan Mianyang621000 China Chongqing Feiyang Measurement and Control Technology Research Institute Co. Ltd Chongqing402760 China China West Normal University China

Prussian blue analogs (PBAs) are promising catalysts in heterogeneous oxidation processes. The catalytic activity of PBAs relies on exposed crystal facets and accessible active sites on the PBA framework. Employing a heteroatom doping strategy using a rare earth element (La), we regulated the crystal structure and optimized the catalytic activity of CoFe PBA for peroxymonosulfate (PMS)-driven degradation of the antibiotic norfloxacin (NOR). The La atoms were mainly doped in the interstitial sites of the CoFe PBA cubic lattices, without affecting the electron distributions of Co and Fe metals or the face-centered cubic crystal phase of the coordination skeleton. La doping dominated the (111) and (100) as the main exposed facets of CoFeLa PBA, increasing the affinity of CoFeLa PBAs for PMS adsorption due to the higher surface energy of the two facets. Consequently, the activation energy considerably declined from 77.57 kJ/mol in CoFe PBA to 51.66 kJ/mol in CoFeLa PBA and the catalytic activity for PMS-driven NOR degradation was largely improved. 1O2 was proven as the main reactive specie responsible for NOR degradation in the PMS/CoFeLa system, and O2·− and SO4·− were verified as the important intermediates to generate 1O2. Thus, La doping can effectively enhance the catalytic performance of PBA-based catalysts for organic pollutant removal. © 2024, The Authors. All rights reserved.

关键词： Activation energy

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TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework

arXiv

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arXiv 2023年

作者： Zhao, Yang Yang, Jiaxi Wang, Wenbo Yang, Helin Niyato, Dusit 2 Fusionopolis Way Innovis #08-04 Singapore138634 Singapore Shenzhen Institute for Advanced Study School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650050 China Department of Information and Communication Engineering Xiamen University Xiamen361005 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Accurate Detection Method for Voltage Traveling Wave Based on L1-Regularization and Least Squares Optimization

Accurate Detection Method for Voltage Traveling Wave Based o...

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International Conference on Advanced Power System Automation and Protection (APAP)

作者： Zhaohui Chen Xu Chen Xiaobing Ding Wei Liu Tao Tang Xiaohan Li Weiguo Yu Power Dispatching and Communication Center of CSG Guangzhou China Electric Power Dispatching and Control Center of Guangdong Power Grid Corporation Guangzhou China School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China CYG SUNRI CO. LTD Shenzhen China

Aiming at the problem that the secondary traveling wave obtained by the voltage traveling wave sensor cannot truly reflect the waveform and fault information of the primary traveling wave, this paper proposes an accurate detection method for voltage traveling wave based on L 1 -regularization and least squares optimization. Firstly, the forward transfer function model of the special voltage traveling wave sensor is constructed, and then the inversion model is derived. Then, discrete deconvolution method and L 1 -regularization and least squares inversion algorithm are used to obtain the primary traveling wave signal. Simulation and experimental results show that the proposed method can effectively solve the ill-posed problem in waveform inversion. The similarity between the inversion and simulation of the primary traveling wave is high, and the accurate detection of fault transient traveling wave information can be realized.

关键词：

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Robotic Arm control System Based on Brain-Muscle Mixed Signals

Research Square

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Research Square 2021年

作者： Cheng, Li-Wei Li, Duan-Ling Yu, Gong-Jing Zhang, Zhong-Hai Yu, Shu-Yue Key Laboratory of Measurement and Control of Complex Engineering Systems China School of Modern Post School of Automation Beijing University of Posts and Telecommunications Beijing100876 China Beijing Aerospace Measurement & Control Technology Co. Ltd. Beijing100041 China

Aiming at the existing problems of BCI (brain computer interface), such as single input signal source, low accuracy of feature recognition, and less output control instructions, this paper proposes a robotic arm control system based on EEG (electroencephalogram) and EMG (electromyogram) mixed signals. The system flow is as follows: Firstly, the EMG signal of the unilateral arm and the EEG signal of the left and right hand motor imagery is collected synchronously. Then the collected EEG and EMG signals are extracted and classified, and the corresponding classification instructions are obtained. Finally, the multi-instruction real-time control of the robotic arm is realized under the classification instruction. The experimental verification results show that: The 10 subjects all realized the real-time multicommand control of the robotic arm, and the average recognition accuracy of each action reached more than 94%. The proposed system enriches the diversity of hybrid BCI and provides a theoretical basis and application foundation for the extended application of BCI in robotic arm control. © 2021, CC BY.

关键词： Brain computer interface

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FGF3D: A Point Cloud 3D Object Detection Algorithm Based on Fine-Grained Feature Fusion

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IEEE Transactions on Instrumentation and measurement 2025年

作者： Li, Bing Xu, Yunheng Wang, Shuoheng Wang, Xinwen Chen, Jie Li, Xinde Li, Yingsong Huang, ZhiXiang Anhui University School of Electronic and Information Engineering Hefei230601 China Southeast University School of Automation Nanjing210096 China Nanjing Center for Applied Mathematics Nanjing211135 China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230601 China 38th Research Institute of China Electronics Technology Group Corporation Hefei230088 China Southeast University Key Laboratory of Measurement and Control of CSE School of Automation Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

In the existing two-stage point cloud 3D object detection task, there are problems such as the lack of fine-grained and insufficient sparsity of point cloud features extracted by the backbone network, the lack of spatial-channel fine-grained fusion of bird-eye-view (BEV) features, and the lack of multi-dimensional representation of ROI pooling features. We innovatively propose a new two-stage algorithm based on fine-grained feature fusion, called FGF3D. We construct a sparsegrained aggregation network (SGANet) to elevate the representation of point cloud features. Furthermore, we innovatively propose a spatial-channel fine-grained fusion module (SCFFM) to enrich BEV features and efficiently extract fine-grained features. Further, we propose a global-local feature pooling fusion module (GLFP), which effectively fuses local detail object features and global features, enhances multi-dimensional representations, and facilitates the refined regression of proposal boxes. Without bells and whistles, FGF3D attains excellent performance on two autonomous driving benchmarks, with detection accuracies of up to 64.10% on KITTI, 57.82% on ONCE. © 2025 IEEE. All rights reserved.

