Implementation of Levenberg-Marquardt Based Multilayer Perceptron (MLP) for Detection and Classification of Power Quality Disturbances

Anwar, Irfanudin Nor; Daud, Kamarulazhar; Samat, Ahmad Asri Abd; Soh, Zainal Hisham Che; Omar, Abdul Malek Saidina; Ahmad, Fadzil

doi:10.1109/iccsce54767.2022.9935584

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The Multi-Layer Perceptron (MLP) serves as a conduit for interchanging and amalgamating distinct features within the same group. It encompasses a series of layers including the fully connected, activation, and standardization layers [28][29][30]. The training process employs backpropagation, iteratively adjusting weights and biases.…”

Section: Human Pose Feature Information Fusionmentioning

confidence: 99%

A Hybrid Human Activity Recognition Method Using an MLP Neural Network and Euler Angle Extraction Based on IMU Sensors

Mao,

Yan,

Guo

et al. 2023

Applied Sciences

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Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. It has propelled the extensive application of HAR across various domains. In the healthcare sector, HAR finds utility in monitoring and assessing movements during rehabilitation processes, while in the sports science field, it contributes to enhancing training outcomes and preventing exercise-related injuries. However, traditional sensor fusion algorithms often require intricate mathematical and statistical processing, resulting in higher algorithmic complexity. Additionally, in dynamic environments, sensor states may undergo changes, posing challenges for real-time adjustments within conventional fusion algorithms to cater to the requirements of prolonged observations. To address these limitations, we propose a novel hybrid human pose recognition method based on IMU sensors. The proposed method initially calculates Euler angles and subsequently refines them using magnetometer and gyroscope data to obtain the accurate attitude angle. Furthermore, the application of FFT (Fast Fourier Transform) feature extraction facilitates the transition of the signal from its time-based representation to its frequency-based representation, enhancing the practical significance of the data. To optimize feature fusion and information exchange, a group attention module is introduced, leveraging the capabilities of a Multi-Layer Perceptron which is called the Feature Fusion Enrichment Multi-Layer Perceptron (GAM-MLP) to effectively combine features and generate precise classification results. Experimental results demonstrated the superior performance of the proposed method, achieving an impressive accuracy rate of 96.13% across 19 different human pose recognition tasks. The proposed hybrid human pose recognition method is capable of meeting the demands of real-world motion monitoring and health assessment.

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Section: Human Pose Feature Information Fusionmentioning

confidence: 99%

A Hybrid Human Activity Recognition Method Using an MLP Neural Network and Euler Angle Extraction Based on IMU Sensors

Mao,

Yan,

Guo

et al. 2023

Applied Sciences

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“…The concept of the MLP algorithm consists of an input layer, a hidden layer, and an output layer [44]. The performance of the MLP algorithm presents calculations that adopt the performance of the feedforward algorithm [45]. The working principles of MLP include (1) each neuron has a non-linear activation function, (2) MLP has one or more hidden layers, (3) the analysis process goes through the input layer to the output layer, and (4) the MLP model has weights (synaptic) [46].…”

Section: Multilayer Perceptron (Mlp) Algorithmmentioning

confidence: 99%

Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children

Yanto,

Febri Hadi,

Syafri Arlis

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Malnutrition is one of the problems that occurs in children due to a lack of nutritional intake. Indonesia contributed 36%, making it the fifth country with the largest cases of malnutrition in the world. On this basis, a solution is needed to reduce the growth rate of malnutrition cases. This research aims to carry out classification analysis to determine nutritional status by optimizing machine learning (ML) performance. The ML classification analysis process will later utilize the performance of the artificial neural network (ANN) method with the Multilayer Perceptron (MLP) algorithm. ML performance can be optimized using the Pearson’s correlation (PC) method to produce optimal classification analysis patterns. This research data set uses child nutrition case data from 576 patients sourced from the M. Djamil Padang Province Regional General Hospital (RSUP). The data set is divided into 417 training data and 159 test data. On the basis of the tests that have been carried out, the performance of the PC method can provide precise and accurate analysis patterns. This analysis pattern has also been able to provide a fairly good level of accuracy, namely 95%. Not only that, this research is also able to present analysis patterns with the best ANN architectural model in classifying nutritional status. Based on the overall results, this research can be used as an alternative solution to the treatment of nutritional problems in children.

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“…Neural networks are trained through learning algorithms to solve a given problem, otherwise called learning from experience. The Levenberg-Marquardt (LM) algorithm is the fastest learning algorithm in achieving convergence [30], [31]. This work is used to determine network configuration and network capabilities in overcoming voltage sag.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

Siregar,

Muhammad,

Arief

et al. 2023

IJECE

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Power quality is one of the problems in power systems, caused by increased nonlinear loads and short circuit faults. Short circuits often occur in power systems and generally cause voltage sags that can damage sensitive loads. Dynamic voltage restorer (DVR) is an efficient and flexible solution for overcoming voltage sag problems. The control system on the DVR plays an important role in improving the quality of voltage injection applied to the network. DVR control systems based on particle swarm optimization (PSO) and artificial neural networks (ANN) were proposed in this study to assess better controllers applied to DVRs. In this study, a simulation of voltage sag due to a 3-phase short-circuit fault was carried out based on a load of 70% of the total load and a fault location point of 75% of the feeder’s length. The simulation was carried out on the SB 02 Sibolga feeder. Modeling and simulation results are carried out with MATLAB-Simulink. The simulation results show that DVR-PSO and DVR-ANN successfully recover voltage sag by supplying voltage at each phase. Based on the results of the analysis shows that DVR-ANN outperforms DVR-PSO in quality and voltage injection into the network.

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Implementation of Levenberg-Marquardt Based Multilayer Perceptron (MLP) for Detection and Classification of Power Quality Disturbances

Cited by 3 publications

References 7 publications

A Hybrid Human Activity Recognition Method Using an MLP Neural Network and Euler Angle Extraction Based on IMU Sensors

A Hybrid Human Activity Recognition Method Using an MLP Neural Network and Euler Angle Extraction Based on IMU Sensors

Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children

Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

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