检索结果-内蒙古大学图书馆

15th Global Information Infrastructure and Networking Symposium, GIIS 2025

作者： Keyela, Patatchona Cherkaoui, Soumaya Polytechnique Montreal Computer Engineering and Software Engineering Dept. Montreal Canada

ISBN: (纸本)9798331520113

RAN slicing technology is a key aspect of the Open RAN paradigm, allowing simultaneous and independent provision of various services such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) through virtual networks that share a single radio access infrastructure. Efficient resource allocation is crucial for RAN slicing, as each service has specific quality of service (QoS) requirements, and a balance between different services must be maintained. Although heuristic and reinforcement learning (RL) techniques have been explored to achieve efficient resource allocation, these approaches face notable limitations: heuristic algorithms face complexity issues that limit their effectiveness in large networks, RL solutions are constrained by their dependency on training data and struggle to adapt to new scenarios and environments. This paper proposes a framework that leverages quantum optimization techniques to optimize radio resource blocks allocation in Open RAN slicing for URLLC and eMBB services. We provide a classical problem formulation and the quantum implementation using the constrained quadratic model on Dwave quantum annealing platform, showcasing the potential of quantum optimization techniques to deliver in real-time optimal solutions for optimization problems in 5G and beyond networks. © 2025 IEEE.

关键词： Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Deeper Anomaly Detection in Industrial Automation 15

Deeper Anomaly Detection in Industrial Automation

引用

15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Jung, Ho-Seok Jang, Sung Won Baek, Youngmi Changshin University Dept. of Computer Software Engineering Korea Republic of Changshin University Dept. of Computer Engineering Korea Republic of

ISBN: (纸本)9798331507695

The security of industrial networks, particularly in industrial automation systems, is critical for ensuring system reliability and protecting sensitive data. This paper proposes a deeper anomaly detection system using the ResNet34 (Residual Network) model to identify and detect cyber-attacks in industrial networks, specifically focusing on Controller Area Network (CAN) systems. The study highlights the vulnerabilities in industrial communication protocols, such as CAN, Modbus, and Ethernet/IP, which are susceptible to cyber-attacks including replay, modification, and fuzzing attacks. These attacks can disrupt the functioning of industrial systems and cause significant damage. Experimental results show that the proposed model achieves a 100% detection rate for all types of cyber-attacks, demonstrating its effectiveness in recognizing abnormal patterns and responding to changes in network behavior. The results confirm that the ResNet34-based deep anomaly detection model can be a valuable tool for strengthening the security of industrial networks by providing real-time detection of cyber-attacks, thereby ensuring the stability and safety of industrial automation systems. © 2025 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A framework for locating multiple RFID tags using RF hologram tensors

引用

Digital Communications and Networks 2025年第2期11卷 337-348页

作者： Xiangyu Wang Jian Zhang Shiwen Mao Senthilkumar CG Periaswamy Justin Patton Dept. of Electrical and Computer Engineering Auburn University RFID Lab Auburn University Department of Electrical and Computer Engineering Kennesaw State University

In this paper, we present a Deep Neural Network(DNN) based framework that employs Radio Frequency(RF) hologram tensors to locate multiple Ultra-High Frequency(UHF) passive Radio-Frequency Identification(RFID) tags. The RF hologram tensor exhibits a strong relationship between observation and spatial location, helping to improve the robustness to dynamic environments and equipment. Since RFID data is often marred by noise, we implement two types of deep neural network architectures to clean up the RF hologram tensor. Leveraging the spatial relationship between tags, the deep networks effectively mitigate fake peaks in the hologram tensors resulting from multipath propagation and phase wrapping. In contrast to fingerprinting-based localization systems that use deep networks as classifiers, our deep networks in the proposed framework treat the localization task as a regression problem preserving the ambiguity between fingerprints. We also present an intuitive peak finding algorithm to obtain estimated locations using the sanitized hologram tensors. The proposed framework is implemented using commodity RFID devices, and its superior performance is validated through extensive experiments.

关键词： Radio-frequency identification (RFID) Ultra-high frequency (UHF) passive RFID tag RF hologram tensor Indoor localization Deep learning (DL) Swin Transformer Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

引用

UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Analysis of Iron Loss Characteristics of PM-Assisted and Fluid-Shaped Synchronous Reluctance Machines for Micro EVs

引用

Transactions of the Korean Institute of Electrical Engineers 2025年第3期74卷 425-431页

作者： Choi, Jaesung Choi, Gilsu Dept. of Electrical and Computer Engineering Inha University Korea Republic of

This paper examines the iron loss characteristics of a permanent magnet-assisted synchronous reluctance machine (PMa-SynRM) and a fluid-shape synchronous reluctance machine (FS-SynRM) for micro EV applications, emphasizing the impact of DC bias fields and minor loops on iron loss estimation. Iron losses are comparatively evaluated using three methods: the Steinmetz method with constant coefficients, the loss separation method with varying coefficients and FFT, and the hysteresis-based model that accounts for minor loops and DC bias fields. The results show that iron losses per unit stack length are higher in the PMa-SynRM, where the effects of DC bias fields and minor loops are more pronounced. Moreover, the Steinmetz and loss separation methods, which neglect these effects, overestimate motor efficiency by up to 1.5% and 0.3%, respectively. This study highlights the importance of incorporating DC bias and minor loops in iron loss and efficiency calculations for micro EV traction machines. © The Korean Institute of Electrical Engineers.

