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2012 IEEE 14th International Conference on Communication technology

作者： Nan Jing Weihong Bi Lin Wang School of Information Science and Engineering Yanshan University The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

ISBN: (纸本)9781467321006

We consider the problem of pilot design for sparse multipath channel estimation in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in this *** order to reconstruct MIMO sparse channel,the prevalent compressed sensing (CS) theory is *** to random pilot construction,a deterministic pilot de- sign method is exploited to form the measurement matrix by deterministically extracting some rows of Fourier matrix that satisfies Restricted Isometry Property (RIP).Two families of continuous functions,namely f1 -SL0 and f 2-SL0,are investi- gated in this paper to approximate A 0norm for MIMO sparse channel *** results suggest that the de- terministic pilot design scheme performs as well as the random one at both low and high signal noise ratio (SNR).Moreover,f1 - SL0 and f 2-SL0 outperform A 1-dantzig algorithm with faster running time and better anti-noisy performance.

关键词： sparse channel estimation,deterministic pilot design,compressed sensing,MIMO OFDM

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Correction: Enumerating all multi-constrained s–t paths on temporal graph

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Knowledge and Information systems 2024年第5期66卷 3157-3157页

作者： Jin, Yue Chen, Zijun Liu, Wenyuan School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

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A Recurrence method of attribute reduction based on rough set

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ICIC Express Letters 2013年第3 B期7卷 1025-1030页

作者： Zhang, Tao Lu, Jing Chen, Nan Gao, Zhi College of Information Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China College of Foreign Languages Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China Institute of Electrical Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China

According to the classical rough set, the attribute reduction set may be far from satisfactory due to the non-existence of core in the attribute reduction process of decision table. Aiming at this issue, this paper puts forward a recurrence method of attribute reduction;first find out the starting attributes with the highest division as the basic one, and then add the remaining attributes gradually to refine the undivided equivalence class until the non-existence of the samples which must be separated. The paper theoretically proved the relation between the traditional method and recurrence method of attribute reduction on the existence of core. The reliability of the new method was analyzed and illustrated by experiments. © 2013 ICIC International.

关键词： Decision tables

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Mining Important Functions in Software Network by Node Vulnerability

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Journal of Physics: Conference Series 2020年第1期1453卷

作者： Bing Zhang Shengting Sun Xiaobing Hao School of Information Science and Engineering Yanshan University Qinhuangdao Hebei P.R.China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City China 066004

Given that analysis on the vulnerability of functions is helpful to the detection and improvement of software security, this paper aims to propose an efficient methods to identify the vulnerable nodes (ITVN) in different software by the interdependence of functions. First, the dynamic software execution process was constructed as Software Execution Dependency Network (SEDN) based on Complex network theory. Second, by analyzing the dependency relationship among functions, the algorithm calVulAndScoOfNodes (CVSN) was designed to compute the vulnerability and the affected scope of each node for further analysis. Third, in order to measure the functions vulnerability in the whole software network, the algorithm calVulDegreeOfNodes (CVDN) was put forward to calculate the vulnerable degree of each node. Finally, the Vulnerable Nodes in different software were obtained by ITVN. Experimental results show that the vulnerable nodes selected as important nodes are well-reasoned in software network by testing different software, and the measures are effective for evaluating nodes vulnerability.

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Research on the Influence of Improved K-shell Algorithm on Commodity Profit

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Journal of Physics: Conference Series 2020年第1期1631卷

作者： Jing Chen Jincheng Huang College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Key Laboratory for Computer Virtual Technology and system Integration of Hebei Province Qinhuangdao 066004 China Software Engineering Laboratory in Hebei Province Qinhuangdao 066004 China

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN (Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities.

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Research on a Novel Influence Maximization Algorithm Based on Community Structure

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Journal of Physics: Conference Series 2020年第1期1631卷

作者： Jing Chen Jiangchuan Liu College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China Software Engineering Laboratory in Hebei Province Qinhuangdao 066004 China

With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model.

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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A Robot Pick and Place Skill Learning Method Based on Maximum Entropy and DDQN Algorithm

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Journal of Physics: Conference Series 2022年第1期2203卷

作者： Peiliang Wu Yan Zhang Yao Li Bingyi Mao Wenbai Chen Guowei Gao School of Information Science and Engineering Yanshan University Qinhuangdao Hebei 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao Hebei 066004 China School of Automation Beijing Information Science & Technology University Beijing 100192 China

Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the PAP skill based on current methods. In order to improve the learning efficiency of robot PAP skills, this paper proposes a Soft-DDQN-based PAP skill learning method. Firstly, the Soft-DDQN is proposed by introducing maximum entropy into robot DDQN framework, and the learning goal of Soft-DDQN is to maximize reward and information entropy. Secondly, PAP problem is modelled as a discrete form and Soft-DDQN is applied to solve the PAP problem. Finally, in order to verify the efficiency of Soft-DDQN-based PAP skill learning, comparisons have been given from two standard perspectives and shown that Soft-DDQN improves efficiency of PAP skill learning evidently.

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Robust visual tracking via combinative deep learning

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Journal of Physics: Conference Series 2018年第1期1074卷

作者： Jia Wen JiWei Zhao PengFei Liu Hongjun Wang The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao P.R.China Information Science and Engineering College Yanshan University Qinhuangdao P.R.China State Key Laboratory of Software Engineering of Hebei Province Yanshan University Qinhuangdao P.R.China

Object tracking is a hot topic in computer vision. In recent years, a large number of trackers has been proposed, in which the deep learning tracker has achieved excellent performance. The real-time capability of the deep learning tracker is not good enough due to the high-complexity of the network structures. This paper proposed an innovative tracking method to solve this problem. There are three important differences between this tracker and the other deep learning trackers. Firstly, the overcomplete basis in the deep learning tracker results in heavy computational cost. In order to reduce the complexity of the network, fewer units are used in the first hidden layer to replace the overcomplete basis. Secondly, a training method combining two observation models is used in the tracking process. The denoising automatic encoder is used in the first layer and the backpropagation is used in the other layers. This can avoid the diffusion of gradients which is caused by BP and adapt to the change of the targets easier. Thirdly, this tracker using adaptive particle filter to track targets. The number of particles is dynamic changes in tracking process. In this paper, we use different kinds of unlabelled datum to train network and initialize observation model. The observation model uses the samples collected in the tracking to adjust dynamically so as to adapt to the target appearance and complex environment. Compared with the existing methods, the results of experiments in different video sequences show that this tracker has a higher speed and the similar accuracy compared.

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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