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Journal articles on the topic 'This model was based in an artificial neural network (ANN) equivalent circuit approach'

Author: Grafiati

Published: 9 September 2021

Last updated: 29 July 2025

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1

Radojković, Miloš, Giovanni Gugliandolo, Mariangela Latino, Zlatica Marinković, Giovanni Crupi, and Nicola Donato. "Development and Validation of an ANN-Based Approach for Temperature-Dependent Equivalent Circuit Modeling of SAW Resonators." Micromachines 14, no. 5 (2023): 967. http://dx.doi.org/10.3390/mi14050967.

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In this paper, a novel approach is proposed for modeling the temperature-dependent behavior of a surface acoustic wave (SAW) resonator, by using a combination of a lumped-element equivalent circuit model and artificial neural networks (ANNs). More specifically, the temperature dependence of the equivalent circuit parameters/elements (ECPs) is modeled using ANNs, making the equivalent circuit model temperature-dependent. The developed model is validated by using scattering parameter measurements performed on a SAW device with a nominal resonant frequency of 423.22 MHz and under different temper

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Shen, Haowen, Wenyong Zhou, Jinye Wang, et al. "A Novel ANN-PSO Method for Optimizing a Small-Signal Equivalent Model of a Dual-Field-Plate GaN HEMT." Micromachines 15, no. 12 (2024): 1437. http://dx.doi.org/10.3390/mi15121437.

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This study introduces a novel method that integrates artificial neural networks (ANNs) with the Particle Swarm Optimization (PSO) algorithm to enhance the efficiency and precision of parameter optimization for the small-signal equivalent model of dual-field-plate GaN HEMT devices. We initially train an ANN model to predict the S-parameters of the device, and subsequently utilize the PSO algorithm for parameter optimization. Comparative analysis with the NSGA2 and DE algorithms, based on convergence speed and accuracy, underscores the superiority of the PSO algorithm. Ultimately, this ANN-PSO a

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Aló, Richard, and Vladik Kreinovich. "Selected Papers from InTech'04." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (2006): 243–44. http://dx.doi.org/10.20965/jaciii.2006.p0243.

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The main objective of the annual International Conference on Intelligent Technologies (InTech) is to bring together researchers and practitioners who implement intelligent and fuzzy technologies in real-world environment. The Fifth International Conference on Intelligent Technologies InTech'04 was held in Houston, Texas, on December 2-4, 2004. Topics of InTech'04 included mathematical foundations of intelligent technologies, traditional Artificial Intelligent techniques, uncertainty processing and methods of soft computing, learning/adaptive systems/data mining, and applications of intelligent

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4

Nabil, Iftissen, Ali Dali, and Samir Abdelmalek. "Artificial neural network-based predictive control for three-phase inverter systems with RLC filters." International Journal of Power Electronics and Drive Systems (IJPEDS) 16, no. 2 (2025): 949. https://doi.org/10.11591/ijpeds.v16.i2.pp949-960.

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Model predictive control (MPC) is becoming more and more popular in power electronics applications, yet its practical implementation faces challenges due to computational complexity and resource demands. To address these issues, a novel MPC control approach using an artificial neural network (ANN-MPC) is put forth in this research. Using a real-time circuit modeling environment, a power converter with a virtual MPC controller that can regulate both linear and nonlinear loads is first created and run. The input-output data gathered from the virtual MPC is then used to train an artificial neural

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Sakhara, S., M. Brahimi, L. Nacib, and T. M. Layadi. "Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines." Electrical Engineering & Electromechanics, no. 3 (April 23, 2023): 21–27. http://dx.doi.org/10.20998/2074-272x.2023.3.03.

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Introduction. Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classif

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Ромащенко, М. А., Д. В. Васильченко, and Д. А. Пухов. "EQUIVALENT HYBRID DIPOLE MODEL OF ELECTROMAGNETIC INTERFERENCE ESTIMATION BASED ON ARTIFICIAL NEURAL NETWORK." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, no. 3 (June 28, 2023): 106–11. http://dx.doi.org/10.36622/vstu.2023.19.3.015.

