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Refrigerant charge fault diagnosis in the VRF system using Bayesian artificial neural network combined with relieff filter

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APPLIED THERMAL ENGINEERING 2017年 112卷 698-706页

作者： Shi, Shubiao Li, Guannan Chen, Huanxin Liu, Jiangyan Hu, Yunpeng Xing, Lu Hu, Wenju Huazhong Univ Sci & Technol Dept Refrigerat & Cryogen Wuhan Peoples R China Beijing Univ Civil Engn & Architecture Beijing Municipal Key Lab HVAC&R Beijing Peoples R China

A proper refrigerant charge amount (RCA) is critical for a variable refrigerant flow (VRF) system since RCA may affect the operational performance. However, there were few studies of RCA fault for the VRF system in the open literature. Therefore VRF systems are calling for a fault diagnosis strategy. This paper develops a highly efficient fault diagnosis model (FDM), which employs the relieff algorithm for feature ranking (FR) and applies the neural network for fault diagnosis. Firstly, the artificial neural network (ANN) model is built on the N-best features data subset and optimized by the Bayesian regularization algorithm. Secondly, the model is verified by testing data subset, the correct diagnosis rates (CDR) using the N-best features data subset can be obtained. The optimal FDM is selected in consideration of CDR and the computational efficiency. Finally, optimal FDM is further optimized by selecting the best hidden neurons. The results show that the CDR of the FDM based on 6-best features is sufficiently high in comparison to the CDR achieved when 22 features are used, while the training time decreases by 98.8%. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis Variable refrigerant flow system relieff algorithm Bayesian neural network Refrigerant charge amount fault

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An Unsupervised Feature Selection Method Based on Improved relieff and Bisecting K-means 6

An Unsupervised Feature Selection Method Based on Improved R...

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6th IEEE International Conference on Network Infrastructure and Digital Content (IEEE IC-NIDC)

作者： Yang, Junyuan Wang, Xiaoxiang Wang, Dongyu Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9781538660676

Due to the fact that a large number of data are unlabeled with many high-dimensional in practical machine learning problems, this paper proposes an unsupervised feature selection model BK-IRIT based on Bisecting K-means and the improved relieff algorithm considering the redundancy information between features, combined with the idea of iteration. The model uses Bisecting K-means to label the unlabeled data, uses improved relieff algorithm for feature selection, and iterates the above process until the desired subset of features is output. Experiments show that this unsupervised feature selection method has good performance.

关键词： Unsupervised Feature Selection Bisecting K-means relieff algorithm Iteration

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relieff Based Feature Selection In Content-Based Image Retrieval

ReliefF Based Feature Selection In Content-Based Image Retri...

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International MultiConference of Engineers and Computer Scientists (IMECS 2012)

作者： Sarrafzadeh, Abdolhossein Atabay, Habibollah Agh Pedram, Mir Mosen Shanbehzadeh, Jamshid Unitec Dept Comp Auckland New Zealand Tarbiat Moallem Univ Tehran Tehran Iran

ISBN: (纸本)9789881925114

content based image retrieval systems usually extract low level features to retrieve similar images. But in most cases, selection of suitable features according to their impact on the classification accuracy has been less considered. This paper studies the effects of reducing the number of features and selecting the most effective subset of features in the context of content-based image classification and retrieval of objects. We use Legendre moments to extract features, relieff algorithm to select the most relevant and non redundant features and support vector machine to classify images. The experimental results on Coil-20 image dataset, shows that by selecting much lower number of features when employing relieff, we can improve retrieval in terms of speed and accuracy.

关键词： Content Based Image Retrieval Feature Selection relieff algorithm Exact Legendre Moments Support Vector Machine

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A Feature Gene Selection Method Based on relieff and PSO 10

A Feature Gene Selection Method Based on ReliefF and PSO

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10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Liu Mengdi Xu Liancheng Yi Jing Huang Jie Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China Shandong Prov Key Lab Distributed Comp Software N Jinan 250358 Shandong Peoples R China Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250014 Shandong Peoples R China

ISBN: (纸本)9781538651148

Due to the shortcomings of low efficiency and low accuracy of DNA microarray data, the paper proposes a feature gene selection algorithm based on relieff and PSO (RefFPSO). Firstly, relieff is used as the feature pre-filter to delete the genes with low correlation with the classification target. Then PSO is used as the search algorithm. Finally, the classification accuracy of SVM is used as the evaluation function of the feature subset to get the final optimal gene subset. Experiments show that this method can effectively put forward irrelevant genes and use fewer characteristic genes to obtain higher classification accuracy.

