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Differential Privacy-Based Data Collection for Improving Data Utility and Reducing Computational Overhead

Transactions of the Korean Institute of electrical Engineers 2025年第1期74卷 102-108页

作者： Kim, Jong Wook Dept. of Computer Science Sangmyung University Korea Republic of

With the rise of real-time data collection through mobile devices such as smartphones, user-driven decision-making systems in various fields such as transportation and healthcare have advanced significantly. However, increasing concerns about the privacy of sensitive information have emerged, leading to the widespread use of differential privacy (DP) to mitigate these risks. Among DP-based data collection methods, local differential privacy (LDP) and distributed differential privacy (DDP) are prominent. While LDP provides low computational overhead, it compromises data utility, while DDP provides better utility at the cost of higher computational overhead. To address these limitations, this paper introduces a DP-compliant (θ,λ) data collection method. The proposed approach collects a fraction of θ of the total items and a fraction of λ of the total user data, achieving improved data utility compared to LDP while significantly reducing computational overhead compared to DDP, resulting in more efficient data collection. Experimental results on real-world datasets show that the proposed method not only improves data utility over LDP, but also effectively reduces computational overhead over DDP. Copyright © The Korean Institute of electrical Engineers.

关键词： Data collection

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computer Vision-Based Object Detection and dept. Estimation to Generate Point Clouds and Estimate Distances

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Transactions of the Korean Institute of electrical Engineers 2025年第1期74卷 164-169页

作者： Eom, Tae-Hyun Paik, Woojin Dept. of Computer Engineering Konkuk University Glocal Campus Korea Republic of Dept. of Computer Engineering Konkuk University Glocal Campus Korea Republic of

This research proposes a distance estimation method using Mono Camera-based object detection and dept. estimation to generate Point Cloud data. The study aims to enhance the applicability of Mono Cameras in autonomous vehicles and robots, reducing costs compared to Stereo Camera systems. The method utilizes YOLOv8 for object detection and dept. Anything V2 for dept. estimation. Results indicate that while the proposed method offers potential, it exhibits higher error rates in distance estimation compared to Stereo Camera-based approaches, primarily due to the limitations of current dept. estimation technologies. The study highlights the need for further improvements in dept. estimation models, particularly to address environmental factors such as lighting. Additionally, the research demonstrates that combining dept. estimation with bounding box methods helps reduce estimation errors, showing promise for more stable performance. Future work will focus on improving dept. estimation accuracy and making the proposed method more efficient for real-time applications, with the potential to integrate into Visual SLAM, a key technology in autonomous driving systems. © 2025 Korean Institute of electrical Engineers. All rights reserved.

关键词： Stereo image processing

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Statistical Thresholding of Ultrasonic Amplitude Maps for Automated Damage Segmentation

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Journal of Aeronautics, Astronautics and Aviation 2025年第3期57卷 319-327页

作者： Yn, Lee Shi Zaludin, Zairil Harley, Joel B. Lee, Jung-Ryul Harmin, Mohammad Yazdi Ciang, Chia Chen Dept. of Aerospace Engineering Universiti Putra Malaysia Selangor Serdang43400 Malaysia Dept. of Electrical and Computer Engineering University of Florida Florida United States Dept. of Aerospace Engineering KAIST Daejeon Korea Republic of

Amplitude maps generated by ultrasound imaging are frequently utilized to visualize invisible damages in thin-walled aero-mechanical structures. Accurate evaluation of damage size from these maps is crucial;however, a reliable automated method for this purpose under the influence of imaging noise is not available. To address this issue, four threshold calculation methods based on statistical analysis of noise content in an amplitude map were developed. These candidates were numerically optimized using amplitude maps containing damages of various sizes and Gaussian noise of differing intensities. The candidate demonstrating the greatest immunity to parameter variations and the highest potential for accurate damage size evaluation was identified. This candidate was then parametrically optimized and benchmarked against k-means clustering. The results demonstrate that the newly proposed statistical thresholding method outperformed k-means clustering, consistently providing high accuracy above 99% even for damages as small as 2 mm and signal-to-noise ratios (SNR) as low as 3 dB. © 2025 The Aeronautical and Astronautical Society of the Republic of China. All rights reserved.

