Vibration-Based Terrain Identification for Planetary Exploration Robots Using Support Vector Machine

2012 ◽  
Vol 220-223 ◽  
pp. 1171-1174
Author(s):  
Qiang Li ◽  
Kai Xue ◽  
He Xu ◽  
Wen Lin Pan ◽  
Zhi Xu Li
Keyword(s):  
Support Vector Machine ◽  
Original Data ◽  
Front Wheel ◽  
Planetary Exploration ◽  
Support Vector ◽  
Terrain Classification ◽  
Feature Extraction Method ◽  
Four Dimensions ◽  
Acceleration Signal ◽  
Multi Class Classification

Human ability to explore planets (e.g. the moon, Mars) depends on the autonomous mobile performance of planetary exploration robots, so studying on terrain classification is important for it. Vibration-based terrain classification unlike vision classification affected by lighting variations, easily cheated by covering of surface, it analyses the vibration signals from wheel-terrain interaction to classify. Three accelerometers in x, y, z direction and a microphone in z direction were mounted to arm of the left-front wheel. The robot drove on the sand, gravel, grass, clay and asphalt at six speeds, three groups of acceleration signal and one group of sound pressure signal were received. The original signals were dealt using Time Amplitude Domain Analysis. Original data were divided into segments, each segment was a three centimeters distance of driving; eleven features from every segment were normalized. The data from four sensors were merged into a forty-four dimensions feature vector. Ten one against one classifiers of Support Vector Machine (SVM) were used to classify; one against one SVM program from LibSVM was applied to multi-class classification using voting strategy in MATLAB. Facing to the same number of votes, we propose a new algorithm. Experimental results demonstrate the effectiveness of the feature extraction method and the multi-class SVM algorithm.

Vibration-based Terrain Classification for Mobile Robots Using Support Vector Machine

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 660 ◽  
Author(s):  
Qiang LI ◽  
Kai XUE ◽  
He XU ◽  
Wenlin PAN ◽  
Tianlong WANG
Keyword(s):  
Support Vector Machine ◽  
Mobile Robots ◽  
Support Vector ◽  
Terrain Classification

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

A Method Based on Support Vector Machine for Feature Selection of Latent Semantic Features

2011 ◽  
Vol 181-182 ◽  
pp. 830-835
Author(s):  
Min Song Li
Keyword(s):  
Support Vector Machine ◽  
Text Categorization ◽  
Latent Semantic Indexing ◽  
Classification Performance ◽  
Compact Representation ◽  
Support Vector ◽  
Semantic Features ◽  
Semantic Indexing ◽  
Feature Extraction Method ◽  
Feature Subspace

Latent Semantic Indexing(LSI) is an effective feature extraction method which can capture the underlying latent semantic structure between words in documents. However, it is probably not the most appropriate for text categorization to use the method to select feature subspace, since the method orders extracted features according to their variance,not the classification power. We proposed a method based on support vector machine to extract features and select a Latent Semantic Indexing that be suited for classification. Experimental results indicate that the method improves classification performance with more compact representation.

scholarly journals Integration of synthetic minority oversampling technique for imbalanced class

2019 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Noviyanti Santoso ◽  
Wahyu Wibowo ◽  
Hilda Hikmawati
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Class Imbalance ◽  
Original Data ◽  
Support Vector ◽  
Classification Methods ◽  
Problematic Issue ◽  
Imbalanced Class ◽  
F Measure

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.

Pipeline Leakage Recognition Based on the Projection Singular Value Features and Support Vector Machine

2010 ◽  
Author(s):  
Mingda Wang ◽  
Laibin Zhang ◽  
Wei Liang ◽  
Jinqiu Hu
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Field Experiments ◽  
Small Sample ◽  
Singular Value ◽  
Support Vector ◽  
Leakage Detection ◽  
Feature Extraction Method ◽  
Standard Bases ◽  
Value Decomposition

Identification of negative pressure waveform is the key of pipeline leakage detection. The feature extraction and the choice of the classifier are two main contents to solve the recognition problem. In this paper, a new feature extraction method based on the Projection Singular Value is presented. First of all, the two orthogonal singular value decomposition matrixes of the typical leakage waveform are extracted as the standard bases. Then the projection singular value features of the other pressure wave matrixes are extracted by being projected to the two standard bases. As the pipeline leakage is a small probability event, it is difficult to obtain the leakage samples. A multi-classification Support Vector Machine, which has the advantage of small sample learning, is constructed to classify these features in this paper. The field experiments indicate that the leakage detection based on this feature extraction and recognition model has a higher accuracy of leakage recognition.

Gas Pipeline Corrosion Prediction Based on Modified Support Vector Machine and Unequal Interval Model

2013 ◽  
Vol 373-375 ◽  
pp. 1987-1994 ◽  
Author(s):  
Wei Dong Zhang ◽  
Bin Shen ◽  
Yi Bo Ai ◽  
Bin Yang
Keyword(s):  
Support Vector Machine ◽  
Oil And Gas ◽  
Prediction Method ◽  
Original Data ◽  
Gas Pipeline ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Grey Prediction ◽  
Interval Model ◽  
Pipeline Corrosion

The corrosion is an important problem for the service safety of oil and gas pipeline. This research focuses. This paper proposed a new prediction algorithm on corrosion prediction of gathering gas pipeline, which combined modified Support Vector Machine (SVM) with unequal interval model. Firstly, grey prediction method with unequal interval model was used to pretreatment original data because there is unequal interval problem in actual collected data of pipeline. Secondly, improved Support Vector Regression (SVR) based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) has been proposed to resolve parameters selection problem for SVR. Finally, the corrosion prediction model of gas pipeline has been proposed which combined improved SVR and unequal interval grey prediction method. The experiment results show this algorithm could increase precision of the pipeline corrosion prediction compared with the traditional SVM. This research provides reliable basis for in-service pipeline life prediction and confirming inspecting cycle.

Sign in / Sign up

Export Citation Format

Share Document