• 한국기후변화학회지
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• 제6권1호
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• pp.1-10
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• 2015

Climate change lead to environmental pollution caused by the radical economic growth and development of industry. The amount of damage from abnormal climate is increasing rapidly for this reason in Korea. In particular, the cities is a lot of carbon emission quantity from the radical growth. Thus the government present "low carbon green growth" for eco-friendly city planning. As one of the important factors effecting climate change, active researches on land use change is performed. In this study, we knew land use change of each scenarios using land use equilibrium model which is kind of predictive model of land use in Japan. First, we selected study area to Jeju lsland. For this study, indicators for input data were selected and spatial data for input data were established using GIS program. Second, we established future scenarios based in 2040s. There are 2 future scenarios: dispersion scenario, compact scenario. Third, we compared with residential area of current and residential area for future scenarios. Results showed that residential area of the difference between current and dispersion scenario were 1,230 ha and residential area of the difference between current and compact scenario were 1,515 ha. Finally, for comparing carbon dioxide absorption volume between dispersion scenarios and compact scenarios, we calculated carbon dioxide absorption volume according to residential area decreased of each future scenarios. Results showed that carbon dioxide absorption volume in dispersion scenario was 477,878 ton and carbon dioxide absorption volume in compact scenario was 588,606 ton. Therefore, the study showed that land use equilibrium model is expected to put to use for future enhancement in creating data for climate change stabilization. And it is also expected to be utilized for city planning research in Korea.

• 한국콘텐츠학회논문지
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• 제18권6호
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• pp.333-341
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• 2018

최근 사용자 중심의 스마트시티 서비스에 대한 관심이 높아지고 있다. 본 연구는 공간기반의 커뮤니케이션을 통한 스마트시티 서비스 시나리오 도출을 위해 공간정보유형을 검토하고, 사용자 중심의 체험요소 시나리오 도출을 위한 방향성을 마련하고자 한다. 본 연구결과는 향후 스마트시티 테스트베드에 적용 가능한 보행지도, 워킹맵으로서 시민이 스마트시티를 체험할 수 있는 공간시나리오를 제시하기 위한 기초자료를 제시하는 데 의의를 둔다. 특히 보행지도인 워킹맵을 통해 스마트시티 테스트베드 내 보행시나리오를 시뮬레이션 함으로서 사용자 요구사항을 기반으로 하는 서비스 방향을 실험하였다. 본 연구를 통해 기존 인프라를 통한 스마트시티 서비스가 다목적으로 활용될 수 있음을 확인하였다. 본 연구에서 제시된 공간정보 및 체험요소 연계모델과 페르소나 모델을 통한 워킹맵은 추후 스마트시티 테스트베드에 적용 가능한 예비시나리오로서 활용될 수 있다.

• 한국수자원학회논문집
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• 제46권8호
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• pp.833-842
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• 2013

분위사상법(QM, Quantile Mapping)은GCM(Global Climate Model) 자료의 계통적 오차를 보정하여 보다 신뢰성 높은 자료로 재생성하기 위해 활용되고 있다. 이 기법은 사상(mapping)시키려는 대상(object) 자료의 통계분포모수가 정상적(stationarity)이라는 가정 하에 대상 자료의 누적확률분포(CDF, Cumulative Distribution Function)를 목표(target) CDF에 통계적으로 투영시키는 것이 일반적이다. 따라서 GCM에서 제공되는 미래 기후시나리오의 강우시계열과 같이 비정상성(non-stationarity)을 갖는 장기 시계열자료에 대한 적용에는 문제점을 보이고 있다. 본 연구에서는 비정상성을 갖는 장기시계열자료의 오차보정을 위해 통계분포모수에 경향성을 부여하는 비정상성 분위사상법(NSQM, Nonstationary Quantile Mapping)을 적용하였다. NSQM 적용을 위한 확률분포로 수문분야에서 광범위하게 쓰이고 있는Gamma 분포를 선정하였으며, 대상 시나리오는 CCCma (Canadian Centre for Climate modeling and analysis)에서 제공하고 있는 CGCM3.1/T63모형의 20C3M(reference scenario)과 SRES A2 시나리오(projection scenario)를 활용하였다. 한강유역 내 관측기간이 충분한 10개의 지상관측소로부터 강우량을 수집하였다. 또한 6월과 10월사이에 연 강수량의 65% 이상이 집중되는 한반도의 계절성을 반영하기 위해 홍수기(6~10월)와 비홍수기(11~5월)를 구분하였고, 기준기간(Baseline)은 1973~2000년, 전망기간(Projection)은 2011~2100년으로 구분하였다. 다양한 목표분포의 설정을 통하여 NSQM의 적용성을 평가하고자 하였으며, 전망기간은 FF시나리오(Foreseeable Future Scenario, 2011~2040년), MF시나리오(Mid-term Future Scenario, 2041~2070년), LF시나리오(Long-term Future Scenario, 2071~2100년)의 3개의 구간으로 설정하여 기준기간과 전망기간의 연평균 강우량에 대한 경향성분석을 실시하였다. 그 결과NSQM이 FF시나리오에서 330.1mm(25.2%), MF시나리오에서 564.5mm(43.1%), LF시나리오에서 634.3mm(48.5%)로 증가하는 전망결과를 나타내고 있었다. 정상성기법을 적용한 결과, 전망기간 중 전체적으로는 동일한 평균값을 갖는 목표통계모수를 사용한다고 하여도, 전망전반부에서 과다하고, 후반부에서 오히려 과소한 전망을 보여주고 있었다. 이러한 결과는 비정상성기법을 사용함으로써 상당부분 개선될 수 있음을 확인하였다.