关键词： Object detection

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Reliability Modeling Methods Using Field Operation Data of Smart Electricity Meters Based on Wiener Process

Reliability Modeling Methods Using Field Operation Data of S...

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2020 Global Reliability and Prognostics and Health Management, PHM-Shanghai 2020

作者： Xianguang, Dong Yanjie, Dai Qing, Wang Zhiru, Chen Yan, Du Pingxin, Wang Jia, Qi China State Grid Shandong Electric Power Company Electric Power Research Institute China School of Measurement and Communication Engineering Harbin University of Science and Technology Jinan China

ISBN: (纸本)9781728159454

The reliability of smart electricity meters was significantly affected by environment, field operation testing of smart electricity meters was conducted in high altitude locations, and one year operation data including power error were collected. This paper investigated the degradation behaviors of smart electricity meters with the help of Wiener process. The variation coefficients were introduced to analyze the characteristics of unknown parameters in the degradation model. Reliability estimates were computed according to the maximum likelihood and were compared to the results from different parameter characteristics. A case study validates that the reliability modelling method and provide guidelines for design, maintenance and applications of smart electricity meters. © 2020 IEEE.

关键词： Reliability

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Thymine-Hg2+-Thymine Strategy in Mof-Based Electrochemical Aptamer Sensor for Paes Detection

SSRN

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SSRN 2024年

作者： Liu, Xiaofang Diao, Zhan Liu, Huan Wang, Qun Lei, Jincan Huo, Danqun Hou, Jingzhou Hou, Changjun Key Laboratory for Biorheological Science and Technology Ministry of Education Bioengineering College Chongqing University Chongqing400044 China Chongqing Engineering and Technology Research Center of Intelligent Rehabilitation and Eldercare Chongqing City Management College Chongqing401331 China Key Laboratory of Condiment Supervision Technology for State Market Regulation Chongqing Institute for Food and Drug Control Chongqing401121 China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

Phthalate acid esters (PAEs), known food contaminants, can lead to reproductive problems, respiratory diseases, childhood obesity, and neuropsychological disorders. This work aims to develop an electrochemical aptamer sensor for the detection of DAP and its analogs. To achieve specific recognition ability for PAEs, We have introduced the thymidine-Hg2+-thymidine strategy for the first time in an electrochemical aptamer sensor, incorporating metal-organic frameworks (MOFs) as the carrier for the aptamer. Additionally, nanoamplification technology was applied by immobilizing MXene on the electrode to intensify the sensor's current signal. In the presence of PAEs, these molecules specifically bound to the aptamers, resulting in competitive release of Hg2+ and aptamers connected to Hg2+, subsequently reducing current signaling. Under optimal experimental conditions, the designed sensor achieved a linear range from 1.65×10-5 mg/mL to 3×10-2 mg/ml and an extremely low detection limit of 8.94×10-6 mg/mL. Furthermore, it exhibited good reliability and practicality in detecting actual samples. © 2024, The Authors. All rights reserved.

关键词： Esters

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Design and Implementation of Telemedicine System Using Light Fidelity and PIC16F877A Microcontroller

Design and Implementation of Telemedicine System Using Light...

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International Conference on Information Science and technology (ICIST)

作者： M.R. Ezilarasan Man-Fai Leung Xiangguang Dai Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Instituteof Science and Technology Chennai India Faculty of Science and Engineering School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

Medical Body Area Networks (MBANs) increasingly utilize wireless communication technologies to enhance the freedom and convenience for patients and caregivers. Currently, radio frequency (RF) is the prevalent medium in healthcare applications; however, the potential for electromagnetic interference with precision medical equipment remains a concern. This study introduces a novel design and implementation of a medical healthcare information system using the newly developed Wireless Visible Light communication (VLC) technology, also known as Light Fidelity (Li-Fi). Visible Light Emitting Diodes (LEDs), expected to soon surpass traditional incandescent and fluorescent bulbs as the primary light source due to their energy efficiency, facilitate this technology. In this research, patient health is monitored using sensors, with results displayed on an LCD. An LED lightbulb transmits the data as light signals, which are captured by a photo-detector at the receiver's end. This setup not only displays the output on an LCD but also alerts caregivers through an auditory signal, beeping every 15 seconds for respiration and every 30 seconds for heart rate monitoring. Moreover, these lighting fixtures serve dual functions: illumination and wireless data transmission, using the rapid switching capability of LEDs. This prototype VLC-based system offers a promising alternative for data services and monitoring in hospital areas where RF use is restricted.

关键词： Wireless communication Radio frequency Wireless sensor networks Lighting Light emitting diodes Light fidelity Liquid crystal displays communication system security Monitoring Visible light communication

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