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A Nonlinear Autoregressive Exogenous Neural Network (NARX) Model for the Prediction of the pH Neutralization Process for Palm Oil Mill Effluent 3rd

A Nonlinear Autoregressive Exogenous Neural Network (NARX) M...

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3rd International Conference on control, instrumentation and mechatronics engineering, CIM 2022

作者： Zainal, Azavitra Wahab, Norhaliza Abdul Yusof, Mohd Ismail Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Instrumentation and Control Engineering Section Malaysian Institute of Industrial Technology Universiti Kuala Lumpur Johor Bahru Malaysia

ISBN: (纸本)9789811939228

This paper introduces a Nonlinear Autoregressive Exogenous Neural Network (NARX) to predict the pH value of the Palm Oil Mill Effluent (POME). NARX is a computing tool that is widely used for nonlinear time series problems, the techniques that can predict efficient and good performance. In this paper, the pH neutralization process is a MISO (Multiple Input Single Output) systems, the inputs of which are the dosing stroke rates of acid and base, and the output value is the pH value. The neural network was built and trained using the experimental data collected in an open-loop test. The neural network structure for modeling the pH neutralization was identified and the training and validation of the neural network structure were analyzed. The result showed that the NARX modeling was able to predict the pH based on the acid and base dosing stroke rate with an overall regression of 0.9934 and MSE values of 0.000924197. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Partial Discharge Classification With 1D Convolutional Neural Network 14

Partial Discharge Classification With 1D Convolutional Neura...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Cheypoca, Thepjit Promphanich, Wiboon Thway, Aung Ye Hankae, Angelina Phimpissada Jeenmuang, Siwakorn Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Instrumentation and Control Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Computer Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Electrical Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Mechatronics Engineering Dept. Bangkok Thailand

ISBN: (纸本)9798350374605

This paper introduces a novel approach for classifying with the 1D Convolutional Neural Network model for partial discharge patterns, that consists of corona discharge, surface discharge and internal discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The Artificial Neural Network for the classification model was constructed. Moreover, 2×1D CNN feature extraction was utilized to reduce the curse of dimensionality in the dense layer of the proposed PD classification model. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed. © 2024 IEEE.

关键词： Convolutional neural networks

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Partial Discharge Classification with Transformer Neural Networks 14

Partial Discharge Classification with Transformer Neural Net...

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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

ISBN: (纸本)9798350374605

This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed © 2024 IEEE.

关键词： Electric corona

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Partial Discharge Classification with Transformer Neural Networks

Partial Discharge Classification with Transformer Neural Net...

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International Conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： Thepjit Cheypoca Wiboon Promphanich Aung Ye Thway Angelina Phimpissada Hankae Siwakorn Jeenmuang Norasage Pattanadech Instrumentation and Control Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Computer Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Electrical Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Mechatronics Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350374605

ISBN: (纸本)9798350386165

关键词： Partial discharges Phased arrays Partial discharge measurement Surface discharges Training data Pattern classification Transformers Discharges (electric) Biological neural networks Testing

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Partial Discharge Classification With 1D Convolutional Neural Network

Partial Discharge Classification With 1D Convolutional Neura...

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International Conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： Thepjit Cheypoca Wiboon Promphanich Aung Ye Thway Angelina Phimpissada Hankae Siwakorn Jeenmuang Norasage Pattanadech Instrumentation and Control Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Computer Engineering Dept. School of EngineeringKing Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Electrical Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Mechatronics Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350374605

ISBN: (纸本)9798350386165

关键词： Partial discharges Partial discharge measurement Surface discharges Neurons Training data Discharges (electric) Data models Convolutional neural networks Integrated circuit modeling Testing

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Temperature and strain sensitivities of surface and hybrid acoustic wave Brillouin scattering in optical microfibers

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Chinese Physics B 2022年第9期31卷 333-339页

