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arXiv 2019年

作者： Zhao, Na Li, Jie Wang, Jian Li, Tong Yu, Yong Zhou, Tao Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China Electric Power Research Institute of Yunnan Power Grid Kunming650217 China College of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China School of Big Data Yunnan Agricultural University Kunming650201 China CompleX Lab University of Electronic Science and Technology of China Chengdu611471 China

Heterogeneous nature of real networks implies that different edges play different roles in network structure and functions, and thus to identify significant edges is of high value in both theoretical studies and practical applications. We propose the so-called second-order neighborhood (SN) index to quantify an edge's significance in a network. We compare SN index with many other benchmark methods based on 15 real networks via edge percolation. Results show that the proposed SN index outperforms other well-known methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Solvents

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Identifying the Predictive Capability of Machine Learning Classifiers for Designing Heart Disease Detection System

Identifying the Predictive Capability of Machine Learning Cl...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： AMIN UL HAQ JIANPING LI JALALUDDIN KHAN MUHAMMAD HAMMAD MEMON SHADMA PARVEEN MORDECAI FOLARIN RAJI WASIF AKBAR TANVIR AHMAD SANA ULLAH LATIPOVA SHOISTA HAPPY N. MONDAY School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Management Science and Economics University of Electronic Science and Technology of China School of Control and Computer Engineering North China Electric Power University Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

The diagnosis of heart diseases through invoice based techniques as well as ordinary medical based methods are not reliable. On other hand, non-invoice based techniques are more effective for heart disease diagnosis. Therefore, we check the capability of various Machine Learning (ML) classifiers and deep learning classifier for heart disease identification in this paper. Six machine-learning classifiers and BPNN were used in order to check which one classifier is more effective for diagnosis the heart disease. The feature selection algorithm Relief was used for selection of important features and on these selected features, classifiers performances were also computed. Ensemble machine learning techniques (boosting, bagging, stacking) were used to further increase the classifiers performance. Furthermore, cross-validation techniques k-folds was also used. Additionally backward propagation neural network (BPNN) was also used for classification purpose because deep learning algorithm not need feature selection algorithms and it automatically select important features for good result. Based on model performance evaluation metrics the SVM (RBF) performed excellently on full features achieved accuracy 86%, and 88% accuracy on selected features as compared other classifiers. Through Ensemble learning techniques, SVM obtained the classification accuracy 92.30%. The BPNN achieved 93% classification accuracy. Thus the performance of deep neural networks learning algorithm is better than traditional machine learning algorithms. As per our experimental results shows that the performance of BPNN based diagnosis system is more effective for heart disease diagnosis.

关键词： Heart Classification algorithms Machine learning Diseases Machine learning algorithms Training Feature extraction

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A Comparative Study of ARIMA and RNN for Short Term Wind Speed Forecasting

A Comparative Study of ARIMA and RNN for Short Term Wind Spe...

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International Conference on Computing and Networking technology (ICCNT)

作者： Shivani K.S. Sandhu Anil Ramachandran Nair Power Systems Engineering National Institute of Technology Kurukshetra Haryana India National Institute of Technology Kurukshetra Haryana India Principal Research Scientist R&D Division Toshiba Software India Pvt. Ltd. (TSIP) Bangalore India

The requirement of electricity is increasing day by day due to industrial and technological development and due to that the gap between generation and demand is also increasing. To fulfil the current electricity demand we are switching towards renewable sources of energy as nonrenewable sources are limited in stock and are depleted day by day. Moreover, renewable sources are more cleaner and environment friendly as they cause small or no carbon emission. Wind energy plays a larger role in providing electricity to industrial and domestic consumers. But wind has a stochastic nature and so we are not sure about how much energy it will give in the coming minutes or hours or days or months or years. Thus, we need an effective forecasting model, which helps in minimizing the use of conventional power plants (by unit commitment) and also optimizing the plants (by economic dispatch). This paper presents a comparative study of a time series model (ARIMA, i.e. Auto Regressive Integrated Moving Average) and a deep learning model (RNN, i.e. Recurrent Neural Network). Here, RNN is used with the combination of LSTM (Long short-term memory).

关键词： Predictive models Wind speed Forecasting Time series analysis Wind forecasting Data models Artificial neural networks

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Development Of Cross-Sectoral Active Network Management Systems Using New engineering Paradigms

Development Of Cross-Sectoral Active Network Management Syst...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Marcel Ludwig Jan Mehlich Schaugar Azad Markus Zdrallek Christian Thommessen Nicolas Witte Nils Loose Ralf Settertobulte Uwe Weber Institute of Power System Engineering University of Wuppertal Wuppertal Germany Chair of Energy Technology University of Duisburg-Essen Duisburg Germany Research Group for Business Informatics and Software Engineering University of Duisburg-Essen Essen Germany Stadtwerke Lemgo GmbH Lemgo Germany

An Active Network Management system comprises a large number of different technical systems that interact in several ways to pursue local objectives. The Smart Grid Architecture Model was developed for the description of the coupling of these technical systems and scenarios within an Active Network Management system. In this paper, this modelling paradigm is applied for the requirements engineering and a uniform description of a cross-sectoral Active Network Management system. Essential requirements are shown and described in relation to the two upper layers of the Smart Grid Architecture Model. Based on the requirements engineering results, the implementation of a multi agent system is proposed. For the development of this multi agent system, a new software engineering paradigm is applied. Finally, the functionality use-case developed cross-sectoral Active Network system are described, which will be investigated in a later field test.