关键词： Thin walled structures

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IEEE Vehicular Technology Society Bylaws Changes [Society News]

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IEEE VEHICULAR TECHNOLOGY MAGAZINE 2025年第1期20卷 111-112页

作者： Rubinstein, Tom Dept. of Electrical & Computer Engineering Auburn University Auburn AL USA

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

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Analysis of Magnetization Characteristic Based B-H Curve Fitting According to the Operation Conditions for Traction Motor

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Transactions of the Korean Institute of electrical Engineers 2025年第2期74卷 310-315页

作者： Kim, Young-Hyun Lee, Jung-Ho Dept. of Electrical Engineering Hanbat University Korea Republic of Dept. of Electrical Engineering Daejeon Institute of Science and Technology Korea Republic of

This paper examines the demagnetization characteristics of rare earth permanent magnets (PMs) used in electric machinery. An analytical model was designed considering a controllable shape, temperature, and the external magnetic field in PMs used in the rotor of an interior permanent magnet synchronous motor (IPMSM) for driving an electric railway vehicle (ERV). The demagnetization process was analysed based on the magnetization and demagnetization theory for PM materials in computer simulation. The result of the analysis can be used to calculate the magnetization and demagnetization phenomenon according to the input B-H curves. So, this paper presents the conditions of demagnetization by the external magnetic field in the running and stopped states and proposes a simulation method that can analyse demagnetization phenomena according to each condition and be used to design the IPMSM that maximizes efficiency and torque characteristics. This was experimentally verified. Copyright © The Korean Institute of electrical Engineers.

关键词： Permanent magnets

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J-NECORA: A Framework for Optimal Resource Allocation in Cloud-Edge-Things Continuum for Industrial Applications With Mobile Nodes

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IEEE Internet of Things Journal 2025年第11期12卷 16525-16542页

作者： Pettorali, Marco Righetti, Francesca Vallati, Carlo Das, Sajal K. Anastasi, Giuseppe Univ. of Pisa Dept. of Information Engineering Italy Missouri Univ. of Science and Technology Dept. of Computer Science RollaMO United States

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of MNs. If the optimal allocation does not exist, J-NECORA provides a best-effort allocation that minimizes the delay. The proposed framework is highly modular and flexible, and can be easily customized to different scenarios. We validated its effectiveness through simulation experiments, focusing on a specific use case. For the considered scenarios, the performance predicted by our framework matches quite closely the simulation results. © 2014 IEEE.

关键词： Cloud platforms

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Improved Object Detection Through a Learning-Based Joint-Initiative Supervised Autonomy (JISA) Framework

Improved Object Detection Through a Learning-Based Joint-Ini...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Tarabay, Andrea Daher, Naseem American University of Beirut Dept. of Electrical and Computer Engineering Beirut Lebanon American University of Beirut Dept. of Electrical and Computer Engineering Dept. of Mechanical Engineering Beirut Lebanon

ISBN: (纸本)9798331529604

Object detection is faced with accuracy and robustness limitations due to unstructured environments with objects of different sizes and occlusions. Despite advancements in object detection models, these models have not reached a point to possess a comparable level of perception to humans, thus limiting their ability to effectively address challenges in diverse environments. Hence, adopting a human-in-the-loop approach in autonomous systems can handle complex perception scenarios. To tackle these issues, the Joint-Initiative Supervised Autonomy (JISA) framework is used to allow a human supervisor to intervene once the agent is uncertain of its predictions based on its self-confidence (SC) metric, or when the human deems the prediction incorrect based on his/her situation awareness (SA). However, the existing JISA framework formally defines the levels of autonomy between the human and the agent, but does not provide a means for learning from human interventions. In this paper, we present an enhanced version of JISA in object detection applications by implementing a learning scheme that is inspired by the Interactive Corrections and Reinforcement for an Epistemic and Aleatoric uncertainty-aware Teaching (ICREATe) algorithm. To address these challenges, we evaluate the results of the proposed framework and demonstrate that the model effectively learns and improves its performance through human feedback. © 2025 IEEE.

