检索结果-内蒙古大学图书馆

2022 IEEE International Conference on electrical, Computer, and Energy Technologies, ICECET 2022

作者： Choi, Yunyeong Lee, Jungwon Shin, Hyungsoon Sun, Wookyung Ewha Womans University Dept. of Electronic and Electrical Engineering Seoul Korea Republic of Seoul National University Dept. of Electrical and Computer Engineering Seoul Korea Republic of Ewha Womans University Smart Factory Multidisciplinary Program Seoul Korea Republic of

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

This paper proposes a new authentication method that enables mutual verification between two different PUFs without a server. The output signal (response) of one PUF is hidden in the opponent's input signal (challenge) list by interlinking the challenge and response information of the two PUFs. In this way, when sending and receiving authentication information, a challenge list index is used instead of exposing the challenge or response information to the network directly. The verification information for checking the validity of the received index is protected with encryption using a hash function. Also, two novel PUF systems, key-map PUF and cube PUF, are presented that improve the security of the PUF-based system using previous authentication information. The proposed authentication method and PUF systems can be used for IoT devices. © 2022 IEEE.

关键词： Hash functions

来源：评论

学校读者我要写书评

暂无评论

Low-complexity Anomaly Detection Method based on Feature Importance using Shapley Value

Low-complexity Anomaly Detection Method based on Feature Imp...

引用

International Conference on Ubiquitous and Future Networks, ICUFN

作者： Joohong Rheey Hyunggon Park Dept. Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea

The increasing popularity of Internet of Things (IoT) devices has brought significant security challenges to IoT networks. However, most deep learning-based anomaly detection solutions often require high computation performance so that it is difficult to be implanted on low-end IoT devices with limited power and memory capacity. In this paper, we propose a low-complexity network anomaly detection method based on feature selection using the Shapley value for the Isolation Forest algorithm. The proposed feature selection method using the Shapley value can reduce the dimension of input data, thereby improving the performance with reduced computational complexity. We provide simulation results to demonstrate the effectiveness of the proposed method. The results show that the proposed method based on Isolation Forest achieves comparable performance to the deep learning method based on neural networks while using fewer dimensions than the deep learning method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Service-Based Optimal Group Resource Allocation Strategy

Service-Based Optimal Group Resource Allocation Strategy

引用

International Conference on Ubiquitous and Future Networks, ICUFN

作者： Chaeyeon Cha Hyunggon Park Dept. Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea

With the increasing development of the Internet of Things (IoT), there is a growing need for efficient resource allocation to the many devices in the IoT system. This need is particularly acute in the case of time-sensitive applications, where fast resource allocation is essential. However, the resource allocation based on the Nash bargaining solution (NBS) necessitates exponentially increasing computational complexity for user dynamic systems. We propose an approach that makes groups based on the requested services and predicts a disagreement point whenever the group size changes, with the aim of reducing the computational complexity to find the NBS. Through simulation results, we demonstrate that our proposed approach outperforms the existing method in terms of execution time to find the NBS, maintaining the optimality in finding NBS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SRTube: Video-Language Pre-Training with Action-Centric Video Tube Features and Semantic Role Labeling

SRTube: Video-Language Pre-Training with Action-Centric Vide...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ju-Hee Lee Je-Won Kang Dept. of Electronic and Electrical Engineering and Graduate Program in Smart Factory Ewha W. University Seoul South Korea

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In recent years, large-scale video-language pre-training (VidLP) has received considerable attention for its effectiveness in relevant tasks. In this paper, we propose a novel action-centric VidLP framework that employs video tube features for temporal modeling and language features based on semantic role labeling (SRL). Our video encoder generates multiple tube features along object trajectories, identifying action-related regions within videos, to over-come the limitations of existing temporal attention mechanisms. Additionally, our text encoder incorporates high-level, action-related language knowledge, previously under-utilized in current VidLP models. The SRL captures action-verbs and related semantics among objects in sentences and enhances the ability to perform instance-level text matching, thus enriching the cross-modal (CM) alignment process. We also introduce two novel pre-training objectives and a self-supervision strategy to produce a more faithful CM representation. Experimental results demonstrate that our method outperforms existing VidLP frameworks in various downstream tasks and datasets, establishing our model a baseline in the modern VidLP framework.

