2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献_第10页

Tracking User Application Activity by using Machine Learning Techniques on Network Traffic 在网络流量上使用机器学习技术跟踪用户应用程序活动

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669040

Sina Fathi Kazerooni, Yagiz Kaymak, R. Rojas-Cessa

引用次数: 5

Convolutional Neural Network Approach for Aircraft Noise Detection 基于卷积神经网络的飞机噪声检测

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669006

Ju-won Pak, Min-koo Kim

引用次数: 6

Outlier Geometric Angle Detection Algorithm 离群几何角度检测算法

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669090

Zhongyang Shen

引用次数: 1

ICAIIC 2019 Committee ICAIIC 2019委员会

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/icaiic.2019.8669013

引用次数: 0

A Survey of Blockchain and Its Applications 区块链及其应用综述

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669067

Qalab E. Abbas, Sung-Bong Jang

引用次数: 33

Framework of Big data Analysis about IoT-Home-device for supporting a decision making an effective strategy about new product design 物联网家庭设备大数据分析框架，支持新产品设计决策制定有效策略

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669086

Jong-jin Jung, Kyung Won Kim, Jongbin Park

{"title":"Framework of Big data Analysis about IoT-Home-device for supporting a decision making an effective strategy about new product design","authors":"Jong-jin Jung, Kyung Won Kim, Jongbin Park","doi":"10.1109/ICAIIC.2019.8669086","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669086","url":null,"abstract":"This paper introduces a framework of big data analysis about IoT home devices which are delivered to the consumer through several distribution channels, are used by a home user in the smart home, and are repaired in A/S center (repair shop). We collect big data and make an analysis at three major stages that are distribution stage, customer-usage stage, and A/S stage. The ultimate purpose of the presented framework is to help the small/medium companies to make an elastic strategy for the new product. Therefore they can make a more effective decision at three major stages. For example, they can reduce redundancy about a distribution channel, they can adjust a quantity of warehousing, release, stock. They can make a decision on what to upgrade the new next device, how to increase durability, and so on. For these purposes, this framework consists of three subsystems. 1) A data crawler that collects and stores big data about IoT-home devices at three major stages, 2) A big data analyzer about IoT-home device with an appreciate analytic model, 3) A visualization of insights, which help a user to understand the analytic output.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132848367","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

Evolutionary Ensemble LSTM based Household Peak Demand Prediction 基于进化集成LSTM的家庭峰值需求预测

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8668971

Songpu Ai, Antorweep Chakravorty, Chunming Rong

{"title":"Evolutionary Ensemble LSTM based Household Peak Demand Prediction","authors":"Songpu Ai, Antorweep Chakravorty, Chunming Rong","doi":"10.1109/ICAIIC.2019.8668971","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8668971","url":null,"abstract":"The popularization of electric vehicle, the commercialization of micro-generation, and the advance of local storage lead great challenges to the local power grid on household and neighbourhood level. A potential solution is to construct a home/neighbourhood energy management system (HEMS) to coordinate all available electrical equipment together using AI. As a portion of HEMS, peak demand prediction is critically important on triggering load scheduling among the household power environment to achieve better electricity usage curve. Long short-term memory (LSTM) network as an eminent type of machine learning method is generally considered to be capable on forecasting based on time series data including temporal dynamic behaviours with unknown lags. Various LSTM networks are adopted in existing researches to provide predictions in energy informatics field. However, the presented network structures are commonly selected through empirical or enumerative approaches. The utilized networks are generally carefully tuned as case by case studies. In this article, an evolutionary ensemble LSTM (EELSTM) method is proposed to pool LSTM networks with the same structure or with similar structures to obtain a more reliable prediction automatically. Experimental study demonstrates that networks with suitable structures and initialization are selected out through the learning process. A better performed peak demand prediction is achieved comparing with single LSTM unit network. In addition, the evolutionary parameters have variant impacts on the model performance.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127261701","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

A layer-wise Perturbation based Privacy Preserving Deep Neural Networks 一种基于分层扰动的隐私保护深度神经网络

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669014

Tosin A. Adesuyi, Byeong-Man Kim

引用次数: 15

DCS-PCA based Data Transmission in Smart Grid 基于DCS-PCA的智能电网数据传输

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669060

Dengjun Zhu, Jinlong Yan, Haiwei Yuan, Yongjun Ma, Xufeng Hu

{"title":"DCS-PCA based Data Transmission in Smart Grid","authors":"Dengjun Zhu, Jinlong Yan, Haiwei Yuan, Yongjun Ma, Xufeng Hu","doi":"10.1109/ICAIIC.2019.8669060","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669060","url":null,"abstract":"The safety of high speed sensor data transmission is an important part of smart grid. Due to the development of the diversity and scale, there has been an ever-increasing need of data transmission algorithms in both academia and industry. Past research shows that with the increasing types and number of sensors deployed, there are problems such as low transmission efficiency and excessive energy consumption. When the collected data are transferred back to the background server, sensor nodes face the problem of high storage pressure. Distributed technology can alleviate the transmission and storage pressure of signal nodes. Therefore, this paper proposes an optimization algorithm which can reduce the amount of data, energy consumption and improve transmission rate. In addition, for further improving the accuracy of restored data, principal component analysis (PCA) is utilized to generate adaptive sparse matrix for different types of sensors. Through selecting different sparse matrices, our experiments show that the technology can significantly reduce the transmission of data and ensure the accuracy of data reconstruction.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122969920","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 Denoising Autoencoder based wireless channel transfer function estimator for OFDM communication system 基于去噪自编码器的OFDM通信系统无线信道传递函数估计器

2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2019-02-01 DOI: 10.1109/ICAIIC.2019.8669044

T. Wada, Takao Toma, Mursal Dawodi, J. Baktash

{"title":"A Denoising Autoencoder based wireless channel transfer function estimator for OFDM communication system","authors":"T. Wada, Takao Toma, Mursal Dawodi, J. Baktash","doi":"10.1109/ICAIIC.2019.8669044","DOIUrl":"https://doi.org/10.1109/ICAIIC.2019.8669044","url":null,"abstract":"This paper proposes a channel estimation method for Orthogonal Frequency Division Multiple Access (OFDM) communication system by utilizing a Neural Network (NN) based a Machine Learning (ML). Especially, Autoencoder is utilized to estimate Channel Transfer Function (CTF) and to reduce a noise on the estimate. Japanese Digital TV broadcast system is assumed as target system. Then 8k FFT/IFFT is used and number of sub-carriers are 5617 such as mode3 in Integrated Services Digital Broadcasting-Terrestrial (ISDB-T) spec. 5617 complex CTF points must be estimated by limited number of scattered pilot sub-carriers. Assumed channel condition is 2 wave multipath channel with Additive White Gaussian Noise (AWGN). The multipath parameters are randomly generated. To train the autoencoder, 5000 CTFs are generated and pre-training was performed. System performance was evaluated by measuring Bit Error Rate (BER). The system with conventional frequency-domain interpolator and the system with autoencoder based were compared. According to BER simulation results, the autoencoder based system has shown lower BER than the conventional. At BER=10$^{-5}$, autoencoder system shows roughly 2dB gain than conventional system.","PeriodicalId":273383,"journal":{"name":"2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123964493","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: 8