• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.392-399
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• 2013

Due to the development of IT technology and the information age, a dependency of the network over the most of our lives have grown to a greater extent. Although it provides us to get various useful information and service, it also has negative effectiveness that can provide network intruder with vulnerable roots. In other words, we need to urgently cope with theses serious security problem causing service disableness or system connected to network obstacle with exploiting various packet information. Many experts in a field of security are making an effort to develop the various security solutions to respond against these threats, but existing solutions have a lot of problems such as lack of storage capacity and performance degradation along with the massive increase of packet data volume. Therefore we propose the packet analysis model to apply issuing Big Data technology in the field of security. That is, we used NoSQL which is technology of massive data storage to collect the packet data growing massive and implemented the packet analysis model based on K-means clustering using MapReudce which is distributed programming framework, and then we have shown its high performance by experimenting.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.107-123
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• 2014

Current text mining techniques suffer from the problem that the conventional text representation models cannot express the semantic or conceptual information for the textual documents written with natural languages. The conventional text models represent the textual documents as bag of words, which include vector space model, Boolean model, statistical model, and tensor space model. These models express documents only with the term literals for indexing and the frequency-based weights for their corresponding terms; that is, they ignore semantical information, sequential order information, and structural information of terms. Most of the text mining techniques have been developed assuming that the given documents are represented as 'bag-of-words' based text models. However, currently, confronting the big data era, a new paradigm of text representation model is required which can analyse huge amounts of textual documents more precisely. Our text model regards the 'concept' as an independent space equated with the 'term' and 'document' spaces used in the vector space model, and it expresses the relatedness among the three spaces. To develop the concept space, we use Wikipedia data, each of which defines a single concept. Consequently, a document collection is represented as a 3-order tensor with semantic information, and then the proposed model is called text cuboid model in our paper. Through experiments using the popular 20NewsGroup document corpus, we prove the superiority of the proposed text model in terms of document clustering and concept clustering.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.49-55
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• 2019

An open office or a shared office is emerging as the emphasis on the collaborative and communicative work environments is increasing. In the past, the user maintained the space, but the maintenance of indoor space became difficult because there is no fixed user. Indoor space information can be collected using the ICBM framework system. The facility management can achieve this with data. Therefore, this study proposed a framework based on ICBM (Internet of Things, Cloud, Big Data, and Mobile) for verifying the possibility of a smart facility management system for indoor space. IoT (Internet of Things) technology was used to measure the indoor temperature, humidity, occupancy, and brightness continuously, and provided the data to Web API via WiFi. Data acquired automatically via IoT, existing maintenance data, and spatial information were integrated through the Cloud. Big data collected by sensors were processed as meaningful spatial information for maintenance. Indoor space information and maintenance information can be delivered to the manager through the mobile. Based on the collected data, room occupancy recognition is limited due to a range of ultrasonic wave sensors. On the other hand, brightness represents the space conditions. The difference between lighting on/off, weekday and weekend can be shown. The temperature data and the relative humidity data were collected steadily to evaluate the comfort.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.157-164
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• 2015

This paper describes subspace clustering of categorical data for convergence and integration. Because categorical data are not designed for dealing only with numerical data, The conventional evaluation measures are more likely to have the limitations due to the absence of ordering and high dimensional data and scarcity of frequency. Hence, conditional entropy measure is proposed to evaluate close approximation of cohesion among attributes within each cluster. We propose a new objective function that is used to reflect the optimistic clustering so that the within-cluster dispersion is minimized and the between-cluster separation is enhanced. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four algorithms, using three evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the proposed algorithm outperforms the algorithms that were considered int the evaluation, regarding the considered metrics.

• The Journal of Bigdata
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• v.2 no.2
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• pp.33-46
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• 2017

Service part has the attribute causing a difficulty of the systematic management like a kind of diversity, uncertainty of demand, high request for quick response against general complete product. Especially, order picking is recognized as the most important work in the warehouse of the parts since inbound cycle of the service part long but outbound cycle is relatively short. But, increasing work efficiency in the warehouse has a limitation that cycle, frequency and quantity for the outbound request depend on the inherent features of the part. Through this research, not only are the types of the parts classified with the various and specified data but also the method is presented that it minimizes (that) the whole distances of the order picking and store location about both inbound and outbound by developing the model of the demand prediction. Based on this study, I expect that all of the work efficiency and the space utilization will be improved without a change of the inbound and outbound quantity in the warehouse.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.45-56
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• 2022

