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GRU-based Buzzer Ensemble for Abnormal Detection in Industrial Control Systems

computers, Materials & Continua 2023年第1期74卷 1749-1763页

作者： Hyo-Seok Kim Chang-Gyoon Lim Sang-Joon Lee Yong-Min Kim Interdisciplinary Program of Information Security Chonnam National UniversityGwangju61186Korea Major in Computer Engineering Chonnam National UniversityYeosu59626Korea School of Business Administration Chonnam National UniversityGwangju61186Korea Department of Electronic Commerce Chonnam National UniversityYeosu59626Korea

Recently,Industrial Control Systems(ICSs)have been changing from a closed environment to an open environment because of the expansion of digital transformation,smart factories,and Industrial Internet of Things(IIoT).Since security accidents that occur in ICSs can cause national confusion and human casualties,research on detecting abnormalities by using normal operation data learning is being actively *** single technique proposed by existing studies does not detect abnormalities well or provide satisfactory *** this paper,we propose a GRU-based Buzzer Ensemble for AbnormalDetection(GBE-AD)model for detecting anomalies in industrial control systems to ensure rapid response and process *** newly proposed ensemble model of the buzzer method resolves False Negatives(FNs)by complementing the limited range that can be detected in a single model because of the internal models composing *** the internal models remain suppressed for False Positives(FPs),GBE-AD provides better *** addition,we generated mean prediction error data in GBE-AD and inferred abnormal processes using soft and hard *** confirmed that the detection model’s Time-series Aware Precision(TaP)suppressed FPs at 97.67%.The final performance was 94.04%in an experiment using anHIL-basedAugmented ICS(HAI)Security Dataset(ver.21.03)among public datasets.

关键词： Industrial control system abnormal detection ensemble learning HAI dataset

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Meta-learning Based Obstructive Sleep Apnea Detection Using Single-Lead ECG Signals 14

Meta-learning Based Obstructive Sleep Apnea Detection Using ...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： He, Yinxian Zhou, Yu Kang, Kyungtae Hanyang University Major in Bio Artificial Intelligence Dept. of Computer Science and Engineering Ansan Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan Korea Republic of

ISBN: (纸本)9798350313277

As a respiratory syndrome correlated with some cardiovascular diseases, obstructive sleep apnea (OSA) not only destroys the quality of our sleep, but also induces a variety of major chronic diseases such as heart disease, and diabetes, and even causes sudden death during sleep. Many studies have been conducted on the classification of OSA from normal events by machine learning. However, we found that differences in patients caused by the individuality of the ECG patterns and variability in the ECG do not create optimal rules for OSA classification by ECG signals. It is necessary to reduce the impact of individual differences in classification. In this study, we propose a method based on meta-learning to detect OSA using a 2D time-frequency scalogram that is converted from a single-lead ECG signal. According to the experiment results, we achieved 68.10% accuracy, 69.19% sensitivity, 67.71% specificity, and a 0.694 F1 score. The results showed this method based on meta-learning using the Siamese network is feasible without being pre-trained by massive 2D scalogram representations. © 2023 IEEE.

关键词： Electrocardiogram Medical diagnoses Obstructive sleep apnea Siamese network

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Mer for cognition: An emotion analysis algorithm in identifying moods of song and valence through musical features 12

Mer for cognition: An emotion analysis algorithm in identify...

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2023 12th International Conference on Information and Electronics engineering, ICIEE 2023

作者： Leysa, C.M. De Castro, J. Gerardo, B. Byun, Yung-Cheol Dionio, Rosel L. College of Information Communications Technology West Visayas State University Philippines Department of Computer Engineering Major of Electronic Engineering Jeju National University Institute of Information Science Technology Jeju63243 Korea Republic of Computer Science Department Capiz State University Roxas City Philippines

This study aimed to classify the moods of the song based on musical features that include Acousticness, danceability, energy, Instrumentalness, loudness, liveliness, valence, tempo, and Speechiness. The researcher used the circumplex plane of James Russell, which has four quadrants. Quadrant 1 (Q1) says that when arousal is higher than 5 and valence is lower than 5, the mood is happy. Quadrant 3 (Q3) says that when arousal is lower than 5 and valence is higher than 5, the mood is sad, and Quadrant 4 (Q4) says that when arousal is lower than 5 and valence is higher than 5, the mood is calm. The desktop application music player was integrated to automatically categorize the moods of the songs after testing and training. The leverage of the TensorFlow algorithm will be the basis for classification. © Published under licence by IOP Publishing Ltd.

