Defect Identification and Classification for Plasma Display Panels

2013 ◽  
Vol 694-697 ◽  
pp. 1197-1201 ◽  
Author(s):  
Du Hyung Cho ◽  
Seok Lyong Lee
Keyword(s):  
Support Vector ◽  
Svm Classifier ◽  
Defect Inspection ◽  
Production Lines ◽  
Defect Identification ◽  
Plasma Display Panels ◽  
Different Types ◽  
Plasma Display ◽  
Image Analysis Techniques

The defect inspection is a crucial process for the plasma display panel (PDP) production that significantly influences the quality of final products. In this paper, we propose a defect identification and classification method that extracts and classifies defects using various image analysis techniques. First, we identify defects through binarization of images using Gaussian filter. Then, those defects are classified into seven different types by analyzing geometric characteristics of defects and utilizing a support vector machine (SVM) classifier. The experimental results using separate sets of training and test PDP images obtained from production lines are quite promising. Our method identifies defects effectively enough to be used in the real environment. It also achieves a high correctness in classifying various types of defects.

scholarly journals Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life

2020 ◽  
Vol 10 (9) ◽  
pp. 3282
Author(s):  
Angela Shin-Yu Lien ◽  
Yi-Der Jiang ◽  
Jia-Ling Tsai ◽  
Jawl-Shan Hwang ◽  
Wei-Chao Lin
Keyword(s):  
Quality Of Life ◽  
Feature Selection ◽  
Diabetic Nephropathy ◽  
Sleep Quality ◽  
Support Vector ◽  
Svm Classifier ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
The Relationship

Fatigue and poor sleep quality are the most common clinical complaints of people with diabetes mellitus (DM). These complaints are early signs of DM and are closely related to diabetic control and the presence of complications, which lead to a decline in the quality of life. Therefore, an accurate measurement of the relationship between fatigue, sleep status, and the complication of DM nephropathy could lead to a specific definition of fatigue and an appropriate medical treatment. This study recruited 307 people with Type 2 diabetes from two medical centers in Northern Taiwan through a questionnaire survey and a retrospective investigation of medical records. In an attempt to identify the related factors and accurately predict diabetic nephropathy, we applied hybrid research methods, integrated biostatistics, and feature selection methods in data mining and machine learning to compare and verify the results. Consequently, the results demonstrated that patients with diabetic nephropathy have a higher fatigue level and Charlson comorbidity index (CCI) score than without neuropathy, the presence of neuropathy leads to poor sleep quality, lower quality of life, and poor metabolism. Furthermore, by considering feature selection in selecting representative features or variables, we achieved consistence results with a support vector machine (SVM) classifier and merely ten representative factors and a prediction accuracy as high as 74% in predicting the presence of diabetic nephropathy.

scholarly journals Automatic Classification for Fruits’ Types and Identification of Rotten Ones Using k-NN and SVM

2019 ◽  
Vol 15 (03) ◽  
pp. 47 ◽  
Author(s):  
Ann Nosseir ◽  
Seif Eldin A. Ahmed
Keyword(s):  
Texture Feature ◽  
Texture Features ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
A Value ◽  
Different Types ◽  
Linear Svm ◽  
Occurrence Matrix

Having a system that classifies different types of fruits and identifies the quality of fruits will be of a value in various areas especially in an area of mass production of fruits’ products. This paper presents a novel system that differentiates between four fruits types and identifies the decayed ones from the fresh. The algorithms used are based on the colour and the texture features of the fruits’ images. The algorithms extract the RGB values and the first statistical order and second statistical of the Gray Level Co-occurrence Matrix (GLCM) values. To segregate between the fruits’ types, Fine, Medium, Coarse, Cosine, Cubic, and Weighted K-Nearest Neighbors algorithms are applied. The accuracy percentages of each are 96.3%, 93.8%, 25%, 83.8%, 90%, and 95% respectively.  These steps are tested with 46 pictures taken from a mobile phone of seasonal fruits at the time i.e., banana, apple, and strawberry. All types were accurately identifying.  To tell apart the decayed fruits from the fresh, the linear and quadratic Support Vector Machine (SVM) algorithms differentiated between them based on the colour segmentation and the texture feature algorithms values of each fruit image. The accuracy of the linear SVM is 96% and quadratic SVM 98%.

scholarly journals Structuring of tactile sensory information for category formation in robotics palpation