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предлагается новая эквивалентная гибридная дипольная модель с искусственной нейронной сетью (ИНС) для оценки электромагнитных помех (ЭМП), генерируемых в процессе работы устройства. Традиционная дипольная модель обычно не учитывает эффекты многократного отражения и дифракцию между источником электромагнитных помех и его близлежащими компонентами, вследствие чего в некоторых случаях это приводит к неточному результату расчетов. В предлагаемом методе функция диполя Грина берется в качестве входных данных, а излучаемое электромагнитное поле берется в качестве выходных данных ИНС. Применение мощны

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7

S., Sakhara, Brahimi M., Nacib L., and M. Layadi T. "Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines." Electrical Engineering & Electromechanics, no. 3 (April 23, 2023): 21–27. https://doi.org/10.20998/2074-272X.2023.3.03.

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<strong><em>Introduction.</em></strong><em> Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The <strong>aim</strong> of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objecti

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Hemalatha, N., and S. Nageswari. "A New Approach of Position Sensorless Control for Brushless DC Motor." Current Signal Transduction Therapy 15, no. 1 (2020): 65–76. http://dx.doi.org/10.2174/1574362413666180713092948.

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Background: Position sensorless control technique for Permanent Magnets-Brush Less Direct Current (PM-BLDC) motor drive is considered in this paper. Materials and Methods: A new estimation based on sensorless technique is proposed for PMBLDC motor. Artificial Neural Network (ANN) is aided for the purpose. Results: The inputs to the ANN are the voltages of PM-BLDC motor and it estimates the sample signals to feed Zero Crossing Point (ZCP) detection circuit. The ZCP detection circuit provides ZCP signals for commutation logic which gives the commutation sequence to power switches. In order to pr

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9

Daylak, Funda, and Serdar Ozoguz. "Automated Neural Network-Based Optimization for Enhancing Dynamic Range in Active Filter Design." Electronics 14, no. 4 (2025): 786. https://doi.org/10.3390/electronics14040786.

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This study presents an automated circuit design approach using neural networks to optimize the dynamic range (DR) of active filters, illustrated through the design of a 7th-order Chebyshev low-pass filter. Traditional design methods rely heavily on designer expertise, often resulting in time-intensive and energy-consuming processes. Two techniques are proposed: inverse modeling and forward modeling. In inverse modeling, artificial neural networks (ANNs) predict circuit parameters to meet specific performance goals. A randomly selected subset, comprising 0.05% of the 1,953,125 possible circuit

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Zulueta, Asier, Ekaitz Zulueta, Javier Olarte, Unai Fernandez-Gamiz, Jose Manuel Lopez-Guede, and Saioa Etxeberria. "Electrochemical Impedance Spectrum Equivalent Circuit Parameter Identification Using a Deep Learning Technique." Electronics 12, no. 24 (2023): 5038. http://dx.doi.org/10.3390/electronics12245038.

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Physical models are suitable for the development and optimization of materials and cell designs, whereas models based on experimental data and electrical equivalent circuits (EECs) are suitable for the development of operation estimators, both for cells and batteries. This research work develops an innovative unsupervised artificial neural network (ANN) training cost function for identifying equivalent circuit parameters using electrochemical impedance spectroscopy (EIS) to identify and monitor parameter variations associated with different physicochemical processes that can be related to the

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SADEGHKHANI, IMAN, ABBAS KETABI, and RENE FEUILLET. "CONTROL OF SHUNT REACTOR OVERVOLTAGES BY CONTROLLED SWITCHING DURING POWER SYSTEM RESTORATION." Journal of Circuits, Systems and Computers 21, no. 07 (2012): 1250051. http://dx.doi.org/10.1142/s021812661250051x.

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The shunt reactors located on both line terminals and substation bus-bars are commonly used on long extra high voltage (EHV) transmission systems for controlling voltage during load variations. In a small power system that appears in an early stage of a black start of a power system, an overvoltage could be caused by core saturation on the energization of a shunt reactor with residual flux. The most effective method for the limitation of the switching overvoltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switc

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Bouakoura, Mohamed, Mohamed-Said Naït-Saïd, and Nasreddine Nait-Said. "Incipient Inter-Turn Short Circuit Fault Estimation Based on a Faulty Model Observer and ANN-Method for Induction Motor Drives." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 4 (2019): 374–83. http://dx.doi.org/10.2174/2352096511666180705113021.

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Background: According to statistics, short circuit faults are the second most frequent faults in induction motors. Thus, in this paper, we investigated inter turn short circuit faults in their early stage. Methods: A new equivalent model of the induction motor with turn to turn fault on one phase has been developed. This model has been used to establish two schemes to estimate the severity of the short circuit fault. In the first scheme, the faulty model is considered as an observer, where a correction of an error between the measured and the estimated currents is the kernel of the fault sever

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13

Karki, Bishal, Sayla Prova, Mayzan Isied, and Mena Souliman. "Neural Network Approach for Fatigue Crack Prediction in Asphalt Pavements Using Falling Weight Deflectometer Data." Applied Sciences 15, no. 7 (2025): 3799. https://doi.org/10.3390/app15073799.

