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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Akbar, Muhamad Nurmaini, Siti Partan, Radiyati Umi Universitas Sriwijaya Program of Engineering Science Fakulty of Engineering Palembang Indonesia Universitas Sriwijaya Intelligent System Research Group Palembang Indonesia Universitas Sriwijaya Faculty of Medicine Palembang Indonesia

ISBN: (纸本)9798350307306

Although deep learning has resulted in tremendous success for image classification processing, speech processing, and video detection processing applications in recent years, most of the training uses sub-optimal hyperparameters, requiring unnecessarily long training time. The Setting hyperparameters remains a black box which requires considerable experience to acquire. This study proposes several efficient ways to adjust hyperparameters that significantly reduce training time and improve model performance. Hyperparameters are used for the classification of arrhythmias. Classification is used for 16 classes that get an accuracy value 98.88%. Apart from tuning learning rate and batch size, this research also tried several scenarios of optimizer, ratio training set, validation set, and testing set;where the ratio 70 : 10 : 20 makes a significant contribution to the accuracy value. © 2023 IEEE.

关键词： Electrocardiograms

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A Vision-Based Neural Networks Model for Turbine Trench-Filler Diagnosis 1

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7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023

作者： Isaza, Cesar Guerrero-Garcia, Fernando Anaya, Karina Jenab, Kouroush Ortega-Moody, Jorge Networking and Telecommunication Academic Program Polytechnic University of Queretaro Queretaro Mexico Productive System Master Academic Program Polytechnic University of Queretaro Queretaro Mexico School of Engineering and Computer Science Morehead State University MoreheadKY40351 United States

ISBN: (数字)9783031396199

ISBN: (纸本)9783031396182

Vision-based neural networks as artificial intelligence models have been critical in many manufacturing industries, including automotive, food, and aerospace. Machine vision and deep learning have provided practical, promising, and accessible innovations to address many problems during manufacturing and diagnostics. Recent studies offer beneficial results in implementing industrial artificial intelligence systems that require determining, comparing, and evaluating optimal technological solutions. The aerospace industry has to deal with the problem of diagnosing critical components that continually need the expertise of a trained human being. On the other hand, automatic diagnostics are becoming a critical technology to deal with this problem. However, these systems require a particular configuration of computer vision algorithms which, in the case of turbine trench-filling components, have yet to be added to the literature. Considering the above, we report in this paper a new methodology that uses a pre-trained deep neural network framework available online to automatically diagnose geometric nonconformities in aeronautical components that protect the turbines from vibrations and reduce noise from the engines to the aircraft deck. The method is based on the following stages: a computer vision system with a monochrome camera to acquire images, training of deep neural networks with transfer learning, and a stage to analyze nonconformities automatically. In addition, a benchmark of several pre-trained deep neural networks is presented to address the problem. According to the experimental results, the YOLO deep neural network topology significantly contributes to automatic diagnosis with high precision and exact rate. Finally, we show that integrating deep neural networks with control graphs is a promising strategy to optimize online diagnostics in the production lines of the aeronautical industry. © The Author(s), under exclusive license to Springer Nature Switzerland

关键词： Deep neural networks

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optimization of Configuration Hybrid Power Generation system at Oil Refinery 4

optimization of Configuration Hybrid Power Generation System...

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4th International Conference on High Voltage engineering and Power systems, ICHVEPS 2023

作者： Syafputra, Muhamad Apriyudi Waluyo, Joko Imawan, Roni Universitas Gadjah Mada Magister Program in System Engineering Yogyakarta Indonesia Universitas Gadjah Mada Department of Mechanical and Industrial Engineering Yogyakarta Indonesia Universitas Gadjah Mada Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350318678

The oil refinery industry has a potential emission reduction by combining its fossil fuel with Renewable Energy usage. One alternative to reduce carbon emissions is the hybrid power system using solar panels for electricity generation. This research will observe the optimum scenario for utilizing a hybrid power system. The research subject is observed refinery facilities that will be built in Indonesia, where the electricity source used formerly comes from fossils. The monitored parameters are Cost of Energy (COE) and carbon emission. optimization involves three scenarios involving basic and hybrid configurations. The basic scenario is the initial configuration of the electricity supply with power source from the generator and PLN (national's utility). The first scenario is a hybrid power plant utilizing the roofs of existing buildings in the refinery area. The second is a hybrid power plant with a battery to supply electricity at night, at the capacity of renewable energy (RE) of 20% and 40%. Compared to the basic scenario, scenario 1 increases COE by 0.17% and reduces carbon emission at 0.3% compared to the basic scenario. Scenario 2 with 20% and 40% of renewable energy are obtained at COE of 4.7% and 44.3%, while reducing emissions occurred at 38% and 52.7%, respectively. The scenario of a hybrid power system with a 20% renewable energy is the optimal result compared to the other scenarios. © 2023 IEEE.