2020 ◽  
Vol 44 (8) ◽  
pp. 1377-1393
Author(s):  
Luca Scimeca ◽  
Perla Maiolino ◽  
Ed Bray ◽  
Fumiya Iida
Keyword(s):  
Sensory Information ◽  
Tactile Sensor ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Category Formation ◽  
Physical Interactions ◽  
Robotic Palpation ◽  
Motion Strategy

Abstract This paper proposes a framework to investigate the influence of physical interactions to sensory information, during robotic palpation. We embed a capacitive tactile sensor on a robotic arm to probe a soft phantom and detect and classify hard inclusions within it. A combination of PCA and K-Means clustering is used to: first, reduce the dimensionality of the spatiotemporal data obtained through the probing of each area in the phantom; second categorize the re-encoded data into a given number of categories. Results show that appropriate probing interactions can be useful in compensating for the quality of the data, or lack thereof. Finally, we test the proposed framework on a palpation scenario where a Support Vector Machine classifier is trained to discriminate amongst different types of hard inclusions. We show the proposed framework is capable of predicting the best-performing motion strategy, as well as the relative classification performance of the SVM classifier, solely based on unsupervised cluster analysis methods.

scholarly journals AUTOMATED SHAPE-BASED PAVEMENT CRACK DETECTION APPROACH

Transport ◽  
2018 ◽  
Vol 33 (3) ◽  
pp. 598-608 ◽  
Author(s):  
Teng Wang ◽  
Kasthurirangan Gopalakrishnan ◽  
Omar Smadi ◽  
Arun K. Somani
Keyword(s):  
Crack Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
False Alarms ◽  
Polynomial Curve ◽  
Detection Approach ◽  
Vector Machines ◽  
Different Types ◽  
Surface Irregularities ◽  
Pavement Crack Detection

Pavements are critical man-made infrastructure systems that undergo repeated traffic and environmental loadings. Consequently, they deteriorate with time and manifest certain distresses. To ensure long-lasting performance and appropriate level of service, they need to be preserved and maintained. Highway agencies routinely employ semiautomated and automated image-based methods for network-level pavement-cracking data collection, and there are different types of pavement-cracking data collected by highway agencies for reporting and management purposes. We design a shape-based crack detection approach for pavement health monitoring, which takes advantage of spatial distribution of potential cracks. To achieve this, we first extract Potential Crack Components (PCrCs) from pavement images. Next, we employ polynomial curve to fit all pixels within these components. Finally, we define a Shape Metric (SM) to distinguish crack blocks from background. We experiment the shape-based crack detection approach on different datasets, and compare detection results with an alternate method that is based on Support Vector Machines (SVM) classifier. Experimental results prove that our approach has the capability to produce higher detections and fewer false alarms. Additional research is needed to improve the robustness and accuracy of the developed approach in the presence of anomalies and other surface irregularities.

scholarly journals A Feature Extraction Method of Ship-Radiated Noise Based on Fluctuation-Based Dispersion Entropy and Intrinsic Time-Scale Decomposition

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 693 ◽  
Author(s):  
Zhaoxi Li ◽  
Yaan Li ◽  
Kai Zhang
Keyword(s):  
Feature Extraction ◽  
Time Scale ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Intrinsic Time ◽  
Different Types ◽  
Time Scale Decomposition

To improve the feature extraction of ship-radiated noise in a complex ocean environment, fluctuation-based dispersion entropy is used to extract the features of ten types of ship-radiated noise. Since fluctuation-based dispersion entropy only analyzes the ship-radiated noise signal in single scale and it cannot distinguish different types of ship-radiated noise effectively, a new method of ship-radiated noise feature extraction is proposed based on fluctuation-based dispersion entropy (FDispEn) and intrinsic time-scale decomposition (ITD). Firstly, ten types of ship-radiated noise signals are decomposed into a series of proper rotation components (PRCs) by ITD, and the FDispEn of each PRC is calculated. Then, the correlation between each PRC and the original signal are calculated, and the FDispEn of each PRC is analyzed to select the Max-relative PRC fluctuation-based dispersion entropy as the feature parameter. Finally, by comparing the Max-relative PRC fluctuation-based dispersion entropy of a certain number of the above ten types of ship-radiated noise signals with FDispEn, it is discovered that the Max-relative PRC fluctuation-based dispersion entropy is at the same level for similar ship-radiated noise, but is distinct for different types of ship-radiated noise. The Max-relative PRC fluctuation-based dispersion entropy as the feature vector is sent into the support vector machine (SVM) classifier to classify and recognize ten types of ship-radiated noise. The experimental results demonstrate that the recognition rate of the proposed method reaches 95.8763%. Consequently, the proposed method can effectively achieve the classification of ship-radiated noise.

