Intelligent Fault Detection and Identification System for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works

Predviđanje modula loma i modula elastičnosti toplinski obrađenog drva anatolskog kestena (Castanea sativa) modelom razvrstavanja fuzzy logikom

In this study, test samples prepared from Anatolian chestnut (Castanea sativa) wood were first exposed to heat treatment at 130, 145, 160, 175, 190 and 205 ºC for 3, 6, 9 and 12 hours. Then the values of the samples of the modulus of rupture (MOR) and modulus of elasticity (MOE) were determined and evaluated by multiple variance analysis. The aim of this study was to establish the effects of heat treatment on the MOR and MOE values of wood samples by using fuzzy logic classifier. Secondly, input and output values and rule base of the fuzzy logic classifier model were built by using the results obtained from the experiment. The developed fuzzy classifier model could predict the MOR and MOE values of test samples at the accuracy levels of 92.64 % and 90.35 %, respectively. The model could be especially employed in manufacturing stages of timber industry.U radu se prikazuju istraživanju u kojima su, prije svega, pripremljeni uzorci od drva kestena te izloženi zagrijavanju na temperaturama od 130, 145, 160, 175, 190 i 205 ºC tijekom 3, 6, 9 i 12 sati. Nakon toga uzorcima su određeni modul loma (MOR) i modul elastičnosti (MOE) te je napravljena analiza varijanci dobivenih vrijednosti. Cilj provedene studije bio je utvrditi učinak toplinske obrade drva na MOR i MOE vrijednosti drvnih uzoraka uporabom modela razvrstavanja neizrazitom (fuzzy) logikom. Ulazne i izlazne vrijednosti te osnovna pravila modela neizrazitog razvrstavanja defi nirani su uporabom rezultata dobivenih eksperimentom. Razvijeni model neizrazitog (fuzzy) razvrstavanja moguće je primijeniti za predviđanje MOR i MOE vrijednosti drvnih uzoraka s točnošću od 92,64 % i 90,35 %. Model može biti primijenjen u proizvodnim uvjetima, posebice u procesu proizvodnje piljene drvne građe

Classification of MRI Brain images using GLCM, Neural Network, Fuzzy Logic & Genetic Algorithm

Detection of Brain abnormality could be a vital and crucial task in medical field. Resonance Imaging Brain image detection method offers the knowledge of the various abnormalities in Brain. This helps the doctors in treatment coming up with. Within the previous work, within the field of medical image process several scientist and soft computing techniques have totally different strategies like totally automatic and semiautomatic. During this projected technique, 2 totally different classification strategies are used along for the classification of magnetic resonance imaging Brain pictures. Those classification strategies square measure Neural Network and fuzzy logic. With this projected hybrid technique Genetic algorithmic program is employed for the optimization. Projected technique consists of various stages. Knowledge assortment through numerous hospitals or repository sites and convert original data pictures into gray scale image. Gray Level Co-occurrence Matrix technique is employed for the extraction of the options from the gray scale image. Optimization technique Genetic algorithmic program is especially used for reducing the options that square measure extracted by GLCM for simple classification and reducing the convergence time or computation time. there\'s a hybrid classifier is employed for classification of magnetic resonance imaging brain pictures specifically Neural and Fuzzy classifier. DOI: 10.17762/ijritcc2321-8169.15060

A branching fuzzy-logic classifier for building optimization

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (p. 109-110).We present an input-output model that learns to emulate a complex building simulation of high dimensionality. Many multi-dimensional systems are dominated by the behavior of a small number of inputs over a limited range of input variation. Some also exhibit a tendency to respond relatively strongly to certain inputs over small ranges, and to other inputs over very large ranges of input variation. A branching linear discriminant can be used to isolate regions of local linearity in the input space, while also capturing the effects of scale. The quality of the classification may be improved by using a fuzzy preference relation to classify input configurations that are not well handled by the linear discriminant.by Matthew A. Lehar.Ph.D

