Statement spectrum with two dimensional eigenvalues for intelligent software fault localization

2021 ◽  
pp. 1-16
Author(s):  
Shengbing Ren ◽  
Xing Zuo ◽  
Jun Chen ◽  
Wenzhao Tan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Rbf Neural Network ◽  
Fault Localization ◽  
Test Case ◽  
Two Dimensional ◽  
Intelligent Software ◽  
Localization Model ◽  
Software Fault ◽  
Program Spectrum

The existing Software Fault Localization Frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and precision of fault localization need to be improved. To solve these problems, a framework 2DSFLF proposed in this paper and used to evaluate the effectiveness of software fault localization techniques (SFL) in two-dimensional eigenvalues takes both dynamic and static features into account to construct the two-dimensional eigenvalues statement spectrum (2DSS). Firstly the statement dependency and test case coverage are extracted by the feature extraction of 2DSFLF. Subsequently these extracted features can be used to construct the statement spectrum and data flow spectrum which can be combined into the optimized spectrum 2DSS. Finally an estimator which takes Radial Basis Function (RBF) neural network and ridge regression as fault localization model is trained by 2DSS to predict the suspiciousness of statements to be faulty. Experiments on Siemens Suit show that 2DSFLF improves the efficiency and precision of software fault localization compared with existing techniques like BPNN, PPDG, Tarantula and so fourth.

Effective Software Fault Localization Using an RBF Neural Network

2012 ◽  
Vol 61 (1) ◽  
pp. 149-169 ◽  
Author(s):  
W. Eric Wong ◽  
Vidroha Debroy ◽  
Richard Golden ◽  
Xiaofeng Xu ◽  
Bhavani Thuraisingham
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Fault Localization ◽  
Software Fault

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

A novel face recognition approach based on kernel discriminative common vectors (KDCV) feature extraction and RBF neural network

2008 ◽  
Vol 71 (13-15) ◽  
pp. 3044-3048 ◽  
Author(s):  
Xiao-Yuan Jing ◽  
Yong-Fang Yao ◽  
Jing-Yu Yang ◽  
David Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Face Recognition ◽  
Rbf Neural Network

A NEURAL BASED HUMAN FACE RECOGNITION SYSTEM USING AN EFFICIENT FEATURE EXTRACTION METHOD WITH PSEUDO ZERNIKE MOMENT

2002 ◽  
Vol 11 (03) ◽  
pp. 283-304 ◽  
Author(s):  
JAVAD HADDADNIA ◽  
KARIM FAEZ ◽  
MAJID AHMADI
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Extraction Method ◽  
Rbf Neural Network ◽  
Zernike Moment ◽  
Human Face ◽  
Shape Information ◽  
Feature Extraction Method ◽  
Face Features ◽  
Face Localization

This paper introduces an efficient method for the recognition of human faces in 2D digital images using a feature extraction technique that combines the global and local information in frontal view of facial images. The proposed feature extraction includes human face localization derived from the shape information. Efficient parameters are defined to eliminate irrelevant data while Pseudo Zernike Moments (PZM) with a new moment orders selection method is introduced as face features. The proposed method while yields better recognition rate, also reduces the classifier complexity. This paper also examines application of various feature domains as face features using the face localization method. These include Principle Component Analysis (PCA) and Discrete Cosine Transform (DCT). The Radial Basis Function (RBF) neural network has been used as the classifier and we have shown that the proposed feature extraction method requires an RBF neural network classifier with a simpler structure and faster training phase that is less sensitive to select training and testing images. Simulation results on the Olivetti Research Laboratory (ORL) database and comparison with other techniques indicate the effectiveness of the proposed method.

Application of Artificial Neural Network to Flaw Classification in Ultrasonic Testing

2011 ◽  
Vol 328-330 ◽  
pp. 1876-1880
Author(s):  
Yuan Chen ◽  
Hong Wei Ma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Ultrasonic Testing ◽  
Probabilistic Neural Network ◽  
Rbf Neural Network ◽  
Slag Inclusion ◽  
Steel Tube ◽  
Flaw Classification ◽  
Artificial Neural

Aiming at the difficult question of flaw qualitative analysis during industrial ultrasonic testing, a method of flaw classification based on the combination of wavelet packet transform (WPT) with artificial neural network (ANN) is proposed in this paper. Firstly, WPT is applied to feature extraction of ultrasonic flaw echo signals, and then BP neural network (BPNN), RBF neural network (RBFNN) and probabilistic neural network (PNN) are respectively used to perform flaw classification by means of the features. To validate the method above, some experiments of feature extraction and flaw classification are performed utilizing a series sample of butt girth welds of seamless steel tube with four types of welding flaws, such as crack, stomata, incomplete penetration and slag inclusion. The results show that the accuracy of flaw classification by three kinds of neural networks respectively reached to 91.25%, 92.50% and 93.75%, and the better classification effect is obtained.

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