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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Merin, Jovencio Alon, Alvin Sarraga Pinkihan, Joel Sandil, Ricky C. Delos Santos Ligayo, Michael Angelo Arago, Ryan Flores Sevilla, Rovenson Cunanan, Christopher Technological University of the Philippines Dept. of Electrical Engineering Manila Philippines Center for AI Research Department of Trade and Industry Makati City Philippines College of Computing Sciences Ifugao State University Ifugao Philippines College Preparatory Program Kingdom of Saudi Arabia Saudi Arabia College of Engineering Quezon City University Quezon City Philippines Technological University of the Philippines Department of Electrical Engineering Manila Philippines Bulacan State University Christopher Franco Cunanan Computer Engineering Department Bulacan Philippines

ISBN: (纸本)9798350369106

This paper introduces a novel approach for power system inspection using Mask-RCNN, an advanced model for instance segmentation. The accuracy of our model in identifying and diagnosing damaged low- to medium-voltage insulators is exceptional, as evidenced by its Mean Average Precision (mAP) score of 98.7%• Throughout the training process, the model exhibited minimal loss metrics, indicating its proficiency in predicting bounding boxes precisely, segmenting, classifying, and aligning masks. The model exhibited consistent reliability in distinguishing between undamaged and damaged components during the inference process, as seen by its consistently high detection rates for both 'normal intact' and 'damaged broken' insulators. The results demonstrate the practical usefulness of the model and offer promising prospects for enhancing power system maintenance and inspection protocols. The model's consistent and accurate identification of damaged insulators has the potential to significantly enhance the safety and efficiency of power systems, thereby revolutionizing the industry. © 2024 IEEE.

关键词： Insulator contamination

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Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Communications Using Rate Splitting Multiple Access

Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Com...

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IEEE Conference on Vehicular Technology (VTC)

作者： Aamer Mohamed Huroon Yu-Chih Huang Li-Chun Wang Department of Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Institute of Communications Engineering National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University

In this paper, we study the transmission strategy of a ground-based beyond diagonal reconfigurable intelligent surface (BD-RIS), a.k.a RIS 2.0, in a network where multiple unmanned aerial vehicles (UAVs) simultaneously transmit signals to the respective groups of users. It is assumed that each group is assigned subcarriers orthogonal to those assigned to other groups and rate splitting multiple access (RSMA) is adopted within each group. A corresponding mixed integer nonlinear programming problem (MINLP) is formulated, which aims to jointly optimize 1) allocation of BD-RIS elements to groups, 2) BD-RIS phase rotations, 3) rate allocation in RSMA, and 4) precoders. To solve the problem, we propose using generalized benders decomposition (GBD) augmented with a manifold-based algorithm. GBD splits the MINLP problem into two sub-problems, namely the primal and the relaxed master problem, which are solved alternately and iteratively. In the primal problem, we apply block coordinate descent (BCD) to manage the coupling of variables effectively. Moreover, we recognize the manifold structure in the phase rotation constraint of BD-RIS, enabling the Riemannian conjugate gradient (RCG). Simulation results demonstrate the effectiveness of the proposed approach in maximizing spectral efficiency.

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Design of a 3 Degree-of-Freedom Upper-Limb Active Exoskeleton with Cable-Driven Actuators for Neuromotor Rehabilitation 1

Design of a 3 Degree-of-Freedom Upper-Limb Active Exoskeleto...

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1st IEEE Colombian Caribbean Conference, C3 2023

作者： Dias, Eduardo Ulhoa, Pedro Andrade, Rafhael Universidade Federal Do Espírito Santo Department of Mechanical Engineering. Vitória Brazil Universidade Federal Do Espírito Santo Department of Electrical Engineering Vitória Brazil Universidade Federal Do Espírito Santo Graduate Program in Mechanical Engineering Vitória Brazil

ISBN: (纸本)9798350341799

Exoskeletons are wearable mechanisms which, parallel to the wearer's joints, modulate movements through active or passive torque application. While existing upper-limb exoskeletons offer benefits in strength augmentation and therapy, they often suffer from limited degrees of freedom and excessive bulk, hindering their utility in neuromotor rehabilitation. This study introduces a three degree-of-freedom active upper-limb exoskeleton optimized for neuromotor rehabilitation. Employing anthropometric data and rigorous kinematic analysis, the design accommodates elbow flexion/extension, forearm pronation/supination, and hand gripping. The upper limb dynamics, kinematics, and physiology were evaluated to provide adequate joint ranges of motion. Cable-driven actuators in the elbow and forearm joints were designed to provide low worn weight through the relocation of the motor units away from the user's joints. The result is a lightweight, compact exoskeleton that provides optimal conditions for neuromotor rehabilitation. Finally, a preliminary control strategy was tested using myoelectric signals to trigger motion. © 2023 IEEE.

