Feature Vector Identification and Prognostics of SAC305 PCB’s for Varying Conditions of Temperature and Vibration Loads

2020 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Feature Vector ◽  
Sac305 Solder ◽  
Feature Vectors ◽  
Operating Environments ◽  
Electronic Board ◽  
Different Types ◽  
Varying Environments ◽  
Temperature Constant ◽  
Temperature Levels ◽  
Strain Signal

Abstract This study focuses on the feature vector identification of SAC305 solder alloy PCB’s of two different configurations during varying conditions of temperature and vibration. The feature vectors are identified from the strain signals, that are acquired from four symmetrical locations of the PCB at regular intervals during vibration. The changes in the vibration characteristics of the PCB are characterized by three different types of experiments. First type of analysis emphasizes the vibration characteristic for varying conditions of acceleration levels keeping the temperature constant during vibration. The second analysis studies the characteristics changes for varying temperature levels by keeping the acceleration levels constant. Finally, the third analysis focuses on the combined changes in temperature and acceleration levels for the board during vibration. The above analyses try to imitate the actual working conditions of an electronic board in an automobile which is subjected to varying environments of temperature and vibration. The strain signals acquired during each of these experiments are compared based on both time and frequency domain characteristics. Different statistical and frequency based techniques were used to identify the variations in the strain signal with changes in the environment and loading conditions. The feature vectors of failure at a constant working condition and load were identified and as an extension to the previous work, the effectiveness of the feature vectors during these varying conditions of temperature and acceleration levels are investigated using the above analyses. The feature vector of a PCB under varying conditions of temperature and load are identified and compared with different operating environments.

scholarly journals Intelligent recognition of acoustic emission signals from damage of glass fiber-reinforced plastics

2020 ◽  
Vol 29 ◽  
pp. 2633366X2097468
Author(s):  
Qiufeng Li ◽  
Tiantian Qi ◽  
Lihua Shi ◽  
Yao Chen ◽  
Lixia Huang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Glass Fiber ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Feature Vectors ◽  
Reinforced Plastics ◽  
Glass Fiber Reinforced ◽  
Fiber Reinforced Plastics ◽  
Intelligent Recognition ◽  
Ae Signals

Glass fiber-reinforced plastics (GFRP) is widely used in many industrial fields. When acoustic emission (AE) technology is applied for dynamic monitoring, the interfering signals often affect the damage evaluation results, which significantly influences industrial production safety. In this work, an effective intelligent recognition method for AE signals from the GFRP damage is proposed. Firstly, the wavelet packet analysis method is used to study the characteristic difference in frequency domain between the interfering and AE signals, which can be characterized by feature vector. Then, the model of back-propagation neural network (BPNN) is constructed. The number of nodes in the input layer is determined according to the feature vector, and the feature vectors from different types of signals are input into the BPNN for training. Finally, the wavelet packet feature vectors of the signals collected from the experiment are input into the trained BPNN for intelligent recognition. The accuracy rate of the proposed method reaches to 97.5%, which implies that the proposed method can be used for dynamic and accurate monitoring of GFRP structures.

A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images

2017 ◽  
Vol 8 (2) ◽  
pp. 52-69 ◽  
Author(s):  
Komal Sharma ◽  
Jitendra Virmani
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Renal Disease ◽  
Support System ◽  
Classification Accuracy ◽  
Feature Vector ◽  
Texture Feature ◽  
Renal Ultrasound ◽  
Ultrasound Images ◽  
Feature Vectors

Early detection of medical renal disease is important as the same may lead to chronic kidney disease which is an irreversible stage. The present work proposes an efficient decision support system for detection of medical renal disease using small feature space consisting of only second order GLCM statistical features computed from raw renal ultrasound images. The GLCM mean feature vector and GLCM range feature vector are computed for inter-pixel distance d varying from 1 to 10. These texture feature vectors are combined in various ways yielding GLCM ratio feature vector, GLCM additive feature vector and GLCM concatenated feature vector. The present work explores the potential of five texture feature vectors computed using GLCM statistics exhaustively for differential diagnosis between normal and MRD images using SVM classifier. The result of the study indicates that GLCM range feature vector computed with d = 1 yields the highest overall classification accuracy of 85.7% with individual classification accuracy values of 93.3% and 77.9% for normal and MRD classes respectively.

