A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification

Sindi, Hatem; Rawa, Muhyaddin; Öztürk, Şaban; Polat, Kemal

doi:10.1016/j.eswa.2021.114785

Cited by 59 publications

(19 citation statements)

References 52 publications

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“…1DCNN is one of the deep learning architectures widely used in signal processing applications [38,39]. Since Using the EEG signal data obtained from the proposed P300 system in this study, a feature extraction consisting of the combination of spectral entropy and instantaneous frequency was carried out.…”

Section: Resultsmentioning

confidence: 99%

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Selvi¹,

Ferikoğlu²,

Erdoğan³

2021

Turk J Elec Eng & Comp Sci

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Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-Computer Interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions, yet have lost their motor and communication abilities. In this study, a BCI system is proposed to classify using Bi-directional Long Short Term Memory (Bi-LSTM) neural networks.In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, EEG data of 10 participants are collected with Emotiv EPOC+ device using 2x2 visual stimulus matrix prepared on Unity. Each symbol of the 2x2 matrix includes stimulus such as Doctor, Police, Fireman and Family.These stimuluses are demonstrated to participants with a fixed order. As data collection protocol, 200ms stimulus time and 300ms interstimulus interval are used. As the performance success of classification, the average accuracy rates are obtained to be 98.6% for training set and 96.9% for the test set. In addition, in classification of P300 EEG signals, the results obtained via Bi-LSTM are compared with the results obtained using 1 Dimensional Convolutional Neural Networks (1DCNN) and Support Vector Machines (SVM) classification methods. Moreover, in the study, information transfer rate (ITR) is provided as 40.39 at an acceptable level.

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Section: Resultsmentioning

confidence: 99%

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Selvi¹,

Ferikoğlu²,

Erdoğan³

2021

Turk J Elec Eng & Comp Sci

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“…This section details the research work reported in the literature related to the detection and classification of PQ disturbances, with emphasis on multiple PQ disturbances. In [6], the authors designed a hybrid technique based on the concept of deep learning, combining the 1-Dimensional (1-D) power signals and 2-Dimensional (2-D) signal images to identify and classify the PQ disturbances. This method has high classification accuracy and moderate complexity of computation.…”

Section: Related Research Workmentioning

confidence: 99%

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

Swarnkar¹,

Mahela²,

Khan³

et al. 2022

Energy Engineering

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A multiple power quality (MPQ) disturbance has two or more power quality (PQ) disturbances superimposed on a voltage signal. A compact and robust technique is required to identify and classify the MPQ disturbances. This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality (MPQ) disturbance using stockwell transform (ST) and hilbert transform (HT). This will reduce the computational time to identify the MPQ disturbances, which makes the algorithm fast. A MPQ identification index (IPI) is computed using statistical features extracted from the voltage signal using the ST and HT. IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances. A MPQ time location index (IPL) is computed using the features extracted from the voltage signal using ST and HT. IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time. Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio (SNR). The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree (RBDT) is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances. MATLAB software is used to perform the study.

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“…At the same time, it is required that the core information of the original data will not be lost in the new space, and the redundant components should be removed. Its core work is to find a set of orthogonal transformation bases in the original space and construct a new coordinate system in the new space with the variance of the original data as a reference [ 14 , 15 ]. Data dimensionality reduction is achieved by mapping N-dimensional features to k-dimensional features.…”

Section: Design Of Deep Neural Network Architecturementioning

confidence: 99%

Internet of things-enabled real-time health monitoring system using deep learning

Liu

Wang

et al. 2021

Neural Comput & Applic

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Smart healthcare monitoring systems are proliferating due to the Internet of Things (IoT)-enabled portable medical devices. The IoT and deep learning in the healthcare sector prevent diseases by evolving healthcare from face-to-face consultation to telemedicine. To protect athletes’ life from life-threatening severe conditions and injuries in training and competitions, real-time monitoring of physiological indicators is critical. In this research work, we present a deep learning-based IoT-enabled real-time health monitoring system. The proposed system uses wearable medical devices to measure vital signs and apply various deep learning algorithms to extract valuable information. For this purpose, we have taken Sanda athletes as our case study. The deep learning algorithms help physicians properly analyze these athletes’ conditions and offer the proper medications to them, even if the doctors are away. The performance of the proposed system is extensively evaluated using a cross-validation test by considering various statistical-based performance measurement metrics. The proposed system is considered an effective tool that diagnoses dreadful diseases among the athletes, such as brain tumors, heart disease, cancer, etc. The performance results of the proposed system are evaluated in terms of precision, recall, AUC, and F1, respectively.

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A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification

Cited by 59 publications

References 52 publications

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

Internet of things-enabled real-time health monitoring system using deep learning

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