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Internal crack detection of castings: a study based on relief algorithm and Adaboost-SVM

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第9-10期108卷 3313-3322页

作者： Jin, Cheng Kong, Xianguang Chang, Jiantao Cheng, Han Liu, Xiaojia Xidian Univ Sch Mechanoelect Engn Xian 710071 Peoples R China Shanghai Spaceflight Precis Machinery Inst Res Ctr Shanghai 201600 Peoples R China

The internal crack defect is prone to appear in the inner part of casting products during the production process due to the influence of casting craft and on-site environment. In order to ensure the internal quality of the casting products, nondestructive examination and defect detection technology should be used to detect the internal crack of the casting product. The existing defect detection technologies have some problems such as poor generalization and low accuracy. Therefore, an internal crack defect detection method based on the relief algorithm and Adaboost-SVM is proposed in this paper. Firstly, casting image is preprocessed by grayscale transformation, bilateral filtering, and adaptive image segmentation. Secondly, HOG feature, invariant moment feature, and LBP feature are extracted, and sensitive feature set is selected by relief algorithm. Finally, the Adaboost-SVM is used to construct the internal crack detection model to realize the crack detection with high generalization and accuracy. The effectiveness of the method is verified by the casting image dataset collected in the actual industrial field. The experimental result reveals that the proposed method could not only extract sensitive feature set but also has better classification performance and generalization ability than other common classifiers.

关键词： Internal crack detection Feature extraction relief algorithm Adaboost-SVM

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Object-oriented feature selection of high spatial resolution images using an improved relief algorithm

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MATHEMATICAL AND COMPUTER MODELLING 2013年第3-4期58卷 619-626页

作者： Jia, Jianhua Yang, Ning Zhang, Chao Yue, Anzhi Yang, Jianyu Zhu, Dehai Xian Univ Sci & Technol Coll Geomat Xian 710054 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

In object-oriented classification approaches, small sample size and high dimensionality of features are the two main characteristics. In order to effectively use the rich features of the image objects, in this study, the relief algorithms were improved in terms of aspects of randomly drawing samples, the influence of sample quantity variance, and iteration times to evaluate the features. Through experiments in WorldView-2 images, the two classification results were contrasted between 36 dimension feature sets selected from Initials and initial 111 dimension feature sets. The results showed that the dimension disaster was avoided efficiently, while the precision and speed of classification were improved as well. Respectively, the overall accuracy and kappa coefficient using the optimized feature sets with the improved relief algorithm were increased by 6% and 11% compared with using all feature sets. Therefore, the feature space of object-oriented classification is effectively optimized with the proposed methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Feature selection relief algorithm Object-oriented classification WorldView-2

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Day-ahead Electricity Price Forecasting Using the relief algorithm and Neural Networks

Day-ahead Electricity Price Forecasting Using the Relief Alg...

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5th International Conference on the European Electricity Market

作者： Amjady, Nima Daraeepour, Ali Semnan Univ Semnan Iran

ISBN: (纸本)9781424417438

In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of a feature selection technique and Neural Network (NN) is proposed for this purpose. The feature selection method is a modified version of the relief algorithm, proposed for the feature selection of price forecasting. Then, by means of the most relevant, explanatory and irredundant features, a Neural Network (NN) predicts the next values of the price signal. The adjustable parameters of the whole method are fine-timed by a cross-validation technique. The proposed method is examined on PJM electricity market, forecasting day-ahead locational marginal prices (LMPs), and compared with some of the most recent price forecast methods especially some other popular and validated feature selection techniques. These comparisons indicate the validity and robustness of the proposed forecasting method.

关键词： Electricity Markets Neural-Network (NN) Price Forecasting relief algorithm

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Method of Extracting Characteristic Parameters of Medium-speed Maglev Train Levitation Controller based on relief algorithm 32

Method of Extracting Characteristic Parameters of Medium-spe...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Wang, Guangyi Xu, Jie Xu, Lili Liang, Dong Xu, Yazhi Beijing Jiaotong Univ State Key Lab Rail Control & Safety Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing Res Ctr Urban Traff Informat Sensing & Se Beijing 100044 Peoples R China PRC MOT Res & Dev Ctr Transport Ind Technol & Equipment U Beijing 100044 Peoples R China CRRC TANGSHAN CO LTD Tangshan 064000 Hebei Peoples R China