Role of submicron residual fillers in improving optical reflectance of barrier rib glasses for plasma display panels

2006 ◽  
Vol 21 (7) ◽  
pp. 1753-1758 ◽  
Author(s):  
Hyunho Shin ◽  
Sang-Gon Kim ◽  
Jong-Sung Park ◽  
Hyun Suk Jung ◽  
Kug Sun Hong ◽  
...  
Keyword(s):  
Glass Matrix ◽  
Optical Reflectance ◽  
Sintered Glass ◽  
Plasma Display Panels ◽  
Different Types ◽  
Plasma Display ◽  
Barrier Rib ◽  
Predicted Values ◽  
High Reflectance

The improvement of optical reflectance of BaO–ZnO–B2O3–SiO2 (BZBS) glass by the addition of various types of micron-scale crystalline fillers (TiO2, SiO2, ZrO2, Al2O3, MgO, and cordierite) was investigated for application of the materials to barrier ribs of plasma display panels. The fillers were partially dissolved during sintering, yielding an increased local volume of the fillers in the submicron range, filler rearrangement along boundaries of sintered glass frits, and rather irregular and rugged filler shapes differing from the original morphologies. The measured optical reflectance of the various filler added specimens was within the 30–70% range, which was much higher than the predicted values (less than 10%) based on the rule of mixture of the refractive index. Here we report that the high reflectance of the barrier rib glass for plasma display panels is explained by light scattering by the increased submicron portion of the partially dissolved residual fillers, the size of which is similar to the visual spectrum range (0.4–0.7 μm). The order of reflectance improvement among different types of filler-embedded specimens was consistent with that of the degree of dispersion of the residual fillers in the glass matrix.

scholarly journals A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 139
Author(s):  
Tanmay Basu ◽  
Simon Goldsworthy ◽  
Georgios V. Gkoutos
Keyword(s):  
Language Processing ◽  
Systematic Reviews ◽  
Data Extraction ◽  
Research Question ◽  
Relevant Literature ◽  
Support Vector ◽  
Svm Classifier ◽  
Geometric Errors ◽  
Training Corpus ◽  
Different Types

The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors.

Real-Time EEG Device for Epilepsy Detection Using Wavelet Transform and Support Vector Machine

2021 ◽  
pp. 104-135
Author(s):  
Sharad Sarjerao Jagtap ◽  
Rajesh Kumar M.
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Real Time ◽  
Low Cost ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Feature Extraction Technique

This chapter gives an effective and efficient technique that can detect epilepsy in real time. It is low cost, low power, and real-time devices that can easily detect epilepsy. Along with EEG device, one can upgrade with GSM module to alert the doctors and parents of patients about its occurrence to prevent a sudden fall, which may cause injury and death. The accuracy of this EEG device depends on the quality of feature extraction technique and classification algorithm. In this chapter, support vector machine (SVM) is used as a classifier. Wavelet transform gives feature extraction, which helps to train data and to detect normal or seizure patients. Discrete wavelet transform (DWT) decomposes the signals into three decomposition levels. In this detection, mean, median, and non-linear parameter entropy were calculated for every sub-band as key parameters. The extracted features are then applied to SVM classifier for the classification. Better accuracy of classification is obtained using wavelet and SVM classifier.

A Wind Power Forecasting Method Based on Improved Support Vector Machine

2014 ◽  
Vol 556-562 ◽  
pp. 1964-1967
Author(s):  
Hong Chang Ke ◽  
Hui Wang ◽  
De Gang Kong
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Method ◽  
Different Types ◽  
Wind Forecasting ◽  
Improved Support Vector Machine ◽  
Range Of Variation ◽  
Maximum Minimum

For the deficiencies of traditional wind forecasting method, a wind forecasting method based on improved support vector machine which support any parameters input. The method can compatible with many different types and magnitude data and construct a loop iteration mechanism, based on the wind farm's own situation which constantly prompts to enter the wind speed, season, temperature, humidity, light until it reaches the desired accuracy. For wind power consumptive capacity, the method proposed can obtain the maximum, minimum wind parameters and the range of variation, diverse support vector machine regression model can be produced by learning, which can predict the future of wind energy within a period of time. The method can improve the quality of wind power and grid scheduling, for maintaining grid stability has a very important role.

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