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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural Computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Image-based Cutting Tool Health Diagnosis in Multi-machine Settings 6

Image-based Cutting Tool Health Diagnosis in Multi-machine S...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Mishra, Debasish Pattipati, Krishna R. Bollas, George M. Data Science and Information Systems Krea University 5655 Central Expressway Sri City517646 India Electrical and Computer Engineering University of Connecticut 371 Fairfield Way Storrs06269 United States Chemical and Biomolecular Engineering University of Connecticut 159 Discovery Drive Storrs06269 United States

Manufacturers are increasingly focused on evaluating the condition of cutting tools during machining processes to ensure high-quality production. This study introduces a method for accurately and cost-effectively diagnosing the health of cutting tools. The method employs image similarity indices-such as Bhattacharyya distance, Fourier transform, and Cosine similarity-to compute indicators of tool health from wavelet coefficient maps of cutting signals. These indicators are then used in an unsupervised machine learning (ML) algorithm, agglomerative clustering, to categorize the tool health state. To validate the method, run-to-failure tests of the tool were conducted on two different machines with varying specifications and machining parameters. The results demonstrate a strong association between the computed indicators and the tool health, regardless of the differences between the two machines and their cutting settings. This is due to the reliance of the method on the physics of machining processes to derive the health indicators. The ML model effectively categorizes the progressive health of cutting tools and provides easily interpretable results on the health of the tool due to the explainability of the indicators. © 2025 The Authors. Published by Elsevier B.V.

关键词： Clustering Machine Learning Precision machining Similarity Tool condition monitoring Unsupervised

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Intelligent Additive Manufacturing Architecture for Enhancing Uniformity of Surface Roughness and Mechanical Properties of Laser Powder Bed Fusion Components

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IEEE Transactions on Automation Science and engineering 2023年第4期20卷 2527-2538页

作者： Tran, Hong-Chuong Lo, Yu-Lung Yang, Haw-Ching Hsiao, Hung-Chang Cheng, Fan-Tien Kuo, Tsung-Han Southern Taiwan University of Science and Technology Department of Mechanical Engineering Tainan71005 Taiwan National Cheng Kung University Department of Mechanical Engineering Tainan70101 Taiwan National Kaohsiung First University of Science and Technology Department of Electrical Engineering Kaohsiung82445 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan70101 Taiwan

In the Laser Powder Bed Fusion (L-PBF) process, 3D components with complex geometries are fabricated in a layer-by-layer fashion by using a controlled laser beam to selectively melt particular regions of the metal powder bed. However, due to the stochastic nature of the L-PBF process, the top surface roughness of each solidified layer tends to be different even when the optimal processing conditions for the different positions on the build plate are employed. As a result, the mechanical properties of the built components frequently vary from one component to the next. Accordingly, this study proposes an Intelligent Additive Manufacturing Architecture (IAMA) for controlling the surface roughness of each build layer through an appropriate adjustment of the laser re-melting parameters. The IAMA architecture comprises five modules, namely In-Situ Metrology (ISM), Ex-Situ Metrology (ESM), Automatic Virtual Metrology (AVM), Additive Manufacturing Simulation (AMS) and Intelligent Compensator (IC). The feasibility of the proposed architecture is demonstrated by comparing the top surface roughness of cubic and mechanical strengths of tensile test samples built using the proposed method with those built using a traditional L-PBF approach without surface roughness control. It is found that the samples fabricated using the IAMA approach have an average top surface roughness of 1.6μm and a standard deviation is 0.7μm. By contrast, the samples produced using the traditional L-PBF approach have an average surface roughness of 13.45μm and a standard deviation of 2.5μm. In addition, the specimens produced with the assistance of IAMA architecture have an average tensile strength of 1013 MPa with a standard deviation of 69.5 MPa, while those printed without surface roughness control have an average tensile strength of 903 MPa with a standard deviation of 101.4 MPa Note to Practitioners - As L-PBF produce part in a layer-by-layer manner, therefore, the roughness on the top surface of p

关键词： Surface roughness

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Code Writing Problems for Basic Object-Oriented Programming Study in Java Programming Learning Assistant System 12

