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Detection of DC Series Arc Fault Based on VMD and ELM 4

Detection of DC Series Arc Fault Based on VMD and ELM

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2019 4th international Seminar on computer Technology, Mechanical and Electrical Engineering, ISCME 2019

作者： Ma, Tao Tian, Ersheng Liu, Zhenxing Liu, Shuxin Guo, Tianhong Wang, Taowei Fu, Long State Grid xiong'An New Area Electric Power Supply Company Baoding Hebei71600 China Xuji Group Corporation Xuchang Henan461000 China Institute of Electrical Apparatus New Technology and Application Shenyang University of Technology Shenyang Liaoning110870 China

With the increase of domestic electrical equipment, the incidence of electrical fires has also increased, and research on fault arc detection has become a hot topic today. In this paper, a method combining variational mode decomposition (VMD), and extreme learning machine (ELM) is proposed to detect arc faults accurately. the characteristic signals of the resistance, capacitance and inductive load under normal conditions and arc fault conditions were collected by experiments. then, the current data was processed by variational mode decomposition (VMD). Due to the different spectral characteristics of normal mode, arc fault mode and switching transient mode, the intrinsic mode function (IMF) under arc fault mode can be selected. Finally, according to the characteristic of determined IMF components, a new arc fault criterion was proposed for general DC arc detection. the experimental results verified that the proposed method can detect arc faults accurately. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Variational mode decomposition

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application of the Modified Imputation Method to Missing Data to Increase Classification Performance 4

Application of the Modified Imputation Method to Missing Dat...

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4th IEEE international conference on computer and Communication Systems (ICCCS)

作者： Caparino, Elenita T. Sison, Ariel M. Medina, Ruji P. Technol Inst Philippines Grad Programs Quezon City Philippines Emilio Aguinaldo Coll Sch Engn & Technol Manila Philippines

ISBN: (纸本)9781728113227

Incomplete data or missing data diminishes the effectivity of statistical results, and may cause bias estimates, which in turn leads to unsound judgment. Inefficiency and impediments in data treatment analysis, which are among the predicaments linked with missing values, may affect the supervised learning process and reduce the classification accuracy and performance of the prediction model in a data mining task. this study applied the modified imputation method-which was previously tested with well-known imputation algorithms-to renowned classification techniques namely Naive Bayes, One-R, k-Nearest Neighbor (kNN), C4.5, and Support Vector machine (SVM) using open data sets from the UCI Repository. the level of performance in terms of precision, accuracy, and Receiver Operating Characteristics (ROC) using Weka tool, before and after imputation was examined. this study manifests that there was an improvement in the classification performance upon the application of the modified imputation method on datasets during preprocessing, compared to that of datasets with missing values.

关键词： classification data mining imputation missing values machine learning

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13th international conference on Intelligent Robotics and applications, ICIRA 2020

13th International Conference on Intelligent Robotics and Ap...

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13th international conference on Intelligent Robotics and applications, ICIRA 2020

ISBN: (纸本)9783030666446

the proceedings contain 45 papers. the special focus in this conference is on 13th international conference on Intelligent Robotics and applications, ICIRA 2020. the topics include: Master-Slave Control of a Bio-Inspired Biped Climbing Robot;leveraging Blockchain for Spoof-Resilient Robot Networks;ImPL-VIO: An Improved Monocular Visual-Inertial Odometry Using Point and Line Features;a Matching Algorithm for Featureless Sparse Point Cloud Registration;progressive Attentional learning for Underwater Image Super-Resolution;movie Genre Filtering for Automated Parental Control;Bridging Explainable machine Vision in CAD Systems for Lung Cancer Detection;extending Egocentric Vision into Vehicles: Malaysian Dash-Cam Dataset;kinematic Calibration for Industrial Robot Using a Telescoping Ballbar;Variable Impedance Control of Manipulator Based on DQN;simulation of Human Upright Standing Push-Recovery Based on OpenSim;Design and Development of sEMG-Controlled Prosthetic Hand with Temperature and Pressure Sensory Feedback;two-Wheel Balancing Robot Foot Plate Control Using Series Elastic Actuator;Kinematics Analysis of a New Spatial 3-DOF Parallel Mechanism;iterative Phase Correction Method and Its application;Design of USV for Search and Rescue in Shallow Water;4-Leg Landing Platform with Sensing for Reliable Rough Landing of Multi-copters;Determination of Singularity Occurrence and Characteristic Analysis of Dual 6-DOF Manipulator Using Manipulability and Manipulability Ellipsoid;rapid Actuation for Soft Pneumatic Actuators Using Dynamic Instability Mechanism;the Point Position and Normal Detection System and Its application;6D Pose Estimation for Texture-Less Industrial Parts in the Crowd;multi-scale Crack Detection Based on Keypoint Detection and Minimal Path Technique;a Self-correction Based Algorithm for Single-Shot Camera Calibration;preface.