A Survey on the Project in title

In this paper we present a survey of work that has been done in the project ldquo;Unsupervised Adaptive P300 BCI in the framework of chaotic theory and stochastic theoryrdquo;we summarised the following papers, (Mohammed J Alhaddad amp; 2011), (Mohammed J. Alhaddad amp; Kamel M, 2012), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2013), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2013), (Mohammed J Alhaddad, Kamel, amp; Al-Otaibi, 2014), (Mohammed J Alhaddad, Kamel, amp; Bakheet, 2014), (Mohammed J Alhaddad, Kamel, amp; Kadah, 2014), (Mohammed J Alhaddad, Kamel, Makary, Hargas, amp; Kadah, 2014), (Mohammed J Alhaddad, Mohammed, Kamel, amp; Hagras, 2015).We developed a new pre-processing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks. The new denoising technique is based on a modified version of the spectral subtraction denoising and works on each temporal signal channel independently thus offering seamless integration with existing pre-processing and allowing low channel counts to be used. We also developed a novel approach for brain-computer interface data that requires no prior training. The proposed approach is based on interval type-2 fuzzy logic based classifier which is able to handle the usersrsquo; uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximize the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. The basic principle of this new class of techniques is that the trial with true activation signal within each block has to be different from the rest of the trials within that block. Hence, a measure that is sensitive to this dissimilarity can be used to make a decision based on a single block without any prior training. The new methods were verified using various experiments which were performed on standard data sets and using real-data sets obtained from real subjects experiments performed in the BCI lab in King Abdulaziz University. The results were compared to the classification results of the same data using previous methods. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed. It will be shown that the produced type-2 fuzzy logic based classifier will learn simple rules which are easy to understand explaining the events in question. In addition, the produced type-2 fuzzy logic classifier will be able to give better accuracies when compared to BLDA or RFLDA on various human subjects on the standard and real-world data sets

Fuzzy logic filtering of radar reflectivity to remove non-meteorological echoes using dual polarization radar moments

The ability of a fuzzy logic classifier to dynamically identify non-meteorological radar echoes is demonstrated using data from the National Centre for Atmospheric Science dual polarisation, Doppler, X-band mobile radar. Dynamic filtering of radar echoes is required due to the variable presence of spurious targets, which can include insects, ground clutter and background noise. The fuzzy logic classifier described here uses novel multi-vertex membership functions which allow a range of distributions to be incorporated into the final decision. These membership functions are derived using empirical observations, from a subset of the available radar data. The classifier incorporates a threshold of certainty (25 % of the total possible membership score) into the final fractional defuzzification to improve the reliability of the results. It is shown that the addition of linear texture fields, specifically the texture of the cross-correlation coefficient, differential phase shift and differential reflectivity, to the classifier along with standard dual polarisation radar moments enhances the ability of the fuzzy classifier to identify multiple features. Examples from the Convective Precipitation Experiment (COPE) show the ability of the filter to identify insects (18 August 2013) and ground clutter in the presence of precipitation (17 August 2013). Medium-duration rainfall accumulations across the whole of the COPE campaign show the benefit of applying the filter prior to making quantitative precipitation estimates. A second deployment at a second field site (Burn Airfield, 6 October 2014) shows the applicability of the method to multiple locations, with small echo features, including power lines and cooling towers, being successfully identified by the classifier without modification of the membership functions from the previous deployment. The fuzzy logic filter described can also be run in near real time, with a delay of less than 1 min, allowing its use on future field campaigns

Multimodal Behavioral Biometric Authentication in Smartphones for Covid-19 Pandemic

The usage of mobile phones has increased multi-fold in recent decades, mostly because of their utility in most aspects of daily life, such as communications, entertainment, and financial transactions. In use cases where users’ information is at risk from imposter attacks, biometrics-based authentication systems such as fingerprint or facial recognition are considered the most trustworthy in comparison to PIN, password, or pattern-based authentication systems in smartphones. Biometrics need to be presented at the time of power-on, they cannot be guessed or attacked through brute force and eliminate the possibility of shoulder surfing. However, fingerprints or facial recognition-based systems in smartphones may not be applicable in a pandemic situation like Covid-19, where hand gloves or face masks are mandatory to protect against unwanted exposure of the body parts. This paper investigates the situations in which fingerprints cannot be utilized due to hand gloves and hence presents an alternative biometric system using the multimodal Touchscreen swipe and Keystroke dynamics pattern. We propose a HandGlove mode of authentication where the system will automatically be triggered to authenticate a user based on Touchscreen swipe and Keystroke dynamics patterns. Our experimental results suggest that the proposed multimodal biometric system can operate with high accuracy. We experiment with different classifiers like Isolation Forest Classifier, SVM, k-NN Classifier, and fuzzy logic classifier with SVM to obtain the best authentication accuracy of 99.55% with 197 users on the Samsung Galaxy S20. We further study the problem of untrained external factors which can impact the user experience of authentication system and propose a model based on fuzzy logic to extend the functionality of the system to improve under novel external effects. In this experiment, we considered the untrained external factor of ‘sanitized hands’ with which the user tries to authenticate and achieved 93.5% accuracy in this scenario. The proposed multimodal system could be one of the most sought approaches for biometrics-based authentication in smartphones in a COVID-19 pandemic situation

