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Effect of Cotton Dust Morphology on Photovoltaic Module Performance

Effect of Cotton Dust Morphology on Photovoltaic Module Perf...

引用

2024 International Conference on Emerging Trends in Smart Technologies, ICETST 2024

作者： Ali, Basit Khan, M. Faisal Ishaque, Kashif Faculty Of Engineering Sciences & Technology Hamdard University Graduate School Of Engineering Sciences & Information Technology Karachi Pakistan Bahria University Department Of Electrical Engineering Karachi Pakistan Faculty Of Engineering Sciences & Technology Hamdard University Department Of Electrical Engineering Karachi Pakistan Muhammad Ali Jinnah University Department Of Electrical And Computer Engineering Karachi Pakistan

ISBN: (纸本)9798331515850

The use of renewable energy is increasing due to the high cost of conventional fuels and climate issues. Textile industries, specifically in Asian region, are shifting towards large PV plants in order to acquire cheap and clean energy. However, cotton dust presence in the textile mill environment causes performance issues for PV modules. This study examines the cotton dust particle effect on PV module by employing an experimental methodology. An indoor experimental setup is utilized to test the PV modules in two schemes, that are clean and dusty. The experiment is conducted for eight days with an hourly interval. It is observed that PV power output reduces significantly up to 15% in eight days as compared to clean PV module. The deposited cotton dust particles are also physically examined through compound microscope in order to determine its physical nature. It is being observed that cotton particles are composed of long, smooth fibers with irregular shapes. The particles are highly influenced by humidity, and their own high content of cellulose makes them vulnerable to stick on the PV surface and forming clumps and aggleromates that develop the hotspots on the PV surface. Along with that, as time progresses the cotton dust opaqueness also increases, ultimately reducing the transmittance and decreasing PV module performance. This study helps to devise a unique cleaning patterns and methods for textile units to enhance performance output. © 2024 IEEE.

关键词： Textile industry

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Frequency Bandwidth Reduction of High-Frequency Intra-Body Propagation Characteristics Used for Attacker's Touch Detection in Fingerprint Authentication 12

Frequency Bandwidth Reduction of High-Frequency Intra-Body P...

引用

12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Tomura, Shunichi Yamada, Koki Yoshida, Takahiro Tokyo University of Science Graduate School of Engineering Department of Electrical Engineering Tokyo Japan Tokyo University of Agriculture and Technology Department of Electrical Engineering and Computer Science Tokyo Japan Tokyo University of Science Faculty of Engineering Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350340181

In previous studies, we have proposed the attacker's touch detection method in fingerprint authentication using high-frequency intra-body propagation characteristics to detect attacks in that an attacker holds the user's finger/wrist and presses the user's finger to a fingerprint scanner. However, the previous method used a wide range of frequency bands from 300 kHz to 10 MHz, and this caused problems in the circuit scale, cost, and size of the device in development for practical use. Therefore, we searched effective frequency bands and applied K-nearest neighbor with data augmentation in this study. As results, we achieved a 99.1 % touch detection rate with a single frequency and a 99.9 % touch detection rate with a combination of two frequencies. We achieved a significant reduction in the frequency bandwidth of the propagation characteristics. © 2023 IEEE.

关键词： Bandwidth

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Balancing Dual DC Voltage Control in Quasi Single-Stage Converter for Low-Voltage Energy Storage System Grid-Connected 20

Balancing Dual DC Voltage Control in Quasi Single-Stage Conv...

引用

20th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Manokum, Warissara Kumsuwan, Yuttana Faculty of Engineering Chiang Mai University Graduate Program in Electrical Engineering Department of Electrical Engineering Chiang Mai Thailand Provincial Electricity Authority Bangkok Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand

ISBN: (纸本)9798350310467

The aim of a bidirectional quasi single-stage converter (QSSC) for low-voltage energy storage systems that are grid-connected is to reduce the power conversion stage of the dc-dc converter, which results in improved overall efficiency. The QSSC configuration relies on a modified modulation strategy due to the unequal dual dc port (upper and lower dc ports) voltage. The control for ensuring that the dual dc port voltages are equal is presented in this paper. When the lower dc port is varied in response to battery voltage, another is controlled to track in response to the lower dc port, so that the voltage ratio between the upper and lower dc port is always equal to one. Furthermore, power sharing in a dual dc port is equal, which means that a dc-dc converter automatically operates at 50% of the battery power. Thus, the existing modulation can be used and the power stage conversion of the dc-dc converter can be decreased. The proposed control for the QSSC is verified with simulation results. © 2023 IEEE.

