2020 International Conference on Information and Communication Technology Convergence (ICTC)最新文献_第5页

Blockchain-based Farming Activities Tracker for Enhancing Trust in the Community Supported Agriculture Model 基于区块链的农业活动跟踪器，以增强对社区支持农业模式的信任

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289297

Duc-Hiep Nguyen, N. H. Tuong, Hoang-Anh Pham

{"title":"Blockchain-based Farming Activities Tracker for Enhancing Trust in the Community Supported Agriculture Model","authors":"Duc-Hiep Nguyen, N. H. Tuong, Hoang-Anh Pham","doi":"10.1109/ICTC49870.2020.9289297","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289297","url":null,"abstract":"Community Supported Agriculture (CSA) model is an efficient solution that not only solves the problem of the agricultural product’s origin but also provides a method to share the market risk between consumers and producers. In the CSA model, consumers order and pay for products in advance. Then, the producers will send packages of fresh products to consumers after a fixed period corresponding to the paid amount. However, the critical weakness of the CSA model is the lack of solutions for both sides to demonstrate the product’s quality, which makes consumers unsure about the condition of receiving products. In consequence, consumers become hesitant to order and pay money in advance. In this paper, we will propose a novel solution that allows consumers to track their products through agricultural diaries recorded by farmers every day. The key difference of the proposed solution is to leverage Blockchain technology advantages in authenticating and protecting the integrity of information. Such that, consumers could track all steps in the production process quickly and reliably. Meanwhile, producers can build and increase their enterprise branding by transferring product information transparently and responsibly.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"120 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":"131562369","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

Deep Learning for Constellation-based Modulation Classification under Multipath Fading Channels 多径衰落信道下基于星座的深度学习调制分类

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289413

Thien Huynh-The, Cam-Hao Hua, Van-Sang Doan, Viet Quoc Pham, Toan-Van Nguyen, Dong-Seong Kim

{"title":"Deep Learning for Constellation-based Modulation Classification under Multipath Fading Channels","authors":"Thien Huynh-The, Cam-Hao Hua, Van-Sang Doan, Viet Quoc Pham, Toan-Van Nguyen, Dong-Seong Kim","doi":"10.1109/ICTC49870.2020.9289413","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289413","url":null,"abstract":"Modulation classification, an intermediate step between signal detection and demodulation, is commonly deployed in many modern wireless communication systems. Although many approaches have been introduced in the last decades for identifying the modulation format of the incoming signal, they have the obstacle of mining radio characteristics for most traditional machine learning algorithms. To effectively handle this limitation, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. A convolutional neural network (CNN), namely CRNet, is developed to proficiently learn the most relevant radio characteristics from transformed gray-scale constellation image by cross-residual connection, a novel structure for associating the intrinsic information between two processing flows specified by regular and grouped convolutional layers. Based on the experimental evaluation, CRNet achieves the classification rate of approximately 90% at +10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further performs more accurately than some existing deep models for constellation-based modulation classification.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"2 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":"131727736","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

Application Layer Benefits of Redundant Disjoint Paths in a Real-Time Ethernet 实时以太网中冗余不相交路径的应用层优势

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289616

Stefan Farthofer, Diego Teixeira Barreto Lima, J. Du

引用次数: 2

RF-Based UAV Surveillance System: A Sequential Convolution Neural Networks Approach 基于射频的无人机监视系统:一种序列卷积神经网络方法

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289281

Rubina Akter, Van-Sang Doan, Godwin Brown Tunze, Jae-Min Lee, Dong-Seong Kim

{"title":"RF-Based UAV Surveillance System: A Sequential Convolution Neural Networks Approach","authors":"Rubina Akter, Van-Sang Doan, Godwin Brown Tunze, Jae-Min Lee, Dong-Seong Kim","doi":"10.1109/ICTC49870.2020.9289281","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289281","url":null,"abstract":"In recent years, popularity of commercial unmanned air vehicles (UAVs) or drones enormously increased due to their ductility and availability in various applications domains. This also results in some security threats to sensitive area, that urgently needs proper investigation and surveillance system to protect the security sensitive institutions. In this paper, we propose a drone detection system which can detect drones and identify different types of drone respectively. The proposed network structure is constituted based on sequential convolution neural network (CNN) with several one-dimensional layer to successively learn the different scales feature map of radio frequency signals, collected from drone. To train the proposed CNN model, we use challenging DroneRF dataset, a free accessible database containing background noise and three different drone’s radio frequency signals. The empirical results verify that the proposed model can detects all UAVs correctly and outperforms the existing RF based CNN model with average classification rate of 92.5% along with 93.5% F1 score.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"150 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":"132976109","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

Intelligent Error Recovery Flow Prediction for Low Latency NAND Flash Memory System 低延迟NAND闪存系统的智能错误恢复流预测

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289409

Bogyeong Kang, Jeongju Jee, Hyuncheol Park

引用次数: 3

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ictc49870.2020.9289159

引用次数: 0

Analysis of forest loss by Sentinel-1 SAR time series 基于Sentinel-1 SAR时间序列的森林损失分析

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289607

Jung-Hoon Lee, J. Sumantyo, M. Waqar, Jae-Hyun Kim

引用次数: 3

Train Throughput Analysis of Distributed Reinforcement Learning 分布式强化学习的训练吞吐量分析

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289179

Sooyoung Jang, Noh-Sam Park

{"title":"Train Throughput Analysis of Distributed Reinforcement Learning","authors":"Sooyoung Jang, Noh-Sam Park","doi":"10.1109/ICTC49870.2020.9289179","DOIUrl":"https://doi.org/10.1109/ICTC49870.2020.9289179","url":null,"abstract":"Distributed deep reinforcement learning can increase the train throughput, which is defined as the timesteps per second used for training, easily by just adding computing nodes to a cluster, which makes it an essential technique for solving complex problems. The more complicated the virtual learning environment and the policy network become, the more the CPU computing power in the rollout phase and the GPU computing power in the policy update phase is required. Recall that the reinforcement learning iterates the phases of acquiring data through rollout in the virtual learning environment and updating the policy from that data over millions of iterations. In this paper, the train throughput analysis is performed with RLlib and IMPALA on two different problems: CartPole, a simple problem, and Pong, a relatively complex problem. The effects of various scalability metrics, clustering, and observation dimensions on train throughput are analyzed. Throughout the analysis, we show that 1) the train throughput varies significantly according to the scalability metrics, 2) it is vital to monitor the bottleneck in the train throughput and configure the cluster accordingly, and 3) when the GPU computing power is the bottleneck, reducing the observation dimensions can be a great option as the train throughput increases up to 3 times by reducing the dimension from 84 to 42.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"25 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":"134506721","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 Comparison of Congestion Control Algorithms in Emulated Wi-Fi Networks 仿真Wi-Fi网络中拥塞控制算法的比较

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289548

Yuki Horie, Do Thi Thu Hien, Kien Nguyen, H. Sekiya

引用次数: 1

Performance of RPL Routing Protocol over Multihop Power Line Communication Network 多跳电力线通信网络中RPL路由协议的性能研究

2020 International Conference on Information and Communication Technology Convergence (ICTC) Pub Date : 2020-10-21 DOI: 10.1109/ICTC49870.2020.9289175

Mingyu Park, Gunmo Jeong, Heedeok Son, Jeongyeup Paek

引用次数: 5