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An Efficiency Enhanced Graphene/n-Si Schottky Junction for Solar Cells

Chinese Physics Letters 2018年第7期35卷 58-61页

作者：李刚程宏伟郭丽芳王开鹰程再军 MicroNano System Research Center College of Information and Computer & Key Lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Taiyuan 030024 School of Optoelectronic and Communication Engineering Xiamen University of Technology Xiamen 361024

A novel and facile oxidation-induced self-doping process of graphene-silicon Schottky junction by nitric acid（HNO3） vapor is reported. The HNO3 oxidation process makes graphene p-type self-doped, and leads to a higher built-in potential and conductivity to enhance charge transfer and to suppress charge carrier recombination at the graphene-silicon Schottky junction. After the HNO3 oxidation process, the open-circuit voltage is increased from the initial value of 0.36 V to the maximum value of 0.47 V, the short-circuit current is greatly increased from 0.80μA to 7.71μA, and the ideality factor is optimized from 4.4 to 1.0. The enhancement of the performance of graphene-Si solar cells may be due to oxidation-induced p-type self-doping of graphene-Si junctions.

关键词： Graphene

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Exploring the Pan-Relationship Between Disease and Symptom Related to Coronary Heart Disease from Chinese Electronic Medical Records 15th

Exploring the Pan-Relationship Between Disease and Symptom R...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Yang, Ronggen Wang, Yichen Wang, Bo Gong, Lejun Faculty of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210046 China School of Geography and Bioinformatics Nanjing University of Posts and Telecommunication Nanjing China

ISBN: (纸本)9783030267650

Electronic Medical Records (EMRs) arises from the clinical treatment process, which reflects a patient’s physical condition, the disease, the treatment process, etc. It has an important significance for the recovery of the patient. As the amount of electronic medical records continues to increase, people are actively looking for ways to get the valid data contained in electronic medical records. More effective methods are urgently needed for processing EMRs. In this paper, 108 real EMRs of coronary heart disease were analyzed, through named entity recognition and entity relation extraction. Pan-relationship between entities was defined in the process. Experimental results showed the analysis of pan-relation between coronary heart disease and symptom is helpful for treatment of coronary heart disease. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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Advanced NOMA Assisted Semi-Grant-Free Transmission Schemes for Randomly Distributed Users

arXiv

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arXiv 2020年

作者： Lu, Huabing Xie, Xianzhong Shi, Zhaoyuan Lei, Hongjiang Yang, Helin Cai, Jun The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing246011 China The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has recently received significant research attention due to its outstanding ability of serving grant-free (GF) users with grant-based (GB) users' spectrum, which greatly improves the spectrum efficiency and effectively relieves the massive access problem of 5G and beyond networks. In this paper, we first study the outage performance of the greedy best user scheduling SGF scheme (BU-SGF) by considering the impacts of Rayleigh fading, path loss, and random user locations. In order to tackle the admission fairness problem of the BU-SGF scheme, we propose a fair SGF scheme by applying cumulative distribution function (CDF)-based scheduling (CS-SGF), in which the GF user with the best channel relative to its own statistics will be admitted. Moreover, by employing the theories of order statistics and stochastic geometry, the outage performances of both BU-SGF and CS-SGF schemes are analyzed. Theoretical results show that both schemes can achieve full diversity orders only when the served users' data rate is capped, which severely limits the rate performance of SGF schemes. To further address this issue, we propose a distributed power control strategy to relax such data rate constraint, and derive analytical expressions of the two schemes' outage performances under this strategy. Finally, simulation results validate the fairness performance of the proposed CS-SGF scheme, the effectiveness of the power control strategy, and the accuracy of the theoretical analyses. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectrum efficiency

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Differential Privacy-preserving Distributed Machine Learning

Differential Privacy-preserving Distributed Machine Learning

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IEEE Annual Conference on Decision and control

作者： Xin Wang Hideaki Ishii Linkang Du Peng Cheng Jiming Chen State Key Lab. of Industrial Control Technology Zhejiang University Hangzhou P. R. China Dept. of Computer Science Tokyo Institute of Technology Yokohama Japan

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Distributed machine learning (DML) has received widespread attentions, where a shared prediction model is collaboratively learned by multiple servers. However, since the data used for model training often contains users' sensitive information, DML faces potential risks of privacy disclosure. Particularly, when servers are untrustworthy, it is critical while challenging to guarantee users to obtain privacy preservation that is self-controllable and does not weaken in strength during the whole DML process. In this paper, we propose a privacypreserving solution for DML, where privacy protection is achieved through data randomization at the users' side and a modified alternating direction method of multipliers (ADMM) algorithm is designed for servers to mitigate the effect of data perturbation. We prove that this solution provides differential privacy guarantee and preserves the convergence property of a general ADMM paradigm. Also, we provide extensive theoretical analysis about the performance of the trained model. Numerical experiments using standard classification datasets are finally conducted to validate the theoretical results.

