scholarly journals Novelty Detection on Power Quality Disturbances Monitoring

2021 ◽  
Vol 19 ◽  
pp. 211-216
Author(s):  
A. D. Gonzalez-Abreu ◽  
◽  
M. Delgado-Prieto ◽  
J.J. Saucedo-Dorantes ◽  
R.A. Osornio-Rios
Keyword(s):  
Power Quality ◽  
Nearest Neighbor ◽  
Novelty Detection ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Power Quality Monitoring ◽  
Detection And Identification ◽  
Power Quality Disturbances

Complex disturbance patterns take place over the corresponding power supply networks due to the increased complexity of electrical loads at industrial plants. Such complex patterns are the result of a combination of simpler standardized disturbances. However, their detection and identification represent a challenge to current power quality monitoring systems. The detection of disturbances and their identification would allow early and effective decision-making processes towards optimal power grid controls or maintenance and security operations of the grid. In this regard, this paper presents an evaluation of the four main techniques for novelty detection: k-Nearest Neighbor, Gaussian Mixture Models, One-Class Support Vector Machine, and Stacked Autoencoder. A set of synthetic signals have been considered to evaluate the performance and suitability of each technique as an anomaly detector applied to power quality disturbances. A set of statistical features have been considered to characterize the power line. The evaluation of the techniques is carried out throughout different scenarios considering combined and single disturbances. The obtained results show the complementary performance of the considered techniques in front of different scenarios due to their differences in the knowledge modelization.

AUTOMATED IDENTIFICATION OF EPILEPTIC AND ALCOHOLIC EEG SIGNALS USING RECURRENCE QUANTIFICATION ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240028 ◽  
Author(s):  
EE PING NG ◽  
TEIK-CHENG LIM ◽  
SUBHAGATA CHATTOPADHYAY ◽  
MURALIDHAR BAIRY
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Gaussian Mixture ◽  
Recurrence Quantification Analysis ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

Epilepsy is a common neurological disorder characterized by recurrence seizures. Alcoholism causes organic changes in the brain, resulting in seizure attacks similar to epileptic fits. Hence, it is challenging to differentiate the cause of fits as epileptic or alcoholism, which is important for deciding on the treatment in the neurology ward. The focus of this paper is to automatically differentiate epileptic, normal, and alcoholic electroencephalogram (EEG) signals. As the EEG signals are non-linear and dynamic in nature, it is difficult to tell the subtle changes in these signals with the help of linear techniques or by the naked eye. Therefore, to analyze the normal (control), epileptic, and alcoholic EEG signals, two non-linear methods, such as recurrence plots (RPs) and then recurrence quantification analysis (RQA) are adopted. Approximately 10 RQA parameters have been used to classify the EEG signals into three distinct classes, i.e., normal, epileptic, and alcoholic. Six classifiers, such as support vector machine (SVM), radial basis probabilistic neural network (RBPNN), decision tree (DT), Gaussian mixture model (GMM), k-nearest neighbor (kNN), and fuzzy Sugeno classifiers have been developed to accomplish this task. Results show that the GMM classifier outperformed the other classifiers with a classification sensitivity of 99.6%, specificity of 98.3%, and accuracy of 98.6%.

scholarly journals Signal-piloted processing and machine learning based efficient power quality disturbances recognition

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252104
Author(s):  
Saeed Mian Qaisar
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Data Storage ◽  
Computational Cost ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Processing Load ◽  
Suggested Approach ◽  
Power Quality Disturbances

Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing PQDs identification is time-invariant. It can result in a huge amount of unnecessary information being collected, processed, and transmitted. Consequently, needless processing activities, power consumption and latency can occur. In this paper, a novel combination of signal-piloted acquisition, adaptive-rate segmentation and time-domain features extraction with machine learning tools is suggested. The signal-piloted acquisition and processing brings real-time compression. Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. Additionally, a reduced computational cost and latency of classifier is promised. The classification is accomplished by using robust machine learning algorithms. A comparison is made among the k-Nearest Neighbor, Naïve Bayes, Artificial Neural Network and Support Vector Machine. Multiple metrics are used to test the success of classification. It permits to avoid any biasness of findings. The applicability of the suggested approach is studied for automated recognition of the power signal’s major voltage and transient disturbances. Results show that the system attains a 6.75-fold reduction in the collected information and the processing load and secures the 98.05% accuracy of classification.