Real Time Recognition of Elderly Daily Activity using Fuzzy Logic through Fusion of Motion and Location Data

One of the major problems that may encounter old people at home is falling. Approximately, one of three adults of the age of 65 or older falls every year. The World Health Organization reports that injuries due to falls are the third most common cause of chronic disability. In this paper, we proposed an approach to indoor human daily activity recognition, which combines motion and location data by using a webcam system, with a particular interest to the problem of fall detection. The proposed system identifies the face and the body in a given area, collects motion data such as face and body speeds and location data such as center of mass and aspect ratio; then the extracted parameters will be fed to a Fuzzy logic classifier that classify the fall event in two classes: fall and not fall

EMG signal classification using wavelet transform and fuzzy logic classifier

Deri yüzeyinde algılanan elektromiyografik (EMG) işaretleri, kas liflerinin kasılması sonucu oluşan çok sayıda aksiyon potansiyellerin birleşimidir. Şimdiye kadar biyomedikal mühendisliğinde çeşitli uygulama alanları bulmuştur. Bu uygulamalardan biri de protez kontrolüdür.Bu çalışmanın hedefi, öznitelik çıkartma yöntemi olarak zaman-frekans domeni analiz yöntemlerini kullanarak protez koluna ait dört farklı hareket için EMG işaretlerini daha iyi sınıflamayı gerçekleştirmektir. Bunun için boyut azaltma ve bulanık sınıflama yöntemleri de incelenmiştir. Sınıflama problemi öznitelik çıkartma, boyut azaltma ve örüntü sınıflama aşamalarına ayrılır. Dalgacık dönüşümü öznitelik çıkartma yöntemi olarak büyük üstünlük sağlar. Özniteliklerin çıkartma aşamasında yüksek boyuta sahip olmalarından dolayı sınıflama başarısı, Ana Bileşenler Analizi (ABA) ve Bağımsız Bileşenler Analizi (BBA) gibi uygun boyut azaltma yöntemleriyle gerçekleştirilebilir. Anahtar Kelimeler: Yüzey elektromiyografik işaret, dalgacık dönüşümü, bulanık öbekleştirme, boyut azaltma, işaret sınıflama.The electromyographic (EMG) signal observed at the surface of the skin is the sum of thousands of small potentials generated in the muscle fibers. After this signal are processed it can be used as a control source of artificial limbs. The objective of this work is to achieve better classification for four different movement of a prosthetic limb making an analysis of time-frequency domain methods as a feature extraction tools in the problem of the EMG signal while investigating the related dimensionality reduction and fuzzy classification methods. The classification problem may be divided into the stages of feature extraction, dimensionality reduction, and pattern classification. It is shown that wavelet transform (WT) provide a powerful framework for feature extraction. Because of high dimension of features at the extraction stage, the success of classification can be achieved by employing suitable dimensionality reduction methods which are Principal Component Analysis and Independent Component Analysis outperform WT features. The other stage is the pattern classification in which fuzzy clustering methods and artificial neural networks (ANN) are used. The clustering methods are used to obtain membership values of the EMG signals for each class or cluster. The values are necessary during the classification stage. As classifier, Fuzzy K-Nearest Neighbor classifier is used. ANN are used to compare these methods as classifier.Keywords: Surface electromyographic signal, wavelet transform, fuzzy clustering, dimensionality reduction, signal classification