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Fatigue cracking is a major issue in asphalt pavements, reducing their lifespan and increasing maintenance costs. This study develops an artificial neural network (ANN) model to predict the onset and progression of fatigue cracking. The model is calibrated utilizing Falling Weight Deflectometer (FWD) testing data, alongside essential pavement characteristics such as layer thickness, air void percentage, asphalt binder proportion, traffic loads (Equivalent Single Axle Loads or ESALs), and mean annual temperature. By analyzing these factors, the ANN captures complex relationships influencing fat

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14

Wang, Tingting, Xin Liu, Dongchen Qin, and Yuechen Duan. "Thermal Modeling and Prediction of The Lithium-ion Battery Based on Driving Behavior." Energies 15, no. 23 (2022): 9088. http://dx.doi.org/10.3390/en15239088.

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Real-time monitoring of the battery thermal status is important to ensure the effectiveness of battery thermal management system (BTMS), which can effectively avoid thermal runaway. In the study of BTMS, driver behavior is one of the factors affecting the performance of the battery thermal status, and it is often neglected in battery temperature studies. Therefore, it is necessary to predict the dynamic heat generation of the battery in actual driving cycles. In this work, a thermal equivalent circuit model (TECM) and an artificial neural network (ANN) thermal model based on the driving data,

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15

Massaro, Alessandro. "ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems." AI 5, no. 2 (2024): 533–49. http://dx.doi.org/10.3390/ai5020027.

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In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and

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16

Sultana, Salma, Hakan Yasarer, Waheed Uddin, and Rulian Barros. "International Roughness Index Modeling For Jointed Plain Concrete Pavement Using Artificial Neural Network." IOP Conference Series: Materials Science and Engineering 1203, no. 3 (2021): 032034. http://dx.doi.org/10.1088/1757-899x/1203/3/032034.

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Abstract Climate attributes such as precipitation, extreme temperature, and freeze-thaw cycles along with traffic loads cause pavement distresses. The maintenance need for pavements is decided based on the pavement condition rating such as International Roughness Index (IRI). Generally, an IRI rating less than 2.68 m/km is acceptable, and a rating greater than 2.68 m/km is considered unacceptable and classified as “very poor” condition of the pavement. It is imperative to be able to accurately predict pavement conditions to prepare proper Maintenance and Rehabilitation (M&R) programs for t

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17

Singh, Vishal, Dineshkumar Harursampath, Sharanjeet Dhawan, Manoj Sahni, Sahaj Saxena, and Rajnish Mallick. "Physics-Informed Neural Network for Solving a One-Dimensional Solid Mechanics Problem." Modelling 5, no. 4 (2024): 1532–49. http://dx.doi.org/10.3390/modelling5040080.

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Our objective in this work is to demonstrate how physics-informed neural networks, a type of deep learning technology, can be utilized to examine the mechanical properties of a helicopter blade. The blade is regarded as a one-dimensional prismatic cantilever beam that is exposed to triangular loading, and comprehending its mechanical behavior is of utmost importance in the aerospace field. PINNs utilize the physical information, including differential equations and boundary conditions, within the loss function of the neural network to approximate the solution. Our approach determines the overa

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18

Aissat, Miloud Elhadj Ali, Abdelkader Mostefa, Mohamed Khodja, Karim Belalia, and Bouziane Meliani. "Detection of partial shading and short-circuit faults in a pv system using the artificial neural network multilayer perceptron technique." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e11814. https://doi.org/10.54021/seesv5n2-684.

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Photovoltaic (PV) systems play a crucial role in renewable energy production but face challenges due to environmental factors such as partial shading and short circuits, which reduce efficiency and reliability. Continuous monitoring and early fault detection are essential to mitigate these issues. In this work, we propose a fault detection method that focuses on identifying partial shading and short-circuit faults using an artificial neural network (ANN), specifically a Multi-Layer Perceptron (MLP). The MLP model is trained to classify these faults under various environmental scenarios, includ

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Wang, Qian-Kun, Yi-Jun He, Jia-Ni Shen, Zi-Feng Ma, and Guo-Bin Zhong. "A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach." Energy 138 (November 2017): 118–32. http://dx.doi.org/10.1016/j.energy.2017.07.035.

