• 한국ITS학회 논문지
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• 제17권4호
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• pp.14-25
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• 2018

최근 빅데이터 시대의 도래와 함께 교통사고와 관련된 요인을 설명하기 용이해졌다. 이에따라 최신 분석 기법을 적용하여 교통사고 자료를 분석하고 시사점을 도출할 필요가 있다. 본 연구의 목적은 고속도로 교통사고 자료를 이용하여 고속도로의 주요 분석 단위인 콘존의 교통사고 건수를 예측하기 위하여 음이항 회귀모형과 딥 러닝을 이용한 기법을 적용하고 예측 성능을 비교하였다. 예측 성능 비교 결과, 딥 러닝 모형의 MOE들이 음이항 회귀모형에 비해 다소 우수한 것으로 나타났으나, MAD 기준으로 차이는 미미한 것으로 나타났다. 하지만 딥 러닝을 이용할 경우 다른 독립변수들을 추가하는 것이 용이하고, 모형의 구조 등을 변경할 경우 예측 신뢰도를 더욱 증가시킬 수 있을 것으로 판단된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1510-1532
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• 2017

In smart grid, privacy implications to individuals and their families are an important issue because of the fine-grained usage data collection. Wireless communications are utilized by many utility companies to obtain information. Network coding is exploited in smart grids, to enhance network performance in terms of throughput, delay, robustness, and energy consumption. However, random linear network coding introduces a new challenge for privacy preserving due to the encoding of data and updating of coefficients in forwarder nodes. We propose a distributed privacy preserving scheme for random linear network coding in smart grid that considers the converged flows character of the smart grid and exploits a homomorphic encryption function to decrease the complexities in the forwarder node. It offers a data confidentiality privacy preserving feature, which can efficiently thwart traffic analysis. The data of the packet is encrypted and the tag of the packet is encrypted by a homomorphic encryption function. The forwarder node random linearly codes the encrypted data and directly processes the cryptotext tags based on the homomorphism feature. Extensive security analysis and performance evaluations demonstrate the validity and efficiency of the proposed scheme.

• KNOM Review
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• 제22권3호
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• pp.1-12
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• 2019

최근 빅데이터와 AI를 이용한 연구들이 ICT 분야에서 주요 이슈로 부상하고 있다. 하지만 연구를 위한 빅데이터의 크기가 기하급수적으로 증가하면서 기존 네트워크 방식의 데이터 전송에 대해 사용자들은 빅데이터를 송수신하는데 걸리는 시간은 하드디스크를 복사하여 보내는 시간보다 느리다는 문제를 제기한다. 이에 따라 연구자들은데이터를 고속으로 전송하고, 다양한 네트워크의 구조를 수용할 수 있는 동적이고 유연한 네트워크 기술을 요구한다. SDN/NFV 기술은 네트워크를 프로그래밍하여 사용자들의 요구에 적절한 네트워크를 제공할 수 있는 기술로써, 네트워크의 유연성 및 보안성 문제를 해결할 수 있다. 또한 AI를 수행하는데 있어 문제가 되는 중앙집중적 방식의데이터 처리는 실시간성을 보장할 수 없고, 트래픽이 증가하는 경우 네트워크 지연이 발생한다. 이를 해결하기 위해 중앙집중적 방식을 탈피한 Edge-Computing 기술을 이용하여 해결할 수 있다. 본 논문에서는 SDN, NFV, Edge-Computing 기술에 대한 개념 및 연구 동향에 대해 알아보고, 세 가지 기술을 접목시켜 사용되는 데이터 중심 네트워크 기술 동향에 대해 분석한다.

• 한국정보시스템학회지:정보시스템연구
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• 제26권3호
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• 국제초고층학회논문집
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• 제10권1호
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• pp.1-8
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• 2021

Currently, big data and open data, together with traditional measured data, have come to constitute a new data environment, expanding new technical paths for quantitative analysis of the street environment. Streets provide precious linear public space in high-density residential areas. Pedestrian activities are the main body of street vitality. In this paper, 441 street segments were selected from 21 residential districts in high-density downtown area of Shanghai as cases, to quantitatively evaluate the influencing factors of pedestrian activities. Bivariate analysis was performed, and the results showed that street vitality was not only correlated with a highly populated environment, but also with other factors. In particular, the density of entrances and exits of residential properties, the proportion of walkable areas, and the density of retail and service facilities, were correlated with the vitality of street segments. The magnitudes of correlation between the street environmental factors and the pedestrian traffic differed across various trip purposes. Segment connectivity factors were more correlated with walking for leisure than for transportation. While public transportation factors were mainly correlated with walking for transportation, vehicular traffic factors were negatively correlated with walking for leisure.

