Planar Feature Extraction and Fitting Method Based on Density Clustering Algorithm

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

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Feature Extraction and Mapping Construction for Mobile Robot via Ultrasonic MDP and Fuzzy Model

Sensors ◽

10.3390/s18113673 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3673 ◽

Cited By ~ 2

Author(s):

Zhili Long ◽

Ronghua He ◽

Yuxiang He ◽

Haoyao Chen ◽

Zuohua Li

Keyword(s):

Feature Extraction ◽

Clustering Algorithm ◽

Mobile Robotics ◽

Low Cost ◽

Fuzzy Model ◽

Fuzzy Modeling ◽

Fuzzy Classification ◽

Classification Model ◽

Service Robot ◽

Ultrasonic Array

This paper presents a modeling approach to feature classification and environment mapping for indoor mobile robotics via a rotary ultrasonic array and fuzzy modeling. To compensate for the distance error detected by the ultrasonic sensor, a novel feature extraction approach termed “minimum distance of point” (MDP) is proposed to determine the accurate distance and location of target objects. A fuzzy model is established to recognize and classify the features of objects such as flat surfaces, corner, and cylinder. An environmental map is constructed for automated robot navigation based on this fuzzy classification, combined with a cluster algorithm and least-squares fitting. Firstly, the platform of the rotary ultrasonic array is established by using four low-cost ultrasonic sensors and a motor. Fundamental measurements, such as the distance of objects at different rotary angles and with different object materials, are carried out. Secondly, the MDP feature extraction algorithm is proposed to extract precise object locations. Compared with the conventional range of constant distance (RCD) method, the MDP method can compensate for errors in feature location and feature matching. With the data clustering algorithm, a range of ultrasonic distances is attained and used as the input dataset. The fuzzy classification model—including rules regarding data fuzzification, reasoning, and defuzzification—is established to effectively recognize and classify the object feature types. Finally, accurate environment mapping of a service robot, based on MDP and fuzzy modeling of the measurements from the ultrasonic array, is demonstrated. Experimentally, our present approach can realize environment mapping for mobile robotics with the advantages of acceptable accuracy and low cost.

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A cluster-based feature selection method for image texture classification

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v14.i3.pp1433-1442 ◽

2019 ◽

Vol 14 (3) ◽

pp. 1433 ◽

Cited By ~ 2

Author(s):

Abbas F. H. Alharan ◽

Hayder K. Fatlawi ◽

Nabeel Salih Ali

Keyword(s):

Feature Extraction ◽

Feature Selection ◽

Clustering Algorithm ◽

Gabor Filter ◽

Texture Feature ◽

Image Feature ◽

Image Texture ◽

Support Vector ◽

Texture Image ◽

Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

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Application of Density Clustering Algorithm Based on Greedy Strategy in Hot Spot Mining of Taxi Passengers

Transactions on Edutainment XVI - Lecture Notes in Computer Science ◽

10.1007/978-3-662-61510-2_10 ◽

2020 ◽

pp. 105-113

Author(s):

Yiping Bao ◽

Jianglin Luo ◽

Qingqing Wang

Keyword(s):

Clustering Algorithm ◽

Hot Spot ◽

Greedy Strategy ◽

Density Clustering

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Multi-Radius Density Clustering Algorithm Based on Outlier Factor

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.472.427 ◽

2014 ◽

Vol 472 ◽

pp. 427-431

Author(s):

Zong Lin Ye ◽

Hui Cao ◽

Li Xin Jia ◽

Yan Bin Zhang ◽

Gang Quan Si

Keyword(s):

Clustering Algorithm ◽

Spatial Clustering ◽

Similar Process ◽

The Core ◽

Dbscan Algorithm ◽

Proposed Model ◽

Density Clustering ◽

Relationship Of ◽

Core Points ◽

The Relationship

This paper proposes a novel multi-radius density clustering algorithm based on outlier factor. The algorithm first calculates the density-similar-neighbor-based outlier factor (DSNOF) for each point in the dataset according to the relationship of the density of the point and its neighbors, and then treats the point whose DSNOF is smaller than 1 as a core point. Second, the core points are used for clustering by the similar process of the density based spatial clustering application with noise (DBSCAN) to get some sub-clusters. Third, the proposed algorithm merges the obtained sub-clusters into some clusters. Finally, the points whose DSNOF are larger than 1 are assigned into these clusters. Experiments are performed on some real datasets of the UCI Machine Learning Repository and the experiments results verify that the effectiveness of the proposed model is higher than the DBSCAN algorithm and k-means algorithm and would not be affected by the parameter greatly.

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