Code Writing Problems for Basic Object-Oriented Programming ...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Wai, Khaing Hsu Funabiki, Nobuo Aung, Soe Thandar Lu, Xiqin Jing, Yanhui Sandi Kyaw, Htoo Htoo Kao, Wen-Chung Okayama University Department of Information and Communication Systems Okayama Japan Tokyo University of Agriculture and Technology Department of Computer and Information Science Tokyo Japan National Taiwan Normal University Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350340181

Nowadays, Java has been extensively used in industries as a reliable and portable object-oriented programming (OOP) language. To support novice students in learning Java programming, we have developed a web-based system called Java Programming Learning Assistant System (JPLAS). JPLAS offers the Code Writing Problem (CWP) among several types of exercise problems. A CWP instance requests a student to write a source code that will pass the provided test code in the assignment where the accuracy of the code is verified by running them on JUnit. In Java programming, every student should master basic OOP concepts, which can be challenging for novice students. This paper presents CWP instances for studying six basic OOP concepts including encapsulation, polymorphism, constructor, inheritance, abstract, and interface in JPLAS. Our preliminary evaluations of them confirm their validity. © 2023 IEEE.

关键词： Object oriented programming

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Development of U-Net Architecture for Audio Super Resolution 9

Development of U-Net Architecture for Audio Super Resolution

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Gunawan, Teddy Surya Sarif, Muhammad Rusdy Mohd Kartiwi, Mira Ahmad, Yasser Asrul International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia

ISBN: (纸本)9798350325218

Audio processing is used in a wide range of applications, including telecommunications and music streaming. Audio quality degradation during transmission and processing is a common issue in these fields, often caused by bandwidth constraints and the use of subpar equipment. This problem is exacerbated when the task requires converting low-quality audio input to high-resolution output, which is difficult for deep neural networks to do. To improve audio superresolution, this paper proposes a novel solution to this problem by embedding a U-Net-based architecture model within deep neural networks. Over 100 iterations, the U-Net architecture was trained, with loss values and Mean Squared Error (MSE) monitored at each epoch. A diverse dataset of audio signals with varying Signal-to-Noise Ratio (SNR) values ranging from 1 dB to 30 dB was used. The model's average SNR of 17.29 dB exceeds thresholds where listener detection of enhancements becomes difficult, demonstrating its ability to preserve subtle audio details. Furthermore, the Log-Spectral Distance (LSD) values revealed a mere 1.41 dB difference between the actual and reconstructed spectrograms, indicating that the model can recover lost information during upsampling. This research suggests a promising method for improving audio quality, particularly when bandwidth constraints or insufficient equipment prevent high-resolution audio transmission and processing. © 2023 IEEE.

关键词： Signal to noise ratio

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Q-Net: A Quantitative Susceptibility Mapping-Based Deep Neural Network for Differential Diagnosis of Brain Iron Deposition in Hemochromatosis 31

Q-Net: A Quantitative Susceptibility Mapping-Based Deep Neur...

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31st IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2024

作者： Zabihi, Soheil Rahimian, Elahe Khademi, Sadaf Sharma, Soumya Sethi, Sean K. Gharabaghi, Sara Asif, Amir Haacke, E. Mark Jog, Mandar S. Mohammadi, Arash Concordia University Electrical and Computer Engineering Montreal Canada Concordia University Concordia Institute for Information Systems Engineering Montreal Canada Western University London Movement Disorders Centre London Health Sciences Centre London Canada Bingham Farms SpinTech MRI MI United States

ISBN: (纸本)9798331515942

Brain iron deposition, in particular deep gray matter nuclei, increases with advancing age. Hereditary Hemochromatosis (HH) is the most common inherited disorder of systemic iron excess in Europeans and recent studies claimed high brain iron accumulation in patient with Hemochromatosis. In this study, we focus on Artificial Intelligence (AI)-based differential diagnosis of brain iron deposition in HH via Quantitative Susceptibility Mapping (QSM), which is an established Magnetic Resonance Imaging (MRI) technique to study the distribution of iron in the brain. Our main objective is investigating potentials of AI-driven frameworks to accurately and efficiently differentiate individuals with Hemochromatosis from those of the Healthy Control (HC) group. More specifically, we developed the Q-Net framework, which is a data-driven model that processes information on iron deposition in the brain obtained from multi-echo gradient echo imaging data and anatomical information on T1-Weighted images of the brain. We illustrate that the Q-Net framework can assist in differentiating between someone with HH and HC of the same age, something that is not possible by just visualizing images. The study is performed based on a unique dataset that was collected from 52 subjects with HH and 47 HC. The Q-Net provides a differential diagnosis accuracy of 83.16% and 80.37% in the scan-level and image-level classification, respectively. © 2024 IEEE.

