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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Ogar, Emmanuel Ede Chaitusaney, Surachai Benjapolakul, Watit Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Chulalongkorn University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350352290

The natural abundance of solar energy and its favorable environmental impacts have led to growing acceptance and utilization as the world transitions from a fossil fuel-based economy to a cleaner, more sustainable, and ecologically friendly alternative. An increasing number of solar photovoltaic projects have emerged to capitalize on these benefits. However, factors such as weather conditions, exposure to natural elements, device deterioration, improper installations, and maintenance concerns can eventually compromise these systems' power-generating performance and dependability over time. This study demonstrates how the random forest machine learning classifier can be used to monitor and categorize the performance of solar PV plants to ensure their reliability and efficiency. The study collected and preprocessed a field dataset containing various environmental and electrical parameters. These parameters include plant capacity, active power, generating capacity, ambient temperature, global radiation, and module temperature. This data was used to train a random forest machine learning algorithm to categorize the operational condition of the solar power plant. The results demonstrate that the model efficiently classified the operational condition of the solar power plant by effectively identifying conditions such as optimal mode, moderate sub-optimal mode, severe sub-optimal mode, critical mode, emergency mode, and offline mode, achieving 99.92% accuracy on the test set. The proposed method provides an effective technique for estimating and monitoring the generating capacity and operational condition of solar power plants in near real time, thereby serving as a distinctive instrument for boosting the efficiency and reliability of solar power generation. © 2024 IEEE.

关键词： artificial intelligence Capacity monitoring machine learning Random Forest algorithm Solar cell

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Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using machine learning

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IEEE Access 2025年 13卷 86768-86780页

作者： Saleem, Muhammad Asim Javeed, Ashir Benjapolakul, Watit Srisiri, Wattanasak Chaitusaney, Surachai Kaewplung, Pasu Chulalongkorn University Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden

Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classification. The NN component extracts high-level features, while XGBoost uses gradient-boosted decision trees for accurate predictions, combining the strengths of deep learning and boosting techniques for improved accuracy. The N-XGB model achieves an accuracy of 94.8% and an average F1 score of 0.95 on a real-world dataset that includes wildfires, floods and earthquakes, significantly outperforming baseline models such as random forest, Support vector machine and logistic regression 85% accuracy. The balanced F1 scores for wildfires 0.96, floods 0.93, and earthquakes 0.96 demonstrate the model's robustness in multi-class classification. The Synthetic Minority Oversampling Technique (SMOTE) balances datasets and improves model efficiency and capability. The proposed N-XGB model provides a reliable and accurate solution for predicting disasters and contributes to improving preparedness, resource allocation and risk management strategies. © 2013 IEEE.

关键词： Risk management

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Solar Power Plant Capacity Monitoring Using Random Forest machine learning Algorithm

Solar Power Plant Capacity Monitoring Using Random Forest Ma...

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IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Emmanuel Ede Ogar Surachai Chaitusaney Watit Benjapolakul Department of Electrical Engineering Faculty of Engineering Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

关键词： Temperature sensors Temperature measurement Performance evaluation machine learning algorithms Accuracy Solar energy Real-time systems Classification algorithms Monitoring Random forests

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Performance Assessment of Supervised machine learning-Based Approaches for Photovoltaic Diagnosis

Performance Assessment of Supervised Machine Learning-Based ...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information technology, ECTI-CON

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

There has been a notable increase in the adoption of solar energy as a sustainable energy choice in recent years due to its positive environmental impact, and widespread availability. However, factors such as weather, component deterioration, improper installation, and maintenance concerns gradually create conditions in the system that threaten its performance and reliability. This study provides a performance evaluation of different supervised machine learning methods for diagnosing photovoltaic cells. Solar power plant datasets containing environmental and electrical parameters were collected, pre-processed, and used to train various machine learning models, including Decision Tree, Long-Short Term Memory, Multilayer Perceptron, Temporal Convolutional Network, Convolutional Neural Network, Naïve Bayes, Support Vector machine, K-Nearest Neighbor, Random Forest, and Linear Discriminant Analysis algorithms. The models are evaluated using accuracy, precision, recall, f1-score, and confusion matrix metrics. Results demonstrated that the models diagnosed short circuit, abnormal voltage, partial shading, and hotspot faults with varying degrees of accuracy.

