• Title/Summary/Keyword: Digital Network

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Prediction for Nonlinear Time Series Data using Neural Network (신경망을 이용한 비선형 시계열 자료의 예측)

  • Kim, Inkyu
    • Journal of Digital Convergence
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    • v.10 no.9
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    • pp.357-362
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    • 2012
  • We have compared and predicted for non-linear time series data which are real data having different variences using GRCA(1) model and neural network method. In particular, using Korea Composite Stock Price Index rate, mean square errors of prediction are obtained in genaralized random coefficient autoregressive model and neural network method. Neural network method prove to be better in short-term forecasting, however GRCA(1) model perform well in long-term forecasting.

Bitmap-based Routing Protocol for Improving Energy and Memory Efficiency (에너지 및 메모리 효율성을 개선한 비트맵기반 라우팅 프로토콜)

  • Choi, Hae Won;Kim, Sang Jin;Ryoo, Myung Chun
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.5 no.3
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    • pp.59-67
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    • 2009
  • This paper proposes a improved bitmap routing protocol, which finds the best energy efficient routing path by minimizing the network overheads and prolongs the overall network lifetime. Jung proposed a bitmap scheme for sensor networks. His scheme uses a bitmap table to represent the connection information between nodes. However, it has a problem that the table size is depends on the number of nodes in the sensor networks. The problem is very serious in the sensor node with a limited memory. Thereby, this paper proposes a improved bitmap routing protocol to solve the problem in Jung's scheme. Proposed protocol over the memory restricted sensor network could optimize the size of bitmap table by applying the deployed network property. Proposed protocol could be used in the diversity of sensor networks due to it has minimum memory overheads.

Implementation of Wireless Network simulator considering a User's Call Characteristics (사용자 통화 특성을 고려한 무선 네트워크 시뮬레이터 구현)

  • Yoon, Young Hyun
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.5 no.3
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    • pp.107-115
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    • 2009
  • Traditionally, simulation method is used to test and evaluate the performance of communication protocol or functional elements for mobile communication service. In this paper, wireless network simulator is implemented using the C++ object-oriented programming language. This simulator can simulate wireless data services, like as ad-hoc networks, by considering the user's mobility. In this paper, the simulator includes network traffic model to reflect wireless data service and traffic source model to represent a user's mobility similar to real service environment and traffic characteristics can be reflected on the simulation, and also more accurate simulation results can be got through that. In addition, by using object-oriented techniques, new service feature or environment can be easily added or changed so that the developed mobile communication simulator can reflect the real service environment all the time. This simulator can be used in adjusting the characteristics of wireless data hosts following the mobility of the user, and also can be used in building new wireless ad-hoc network routing protocols.

Simple AI Robust Digital Position Control of PMSM using Neural Network Compensator (신경망 보상기를 이용한 PMSM의 간단한 지능형 강인 위치 제어)

  • 윤성구
    • Proceedings of the KIPE Conference
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    • 2000.07a
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    • pp.620-623
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    • 2000
  • A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a fedforward recall and error back-propagation training. Since the total number of nodes are only eight this system can be easily realized by the general microprocessor. During the normal operation the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. IN addition the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Singal Processor DS1102 Board (TMS320C31) The basic DSP software is used to write C program which is compiled by using ANSI-C style function prototypes.

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Design and Implementation of a Knowledge Base for Intelligence Service in IoV (차량인터넷에서 지능형 서비스 제공을 위한 지식베이스 설계 및 구축)

  • Ryu, Minwoo;Cha, Siho
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.13 no.4
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    • pp.33-40
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    • 2017
  • Internet of Vehicles (IoV) is a subset of Internet of Things (IoT) and it is an infrastructure for vehicles. Therefore, IoV consists of three main network including inter-vehicle network, intra-vehicle network, and vehicular mobile internet. IoV mainly used in urban traffic environment to provide network access for drivers, passengers and traffic management. Accordingly, many research works have focused on network technology. But, recent concerted efforts in academia and industry point to paradigm shift in IoV system. In this paper, we proposed a knowledge base for intelligence service in IoV. A detailed design and implementation of the proposed knowledged base is illustrated. We hope this work will show power of IoV as a disruptive technology.