• 한국대기환경학회지
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• 제32권1호
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• pp.9-20
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• 2016

Ozone has been a problem in big cities. That is secondary air pollutant produced by nitrogen oxide and VOCs in the atmosphere. In order to solve this, the first to be the analysis of the $NO_x$ and VOCs. The main source of nitrogen oxide is the road mobile. Industrial sources in Seoul are particularly low, and mobile traffics on roads are large, so 45% of total $NO_x$ are estimated that road mobile emissions in Seoul. Thus, it is necessary to clarify the relation with the activity of road mobile source and $NO_x$ concentration. In this study, we analyzed the 4 locations with roadside automatic monitoring systems in their center. The V.K.T. calculating areas are set in circles with 50 meter spacing, 50 meter to 500 meter from their center. We assumed the total V.K.T. in the set radius affect the $NO_x$ concentration in the center. We used the hourly $NO_x$ concentrations data for the 4 observation points in July for the interference of the other sources are minimized. We used the intersection traffic survey data of all direction for construction of the V.K.T. data, the mobile activities on the roads. ArcGIS application was used for calculating the length of roads in the set radius. The V.K.T. data are multiplied by segment traffic volume and length of roads. As a result, the $NO_x$ concentration can be expressed as linear function formula for V.K.T. with high predictive power. Moreover we separated background concentration and concentrations due to road mobile source. These results can be used for forecasting the effect of traffic demand management plan.

• 한국안전학회지
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• 제31권6호
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• pp.121-128
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• 2016

Social Media transformed the mass media based information traffic, and it has become a key resource for finding value in enterprises and public institutions. Particularly, in regards to disaster management, the necessity for public participation policy development through the use of social media is emphasized. National Disaster Management Research Institute developed the Social Big Board, which is a system that monitors social Big Data in real time for purposes of implementing social media disaster management. Social Big Board collects a daily average of 36 million tweets in Korean in real time and automatically filters disaster safety related tweets. The filtered tweets are then automatically categorized into 71 disaster safety types. This real time tweet monitoring system provides various information and insights based on the tweets, such as disaster issues, tweet frequency by region, original tweets, etc. The purpose of using this system is to take advantage of the potential benefits of social media in relations to disaster management. It is a first step towards disaster management that communicates with the people that allows us to hear the voice of the people concerning disaster issues and also understand their emotions at the same time. In this paper, Korean language text mining based Social Big Board will be briefly introduced, and disaster issue detection model, which is key algorithms, will be described. Disaster issues are divided into two categories: potential issues, which refers to abnormal signs prior to disaster events, and occurrence issues, which is a notification of disaster events. The detection models of these two categories are defined and the performance of the models are compared and evaluated.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
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• pp.68-71
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• 2009

All-IP network requires change of the existing IP network engineering methods as the convergence service market between communication and broadcasting industries using IP network is growing rapidly. Especially the video services like IPTV require more strict transmission quality and higher bandwidth than the existing data services. So it is difficult to design All-IP network by the over-provisioning method which used to be used for the existing IP network design. It also requires a heavy investment which becomes one of big obstacles to the IPTV service expansion. In order to reduce the investment costs, it is required to design an optimized network by maximizing the utilization of the network resources and at the same time maintaining the customer satisfaction in terms of service quality. In this paper, we first analyze the effects of IPTV traffic on the existing internet. Then we compare two traffic engineering technologies, which are dimensioning without admission control and dimensioning with admission control, on the All-IP network design by simulation. Finally, we suggest cost effectiveness of traffic engineering technologies for designing the All-IP network.

• 정보보호학회논문지
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• 제14권4호
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• pp.111-121
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• 2004

인터넷의 급속한 발달과 더불어 네트워크 환경에서의 침입은 빠르게 증가하고 있으며, 그 피해 또한 급격히 증가하고 있다. 최근의 인터넷 공격은 특정 호스트나 네트워크에 대한 피해를 초래할 뿐만 아니라, 네트워크 전반의 성능저하를 유발한다. 기존의 침입 탐지 시스템은 각 지역망 및 특정한 대상 시스템을 보호하기 위한 솔루션들로, 기간망 수준의 실시간 공격 탐지에 적용하기 힘든 문제점을 가지고 있다. 본 논문에서는 네트워크 수준의 실시간 공격탐지를 위하여 각 포트별 트래픽을 대상으로 지수평활법을 적용하는 광대역 네트워크 침입 탐지 기법 제안하였다. 8일간의 기간망의 트래픽 데이터를 대상으로 한 실험에서, 제안한 기법은 공격으로 추정되는 급격한 트래픽의 증가를 적절히 탐지함을 보여주었다.

• 한국ITS학회 논문지
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• 제22권1호
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• pp.103-114
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• 2023

본 연구에서는 도시부 도로의 다양한 자료를 수집하여 통행속도 변화에 대한 영향을 분석하였고, 이와 같은 빅데이터를 활용하여 GRU 기반의 단기 통행속도 예측 모형을 개발하였다. 그리고 Baseline 모형과 이중지수평활 모형을 비교 모형으로 선정하여 RMSE 지표로 예측 오차를 평가하였다. 모형 평가 결과, Baseline 모형과 이중지수평활 모형의 RMSE는 평균 7.46, 5.94값으로 각각 산출되었다. 그리고 GRU 모형으로 예측한 평균 RMSE는 5.08 값이 산출되었다. 15개 링크별로 편차가 있지만, 대부분의 경우 GRU 모형의 오차가 최소의 값을 나타내었고, 추가적인 산점도 분석 결과도 동일한 결과를 제시하였다. 이러한 결과로부터 도시부 도로의 통행속도 정보 생성 과정에서 GRU 기반의 예측 모형 적용 시 예측 오차를 감소시키고 모형 적용 속도의 개선을 기대할 수 있을 것으로 판단된다.