关键词： Self-supervised learning

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Enhancing the Kubernetes Platform with a Load-Aware Orchestration Strategy

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SN computer science 2025年第3期6卷 1-15页

作者： Marchese, Angelo Tomarchio, Orazio Dept. of Electrical Electronic and Computer Engineering University of Catania Catania Italy

Microservice architecture is increasingly favored for building large-scale applications designed for deployment in distributed and resource-constrained cloud-to-edge computing environments. As a cloud-native approach, microservices are particularly powerful due to their loosely coupled, independently deployable, and scalable characteristics. These features enable distributed deployment and flexible integration across robust cloud data centers and heterogeneous, often resource-limited, edge nodes. To fully leverage these advantages, there is a need for advanced placement algorithms that can optimize application performance by utilizing the strengths of microservices. In response to these challenges, we propose to extend Kubernetes with a load-aware orchestration strategy, which enhances its capability to deploy microservice applications within shared clusters characterized by dynamic resource usage patterns. Our approach orchestrates applications dynamically, based on real-time resource usage, allowing for continuous adjustment of their placement to match evolving conditions. Evaluation of a prototype system in a testbed environment demonstrates that this approach offers substantial benefits compared to the standard Kubernetes scheduler. © The Author(s) 2025.

关键词： Cloud continuum Container technology Kubernetes descheduler Kubernetes scheduler Microservice architecture

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A Serendipity Recommendation Method for Book Categories Using BERT to Strengthen the Web Service of the Book

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Journal of Web engineering 2025年第2期24卷 199-216页

作者： Kim, Youngmo Kim, Seok-Yoon Park, Byeongchan Dept. of Computer Science and Engineering Soongsil University South Korea

In the field of book search, research on a web service-based user-customized book recommendation system is being conducted to respond to increasingly diverse user requirements. The collaborative filtering algorithm, which is mainly used for book recommendation, has a problem in that it is difficult to reflect the user’s recent interest without considering the changes in preference over time, and the user’s satisfaction decreases because it repeatedly recommends only similar items. In this paper, we propose a book recommendation method using category similarity based on deep learning. The proposed method is to predict books to be used next time by inputting users’ past and current book usage history through BERT, a natural language processing model, and to recommend popular books in other categories with high similarity to the predicted book category in the BERT model to reflect serendipity. This method reflects serendipity, which can lead to users’ recent interests and practical preferences, so that recommendation accuracy and user satisfaction can be satisfied at the same time. © 2025 River Publishers.

关键词： BERT book category recommendation serendipity Web service

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A Study on Verification of Sewage Treatment Expert Operating Experience Using RNN

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Transactions of the Korean Institute of electrical Engineers 2025年第4期74卷 691-699页

作者： Nahm, Eui-Seok Dept. of AI Computer Engineering Far East University Korea Republic of

In this paper, we proposed a data quality and diversity guarantee plan and a method to verify the expert operation experience using RNN and a data quality and diversity guarantee plan to 1) solve the two problems of the sewage treatment expert driving experience and 2) the limitation of situation recognition and judgment. As a solution to the lack of data quality and diversity, not all data are used, but a method of filtering and using only the part that is the basis for determining the expert's experience was proposed. In addition, RNN was used to solve the problem of the limitation of situation recognition and judgment. The reason why RNN is used among various neural networks is that RNN is a neural network architecture for deep learning that predicts time series or sequential data, and sewage treatment data is also time series or sequential data. According to the proposed technique, a RNN model with a reliability of more than 8% was implemented, and the effectiveness of the proposed technique was verified by applying two experiences and one incorrect experience of actual operation experts of the target sewage treatment plant. As a result of the verification, both the actual driving expert's experiences 1 and 2 were verified as valid, and for incorrect experiences, there were 3,442 data that matched the expert's experience and 4,444 data that did not match, so the proposed method can verify the expert's experience. © The Korean Institute of electrical Engineers.

关键词： Sewage treatment plants

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