FORECASTING OF FINANCIAL TIME SERIES BY A DIGITAL FILTER AND A NEURAL NETWORK

  • Saito, Susumu;Kanda, Shintaro
    • Proceedings of the Korea Society for Simulation Conference
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    • 2001.10a
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    • pp.313-317
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    • 2001
  • The approach to predict time series without neglecting the fluctuation in a short period is tried by using a digital FIR filter and a neural network. The differential waveform of the Nikkei average closing price is filtered by the FIR band-pass filter of 101 length. It is filtered into the five frequency bands of 0-1Hz, 1-2Hz, 2-3Hz, 3-4Hz and 4-5Hz by setting the sampling frequency 10Hz. The each filtered waveform is learned and forecasted by the neural network. The neural network of the back propagation method is adopted in the learning the waveform. By inputting the data of 20 days in the past, the prediction of 10 days ahead is carried out. After learning the time series of each frequency band by the neural network, the predicted data far each frequency band are obtained. The predicted waveforms of each frequency band are synthesized to obtain a final forecast. The waveform can be forecasted well as a whole.

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A Study about Web Traffic Performance on wired and wireless network (유무선 혼합망에서 웹 트래픽 성능에 관한 연구)

  • Kim, Chang Hee
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.7 no.2
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    • pp.47-58
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    • 2011
  • Snoop was suitably designed for wired and wireless network as having snoop agent module in BS(Base Station) which is connecting to both wired and wireless network in order to supplement the problems of TCP. This study examined performance difference by using the web traffic taken wide possession in current internet traffic. The web traffic has greater amount of traffic, shorter life time, and smaller size than other traffics. This study found that snoop producing performance improvement of wireless network in the network mixed with the wired and wireless lead performance loss when transmitting web traffic. This study found that in case of web traffic is transmitted it bring a performance improvement of web traffic as computing BWE((Bandwidth Expansion), and also found that this study prove of performance improvement by decreasing local retranmission.

Effects of Switching Costs on Loyalty to Social Network Sites: Resource Based Approach

  • Namn, Su-Hyeon;Jung, Chul-Ho
    • Journal of Digital Convergence
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    • v.9 no.1
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    • pp.25-36
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    • 2011
  • This paper examines user's loyalty to social network sites (SNS) from switching costs (SC) incurred by both technology and social factors. We propose a research model specifying that the perceived values of resources of the factors affect the SC and the SC determine user's loyalty. Empirical results show that technology variables of ease of use and privacy controllability, and social variables such as network size, usefulness of SNS activities, and awareness of network status have significant effect on SC. In particular, ease of use is negatively associated with SC. Since it is shown that in overall the impact of social factors is stronger than that of technology factors, we can interpret that technological superiority itself does not lead to the success of SNS. Contributions of this paper are: 1) application of SC in SNS research from the resource based perspective, which can be used for developing strategies of sustainable SNS, and 2) provision of different perspective toward the variable of ease of use, which has been considered an important factor of technology acceptance.

The Effects of Social Network Positions on Individual Performance (사회적 네트워크가 성과에 미치는 영향)

  • Kim, Changsik;Kim, Tae kyung;Kwahk, Keeyoung
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.14 no.2
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    • pp.133-141
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    • 2018
  • The purpose of this study was to propose a model of knowledge transfer in IT outsourcing. In this study, structural holes were chosen as antecedent factors, and job performance as a consequence factor. We conducted a survey in which we collected data from 42 respondents working in one of the leading IT companies in Seoul, South Korea. The data were analyzed using UCINET 6 and SmartPLS 2.0. The antecedent factors (structural holes in closeness network and in professional network) turned out to be statistically significant. Knowledge transfer considerably influenced job performance. Lastly, implications and limitations of these findings were discussed, and directions for future research were suggested.

Sentiment Analysis on Movie Reviews Using Word Embedding and CNN (워드 임베딩과 CNN을 사용하여 영화 리뷰에 대한 감성 분석)

  • Ju, Myeonggil;Youn, Seongwook
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.15 no.1
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    • pp.87-97
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    • 2019
  • Reaction of people is importantly considered about specific case as a social network service grows. In the previous research on analysis of social network service, they predicted tendency of interesting topic by giving scores to sentences written by user. Based on previous study we proceeded research of sentiment analysis for social network service's sentences, which predict the result as positive or negative for movie reviews. In this study, we used movie review to get high accuracy. We classify the movie review into positive or negative based on the score for learning. Also, we performed embedding and morpheme analysis on movie review. We could predict learning result as positive or negative with a number 0 and 1 by applying the model based on learning result to social network service. Experimental result show accuracy of about 80% in predicting sentence as positive or negative.