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20

Huang, Yiming, Bin Li, Zhaohui Wu, and Wenchao Liu. "Symbolic Regression Based on Kolmogorov–Arnold Networks for Gray-Box Simulation Program with Integrated Circuit Emphasis Model of Generic Transistors." Electronics 14, no. 6 (2025): 1161. https://doi.org/10.3390/electronics14061161.

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In this paper, a novel approach to symbolic regression using Kolmogorov–Arnold Networks (KAN) for developing gray-box Simulation Program with Integrated Circuit Emphasis models of generic transistors is proposed. Unlike traditional black-box models, such as artificial neural networks, (ANN), the developed KAN-based model enhances interpretability by generating explicit mathematical expressions while maintaining high accuracy in device modeling. By combining the computational efficiency of neural network approaches with the transparency of formula-based modeling, the SPICE model generation is s

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Zubaidi, Salah, Hussein Al-Bugharbee, Sandra Ortega-Martorell, et al. "A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach." Water 12, no. 6 (2020): 1628. http://dx.doi.org/10.3390/w12061628.

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Accurate and reliable urban water demand prediction is imperative for providing the basis to design, operate, and manage water system, especially under the scarcity of the natural water resources. A new methodology combining discrete wavelet transform (DWT) with an adaptive neuro-fuzzy inference system (ANFIS) is proposed to predict monthly urban water demand based on several intervals of historical water consumption. This ANFIS model is evaluated against a hybrid crow search algorithm and artificial neural network (CSA-ANN), since these methods have been successfully used recently to tackle a

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Bedra, Sami, Siham Benkouda, and Tarek Fortaki. "Analysis of a circular microstrip antenna on isotropic or uniaxially anisotropic substrate using neurospectral approach." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 1/2 (2013): 567–80. http://dx.doi.org/10.1108/compel-10-2012-0225.

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Purpose – The paper aims to propose an artificial neural network (ANN) in conjunction with spectral domain formulation for fast and accurate determination of the resonant frequency and quality factor of circular microstrip antenna printed on isotropic or anisotropic substrate. This neurospectral approach reduces the problem complexity. Design/methodology/approach – The moment method implemented in the spectral domain provides good accuracy but its computational cost is high due to the evaluation of the slowly decaying integrals and the iterative nature of the solution process. The paper introd

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23

Shahabi, Mahmood, Mohammad Ali Ghorbani, Sujay Raghavendra Naganna, et al. "Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction." Complexity 2022 (June 13, 2022): 1–15. http://dx.doi.org/10.1155/2022/3123475.

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The potential of the soil to hold plant nutrients is governed by the cation-exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised with a hybrid genetic algorithm-neural network (MM-GANN) was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models (i.e., artificial neural network (ANN) and extreme learning machine (ELM)) were implemented for incorporation into t

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Mishra, Vimal, and R. B. Mishra. "Handling of Infinitives in English to Sanskrit Machine Translation." International Journal of Artificial Life Research 1, no. 3 (2010): 1–16. http://dx.doi.org/10.4018/jalr.2010070101.

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The development of Machine Translation (MT) system for ancient language like Sanskrit is a fascinating and challenging task. In this paper, the authors handle the infinitive type of English sentences in the English to Sanskrit machine translation (EST) system. The EST system is an integrated model of a rule-based approach of machine translation with Artificial Neural Network (ANN) model that translates an English sentence (source sentence) into the equivalent Sanskrit sentence (target sentence). The authors use feed forward ANN for the selection of Sanskrit words, such as nouns, verbs, objects

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Overhagen, Christian, and Kaiqi Fu. "Data-Driven Roll Pass Design of Wire Rod Mills." Key Engineering Materials 926 (July 22, 2022): 589–601. http://dx.doi.org/10.4028/p-dwm928.

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The classical approach for roll pass design of a wire rod mill employs an iterative technique incorporating spread calculation and rectangular equivalent pass methods. This method comes to its limits in terms of computational efficiency and numerical stability when a complete pass design for a wire rod mill with lots of different final dimensions and materials must be designed. To improve the pass design technique, a fast data-driven method for pass design based on synthetic data generated by the classical pass design model was created. The results are compared to the original training data, a

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Nam, Jae-Won, Young-Kyun Cho, and Youn Kyu Lee. "Regression Model-Based AMS Circuit Optimization Technique Utilizing Parameterized Operating Condition." Electronics 11, no. 3 (2022): 408. http://dx.doi.org/10.3390/electronics11030408.