关键词： Brain mapping

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Adversarial Attacks in Problem Space for VBA Code Samples 1st

Adversarial Attacks in Problem Space for VBA Code Samples

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1st International Symposium on Intelligent Computing and Networking, ISICN 2024

作者： Fehrman, Brian Akowuah, Francis Xu, Shengjie Department of Electrical Engineering and Computer Science South Dakota Mines Rapid CitySD57701 United States Department of Management Information Systems San Diego State University San DiegoCA92182 United States

ISBN: (纸本)9783031674464

Machine learning is being used more frequently in cyber security. One area is in antivirus software. Learning-based antivirus software is not perfect, since threat actors can use adversarial data to craft attacks against these learning-based security products. This paper focuses on using machine learning to craft attacks against Microsoft (MS) Windows Defender. The goal of this work is to provide tooling and knowledge for: security vendors to tighten the security of their products, security professionals to aid in showcasing risks to customers, and law enforcement to help discover the locations and identities of cyber criminals. The attacks focus on modifying Visual Basic for Applications (VBA) code samples to flip Defender’s classification of those files. The modifications are done to the code itself (i.e., problem space) rather than just in feature space. The project attempts to extract information from MS Windows Defender to help craft the attacks. Preliminary results showed that the classification of some of the files was changed after targeted modification and that the attacks were feasible. Additional work will be conducted to further increase the reliability of the attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Invertibility of Discrete-Time Linear systems with Sparse Inputs

Invertibility of Discrete-Time Linear Systems with Sparse In...

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IEEE Conference on Decision and Control

作者： Kyle Poe Enrique Mallada René Vidal Applied Mathematics and Computational Science Group University of Pennsylvania PA Mallada is with the Department of Electrical and Computer Engineering Johns Hopkins University MD Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control.

关键词： Linear systems Geometry Sufficient conditions Systematics Heuristic algorithms Standards

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Dispersion compensating fiber with Brillouin fiber laser generation

Dispersion compensating fiber with Brillouin fiber laser gen...

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2023 Al-Sadiq International Conference on Communication and information Technology, AICCIT 2023

作者： Ali, Hasanain Naser Abd Harun, Sulaiman Wadi Arsad, Norhana Abdali, Taj-Aldeen Naser Al-Khaleefa, Ahmed Salih Hassan, Mustafa H Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Bangi Malaysia Faculty of Engineering University of Malaya Photonics Engineering Laboratory Department of Electrical Engineering Kuala Lumpur Malaysia University of Misan Department of Electronic Computer Center Misan Iraq University of Misan Faculty of Education Department of Physics Misan Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq

ISBN: (纸本)9798350341881

Short linewidth light lasers are essential for a wide range of applications, including quantum computing, spectroscopy, and sensing. Stimulated Brillouin scattering is an intriguing method for achieving extremely coherent light laser output. The demonstration of an unstable pulsed Brillouin erbium fiber laser (BFL) operating at kHz is shown here. The BFL works at 1550.1 nm, which is 0.09 nm higher than the Brillouin pump wavelength since the dispersion compensating fiber was pumped over the 24.77 mW threshold. It has an extreme power of 0.7 dBm, a side-mode suppression ratio of 29 dB, and a pump power of 59.12 mW at 980 nm. Due to the intrinsic instability in relaxation oscillation, which generates the nonlinear self-pulsing mechanism in the BEFL cavity, the BEFL produced a pulse train running at 200.9 kHz with a 24.77 mW pump. The pulse frequency was raised to 3.02 MHz. The pump power has been increased to 59.12 mW. Yet, as the pump power was raised further, more than one pulse was produced. This is the first example of generating kHz pulses in a BFL cavity. The laser system is straightforward, small, and all-fiber. These are the attempts of experimental work to realize the BFL with Q-switched multi-wavelength output using a specific length of DCF to generate the pulses. © 2023 IEEE.

关键词： Fiber lasers

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