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Superior performance of ZnGaO solar-blind photodetectors by Implementing TFT structure and tunable ZnO cycle ratio

Materials Today Chemistry 2024年 38卷

作者： Fan, Hui-Chen Wang, Chen Xu, Yi-Hong Fan, Teng-Min Huang, Pao-Hsun Ruan, Yu-Jiao Wu, Ting-Zhu 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 Xiamen 361024 China School of Ocean Information Engineering Jimei University Jimei District Xiamen 361021 Fujian China National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen 361024 China National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen 361005 China Department of Photonics & Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou 54561 Taiwan Department of Biomedical Engineering Da-Yeh University Changhua 51591 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 Elsevier Ltd

关键词： High performance Plasma enhanced atomic layer deposition TFT-Type solar-blind photodetector ZnGaO film ZnO cycle ratio

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Tigc-Net: Transformer-Improved Graph Convolution Network for Spatio-Temporal Prediction

SSRN

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SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

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Motion Attitude Analysis Method of Bullets Based on Field Shadow Image

引用

IOP Conference Series: Materials Science and engineering 2019年第3期612卷

作者： Yijie Zhou Hongju Gao Zhonghui Sun Zhiwang Qiao Changan Di School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China Beijing Tianying Measurement and Control Technology LTD Beijing China China Baicheng Weapon Test Center Baicheng China

Aimed at obvious interference noise of shadow images of bullet shot in outside field, and larger extraction error of bullet attitude features of shadow images and other problems, intersection point location method based on axis line and edge is adopted to extract features of bullet tip, and the method based on ellipse fitting is adopted to extract features of bullet tail in the Paper, and then extracted feature information is used to calculate motion attitude of bullets. The method can be applied to shadow image analysis of bullets under field environment well with accurate feature extraction, good consistency and high analysis precision and other features.

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Emerging contaminants:A One Health perspective

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The Innovation 2024年第4期5卷 140-170,139页

作者： Fang Wang Leilei Xiang Kelvin Sze-Yin Leung Martin Elsner Ying Zhang Yuming Guo Bo Pan Hongwen Sun Taicheng An Guangguo Ying Bryan WBrooks Deyi Hou Damian EHelbling Jianqiang Sun Hao Qiu Timothy MVogel Wei Zhang Yanzheng Gao Myrna JSimpson Yi Luo Scott XChang Guanyong Su Bryan MWong Tzung-May Fu Dong Zhu Karl JJobst Chengjun Ge Frederic Coulon Jean Damascene Harindintwali Xiankui Zeng Haijun Wang Yuhao Fu Zhong Wei Rainer Lohmann Changer Chen Yang Song Concepcion Sanchez-Cid Yu Wang Ali El-Naggar Yiming Yao Yanran Huang Japhet Cheuk-Fung Law Chenggang Gu Huizhong Shen Yanpeng Gao Chao Qin Hao Li Tong Zhang Natàlia Corcoll Min Liu Daniel SAlessi Hui Li Kristian KBrandt Yolanda Pico Cheng Gu Jianhua Guo Jianqiang Su Philippe Corvini Mao Ye Teresa Rocha-Santos Huan He Yi Yang Meiping Tong Weina Zhang Fidèle Suanon Ferdi Brahushi Zhenyu Wang Syed AHashsham Marko Virta Qingbin Yuan Gaofei Jiang Louis A.Tremblay Qingwei Bu Jichun Wu Willie Peijnenburg Edward Topp Xinde Cao Xin Jiang Minghui Zheng Taolin Zhang Yongming Luo Lizhong Zhu Xiangdong Li DamiàBarceló Jianmin Chen Baoshan Xing Wulf Amelung Zongwei Cai Ravi Naidu Qirong Shen Janusz Pawliszyn Yong-guan Zhu Andreas Schaeffer Matthias C.Rillig Fengchang Wu Gang Yu James M.Tiedje State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Department of Chemistry Hong Kong Baptist UniversityHong KongChina HKBU Institute of Research and Continuing Education Shenzhen Virtual University ParkShenzhenChina Technical University of Munich TUM School of Natural SciencesInstitute of Hydrochemistry85748 GarchingGermany School of Resources&Environment Northeast Agricultural UniversityHarbin 150030China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVICAustralia Faculty of Environmental Science&Engineering Kunming University of Science&TechnologyKunming 650500China Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory of Environmental Theoretical Chemistry South China Normal UniversityGuangzhouGuangdong 510006China Department of Environmental Science Baylor UniversityWacoTXUSA Center for Reservoir and Aquatic Systems Research(CRASR) Baylor UniversityWacoTXUSA School of Environment Tsinghua UniversityBeijing 100084China School of Civil and Environmental Engineering Cornell UniversityIthacaNY 14853USA Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of TechnologyHangzhou 310014China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Laboratoire d’Ecologie Microbienne Universite Claude Bernard Lyon 1UMR CNRS 5557UMR INRAE 1418VetAgro Sup69622 VilleurbanneFrance Department of Pla