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Multifactorial evolutionary prediction of phenology and pests: Can machine learning help? 16

Multifactorial evolutionary prediction of phenology and pest...

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16th international conference on Web Information Systems and Technologies, WEBIST 2020

作者： Lacueva-Pérez, Francisco José Artigas, Sergio Ilarri Barriuso Vargas, Juan José Lezaun, Gorka Labata Del Hoyo Alonso, Rafael ITAINNOVA Instituto Tecnológico de Aragón PT. Walqa Huesca Spain Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain I3A University of Zaragoza Zaragoza Spain CITA - Universidad de Zaragoza Zaragoza Spain Zaragoza Spain

ISBN: (纸本)9789897584787

Agriculture is a key primary sector of economy. Developing and applying techniques that support a sustainable development of the fields and maximize their productivity, while guaranteeing the maximum levels of health and quality of the crops, is necessary. Precision agriculture refers to the use of technology to help in the decision-making process and can lead to the achievement of these goals. In this position paper, we argue that machine learning (ML) techniques can provide significant benefits to precision agriculture, but that there exist obstacles that are preventing their widespread adoption and effective application. Particularly, we focus on the prediction of phenology changes and pests, due to their important to ensure the quality of the crops. We analyze the state of the art, present the existing challenges, and outline our specific research goals. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

关键词： Precision agriculture

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7th international conference on Augmented Reality, Virtual Reality, and computer Graphics, AVR 2020

7th International Conference on Augmented Reality, Virtual R...

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7th international conference on Augmented Reality, Virtual Reality, and computer Graphics, AVR 2020

ISBN: (纸本)9783030584641

the proceedings contain 60 papers. the special focus in this conference is on Augmented Reality, Virtual Reality, and computer Graphics. the topics include: Using augmented reality to train flow patterns for pilot students - an explorative study;scalable integration of image and face based augmented reality;usability of an input modality for ar;asymmetrical multiplayer versus single player: Effects on game experience in a virtual reality edutainment game;state of the art of non-vision-based localization technologies for ar in facility management;ai4ar: An ai-based mobile application for the automatic generation of ar contents;ar-based visual aids for suas operations in security sensitive areas;global-map-registered local visual odometry using on-the-fly pose graph updates;combining hololens and leap-motion for free hand-based 3d interaction in mr environments;mixed reality annotations system for museum space based on the uwb positioning and mobile device;a glasses-based holographic tabletop for collaborative monitoring of aerial missions;a comparative study of the influence of the screen size of mobile devices on the experience effect of 3d content in the form of ar/vr technology;photogrammetric 3d reconstruction of small objects for a real-time fruition;adaptive detection of single-color marker with webgl;procedural content generation via machine learning in 2d indoor scene;users’ adaptation to non-standard controller schemes in 3d gaming experiences;3d dynamic hand gestures recognition using the leap motion sensor and convolutional neural networks;rgb-d image inpainting using generative adversarial network with a late fusion approach;3d radial layout for centrality visualization in graphs;preface.

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7th international conference on Augmented Reality, Virtual Reality, and computer Graphics, AVR 2020

7th International Conference on Augmented Reality, Virtual R...

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7th international conference on Augmented Reality, Virtual Reality, and computer Graphics, AVR 2020

ISBN: (纸本)9783030584672

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Comparison of machine learning Methods for Android Malicious Software Classification based on System Call 4

Comparison of Machine Learning Methods for Android Malicious...

