2020 International Conference on Information and Communication Technology Convergence (ICTC)最新文献_第6页

Zero-Keep Filter Pruning for Energy Efficient Deep Neural Network 高效能量深度神经网络的零保持滤波剪枝

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289201

Yunhee Woo, Dongyoung Kim, Jaemin Jeong, Y. Ko, Jeong-Gun Lee

{"title":"Zero-Keep Filter Pruning for Energy Efficient Deep Neural Network","authors":"Yunhee Woo, Dongyoung Kim, Jaemin Jeong, Y. Ko, Jeong-Gun Lee","doi":"10.1109/ICTC49870.2020.9289201","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289201","url":null,"abstract":"Recent deep learning models succeed to achieve high accuracy and fast inference time, but they require high-performance computing resources because of a large number of parameters. However, not all systems have high-performance hardware. Sometimes, deep learning model needs to be run on edge devices such as IoT devices or smartphones. The edge devices have limited performance and energy consumption. On these devices, the amount of computation must be reduced. Pruning is one of the well-known approaches to solve this problem. In this work, we propose \"zero-keep filter pruning\" for an energy-efficient deep neural network. The proposed method maximizes the number of zero elements in filters by replacing small values with zero and pruning the filter that has the lowest number of zeros. In the conventional approach, the filters that have the highest number of zeros are generally pruned. As a result, through this zero-keep filter pruning, we can have the filters that have many zeros in a model. We compared the results of the proposed method with the random filter pruning and proved that our method shows better performance with much fewer non-zero elements with marginal accuracy drop. We also compare the number of remained filters with random and proposed pruning methods after pruning. Finally, we discuss a possible multiplier architecture, zero-skip multiplier circuit, which skips the multiplications with zero to accelerate and reduce energy consumption.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127410535","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: 1

Neural Network-based LED Detection in Vehicular System for High Data Rate in OCC 基于神经网络的车载系统 LED 检测，实现 OCC 中的高数据速率

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289325

Md. Faisal Ahmed, M. Shahjalal, Md. Osman Ali, Y. Jang

引用次数: 1

MR-Block: A Blockchain-Assisted Secure Content Sharing Scheme for Multi-User Mixed-Reality Applications in Internet of Military Things MR-Block:面向军事物联网多用户混合现实应用的区块链辅助安全内容共享方案

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289327

A. Islam, M. Masuduzzaman, Arifa Akter, S. Shin

{"title":"MR-Block: A Blockchain-Assisted Secure Content Sharing Scheme for Multi-User Mixed-Reality Applications in Internet of Military Things","authors":"A. Islam, M. Masuduzzaman, Arifa Akter, S. Shin","doi":"10.1109/ICTC49870.2020.9289327","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289327","url":null,"abstract":"Internet of Things (IoT) established the foundation of the smart world via its diverse functionalities (e.g., remote data acquisition, automated task execution). This trend continues to military applications too by introducing internet of military things (IoMT). Due to being at infancy level, new technologies require to move forward and assist IoMT to gain maturity. Mixed reality (MR) can be a potential technology which fuses the virtual and actual world. MR can improve the quality of service (QoS) in terms of inventory management, remote mission handling, battlefield assistance, etc. However, data among these applications is surrounded by cyber threats (e.g., illegal data modification, unauthorised data access). Blockchain is another promising technology which brings security in the distributed world. A content sharing scheme for MR application is proposed on the top of blockchain to bring security in the multi-user environment in IoMT. A smart contract is employed to manage security in accessing data by different users. Moreover, an experimental environment is set to observe the performance of the proposed scheme. The analysis manifests that proposed scheme maintains security without affecting the regular performance.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115439282","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: 11

Implementation of a Collision Avoidance System To Assist Safe Driving Based on Data Fusion in Vehicular Networks 基于数据融合的车联网避碰辅助安全驾驶系统实现