关键词： Computer vision Attention mechanisms Computational modeling Semantics Electron tubes Trajectory Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Mobile Sleep Stage Analysis Using Multichannel Wearable Devices Integrated with Stretchable Transparent Electrodes

引用

ACS Sensors 2025年 2025 May 15页

作者： Um, Hyun-Kyung Noh, Eunseo Yoo, Chaehwa Lee, Hyang Woon Kang, Je-Won Lee, Byoung Hoon Lee, Jung-Rok Department of Mechanical and Biomedical Engineering Ewha Womans University Seoul 03760 South Korea Graduate Program in Smart Factory Ewha Womans University Seoul 03760 South Korea Department of Chemical Engineering and Materials Science Ewha Womans University Seoul 03760 South Korea Graduate Program in System Health Science and Engineering Ewha Womans University Seoul 03760 South Korea School of Electrical Engineering Chungbuk National University Cheongju 28644 South Korea Department of Neurology and Medical Science School of Medicine Ewha Womans University Seoul 03760 South Korea Ewha Medical Research Institute Ewha Womans University Seoul 03760 South Korea Department of Electronic and Electrical Engineering Ewha Womans University Seoul 03760 South Korea

The prevalence of sleep disorders in the aging population and the importance of sleep quality for health have emphasized the need for accurate and accessible sleep monitoring solutions. Polysomnography (PSG) remains the clinical gold standard for diagnosing sleep disorders;however, its discomfort and inconvenience limit its accessibility. To address these issues, a wearable device (WD) integrated with stretchable transparent electrodes (STEs) is developed in this study for multisignal sleep monitoring and artificial intelligence (AI)-driven sleep staging. Utilizing conductive and flexible STEs, the WD records multiple biological signals (electroencephalogram [EEG], electrooculogram [EOG], electromyogram [EMG], photoplethysmography, and motion data) with high precision and low noise, comparable to PSG (<4 μV_RMS). It achieves a 73.2% accuracy and a macro F1 score of 0.72 in sleep staging using an AI model trained on multisignal inputs. Notably, accuracy marginally improves when using only the EEG, EOG, and EMG channels, which may simplify future device designs. This WD offers a compact, multisignal solution for at-home sleep monitoring, with the potential for use as an evaluation tool for personalized sleep therapies. © 2025 American Chemical Society.

关键词： biopotential signals deep-learning algorithm electroencephalography sleep stage analysis stretchable transparent electrodes wearable devices

来源：评论

学校读者我要写书评

暂无评论

Game-Theoretic Lightweight Autoencoder Design for Intrusion Detection

Game-Theoretic Lightweight Autoencoder Design for Intrusion ...

引用

IEEE Conference on Wireless Communications and Networking

作者： Joohong Rheey Hyunggon Park Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In response to the security threats in wireless networks with concurrent device connections, deploying Intrusion Detection Systems (IDS) at the network edge is a promising strategy. However, this approach must take into account the resource constraints incurred by power-limited edge devices, requiring a lightweight solution to IDS. At the same time, the lightweight IDS solution has to minimize performance degradation as higher detection performance is also a key requirement of IDS. In this paper, we design a lightweight autoencoder with explainability, employing the Shapley value to measure unit importance and link importance. This approach can selectively activate only critical components, thereby reducing the complexity for IDS while effectively lowering its performance degradation. We confirm that the proposed algorithm is robust against the harsh sparsity of the autoencoder. Moreover, the sparsity of the proposed lightweight autoencoder can be easily manageable, such that it can be controlled to satisfy the potential constraints of power-limited edge devices. Therefore, the solution is a suitable algorithm for IDS that can be deployed on edge devices in wireless networks.

关键词： Performance evaluation Degradation Measurement units Accuracy Image edge detection Wireless networks Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

引用

IEIE Transactions on smart Processing and Computing 2023年第2期12卷 122-154页

作者： Lee, Minhun Song, HyeonJu Park, Jeeyoon Jeon, Byeungwoo Kang, Jungwon Kim, Jae-Gon Lee, Yung-Lyul Kang, Je-Won Sim, Donggyu Department of Computer Engineering Kwangwoon University Seoul Korea Republic of Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Kyunggi-do Korea Republic of Communication and Media Research Laboratory Electronics and Telecommunications Research Institute Deajeon Korea Republic of School of Electronics and Information Engineering Korea Aerospace University Kyunggi-do Korea Republic of Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data. © 2023 Institute of electronics and Information Engineers. All rights reserved.

