ETRI Journal最新文献_第3页

Peak-to-average power ratio reduction of orthogonal frequency division multiplexing signals using improved salp swarm optimization-based partial transmit sequence model 基于改进salp群优化的部分发射序列模型降低正交频分复用信号的峰均功率比

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2025-01-21 DOI: 10.4218/etrij.2023-0347

Vandana Tripathi, Prabhat Patel, Prashant Kumar Jain, Shailja Shukla

{"title":"Peak-to-average power ratio reduction of orthogonal frequency division multiplexing signals using improved salp swarm optimization-based partial transmit sequence model","authors":"Vandana Tripathi, Prabhat Patel, Prashant Kumar Jain, Shailja Shukla","doi":"10.4218/etrij.2023-0347","DOIUrl":"https://doi.org/10.4218/etrij.2023-0347","url":null,"abstract":"Several peak-to-average power ratio (PAPR) reduction methods have been used in orthogonal frequency division multiplexing (OFDM) applications. Among the available methods, partial transmit sequence (PTS) is an efficient PAPR reduction method but can be computationally expensive while determining optimal phase factors (OPFs). Therefore, an optimization algorithm, namely, the improved salp swarm optimization algorithm (ISSA), is incorporated with the PTS to reduce the PAPR of the OFDM signals with limited computational cost. The ISSA includes a dynamic weight element and Lévy flight process to improve the global exploration ability of the optimization algorithm and to control the global and local search ability of the population with a better convergence rate. Three evaluation measures, namely, the complementary cumulative distribution function (CCDF), bit error rate (BER), and symbol error rate (SER), demonstrate the efficacy of the PTS-ISSA model, which achieves a lower PAPR of 3.47 dB and is superior to other optimization algorithms using the PTS method.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 2","pages":"256-269"},"PeriodicalIF":1.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A graph neural network model application in point cloud structure for prolonged sitting detection system based on smartphone sensor data 基于智能手机传感器数据的久坐检测系统点云结构中的图神经网络模型应用

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2025-01-21 DOI: 10.4218/etrij.2023-0190

Mardi Hardjianto, Jazi Eko Istiyanto, A. Min Tjoa, Arfa Shaha Syahrulfath, Satriawan Rasyid Purnama, Rifda Hakima Sari, Zaidan Hakim, M. Ridho Fuadin, Nias Ananto

{"title":"A graph neural network model application in point cloud structure for prolonged sitting detection system based on smartphone sensor data","authors":"Mardi Hardjianto, Jazi Eko Istiyanto, A. Min Tjoa, Arfa Shaha Syahrulfath, Satriawan Rasyid Purnama, Rifda Hakima Sari, Zaidan Hakim, M. Ridho Fuadin, Nias Ananto","doi":"10.4218/etrij.2023-0190","DOIUrl":"https://doi.org/10.4218/etrij.2023-0190","url":null,"abstract":"The prolonged sitting inherent in modern work and study environments poses significant health risks, necessitating effective monitoring solutions. Traditional human activity recognition systems often fall short in these contexts owing to their reliance on structured data, which may fail to capture the complexity of human movements or accommodate the often incomplete or unstructured nature of healthcare data. To address this gap, our study introduces a novel application of graph neural networks (GNNs) for detecting prolonged sitting periods using point cloud data from smartphone sensors. Unlike conventional methods, our GNN model excels at processing the unordered, three-dimensional structure of sensor data, enabling more accurate classification of sedentary activities. The effectiveness of our approach is demonstrated by its superior ability to identify sitting, standing, and walking activities—critical for assessing health risks associated with prolonged sitting. By providing real-time activity recognition, our model offers a promising tool for healthcare professionals to mitigate the adverse effects of sedentary behavior.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 2","pages":"290-302"},"PeriodicalIF":1.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial feature recognition and layout method based on improved CenterNet and LSTM frameworks 基于改进CenterNet和LSTM框架的空间特征识别与布局方法