关键词： Renewable energy resources

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Underwater Robot Navigation Through Compensation of Sensor Misalignment in Non-linear Space 23

Underwater Robot Navigation Through Compensation of Sensor M...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Jeong, Da Bin Choi, Woong Ko, Nak Yong Interdisciplinary Program in IT- Bio Convergence System Graduate School Chosun University Department of Electronic Engineering Gwangju61452 Korea Republic of College of Ict Construction Welfare Convergence Kangnam University Gyeonggi-do16979 Korea Republic of Chosun University Interdisciplinary Program in IT-Bio Convergence System Department of Electronic Engineering Gwangju61452 Korea Republic of

ISBN: (纸本)9788993215274

This paper presents a novel approach to estimate and compensate for the misalignment between the Inertial Measurement Unit (IMU) and Doppler Velocity Log (DVL) sensors. Estimating the location and attitude of an ROV usually combines the attitude by IMU and the velocity by the DVL provided in the sensor coordinate system. The attitude is regarded as the attitude of the ROV and the velocity in the sensor coordinate frame is converted to the velocity in the inertial coordinate frame. Misalignment in the DVL and IMU causes errors in the converted velocity and results in the degradation of the navigation performance. The proposed method represents the misalignment using unit quaternion. Especially, the method utilizes the Lie theory to deal with the operations on unit quaternions which constitute a Lie group, called the 3-sphere group. Simulation and testing of the method in a marine environment validate its effectiveness and practical applicability. © 2023 ICROS.

关键词： Remotely operated vehicles

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Vessel-position Tracking in 3-sphere Space Using an Interacting Multiple Model Based on Lie Theory

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Journal of Institute of Control, Robotics and systems 2024年第6期30卷 643-649页

作者： Jeong, Da Bin Ko, Nak Yong Department of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Korea Republic of

This paper proposes a method for tracking vessel positions using an interacting multiple model approach based on Lie theory in 3-sphere space. Attitude increments and errors are evaluated and corrected using Lie algebra, as well as exponential and logarithmic operations. The covariance of attitudes is appropriately represented in Lie algebra. The proposed method is compared with two methods, i.e., the unscented Kalman filter (UKF) in which the attitude is represented by a unit quaternion (quaternion-utilizing UKF) and the UKF with a single model that uses Lie theory (Lie theory-incorporating UKF). The quaternion-utilizing UKF represents the attitude using quaternions without incorporating Lie theory, whereas the Lie theory-incorporating UKF represents the attitude as a unit quaternion and utilizes Lie theory. The comparisons indicate that the proposed method effectively reduces tracking errors. Additionally, the proposed method adapts to changes in maneuvering conditions using optimized model probabilities, ensuring timely and appropriate responses. The robustness and effectiveness of the proposed method in various maneuvering scenarios demonstrate its potential for enhancing vessel tracking accuracy in real-world applications. © ICROS 2024.

关键词： Exponential functions

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Bibliometric Analysis of Trends and Directions in Machine Learning Applied to FinTech 4

Bibliometric Analysis of Trends and Directions in Machine Le...

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4th International Conference on Electronic and Electrical engineering and Intelligent system, ICE3IS 2024

作者： Arif, Erman Suherman, Suherman Widodo, Aris Puji Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350378368

Our article presents a comprehensive analysis of trends and directions in the application of machine learning in the FinTech sector. By utilizing data from academic databases such as Scopus, our objective is to showcase the advancements in this interdisciplinary research area. The study identifies key trends, influential authors, leading journals, and significant collaborative networks that shape the field. The methodology employed involves quantitative bibliometric analysis to evaluate publications from the past five years. The results indicate a substantial increase in the number of studies during this period, highlighting the importance of machine learning in addressing critical financial challenges, including fraud detection, algorithmic trading, credit scoring, and risk management. Additionally, we delve into the main themes, countries, and institutions that dominate the current research agenda. This analysis provides valuable insights into the present state and future direction of machine learning in FinTech, serving as a resource for researchers and practitioners seeking to comprehend and contribute to this dynamic and rapidly evolving field. We also discuss implications for future research, emphasizing the necessity for continuous innovation and interdisciplinary collaboration to maximize the potential of machine learning in transforming financial services. © 2024 IEEE.

关键词： Adversarial machine learning

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Technology-Enhanced Learning for User Security Awareness Using AI-based Naive RAG: A Design and Prototype

Technology-Enhanced Learning for User Security Awareness Usi...