• 한국환경교육학회지:환경교육
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• 제20권3호
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• pp.31-44
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• 2007

The solution of the environment problem is the common issue all over the world, for this reason the necessity of the environmental education of school has emphasized. On this a variety method for environmental education is needed, this paper planned and applied the 'ecology & environment' for high school which are based on GBS theory and presented a new model of environment education. GBS(Goal-Based Scenario) is that learners are presented with an end goal that is motivating and challenging. This goal is structured such that, in order to successfully meet it learners are required to build a predetermined core set of skills and knowledge by process mission and scenario. GBS is an active learning environment in which learners are trained in study that have a real-world context. When they are back in real-world they have increased ability to apply what was learned by reflecting on the GBS learning experience. This study was designed on GBS theory and taught a class by using internet Blog. As a result, when carefully reviewing the materials such as final presentation reflect journal, we conclude that the students' awareness of a learning environment is improved and the students seems to try to apply the learning outcome to a real life.

• 한국정보처리학회논문지
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• 제5권5호
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• pp.1119-1129
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• 1998

본 논문은 하이퍼미디어 시나리오 수행 시간 예측 방법을 제안한다. 제안된 방법은 하이퍼미디어 시나리오의 흐름을 "확률분포표를 갖는 페트리 넷(Petri Net with probability Distribution Table : PNDT)" 으로 모델링하고, PNDT 모델에서 시나리오 진행을 몬테칼로 방법을 적용하여 시뮬레이션함으로써 시나리오의 시작부터 종료에 이르기까지의 소요 시간을 계산한다. 시나리오 수행 시간은 정보제공자와 사용자 모두에게 귀중하게 사용된다. 사용자는 이를 참조하여 시간 사용 계획을 세울 수 있고, 정보제공자는 이를 참조하여 사용자에게 가장 효율적인 감상 스케쥴을 작성하여 줄 수 있다. 그럼에도 불구하고, 지금까지 하이퍼미디어 시나리오 수행 시간을 예측하는 방법에 대한 연구는 전무하다.

• Asia pacific journal of information systems
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• 제26권3호
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• pp.427-448
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• 2016

The stochastic phenomena of traffic network condition, such as traffic speed and density, are affected not only by exogenous traffic control but also by endogenous changes in service time during congestion. In this paper, we propose a mixed M/G/1 queuing model by introducing a condition-varying parameter of traffic congestion to reflect structural changes in the traffic network. We also develop congestion indices to evaluate network efficiency in terms of traffic flow and economic cost in traffic operating system using structure-changing queuing model, and perform scenario analysis according to various traffic network improvement policies. Empirical analysis using Korean highway traffic operating system shows that our suggested model better captures structural changes in the traffic queue. The scenario analysis also shows that occasional reversible lane operation during peak times can be more efficient and feasible than regular lane extension in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.1100-1118
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• 2021

In a wide range of ML applications, the training data contains privacy-sensitive information that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML model inherent to the data. As the structure of the model has been fine-tuned by training data, the model can be abused for accessing the data by the estimation in a reverse process called model inversion attack (MIA). Although, MIA has been applied to shallow neural network models of recognizers in literature and its threat in privacy violation has been approved, in the case of a deep learning (DL) model, its efficiency was under question. It was due to the complexity of a DL model structure, big number of DL model parameters, the huge size of training data, big number of registered users to a DL model and thereof big number of class labels. This research work first analyses the possibility of MIA on a deep learning model of a recognition system, namely a face recognizer. Second, despite the conventional MIA under the white box scenario of having partial access to the users' non-sensitive information in addition to the model structure, the MIA is implemented on a deep face recognition system by just having the model structure and parameters but not any user information. In this aspect, it is under a semi-white box scenario or in other words a gray-box scenario. The experimental results in targeting five registered users of a CNN-based face recognition system approve the possibility of regeneration of users' face images even for a deep model by MIA under a gray box scenario. Although, for some images the evaluation recognition score is low and the generated images are not easily recognizable, but for some other images the score is high and facial features of the targeted identities are observable. The objective and subjective evaluations demonstrate that privacy cyber-attack by MIA on a deep recognition system not only is feasible but also is a serious threat with increasing alert state in the future as there is considerable potential for integration more advanced ML techniques to MIA.