A Reconfigurable 3D Engineering Shape Search System: Part II — Database Indexing, Retrieval, and Clustering

2003 ◽  
Author(s):  
Kuiyang Lou ◽  
Subramaniam Jayanti ◽  
Natraj Iyer ◽  
Yagnanarayanan Kalyanaraman ◽  
Sunil Prabhakar ◽  
...  
Keyword(s):  
Relevance Feedback ◽  
3D Model ◽  
Feature Vector ◽  
3D Models ◽  
Semantic Gap ◽  
Multidimensional Index ◽  
Search System ◽  
Feature Vectors ◽  
Database Indexing ◽  
Database Clustering

This paper introduces database and related techniques for a reconfigurable, intelligent 3D engineering shape search system, which retrieves similar 3D models based on their shape content. Feature vectors, which are numeric “fingerprints” of 3D models, and skeletal graphs, which are the “minimal representations of the shape content” of a 3D model, represent the shape content. The Euclidean distance of the feature vectors, as well as the distance between skeletal graphs, provides indirect measures of shape similarity between the 3D models. Critical database issues regarding 3D shape search systems are discussed: (a) database indexing, (b) semantic gap, (c) subjectivity of similarity, and (d) database clustering. An Rtree based multidimensional index is used to speed up the feature-vector based search operation, while a decision treebased approach is used for efficiently indexing/searching skeletal graphs. Interactions among users and the search system, such as relevance feedback and feature vector reconfiguration, are used to bridge the semantic gap and to customize the system for different users. Database clustering of the R-tree index is compared with that generated by a selforganizing map (SOM). Synthetic databases and real 3D model databases are employed to investigate the efficiency of the multidimensional index and the effectiveness of relevance feedback.

A Method of Extracting Feature of Phonic Signal Based on the Matrix Analysis

2013 ◽  
Vol 457-458 ◽  
pp. 1200-1203
Author(s):  
Yang Xu ◽  
Fang Chao Hu
Keyword(s):  
Embedded System ◽  
Feature Vector ◽  
Recognition Rate ◽  
Audio Signal ◽  
Coefficient Matrix ◽  
Matrix Analysis ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
The Matrix ◽  
The Embedded System

In the speech recognition technology, feature extraction is essential for the system recognition rate, taking amount of strategies to find the better feature vectors are most researchers target. This paper presents a method of extracting feature of audio signal based on the discrete wavelet transform, then decomposed the coefficient matrix by the matrix analysis way, through this method to find a new thinking on the way of extracting feature vector. The method can be achieved in the procedure. The main purpose is to reduce the dimension of feature vector, make the vector briefer, and then reduce the computing complexity in the embedded system. This method can reduce the feature vectors dimension, accelerated the computing velocity.

Chinese Person Name Disambiguation Based on Two-Stage Clustering

2016 ◽  
Vol 20 (5) ◽  
pp. 755-764
Author(s):  
Jie Zhou ◽  
◽  
Bicheng Li ◽  
Yongwang Tang ◽  
Keyword(s):  
High Precision ◽  
Feature Vector ◽  
Social Relation ◽  
Statistical Properties ◽  
Experimental Results ◽  
Vector Representation ◽  
Name Disambiguation ◽  
Two Stage ◽  
Different Types ◽  
Cluster Feature

