scholarly journals Linear discriminate analysis and k-nearest neighbor based diagnostic analytic of harmonic source identification

2021 ◽  
Vol 10 (1) ◽  
pp. 171-178
Author(s):  
Mohd Hatta Jopri ◽  
Abdul Rahim Abdullah ◽  
Mustafa Manap ◽  
M. Badril Nor Shah ◽  
Tole Sutikno ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Geometric Mean ◽  
Performance Criteria ◽  
K Nearest Neighbor ◽  
Linear Discriminate Analysis ◽  
Discriminate Analysis ◽  
Time Frequency ◽  
Frequency Representation ◽  
S Transform ◽  
Harmonic Source

The diagnostic analytic of harmonic source is crucial research due to identify and diagnose the harmonic source in the power system. This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis (LDA) and k-nearest neighbor (KNN) in identifying and diagnosing the harmonic sources. Voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for ML. Several unique cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, each ML algorithm is executed 10 times due to prevent any overfitting result and the performance criteria are measured consist of the accuracy, precision, geometric mean, specificity, sensitivity, and F measure are calculated.

scholarly journals Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification

2020 ◽  
Vol 20 (3) ◽  
pp. 1626
Author(s):  
M. H Jopri ◽  
MR Ab Ghani ◽  
A.R Abdullah ◽  
Tole Sutikno ◽  
M Manap ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Geometric Mean ◽  
Naïve Bayes ◽  
Linear Discriminate Analysis ◽  
Discriminate Analysis ◽  
Time Frequency ◽  
Frequency Representation ◽  
S Transform ◽  
Harmonic Voltage ◽  
Harmonic Source

<span>The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources.  The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (H<sub>V</sub>) and harmonic current (H<sub>C</sub>) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.</span>

scholarly journals K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

2021 ◽  
Vol 9 (6) ◽  
pp. 2650-2657
Author(s):  
Mohd Hatta Jopri ◽  
Mohd Ruddin Ab Ghani ◽  
Abdul Rahim Abdullah ◽  
Mustafa Manap ◽  
Tole Sutikno ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Single Point ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
System A ◽  
Frequency Representation ◽  
S Transform ◽  
Harmonic Source

This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.

scholarly journals Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

2020 ◽  
Vol 20 (1) ◽  
pp. 1
Author(s):  
Mohd Hatta Jopri ◽  
Abdul Rahim Abdullah ◽  
Jingwei Too ◽  
Tole Sutikno ◽  
Srete Nikolovski ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Current Source ◽  
Naïve Bayes ◽  
Support Vector ◽  
Time Frequency ◽  
Frequency Representation ◽  
S Transform ◽  
Harmonic Voltage ◽  
Harmonic Source

<span>A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and Naïve Bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to different partitions and to prevent any overfitting result.</span>

Complex matrix interpolation model of the S-transform for electric load movement forecast

2014 ◽  
Vol 05 (03) ◽  
pp. 1450011
Author(s):  
Zong-Chang Yang
Keyword(s):  
Nearest Neighbor ◽  
Interpolation Method ◽  
Electric Load ◽  
Complex Matrix ◽  
Time Frequency ◽  
Frequency Representation ◽  
S Transform ◽  
Load Movement ◽  
Matrix Interpolation ◽  
Symmetric Property

Electric load movement forecast is increasingly importance for the industry. This study addresses the load movement forecast modeling based on complex matrix interpolation of the S-transform (ST). In complex matrix of time-frequency representation of the ST, each row follows conjugate symmetric property and each column appears a certain degree of similarity. Based on these characteristics, a complex matrix interpolation method for the time-frequency representation of the ST is proposed to interpolate each row of the complex matrix based on the conjugate symmetric property, and then to perform nearest-neighbor interpolation on each column. Then with periodic extension for daily and yearly electric load movement, a forecast model employing the complex matrix interpolation of the ST is introduced. The forecast approach is applied to predict daily load movement of the European Network on Intelligent Technologies (EUNITE) load dataset and annual electric load movement of State Gird Corporation of China and its branches in 2005 and 2006. Result analysis indicates workability and effectiveness of the proposed method.

scholarly journals Detection of Ventricular Fibrillation Using the Image from Time-Frequency Representation and Combined Classifiers without Feature Extraction

2018 ◽  
Vol 8 (11) ◽  
pp. 2057 ◽  
Author(s):  
Azeddine Mjahad ◽  
Alfredo Rosado-Muñoz ◽  
Juan Guerrero-Martínez ◽  
Manuel Bataller-Mompeán ◽  
Jose Francés-Villora ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Ventricular Fibrillation ◽  
Real Time ◽  
Nearest Neighbor ◽  
Diagnostic System ◽  
Image Data ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
Hierarchical Method ◽  
Frequency Representation

