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A short-term output power prediction model of wind power based on deep learning of grouped time series

European Journal of Electrical engineering 2020年第1期22卷 29-38页

作者： Wang, Yongsheng Gao, Jing Xu, Zhiwei Li, Leixiao College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

The output power prediction of wind power plants is an important guarantee to improve the utilization rate of wind energy and reduce wind curtailment. However, due to the strong randomness of wind energy, the ultra-short-term prediction accuracy of wind power output is poor. In view of the problem above, a prediction model based on deep learning of grouped time series (LW-CLSTM) was proposed in this paper. Based on this model, the authors attempted to explore a prediction method of wind power output. For this, first the multivariate data of wind power was fused, cleaned, dimension-reduced, and standardized, and the time period characteristics of the output power itself were extracted. Afterwards, it proposes a time sliding window (TSW) algorithm, and constructs a neural network input data set. Then a deep neural network prediction model combining the Convolutional Neutral Network (CNN) and Long-Short Term Memory (LSTM) was established, and the regression evaluation criteria for output power forecast accuracy in wind power production were designed, to compare the proposed model with four other prediction models. Finally, the experiments on the TensorFlow platform using real data show that this model has better prediction accuracy than the other four models, reaching a prediction accuracy rate of 92.5%, which verified the effectiveness of this prediction method. © 2020 International Information and engineering Technology Association. All rights reserved.

关键词： Wind power

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Face Analysis for Coronary Heart Disease Diagnosis 12

Face Analysis for Coronary Heart Disease Diagnosis

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12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Lin, Shisong Zhuang, Xiaodong Huang, Shiyun Liu, Yahui Shen, Linlin Liao, Xinxue Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Cardiology department First affiliated hospital of Sun Yat-sen University Guangzhou China NHC Key Laboratory of Assisted Circulation Sun Yat-sen University Guangzhou China Shenzhen National Medical Science and Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9781728148526

CHD (Coronary Heart Disease) is one of the leading causes of cardiovascular disease deaths. Invasive coronary arteriography is one of the widely used approaches to diagnose CHD. However, the time cost and expenses for most of the diagnosis methodologies are high and some patients are reluctant to do such a diagnosis. In this paper, we aim to develop a novel low cost method to predict CHD. As suggested by the cardiologists, there are substantial differences between the facial images of patients with CHD and those of healthy subjects. In this paper, we conduct an automatic analysis of the texture features extracted from eight ROIs (Regions of Interests) of the face images. Based on the texture features, random forest and decision tree are used to predict whether the subject has a CHD, or not. The experimental results on a set of 1528 face images collected from 309 subjects suggest that, our approach achieved a promising, i.e. 72.73%, prediction accuracy. © 2019 IEEE.

关键词： Diagnosis

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Automatic Primary Gross Tumor Volume Segmentation for Nasopharyngeal Carcinoma using ResSE-UNet

Automatic Primary Gross Tumor Volume Segmentation for Nasoph...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Zhihao Jin XueChen Li Linlin Shen Jinyi Lang Jie Li Junxiang Wu Peng Xu Jiang Duan College of Computer Science and Software Engineering Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen PR China Sichuan Cancer Hospital & Institute Sichuan Cancer Center School of Medicine University of Electronic Science and Technology of China Radiation Oncology Key Laboratory Of Sichuan Province Chengdu PR China School of economic information engineering Southwestern university of finance and economics Chengdu PR China

ISBN: (数字)9781728194295

ISBN: (纸本)9781728194301

Nasopharyngeal carcinoma (NPC) is an endemic disease within specific regions in the world. Radiotherapy is the standard treatment for NPC and accurate segmentation of primary gross tumor volume (GTV) is a critical process of continue therapy. In this paper we proposed a ResSE-UNet network and a Ternary Cross-Entropy (TCE) loss function for delineation of GTV. ResSE-UNet employed ResSE blocks to replace convolutional blocks in the original UNet to extract better features, and reduced the number of down-sampling processing to keep relatively high resolution of the images. TCE combined dice loss and Binary cross-entropy loss for larger gradient and better stability in training. The experimental results showed that among all combinations of networks and loss functions, the ResSE-UNet with TCE loss achieved the best segmentation performance, i.e. about 0.84 DSC can be obtained.