关键词： Actuators

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Detection of polymeric insulator defects based on YOLOv8

Detection of polymeric insulator defects based on YOLOv8

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International Conference on High Voltage engineering and Application, ICHVE

作者： Samuel C. Nóbrega George R. S. Lira Pablo B. Vilar Post-Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Department of Electrical Engineering Federal University of Campina Grande Campina Grande Brazil

ISBN: (数字)9798350374988

ISBN: (纸本)9798350374995

This work proposes using the You Only Look Once (YOLO) architecture for defect detection in suspension polymeric insulators from images. Firstly, an image dataset of 138 kV polymeric suspension insulators was developed. Then, the dataset was augmented by horizontal and vertical flips to increase the training data. Afterward, YOLOv8n and YOLOv8s architectures were trained and used to detect insulator defects from the captured images. The developed models can detect four insulator defects: exposed rod, end fitting corrosion, cracks, and damaged sheds. Finally, the trained models were evaluated based on precision, recall, mean Average Precision (mAP), and confusion matrix. The results showed that YOLOv8s presented satisfactory results and potential for deploying and experimentation on UAVs to monitor polymeric insulators.

关键词： YOLO Training Sensitivity Computational modeling Corrosion Fitting Training data

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Predicting Types of Urinary Crystals and Diameter Using Image Processing Techniques and Deep Learning Models 24

Predicting Types of Urinary Crystals and Diameter Using Imag...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Huang, Yu-Ju Tseng, Yi-Shiou Chen, Tzu-Jung Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Surgery Far Eastern Memorial Hospital Graduate Institute of Medicine Yuan Ze University Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Kidney stones are primarily crystals formed from ion oversaturation in urine. Currently, the diagnosis of kidney stones involves experienced professionals manually interpreting images of urinary crystals under a microscope. In recent years, with the rapid development of artificial intelligence (AI), many researchers have proposed various methods to assist in diagnosis, hoping to improve medical quality. This study explored the application of AI in the medical field, aiming to automate the interpretation of urinary crystal images, such as red blood corpuscles (RBCs), calcium oxalate monohydrate (COM), calcium oxalate dihydrate octahedron (COD-OCT), calcium oxalate dihydrate dodecahedron (COD-DOD), debris, and others, and to use image processing techniques to classify urinary crystal types. This research is expected to assist medical personnel in quickly determining the categories of urinary crystals. Ultimately, our model achieved an F1-score of 0.93, indicating that this architecture has significant potential. © 2024 Copyright ACM.

关键词： Deep learning

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MarUCOD: Unknown but Concerned Object Detection in Maritime Environments 27th

MarUCOD: Unknown but Concerned Object Detection in Maritim...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yoon, Hajung Lee, Yoonji Lee, Hwijun Um, Daeho Choi, Hong Seok Choi, Jin Young Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of NexReal Inc. 178 Digital-ro Geumcheon-gu Seoul Korea Republic of

ISBN: (纸本)9783031784460

Detection of unknown but concerned objects such as diverse unknown suspicious objects in the sea area is a critical problem in military defense applications, but the problem is challenging because 1) a pre-trained detector cannot easily detect unknown but concerned objects, 2) it detects too many unconcerned objects such as usual objects in the coastal land area, and 3) we cannot easily establish a well-performing discriminator to divide the detected objects into unknown and known objects, as well as concerned and unconcerned objects because the unknown objects are not available, whereas the concerned and unconcerned objects are not clearly defined. To tackle this challenge, this paper proposes a real-time framework for unknown but concerned object detection in maritime environments by integrating object detection, segmentation, and out-of-distribution (OOD) detection techniques. In our framework, an object detector finds all object-like foregrounds by setting a low threshold and a segmentation deep-learning network filters out unconcerned foregrounds detected in the coastal land area. After that, to discriminate known or unknown objects among concerned objects detected in the sea area, a discriminator performs unsupervised OOD detection using bisecting K-means clustering. To boost the performance of the proposed framework, we apply a pre-processing scheme and a contrastive separation loss for segmentation. The proposed framework achieves a high detection rate of unknown but concerned objects with minimal detection of unconcerned objects (i.e., minimal false positives), surpassing baseline methods and demonstrating potential for enhanced maritime safety and security. The codes are open at https://***/AIX-Coast-Defense-PIL/MarUCOD. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： K-means clustering

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Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP

Semantic Token Reweighting for Interpretable and Controllabl...