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4th international conference on Sustainable Information Engineering and Technology (SIET)

作者： Anshori, Mochammad Mar'i, Farhanna Bachtiar, Fitra A. Brawijaya Univ Fac Comp Sci Malang Indonesia

ISBN: (纸本)9781728138800

the development of the Android operating system is very rapid accompanied by the development of various types of malicious software (malware). the malware application can enter automatically into an Android device in an unintentional way by Android smartphone users so that there are many cases of data theft that are very detrimental to the user. In this study, malware detection will be based on the system call feature on Android using several machine learning methods, namely Support Vector machine (SVM), Naive Bayes, Decision Tree, Random Forest, Log Regression, and K-nearest Neighbor (KNN). the purpose of this study is to find out the machine learning method that can provide the best value of accuracy, TPR, and FPR in resolving the problem of malware detection on android by classification of types of malware using a system call on Android. Based on the results of this study, it can be seen that the Random Forest (RF) method can classify malware in an android system by conducting early detection that produces an accuracy value of 76%, Random Forest has proven to have reliable performance in case of classification and also has advantages such as fast computation time and high accuracy also proved to be better than other machine learning methods, namely SVM, Naive Bayes, Decision Tree, Log Regression, and K-nearest Neighbor (KNN), which each produced an accuracy value of 71.67%, 66.83%, 69.33%, 70.83% and 71.67%.

关键词： Classification Malware Android Support Vector machine

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Latent Class Models on Business Analytics 4

Latent Class Models on Business Analytics

引用

4th IEEE/ACIS international conference on Big Data, Cloud Computing, and Data Science, BCD 2019

作者： Goto, Masayuki Department of Industrial Management and Systems Engineering Waseda University Faculty of Creative Science and Engineering Tokyo Japan

ISBN: (纸本)9781728108865

this paper discusses the systematic application of the latent class model on business analytics. the latent class model is one of the effective statistical model classes on business analytics to represent essential statistical structures by learning the sparse and high dimensional data. this model class is useful for the purpose of reduction of feature dimension and cancellation of sparseness of the data. this is because many practical data can be assumed to consist of several unobserved subgroups. For example, a customers set, which is a target in the field of marketing analysis, consists of several subgroups with different characteristics and preferences. In addition, data clustering can also be realized by estimated belonging probabilities to latent classes of each *** paper gives a general form of the latent class model and discuss how to construct the model structure and apply to a real problem in business analytics. After describing important points to be noted in analysis based on a latent class model, several practical examples are also shown. © 2019 IEEE.

关键词： machine learning

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4th Annual international conference on Information System and Artificial Intelligence - Chapter 1: computer Science and Information System

4th Annual International Conference on Information System an...

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2019 4th Annual international conference on Information System and Artificial Intelligence, ISAI 2019

the proceedings contain 222 papers. the topics discussed include: research on enterprise risk identification and early warning system based on big data background;modeling and optimization of heterogeneous vehicle routing problem in emergency resource distribution after disaster;empirical study of the impact of QFII shareholdings on board governance and corporate performances - based on Chinese listed companies data;multi-person pose estimation via learning feature integration;fuzzy twin support vector machine based on intra-class hyperplane;the research on the simulation of VHF radio broadcasting loss;and saliency object detection based on regions merging and its application in image retrieval.

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Feature Selection and Prediction Model for Type 2 Diabetes in the Chinese Population with machine learning 20

Feature Selection and Prediction Model for Type 2 Diabetes i...

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4th international conference on computer Science and application Engineering, CSAE 2020

作者： Hou, Jiaqi Sang, Yongsheng Liu, Yuping Lu, Li State Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China College of Computer Science Sichuan University Chengdu China Health Management Center Sichuan Provincial People's Hospital Affiliated University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9781450377720

Diabetes is a chronic disease characterized by hyperglycemia. Based on the rising incidence of the disease in recent years, diabetes is affecting more and more families. In 2017 alone, it caused 5 million deaths and cost $850 billion in global healthcare. In this paper, we proposed a method to predict the prevalence of diabetes based on a selected set of features from physical examination data. We used Fisher's score, RFE and decision tree to select features. Random forest, logistic regression, SVM and MLP were used to predict the prevalence of diabetes. EA and Fisher' s score helped us to reduce dimensions. We used random forest to classify diabetes accurately. Our results show that the highest accuracy (0.987) can be achieved by using random forest with 85 features. the prediction accuracy using Fisher's Score with 19 features also reached 0.986. We finally selected 5 features based on our method to form a new dataset for diabetes prediction. the 5 features are fasting plasma glucose, HbA1c, HDL, total cholesterol level and hypertension. the values of accuracy, precision, sensitivity, F1 score, MCC and AUC were 0.977, 0.968, 0.812, 0.883, 0.875, and 0.905, respectively. Results show that our method can be successfully used to select features for diabetes classifier and improve its performance, which will provide support for clinicians to quickly identify diabetes. © 2020 ACM.

关键词： Feature Selection

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