关键词： Energy storage

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Medium-Term Load Forecasting Model Using Radial Basis Function Algorithm 2

Medium-Term Load Forecasting Model Using Radial Basis Functi...

引用

2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Pamela, Astrid Suriadi, Suriadi Tarmizi, Tarmizi Graduate School of Electrical Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (纸本)9798350343069

This study developed two medium-term electricity load forecasting models for Banda Aceh city using Radial Basis Function method and Artificial Neural Network. There are three load groups used to create the model, including social, household and industrial load groups. The dataset used consists of 4 (four) input variables and one target. This research developed 3 training functions that were applied to both models to obtain the best forecast model. The training functions include Lavenberg-Marquardt, Gradient Descent and Adaptive Learning Rate, and Gradient Descent with momentum and adaptive learning rate. Based on the simulation and its MAPE value, the radial basis function algorithm is highly suitable for implementing mid-term electricity load forecasting modeling. © 2023 IEEE.

关键词： Radial basis function networks

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Neural Network-Based Analysis of Mammography Images for Identifying Breast Cancer Histological Subtypes

Neural Network-Based Analysis of Mammography Images for Iden...

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Alqaaf, Muhammad Technology, Ahmad Kamal Nasution graduate School Of Science And Supriyanti, Retno Asaduzzaman, Abu Ono, Naoaki Altaf-Ul-Amin, Md. Kanaya, Shigehiko Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Jenderal Soedirman University Department of Electrical Engineering Banyumas Indonesia Wichita State University Department of Electrical and Computer Engineering Wichita United States

ISBN: (纸本)9798350391213

Breast cancer (BC) is a complex disease with multiple histological subtypes that exhibits distinct biological and clinical characteristics. Accurate identification of these subtypes is crucial for the implementation of personalized treatment strategies and the improvement of patient outcomes. This study aimed to leverage the potential of neural networks and mammography data to identify specific histological subtypes of BC, with a focus on distinguishing between in situ and invasive carcinoma. We used the Digital Database for Screening Mammography (DDSM) and its curated subset, CBIS-DDSM, which provides mammography images of normal and malignant cases with verified pathological information. A convolutional neural network (CNN) architecture was designed to extract relevant features from mammography images. The model was trained using data augmentation techniques to enhance diversity and mitigate overfitting, resulting in training and validation accuracies ranging from 0.98 to 1.0. The features extracted by the trained model were then used for clustering analysis using Kmeans on PCA (KM-PCA), which identified nine well-separated clusters with minimal overlap. The clustering results were validated using a subset of mammography images with confirmed invasive carcinoma types from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Two validation approaches were incorporated to strengthen the hypothesis and further validate the results. Overall, this study demonstrates the potential of combining neural networks and mammography data to accurately identify BC histological subtypes, which could pave the way for personalized and effective treatment strategies. © 2024 IEEE.

关键词： Lung cancer

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Prediction Analysis of Greeting Gestures based on Data Augmentation and LSTM 23

Prediction Analysis of Greeting Gestures based on Data Augme...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wibow, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of System Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798331528041

The field of Human Activity Recognition (HAR) is growing significantly in several areas but little research focuses on cultural behavior. How machine learning can explain human activity as a promotional tool in understanding cultural differences in a region is very challenging. Studies using Recurrent Neural Network (RNN), especially Long Short-Term Memory (LSTM), require large data when used to research human activity recognition. Using small data is a challenge when using LSTM. This study aims to determine which data augmentation is most appropriate when used using LSTM to predict Japanese greeting gestures, namely eshaku, keirei, saikeirei, waving hands, and Indonesian greeting gestures, namely the movement of bringing both hands together in front of the chest with a slight bow of the head. This study proposes to compare several data augmentations, namely jittering, scaling, permutation, cropping, reverse, time shifting, frequency domain, and synthetic data generation. We evaluate model performance using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) metrics. The experimental results show that Time Shifting has the best MSE, RMSE, and MAE values compared to others and Frequency Domain has the best R2 values compared to others. This study concludes that Time Shifting and Frequency Domain are the best data augmentation methods when using LSTM in predicting greeting gestures using small data. These findings provide valuable insights into the effectiveness of various data augmentation methods in motion prediction tasks with limited data sets, as well as the importance of selecting appropriate data augmentation methods in human motion analysis. Future work could use data from two or more people using model explanations generated by machine learning and developing a new method for cultural behavior. © 2024 IEEE.

关键词： Mean square error

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Light Shift Detection Using a Multiple Photodetection Method Suitable for a Chip-Scale Atomic Clock

Light Shift Detection Using a Multiple Photodetection Method...

