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Optimal Integration of Metaheuristic Techniques with Correction-Based Approaches for Profit Maximization in Combinatorial Production Planning: A Comparative Analysis

Optimal Integration of Metaheuristic Techniques with Correct...

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International Conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Arijit Mukherjee Surojit Mukherjee Deptartment of Electrical Engineering Future Institute of Technology Kolkata India Post-Graduate Program in Management Great Lakes Institute of Management Chennai India

To enhance production planning efficiency, it’s crucial to have well-structured models that align with metaheuristic and other optimization techniques. This research conducts a comparative analysis of three unique metaheuristic methods: sanitized-teaching-learning-based optimization (sTLBO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations. The primary focus lies on profit-maximizing production planning within a single-level framework, encompassing intricate domain constraints, nonlinear costs, budget limitations and raw material constraints. The paper introduces three correction-based solution approaches. Upon evaluating 300 distinct instances of the problem, the study reveals that s-TLBO integrated with correction-based approach 1 and 3 consistently outperforms the other methods when it comes to devising optimal production plans for a Saudi Arabian petrochemical industry. This industry scenario involves 24 products, 54 processes, and three raw materials.

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Interpretable Machine Learning for Multi-Class Crop Yield Prediction 3

Interpretable Machine Learning for Multi-Class Crop Yield Pr...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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Downstream Task Performance of Pre-Trained Model For Outdoor Audio Event Classification 9

Downstream Task Performance of Pre-Trained Model For Outdoor...

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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Rochadiani, Theresia Herlina Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wawan Bina Nusantara University Binus Graduate Program- Computer Science Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350369922

Our surroundings' auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models - three, four, and five classes - with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Quantum Wrapper Networking: Investigating the Noise Impact of Classical Headers on the Quantum Payload

Quantum Wrapper Networking: Investigating the Noise Impact o...

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2024 Frontiers in Optics, FiO 2024

作者： Gül, Gamze On, Mehmet Berkay Tan, Shannon G. Proietti, Roberto Kanter, Gregory S. Yoo, S.J. Ben Kumar, Prem Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy

We study the impact in time and frequency domains of classical headers on quantum payloads in quantum wrapper networking. We identify and characterize in-fiber scattering processes that produce noise photons degrading...

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Polymethyl Methacrylate-based Light Collector for Enhanced Indoor Visible Light Communication

Polymethyl Methacrylate-based Light Collector for Enhanced I...

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Optics and Photonics Conference (SBFoton IOPC), SBFoton International

作者： David Esteban Farfán-Guillén João Pedro Duarte Jankosz Neri Volpato Alexandre de Almeida Prado Pohl Graduate Program in Electrical and Computer Engineering (CPGEI) Federal University Technology-Parana Curitiba Brazil Graduate Program in Mechanical Engineering and Material Science Federal University Technology-Parana. Curitiba Brazil

A new polymethyl methacrylate-based light collector has been designed and fabricated with additive manufacturing to optimize the efficiency of VLC systems. It enhances the area of detection, leading to a ~ 1.9x increa... 详细信息

ISBN: (数字)9798350388176

ISBN: (纸本)9798350388183

关键词： Optical device fabrication Optical variables measurement Three-dimensional printing Optics Optical receivers Optical sensors Reliability Visible light communication Testing Photonics

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Mechanism of Threshold Voltage Instability in Double Gate α-IGZO Nanosheet TFT under Bias and Temperature Stress

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IEEE Journal of the Electron Devices Society 2024年 12卷 464-471页

