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Network Based Diffusion Model (네트워크 기반 확산모형)

  • Joo, Young-Jin
    • Korean Management Science Review
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    • v.32 no.3
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    • pp.29-36
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    • 2015
  • In this research, we analyze the sensitivity of the network density to the estimates for the Bass model parameters with both theoretical model and a simulation. Bass model describes the process that the non-adopters in the market potential adopt a new product or an innovation by the innovation effect and imitation effect. The imitation effect shows the word of mouth effect from the previous adopters to non-adopters. But it does not divide the underlying network structure from the strength of the influence over the network. With a network based Bass model, we found that the estimate for the imitation coefficient is highly sensitive to the network density and it is decreasing while the network density is decreasing. This finding implies that the interpersonal influence can be under-looked when the network density is low. It also implies that both of the network density and the interpersonal influence are important to facilitate the diffusion of an innovation.

A Design of Peer-to-Peer Network Security Model using Fingerprint Recognition (지문 인식을 이용한 Peer to Peer Network 보안 모델의 설계)

  • 박정재;구하성
    • Proceedings of the Korea Multimedia Society Conference
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    • pp.481-487
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    • 2001
  • 본 논문은 현재까지 제시되어진 peer to peer network model들을 정리하고 대표적인 peer to peer network model에 지문인식을 적용하여 개인에 대한 신원 인증 절차를 수행함으로써 보안에 대한 새로운 해결책을 제안하였다. 기존의 peer to peer network model은 개인 대 개인간의 효율적인 network검색 기능과 분산 computing 환경을 제공하지만 보안에 관해서는 아직까지도 많은 연구가 필요하다. 본 연구에서는 기존의 peer to peer network model들에 지문인식을 사용한 새로운 보안 model을 설계하였다.

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A Study on the Rainfall Forecasting Using Neural Network Model in Nakdong River Basin - A Comparison with Multivariate Model- (낙동강유역에서 신경망 모델을 이용한 강우예측에 관한 연구 - 다변량 모델과의 비교 -)

  • Cho, Hyeon-Kyeong;Lee, Jeung-Seok
    • Journal of the Korean Society of Industry Convergence
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    • v.2 no.2
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    • pp.51-59
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    • 1999
  • This study aims at the development of the techniques for the rainfall forecasting in river basins by applying neural network theory and compared with results of Multivariate Model (MVM). This study forecasts rainfall and compares with a observed values in the San Chung gauging stations of Nakdong river basin for the rainfall forecasting of river basin by proposed Neural Network Model(NNM). For it, a multi-layer Neural Network is constructed to forecast rainfall. The neural network learns continuous-valued input and output data. The result of rainfall forecasting by the Neural Network Model is superior to the results of Multivariate Model for rainfall forecasting in the river basin. So I think that the Neural Network Model is able to be much more reliable in the rainfall forecasting.

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Study of Virtual Goods Purchase Model Applying Dynamic Social Network Structure Variables (동적 소셜네트워크 구조 변수를 적용한 가상 재화 구매 모형 연구)

  • Lee, Hee-Tae;Bae, Jungho
    • The Journal of Distribution Science
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    • v.17 no.3
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    • pp.85-95
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    • 2019
  • Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.

Proposal of Open Network Service Model as a New Business Model of Telecom Operator (통신사업자의 새로운 사업 모델로서의 개방형 네트워크 서비스 모델 제안)

  • Jin, Myung Sook;Oh, Suk
    • Journal of the Korea Society of Digital Industry and Information Management
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    • v.6 no.2
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    • pp.81-89
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    • 2010
  • The major worldwide communication network operators have designed and are building up the NGN with various network capabilities, which conventional Internet do not have. The open network service model makes these network capabilities available to the third party of the value added service providers through the standardized API providing users with more intelligent and enhanced services. This paper proposes the open network service model as NaaS (Network as a Service) and examines service models of several levels. It is believed that these efforts presented in this paper will make the network operators expand their service ranges through the opening of invested network resources producing more various communication services for users.