IF 1.6 4区计算机科学

ETRI Journal Pub Date : 2025-01-05 DOI: 10.4218/etrij.2024-0192

Yuxuan Gu, Fengyu Liu, Xiaodi Yi, Lewei Yang, Yunshu Wang

{"title":"Spatial feature recognition and layout method based on improved CenterNet and LSTM frameworks","authors":"Yuxuan Gu, Fengyu Liu, Xiaodi Yi, Lewei Yang, Yunshu Wang","doi":"10.4218/etrij.2024-0192","DOIUrl":"https://doi.org/10.4218/etrij.2024-0192","url":null,"abstract":"Existing spatial feature recognition and layout methods primarily identify spatial components manually, which is time-consuming and inefficient, and the constraint relationship between objects in space can be difficult to observe. Consequently, this study introduces an advanced spatial feature recognition and layout methodology employing enhanced CenterNet and LSTM (Long Short-Term Memory) frameworks, which is bifurcated into two major components—first, HCenterNet-based feature recognition enhances feature extraction through an attention mechanism and feature fusion technology, refining the identification of small targets within complex background areas; second, a GA-BiLSTM (Genetic Algorithm - Bidirectional LSTM)-based spatial layout model uses a bidirectional LSTM network optimized with a genetic algorithm (GA), aimed at fine-tuning the network parameters to yield more accurate spatial layouts. Experiments verified that compared with the CenterNet model, the recognition performance of the proposed HCenterNet-DIoU model improved by 7.44%. Moreover, the GA-BiLSTM model improved the overall layout accuracy by 10.08% compared with the LSTM model. Time cost analysis also confirmed that the proposed model could meet the real-time requirements.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 4","pages":"721-736"},"PeriodicalIF":1.6,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance analysis of wireless-powered cell-free massive multiple-input multiple-output system with spatial correlation in Internet of Things network 物联网网络中具有空间相关性的无线供电无蜂窝大规模多输入多输出系统的性能分析

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2025-01-05 DOI: 10.4218/etrij.2023-0216

Haiyan Wang, Xinmin Li, Yuan Fang, Xiaoqiang Zhang

引用次数: 0

Detection and segmentation framework for defect detection on multi-layer ceramic capacitors 多层陶瓷电容器缺陷检测与分割框架

IF 1.6 4区计算机科学

ETRI Journal Pub Date : 2024-12-13 DOI: 10.4218/etrij.2024-0066

Hyun-Jae Kim, Sung-Bin Son, Heung-Seon Oh

{"title":"Detection and segmentation framework for defect detection on multi-layer ceramic capacitors","authors":"Hyun-Jae Kim, Sung-Bin Son, Heung-Seon Oh","doi":"10.4218/etrij.2024-0066","DOIUrl":"https://doi.org/10.4218/etrij.2024-0066","url":null,"abstract":"Detecting defective multi-layer ceramic capacitors (MLCCs) during the inspection stage is a crucial production task to effectively manage production yield and maintain quality. However, this task presents two challenges: the necessity of pixel-level segmentation in high-resolution images and unexplored defect patterns. To address these challenges, this paper introduces an MLCC defect-detection framework based on deep learning with an MLCC dataset we constructed and a comprehensive analysis of MLCC images. Our framework employs an object-detection model to identify dielectric regions in input MLCC images, followed by a semantic segmentation model to create dielectric masks for calculating the margin ratio. This approach follows the traditional inspection process but can be performed without specialized personnel. Furthermore, we generated pseudo-defect images using generative adversarial networks to obtain sufficient training data. Experiments demonstrate the effectiveness of our framework, which achieved a defect-detection accuracy of 93.1%, as revealed by an in-depth error analysis.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 4","pages":"685-694"},"PeriodicalIF":1.6,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network function parallelism configuration with segment routing over IPv6 based on deep reinforcement learning 基于深度强化学习的 IPv6 分段路由网络功能并行性配置

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-12-11 DOI: 10.4218/etrij.2023-0511

