scholarly journals A Nuclide Recognition Method for Nuclear Robot System

2021 ◽  
Vol 2095 (1) ◽  
pp. 012075
Author(s):  
Siyi Zhou ◽  
Jiangmei Zhang ◽  
Xinghua Feng ◽  
Caolin Zhang
Keyword(s):  
Support Vector Machine ◽  
Energy Spectrum ◽  
Mahalanobis Distance ◽  
Support Vector ◽  
Two Dimensional ◽  
Recognition Method ◽  
The Real ◽  
Real Environment ◽  
Spectrum Curve ◽  
Spectrum Data

Abstract In the real energy spectrum attenuation environment, many traditional nuclide identification methods for nuclear robot systems have problems such as using only part of the energy spectrum curve, being susceptible to noise, and having low recognition accuracy. Proposes an energy spectrum nuclide recognition method based on S-transform (ST) and Mahalanobis distance-based support vector machine (MSVM). Regarding the energy spectrum curve as a non-stationary signal, combined with the widely used S transformation method in signal transformation, the energy spectrum data is two-dimensional, Then use two-dimensional principal component analysis(2D-PCA) to reduce the dimension of the two-dimensional energy spectrum data for feature extraction, and design a support vector machine (SVM) classifier based on Mahalanobis distance to realize the identification of energy spectrum nuclides. Finally, experiments are carried out with simulated nuclide energy spectrum data based on Geant4. The experimental results show that this method effectively improves the accuracy of energy spectrum nuclide recognition by using full spectrum information. At the same time, experiments are carried out on the nuclide energy spectrum data of different detection distances obtained by the NaI detector in the real environment, and it is verified that the algorithm proposed in this paper also has a good recognition performance for the nuclide energy spectrum collected in the real environment.

Combining Block DCV and Support Vector Machine for Ear Recognition

2016 ◽  
Vol 8 (2) ◽  
pp. 36-44 ◽  
Author(s):  
Zhao Hailong ◽  
Yi Junyan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Construction Method ◽  
Experimental Results ◽  
Support Vector ◽  
Recognition Method ◽  
Ear Recognition ◽  
Human Ear ◽  
Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

scholarly journals Research on Fault Diagnosis Model of Rotating Machinery Vibration Based on Information Entropy and Improved SVM

2019 ◽  
Vol 118 ◽  
pp. 02036 ◽  
Author(s):  
Hankun Bing ◽  
Yuzhu Zhao ◽  
Le Pang ◽  
Minmin Zhao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Time Domain ◽  
Information Entropy ◽  
Singular Value ◽  
Support Vector ◽  
Learning Platform ◽  
Diagnosis Model ◽  
Concept Of Information

Based on the concept of information entropy, this paper analyzes typical nonlinear vibration fault signals of steam turbine based on spectrum, wavelet and HHT theory methods, and extracts wavelet energy spectrum entropy, IMF energy spectrum entropy, time domain singular value entropy and frequency domain power spectrum entropy as faults. The feature is supported by a support vector machine (SVM) as a learning platform. The research results show that the fusion information entropy describes the vibration fault more comprehensively, and the support vector machine fault diagnosis model can achieve higher diagnostic accuracy.

scholarly journals Hydrometeor classification from two-dimensional video disdrometer data

2014 ◽  
Vol 7 (9) ◽  
pp. 2869-2882 ◽  
Author(s):  
J. Grazioli ◽  
D. Tuia ◽  
S. Monhart ◽  
M. Schneebeli ◽  
T. Raupach ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Visual Inspection ◽  
Particle Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Time Intervals ◽  
Classification Technique ◽  
Statistical Behavior ◽  
Over Time

Abstract. The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.

COMPUTER INTRUSION DETECTION WITH CLASSIFICATION AND ANOMALY DETECTION, USING SVMs

2003 ◽  
Vol 17 (03) ◽  
pp. 441-458 ◽  
Author(s):  
MIKE FUGATE ◽  
JAMES R. GATTIKER
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Mahalanobis Distance ◽  
Supervised Classification ◽  
Cyber Attacks ◽  
Support Vector ◽  
Joint Performance ◽  
Modeling And Prediction ◽  
Computer Intrusion Detection

This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. First, issues in supervised classification are discussed, then the incorporation of anomaly detection enhancing the modeling and prediction of cyber-attacks. SVM methods are seen as competitive with benchmark methods and other studies, and are used as a standard for the anomaly detection investigation. The anomaly detection approaches compare one class SVMs with a thresholded Mahalanobis distance to define support regions. Results compare the performance of the methods and investigate joint performance of classification and anomaly detection. The dataset used is the DARPA/KDD-99 publicly available dataset of features from network packets, classified into nonattack and four-attack categories.

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