Person name clustering disambiguation is the process that partitions name mentions according to corresponding target person entities in reality. The existed methods can not realize effective identification of important features to disambiguate person names. This paper presents a method of Chinese person name disambiguation based on two-stage clustering. This method adopts a stage-by-stage processing model to identify and utilize different types of important features. Firstly, we extract three kinds of core evidences namely direct social relation, indirect social relation and common description prefix, recognize document-pairs referring to the same person entity, and realize initial clustering of person names with high precision. Then, we take the result of initial clustering as new initial input, utilize the statistical properties of multi-documents to recognize and evaluate important features, and build a double-vector representation of clusters (cluster feature vector and important feature vector). Based on the processes above, the final clustering of person names is generated, and the recall of clustering is improved effectively. The experiments have been conducted on the dataset of CLP2010 Chinese person names disambiguation, and experimental results show that this method has good performance in person name clustering disambiguation.

scholarly journals Walsh Transform based Feature vector generation for Image Database Classification

2016 ◽  
Vol 6 (3) ◽  
pp. 1176
Author(s):  
Jagruti Ketan Save
Keyword(s):  
Nearest Neighbor ◽  
Feature Vector ◽  
Similarity Measures ◽  
Image Database ◽  
Training Data ◽  
Walsh Transform ◽  
Test Image ◽  
Feature Vectors ◽  
Vector Generation ◽  
Automated Classifier

Thousands of images are generated everyday, which implies the need to build an easy, faster, automated classifier to classify and organize these images. Classification means selecting an appropriate class for a given image from a set of pre-defined classes. The main objective of this work is to explore feature vector generation using Walsh transform for classification. In the first method, we applied Walsh transform on the columns of an image to generate feature vectors. In second method, Walsh wavelet matrix is used for feature vector generation. In third method we proposed to apply vector quantization (VQ) on feature vectors generated by earlier methods. It gives better accuracy, fast computation and less storage space as compared with the earlier methods. Nearest neighbor and nearest mean classification algorithms are used to classify input test image. Image database used for the experimentation contains 2000 images. All these methods generate large number of outputs for single test image by considering four similarity measures, six sizes of feature vector, two ways of classification, four VQ techniques, three sizes of codebook, and five combinations of wavelet transform matrix generation. We observed improvement in accuracy from 63.22% to 74% (55% training data) through the series of techniques.

Remaining Useful Life Estimation and Prognostication of SAC305 PCB's for Dynamic Conditions of Temperature and Vibration Loads

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Remaining Useful Life ◽  
Series Data ◽  
Sac305 Solder ◽  
Feature Vectors ◽  
Useful Life

Abstract This study focuses on the feature vector identification and Remaining Useful Life (RUL) estimation of SAC305 solder alloy PCB's of two different configurations during varying conditions of temperature and vibration. The feature vectors are identified using the strain signals acquired from four symmetrical locations of the PCB at regular intervals during vibration. Two different types of experiments are employed to characterize the PCB's dynamic changes with varying temperature and acceleration levels. The strain signals acquired during each of these experiments are compared based on both time and frequency domain characteristics. Different statistical and frequency-based techniques were used to identify the strain signal variations with changes in the environment and loading conditions. The feature vectors in predicting failure at a constant working temperature and load were identified, and as an extension to this work, the effectiveness of the feature vectors during varying conditions of temperature and acceleration levels are investigated. The remaining Useful Life of the packages was estimated using a deep learning approach based on Long Short Term Memory (LSTM) network. This technique can identify the underlying patterns in multivariate time series data that can predict the packages' life. The autocorrelation function's residuals were used as the multivariate time series data in conjunction with the LSTM deep learning technique to forecast the packages' life at different varying temperatures and acceleration levels during vibration.

scholarly journals Bootstrapping Distributional Feature Vector Quality

2009 ◽  
Vol 35 (3) ◽  
pp. 435-461 ◽  
Author(s):  
Maayan Zhitomirsky-Geffet ◽  
Ido Dagan
Keyword(s):  
Feature Vector ◽  
Similarity Measures ◽  
Feature Reduction ◽  
Feature Weighting ◽  
Superior Performance ◽  
Weighting Functions ◽  
Word Similarity ◽  
Feature Vectors ◽  
Distributional Similarity