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Device-free motion & trajectory detection via RFID

Device-free motion & trajectory detection via RFID

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作者： Liang, Xiaoxuan Huang, Zhangqin Yang, Shengqi Qiu, Lanxin Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology No. 100 Pingleyuan Chaoyang District Beijing100124 China Beijing Engineering Research Center for IoT Software and Systems State Grid Zhejiang Electric Power Company Information and Telecommunication Branch Beijing University of Technology No. 8 Huanglong Road Xihu District Hangzhou Zhejiang Province310012 China

Compared with traditional methods that employ inertial sensors or wireless sensors, device-free approaches do not require that people carry devices, and they are considered a useful technique for indoor navigation and posture recognition. However, few existing methods can detect the trajectory and movements of humans at the same time. In this study, we propose a scheme called PADAR for addressing these two problems simultaneously by using passive radio frequency identification (RFID) tags but without attaching them to the human body. The idea is based on the principle of radio tomographic imaging, where the variance in a tag's backscattered radio frequency signal strength is influenced by human movement. We integrated a commodity off-the-shelf RFID reader with a two-dimensional phased array antenna and a matrix of passive tags to evaluate the performance of our scheme. We conducted experiments in a simulated indoor environment. The experimental results showed that PADAR achieved an accuracy of over 70%. © 2018 ACM.

关键词： Antenna phased arrays

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Improvement and Application of Apriori Algorithm Based on Equalization

Improvement and Application of Apriori Algorithm Based on Eq...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Libo Xu Lin Qiao Feng Zhao Bo Yang Qiong Wang Ping Ding Lei Li State Grid Liaoning Electric Power Co. Ltd Shenyang Liaoning China Anhui Jiyuan Software Co. Ltd Heifei Anhui China State Grid Gansu Electric Power Co. Ltd Lanzhou Gansu China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China

ISBN: (数字)9781728145280

ISBN: (纸本)9781728145297

In the era of information explosion, data collection and analysis have become more and more important, and association rule mining has become an important research area of the data mining field. As a classic association rule algorithm, Apriori often falls into the problem of frequently scanning the database and generating a large number of candidate sets. Aiming at these problems, an improved Apriori algorithm MD_Apriori based on the idea of equalization is proposed, which adopts the vertical data structure to mine frequent items and then the proposed approach is applied to mining frequent items of massive alarm information. Experimental results show that the proposed algorithm is significantly superior to the current association rule mining algorithms.

关键词： Conferences Data science Cyberspace

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A research on face cognition method with deep ensemble learning and feedback mechanism

A research on face cognition method with deep ensemble learn...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Fan Yeping Yang Desheng Ma Dong Jiao Dian Chi Xiaojin Chen Mengxian Anhui Jiyuan Software CO. LTD State Grid Communication Industry Group CO. LTD Hefei China School of Electric Engineering and Automation Hefei University of Technology Hefei China State Grid Wenzhou Power Supply Company Wenzhou China

ISBN: (数字)9781728130668

ISBN: (纸本)9781728130675

Face recognition technology is an important research field for deep learning. In order to overcome the shortcomings of traditional open-loop face cognition mode and deep neural network structure, and to imitate human cognition model with real-time evaluation of cognitive results and self-optimized regulation of feature space and classification criteria, drawing on the theory of closed-loop control theory, this paper explores a face intelligent cognition method with deep ensemble learning and feedback mechanism. Firstly, based on the DEEPID neural network, a multi-layer feature space of face images from the global to the local is established. Secondly, based on feature separability evaluation and variable precision rough set theory, a face cognition decision information system model with dynamic feature representation is established from the perspective of information theory, to compact the multilayer feature space. Thirdly, the ensemble random vector functional-link net is used to construct the classification criterion for the compacted multi-layer feature space. Finally, the entropy measure index of face recognition results is constructed to provide a quantitative basis for the self-optimization adjustment mechanism of face feature space and classification criteria. The experimental results show that the proposed method can effectively improve the recognition rate of face images compared with the existing methods.