关键词： DNA microarray data relieff algorithm particle swarm optimization feature selection

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The Construction of a Majority-Voting Ensemble Based on the Interrelation and Amount of Information of Features

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COMPUTER JOURNAL 2020年第11期63卷 1756-1774页

作者： Aydin, Fatih Aslan, Zafer Kirklareli Univ Dept Software Engn Fac Engn Kirklareli Turkey Istanbul Aydin Univ Dept Comp Engn Fac Engn Istanbul Turkey

In this paper, we introduced a new ensemble learning algorithm called VIBES, which is better in terms of performance when compared to 85 machine learning algorithms in WEKA tool. This new algorithm is based on three major processes: (i) making an assumption regarding whether features are dependent on or independent of each other, (ii) computing the amount of information of features when it is assumed that they are dependent on each other and then sorting them in a descending manner based on the amount of information, (iii) speeding up the algorithm by optimizing the forward search algorithm that is used in the construction of the final hypothesis from base learner hypotheses. As a result of these processes, it has been seen in the experiments that choosing the relevant assumption can boost learning performance if features are independent of each other;considering features according to the amount of information provides high accuracy and diversity of base learner models. According to experiment results, the algorithm that has been developed has the highest average classification accuracy rate across the 33 datasets. The highest and the lowest average classification accuracy rates have been found to be 89.80 and 78.03 %, respectively.

关键词： machine learning optimized forward search genetic algorithms Shannon entropy relieff algorithm

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Depth features to recognise dyadic interactions

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IET COMPUTER VISION 2018年第3期12卷 331-339页

作者： Keceli, Ali Seydi Kaya, Aydin Can, Ahmet Burak Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkey

Usage of depth sensors in activity recognition is an emerging technology in human-computer interaction. This study presents an approach to recognise human-to-human interactions by using depth information. Both hand-crafted features and deep features extracted from depth frames are studied. After selecting and ranking strong features with relieff algorithm, depth frames are assigned to words. Then, interaction sequences are represented as histograms of words and non-linear input mapping is applied over histogram bins to minimise differences among various subjects. Random forest, K-nearest neighbour, and support vector machine (SVM) classifiers are trained using these histograms. The final model is tested on SBU and K3HI datasets and compared with the methods in the literature. In the experiments, joint distances, joint angles and spherical coordinates of the joints were the best performing features. The most successful results are obtained with the composite kernel SVM with relieff and input mapping methods. While relieff algorithm helps to select and rank the best features in the feature set, input mapping reduces differences among interactions of various actors.

关键词： gesture recognition feature extraction image sequences image representation support vector machines depth feature extraction dyadic interaction recognition depth sensors activity recognition human-computer interaction human-to-human interactions hand-crafted features depth frames relieff algorithm interaction sequence representation nonlinear input mapping method random forest K-nearest neighbour support vector machine SVM classifiers joint distances joint angles joint spherical coordinates composite kernel SVM

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Low-Voltage Arc Fault Identification Using a Hybrid Method Based on Improved Salp Swarm algorithm-Variational Mode Decomposition- Random Forest

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IEEE ACCESS 2024年 12卷 15410-15418页

作者： Li, Bin Wu, Jinglong Liaoning Tech Univ Sch Elect & Control Engn Huludao 125105 Peoples R China

The current of the residential series arc fault is affected by the load type, and the fault feature change is not obvious and contains noise. Therefore, the extraction of fault features will affect the arc fault detection results. To solve this problem, an improved salp swarm optimization algorithm combined with variational mode decomposition is proposed to extract the characteristics of current signal, improve the decomposition effect of current signal, and construct a dataset that can fully reflect the characteristics of arc fault. The relieff algorithm is designed to combine minimum redundancy maximum relevance to reduce the feature dimension and eliminate redundancy. Finally, the random forest model is used to diagnose the fault, which can quickly detect the fault and does not cause overfitting. Experiments based on the self-built sample set prove that the arc fault can be quickly detected under different acquisition frequencies and noise environments, and the detection rate is more than 95%.