Due the fact that the required therapy to treat Ventricular Fibrillation (V F) is aggressive (electric shock), the lack of a proper detection and recovering therapy could cause serious injuries to the patient or trigger a ventricular fibrillation, or even death. This work describes the development of an automatic diagnostic system for the detection of the occurrence of V F in real time by means of the time-frequency representation (T F R) image of the ECG. The main novelties are the use of the T F R image as input for a classification process, as well as the use of combined classifiers. The feature extraction stage is eliminated and, together with the use of specialized binary classifiers, this method improves the results of the classification. To verify the validity of the method, four different classifiers in different combinations are used: Regression Logistic with L2 Regularization (L 2 R L R), adaptive neural network (A N N C), Bagging (B A G G), and K-nearest neighbor (K N N). The Hierarchical Method (HM) and Voting Majority Method (VMM) combinations are used. ECG signals used for evaluation were obtained from the standard MIT-BIH and AHA databases. When the classifiers were combined, it was observed that the combination of B A G G , K N N , and A N N C using the Hierarchical Method (HM) gave the best results, with a sensitivity of 95.58 ± 0.41%, a 99.31 ± 0.08% specificity, a 98.6 ± 0.04% of overall accuracy, and a precision of 98.25 ± 0.29% for V F . Whereas a sensitivity of 94.02 ± 0.58%, a specificity of 99.31 ± 0.08%, an overall accuracy of 99.14 ± 0.43%, and a precision of 98.59 ± 0.09% was obtained for V T with a run time between 0.07 s and 0.12 s. Results show that the use of T F R image data to feed the combined classifiers yields a reduction in execution time with performance values above to those obtained by individual classifiers. This is of special utility for V F detection in real time.

scholarly journals Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 991 ◽  
Author(s):  
Md Hasan ◽  
Jong-Myon Kim
Keyword(s):  
Fault Detection ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Time Frequency ◽  
Reduction Techniques ◽  
Spherical Tank ◽  
Frequency Domains ◽  
Dimensionality Reduction Techniques ◽  
Linear Dimensionality Reduction ◽  
Ae Signals

Fault detection in metallic structures requires a detailed and discriminative feature pool creation mechanism to develop an effective condition monitoring system. Traditional fault detection methods incorporate handcrafted features either from the time, frequency or time-frequency domains. To explore the salient information provided by the acoustic emission (AE) signals, a hybrid of feature pool creation and an optimal features subset selection mechanism is proposed for crack detection in a spherical tank. The optimal hybrid feature pool creation process is composed of two major parts: (1) extraction of statistical features from time and frequency domains, as well as extraction of traditional features associated with the AE signals; and (2) genetic algorithm (GA)-based optimal features subset selection. The optimal features subset is then provided to the k-nearest neighbor (k-NN) classifier to distinguish between normal (NC) and crack conditions (CC). Experimental results show that the proposed approach yields an average 99.8% accuracy for heath state classification. To validate the effectiveness of the proposed approach, it is compared to conventional non-linear dimensionality reduction techniques, as well as those without feature selection schemes. Experimental results show that the proposed approach outperforms conventional non-linear dimensionality reduction techniques, achieving at least 2.55% higher classification accuracy.

scholarly journals An Investigation of Insider Threat Mitigation Based on EEG Signal Classification

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6365
Author(s):  
Jung Hwan Kim ◽  
Chul Min Kim ◽  
Man-Sung Yim
Keyword(s):  
Nearest Neighbor ◽  
Nuclear Industry ◽  
Classification Model ◽  
Support Vector ◽  
Insider Threat ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Nuclear Facilities ◽  
Time Frequency ◽  
Threat Mitigation

This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.

A Time–Frequency Representation Model for Seismic Ground Motions

2020 ◽  
Author(s):  
Xi Zhong Cui ◽  
Han Ping Hong
Keyword(s):  
Ground Motions ◽  
Seismic Source ◽  
Model Parameters ◽  
Time Frequency ◽  
Motion Models ◽  
Power Spectral ◽  
Frequency Representation ◽  
Site Characteristics ◽  
S Transform ◽  
Proposed Model

ABSTRACT A probabilistic model of the time–frequency power spectral density (TFPSD) is presented. The model is developed, based on the time–frequency representation of records from strike-slip earthquakes, in which the time–frequency representation is obtained by applying the S-transform (ST). The model for the TFPSD implicitly considers the amplitude modulation and frequency modulation for the nonstationary ground motions; this differs from the commonly used evolutionary PSD model. Predicting models for the model parameters, based on seismic source and site characteristics, are developed. The use of the model to simulate ground motions for scenario seismic events is illustrated, in which the simulation is carried out using a recently developed model that is based on the discrete orthonormal ST and ST. The illustrative example highlights the simplicity of using the proposed model and the physical meaning of some of the model parameters. A model validation analysis is carried out by comparing the statistics of the pseudospectral acceleration obtained from the simulated records to those obtained using a few ground-motion models available in the literature and considered actual records. The comparison indicates the adequacy of the proposed model.

Performance Comparison of VSI Switches Faults Analysis Using STFT and S Transform

2015 ◽  
Vol 785 ◽  
pp. 210-214 ◽  
Author(s):  
M. Manap ◽  
A.R. Abdullah ◽  
N.Z. Saharuddin ◽  
N.A. Abidullah ◽  
Nur Sumayyah Ahmad ◽  
...  
Keyword(s):  
Power Converter ◽  
Performance Comparison ◽  
Voltage Source ◽  
Percentage Error ◽  
Identification System ◽  
Time Frequency ◽  
Signal Parameters ◽  
Detection And Identification ◽  
Frequency Representation ◽  
S Transform

Switches fault in power converter has become compelling issues over the years. To reduce cost and maintenance downtime, a good fault detection technique is an essential. In this paper, the performance of STFT and S transform techniques are analysed and compared for voltage source inverter (VSI) switches faults. The signal from phase current is represented in jointly time-frequency representation (TFR) to estimate signal parameters and characteristics. Then, the degree of accuracy for both STFT and S transform are determined by the lowest value of mean absolute percentage error (MAPE). The results demonstrate that S transform gives better accuracy compare to STFT and is suitable for VSI switches faults detection and identification system.

Sign in / Sign up

Export Citation Format

Share Document