关键词： Face Cognition Feature extraction Face recognition Training Information systems Neural networks

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Boosting Reactive Oxygen Species Generation Using Inter-Facet Edge Rich WO3Arrays for Photoelectrochemical Conversion

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Angewandte Chemie 2022年第1期135卷

作者： He Li Cheng Lin Yilong Yang Chaoran Dong Prof. Yulin Min Prof. Xiaoqin Shi Prof. Luyang Wang Prof. Siyu Lu Prof. Kan Zhang School of Materials Science and Engineering and School of Chemistry and Chemical Engineering Nanjing University of Science and Technology Nanjing 210094 China Contribution: Data curation (lead) Investigation (equal) Methodology (equal) Writing - original draft (equal) Contribution: Investigation (lead) Methodology (equal) Writing - original draft (equal) Contribution: Formal analysis (equal) Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power Shanghai University of Electric Power Shanghai 200090 P. R. China Contribution: Investigation (supporting) Writing - review & editing (supporting) Contribution: Software (lead) College of New Materials and New Energies Shenzhen Technology University Shenzhen Guangdong 518118 P. R. China Contribution: Methodology (supporting) Supervision (supporting) Writing - review & editing (supporting) Green Catalysis Center and College of Chemistry Zhengzhou University Zhengzhou 450000 P. R. China Contribution: Supervision (equal) Contribution: Supervision (lead) Writing - review & editing (lead)

Water oxidation reaction leaves room to be improved in the development of various solar fuel productions, because of the kinetically sluggish 4-electron transfer process of oxygen evolution reaction. In this work, we realize reactive oxygen species (ROS), H 2 O 2 and OH⋅, formations by water oxidation with total Faraday efficiencies of more than 90 % by using inter-facet edge (IFE) rich WO 3 arrays in an electrolyte containing CO 3 2− . Our results demonstrate that the IFE favors the adsorption of CO 3 2− while reducing the adsorption energy of OH⋅, as well as suppresses surface hole accumulation by direct 1-electron and indirect 2-electron transfer pathways. Finally, we present selective oxidation of benzyl alcohol by in situ using the formed OH⋅, which delivers a benzaldehyde production rate of ≈768 μmol h −1 with near 100 % selectivity. This work offers a promising approach to tune or control the oxidation reaction in an aqueous solar fuel system towards high efficiency and value-added product.

关键词： Charge Transfer Oxygenation Photocatalysis Radicals Water Splitting

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Nuclear Clustering Algorithm on State Grid's IT Operation Log

Nuclear Clustering Algorithm on State Grid's IT Operation Lo...

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2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Yao, Feng Li, Ang Ding, Ping Li, Lei State Grid Gansu Electric Power Co. Ltd Lanzhou Gansu730000 China Anhui Jiyuan Software Co. Ltd Hefei Anhui230000 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

ISBN: (纸本)9781728105512

With the continuous evolution of the IT infrastructure of State Grid Data Center and the growing operational data in the electric power system, how to quickly and automatically cluster the operation log in the data center of State Grid has become a key issue in the IT operation and maintenance of the data center. As the most commonly used algorithms in data mining, a clustering algorithm from data mining is adopted to handle the operational log data of State Grid IT data center, which can be used to effectively discover the changes of the topology structure during the operation of the IT infrastructure. Specifically, because the traditional sequential clustering algorithm lacks the ability to discover potential links in logs, this paper proposes a self-destructive nuclear clustering algorithm SDN-means, which aims at the business and data characteristics of the IT infrastructure system of State Grid data center, in order to effectively classify the operational log data of State Grid IT data center during the operation of State Grid. Through the analysis of the running logs of State Grid data center with obvious time series characteristics, the proposed SDN-means algorithm can effectively outperform the existing approaches on the operation of the data center of State Grid. © 2018 IEEE.

关键词： Data mining

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Stable web scraping: An approach based on neighbour zone and path similarity of page elements

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International Journal of Web engineering and technology 2018年第4期13卷 301-333页

作者： Gao, Peng Han, Hao Guo, Junxia Saeki, Motoshi Laboratory of Information Security and Software Engineering NARI Group Corporation State Grid Electric Power Research Institute No. 8 Nanrui Road Gulou District Nanjing Jiangsu210003 China Konica Minolta Tokyo Japan College of Information Science and Technology Beijing University of Chemical Technology 4# 15 Beisanhuan East Road Chaoyang District Beijing100029 China School of Computing Tokyo Institute of Technology Ookayama 2-12-1-W8-71 Meguro Tokyo152-8552 Japan

Web scraping techniques based on XPath enable users to consistently extract information of interest from webpages that do not provide a structured interface. However, XPath-based extraction is likely to fail when encountering page variants, resulting in a high cost of repair. Countermeasures based on pattern matching or model learning often require careful pre-processing, which is not suitable for cases where the target data is frequently re-designated. In this paper, we present a new extraction method for the stable scraping of arbitrary designated data from webpages. Instead of attempting to find the desired data directly, we first determine its approximate location in the changed page, called the neighbour zone. Then we search for the precise location by ranking the path similarity of page elements within the neighbour zone. Experiments on a large set of real-world webpages show that our method has better stability for web scraping, compared with the XPath-based extraction. In the two datasets, 0.118 and 0.891 F1-score were increased respectively. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Extraction

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