关键词： Support vector machines Photovoltaic systems Accuracy Photovoltaic cells Neural networks Voltage Convolutional neural networks

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Localization for V2X communication with noisy distance measurement

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International Journal of Intelligent Networks 2023年第1期4卷 355-360页

作者： Javed, Iram Tang, Xianlun Saleem, Muhammad Asim Javed, Ashir Zia, Muhammad Azam Shoukat, Ijaz Ali School of Computer Science and Technology Chongqing University of Posts and Telecommunications China Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Aging Research Center Karolinska Institutet Stockholm171 65 Sweden Department of Computer Science University of Agriculture Faisalabad Pakistan Riphah College of Computing Riphah International University Faisalabad Campus Pakistan

Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47. © 2023 The Authors

关键词： Extended Kalman filters

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Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet machine learning Model 23

Stroke Home Rehabilitation Approach Using Mobile Application...

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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9798400700712

Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabilities or mobility problems in affected body parts, either on one or both sides of the body. Physiotherapy exercises are the primary treatment and medication for stroke patients, necessitating daily monitoring by a physiotherapist. However, this approach is expensive, and rehabilitation centers and physicians are scarce. Numerous research studies have been conducted to address this issue from various perspectives. This study proposes a Convolutional Neural Network (CNN) based pose net machine learning (ML) model for stroke home rehabilitation using pose detection and classification with a skeleton base model and human pose estimation drawing. We trained our ML model using different human pose images for this model. The tested accuracy of our CNN model is 100% for our exercise pose image test case. Later we build and mobile application for remote rehabilitation. Tested the application with ten different subjects, achieving a 98% for elbow extension, and in the case of elbow flexion and Normal position of balancing both sides, all the persons achieved a 100 % accuracy rate under a laboratory environment. © 2023 ACM.

关键词： Patient treatment

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Undergraduate Students’ Profiles of Cognitive Load in Augmented Reality–Assisted Science learning and Their Relation to Science learning Self-efficacy and Behavior Patterns

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International Journal of Science and Mathematics Education 2024年第2期22卷 419-445页

作者： Lin, Xiao-Fan Wong, Seng Yue Zhou, Wei Shen, Weipeng Li, Wenyi Tsai, Chin-Chung School of Education Information Technology Office 214 GuangDong Engineering Technology Research Center of Smart Learning South China Normal University 55 Zhongshan Dadao Xi Guangzhou 510631 China Guangdong Provincial Institute of Elementary Education and Information Technology Guangzhou China Guangdong Provincial Philosophy and Social Sciences Key Laboratory of Artificial Intelligence and Smart Education Institute for Artificial Intelligence Education Guangzhou China Centre for Internship Training and Academic enrichment (CITrA) University of Malaya Kuala Lumpur Malaysia Program of Learning Sciences National Taiwan Normal University Taipei Taiwan Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan

Research evidence indicated that a specific type of augmented reality–assisted (AR-assisted) science learning design or support might not suit or be effective for all students because students’ cognitive load might differ according to their experiences and individual characteristics. Thus, this study aimed to identify undergraduate students’ profiles of cognitive load in AR-assisted science learning and to examine the role of their distinct profiles in self-efficacy together with associated behavior patterns in science learning. After ensuring the validity and reliability of each measure, a latent profile analysis confirmed that 365 Chinese undergraduates carried diverse dimensions of cognitive load simultaneously. The latent profile analysis findings revealed four fundamental profiles: Low Engagement, Immersive, Dabbling, and Organized, characterized as carrying various respective cognitive loads. The multivariate analysis of variance findings revealed different levels of the six AR science learning self-efficacy dimensions across profiles. Low Engagement students displayed the lowest self-efficacy among all dimensions. Organized students recorded better conceptual understanding and higher-order cognitive skills than Dabbling ones. Students with the Immersive profile had the highest science learning self-efficacy. The lag sequential analysis results showed significant differences in behavior patterns among profiles. Among them, profiles with social interaction, test, and reviewing feedback behavior had a significantly higher score for self-efficacy than those patterns mainly based on test learning and resource visits. This finding provides a unified consideration of students’ diverse profiles and can inform interventions for effective design of AR-assisted science learning to match appropriate strategies to facilitate the science learning effect. © 2023, National Science and technology Council, Taiwan.