scholarly journals Diagnosis and Classification of Brain Tumor

2019 ◽  
Vol 9 (1) ◽  
pp. 1622-1630
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Early Detection ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Detection And Identification

The aim of this paper is to provide an outline on cerebrum (brain) tumor diagnoses for folks that are new to virtual medical image processing. The strange improvement of cells within the brain is brain Tumor. Detection of brain tumor at early stage is feasible with image processing and the development of device learning. For early detection of extraordinary adjustments in tumor cells, Computer tomography, Magnetic resonance imaging strategies are used. Early detection and identification of tumor is the handiest manner to get remedy. Brain tumor is classed into two kinds benign and malignant tumor. Various imaging processing strategies had been proposed in latest couple of years for detection and class of brain tumor. Automatic segmentation method the usage of clustering and convolution neural community gives nice consequences. The PCA has been used for reducing the features from the segmented area gives superb outcomes compared to other techniques. For classification of brain tumors diverse algorithms any such Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor are reviewed. These strategies correctly paintings on CT and MRI images

BAYESIAN SELF-ORGANIZING MAP FOR DATA CLASSIFICATION AND CLUSTERING

2013 ◽  
Vol 11 (05) ◽  
pp. 1350037 ◽  
Author(s):  
XIAOLIAN GUO ◽  
HAIYING WANG ◽  
DAVID H. GLASS
Keyword(s):  
Learning Process ◽  
Bayesian Learning ◽  
Nearest Neighbor ◽  
Information Criterion ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Support Vector ◽  
Self Organizing Map ◽  
K Nearest Neighbor ◽  
Self Organizing

The Bayesian self-organizing map (BSOM) has typically been used for density estimation. In this study, we implemented an adaptation of the model for performing unsupervized and supervised classification. In order to determine the optimal number of neurons to represent the given dataset during the learning process, an extended Bayesian learning process is proposed called the growing BSOM. It starts with two neurons and adds new neurons to the network via a process in which the neuron with the lowest individual log-likelihood is identified. The system has been tested using three synthetic datasets and one real dataset. The experimental results suggest that the BSOM-based approach can achieve better classification performance in comparisons to several widely-used models such as k-nearest neighbor (KNN), support vector machine (SVM) and Gaussian mixture model (GMM). By using the Bayesian information criterion (BIC) as a stopping criterion, the growing BSOM can model the data under study and estimate the number of clusters.

scholarly journals Power Quality Disturbances Classification via Fully-Convolutional Siamese Network and k-Nearest Neighbor

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4732
Author(s):  
Ruijin Zhu ◽  
Xuejiao Gong ◽  
Shifeng Hu ◽  
Yusen Wang
Keyword(s):  
Power Quality ◽  
Nearest Neighbor ◽  
Small Sample ◽  
Small Samples ◽  
K Nearest Neighbor ◽  
Siamese Network ◽  
Power Quality Disturbance ◽  
Power Quality Disturbances ◽  
Small Sample Sizes

The classification of disturbance signals is of great significance for improving power quality. The existing methods for power quality disturbance classification require a large number of samples to train the model. For small sample learning, their accuracy is relatively limited. In this paper, a hybrid algorithm of k-nearest neighbor and fully-convolutional Siamese network is proposed to classify power quality disturbances by learning small samples. Multiple convolutional layers and full connection layers are used to construct the Siamese network, and the output result of the Siamese network is used to judges the category of the signal. The simulation results show that: For small sample sizes, the accuracy of the proposed approach is significantly higher than that of the existing methods. In addition, it has a strong anti-noise ability.

