2020 International Conference on Information and Communication Technology Convergence (ICTC)最新文献_第2页

How to Aggregate Acoustic Delta Features for Deep Speaker Embeddings 如何在深度扬声器嵌入中聚合声学Delta特征

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289205

Youngsam Kim, Jong-hyuk Roh, Kwantae Cho, Sangrae Cho

引用次数: 0

LDPC Coded OFDM-IM Performance Evaluation Under Jamming Attack 干扰下LDPC编码OFDM-IM性能评估

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289150

Ahmet Kaplan, Mehmet Can, I. Altunbas, Günes Karabulut-Kurt, D. Kucukyavuz

引用次数: 1

Score Prediction Network and Graph-based Selection for Semantic Line Detection 分数预测网络和基于图的语义线检测选择

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289236

Dongkwon Jin, Chang-Su Kim

引用次数: 0

Fault Diagnosis of Rotating Machine Using an Indirect Observer and Machine Learning 基于间接观测器和机器学习的旋转机械故障诊断

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289590

Shahnaz TayebiHaghighi, Insoo Koo

{"title":"Fault Diagnosis of Rotating Machine Using an Indirect Observer and Machine Learning","authors":"Shahnaz TayebiHaghighi, Insoo Koo","doi":"10.1109/ICTC49870.2020.9289590","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289590","url":null,"abstract":"Bearing is one of the important mechanical components to reduce friction in rotating machines. Early fault diagnosis in bearings is an important challenge to the prevention of full failure and avoiding disorder of the machine. In this paper, an indirect observer and machine learning technique are adopted for fault identification in bearing. To develop an indirect observer, in the first step, the autoregressive with uncertainty modeling technique is proposed to modeling the RMS (indirect) normal signal of bearing. After that, the robust (sliding fault detection) proportional multi integral with autoregressive external input modeling (ARPMI) observer was used to solve the unknown signal estimation in bearing. Besides, the support vector machine (SVM) technique for fault classification is proposed. The effectiveness of the proposed scheme is validated using Case Western Reverse University (CWRU) dataset. Experimental results show that, the proposed scheme improves the average performance for various rotational speed fault identification by about 10.5% and 13.5% compared with the proportional multi integral with autoregressive external input modeling (APMI) observer and proportional-integral with autoregressive external input modeling (API) observer, respectively.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"183 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":"114954560","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

Construction of Selection-Scheduling of Accessing Node for High Efficiency Information Exchanging in Overloaded Wireless MIMO Switching 重载无线MIMO交换中高效信息交换的访问节点选择调度构建

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289587

Arata Takahashi, O. Takyu, H. Fujiwara

引用次数: 0

Efficient User Clustering and Reinforcement Learning Based Power Allocation for NOMA Systems 基于高效用户聚类和强化学习的NOMA系统功率分配

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289376

Sifat Rezwan, Seokjoo Shin, Wooyeol Choi

{"title":"Efficient User Clustering and Reinforcement Learning Based Power Allocation for NOMA Systems","authors":"Sifat Rezwan, Seokjoo Shin, Wooyeol Choi","doi":"10.1109/ICTC49870.2020.9289376","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289376","url":null,"abstract":"Non-orthogonal multiple access (NOMA) is a popular multiplexing technique for the 5th generation (5G) network due to its spectral efficiency, high reliability, and massive connectivity support. However, some technical challenges in NOMA needs to be addressed properly. Usually, NOMA exploits the channel gains of multiple users to serve them from the same radio resource block at different power levels using a complex power allocation policy. In this paper, we propose a reinforcement learning-based power allocation algorithm with a simple and efficient user clustering technique. We use a Q-learning algorithm among all reinforcement learning techniques, which can easily obtain an optimal strategy to allocate power efficiently to maximize the sum data-rate of the NOMA system. In addition, we propose a channel gain-based user clustering technique that also contributes to the maximization of sum data-rate. To verify the performance of the proposed scheme, we conduct extensive simulations under various environments. We can confirm that the proposed Q-learning algorithm with user clustering achieves the maximum sum data-rate compared to other scenarios.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"91 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":"115348240","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: 5

GPU-specific Task Offloading in the Mobile Edge Computing Network 移动边缘计算网络中特定gpu的任务卸载

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289354

Namkyu Kim, Yunseong Lee, Chunghyun Lee, The-Vi Nguyen, Van Dat Tuong, Sungrae Cho

引用次数: 4

Attack Detection in Fog Layer for IIoT Based on Machine Learning Approach 基于机器学习的工业物联网雾层攻击检测

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289380

Mareska Pratiwi Maharani, P. T. Daely, Jae-Min Lee, Dong-Seong Kim

引用次数: 8

Knowledge Distillation based Compact Model Learning Method for Object Detection 基于知识蒸馏的紧凑模型学习方法在目标检测中的应用

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289463

Jong-gook Ko, Wonyoung Yoo

{"title":"Knowledge Distillation based Compact Model Learning Method for Object Detection","authors":"Jong-gook Ko, Wonyoung Yoo","doi":"10.1109/ICTC49870.2020.9289463","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289463","url":null,"abstract":"Recently, video analysis technology through deep learning has been developing at a very rapid pace, and most of the technology related to improving recognition performance in server environment is being developed. However, in addition to video analysis technology in the existing server environment, the demand of object detection in visual image analysis have been increasing recently in embedded boards of low specification and mobile environments such as smartphones, drones, and industrial boards. Despite the significant improvement in the accuracy of existing object detectors, image processing for real- time applications often requires a lot of runtime. Therefore, many studies are being conducted on lightweight object detection technology, and knowledge distillation is one of the solutions. Efforts such as model compression use fewer parameters, but there is a problem that accuracy is significantly reduced. In this paper, we propose method to improve the performance of lightweight mobilenet-SSD models in object detection by using knowledge transfer methods. We conduct evaluation with PASCAL VOC dataset. Our results show detection accuracy improvement in object detection.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"8 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":"114169927","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

An Approach to Automatic Classification of Construction Workers by Degree of Risk 基于危险程度的建筑工人自动分类方法研究

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289519

Youhee Choi, Do Hyun Kim

引用次数: 0