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An analog and mixed-signal (AMS) circuit that draws on machine learning while using a regression model differs in terms of the design compared to more sophisticated circuit designs. Technology structures that are more advanced than conventional CMOS processes, specifically the fin field-effect transistor (FinFET) and silicon-on-insulator (SOI), have been proposed to provide the higher computation performance required to meet various design specifications. As a result, the latest research on AMS design optimization has enabled enormous resource savings in AMS design procedures but remains limit

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27

Zhang, Yongnian, Yinhe Chen, Zhenwei Chang, Jie Zhao, Xiaochan Wang, and Jieyu Xian. "Detection of Localized Damage in Tomato Based on Bioelectrical Impedance Spectroscopy." Agronomy 14, no. 8 (2024): 1822. http://dx.doi.org/10.3390/agronomy14081822.

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This paper proposes a method for localized damage detection in tomato, with the objective of enabling the detection of bruises prior to sorting. Bioimpedance spectroscopy technology is employed to assess the extent of localized damage in tomato. An equivalent circuit model is constructed, and the impedance spectroscopy data are obtained by developing a local damage measurement platform for tomatoes using a self-designed circular four-electrode BIS sensor. The electrical parameters are then extracted by fitting the constructed equivalent circuit model to the tomato data. Subsequently, we analyz

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Beckman, Celso Antonio Paé, Heloisa Theresa Teixeira Saliba, and Alexandre Barbosa De Lima. "Electrothermal modeling of lithium-ion batteries using an artificial intelligence technique-based approach for powerwall applications." OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA 22, no. 2 (2024): e3136. http://dx.doi.org/10.55905/oelv22n2-040.

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The Powerwall, an innovative home battery powered by solar energy, is gaining popularity in the United States and Australia due to its ability to provide complete power to homes and serve as a backup during electrical supply interruptions. Recently, lithium-ion (Li-ion) battery technology has received significant attention from the industry and academia, standing out for offering greater energy capacity, power density, efficiency, and lower self-discharge rate compared to other technologies such as NiCd and NiMH batteries. Initially, this research aimed to model the Li-ion cells of the Powerwa

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Valluri, Ashok Babu. "Novel Control Approach with FRT capability for Grid connected HYBRID distributed generation system using ANN controller based DVR." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 2002–21. http://dx.doi.org/10.22214/ijraset.2021.39684.

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Abstract: For ever increasing power demand and depletion of conventional energy resources, Renewable Energy Systems (RES) became an alternative source of electricity to reduce the load stress on the Power Grid. Although several control & design modifications are presented in past literature to improve reliability & performance of through Distribution Generation (DG) technologies, they always fall short in some aspects of voltage stability and Fault Ride Through (FRT) capabilities. The main aim of the project is Protecting Critical load from Grid side altercations which occur due to har

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Shchanikov, S. A. "The use of memristive devices in machine vision systems." Genes & Cells 18, no. 4 (2023): 831–34. http://dx.doi.org/10.17816/gc623429.

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The comparison results of processing units with memristive devices versus modern hardware accelerators of artificial neural networks (ANNs) based on traditional electronic components, as presented in the review [1], demonstrate numerous advantages across all major indicators such as throughput, energy efficiency, accuracy, and others. This report analyzes the current state of memristive devices in addressing machine vision issues. Special attention is paid to the concept of [2] neuromorphic machine vision systems (MVS) based on memristive devices. This concept’s distinct feature lies in its fu

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Beji, Hamdi, Tanguy Messager, and Toufik Kanit. "Equivalent Morphology Concept in Composite Materials Using Machine Learning and Genetic Algorithm Coupling." Journal of Composites Science 8, no. 8 (2024): 297. http://dx.doi.org/10.3390/jcs8080297.

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The objective of this study is to investigate the synergistic integration of machine learning and evolutionary algorithms for the discovery of equivalent morphologies exhibiting analogous behavior within the domain of composite materials. To pursue this objective, two comprehensive databases are meticulously constructed. The first database encompasses randomly positioned inclusions characterized by varying volume fractions and contrast levels. Conversely, the second database comprises microstructures of diverse shapes, such as elliptical, square, and triangular, while maintaining consistent vo

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Shah, Chirag Vinalbhai, and Aravind Ravi. "Advanced Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI." International Journal of Engineering and Computer Science 13, no. 06 (2024): 26229–46. http://dx.doi.org/10.18535/ijecs/v13i06.4837.