Seokwon Jang, Namseok Ko, Yeunwoong Kyung, Haneul Ko, Jaewook Lee, Sangheon Pack

{"title":"Network function parallelism configuration with segment routing over IPv6 based on deep reinforcement learning","authors":"Seokwon Jang, Namseok Ko, Yeunwoong Kyung, Haneul Ko, Jaewook Lee, Sangheon Pack","doi":"10.4218/etrij.2023-0511","DOIUrl":"https://doi.org/10.4218/etrij.2023-0511","url":null,"abstract":"Network function parallelism (NFP) has gained attention for processing packets in parallel through service functions arranged in the required service function chain. While parallel processing efficiently reduces the service function chaining (SFC) completion time, it incurs a higher network overhead (e.g., network congestion) to replicate various packets for processing. To reduce the SFC completion time while maintaining a low network overhead, we propose a deep-reinforcement-learning-based NFP algorithm (DeepNFP) that provides an SFC processing policy to determine the sequential or parallel processing of every service function. In DeepNFP, deep reinforcement learning captures the network dynamics and service function conditions and iteratively finds the SFC processing policy in the network environment. Furthermore, an SFC data plane protocol based on segment routing over IPv6 configures and operates the policy in the SFC data network. Evaluation results show that DeepNFP can achieve 46% of the SFC completion time and 66% of the network overhead compared with conventional SFC and NFP, respectively.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 2","pages":"278-289"},"PeriodicalIF":1.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes UP-Net：用于读取和有效分割扭曲QR码的多头架构

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-12-11 DOI: 10.4218/etrij.2023-0540

Ebrahim Parcham, Mahdi Ilbeygi, Vahid Hajipour, Ali Gharaei, Mahdi Mokhtari, Mostafa Foroutan

引用次数: 0

Performance evaluations of AI-based obfuscated and encrypted malicious script detection with feature optimization 基于特征优化的人工智能模糊加密恶意脚本检测性能评估

IF 1.6 4区计算机科学

ETRI Journal Pub Date : 2024-12-08 DOI: 10.4218/etrij.2024-0255

Kookjin Kim, Jisoo Shin, Jong-Geun Park, Jung-Tae Kim

{"title":"Performance evaluations of AI-based obfuscated and encrypted malicious script detection with feature optimization","authors":"Kookjin Kim, Jisoo Shin, Jong-Geun Park, Jung-Tae Kim","doi":"10.4218/etrij.2024-0255","DOIUrl":"https://doi.org/10.4218/etrij.2024-0255","url":null,"abstract":"In the digital security environment, the obfuscation and encryption of malicious scripts are primary attack methods used to evade detection. These scripts—easily spread through websites, emails, and file downloads—can be automatically executed on users' systems, posing serious security threats. To overcome the limitations of signature-based detection methods, this study proposed a methodology for real-time detection of obfuscated and encrypted malicious scripts using ML/DL models with feature optimization techniques. The obfuscated script datasets were analyzed to identify the unique characteristics, classified into 16 feature sets, to evaluate the optimal features for the best detection accuracy. Although the detection accuracy of these datasets was < 20%, when tested with commercial antivirus services, the experimental results using ML and DL models demonstrated that the proposed light gradient boosting model (LGBM) could achieve the best detection accuracy and processing speed. The LGBM outperformed other artificial intelligence models by achieving 97% accuracy and the minimum processing time in the decoded, obfuscated, and encrypted dataset cases.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 4","pages":"753-770"},"PeriodicalIF":1.6,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to “NEST-C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators” 更正“NEST-C：一个用于具有人工智能加速器的异构计算系统的深度学习编译器框架”

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-12-08 DOI: 10.4218/etr2.12748

Jeman Park, Misun Yu, Jinse Kwon, Junmo Park, Jemin Lee, Yongin Kwon

引用次数: 0

Correction to “Low-complexity patch projection method for efficient and lightweight point-cloud compression” 对 "用于高效、轻量级点云压缩的低复杂度补丁投影法 "的更正

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-11-26 DOI: 10.4218/etr2.12746

引用次数: 0