作者： Aslam, Muhammad Chang, Shu-Wei Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Lee, Wen-Hsi Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan City701401 Taiwan Institute of Pioneer Semiconductor Innovation Industry Academic Innovation School National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Amorphous indium gallium zinc oxide (a-IGZO)-based thin film transistors (TFTs) are increasingly becoming popular because of their potential in futuristic applications, including CMOS technology. Given the demand for CMOS-compatible, ultra-scaled, reliable, and high-performing devices, we fabricate and analyze scaled-channel a-IGZO-TFTs with an optimal double-gate structure, a thin nanosheet-based channel, and an effective high- κ dielectric namely HfO2. The reliably reported double gate IGZO nanosheet TFTs (DG-IGZO-NS-TFTs) are tested under positive and negative bias stress at variant temperatures, and the resulting modulations are analyzed critically. The reported DG-IGZO-NSTFTs exhibit a negative side threshold voltage shift (Δ Vth) accompanied by an increase in Ion/Ion(0) under negative bias temperature stress (NBTS) at elevated temperatures, which indicates the presence of additional charges. An anomalous negative side shifting of the Vth is observed under positive bias temperature stress (PBTS), where diffused hydrogen atoms are identified as introducing excess n-type carriers into the channel and causing the observed Δ Vth. The spectroscopic analysis is performed to establish evidence for the assumed mechanisms, and the role of individual gates is investigated in the context of performance variance under temperature-bias stress. Moreover, the partial reversibility of the stress-induced degradation is experimentally established and methodically discussed. Overall, the reported results offer a comprehensive understanding of scaled-channel DG-NS-IGZO-TFTs, which help shape performance-enhancement strategies, control degradation mechanisms, and define appropriate application scenarios. © 2013 IEEE.

关键词： Thin film transistors

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Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

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IEEE Transactions on Network and Service Management 2023年第3期20卷 3413-3427页

作者： Bhat, Showkat Ahmad Huang, Nen-Fu Hussain, Imtiyaz Sajjad, Uzair National Tsing Hua University College of Electrical and Computer Science Hsinchu1300044 Taiwan National Tsing Hua University Department of Computer Science Hsinchu1300044 Taiwan National Taipei University of Technology Graduate Program in Energy and Opto-Electronic Materials Taipei10608 Taiwan National Taipei University of Technology Department of Energy and Refrigerating Airconditioning Engineering Taipei10608 Taiwan

LoRa's biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this function allows the spreading factor to be modified accordingly. Despite their efficiency and scalability, adaptive data rate algorithms ignore and fail to factor in the complex correlation between ambient weather parameters influencing the communication channel design. In this research, a Bayesian surrogate Gaussian process-based bidirectional LSTM stacked autoencoder model (BSGP-BLSTM-SAE) is proposed to estimate the channel performance indicators such as received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) and to determine the correlation between the ambient weather conditions and performance indicators for the LoRaWAN network. Bayesian optimization algorithm has been used to optimize the hyper-parameters of the developed model. A LoRaWAN experimental multivariate time series dataset has been used for the evaluation of the developed model, which upon testing and validation produces high accuracy in predicting the channel performance indicators and ambient conditions of the experimental LoRaWAN network. The mean absolute error of the developed model was around 0.45. Thus, the proposed model can predict the link performance indicators and thereby assist in real-time optimization of the transmission parameters to enhance the network performance in LoRaWAN-based systems at different ambient conditions. © 2004-2012 IEEE.

关键词： Internet of things

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ADHD Subtype Diagnosis Through Convolutional Neural Networks Evaluation of the Connectivity Networks in Brain fMRI 9th

ADHD Subtype Diagnosis Through Convolutional Neural Networks...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Pedrollo, Guilherme Rodrigues Franco, Alexandre Rosa Balbinot, Alexandre Post Graduate Program in Electrical Engineering Universidade Federal do Rio Grande do Sul RS Porto Alegre90020-000 Brazil Child Mind Institute New YorkNY10022 United States Nathan Kline Institute for Psychiatric Research New YorkNY10962 United States