关键词： Improved salp swarm algorithm minimum redundancy maximum relevance random forest relieff algorithm series arc fault variational mode decomposition

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A paper quality and comment consistency detection model based on feature dimensionality reduction

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ALEXANDRIA ENGINEERING JOURNAL 2022年第12期61卷 10395-10405页

作者： Sheng, Xinlei Huo, Wenjie Zhang, Caijun Zhang, Xin Han, Yang North China Univ Sci & Technol Coll Sci Tangshan 063210 Peoples R China North China Univ Sci & Technol Dev Planning & Discipline Construct Div Tangshan 063210 Peoples R China

As a reflection of the scholar's mastery of basic theories and professional knowledge, the dissertation is an important yardstick for measuring the level of scientific research. At present, the problem of academic misconduct is becoming increasingly prominent, which is not only related to personal academic ethics, but also related to the overall development of the national academic and scientific research field. In the traditional method of evaluating the quality of papers, it is mainly based on the evaluation experts' comments and scores. However, there are cases that the evaluation experts' comments and scores are inconsistent in practice. To address this problem, we proposed a paper quality consistency detection model based on the nearest neighbor analysis dimensionality reduction algorithm. Compared with other traditional models, the experimental results show that the detection accuracy of XGBoost model after dimensionality reduction using nearest neighbor analysis algorithm reaches 85.81%. (c) 2022 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Emotion analysis XGBoost Neighbourhood component analysis relieff algorithm

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A New Framework Combining Local-Region Division and Feature Selection for Micro-Expressions Recognition

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IEEE ACCESS 2020年 8卷 94499-94509页

作者： Zhang, Yanliang Jiang, Hanxiao Li, Xingwang Lu, Bing Rabie, Khaled M. Rehman, Ateeq Ur Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo 454003 Henan Peoples R China Manchester Metropolitan Univ Dept Engn Manchester M1 5GD Lancs England Abdul Wali Khan Univ Dept Comp Sci Mardan 23200 Pakistan

Micro-expressions are deliberate or unconscious movements of people's psychological activities, reflecting the transient facial true expressions. Previous works focus on the whole face for micro-expressions recognition. These methods can extract a number of feature vectors which are relevant or irrelevant to the micro-expressions recognition. Besides, the high-dimension feature vectors can result in longer computational time and increased computational complexity. In order to address these problems, we propose a new framework which combines the local-region division and the feature selection. Based on the proposed framework, the original images can retain more efficient regions and filter out the invalid components of feature vectors. Specifically, with the joint efforts of the facial deformation identification model and facial action coding system, the global region is divided into seven local regions with their corresponding actions units. The relieff algorithm is used to select effective components of feature vectors and reduce the dimension. To evaluate the proposed framework, we conduct experiments on both the Chinese Academy of Sciences Micro-expression II Database and Spontaneous Micro-expression Database with Leave-One-Subject-Out Cross Validation method. The results show that the performance in local combined regions outperforms its counterpart in the global region, and the recognition accuracy is further improved with the combination of feature selection.

关键词： Feature extraction Databases Histograms Face recognition Face Diversity reception Psychology Micro-expression recognition local region division feature selection relieff algorithm

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Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE 2018年第9期232卷 884-900页

作者： Bharti, Puja Mittal, Deepti Ananthasivan, Rupa Thapar Inst Engn & Technol Dept Elect & Instrumental Engn Patiala 147004 Punjab India Manipal Hosp Dept Radiol Bangalore Karnataka India

Chronic liver diseases are fifth leading cause of fatality in developing countries. Early diagnosis is important for timely treatment and to salvage life. Ultrasound imaging is frequently used to examine abnormalities of liver. However, ambiguity lies in visual interpretation of liver stages on ultrasound images. This difficult visualization problem can be solved by analysing extracted textural features from images. Grey-level difference matrix, a texture feature extraction method, can provide information about roughness of liver surface, sharpness of liver borders and echotexture of liver parenchyma. In this article, the behaviour of variants of grey-level difference matrix in characterizing liver stages is investigated. The texture feature sets are extracted by using variants of grey-level difference matrix based on two, three, five and seven neighbouring pixels. Thereafter, to take the advantage of complementary information from extracted feature sets, feature fusion schemes are implemented. In addition, hybrid feature selection (combination of relieff filter method and sequential forward selection wrapper method) is used to obtain optimal feature set in characterizing liver stages. Finally, a computer-aided system is designed with the optimal feature set to classify liver health in terms of normal, chronic liver, cirrhosis and hepatocellular carcinoma evolved over cirrhosis. In the proposed work, experiments are performed to (1) identify the best approximation of derivative (forward, central or backward);(2) analyse the performance of individual feature sets of variants of grey-level difference matrix;(3) obtain optimal feature set by exploiting the complementary information from variants of grey-level difference matrix and (4) analyse the performance of proposed method in comparison with existing feature extraction methods. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The res

关键词： Chronic liver disease computer-aided diagnosis grey-level difference matrix relieff algorithm sequential forward selection feature fusion

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