DEPRESSION DIAGNOSIS SUPPORT SYSTEM BASED ON EEG SIGNAL ENTROPIES

2014 ◽  
Vol 14 (03) ◽  
pp. 1450035 ◽  
Author(s):  
OLIVER FAUST ◽  
PENG CHUAN ALVIN ANG ◽  
SUBHA D. PUTHANKATTIL ◽  
PAUL K. JOSEPH
Keyword(s):  
Support System ◽  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Approximate Entropy ◽  
Gaussian Mixture ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Non Linear

Electroencephalography (EEG) is a measure which represents the functional activity of the brain. We show that a detailed analysis of EEG measurements provides highly discriminant features which indicate the mental state of patients with clinical depression. Our feature extraction method revolves around a novel processing structure that combines wavelet packet decomposition (WPD) and non-linear algorithms. WPD was used to select appropriate EEG frequency bands. The resulting signals were processed with the non-linear measures of approximate entropy (ApEn), sample entropy (SampEn), renyi entropy (REN) and bispectral phase entropy ( P h). The features were selected using t-test and only discriminative features were fed to various classifiers, namely probabilistic neural network (PNN), support vector machine (SVM), decision tree (DT), k-nearest neighbor algorithm (k-NN), naive bayes classification (NBC), Gaussian mixture model (GMM) and Fuzzy Sugeno Classifier (FSC). Our classification results show that, with a classification accuracy of 99.5%, the PNN classifier performed better than the rest of classifiers in discriminating between normal and depression EEG signals. Hence, the proposed decision support system can be used to diagnose, and monitor the treatment of patients suffering from depression.

scholarly journals An Artificial Intelligence-Based Smart System for Early Glaucoma Recognition Using OCT Images

2021 ◽  
Vol 12 (4) ◽  
pp. 32-59
Author(s):  
Law Kumar Singh ◽  
Pooja ◽  
Hitendra Garg ◽  
Munish Khanna
Keyword(s):  
Vision Loss ◽  
Nearest Neighbor ◽  
Peripheral Vision ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Glaucoma Diagnosis ◽  
Detection And Identification

Glaucoma is a progressive and constant eye disease that leads to a deficiency of peripheral vision and, at last, leads to irrevocable loss of vision. Detection and identification of glaucoma are essential for earlier treatment and to reduce vision loss. This motivates us to present a study on intelligent diagnosis system based on machine learning algorithm(s) for glaucoma identification using three-dimensional optical coherence tomography (OCT) data. This experimental work is attempted on 70 glaucomatous and 70 healthy eyes from combination of public (Mendeley) dataset and private dataset. Forty-five vital features were extracted using two approaches from the OCT images. K-nearest neighbor (KNN), linear discriminant analysis (LDA), decision tree, random forest, support vector machine (SVM) were applied for the categorization of OCT images among the glaucomatous and non-glaucomatous class. The largest AUC is achieved by KNN (0.97). The accuracy is obtained on fivefold cross-validation techniques. This study will facilitate to reach high standards in glaucoma diagnosis.

APPLICATION OF NON-LINEAR AND WAVELET BASED FEATURES FOR THE AUTOMATED IDENTIFICATION OF EPILEPTIC EEG SIGNALS

2012 ◽  
Vol 22 (02) ◽  
pp. 1250002 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
S. VINITHA SREE ◽  
PENG CHUAN ALVIN ANG ◽  
RATNA YANTI ◽  
JASJIT S. SURI
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Automatic Monitoring ◽  
Gaussian Mixture ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Fuzzy Classifier ◽  
Linear Features ◽  
Non Linear

Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.

A Comparative Study of Power Quality Monitoring Using Various Techniques

2021 ◽  
pp. 259-288
Author(s):  
Fatma Zohra Dekhandji ◽  
Mohamed Cherif Rais
Keyword(s):  
Signal Processing ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Support Vector ◽  
Labview Software ◽  
Power Quality Monitoring ◽  
Power Quality Disturbances ◽  
Processing Techniques ◽  
Signal Processing Techniques

In recent years, power quality (PQ) has become an increasingly major concern for both electric utilities and the end users. Accordingly, the electrical engineering community has to deal with the analysis, diagnosis, and solution of PQ issues using system approach rather than handling these issues as individual problems. This chapter describes the analysis of PQ using advanced signal processing tools represented in Hilbert and wavelet transforms (HT-WT) and artificial intelligence tools represented in artificial neural network and support vector machine (ANN-SVM) for detection and classification of power quality disturbances, respectively. These techniques were successfully simulated using LABVIEW software capabilities. The results of simulation indicate that the signal processing techniques are effective mechanisms to detect and classify power quality disturbances. At the end, the combination of WT as a tool of detection and features extraction with SVM as a classifier tool resulted as the best combination for PQ monitoring system.

Sign in / Sign up

Export Citation Format

Share Document