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary *** global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the *** response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals,Waste,and Pollution *** review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One Health *** for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international *** actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

关键词： pollution planet contaminants

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Design of Calibration System for Motion Attitude measurement of Large-caliber Projectile

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IOP Conference Series: Materials Science and engineering 2019年第2期677卷

作者： Dongxu Wang Hongju Gao Zhonghui Sun Zhiwang Qiao Changan Di School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China Beijing Tianying Measurement and Control Technology LTD Beijing China China Baicheng Weapon Test Center Baicheng China

To meet the large-caliber projectile motion measurement system calibration requirement, developed a large-caliber projectile motion measurement system calibration system, gives the calibration principle and working process, design a visibility good green light source as the collimating light source, the design targets based on the filtering and PSD field grid position detection module; Aiming at the calibration requirement that the six faces of cavity cross grids frame cannot be shielded, the cavity cross grids adjustment module is designed. Error analysis shows that the calibration error of the system is 0.062mm.

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Design of Half-bridge Bidirectional DC-DC Converter control Loop

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Journal of Physics: Conference Series 2021年第1期1894卷

作者： Huan Cheng Yingna Guo Zhao Ma Sisi Bai School of Electronic Engineering Xi'an Shiyou University Xi'an China Shaanxi Key Laboratory of Measurement and Control Technology for Oil and Gas Wells Xi'an China School of Electronic Engineering Xi'an Aeronautical University Xi'an China

In recent years, the direct current (DC) microgrid system has received extensive attention from industrial and academic circles worldwide. As an essential part of the DC microgrid, the bidirectional DC-DC converter can realize the bidirectional flow of energy according to the needs of the DC microgrid. In this paper, the half-bridge bidirectional DC-DC converter is taken as the research object, and the double closed-loop feedback control loop of the converter in the charging and discharging mode is designed based on the intermittent and fluctuating characteristics of distributed generation. Besides, a simulation model is built using MATLAB to analyze the operation of the converter in different modes and the control effect of the double closed-loop feedback control loop. The results verify the effectiveness and feasibility of the proposed control method.

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Corrigendum to “3D model classification based on few-shot learning” [Neurocomputing 398 (2020) 539–546/21103]

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Neurocomputing 2020年 407卷 480-480页

作者： Jie Nie Ning Xu Ming Zhou Ge Yan Zhiqiang Wei College of Information Science and Engineering Ocean University of China China School of Electrical and Information Engineering Tianjin University China Chengdu Spaceon Measurement & Control Technology Co. Ltd China

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Dual-band nonreciprocal thermal radiation by coupling optical Tamm states in magnetophotonic multilayers

arXiv

引用

arXiv 2021年

作者： Wu, Jun Wu, Feng Zhao, Tiancheng Antezza, Mauro Wu, Xiaohu Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education College of Electrical Engineering Anhui Polytechnic University Wuhu241000 China School of Optoelectronic Engineering Guangdong Polytechnic Normal University Guangzhou510665 China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing100076 China UMR 5221 CNRS-Universitéde Montpellier MontpellierF-34095 France Institut Universitaire de France 1 rue Descartes Paris Cedex 05F-75231 France Shandong Institute of Advanced Technology Jinan250100 China