作者： Yi Liu Yuanqi Gu Yu Ning Pengfei Chen Yao Yao Yajun You Wenjun He Xiujian Chou Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control SystemMinistry of Education and Shanxi ProvinceTaiyuan 030024China Taiyuan University of Technology Institute of Optoelectronic EngineeringCollege of Physics and OptoelectronicsTaiyuan 030024China Strong Digital Technology Co. Ltd.(Thinvent)Nanchang 410000China North University of China Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of EducationTaiyuan 030051China North University of China College of Mechatronics EngineeringTaiyuan 030051China North University of China Science and Technology on Electronic Test and Measurement LaboratorySchool of Instrument and ElectronicsTaiyuan 030051China

Temperature and strain sensitivities of surface acoustic wave(SAW)and hybrid acoustic wave(HAW)Brillouin scat-tering(BS)in 1μm-1.3μm diameter optical microfibers are *** contrast to stimulated Brillouin scattering(SBS)from bulk acoustic wave in standard optical fiber,SAW and HAW BS,due to SAWs and HAWs induced by the coupling of longitudinal and shear waves and propagating along the surface and core of microfiber respectively,facilitate innovative detection in optical microfibers *** highest temperature and strain sensitivities of the hybrid acoustic modes(HAMs)are 1.082 MHz/℃and 0.0289 MHz/με,respectively,which is suitable for microfiber sensing applica-tion of high temperature and strain ***,the temperature and strain sensitivities of the SAMs are less affected by fiber diameter changes,ranging from 0.05 MHz/℃/μm to 0.25 MHz/℃/μm and 1×10^(-4) MHz/με/μm to 5×10^(-4) MHz/με/μm,*** can be found that that SAW BS for temperature and strain sensing would put less stress on manufacturing constraints for optical ***,the simultaneous sensing of temperature and strain can be realized by SAW and HAW BS,with temperature and strain errors as low as 0.30℃-0.34℃and 14.47με-16.25με.

关键词： Brllouin scatering surface acoustic waves hybrid acoustic waves optical microfiber sensing

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A domain decomposition strategy for natural imposition of mixed boundary conditions in port-Hamiltonian systems

arXiv

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arXiv 2025年

作者： de Jong, Sjoerd D.M. Brugnoli, Andrea Rashad, Ramy Zhang, Yi Stramigioli, Stefano Department of Microelectronics Delft University of Technology Delft Netherlands ICA Université de Toulouse ISAE–SUPAERO INSA CNRS MINES ALBI UPS Toulouse France Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Saudi Arabia School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China Robotics and Mechatronics Department University of Twente Netherlands

[Abstract] In this contribution, a finite element scheme to impose mixed boundary conditions without introducing Lagrange multipliers is presented for wave propagation phenomena described as port-Hamiltonian systems. The strategy relies on finite element exterior calculus and a domain decomposition to interconnect two systems with different causalities. The spatial domain is split into two parts by introducing an arbitrary interface. Each subdomain is discretized with a mixed finite element formulation that introduces a uniform boundary condition in a natural way as the input. In each subdomain the spaces are selected from a finite element subcomplex to obtain a stable discretization. The two systems are then interconnected together by making use of a feedback interconnection. This is achieved by discretizing the boundary inputs using appropriate spaces that couple the two formulations. The final systems includes all boundary conditions explicitly and does not contain any Lagrange multiplier. Each subdomain is integrated using an implicit midpoint scheme in an uncoupled way from the other by means of a leapfrog scheme. The proposed strategy is tested on three different examples: the Euler-Bernoulli beam, the wave equation and the Maxwell equations. Numerical tests assess the conservation properties of the scheme and the effectiveness of the methodology. © 2025, CC BY.

