2020 International Conference on Information and Communication Technology Convergence (ICTC)最新文献_第3页

Mass authentication information injection method for effective security management of AMI device 海量认证信息注入方法，实现AMI设备的有效安全管理

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289627

Taehun Kim, Muyong Hyun, Minyong Kim, Sungcheol Kim

{"title":"Mass authentication information injection method for effective security management of AMI device","authors":"Taehun Kim, Muyong Hyun, Minyong Kim, Sungcheol Kim","doi":"10.1109/ICTC49870.2020.9289627","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289627","url":null,"abstract":"AMI is a technology that provides electricity usage and billing information in real time by two-way communication between consumers and a supplier which is a power company. The two-way communication environment can be used as a variety of security threat paths and security vulnerabilities may occur due to the increased adoption of multi-devices by multiple manufacturers. Security threats to AMI can be a major threat to cybersecurity across the country, so countermeasures are needed. In order to solve these problems, an electronic certificate-based AMI device authentication system was established, but a system for managing and injecting keys and certificates for a large number of devices is required. Management technology for security authentication information such as keys and certificates of AMI devices is an important factor in ensuring AMI security safety. Therefore, in this paper, we propose a method to inject mass security authentication information for AMI devices to systematically, effectively manage and safely inject authentication information.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"49 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":"128992712","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

Comparison of Various UEP Techniques for IRNSS Message Structure IRNSS报文结构中各种UEP技术的比较

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289574

Gangsan Kim, Hyunwoo Cho, Hong‐Yeop Song, Sanguk Lee

引用次数: 0

Myanmar Text-to-Speech System based on Tacotron (End-to-End Generative Model) 基于Tacotron(端到端生成模型)的缅甸文转语音系统

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289277

Yuzana Win, Htoo Pyae Lwin, Tomonari Masada

{"title":"Myanmar Text-to-Speech System based on Tacotron (End-to-End Generative Model)","authors":"Yuzana Win, Htoo Pyae Lwin, Tomonari Masada","doi":"10.1109/ICTC49870.2020.9289277","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289277","url":null,"abstract":"The main motivation of this paper is to improve the naturalness of Myanmar text-to-speech system using an end-to-end generative model called Tacotron. We introduce the open-source implementation for Myanmar text-to-speech system with very high natural-sounding. In this paper, there are four main parts: speech corpus creation, data pre-processing, applying end-to-end generative model, and speech synthesis. Firstly, we develop a speech corpus of 8k sentences from a large set of news articles, novel books, daily usages and travel-related expressions for corpus creation. Secondly, we use a syllable segmenter and text normalizer for data pre-processing. Thirdly, we apply end-to-end generative model called Tacotron that synthesizes speech directly from the sequence of text characters. Finally, we use Griffin-Lim algorithm to convert the corresponding text into the output speech. For the subjective evaluation, we compare our synthesized speech output with the original recording speech in both intelligibility and naturalness by using mean opinion score (MOS). The experimental results show that we can obtain the synthesized speech comparable to the similar state-of-the-art synthsizers for other languages.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"37 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":"126976482","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

Design of SW Framework for Trustworthy AI-Data Commons 可信ai数据共享的SW框架设计

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289370

Sunhwan Lim, Young-Ho Suh, Donghwan Park, Sungpil Woo, Chanwon Park

引用次数: 2

A novel method of combining pixel density map and SLIC for low-power display 一种结合像素密度图和SLIC的低功耗显示新方法

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289228

Simon Suh, Young-jin Kim

{"title":"A novel method of combining pixel density map and SLIC for low-power display","authors":"Simon Suh, Young-jin Kim","doi":"10.1109/ICTC49870.2020.9289228","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289228","url":null,"abstract":"OLED displays have different power consumption depending on R, G, and B pixel values. Therefore, if an image is segmented according to saliency and then divided according to color using a super pixel algorithm, low power can be achieved while maintaining human visual satisfaction However, if the image is segmented using saliency and then the segmented image is segmented using the super pixel algorithm, simple linear iterative clustering(SLIC), the pixels that do not have a color value because the segmented image has a different saliency level are also segmented by the super pixel algorithm. So the ability to divide color is poor at segmented image. This paper excludes pixels that do not have color values from the segmentation process when dividing an image including pixels that do not have color values by saliency criteria into super pixels. In addition, by allocating the first search position not evenly in the entire image, but focusing on the pixels with color values, the performance of the super pixel that divides the image according to color in the image divided based on saliency was improved. In terms of low power, the proposed method has similar power savings of about 38% to that of the FDM-oriented SLIC method, but the SSIM, which is structurally similar to the original image, has shown higher.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"247 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":"123186377","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

Deep Reinforcement Learning Based Dynamic Resource Allocation in Cloud Radio Access Networks 基于深度强化学习的云无线接入网动态资源分配

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289530

Rehenuma Tasnim Rodoshi, Taewoon Kim, Wooyeol Choi

{"title":"Deep Reinforcement Learning Based Dynamic Resource Allocation in Cloud Radio Access Networks","authors":"Rehenuma Tasnim Rodoshi, Taewoon Kim, Wooyeol Choi","doi":"10.1109/ICTC49870.2020.9289530","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289530","url":null,"abstract":"Cloud radio access network (C-RAN) is a promising architecture to fulfill the ever-increasing resource demand in telecommunication networks. In C-RAN, a base station is decoupled into baseband unit (BBU) and remote radio head (RRH). The BBUs are further centralized and virtualized as virtual machines (VMs) inside a BBU pool. This architecture can meet the massively increasing cellular data traffic demand. However, resource management in C-RAN needs to be designed carefully in order to reach the objectives of energy saving and to meet the user demand over a long operational period. Since the user demands are highly dynamic in different times and locations, it is challenging to perform the optimal resource management. In this paper, we exploit a deep reinforcement learning (DRL) model to learn the spatial and temporal user demand in C-RAN, and propose an algorithm that resizes the VMs to allocate computational resources inside the BBU pool. The computational resource allocation is done according to the amount of required resources in the associated RRHs of the VMs. Through an extensive evaluation study, we show that the proposed algorithm can make the C-RAN network resource-efficiency while satisfying dynamic user demand.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"39 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":"123234960","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: 4

Transfer Learning using Transformation: Is Large Unlabeled Data Helpful at Segmentation? 使用转换的迁移学习:大量未标记数据对分割有用吗?

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289267

Heejeong Lim, Seongwook Yoon, S. Sull

引用次数: 0

A study on fire detection technology through spectrum analysis of smoke particles 基于烟雾粒子光谱分析的火灾探测技术研究

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289272

Soyoung Park, K. Han, Kangbok Lee

引用次数: 2

The method for providing of parking location guidance service in a smart parking lot 一种在智能停车场中提供停车位置引导服务的方法

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289517

Eun Joo Kim, Woongshik You, C. Pyo

引用次数: 0

Robotic Behavioral Cloning Through Task Building 通过任务构建的机器人行为克隆

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289148

Jinchul Choi, Hyunseok Kim, Youngsung Son, Chan-Won Park, Jun Hee Park

引用次数: 3