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

作者： Kim, Eunji Shim, Kyuhong Chang, Simyung Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm AI Research an initiative of Qualcomm Technologies Inc. Qualcomm Korea YH Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

ISBN: (纸本)9798891761681

A text encoder within Vision-Language Models (VLMs) like CLIP plays a crucial role in translating textual input into an embedding space shared with images, thereby facilitating the interpretative analysis of vision tasks through natural language. Despite the varying significance of different textual elements within a sentence depending on the context, efforts to account for variation of importance in constructing text embeddings have been lacking. We propose a framework of Semantic Token Reweighting to build Interpretable text embeddings (SToRI), which incorporates controllability as well. SToRI refines the text encoding process in CLIP by differentially weighting semantic elements based on contextual importance, enabling finer control over emphasis responsive to data-driven insights and user preferences. The efficacy of SToRI is demonstrated through comprehensive experiments on few-shot image classification and image retrieval tailored to user preferences. © 2024 Association for Computational Linguistics.

关键词： Embeddings

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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry

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Procedia computer Science 2023年 227卷 307-315页

作者： Muhammad Asrol Sofyan Wahyudi Suharjito Christian Harito Ditdit N Utama Muhammad Syafrudin Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Artificial Intelligence Sejong University Seoul 05006 South Korea

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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computer Vision-Based Hydroponic Lettuce Contour and Area Recognition System

Computer Vision-Based Hydroponic Lettuce Contour and Area Re...

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International Conference on electrical engineering and Informatics, ICEEI

作者： Samuel Raymond Pranowo Zener Lie Sukra Muhammad Zacky Asy'ari Marisa Paryasto Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Electrical Engineering Department Computer Engineering Program Telkom University Bandung Indonesia

Agriculture is a vital industry for the people of Indonesia, but there are several obstacles, including limited space in urban areas and inefficient conventional sorting and harvesting methods. Using computer vision to choose hydroponic lettuce that is ready to be harvested and a robotic arm to bring the selected lettuce yields, the produced system aims to solve this problem. The system's computer vision algorithms include color space conversion, intensity transformation, and an algorithm for following borders. This project's research methodology combines a quantitative approach with an experimental approach, which consists of conducting several trials on the built system. Several trials demonstrated that the computer vision software was able to accomplish the specified objectives. The average success rate of the developed computer vision system is 93%, while the average success rate of the robot arm is 85%.

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Residential Power Satisfaction as a Demand Side Management Approach in Puerto Rico 42

Residential Power Satisfaction as a Demand Side Management A...

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42nd IEEE Central America and Panama Convention, CONCAPAN 2024

作者： Pena Becerra, Carlos A. Pacheco Cano, Willian Sitiriche, Marcel Castro Ortiz, Samira University of Puerto Rico Department of Electrical and Computer Engineering Mayaguez Puerto Rico Universidad Ana G. Méndez - Recinto de Gurabo Electrical and Computer Engineering Program Puerto Rico

ISBN: (纸本)9798350366723

Puerto Rico's electrical infrastructure powers over three million residents, yet energy consumption overcomes generation and continues to rise. This imbalance results in an unreliable power supply and frequent blackouts across the island. One solution to this issue is to increase generation capacity by adding new generating units and enhancing transmission networks, though this is expensive. Another approach is to promote the adoption of rooftop solar generation with storage systems through microgrids. Alternatively, demand-side management (DSM) offers a more cost-effective solution by regulating demand and reducing energy use at the consumer level. This paper explores the unique integration of power satisfaction within DSM strategies to reduce energy consumption while preserving user satisfaction. Utilizing an agent-based model to simulate household appliance behavior, we demonstrate how power satisfaction can reduce energy demand. This innovative approach offers a promising pathway toward a more resilient and sustainable energy future for Puerto Rico. © 2024 IEEE.

关键词： Associative storage

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