ISBN: (纸本)9781728158556

Medium-speed maglev train has been widely studied as a new means of transport. The suspension controller is the center of the suspension control system for electromagnetic medium-speed (EMS) maglev trains. Its inner equipment and components are complex and easily damaged. In severe cases, it will directly cause the train to lose its suspension ability. Therefore, efficient and accurate state identification and fault diagnosis algorithms are necessary for suspension controllers. In this paper, the medium and low speed maglev train fault data of Changsha Maglev Airport line is used as the basic data, to analyze the determined eight main faults of suspension controllers for medium-speed maglev trains. These faults including filter failure, single resistance open circuit of charging circuit, double resistance open circuit of charging circuit, protection circuit failure, input power failure, DC / DC converter failure, LC control power supply circuit malfunction, capacitor open circuit of charging circuit. Based on the relief algorithm, the characteristic parameters of the suspension controller faults are extracted with sufficient correlation. Finally, the characteristic selection results are verified by BP neural network, and the accuracy rate is significantly improved by 9.44% to 88.33 %, which proved the effectiveness of the method.

关键词： Medium-Speed Maglev Train Suspension Controller relief algorithm Fault Feature Parameters

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AMI Data Analytics: Customer Charactrization by relief algorithm and Supplementary Tools 25

AMI Data Analytics: Customer Charactrization by Relief algor...

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25th Iranian Conference on Electrical Engineering (ICEE)

作者： Kojury-Naftchali, Mohsen Fereidunian, Alireza Lesani, Hamid Univ Tehran Sch ECE CIPCE SMRL Tehran Iran KN Toosi Univ Technol Fac EE Tehran Iran

ISBN: (纸本)9781509059638

The present market environment is composed of different participants. Suppliers, demand aggregators, independent system operator Gencos, Transcos and so on, are the participants of this environment. Besides, in the literature of competitive electricity market, exchange of enough information among them is inevitable. Such information helps to better decision making for different aims such as cost decreasing, benefit maximizing, more accurate load forecasting, operation guides, planning and so on. This paper concentrates on a procedure helping better customer characterization by analyzing customers consumption data. For this goal, data-mining techniques in corporation with other algorithms are utilized. The result of this study is summarized in enough knowledge about customers consumption behavior classes and allocation of the best pattern for each individual class. Besides, in this study, for determination of the most affecting features, relief algorithm is utilized as a feature selection tool in comparison with peA algorithm. Eventually, performance of the proposed algorithm is evaluated by using a real dataset related to Irish customers.

关键词： Advanced Metering Infrastructure (AMI) relief algorithm data-mining Entropy Index Perincipal Component Analysis(PCA)

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Fault Feature Parameter Extraction Method for Wind Turbine Based on relief algorithm 12

Fault Feature Parameter Extraction Method for Wind Turbine B...

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12th International Conference on Power and Energy Systems (ICPES)

作者： Wang Zhengyu Gong Chao Gong Yu Zhang Yangfan Yang Weixin State Grid Jibei Elect Power Res Inst Beijing Peoples R China

ISBN: (纸本)9781665451451

With the rapid development of wind power technology, the capacity of wind power is growing rapidly and monitoring data of SCADA system is also becoming more comprehensive. Faults of wind turbine components are closely related to work condition, so the overall availability of whole wind farm could be improved by focusing on the wind turbines operating in harsh condition. In this paper, a method based on SCADA data to judge harsh condition is proposed. According to historical data of SCADA and fault record, the fault characteristic parameters are selected by relief algorithm, as the characteristic quantity of failure rate of wind turbine. Taking pitch fault and yaw fault as example, the SCADA data of the actual wind farm are simulated and the effectiveness of the method is verified.

关键词： wind turbine relief algorithm SCADA fault characteristic parameters

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Feature Selection Using Cooperative Game Theory and relief algorithm 8th

Feature Selection Using Cooperative Game Theory and Relief A...

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8th International Conference on Knowledge, Information, and Creativity Support Systems (KICSS)

作者： Gore, Shounak Govindaraju, Venu SUNY Buffalo Dept Comp Sci Buffalo NY 14260 USA

ISBN: (纸本)9783319190907;9783319190891

With the advancements in various data mining and social network-related approaches, datasets with a very high feature-dimensionality are often used. Various information theoretic approaches have been tried to select the most relevant set of features, and hence bring down the size of the data. Most of the times these approaches try to find a way to rank the features, so as to select or remove a fixed number of features. These principles usually assume some probability distribution for the data. These approaches also fail to capture the individual contribution of every feature in a given set of features. In this paper, we propose an approach which uses the relief algorithm and cooperative game theory to solve the problems mentioned above. The approach was tested on NIPS 2003 and UCI datasets using different classifiers and the results were comparable to the state-of-the-art methods.