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This paper proposes a methodology for the design of electronic control unit (ECU) hardware units with increased performance and reliability. Today's vehicles are equipped with dozens of ECUs that significantly influence the system's efficiency, reliability, performance, and safety. With the increased complexity of control algorithms and the environmental constraints that automotive systems operate, the robustness and efficiency of the ECUs are of utmost importance. In this work, an approach is proposed based on combining hardware redundancy, commercial field programmable gate arrays (FPGAs), a

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Shah, Chirag Vinalbhai, and Aravind Ravi. "Advanced Innovations in Electronic Control Units: Enhancing Performance and Reliability with AI." Global Research and Development Journals 9, no. 7 (2024): 25–38. http://dx.doi.org/10.70179/grdjev09i100017.

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This paper proposes a methodology for the design of electronic control unit (ECU) hardware units with increased performance and reliability. Today's vehicles are equipped with dozens of ECUs that significantly influence the system's efficiency, reliability, performance, and safety. With the increased complexity of control algorithms and the environmental constraints that automotive systems operate, the robustness and efficiency of the ECUs are of utmost importance. In this work, an approach is proposed based on combining hardware redundancy, commercial field programmable gate arrays (FPGAs), a

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34

Gbolagade, Musiliu. "Development of an Economic Cost Model for Gold and Associated Minerals Using Economic Analysis and Artificial Intelligence Approach." FUOYE Journal of Engineering and Technology 9, no. 1 (2024): 141–50. http://dx.doi.org/10.4314/fuoyejet.v9i1.22.

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Limited accuracy due to complex geological processes, insufficient data granularity, and challenges in predicting market dynamics impact mineral forecast models. In order to develop a cost-based model for gold and related minerals in Birnin Gwari (Kaduna State) and Kagara (Niger State), this study is being carried out in Nigeria. The created concept aims to increase the profitability and economic viability of open-pit mines operated by craftsmen and mining investors. By maximizing mine life cycles and preventing the early closure of mining sites, this was intended to encourage the best recover

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Jahan, Ibrahim Salem, Vojtech Blazek, Stanislav Misak, Vaclav Snasel, and Lukas Prokop. "Forecasting of Power Quality Parameters Based on Meteorological Data in Small-Scale Household Off-Grid Systems." Energies 15, no. 14 (2022): 5251. http://dx.doi.org/10.3390/en15145251.

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Off-grid power systems are often used to supply electricity to remote households, cottages, or small industries, comprising small renewable energy systems, typically a photovoltaic plant whose energy supply is stochastic in nature, without electricity distributions. This approach is economically viable and conforms to the requirements of the European Green Deal and the Fit for 55 package. Furthermore, these systems are associated with a lower short circuit power as compared with distribution grid traditional power plants. The power quality parameters (PQPs) of such small-scale off-grid systems

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Lazzaretti, André Eugênio, Clayton Hilgemberg da Costa, Marcelo Paludetto Rodrigues, et al. "A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants." Sensors 20, no. 17 (2020): 4688. http://dx.doi.org/10.3390/s20174688.

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Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same

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Crupi, Giovanni, Mariangela Latino, Giovanni Gugliandolo, et al. "A Comprehensive Overview of the Temperature-Dependent Modeling of the High-Power GaN HEMT Technology Using mm-Wave Scattering Parameter Measurements." Electronics 12, no. 8 (2023): 1771. http://dx.doi.org/10.3390/electronics12081771.

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The gallium-nitride (GaN) high electron-mobility transistor (HEMT) technology has emerged as an attractive candidate for high-frequency, high-power, and high-temperature applications due to the unique physical characteristics of the GaN material. Over the years, much effort has been spent on measurement-based modeling since accurate models are essential for allowing the use of this advanced transistor technology at its best. The present analysis is focused on the modeling of the scattering (S-) parameter measurements for a 0.25 μm GaN HEMT on silicon carbide (SiC) substrate at extreme operatin

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d N Santos, Francisco, Nymfa Noppe, Wout Weijtjens, and Christof Devriendt. "Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups." Wind Energy Science 7, no. 1 (2022): 299–321. http://dx.doi.org/10.5194/wes-7-299-2022.