关键词： Servers Data privacy Privacy Machine learning Linear programming Convex functions Minimization

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What is that in your hand? Recognizing grasped objects via forearm electromyography sensing

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–24页

作者： Fan, Junjun Fan, Xiangmin Tian, Feng Li, Yang Liu, Zitao Sun, Wei Wang, Hongan State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Google Research and Machine Intelligence Mountain ViewCA United States TAL AI Lab Beijing China

Knowing the object in hand can offer essential contextual information revealing a user's fine-grained activities. In this paper, we investigate the feasibility, accuracy, and robustness of recognizing the uninstrumented object in a user's hand by sensing and decoding her forearm muscular activities via off-the-shelf electromyography (EMG) sensors. We present results from three studies to advance our fundamental understanding of the opportunities that EMG brings in object interaction recognition. In the first study, we investigated the influence of physical properties of objects such as shape, size, and weight on EMG signals. We also conducted a thorough exploration of the feature spaces and sensor positions which can provide a solid base to rely on for future designers and practitioners for such interactive technique. In the second study, we assessed the feasibility and accuracy of inferring the types of grasped objects via using forearm muscular activity as a cue. Our results indicate that the types of objects can be recognized with up to 94.2% accuracy by employing user-dependent training. In the third study, we investigated the robustness of this approach in a realistic office setting where users were allowed to interact with objects as they would naturally. Our approach achieved up to 82.5% accuracy in discriminating 15 types of objects, even when training and testing phrases were purposefully performed on different days to incorporate changes in EMG patterns over time. Overall, this work contributes a set of fundamental findings and guidelines on using EMG technologies for object-based activity tracking. © 2018 Association for Computing Machinery.

关键词： Object detection

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

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arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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Sensor Fault Detection of Continuous Casting Mold Vibration System Driven by Servo Motor

Sensor Fault Detection of Continuous Casting Mold Vibration ...

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Chinese control Conference (CCC)

作者： Xia Wu Jianxiong Li Yiming Fang Zhiqiang Wang Key Lab of Industrial Computer Control Engineering of Yanshan University Qinhuangdao Hebei Province China

The problem of sensor fault diagnosis and reconstruction of the continuous casting mold vibration displacement system driven by servo motor is investigated in this paper, and a fault reconstruction method based on adaptive sliding mode observer is proposed. Firstly, the matching condition of the observer is satisfied by constructing an auxiliary output, and a new state variable is selected as the low-pass filter with the fault vector output. Then an adaptive sliding mode observer is designed for the auxiliary output system in the present of the unknown upper bound of the fault, in which the adaptive switching gain is updated online. The gain matrix of the observer is obtained by solving a LMI optimization problem. And a linear transforming matrix is introduced to realize the direct estimation of the fault by the principle of the equivalent control output error injection. Finally, simulation results are given to illustrate the effectiveness of the proposed method.

关键词： Observers Casting Mathematical model Servomotors Adaptive systems Vibrations Upper bound

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An Ameliorated Moth-flame Optimization Algorithm

An Ameliorated Moth-flame Optimization Algorithm

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Chinese control Conference

作者： Xiao-dong Zhao Yi-ming Fang Zhuang Ma Miao Xu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

To improve the global search ability under the condition of ensuring convergence speed, it is still a major challenge for most meta-heuristic optimization algorithms. The Moth-Flame Optimization (MFO) algorithm is an innovative nature-inspired algorithm. To improve the precision of the solution and to quicken the convergence speed and to increase the stability of MFO, an ameliorated Moth-flame optimization algorithm (A-MFO) that combines the crisscross optimization algorithm with MFO is proposed to solve this problems that are mentioned above. The performance of proposed A-MFO is demonstrated on six benchmark mathematical function optimization problems regarding superior accuracy and lower computational time achieved compared to other well-known nature-inspired algorithms.

关键词： Optimization Fires Convergence Sociology Statistics Benchmark testing Heuristic algorithms

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A novel multi-objective decomposition particle swarm optimization based on comprehensive learning strategy 36

A novel multi-objective decomposition particle swarm optimiz...

引用

第36届中国控制会议

作者： Lixin Wei Rui Fan Xin Li Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

ISBN: (纸本)9781538629185

Comprehensive learning particle swarm optimization（CLPSO） algorithm has a good performance in overcoming premature convergence and avoiding getting stuck in local minima, which are shortcomings in particle swarm optimization. It can solve complex, multi-modal of single-objective problems, but it has not such performance in handling multi-objective optimization problems because of the difficulty of selective solution mechanism. In this article, a multi-objective decomposition particle swarm optimization based on comprehensive learning strategy is proposed, which uses a comprehensive learning strategy for multi-objective problems to prevent premature convergence;updates the leading particles by decomposition method to enhance the distribution of solutions;adds the archive to preserve non-dominated solutions, and adopts mutation in archive to avoid falling into local optimum. The proposed approach is compared with three multi-objective evolutionary algorithms and the results indicate that the proposed approach is competitive respect to which it is compared in most of the test problems adopted.

关键词： comprehensive learning particle swarm optimization multi-objective optimization decomposition

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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