ISBN: (纸本)9783031494031

Attention-deficit hyperactivity disorder (ADHD) affects at least 5% of the world population. ADHD typically starts in childhood and can affect normal development that can cause serious consequences during adulthood. Moreover, there are three subtypes of ADHD, each with its symptomatic differences that require particular treatment. The development of tools capable of detecting the brain differences between each subtype and a control group can help in directing future neuropsychological studies and guide treatments. Here we investigate the hypothesis of using Convolutional Neural Networks (CNN) as a tool to detect ADHD in connectivity networks of the brain. This research used the Yeo brain parcellation maps from 28 healthy volunteers, 36 combined subtype ADHD, and 26 inattentive type ADHD. The CNN achieved a 64.71% accuracy at detecting the multi-class diagnosis (Control, ADHD combined, or ADHD inattentive) when using the complete connectivity network matrix, surpassing the individual accuracies achieved when using only the within network connectivity measures. However, the separate analysis of each network showed that the Dorsal, Default and Frontoparietal networks have better accuracy of performing the classification compared to using other within brain connectivity measures. The Default Network has shown greater accuracy at identifying ADHD-I than the other within networks or whole brain connectivity measures. These results suggest that the neurological differences of the presence of ADHD and its subtypes may be present and, therefore, affect more than one connectivity network functionality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolution

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An Ensemble-Based Classifier to Determine the Harmonic Contribution Responsibility Between Utility’s Grid and Microgrids

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IEEE Access 2025年 13卷 85873-85881页

作者： Mella, Matheus C. Fernandes, Ricardo A.S Barbosa, Daniel Federal University of São Carlos Graduate Program in Electrical Engineering São Carlos Brazil University of São Paulo São Carlos School of Engineering Department of Electrical and Computer Engineering São Carlos Brazil Federal University of Bahia Department of Electrical Engineering Salvador Brazil

The integration of nonlinear loads and distributed energy resources in microgrids (MG) has increased the levels of harmonic distortions in distribution feeders. Identifying the responsibility for the harmonic distortions measured at the MG’s point of common coupling (PCC) is essential for managing the power quality. For this purpose, the present paper aims to determine whether the responsibility predominates from the utility’s grid or the MG itself. Thus, an ensemble-based approach was proposed, comparing the behaviour of Random Forest and XGBoost classifiers to identify the side with the predominant harmonic contribution. In order to validate the proposed approach, scenarios were simulated in the IEEE 34-node test feeder using the ATP/ATPDraw software, considering the harmonic sources located at different nodes of the feeder. Three-phase voltage and current signals were acquired at MG’s PCC to train and validate the ensembles. However, a feature extraction stage was performed to obtain time and frequency features used as inputs to the predictive models. The results demonstrated that the proposed approach effectively identified the contribution side, achieving an average F1-score above 99% for both ensembles, representing a feasible solution for determining the harmonic distortion responsibilities at the MG’s PCC. © 2013 IEEE.

关键词： Software testing

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Quality Improvements in the electrical Design of Typical DC-DC Electronic Power Converter for High-Voltage Applications 8

Quality Improvements in the Electrical Design of Typical DC-...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Recto, King Harold A. Escoto, Miguel T. Hernandez, Noel B. Ateneo de Manila University Department of Electronics Computer and Communications Engineering Quezon City Philippines Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City Philippines Centro Escolar University Post-doctoral Program in Quality Management of the Graduate School Manila Philippines

ISBN: (纸本)9781665491068

The green transition has brought about a worldwide-shift to the use of renewables as alternative energy sources. Because of this, high voltage DC has been a field of interest in power electronics due to its capability in efficiently transmitting power to reach remote areas. Typical DC-DC electronic power converter design has the capability of boosting voltage level to a certain extent but not enough for high-voltage applications. Using the DMAIC Approach of the Six Sigma, quality improvements in the design was proposed to stabilize operating frequency, bring up the output voltage from 100 VDC to 4,000 VDC and provide at least 10% margin of error in the apparent power measured at the output side to offset inherent losses in the electrical design. These proposed quality improvements were sustained by designing a control circuit that would ensure the attainment of the parameters set in the Define Phase of the DMAIC Approach. The result of the study is a practical 12 VDC to 4,000 VDC boost converter design that has incorporated all the proposed quality improvements. © 2022 IEEE.

关键词： Six sigma

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