Неразрушающий контроль стальных тосов с использованием оптимизированного метода опорных векторов

2021 ◽  
pp. 49-59
Author(s):  
Бин Ли ◽  
Цзювэй Чжан ◽  
Цихан Чен
Keyword(s):  
Noise Reduction ◽  
Color Image ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
False Color ◽  
Defect Image ◽  
Value Decomposition ◽  
Maximum Method

In this paper, to address the problems of poor signal noise reduction and low recognition rate in wire rope leakage magnetic detection, we propose the algorithm MSVDW, which uses a combination of median filtering, singular value decomposition (SVD) and wavelet transform, to denoise the collected three-dimensional MFL signals. Then, false color is used to enhance the image. The image is then localized and segmented using the modulus maximum method. The color moments are extracted from the images and used as the input of the particle swarm algorithm optimized support vector machine (PSO-SVM) for training and recognition. The experimental results show that the noise reduction algorithm proposed in this paper effectively reduces the noise of the leakage signal, the false color image enhances the defect image information, and the algorithm of PSO-SVM greatly improves the recognition rate of defects.

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

scholarly journals Joint Design of the Hardware and the Software of a Radar System with the Mixed Grey Wolf Optimizer: Application to Security Check

2020 ◽  
Vol 12 (18) ◽  
pp. 3097
Author(s):  
Julien Marot ◽  
Claire Migliaccio ◽  
Jérôme Lantéri ◽  
Paul Lauga ◽  
Salah Bourennane ◽  
...  
Keyword(s):  
Error Correcting Code ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Support Vector ◽  
Computational Technique ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Real World Data ◽  
Joint Design ◽  
Security Check

The purpose of this work is to perform the joint design of a classification system including both a radar sensor and an image processing software. Experimental data were generated with a three-dimensional scanner. The criterion which rules the design is the false recognition rate, which should be as small as possible. The classifier involved is support vector machines, combined with an error correcting code. We apply the proposed method to optimize security check. For this purpose we retain eight relevant parameters which impact the recognition performances. To estimate the best parameters, we adapt our adaptive mixed grey wolf algorithm. This is a computational technique inspired by nature to minimize a criterion. Our adaptive mixed grey wolf algorithmwas found to outperform comparative methods in terms of computational load on simulations and with real-world data.

Towards Multi-Classification of Human Motions Using Micro IMU and SVM Training Process

2009 ◽  
Vol 60-61 ◽  
pp. 189-193 ◽  
Author(s):  
Guang Yi Shi ◽  
Yue Xian Zou ◽  
Wen J. Li ◽  
Yu Feng Jin ◽  
Pei Guan
Keyword(s):  
Optimal Parameter ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Human Motion ◽  
Measurement Unit ◽  
Support Vector ◽  
Training Process ◽  
Motion Feature ◽  
Novel Approach ◽  
Multi Classification

This paper introduces a novel approach for human motion recognition via motion feature vectors collected by A Micro Inertial Measurement Unit (µIMU). First, µIMU that is 56x23x15mm3 in size was built. The unit consists of three dimensional MEMS accelerometers, gyroscopes, a Bluetooth module and a Micro Controller Unit (MCU), which can transmit human motion information through a serial port to a computer. Second, a human motion database was setup by recording the motion data from the µIMU. The motions include fall, walk, stand, run and step upstairs. Third, Support Vector Machine (SVM) training process was used for human motion multi-classification. FFT was used for feature generation and optimal parameter searching process was done for the best SVM kernel function. Experimental results showed that for the given 5 different motions, the total correct recognition rate is 92%, of which the fall motion can be classified from others with 100% recognition rate.

scholarly journals Using nonlocal filtering and feature extraction approaches in three-dimensional face recognition by Kinect

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878774 ◽  
Author(s):  
Shahram Mohammadi ◽  
Omid Gervei
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Component Analysis ◽  
Block Matching ◽  
Depth Image ◽  
Support Vector ◽  
Depth Images ◽  
Dimensional Face

To use low-cost depth sensors such as Kinect for three-dimensional face recognition with an acceptable rate of recognition, the challenges of filling up nonmeasured pixels and smoothing of noisy data need to be addressed. The main goal of this article is presenting solutions for aforementioned challenges as well as offering feature extraction methods to reach the highest level of accuracy in the presence of different facial expressions and occlusions. To use this method, a domestic database was created. First, the noisy pixels-called holes-of depth image is removed by solving multiple linear equations resulted from the values of the surrounding pixels of the holes. Then, bilateral and block matching 3-D filtering approaches, as representatives of local and nonlocal filtering approaches, are used for depth image smoothing. Curvelet transform as a well-known nonlocal feature extraction technique applied on both RGB and depth images. Two unsupervised dimension reduction techniques, namely, principal component analysis and independent component analysis, are used to reduce the dimension of extracted features. Finally, support vector machine is used for classification. Experimental results show a recognition rate of 90% for just depth images and 100% when combining RGB and depth data of a Kinect sensor which is much higher than other recently proposed algorithms.

Partial Friction Fault Diagnosis of Electrical Submersible Pump Based on Support Vector Machines

2011 ◽  
Vol 219-220 ◽  
pp. 1689-1692
Author(s):  
Cheng Yao ◽  
Ming Zhong Li ◽  
Guang Fu Liu
Keyword(s):  
Fault Diagnosis ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Grid Method ◽  
Economic Loss ◽  
Small Sample ◽  
Support Vector ◽  
Submersible Pump ◽  
Parameters Selection ◽  
Electrical Submersible Pump

Exact diagnosis of electrical submersible pump （ESP）partial friction fault may avoid magnitude economic loss. Three-dimensional vibration accelerations of ESP were measured with acceleration transducer. Since working well ESPs are deeper in the earth, vibration signals seriously fade. This paper proposed that SVM is employed as classifier and wavelet parameters as features for ESP partial friction fault diagnosis. After SVM parameters selection with grid method, the highest recognition rate is up to 86.7%. Results indicate that SVM is competent not only for small sample recognitions but also for recognitions based on faintness signal. A new method is provided for ESP partial friction fault diagnosis.

Application of Improved Invariant Moments and SVM in the Recognition of Solar Cell Defects

2014 ◽  
Vol 672-674 ◽  
pp. 3-6 ◽  
Author(s):  
Yong Qing Wang ◽  
Dan Tian ◽  
Deng Yuan Song ◽  
Lei Zhang
Keyword(s):  
Support Vector Machine ◽  
Scale Invariance ◽  
Recognition Rate ◽  
Infrared Image ◽  
Automatic Recognition ◽  
Support Vector ◽  
Discrete State ◽  
Invariant Moments ◽  
Defect Image ◽  
Important Means

The infrared image of solar cell's electroluminescence (EL) is one of the important means of hidden defects detection. In order to improve the automatic recognition rate of defect images, this paper adopts improved invariant moments for feature extraction. The scale factor of the improved invariant moments is eliminated by transformation. Therefore they have the properties of translation, rotation and scale invariance simultaneously in discrete state. At the same time, Support Vector Machine (SVM) is used to distinguish the defect image. The system which combined invariant moments with SVM is applied to classify the debris, crack, off-grid, open weld and black pieces. The recognition rate of 5 kinds of defects has reached more than 90%.

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

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