关键词： Image segmentation Computed tomography Convolution Feature extraction Tumors Task analysis Training

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A Hybrid Structural Building Data Mining System Based On the Data Tracking Through Devices

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Journal of Physics: Conference Series 2021年第4期1964卷

作者： A Sangeerani Devi B Ravi Prasad Venkateshwaran Loganathan Swapnil Saurav Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology Dundigal Telangana India Department of Information Technology KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Problem Management Service Now Software India Pvt. Ltd Hyderabad Telangana India

One of several traditional megaprojects is underground construction, given its long building time high building expense and possible risks. In tunnel engineering, trench boring devices are generally used to increase work performance and safety. During the tunnelling process, system has recorded vast volumes of tracking data to ensure building safety. The processing of vast real-time surveillance data also lacks successful techniques, and, in many situations, it must be performed manually that pose possible safety hazards. This paper suggests an approach for hybrid data mining (DM) to automatically process the TBM data for real-time tracking. Three separate DM strategies are merged in order to improve the operation of mining also to help security management. The sequential pattern method is executed to remove connections between TBM parameters in order to give people the expertise needed for an irregular on-site judging. A random forest model is built to identify training data in order to complement knowledge needed for building decision-making system. Finally, neural network models measure the penetration rate (ROP) in order to detect irregular data and to alert early. In the case of a tunnel project in China, the suggested technique was applied, and the findings of the application concluded that the approach offered a reliable and effective way of evaluating TBM protection management data in real time during buildings.

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Developing an Effective Graph Based Block Chain Framework

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Journal of Physics: Conference Series 2021年第4期1964卷

作者： B Ravi Prasad Swapnil Saurav A Sangeerani Devi Venkateshwaran Loganathan Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology Dundigal Telangana India Department of Problem Management Service Now Software India Pvt. Ltd Hyderabad Telangana India Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Information Technology KGiSL Institute of Technology Coimbatore Tamil Nadu India

Blockchain's decentralized characteristic is recognized as a potential technology to deliver secure and safe resources. However, the existing blockchain networks cannot fulfil the transaction given the limited bandwidth for practical application demand. In this article, the cubic design of the conventional public blockchain is strengthened with the Directed Graph Probabilistic model. In the current structure, blocks of the lightweight structure are ordered in grades and width. We develop protocols to position newly created blocks in order to make them more effective and secure. It increases confidentiality and time for authentication of transactions in contrast with standard blockchain protocols and enjoys the accuracy and liveliness of blockchain. This paper introduces two potential methods for targeting opposition parties and we also show that the procedure against them is safe and stable. Experiments will generate three hundred inputs every second that is 64 times the output of Bitcoin's transaction. This time Bitcoin's production will hit 27 times its Ethereum's.

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Command and control organization generation method based on adaptive quantum genetic algorithm

Command and control organization generation method based on ...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Peng Sun Junsheng Wu Xun Wang Zhiqiang Jiao Yichao He School of computer science Northwestern Polytechnical University School of software Northwestern Polytechnical University College of Information and Navigation Air Force Engineering University Xi’an China

Firstly, the design problem of command and control structure before battle is described, and the workload of task command and control complexity measurement decision entity is defined. Secondly, a mathematical model is established with the optimization objective of minimizing the root mean square of workload. In order to solve the mathematical model, the adaptive quantum genetic algorithm is proposed by combining the quantum genetic algorithm with the adaptive strategy. Finally, the effectiveness of the algorithm is verified by experimental simulation.