关键词： Permanent magnets

来源：评论

学校读者我要写书评

暂无评论

Study on a Regression Model for the Electromagnetic Characteristics of SPMSM Based on Convolutional Neural Network with Attention Mechanism

引用

Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 302-309页

作者： Ji, Tae-Hyuk Song, In Seok Kim, Hyung-Woo Jung, Sang-Yong Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Surface-mounted permanent magnet synchronous motors (SPMSMs) are widely used in high-performance system. However, they suffer from torque ripple and cogging torque, which degrade output quality and cause noise and vibration, necessitating optimized design. While finite element analysis offers high accuracy for optimization, it comes with significant computational cost. To address this limitation, this paper proposes an attention mechanism-based convolutional neural network (CNN) regression model to predict SPMSM electromagnetic performance. CNNs capture spatial structures of motor designs, while attention mechanisms highlight key design features, boosting prediction accuracy and efficiency. This study analyzes the effects of attention mechanisms and proposes a CNN-based regression model incorporating them, confirming the effectiveness of the attention mechanism through comparisons with conventional CNN models. © The Korean Institute of Electrical Engineers.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Study on Prediction and Accuracy Improvement of SVR Voltage Control in Distribution System Based on Machine Learning

引用

Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 7-14页

作者： Yoo, Byungchan Choi, Wonna Jung, Seungmin Dept. of Electrical and Computer Engineering University of Seoul Korea Republic of

The proportion of distributed energy sources in the distribution network is rapidly increasing. Along with this, voltage management is becoming more difficult, and stability issues in the power system are being reported. Until recently, the voltage of distribution lines has generally been regulated using voltage regulators, predominantly deployed in sections where rapid voltage drops occur. However, as the characteristics of distributed energy sources are reflected, the efficiency of the voltage regulator diminishes. Therefore, cooperation with substations is necessary for stable operation of the distribution network. This paper aims to predict the operation of a machine learning-based voltage regulator to evaluate voltage fluctuations occurring in distribution lines and the corresponding control operations in advance. Long Short-Term Memory is capable of prediction on time scales. Therefore, the output characteristics of distributed energy sources can be considered. Additionally, to improve prediction accuracy, cases are generated in the OpenDSS-Python environment and additional training is performed on the LSTM model. As a result, it was confirmed that the prediction accuracy improved when the proposed method was applied. The machine learning model was verified using four evaluation indices. © 2025 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Power distribution lines

来源：评论

学校读者我要写书评

暂无评论

A Study on Rotor Shape for Reducing Low-Order Radial Force in Interior Permanent Magnet Synchronous Motors for Air Compressor

引用

Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 56-63页

作者： Cheo, Daeseon Kim, Nam-Ho Cho, Jae-Hoon Kwon, Yong-Jun Jung, Sang-Yong Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In this paper, a rotor design to reduce low order raidal force and electromagnetic vibration in interier permanent magnet synchronous motor (IPMSM) for an air compressor is proposed. First, according to the analysis results of radial force and no-load air-gap flux density of V-type 1-layer and 2-layer models, 2-layer model is selected as the initial model since it exhibits lower low radial force than 1-layer model. Additionally, the no-load air-gap flux density of the 2-layer model is further analyzed, confirming that the low order radial forces affected by 20th order harmonic and the fundamental component of no-load air-gap flux density have a 180° phase difference, canceling each other out. Moreover, the sensitivity anlysis of rotor notches reveals that the 3-Notch model effectively increases magnitude of 20th order harmonic. Based on the aformentionted analysis, as a result, by applying notches to the 2-layer model rotor, the low order radial force and electromagnetic vibration are reduced. Copyright © The Korean Institute of Electrical Engineers.

关键词： Permanent magnets

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Optimization of Torque and Torque Ripple in Double V-Type IPMSM Using a Random Forest-Based Modified PSO-GA Hybrid Method

引用

Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 266-272页

作者： Kwon, Yong-Jun Choi, Dae-Sun Wang, Chang-Hyeon Oh, Ho-Jin Yoon, Han-Joon Jung, Sang-Yong Dept. of Electrical and Computer Engineering SungkyunKwan University Korea Republic of

Interior Permanent Magnet Synchronous Machines (IPMSMs) are widely used not only as drive motors for Electric Vehicles (EVs) but also in various industrial fields due to their high efficiency and high power output characteristics. However, because of the embedded magnets, IPMSMs exhibit significant torque ripple during operation, necessitating the use of optimization algorithms to address this issue. For IPMSMs, which have a large number of design variables, the feasible design space is defined by multiple constraints, increasing the complexity of the design optimization process. Therefore, this paper proposes a Random Forest-based Modified PSO-GA hybrid method to perform optimization in the presence of multiple constraints and applies it to the torque and torque ripple improvement design of a Double V-Type IPMSM. ©The Korean Institute of Electrical Engineers.

关键词： Permanent magnets

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：