A Development of System for Flood Runoff Forecasting using Neural Network Model (신경망 모형을 이용한 홍수유출 예측시스템의 재발)

  • Ahn, Sang-Jin;Jun, Kye-Won
    • Journal of Korea Water Resources Association
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    • v.37 no.9
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    • pp.771-780
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    • 2004
  • The purpose of this study is to test a development of system for flood runoff forecasting using neural network model. As the forecasting models for flood runoff the neural network model was tested with the observed flood data at Gongju and Buyeo stations. The neural network model consists of input layer, hidden layer, and output layer. For the flood events tested rainfall and runoff data were the input to the input layer and the flood runoff data were used in the output layer. To make a choice the forecasting model which would make up of runoff forecasting system properly, real-time runoff of river when flood periods were forecasted by using neural network model and state-space model. A comparison of the results obtained by the two forecasting models indicated the superiority and reliability of the neural network model over the state-space model. The neural network model was modified to work in the Web and developed to be the basic model of the forecasting system for the flood runoff. The neural network model developed to be used in the Web was loaded into the server and was applied to the main stream of Geum river. For the main stage gauging stations mentioned above the applicability of the selected forecasting model, the Neural Network Model, was verified in the Web.

The Design of SNMP SubManager Model Considering Characteristic of Network Traffics (Network 부하 특성을 고려한 SNMP SubManager Model 설계)

  • 하경재;신복덕;강임주
    • Proceedings of the Korean Information Science Society Conference
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    • pp.183-185
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    • 2000
  • 본 논문에서는 SNMP를 이용한 Nerwork Management System(NMS)이 Network을 사용하는 Application에 영향을 주지 않도록 하는 Polling 전략과 Model을 설계하였다. 제안된 System은 Network의 각 Client 정보를 처리하는 Agent와 Data 수집 및 제어를 담당하는 Server로 구성된다. Agent는 SNMP Agent 부분과 Network 상태를 Monitoring 하는 SubManager로 구성되어, Server는 SNMP Agent와의 Polling 및 Polling 정책을 결정하는 부분으로 구성된다. 제안 Model은 SNMP를 이용한 NMS를 도입할 경우, 기존 Network Service에 영향을 주지 않도록 하는 것이 목적이다. 제안된 System에 대한 성능평가를 위해 실존하는 Network을 대상으로 SNMP의 Polling 및 Service의 부하량을 측정하였다.

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Building a network model for a mobile robot using sonar sensors (초음파센서를 이용한 이동로보트의 네트워크환경모델 구성)

  • Chung, Hak-Young;Park, Sol-lip;Lee, Jang-Gyu
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.5
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    • pp.593-599
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    • 1999
  • A mobile robot in FMS environment should be able to nevigate itself. Therefore, path planning is necessary for the mobile robot to perform its tasks without being lost. Path planning using a network model gives oprimal paths to every pair of nodes but building this model demands accurate information of environments. In this paper, a method to build a network model using sonar sensors is presented. The main idea is to build a quad tree model by using sonar sensors and convert the model to a network model for path planning. The new method has been implemented on a mobile robot. Experimental results show that the mobile robot constructs an accurate network model using inaccurate sonar data.

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A Study on the Development of Transportation Module for Mail Transportation Decision Support System (우편수송DSS를 위한 수송 모듈 구축에 관한 연구)

  • 최민구;김영민
    • Journal of the Korea Safety Management and Science
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    • v.3 no.4
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    • pp.145-154
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    • 2001
  • This paper deals with a network model for the efficient transportation of post and consists of the formulation based on the network model and the LINGO programming model including the operations of the post transportation. This network model is represented by using Time Space Network. The generalized formulation is built up with the input variables and the decision variables, which are defined on the basis of the network model. And LINGO programming model to be proposed with DB and LINGO is constructed in consideration of how to manage the post transportation and the intermodal transport. The results of the model implementation were represented on Time Space Network and they are analyzed and verified. The LINGO programming model is used as the module to be set in application software. Specifically with using GEOmania, GIS tool, the LINGO Model is applied to develop the application for Mail Transportation Decision Support System.

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Accelerated Monte Carlo analysis of flow-based system reliability through artificial neural network-based surrogate models

  • Yoon, Sungsik;Lee, Young-Joo;Jung, Hyung-Jo
    • Smart Structures and Systems
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    • v.26 no.2
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    • pp.175-184
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    • 2020
  • Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.