关键词： Image compression

来源：评论

学校读者我要写书评

暂无评论

Dynamic Volumetric Video Coding with Tensor Decomposition

Dynamic Volumetric Video Coding with Tensor Decomposition

引用

IEEE Visual Communications and Image Processing (VCIP)

作者： Ju-Yeon Shin Yeoneui Kim Je-Won Kang Gun Bang Dept. of Electronic and Electrical Engineering and Graduate Program in Smart Factory Ewha Womans University Seoul South Korea Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recently, volumetric video coding based on neural radiance fields has gained significant attention for storing and transmitting three-dimensional (3D) scenes captured from multi-view video. Because the neural networks are trained to produce novel view synthesis of surrounding 3D scenes, compressing the model and then rendering the colors and geometry through the decompressed model can be utilized as a 3D video coding system. However, although this approach provides superior performance compared to conventional 3D video coding standards using dept. video, challenges remain in reducing overall model sizes to improve coding efficiency. In this paper, we propose a novel dynamic volumetric video coding technique that employs a Group of Volume (GoV) to divide multi-view video sequences into smaller chunks, addressing complex temporal dynamics. Our method uses volumetric video features represented with 3D spatial and temporal tensor matrices and vectors and encodes them with the GoVs. The tensors are compressed by existing 2D video codec, allowing for fast rendering and easing deployment. Experimental results validate that our method not only reduces memory footprint but also maintains high-quality rendering as compared to state-of-the-art studies.

关键词： Video coding Solid modeling Three-dimensional displays Image coding Tensors Visual communication Video sequences Rendering (computer graphics) Video codecs Standards

来源：评论

学校读者我要写书评

暂无评论

Implicit Neural Representation for Video Coding Through Progressive Feature Extraction

Implicit Neural Representation for Video Coding Through Prog...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Jihoo Lee Je-Won Kang Department of Electronic and Electrical Engineering Ewha W. University Seoul Korea Graduate Program in Smart Factory Ewha W. University Seoul Korea

Implicit neural representation for video (INRV) utilizes a neural network to represent a video without relying on an explicit pixel-wise representation of RGB values, and, therefore an INRV-based video coding method aims to compress a neural network and transmit its parameters through a bit-stream. In this paper, we propose a novel rate-distortion (R-D) optimized INRV-based video coding method through a progressive feature extraction module (PFEM). The PFEM consists of a series of residual blocks (RBs) that can progressively control the quality of the reconstruction, by searching the optimal network architecture. By increasing the number of RBs, the quality of a reconstruction frame is improved. However, the quality comes at the expense of overheads in transmission. In the proposed method, the number of the RBs are decided to provide the best trade-off between the frame quality and bits. Our decision model is based upon the R-D theory. Experimental results demonstrated that the proposed method provides a superior coding gain to the conventional video coding standards.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Personal Mobility Safe Driving System with Knowledge Distillation

Personal Mobility Safe Driving System with Knowledge Distill...

引用

International Conference on Ubiquitous Robots (UR)

作者： Damin Yeom Heejun Yoon Soyeon Lee Kahyun Lee Department of Electronic and Electrical Engineering Ewha Womans University Seoul South Korea Graduate Program in Smart Factory Ewha Womans University Seoul South Korea

The global personal mobility market has been rapidly expanding due to its convenience. However, the increasing number of accidents involving personal mobility devices has become a growing concern, including falls, collisions with objects, and riders being struck by moving vehicles or objects. In this paper, we propose a deep learning-based safe driving system that considers both user and road images to address this issue. Our system employs CNN-based models to detect whether the user is 1) wearing a helmet and 2) looking ahead in user-side images. At the same time, the roadside image recognizes whether the user is 3) driving on the sidewalk and 4) near the intersection. These tasks are simultaneously performed in parallel to identify the overall situation, which is done by determining the final speed as the minimum speed of speed values extracted from all tasks. Additionally, we employ knowledge distillation techniques to compress the models and enable real-time inference on edge devices, resulting in a fast and accurate system that is well-suited to the characteristics of personal mobility.

关键词：

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：