关键词： Feature selection Game theory Shapley values relief algorithm

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Multi-Support Vector Machine Power System Transient Stability Assessment Based on relief algorithm

Multi-Support Vector Machine Power System Transient Stabilit...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Dai Yuanhang Chen Lei Zhang Weiling Min Yong Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9781467381321

It is difficult to choose a best feature subset for the power system transient stability assessment(TSA) problem, and the existing data mining methods for TSA lack sufficient considerations for these situations that wrongly classify unstable samples as stable ones. In response to these deficiencies, this paper proposes a multi-support vector machine (SVM) power system TSA method based on relief algorithm. Firstly, the proposed method selects several feature subsets with different size based on relief algorithm, then uses these selected feature subsets for SVM training, finally, these trained SVMs are integrated to assess the transient stability of power system. The analysis of a simulation system shows that the proposed method can take full advantage of the useful information provided by different feature subsets, and it can get a significant reduction in "misclassification" samples, which provides a useful reference for using data mining theory for TSA in real power systems.

关键词： Power System Transient Stability Assessment Support Vector Machine relief algorithm

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Unsupervised Texture Feature Classification Based on Cuckoo Search and relief algorithm 9

Unsupervised Texture Feature Classification Based on Cuckoo ...

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9th International Conference on Digital Image Processing (ICDIP)

作者： Wang, Mingwei Wan, Youchuan Ye, Zhiwei Chen, Maolin Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Hubei Peoples R China Hubei Univ Technol Sch Comp Sci Wuhan 430068 Hubei Peoples R China

ISBN: (纸本)9781510613058;9781510613041

Gabor filters and K-means algorithm are two commonly used texture analysis methods. However, the texture feature vector has a high dimension by using Gabor filters, which will influence the operating efficiency. Meanwhile, K-means algorithm is affected by the initial clustering centers, and it may lead to the decrease of classification accuracy. Hence, relief algorithm is applied to make a feature selection for Gabor texture feature, and obtain a suitable texture feature sunset. Furthermore, cuckoo search is used to optimize the clustering center of K-means algorithm, and enhance the accuracy and efficiency of texture recognition. Experimental results demonstrate the effectiveness of the proposed method.

关键词： Texture feature classification Gabor filter K-means algorithm relief algorithm Cuckoo search

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Combined Terrestrial Evapotranspiration Index prediction using a hybrid artificial intelligence paradigm integrated with relief algorithm-based feature selection

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 193卷

作者： Jamei, Mehdi Elbeltagi, Ahmed Maroufpoor, Saman Karbasi, Masoud Jamei, Mozhdeh Jalali, Mohammadnabi Najafzadeh, Negin Shahid Chamran Univ Ahvaz Shohadaye Hoveizeh Campus Technol Fac Engn Dashte Azadegan Iran Mansoura Univ Fac Agr Agr Engn Dept Mansoura 35516 Egypt Zhejiang Univ Coll Environm & Resource Sci Hangzhou 310058 Peoples R China Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran Univ Zanjan Fac Agr Zanjan Iran Ferdowsi Univ Mashhad Water Sci & Engn Dept Mashhad Razavi Khorasan Iran Khuzestan Water & Power Author Ahvaz Iran Univ Tehran Dept Water Engn Aburaihan Campus Tehran Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran

Drought is a common environmental disaster strongly influenced by the potential production of agricultural products, lack of water resources, and yields destructive effects on the economy. In this study, the prediction of a novel monthly Combined Terrestrial Evapotranspiration Index (CTEI) was considered as a measure of all three types of drought (meteorology, hydrology, and agriculture) in the Ganga river basin (GRB) over the period of 14 years. For this purpose, a combination of hydro-meteorological and satellite-based data, including 11 input variables, was implemented. A new Artificial Neural Network (ANN) integrated with a Whale Optimization algorithm (WOA-ANN) and a relief algorithm-based Feature Selection (FS) method was applied to simulate the monthly CTEI index and find the optimum input combinations. Besides, the standalone ANN and Least Square Support Vector Regression (LSSVR) models were examined to validate the WOA-ANN performance. The results indicated that WOA-ANN with (R = 0.9391), (RMSE = 0.241), (WI = 0.968), and (U-95% = 0.669) had a high ability to predict CTEI and reduced the RMSE in the ANN and LSSVR models by 27% and 30%, respectively. WOA-ANN had the best predictive performance, followed by LSSVR and ANN models, respectively, concerning various graphical validations and diagnostic analyses. Besides, the outcome of this research, despite fewer parameters (seven parameters), considerably outperformed the study of (Elbeltagi et al., 2021) with (R = 0.9055 and RMSE = 0.33) accuracy employing 11 inputs.

关键词： Terrestrial Evapotranspiration Index Drought Whale optimization algorithm relief algorithm ANN LSSVR

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