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Abstract. The sustained development over the past decades of the offshore wind industry has seen older wind farms beginning to reach their design lifetime. This has led to a greater interest in wind turbine fatigue, the remaining useful lifetime and lifetime extensions. In an attempt to quantify the progression of fatigue life for offshore wind turbines, also referred to as a fatigue assessment, structural health monitoring (SHM) appears as a valuable contribution. Accurate information from a SHM system can enable informed decisions regarding lifetime extensions. Unfortunately direct measureme

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Maneux, Cristell, Chhandak Mukherjee, Marina Deng, et al. "(Invited) Strategies for Characterization and Parameter Extraction of Vertical Junction-Less Nanowire FETs Dedicated to Design Technology Co-Optimization." ECS Meeting Abstracts MA2023-01, no. 33 (2023): 1863. http://dx.doi.org/10.1149/ma2023-01331863mtgabs.

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In the era of emerging computing paradigms and artificial neural networks, hardware and functionality requirements are in the surge. In order to meet low power and latency criteria, new architectures for in-memory computing are being explored as alternatives to traditional von Neumann machines, which requires technological breakthrough at the semiconductor device level such as vertical gate-all-around junctionless nanowire field effect transistors (VNWFET), that can address many process challenges such as downscaling, short-channel effects, compactness and electrostatic control. Its integratio

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Gharaibeh, Mohammad A. "An artificial intelligence-based approach for identifying the in-plane orthotropic mechanical properties of electronic circuit boards." Journal of Strain Analysis for Engineering Design, March 28, 2024. http://dx.doi.org/10.1177/03093247241240832.

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The finite element modeling of electronic boards is a challenging task due to the complexity of the multi-component board structure. Hence, it is acceptable to attain equivalent orthotropic in-plane mechanical properties and use them throughout the finite element analysis (FEA) simulations. This paper aims to present an artificial intelligence-based methodology, using the artificial neural networks (ANNs), to estimate the in-plane mechanical properties of the printed circuit boards (PCB). In this methodology, the ANN technique used FEA data to find the relationship between the first 10 natural

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Jaguemont, Joris, Ali Darwiche, and Fanny Bardé. "Complete electrothermal and lifetime model of 18650 nickel manganese cobalt cell based on artificial neural network." Explora: Environment and Resource, February 28, 2025, 7228. https://doi.org/10.36922/eer.7228.

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This study presents a comprehensive electrothermal and lifetime model for cylindrical 3 ampere-hours (Ah) lithium-ion cells using artificial neural networks (ANNs) to estimate the cell&rsquo;s lifespan. The model combines an electrothermal component with an ANN-based lifetime prediction approach, offering a holistic representation of cell behavior over its lifetime by incorporating key parameters, including the state of charge, temperature, current, and cycle life. The ANN is trained offline using extensive experimental data collected from Sony cylindrical 3 Ah cells under various operatin

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Bhattacharyya, Arunava. "PERFORMANCE ANALYSIS OF OPTICAL PARALLEL FULL ADDER USING ARTIFICIAL NEURAL NETWORK." JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES 18, no. 12 (2023). http://dx.doi.org/10.26782/jmcms.2023.12.00003.

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A verbal exchange today wishes for quick operational progress. This can be accomplished by replacing devices that are primarily concerned with commutation and logic with photon-based systems instead of the usual data service, the electron. The basic building blocks of superior frames are called gates. With the aid of these gates, various logical and mathematical operations can be performed. All-optical arithmetical and logical processes are eagerly expected in high-speed dialogue frameworks. In this chapter, we’ve introduced parallel models for adding two binary digits that are based on Sagnac

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Xie, Hong Mei, Deng Yun Lei, Zhi Cheng Zhang, Yong Quan Chen, Zhen Hui He, and Yuan Liu. "Compact modeling of metal–oxide TFTs based on the Bayesian search-based artificial neural network and genetic algorithm." AIP Advances 13, no. 8 (2023). http://dx.doi.org/10.1063/5.0160221.

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Thin-film transistors play an important role in ultra-high definition displays. The conventional physical model requires a significant amount of time and resources, while its generalizability is limited. This paper introduces a method for quickly incorporating the characteristics of emerging devices into circuit simulations using an artificial neural network (ANN) model. The multi-layer perceptron (MLP) model, with a simple structure and high modeling efficiency, is employed as a typical ANN model. The pivotal step in using the MLP model is to determine its topology. This hyperparameter proble

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Davidson, Simon, and Steve B. Furber. "Comparison of Artificial and Spiking Neural Networks on Digital Hardware." Frontiers in Neuroscience 15 (April 6, 2021). http://dx.doi.org/10.3389/fnins.2021.651141.