This article presents a novel bootstrapping approach for improving the quality of feature vector weighting in distributional word similarity. The method was motivated by attempts to utilize distributional similarity for identifying the concrete semantic relationship of lexical entailment. Our analysis revealed that a major reason for the rather loose semantic similarity obtained by distributional similarity methods is insufficient quality of the word feature vectors, caused by deficient feature weighting. This observation led to the definition of a bootstrapping scheme which yields improved feature weights, and hence higher quality feature vectors. The underlying idea of our approach is that features which are common to similar words are also most characteristic for their meanings, and thus should be promoted. This idea is realized via a bootstrapping step applied to an initial standard approximation of the similarity space. The superior performance of the bootstrapping method was assessed in two different experiments, one based on direct human gold-standard annotation and the other based on an automatically created disambiguation dataset. These results are further supported by applying a novel quantitative measurement of the quality of feature weighting functions. Improved feature weighting also allows massive feature reduction, which indicates that the most characteristic features for a word are indeed concentrated at the top ranks of its vector. Finally, experiments with three prominent similarity measures and two feature weighting functions showed that the bootstrapping scheme is robust and is independent of the original functions over which it is applied.

Video Steganalysis Exploiting Motion Vector Calibration-Based Features

2012 ◽  
Vol 482-484 ◽  
pp. 168-172 ◽  
Author(s):  
Yu Deng ◽  
Yun Jie Wu ◽  
Lin Na Zhou
Keyword(s):  
Support Vector Machine ◽  
Feature Vector ◽  
Motion Vector ◽  
Support Vector ◽  
Motion Vectors ◽  
Feature Vectors ◽  
Video Steganalysis

The motion vector (MV)-based steganography embeds the secret messages by modifying the motion vectors. So the traditional video steganalytic schemes cannot detect the presence of the hidden messages by MV-based steganography. In this paper, a novel calibration-based steganalytic scheme against MV-based steganography is presented. The features are derived from the shift differences between the original and calibrated MVs, and then the feature vector is constructed. Using the extracted feature vectors, the support vector machine (SVM) is trained to detect the presence of stego videos. Compared with other features, the proposed features have better performance even with the low embedding strength.

scholarly journals Evaluation of Dust Deposition on Parabolic Trough Collectors in the Visible and Infrared Spectrum

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6249
Author(s):  
Rubén Usamentiaga ◽  
Alberto Fernández ◽  
Juan Luis Carús
Keyword(s):  
Solar Energy ◽  
Infrared Spectrum ◽  
Air Pollutants ◽  
Solid Particles ◽  
Dust Deposition ◽  
Parabolic Trough ◽  
High Concentration ◽  
Degradation Factor ◽  
Operating Environments ◽  
Different Types

Solar energy is mostly harnessed in arid areas where a high concentration of atmospheric dust represents a major environmental degradation factor. Gravitationally settled particles and other solid particles on the surface of the photovoltaic panels or thermal collectors greatly reduce the absorbed solar energy. Therefore, frequent cleaning schedules are required, consuming high quantities of water in regions where water precipitation is rare. The efficiency of this cleaning maintenance is greatly improved when methods to estimate the degree of cleanness are introduced. This work focuses on the need for better detecting the degradation created by dust deposition, considering experimental data based on different air pollutants, and analyzing the resulting thermal and visible signatures under different operating environments. Experiments are performed using six different types of pollutants applied to the surface of parabolic trough collectors while varying the pollutant density. The resulting reflectivity in the visible and infrared spectrum is calculated and compared. Results indicate that the pollutants can be distinguished, although the reflectivity greatly depends on the combination of the particle size of the pollutant and the applied amount, with greater impact from pollutants with small particles.

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