关键词： Emotion Recognition

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Prediction Model based on Bagging and Boosting Ensemble Technique for Decision Support System of Autonomous Smart IIoT Smart Aquaponic System 12

Prediction Model based on Bagging and Boosting Ensemble Tech...

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2023 12th International Conference on Information and Electronics engineering, ICIEE 2023

作者： Rodriguez, Rani E. De Castro, Joel T. Sansolis, Evans B. Gerardo, Bobby D. Byun, Yung-Cheol College of Information and Communications Technology West Visayas State University Iloilo5000 Philippines Department of Computer Engineering Major of Electronic Engineering Jeju National University Institute of Information Science Technology Jeju63243 Korea Republic of

The aqua-agriculture food sector, challenged by climate change, exploited biodiversity, food insecurity, water crisis, and global pandemic, urgently needs the sustainability transition and more sustainable practices. Sustainability transitions aim to transform current patterns of production and consumption into sustainable ones while enhancing the ecological and economic situation. This agenda is much possible with the integration of IIoT and AI/ML into the aqua agriculture. In this study, the researchers integrated a Prediction Model based on Bagging and Boosting Ensemble Technique in the Decision Support System of an Autonomous Smart IIoT Smart Aquaponic System that autonomously monitored, controlled, and managed the aquaponic systems. © Published under licence by IOP Publishing Ltd.

关键词： Decision support systems

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Enhancing Predictive Battery Maintenance Through the Use of Explainable Boosting Machine 1

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Jafari, Sadiqa Byun, Yung-Cheol Department of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Jeju National University Institute of Information Science & Technology Jeju63243 Korea Republic of

ISBN: (数字)9783031441462

ISBN: (纸本)9783031441455

Battery Remaining Useful Life (RUL) prediction is crucial for the predictive maintenance of lithium-ion batteries. This paper presents a study on applying an Explainable Boosting Machine (EBM) for RUL prediction of lithium-ion batteries. EBM is a machine learning technique that combines the benefits of gradient boosting and rule-based systems, making it highly interpretable and suitable for applications in safety-critical domains. We evaluated the performance of EBM compared to other machine learning techniques and demonstrated its superiority in accuracy, interpretability, and robustness. The results show that EBM can accurately predict the RUL of lithium-ion batteries;the interpretability of EBM provides insights into the factors that affect battery RUL and enables a better understanding of battery degradation mechanisms. The proposed method highlights the potential of EBM for battery RUL prediction and guarantees the secure and reliable functioning of batteries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Lithium-ion batteries

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Breast Cancer Classification using Random Forest Algorithm 12

Breast Cancer Classification using Random Forest Algorithm

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2023 12th International Conference on Information and Electronics engineering, ICIEE 2023

作者： Batool, Amreen Byun, Yung-Cheol Department of Electronic Engineering Institute of Information Science Technology Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Jeju National University Institute of Information Science Technology Jeju63243 Korea Republic of

This study uses detect breast cancer based on Random Forest (RF). It is crucial to diagnose the illness to identify treatment solutions closely linked to patient safety. Breast cancer is diagnosed using past medical records and various classification methods used in data mining fields today. Each technique performs differently depending on the input feature types and model parameters. Neutral Networks have been proven to be more effective in data analysis and pre-diagnosis without requiring medical knowledge. The study reduces diagnostic variance and increases diagnostic accuracy by overcoming the limitation of individual models. The Random Forest model had a training and validation accuracy of 90% and 91%. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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Device Distribution Scheme of Random Access in Space-Air-Ground Integrated Network for Massive IoT 13

Device Distribution Scheme of Random Access in Space-Air-Gro...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Oh, Joohyun Park, Joohan Youn, Jiseung Im, Jeong-Ju Cho, Sunghyun Major in Bio-Artificial Intelligence Hanyang University Dept. of Applied Artificial Intelligence Ansan Korea Republic of Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of Major in Bio-Artificial Intelligence Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

To support massive random access trials of devices with diverse QoS is a major challenge for massive machine-type communications. Space-air-ground integrated network(SAGIN) can be a promising solution for the congestion issues because the SAGIN can extend preamble pool. In this paper, we propose a device distribution scheme to maximize random access success rate while conserving delay of the packet in SAGIN. The proposed scheme consists of two phases. One is an algorithm in which the device selects the appropriate SAGIN infrastructure to maximize the random access success rate. The other is an algorithm in which devices that have failed previous random access select an infrastructure different from the previously selected infrastructure to resolve congestion. We compare the performance of the proposed algorithm with two benchmark algorithms in terms of random access success rate and delay. © 2022 IEEE.

