2023 International Conference on Electronics, Information, and Communication (ICEIC)最新文献_第6页

Interference Mitigation between Remote Base Stations 远程基站间的干扰缓解

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049866

Sungmo Ku, K. Lee, Chungyong Lee

引用次数: 0

Analysis of performance difference when using knowledge distillation of efficient CNN-based super-resolution algorithm 基于cnn的高效超分辨算法的知识精馏性能差异分析

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049942

Min Yoon, Seunghyun Lee, B. Song

引用次数: 0

Efficient Hardware Acceleration of Chinese Remainder Theorem for Fully Homomorphic Encryption 全同态加密中文剩余定理的高效硬件加速

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049928

Hyun-Wook Kim, Seong-Cheon Park

引用次数: 0

IDS-Extract:Downsizing Deep Learning Model For Question and Answering IDS-Extract:精简深度学习问答模型

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049915

Zikun Guo, Swathi Kavuri, Jeongheon Lee, Minho Lee

{"title":"IDS-Extract:Downsizing Deep Learning Model For Question and Answering","authors":"Zikun Guo, Swathi Kavuri, Jeongheon Lee, Minho Lee","doi":"10.1109/ICEIC57457.2023.10049915","DOIUrl":"https://doi.org/10.1109/ICEIC57457.2023.10049915","url":null,"abstract":"In recent years, Question-answering systems are extensively used in human-computer systems, and the accuracy rate on a large scale is increasing. However, in actual deployment, a large number of parameters are often accompanied by a large amount of memory and long-term processing requirements. Therefore, compressing the data of the model, reducing training time, memory, becomes more and more urgent. we aim to resolve issues: IDS-Extract dynamically sized data to support models and devices with different memory. The proposed technique does efficient data extraction, segments that are not meaningful for model learning on the original dataset and output multiple datasets of adaptive size followed by target training based on model size. We leverage techniques in IG(Integration Gradient), DPR, and SBERT to improve localization performance for answer positions. We compare the model performance of SQuAD and the data set reduced by the IDS extraction technique, and the results prove that our technique can train the model more targeted and obtain higher performance evaluation. We prove that this method has successfully passed the sanity check, and can be directly applied to emotion recognition, two-classification, and multi-classification fields.","PeriodicalId":373752,"journal":{"name":"2023 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121693339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Head Convolutional Neural Network Compression based on High-Order Principal Component Analysis 基于高阶主成分分析的多头卷积神经网络压缩

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049909

Taehyeon Kim, Youjeong Na, Seho Park

{"title":"Multi-Head Convolutional Neural Network Compression based on High-Order Principal Component Analysis","authors":"Taehyeon Kim, Youjeong Na, Seho Park","doi":"10.1109/ICEIC57457.2023.10049909","DOIUrl":"https://doi.org/10.1109/ICEIC57457.2023.10049909","url":null,"abstract":"A multi-head convolutional neural network performs remarkably in various multi-task learning-based computer vision applications. Behind these achievements, a multi-head convolutional neural network utilizes significantly huge parameters and complex neural architecture. This peculiarity of the multi-head convolutional neural networks can make them represent and capture versatile features from images; however, it also creates serious implementation problems when deploying the multi-head convolutional neural network on resource-constrained systems. To handle this problem, we propose a novel neural network compression algorithm that can maintain the core features and remove redundant features in the convolutional layer as an aspect of multi-head convolutional neural network architecture. The proposed neural network compression algorithm computes multidimensional principal components on the convolutional layer of a multi-head convolutional neural network with statistically guaranteed hyper-parameter optimization. Experiments show that the proposed algorithm is able to produce an efficient multi-head convolutional neural network with low computational complexity and negligible performance degradation.","PeriodicalId":373752,"journal":{"name":"2023 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131545567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey of Visual Commonsense Generation Task 视觉常识生成任务综述

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049859

Jinwoo Park, Junyeong Kim

引用次数: 0

Impacts of Clock Constraints on Side-Channel Leakage of HLS-designed AES Circuits 时钟约束对hls设计AES电路侧道泄漏的影响

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049959

Yuto Miura, Takumi Mizuno, Hiroki Nishikawa, Xiangbo Kong, Hiroyuki Tomiyama

引用次数: 0

Analysis on the Neural Network-aided Satellite Resource Allocation Schemes 神经网络辅助卫星资源分配方案分析

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049977

Gyuseong Jo, Satya Chan, S. K. Shin, D. Oh

引用次数: 1

Deep learning classification of focal liver lesions with contrast-enhanced ultrasound from arterial phase recordings 动脉期超声造影记录下肝局灶性病变的深度学习分类

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049872

Namjoon Kim, Won Jae Lee, Hyuk-Jae Lee

{"title":"Deep learning classification of focal liver lesions with contrast-enhanced ultrasound from arterial phase recordings","authors":"Namjoon Kim, Won Jae Lee, Hyuk-Jae Lee","doi":"10.1109/ICEIC57457.2023.10049872","DOIUrl":"https://doi.org/10.1109/ICEIC57457.2023.10049872","url":null,"abstract":"Contrast-enhanced ultrasound (CEUS) has been known as a safe, robust, and cost-effective image modality to diagnose an early sign of hepatocellular carcinoma (HCC). The enhancement patterns on CEUS are composed of arterial, portal venous, and late phases, where the hepatic arterial phase provides information on the degree and pattern of vascularity, and the portal venous and late phases provide important information on the differentiation between benign and malignant liver lesions. The enhancement patterns of HCC on CEUS are hyper-enhanced in the arterial phase. Therefore, we propose learning-based frameworks to differentiate between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) during the arterial phase. We design artificial neural networks to learn the change of characteristics over time for the differentiation of HCC from FNH. We had gathered CEUS videos during the arterial phase for 4 years in Samsung Medical Center (SMC) and picked out only small hepatic lesions under 3 centimeters. From these datasets, the proposed novel 3D-CNN and CNN-LSTM networks show accuracy rates of 100% and 98% for 10-fold and 5-fold cross-validations. In the end, the proposed models are proved to be feasible for accurate automatic classification between HCC and FNH in livers.","PeriodicalId":373752,"journal":{"name":"2023 International Conference on Electronics, Information, and Communication (ICEIC)","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132577259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Research on the Indoor Environment Positioning Algorithm Using Sensor Fusion 基于传感器融合的室内环境定位算法研究

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI: 10.1109/ICEIC57457.2023.10049976

Show-Ling Jang, Byoungman An, Sanghun Yoon, Ki-Taeg Lim

引用次数: 1