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Despite the success of Deep Neural Networks—a type of Artificial Neural Network (ANN)—in problem domains such as image recognition and speech processing, the energy and processing demands during both training and deployment are growing at an unsustainable rate in the push for greater accuracy. There is a temptation to look for radical new approaches to these applications, and one such approach is the notion that replacing the abstract neuron used in most deep networks with a more biologically-plausible spiking neuron might lead to savings in both energy and resource cost. The most common spiki

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Mohd Fahmi, Natasha Munirah, Nor Aira Zambri, Norhafiz Salim, and Sim Sy Yi. "Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja." Journal of Advanced Industrial Technology and Application 02, no. 01 (2021). http://dx.doi.org/10.30880/jaita.2021.02.01.003.

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This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Functio

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Patil, Sneha, Mahesh Goudar, and Ravindra Kharadkar. "Neural network-based estimation of lighting condition in indoor environment with improved brain storm algorithm." Journal of Engineering, Design and Technology ahead-of-print, ahead-of-print (2021). http://dx.doi.org/10.1108/jedt-03-2021-0143.

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Purpose For decades, continuous research work is going on to maximize the power harvested from the sun; however, there is only a limited analysis on exploiting the microwatt output power from indoor lightings. Microelectronic system has power demand in the µW range, and therefore, indoor photovoltaics would be appropriate for micro-energy harvesting appliances. “Energy harvesting is defined as the transfer process by which energy source is acquired from the ambient energy, stored in energy storage element and powered to the target systems”. The theory of energy harvesting is: gathering energy

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Dipankar, Dhabak, and Pandit Soumya. "Adaptive Sampling Algorithm for ANN-based Performance Modeling of Nano-scale CMOS Inverter." August 20, 2011. https://doi.org/10.5281/zenodo.1073529.

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This paper presents an adaptive technique for generation of data required for construction of artificial neural network-based performance model of nano-scale CMOS inverter circuit. The training data are generated from the samples through SPICE simulation. The proposed algorithm has been compared to standard progressive sampling algorithms like arithmetic sampling and geometric sampling. The advantages of the present approach over the others have been demonstrated. The ANN predicted results have been compared with actual SPICE results. A very good accuracy has been obtained.

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Mele, Mattia, Gianmarco Milan, Andrea Paffetti, et al. "Homogenization and artificial neural network prediction of elastic properties in triply periodic minimal surface structures." Progress in Additive Manufacturing, May 23, 2025. https://doi.org/10.1007/s40964-025-01165-7.

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Abstract This study introduces a comprehensive framework for the generation, homogenization, and prediction of linear elastic properties of Triply Periodic Minimal Surface (TPMS)-based unit cells. Hybrid cells are created by combining four fundamental TPMS structures, namely primitive, gyroid, diamond, and I-WP. Finite element analysis is used to calculate the equivalent elastic properties of these structures. A dataset is generated using a full-factorial design of the experiment approach to train an Artificial Neural Network (ANN) for predicting the coefficients of the equivalent stiffness ma

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Sadeghkhani, Iman, Abbas Ketabi, and Rene Feuillet. "Study of Transformer Switching Overvoltages during Power System Restoration Using Delta-Bar-Delta and Directed Random Search Algorithms." International Journal of Emerging Electric Power Systems 13, no. 3 (2012). http://dx.doi.org/10.1515/1553-779x.2996.

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Abstract In this paper an intelligent-based approach is introduced to evaluate harmonic overvoltages during three-phase transformer energization. In a power system that appears in an early stage of a black ‎start of a power system, an overvoltage could be caused by core ‎saturation on the energization of a three-phase transformer with residual flux. ‎Such an overvoltage might damage some equipment and delay ‎power system restoration. A new approach based on worst case determination is proposed to reduce time-domain simulations. Also, an artificial neural network (ANN) has been used to estimate

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Hana, Boudjedir, Yacef Fouad, Bouhali Omar, and Rizoug Nassim. "Dual Neural Network for Adaptive Sliding Mode Control of Quadrotor Helicopter Stabilization." July 31, 2012. https://doi.org/10.5121/ijist.2012.2401.

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An adaptive sliding mode control based on two neural networks is proposed in this paper for Quadrotor stabilization. This approach presents solutions to conventional control drawbacks as chattering phenomenon and dynamical model imprecision. For that reason two ANN for each quadrotor helicopter subsystem are implemented in the control loop, the first one is a Single Hidden Layer network used to approximate on line the equivalent control and the second feed-forward Network is used to estimate the ideal corrective term. The main purpose behind the use of ANN in the second part of SMC is to minim

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