Kirchhoff’s law is one of the most fundamental law in thermal radiation. The violation of traditional Kirchhoff’s law provides opportunities for higher energy conversion efficiency. Various micro-structures have been proposed to realize single-band nonreciprocal thermal emitters. However, dual-band nonreciprocal thermal emitters remain barely investigated. In this paper, we introduce magneto-optical material into a cascading one-dimensional (1-D) magnetophotonic crystal (MPC) heterostructure composed of two 1-D MPCs and a metal layer. Assisted by the nonreciprocity of the magneto-optical material and the coupling effect of two optical Tamm states (OTSs), a dual-band nonreciprocal lithography-free thermal emitter is achieved. The emitter exhibits strong dual-band nonreciprocal radiation at the wavelengths of 15.337 μm and 15.731 μm when the external magnetic field is 3 T and the angle of incidence is 56°. Besides, the magnetic field distribution is also calculated to confirm that the dual-band nonreciprocal radiation originates from the coupling effect between two OTSs. Our work may pave the way for constructing dual-band and multi-band nonreciprocal thermal emitters. Copyright © 2021, The Authors. All rights reserved.

关键词： Conversion efficiency

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Adaptive Hybrid Potential Evapotranspiration (Pet) Prediction Method Based on Automatic Machine Learning

SSRN

引用

SSRN 2024年

作者： Zhang, Chenjia Xu, Tianxin Zhang, Yan Abdu Kaimu, Abullimiti Guo, Lirui Zhang, Yutong Ma, Daokun Xinjiang Agricultural University China College of Information and Electrical Engineering China Agricultural University Haidian District Beijing100089 China School of Yi Language and Culture Xichang University Sichaun Xichang831000 China Beijing Lianchuang Siyuan Measurement and Control Technology Co. Ltd. Beijing Beijing615000 China College of Information Engineering Yangzhou University Hanjiang District Jiangsu Yangzhou225009 China College of Resources and Environment China Agricultural University Haidian District Beijing100094 China

An adaptive hybrid model based on automatic machine learning for short-term PET prediction is proposed coupling neural network and PET formula. 105 standard weather stations in Xinjiang were used as data sets (43 of them had missing data) to test model. According to modules, networks and PET formulas used in the prediction process, corresponding labels were generated in each dataset forecast result. Ratio of training set and test set for each data set was 8:2. Grid search was used to optimize the best hyperparameter combination. In test set, the average absolute error (MAE) and average squared error (MSE) of the model prediction were 0.338mm and 0.270. We prove that the neural network applicability varies among the used data sources, and that GRU and 1 DCNN are more suitable for the selected datasets, while LSTM and MLP are not applicable. We find evidences that applicability of neural networks and PET formulas is independent of geographic region and degree of drought. In 2023, method of rolling prediction for 1-15 days is verified, PET prediction error based on neural network is significantly smaller than useing weather forecast data to calculate PET. In addition, by comparison，we determined that adaptive input length can effectively reduce the prediction error, MAE was 27.52% smaller than fixed input length, and MSE was 45.76% smaller than fixed input length. © 2024, The Authors. All rights reserved.

关键词： Statistical tests

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Online Non-Contact Weighing Duck Carcasses Using 2d Images and Deep Learning

SSRN

引用

SSRN 2022年

作者： Zhao, Yuliang Tian, Kaixuan Chen, Ruoyu Yang, Yongliang School of Control Engineering Northeastern University Hebei Qinhuangdao066001 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China

In the poultry meat processing industry, non-contact weighing methods based on computer vision have become increasingly popular due to their low costs and high efficiency. However, the accuracy of the traditional 2D image weighing techniques is reduced by the overlap and sway of carcasses. In this paper, we propose a more accurate regression model, Vggx, to weigh duck carcasses through image processing and deep learning. We adopt an image segmentation technique to obtain 2D images of a single duck section under a complex background. The images of the same duck are fused together to estimate its 3D information. Then, we use a convolutional neural network regression model to estimate the weight of duck carcass with the acquired 3D information. The Mean Absolute Percentage Error of our model is 1.46% and the average weight error of our model is 23.1 gram. Experimental results demonstrate that our method is high performance. © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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