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Electrochemical behavior of rutin on graphene and ionic liquids composite film modified electrode

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery 2014年 45卷 219-224页

作者： Wang, Hui Wang, Zhiqiang Liu, Gang Zhang, Zhihao Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education China Agricultural University Beijing100083 China Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture Beijing100083 China College of Computer Science and Technology Shandong University of Technology Zibo255049 China

An electrode for the detection of rutin in tea and pill was produced and fabricated, which was modified by the glass carbon electrode (GCE) surface by graphene (GR) nanometer materials and n-octylpyridinum hexafluorophosphate (OPFP). Different electrochemical methods such as cyclic vohammetry, impedance spectroscopy and square wave voltammetry were used to investigate the electrochemical behaviors of rutin on IL/GR/GCE, indicating that greatly improved the electrochemical performance and detection response due to ionic conductivity and adhesiveness of room temperature and the large specific surface area and high electron transfer of graphene. Under the optimal conditions using square wave voltammetry, the rutin oxidative peak current was linearly related to its concentration in the range from 0.05 μmol/L to 11 μmol/L with the detection limit estimated to 0.01 μmol/L(S/N=3) and the linear regression coefficient was 0.997. Finally, the developed electrode was successfully applied to detect the content of rutin in real samples, which displays good stability and excellent anti-interference ability. ©, 2014, Chinese Society of Agricultural Machinery. All right reserved.

关键词： Graphene

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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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Multi-parameter prediction model of the aeroengine based on chaos particle swarm optimization

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Hangkong Taikong ji Minhang Xuekan/Journal of Aeronautics, Astronautics and Aviation, Series B 2012年第1期44卷 43-48页

作者： Niu, Wei Cheng, Juan Wang, Guo-Ging Zhai, Zheng-Jun College of Computer Northwestern Polytechnical University Xi'an 710072 China Xi'an Institute of Applied Optics Xi'an 710065 China National Key Laboratory of Science and Technology on Avionics System Integration Shanghai 200233 China

The fault prediction of weapon is getting more difficult for its complex structure, unique operating environment and multi-plicate faults. Currently although the main fault prediction methods have achieved certain success in practical application, they all fall short in some aspects. In grey forecasting model, the theoretical basis of transformation from discrete form to continuous form and the forecasting stability, which puzzled those who are absorbed in grey theory. Many scholars used all kinds of methods to revise the model, and they have made some progress, but they didn't solve it absolutely in principle. Based on the discrete grey prediction theory and with an analysis of the disadvantages of the discrete grey model, an adaptive prediction model with several characteristic parameters for small samples is proposed. This model reconstructs the initial value based on chaos particle swarm optimization and takes into account the interrelations of the parameters and characteristics of prediction series. The data of a certain aeroengine are taken as an example for prediction and analysis, and the results showed that the model had high availability and precision.

关键词： Forecasting

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Sound source localization technology based on small microphone array

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Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and technology (Natural Science Edition) 2013年第SUPPL.I期41卷 188-191页

作者： Zhao, Sheng Cui, Mufan You, Lei Wang, Hongpeng Engineering Laboratory for Key Technology and System Integration of Internet of Things Harbin Institute of Technology Shenzhen Graduate School Guangdong Shenzhen 518055 China National Computer Network Emergency Response Coordination Center of China Beijing 100029 China

As the traditional time delay estimation algorithm can not accurately estimate the time delay between the microphone and vulnerable to environmental noise and other shortcomings, GCC algorithm was used and its improvement was used for time delay estimation. Ultimately a sound source localization system was implemented, combined with the microphone array designed by the author and the spatial orientation method was proposed. It can be calculated the distance and azimuth between the sound source and the target. After experimental verification, the sound source localization system can achieve high positioning accuracy, so it has some practical value.

关键词： Microphone array

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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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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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Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition

arXiv

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arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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

SSRN

引用

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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