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Improving Prediction Accuracy for Logistic Regression on Imbalanced Datasets

Improving Prediction Accuracy for Logistic Regression on Imb...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Hao Zhang Zhuolin Li Hossain Shahriar Lixin Tao Prabir Bhattacharya Ying Qian College of CSE Kennesaw State University Dept. of Software Engineering Pace University Dept. of Computer Science SUNY at Albany Albany NY USA Dept. of Computer Science East China Normal University

An imbalanced dataset is a dataset that has a majority class which is a class has far more example distributions than other classes. It is difficult to deal with unbalanced datasets in classification problems, and many classification algorithms do not perform well in unbalanced datasets. In this paper, we present our logistic regression analysis with Python on imbalanced datasets and determine different thresholds for classification according to the data proportion of imbalanced datasets.

关键词： Logistics Classification algorithms Credit cards Genetic algorithms Machine learning Companies Data mining

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SG-PBFT: A secure and highly efficient blockchain PBFT Consensus Algorithm for internet of vehicles

arXiv

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arXiv 2021年

作者： Xu, Guangquan Liu, Yihua Xing, Jun Luo, Tao Gu, Yonghao Liu, Shaoying Zheng, Xi Vasilakos, Athanasios V. The Big Data School Qingdao Huanghai University Qingdao266427 China College of Intelligence and Computing Tianjin University Tianjin300350 China The International Engineering Institute Tianjin University Tianjin300350 China The College of Intelligence and Computing Tianjin University Tianjin Tianjin300350 China The school of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China The Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China School of Informatics and Data Science Hiroshima University Japan The Department of Computing Macquarie University SydneyNSW2109 Australia The University of Technology Sydney Australia The Fuzhou University Fuzhou China Lulea University of Technology Lulea Sweden

The Internet of Vehicles (IoV) is an application of the Internet of things (IoT). It faces two main security problems: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To solve these problems, this paper proposes SG-PBFT: a secure and highly efficient PBFT consensus algorithm for Internet of Vehicles, which is based on a distributed blockchain structure. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional PBFT consensus algorithm by using a score grouping mechanism to achieve a higher consensus efficiency. The experimental result shows that our method can greatly improve the consensus efficiency and prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art consensus algorithm (PBFT). Our proposed SG-PBFT is versatile and can be used in other application scenarios which require high consensus efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Correction to: Next-generation UWB antennas gadgets for human health care using SAR

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EURASIP Journal on Wireless Communications and Networking 2021年第1期2021卷 1-1页

作者： Ali, Aysha Maryam Al Ghamdi, Mohammed A. Iqbal, Muhammad Munwar Khalid, Shehzad Aldabbas, Hamza Saeed, Saqib University of Engineering and Technology Taxila Pakistan Computer Science Department Umm Al-Qura University Makkah City Saudi Arabia Computer Engineering Department Bahria University Islamabad Pakistan Software Engineering Department Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt Jordan Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Kingdom of Saudi Arabia

An amendment to this paper has been published and can be accessed via the original article.

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A Fitted Fuzzy-rough Method for Missing Data Imputation

A Fitted Fuzzy-rough Method for Missing Data Imputation

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Daiwei Li Tianrui Li Haiqing Zhang Abdelaziz Bouras Xi Yu Dan Tang School of Information Science and Technology Southwest Jiaotong University College of Software Engineering Chengdu University of Information Technology Chengdu China School of Information Science and Technology Southwest Jiaotong University Chengdu China College of Software Engineering Chengdu University of Information Technology Chengdu China Computer Science Department Qatar University Doha Qatar College of Information Science and Technology Chengdu University Chengdu China

ISBN: (数字)9781728123486

ISBN: (纸本)9781728123493

Missing data imputation is a fundamental task for reducing uncertainty and vagueness in medical dataset. Fuzzy-rough set has taken very important role to accurate representation original information. This paper proposes Fitted fuzzy-rough imputation algorithms called Fitted FRNNI and Fitted VQNNI by introducing weight coefficients to balance fuzzy similarly relations among training and testing instances. Meanwhile, modification fuzzy decisions of nearest neighbors based on lower/upper approximations are studied. Performance analysis is conducted including classification accuracy analysis, the impact of k parameter and weight coefficient of a and β to evaluate the proposed Fitted FRNNI and VQNNI algorithms. Experimental results on 13 benchmark datasets show that the proposed algorithms outperform current leading algorithms.

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