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289528

Haeyoung Lee, Jihoon Yang, K. Moessner

引用次数: 3

Network Intrusion Detection System using Feature Extraction based on Deep Sparse Autoencoder 基于深度稀疏自编码器特征提取的网络入侵检测系统

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289253

Joohwa Lee, Ju-Geon Pak, Myung-suk Lee

{"title":"Network Intrusion Detection System using Feature Extraction based on Deep Sparse Autoencoder","authors":"Joohwa Lee, Ju-Geon Pak, Myung-suk Lee","doi":"10.1109/ICTC49870.2020.9289253","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289253","url":null,"abstract":"The classification function in network intrusion detection systems (NIDSs) is important for determining whether traffic is normal. Accordingly, the detection performances of NIDSs depend on various characteristics. Recently, owing to its considerable advancement, deep learning has been applied to NIDSs. However, this method is associated with slow detection problems owing to the high volumes of traffic and increased data dimensionality. Therefore, we propose a method to classify deep learning based on extracted features, not as a classification but as a preprocessing methodology for feature extraction. A deep sparse autoencoder is used to extract features from a typical unsupervised deep learning autoencoder model classified by the Random Forest (RF) classification algorithm. Improvements to the classification performance and detection speed are confirmed. An accuracy of 99% can be achieved when normal and attack traffic is classified using the latest data and when compared with other algorithms, such as the Pearson–RF, SA–RF, and DSA–SVC. However, as the performance of the sparse class is worse than those of the other classes, additional research is required to improve it.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115835491","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: 10

Second-order Passive Intermodulation Distortion Modeling Using a Quadratic Volterra Filter for Wireless Relay Systems 基于二次Volterra滤波器的无线中继系统二阶无源互调失真建模

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289348

Jinzi Song, Bobae Kim, Zhihui Jin, S. Im

引用次数: 1

DNN-based Phase Noise Compensation for Sub-THz Communications 基于dnn的亚太赫兹通信相位噪声补偿

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289411

Hyeong-Sook Park, E. Choi, Youngseog Song, Song Noh, Kyung-Hee Seo

引用次数: 3

Construction of a machine learning dataset for multiple AI tasks using Korean commercial multimodal video clips 使用韩国商业多模态视频片段构建用于多个AI任务的机器学习数据集

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289319

Saim Shin, J. Jang, Minyoung Jung, Jieun Kim, Yoonyoung Jung, Hyedong Jung

{"title":"Construction of a machine learning dataset for multiple AI tasks using Korean commercial multimodal video clips","authors":"Saim Shin, J. Jang, Minyoung Jung, Jieun Kim, Yoonyoung Jung, Hyedong Jung","doi":"10.1109/ICTC49870.2020.9289319","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289319","url":null,"abstract":"Accordingly a lot of broadcasting medias pursuing various concepts have been appeared and the major type of contents consumed on the web has been changed to multimodal contents, the attempt to actively utilize multimedia content in artificial intelligence research is also starting. This paper introduces a study that constructs a converged information dataset in an integrated form by analyzing various types of multimodal information on video clips. The constructed dataset was released with various semantic labels for artificial intelligence research about various information classification. The labels and descriptions in this dataset include various context, intention and emotion information describing with vision, speech and language in each video clips. The constructed dataset can be resolved the problem of lack of public data for multimodal interaction research with Korean. It is expected that this dataset can be applied in the constructions of various artificial intelligence services like Korean dialogue processing, visual information extractions and various multimodal data analysis tasks.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124372558","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

A Low-complexity Neural BP Decoder with Network Pruning 基于网络剪枝的低复杂度神经BP解码器

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289525

Seokju Han, J. Ha

引用次数: 1

FBMC-OQAM PAPR Reduction Schemes FBMC-OQAM纸张减少方案

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289244

Nahla Ali Mohamed Al Harthi, Zhongfeng Zhang, Seungwon Choi

引用次数: 3