关键词： Lagrange multipliers

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Finite element hybridization of port-Hamiltonian systems

arXiv

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arXiv 2023年

作者： Brugnoli, Andrea Rashad, Ramy Zhang, Yi Stramigioli, Stefano ICA Université de Toulouse ISAE–SUPAERO INSA CNRS MINES ALBI UPS Toulouse France Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Saudi Arabia School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China Robotics and Mechatronics Department University of Twente Netherlands

In this contribution, we extend the hybridization framework for the Hodge Laplacian [Awanou et al., Hybridization and postprocessing in finite element exterior calculus, 2023] to port-Hamiltonian systems describing linear wave propagation phenomena. To this aim, a dual field mixed Galerkin discretization is introduced, in which one variable is approximated via conforming finite element spaces, whereas the second is completely local. The mixed formulation is then hybridized to obtain an equivalent formulation that can be more efficiently solved using a static condensation procedure in discrete time. The size reduction achieved thanks to the hybridization is greater than the one obtained for the Hodge Laplacian as the final system only contains the globally coupled traces of one variable. Numerical experiments on the 3D wave and Maxwell equations illustrate the convergence of the method and the size reduction achieved by the hybridization. © 2023, CC BY-NC-ND.

关键词： Maxwell equations

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Artificial Intelligence and Tactile Healthcare for Mitigating the Impact of COVID-19

IEEE Technology Policy and Ethics

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IEEE Technology Policy and Ethics 2021年第4期6卷 1-7页

作者： Ali Nauman Rashid Ali Yousaf Bin Zikria Sung Won Kim Wireless Information Networking Laboratory (WINLab) Republic of Korea School of Intelligent Mechatronics Engineering Sejong University Republic of Korea Department of Information and Communication Engineering Yeungnam University South Korea Department of Control and Instrumentation Engineering Seoul National University Korea

The COVID-19 epidemic, and the recent related waves of variant outbreaks, have had a significant impact on every field of life. One of the major impacts of COVID is the increased stress on the already exhausted healthcare system. Tactile healthcare is revolutionizing healthcare systems. The 5G and Beyond-5G (B5G) technologies are expected to enable Tactile Healthcare applications, which are time-sensitive and critical. The 5G and B5G communication networks were constructed to support high data rates on an energy-efficient platform, and to provide ultra-reliability and low latency. The use of Reinforcement Learning (RL) algorithms, a type of Machine Learning (ML), can enhance the capability of 5G and B5G networks by optimizing latency and reliability in terms of data delivery. The idea of RL algorithms is to make a system capable of mimicking the human brain and enhancing its abilities. The New Radio (NR) in B5G offers flexible Medium Access control (MAC) frame structures, and scalable numerology. The efficient MAC scheduling approaches are of prime importance for wireless networks. The efficient MAC scheduling protocols and RL algorithms ultimately solve the problems of reliability and latency for Tactile Healthcare applications.

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Enhancing prediction accuracy of Remaining Useful Life in lithium-ion batteries: A deep learning approach with Bat optimizer

Future Batteries

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Future Batteries 2024年 2卷

作者： Shahid A. Hasib S. Islam Md F. Ali Subrata. K. Sarker Li Li Md Mehedi Hasan Dip K. Saha Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Department of Mechatronics Engineering Rajshahi University of Engineering & Technology Bangladesh School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering & Informatics University of Sussex Brighton United Kingdom

Remaining Useful Life (RUL) prediction in lithium-ion batteries is crucial for assessing battery performance. Despite the popularity of deep learning methods for RUL prediction, their complex architectures often pose challenges in interpretation and resource consumption. We propose a novel approach that combines the interpretability of a convolutional neural network (CNN) with the efficiency of a bat-based optimizer. CNN extracts battery data features and characterizes degradation kinetics, while the optimizer refines CNN parameters. Tested on NASA PCoE data, our method achieves exceptional results with minimal computational burden and fewer parameters. It outperforms traditional approaches, yielding an R2-score of 0.9987120 , an MAE of 0.004397067 Ah , and a low RMSE of 0.00656 Ah . The proposed model outperforms traditional deep learning models, as confirmed by comparative analysis.

关键词： Li-ion battery Convolutional neural network Remaining Useful Life prediction Battery degradation model Bat based optimization algorithm

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