关键词： Benchmarking

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A System Design for 3D Visualization of Underwater Multi-Target Tracking Based on Unity 3D and GMPHD Filter

A System Design for 3D Visualization of Underwater Multi-Tar...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Seungho Kim Scott Uk-Jin Lee Department of Computer Science and Engineering Major in Bio-Artificial Intelligence Hanyang University Ansan Republic of Korea Department of Computer Science and Engineering Hanyang University Ansan Republic of Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper proposes a design for a visualization system for underwater multi-target tracking based on the Gaussian Mixture Probability Hypothesis Density (GMPHD) filter using Unity 3D engine. Traditional analysis methods for underwater tracking algorithms rely primarily on two-dimensional graphs and numerical data, making it challenging to intuitively understand the complex behavior of tracking systems in three-dimensional underwater environments. Our proposed system design provides a framework for 3D visualization of target movements, sonar sensor detection areas, and filter estimation results, along with an interactive interface for parameter adjustment. The proposed architecture consists of four main components: simulation core, visualization module, environment simulation, and user interface, which would enable researchers to analyze algorithm performance under various scenarios effectively. Through real-time visualization and parameter adjustment capabilities, the system is designed to offer immediate feedback on how different parameters affect tracking performance. This architectural approach aims to provide researchers with a more intuitive understanding of the GMPHD filter's operation and facilitate the optimization of tracking algorithms for underwater environments. The proposed system design demonstrates the potential of 3D visualization tools in advancing underwater multi-target tracking research and algorithm development.

关键词： Three-dimensional displays Target tracking Data visualization Systems architecture Filtering algorithms Information filters Real-time systems Underwater tracking System analysis and design Sonar detection

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ReRobo Walker: Robotic Walker with Fall Detection and Active Safety 18

ReRobo Walker: Robotic Walker with Fall Detection and Active...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Ding, Damin Wang, Yagang Zhang, Houji Meng, Qiaoling Yu, Hongliu Lin, Yanzhao Chen, Qiu University of Shanghai for Science and Technology Rehabilitation Engineering and Technology Institute School of Optical-Electrical and Computer Engineering Shanghai China Rehabilitation Engineering and Technology Institute School of Optical-Electrical School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Guangzhou Foreign Language School Guangzhou China University of Shanghai for Science and Technology Rehabilitation Engineering and Technology Institute Shanghai China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China Kogakuin University Major of Electrical Engineering and Electronics Graduate School of Engineering Tokyo Japan

ISBN: (纸本)9798350312201

In recent years, Chinese society has become increasingly ageing while the fertility rate continues to decline. This situation has led to a growing public demand for rehabilitation devices such as walking aids. A novel intelligent robotic walker named ReRobo Walker is proposed which can assist groups of elderly people with dysfunctional legs with rehabilitation training and indoor and outdoor walking. We design robust mechanical structures for robotic walker, install special 3D force sensor, 2D LIDAR and other sensors, and design new algorithms to enable intelligent functionality while guaranteeing the safety of robotic walker.(1) Real-time monitoring of the user's physical status, such as falls, through laser range sensors and 3D force sensor;(2) Modelling of the scene through 2D LIDAR for path planning, obstacle avoidance and navigation functions;(3) Precise control through LADRC-based algorithm for uphill assistance, downhill control and prevention of sharp shifts of the robotic walker. Experiment results demonstrate the solid mechanical structure, stable reliability and the effectiveness of intelligent control algorithms of the intelligent robotic walker. © 2023 IEEE.

关键词： Walking aids

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Runtime WCET Estimation Using Machine Learning 23

Runtime WCET Estimation Using Machine Learning

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29th Annual International Conference on Mobile Computing and Networking, MobiCom 2023

作者： Yun, Sangwoon Kang, Kyungtae Dept. of Computer Science and Engineering Hanyang University Ansan60211585 Korea Republic of Dept. of Artificial Intelligence Hanyang Univeristy Ansan60211585 Korea Republic of Major in Bio Artificial Intelligence Korea Republic of

ISBN: (纸本)9781450399906

Accurate task execution time estimation is vital for efficient and dependable operation of safety-critical systems. However, modern automotive functions' complexity challenges conventional estimation methods. To address this, we propose a novel technique that combines execution time and job sequence data using a multi-layer perceptron (MLP) neural network. Leveraging MLP's capabilities, our approach achieves impressive 99.7% prediction accuracy with a mere 38.33 μs latency. Integrating our technique into safety-critical systems optimizes resource allocation and scheduling, enhancing performance and reliability. Importantly, our method extends beyond automotive systems, finding potential in diverse safety-critical domains. By precisely estimating task execution time, we enhance operational efficiency and decision-making in complex systems. © 2023 ACM.

关键词： Real time systems

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