引用

2023 Joint Conference of the European Frequency and Time Forum and IEEE International Frequency Control Symposium, EFTF/IFCS 2023

作者： Fukuoka, Masahiro Goka, Shigeyoshi Tokyo Metropolitan University Graduate School of System Design Department of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350311426

In this study, we demonstrated light shift detection in a running atomic clock using the multi-photodetection method. This method enables the observation of atomic resonances with different intensities using the intensity distribution of the laser beam. As it requires only two photodiodes to detect light shifts, it is easily applicable to miniature vapor-cell-based atomic clocks such as chip-scale atomic clocks and miniature atomic clocks. © 2023 IEEE.

关键词： chip-scale atomic clock coherent population trapping intensity distribution laser light shift VCSEL

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Cloud-Native Mobile Application Applied for Law and Regulations Harmonization 3

Cloud-Native Mobile Application Applied for Law and Regulati...

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

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

ISBN: (纸本)9798350394559

Navigating the complex legal and regulatory landscape requires a sophisticated platform that is not only comprehensive but also user-friendly and enables seamless analysis and document comparison in the legal realm. To address this challenging requirement, this research is dedicated to the user experience (UX) design of state-of the-art cloud-native mobile applications specifically designed for legal and regulatory harmonization. The methodology used is based on design thinking principles and takes advantage of the benefits of cloud computing. These include, but are not limited to, scalability, reliability, and security. The result is a solution that is not only powerful but also cost-effective. The core of this application follows a user-centered design philosophy and involves potential users closely in the design process. A comprehensive evaluation is performed using the System Usability Scale (SUS) methodology to measure the effectiveness and usability of the application. The results of this comprehensive approach are convincing. The application has an impressive 95.5% usage rate, indicating strong user engagement. Furthermore, user satisfaction ratings are particularly high and transformative impact of this application in a complex environment of legal harmonization. Through its innovative design, seamless functionality, and user-centric approach, this application has proven to be not only a technical solution, but also a catalyst for positive change in the legal and regulatory harmonization process. © 2023 IEEE.

关键词： Cost effectiveness

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BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization

BAPO: Base-Anchored Preference Optimization for Overcoming F...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Lee, Gihun Jeong, Minchan Kim, Yujin Jung, Hojung Oh, Jaehoon Kim, Sangmook Yun, Se-Young Graduate School of AI KAIST Korea Republic of Samsung Advanced Institute of Technology Korea Republic of Department of Electrical and Computer Engineering UBC Canada

ISBN: (纸本)9798891761681

While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents further challenges in preserving previous knowledge. This paper examines the impact of personalized preference optimization on LLMs, revealing that the extent of knowledge loss varies significantly with preference heterogeneity. Although previous approaches have utilized the KL constraint between the reference model and the policy model, we observe that they fail to maintain global knowledge and general alignment when facing personalized preferences. To this end, we introduce Base-Anchored Preference Optimization (BAPO), a simple yet effective approach that utilizes the initial responses of reference model to mitigate forgetting while accommodating personalized alignment. BAPO effectively adapts to diverse user preferences while minimally affecting global knowledge or general alignment. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Greeting Gesture Analysis Using Boosting Algorithms and SHAP 13

Greeting Gesture Analysis Using Boosting Algorithms and SHAP

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Wibowo, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of Systems Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350373332

Recently, studies related to human activity recognition have developed which have been applied in various fields. In that field Machine learning and deep learning techniques have been widely used for several classification and prediction tasks. However, the first problem is that there is rarely any research on human activities that reflect the culture of a region, such as greeting gestures. Second, there are few studies that do not provide models resulting from the classification or prediction process using machine learning, which can result in a lack of understanding of how a model from machine learning is generated. This study aims to predict Japanese greeting gestures (eshaku, keirei, saikeirei, waving hand) and Indonesian (placing both hands in front of the chest) using Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting. Then, represent the model of the learning machine using SHapley Additive exPlanations (SHAP) to find out how the learning machine obtain a model. Our findings show the efficacy of this method and show that the model not only predicts greeting gestures with high accuracy but also offers a transparent understanding of the factors influencing its predictions, thus enhancing the interpretability and applicability of machine learning in recognizing culturally significant human activities. In the future, we plan to use machine learning and other Explainable Artificial Intelligence (XAI) methods to study human activities with cultural concepts such as greeting gestures in different styles, create more greeting gesture-based human activity datasets, and examine the ethical implications or considerations of using machine learning in cultural contexts to respect cultural differences. © 2024 IEEE.

关键词： Contrastive Learning

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