Recently, bicycle sharing system is introduced and the usage of shared bicycles is increasing in Suwon city. Despite the need to expand the bicycle road infrastructure, this is not the case. Therefore, this research attempts to propose a method for bicycle lane installation in Suwon city. For this, this research conducted location analysis based on the shared bicycle usage data and trip inducing facility data. Using location analysis results, appropriate routes for bicycle lanes are selected. As a result, two routes are selected. These routes have advantages that it is easy to connect with the existing bicycle roads or traffic inducing facilities and to install using the existing bicycle roads. However, these routes also have disadvantage that traffic congestion may occur due to the occupancy of the existing road space. It is expected that this research may contribute to expansion and maintenance of bicycle lane infrastructure, the bicycle and PM sharing service usage, implementation of sustainable urban transportation systems in Suwon city.

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.17-30
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• 2015

Local governments' spatial information policies are very important in that it can increase the relatedness to upper policy regarding the share, openness and converged utilization of spatial information and contribute to voluntary participation and creative uses linked to big data. However, local governments' spatial information policies require enhancement since it need to update framework spatial data, to derive spatial information service and to share the data. In this research, we compared the spatial information policies and related systems of central and local governments, and analyzed the local governments' spatial information policy enforcement plans and the Seoul metropolitan government's utilization survey on 32 spatial information systems. In the result, for the improvement of local governments' spatial policies, on-demand updating of base map using the as built drawings linked to field work departments, securing up-to-date public domain spatial information through the NSDI system, sharing of spatial information based on the spatial information platform and benchmarking of best practices related to the spatial information based policy participation are suggested.

• Korean Architects
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• s.596
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• pp.28-44
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• 2018

"SH공사(서울주택도시공사)가 내년 1월부터 '청신호 건축사'라는 이름으로 전문가 인력풀(Pool)을 운영하고, 매입임대를 늘려 신축 리모델링에서 역할을 할 수 있도록 제도화할 계획입니다. 그동안 SH공사가 '주거복지'에 주력해 왔는데, 인공지능과 빅데이터 등을 결합해 앞으로는 한 단계 발전한 '공간복지'로 정책기조를 확대하려 합니다. 이 일을 수행키 위해서 도시설계가, 조경가도 투입될 것입니다." 김세용 SH공사 사장은 최근 서울시 강남구 개포로 SH공사 사옥에서 본지와 인터뷰를 하고, '디자인건축'을 표방한 '청신호 건축사' 전문가인력풀(Pool)을 운영하겠다고 밝혔다. SH공사는 20 30대 청년과 신혼부부에게 공급하는 임대주택에는 내년부터 '청신호'라는 자체 브랜드를 적용한다. 올 1월 취임한 김세용 SH공사 사장은 서울시의 캠퍼스타운조성 시범사업을 총괄 지휘한 바 있다. 이 같은 경험을 토대로 ▲ SH공사 브랜드 가치 제고 ▲ 주택품질 개선 ▲ 임대주택사업 추진 방식 다양화 등을 위한 사업들을 이끌고 있는데, 특히 기존에는 단순히 주거기능만 제공하던 공공아파트를 공동체 생활기능이 접목된 커뮤니티로 발전시키는 '공간복지'를 강화하는데 역량을 집중하고 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.219-222
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• 2019

4차 산업혁명이 언급되면서 3D 프린터, 로봇, 인공지능, 빅데이터 등 신기술이 발전하고 이는 사회 구조를 변화시킬 것으로 예상된다. 이에 새로운 디지털 기기, 기술들을 이해하는 디지털 리터러시가 중요해지고 있다. 2015 OECD PISA에서 ICT 배경조사 중 한국은 접근성, 활용성 부분과 2018 국가수준 디지털 리터러시 수준 측정연구, 학생들의 디지털 기기의 인식 여부 및 활용 여부 실태조사를 바탕으로 디지털 기기 활용 공간 구성 및 디지털 기기 활용 콘텐츠를 다음과 같이 제안하였다. 먼저 구체적 조작기인 초등학생임을 고려하여 디지털 기기 위주 프로그램으로 선정하였다. 디지털 기기 활용 공간은 기반 기기로 컴퓨터(노트북), 유 무선 인터넷, 디지털 기기로 3D 프린터 및 3D 프린팅 펜, 스마트폰 기반 VR기기, AR마커, 블록코딩 기반 피지컬 컴퓨팅 교구 등을 이용해서 구성하였다. 활용하는 콘텐츠는 학생들의 인식 여부 및 수준에 맞춰 체험 위주의 프로그램과 학습, 콘텐츠 제작, 문제해결 프로그램으로 제시하였다.