关键词： Augmented reality Behavior Cognitive load Science learning Self-efficacy

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PROTLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training

arXiv

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arXiv 2024年

作者： Zhuo, Le Chi, Zewen Xu, Minghao Huang, Heyan Zheng, Heqi He, Conghui Mao, Xian-Ling Zhang, Wentao School of Computer Science and Technology Beijing Institute of Technology China Beihang University China Center for Machine Learning Research Peking University China State Grid Smart Grid Research Institute Co. Ltd China Shanghai Artificial Intelligence Laboratory China

We propose PROTLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. PROTLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs where the natural language text is interspersed with an arbitrary number of proteins. Besides, we propose the protein-as-word language modeling approach to train PROTLLM. By developing a specialized protein vocabulary, we equip the model with the capability to predict not just natural language but also proteins from a vast pool of candidates. Additionally, we construct a large-scale interleaved protein-text dataset, named InterPT, for pre-training. This dataset comprehensively encompasses both (1) structured data sources like protein annotations and (2) unstructured data sources like biological research papers, thereby endowing PROTLLM with crucial knowledge for understanding proteins. We evaluate PROTLLM on classic supervised protein-centric tasks and explore its novel protein-language applications. Experimental results demonstrate that PROTLLM not only achieves superior performance against protein-specialized baselines on protein-centric tasks but also induces zero-shot and in-context learning capabilities on protein-language tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Proteins

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Brain Cancer Tumor Detection by U-Net Deep learning Algorithm from MRI Images 12

Brain Cancer Tumor Detection by U-Net Deep Learning Algorith...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Das, Utpal Chandra Benjapolakul, Watit Gupta, Manoj Vitoonpong, Timporn Suanpang, Pannee Somthawinpongsai, Chanyanan Butdisuwan, Sujin Nanthaamornphong, Aziz Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Dept. of Rehabilitation Medicine King Chulalongkorn Memorial Hospital Dept. of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Department of Information Technology Faculty of Science and Technology Suan Dusit University Bangkok Thailand Shinawatra University Pathum Thani12160 Thailand College of Computing Prince of Songkla University Phuket Campus Phuket Thailand

ISBN: (纸本)9798350387315

This research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas. © 2024 IEEE.

关键词： Gene expression

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Sunflower Disease Identification using Deep learning: A data-driven approach

Sunflower Disease Identification using Deep Learning: A data...

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International Conference on Computer and Information technology (ICCIT)

作者： Amir Sohel Md. Murshidul Alam Sarker Utpal Chandra Das Pranajit Kumar Das Shah Md Tanvir Siddiquee Sheak Rashed Haider Noori Ngoc Thien Le Computer Science and Engineering Daffodil International University Dhaka Bangladesh Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Electrical Engineering Chulalongkorn University Bangkok Thailand Computer Science and Engineering Sylhet Agricultural University Sylhet Bangladesh

The sunflower (Helianthus annuus) is considered to possess a low to moderate susceptibility to drought conditions. However, production has reduced as a result of some of its illnesses. Therefore, it is necessary to take steps to identify the diseases of plants earlier. Disease identification is a time-consuming process that cannot be achieved simultaneously. In this study we have proposed a novel strategy utilizing deep learning techniques to precisely detect and classify three distinct diseases affecting sunflower leaves, as well as molds, based on image analysis. A collection of 466 images depicting four distinct types, namely Gray mold, downy mildew, leaf scars, and healthy leaves, were gathered from sunflower leaves and molds found in the agricultural fields of Bangladesh. The dataset undergoes several images preprocessing techniques, including rescaling, augmentation, histogram equalization, contrast stretching, gamma correction, Gaussian noise, and Gaussian filtering. Subsequently, feature extraction methodologies are employed for figuring out the underlying cause of infection in a picture. Four separate evaluation metrics have been implemented in order to assess the performance of each classifier. We have employed a total of five deep-learning methodologies named as InceptionV3, VGG19, MobileNetV2, ResNet152V2, DenseNet201. Among the models taken into consideration, DenseNet201 had the most optimal performance, with a notable accuracy rate of 98.7%.

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