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2025 International Conference on Advancement in Data Science, E-learning and Information system, ICADEIS 2025

作者： Pratama, I. Putu Agus Eka Widyantara, I. Made Oka Linawati Gunantara, Nyoman Suakanto, Sinung Irawan, Eka Tresna Udayana University Doctoral Program of Engineering Sciences Faculty of Engineering Bali Badung Indonesia Udayana University Dept. of Electrical Engineering Faculty of Engineering Bali Badung Indonesia Telkom University Dept. of Information System West Java Bandung Indonesia Mayar International Pte. Ltd. Singapore

ISBN: (纸本)9798331513320

The increasing use of Information Technology (IT) in all areas of life, followed by increasing cybersecurity threats on the user side, makes cybersecurity awareness among users an obligation that must be met. Several solutions currently available (guides, discussion forums, workshops) have not been able to provide good results and are lacking in terms of user engagement, adaptability, and optimizing available resources, so technology-based learning is needed, especially AI. This paper introduces the design and prototype of an AI-based learning system, especially with the Naive Retrieval-Augmented Generation (Naive RAG) method with three steps, including Retrieval, Augmented, and Generation. The system combines contextual user questions and answers with internal and domain knowledge systems to explain cybersecurity awareness to users dynamically and personally. The prototype that has been developed is then tested using the Black Box Testing method to evaluate functionality and usability. The test results show that the functionality of the system built has run well with a percentage of success of 100%, so it can be used as a technologybased learning medium for education and learning of user security awareness. © 2025 IEEE.

关键词： Adversarial machine learning

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Beamspace AIC & MDL Algorithm for Based on Concentric Circular Array Antenna

Beamspace AIC & MDL Algorithm for Based on Concentric Circul...

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Heui-Seon Park Suk-seung Hwang Department of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Gwangju Korea Interdisciplinary Program in IT-Bio Convergence System School of Electronic Engineering Chosun University Gwangju Korea

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

In modern wireless communication systems using multiple antenna elements, accurately identifying the signals contained in the received signal is very important. Conventional signal number estimation algorithms operate for the entire set of antenna elements, resulting in high computational complexity. In order to enhance this high computational complexity problem, this paper proposes beamspace based Akaike Information Criterion (AIC) and Minimum Description Length (MDL) algorithms based on a concentric circular array (CCA) antenna for efficient signal number estimation. In addition, computer simulation results are presented to evaluate the signal-number estimation performance of the proposed algorithm.

关键词： Wireless communication Array signal processing Computer simulation Estimation Computational complexity MIMO communication Antenna arrays

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Contact Lens Automatic Optical Inspection Method Based on YOLOv9 13

Contact Lens Automatic Optical Inspection Method Based on YO...

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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： Kim, Tae-Yun Lee, Bumshik Hwang, Suk-Seung Institute of Ai Convergence Chosun University Gwangju Korea Republic of Chosun University Department of Information and Communications Engineering Gwangju Korea Republic of School of Electronic Engineering Chosun University Interdisciplinary Program in IT-Bio Convergence System Gwangju Korea Republic of

ISBN: (纸本)9798350373332

Traditional inspection methods for contact lenses primarily involved manual examination, where workers directly assessed the physical properties and manufacturing defects of the lenses. Consequently, the quality determination relied on the workers' judgment, leading to inconsistencies. In order to address this issue, although Automated Optical Inspection (AOI) was introduced, it has limitations in applying to various types of contact lenses. As part of research to utilize deep learning in AOI, this paper introduces a You Only Look Once (YOLO) v9-based contact lens inspection technique that leverages a small dataset of contact lens images. This method involves training YOLO v9 with the limited number of images that include various defects such as bubbles, cracks, and foreign particles, as well as images of defect-free contact lenses. © 2024 IEEE.

关键词： Contact lenses

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CorrA: Leveraging Large Language Models for Dynamic Obstacle Avoidance of Autonomous Vehicles

arXiv

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arXiv 2025年

作者： Wang, Shanting Typaldos, Panagiotis Malikopoulos, Andreas A. System Engineering Program Cornell University IthacaNY United States School of Civil and Environmental Engineering Cornell University IthacaNY United States School of Civil and Environmental Engineering The System Engineering Program Cornell University IthacaNY United States

In this paper, we present Corridor-Agent (CorrA), a framework that integrates large language models (LLMs) with model predictive control (MPC) to address the challenges of dynamic obstacle avoidance in autonomous vehicles. Our approach leverages LLM reasoning ability to generate appropriate parameters for sigmoid-based boundary functions that define safe corridors around obstacles, effectively reducing the state-space of the controlled vehicle. The proposed framework adjusts these boundaries dynamically based on real-time vehicle data that guarantees collision-free trajectories while also ensuring both computational efficiency and trajectory optimality. The problem is formulated as an optimal control problem and solved with differential dynamic programming (DDP) for constrained optimization, and the proposed approach is embedded within an MPC framework. Extensive simulation and real-world experiments demonstrate that the proposed framework achieves superior performance in maintaining safety and efficiency in complex, dynamic environments compared to a baseline MPC approach. © 2025, CC0.

关键词： Constrained optimization

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