• Journal of information and communication convergence engineering
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• 제9권4호
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• pp.379-384
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• 2011

The scope of the problems that could be solved by monitoring and the improvement of the recognition time is directly correlated to the performance of the management function of the business process. However, the current monitoring process of business activities decides whether to apply warnings or not by assuming a fixed environment and showing expressions based on the design rules. Also, warnings are applied by carrying out the measuring process when the event attribute values are inserted at every point. Therefore, there is a limit for distinguishing the range of occurrence and the level of severity in regard to the new external problems occurring in a complicated environment. Such problems cannot be ed. Also, since it is difficult to expand the range of problems which can be possibly evaluated, it is impossible to evaluate any unexpected situation which could occur in the execution period. In this paper, a process-evaluating model based on the goal scenario is suggested to provide constant services through the current monitoring process in regard to the service demands of the new scenario which occurs outside. The new demands based on the outside situation are analyzed according to the goal scenario for the process activities. Also, by using the meta-heuristic algorithm, a similar process model is found and identified by combining similarity and interrelationship. The process can be stopped in advance or adjusted to the wanted direction.

• Journal of Ecology and Environment
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• 제34권2호
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• pp.203-214
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• 2011

The objectives of this research were to predict land-use/land-cover change at the Sakaerat Environmental Research Station (SERS) and to analyze its consequences on the distribution for Black-crested Bulbul (Pycnonotus melanicterus), which is a popular species for bird-watching activity. The Dyna-CLUE model was used to determine land-use allocation between 2008 and 2020 under two scenarios. Trend scenario was a continuation of recent land-use change (2002-2008), while the integrated land-use management scenario aimed to protect 45% of study area under intact forest, rehabilitated forest and reforestation for renewable energy. The maximum entropy model (Maxent), Geographic Information System (GIS) and FRAGSTATS package were used to predict bird occurrence and assess landscape fragmentation indices, respectively. The results revealed that parts of secondary growth, agriculture areas and dry dipterocarp forest close to road networks would be converted to other land use classes, especially eucalyptus plantation. Distance to dry evergreen forest, distance to secondary growth and distance to road were important factors for Black-crested Bulbul distribution because this species prefers to inhabit ecotones between dense forest and open woodland. The predicted for occurrence of Black-crested Bulbul in 2008 covers an area of 3,802 ha and relatively reduces to 3,342 ha in 2020 for trend scenario and to 3,627 ha for integrated-land use management scenario. However, intact habitats would be severely fragmented, which can be noticed by total habitat area, largest patch index and total core area indices, especially under the trend scenario. These consequences are likely to diminish the recreation and education values of the SERS to the public.

• 수산경영론집
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• 제54권1호
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• pp.37-49
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• 2023

Climate change is a major global problem. Oysters, one of the most representative farmed fish in Korea, are attracting attention as candidates for blue carbon, an alternative to carbon neutrality. This study is analyzed by the SSP scenarios to determine the impact of oyster aquaculture production according to climate change. Based on the analysis, future productions of oysters are predicted by the SSP scenario. Significant differences by the SSP scenario are confirmed through predictive power tests among scenarios. Regression analysis was conducted from January 2001 to December 2014. As a result of the analysis, water temperature, water temperature quadratic term, salinity, salinity quadratic term, and month × water temperature cross term were estimated as significant variables. Oyster production which is predicted by the SSP scenario based on the significant variables from 2015 to 2022 was compared with actual production. The model with the highest predictive power was selected by RMSE and MAPE criteria. The predictive power was compared with the MDM test to determine which model was superior. As a result, based on RMSE and MAPE, the SSP1-2.6 scenario was selected as the best model and the SSP1-2.6, SSP2-4.5, and SSP3-7.0 scenarios all showed the same predictive power based on the MDM test. In conculusion, this study predicted oyster aquaculture production by 2030, not the distant future, due to the short duration of the analytical model. This study was found that oyster aquaculture production increased in all scenarios and there was no significant difference in predictive power by the SSP scenario.