• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.195-200
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• 2004

Computer games use an intelligent method called flocking for boids' group behavioral modeling. Flocking can naturally model group behavioral patterns of unpredictable forms such as birds and fishes using some computer resource. In this paper, we implemented an ecosystem which is composed of predator and prey for group behavioral modeling of real underwater ecosystem. Also fuzzy logic is applied to implement instinct desire of ecosystem elements. As the result, we confirmed that the model can overcome breakdown of ecosystem and model naturally ecosystem behavior.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.54 no.2
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• pp.124-137
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• 2018

Due to the decrease in coastal productivity and deterioration in the quality of ecosystem which result from the excessive overfishing of fisheries resources and the environmental pollution, fisheries resources in the Korean waters hit the dangerous level in respect of quantity and quality. In order to manage sustainable and effective fisheries resources, it is necessary to suggest the potential yield (PY) for clarifying available fisheries resources in the Korean waters. So far, however, there have been few studies on the estimation methods for PY in Korea. In addition, there have been no studies on the comparative analysis of the estimation methods and the substantial estimation methods for PY targeted for large marine ecosystem (LME) For the reasonable management of fisheries resources, it is necessary to conduct a comprehensive study on the estimation methods for the PY which combines population dynamics and ecosystem dynamics. To reflect the research need, this study conducts a comparative analysis of estimation methods for the PY in the Korean waters of the East Sea to understand the advantages and disadvantages of each method, and suggests the estimation method which considered both population dynamics and ecosystem dynamics to supplement shortcomings of each method. In this study, the maximum entropy (ME) model of the holistic production method (HPM) is considered to be the most reasonable estimation method due to the high reliability of the estimated parameters. The results of this study are expected to be used as significant basic data to provide indicators and reference points for sustainable and reasonable management of fisheries resources.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.1
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• pp.111-122
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• 2014

The provided habitat of many services from natural capital is important. But because most ecosystem services tools qualitatively evaluated biodiversity or habitat quality, this study quantitatively analyzed those aspects using the species distribution model (MaxEnt). This study used location point data of the goat(Naemorhedus caudatus), marten(Martes flavigula), leopard cat(Prionailurus bengalensis), flying squirrel(Pteromys volans aluco) and otter(Lutra lutra) from the 3rd National Ecosystem Survey. Input data utilized DEM, landcover classification maps, Forest-types map and digital topographic maps. This study generated the MaxEnt model, randomly setting 70% of the presences as training data, with the remaining 30% used as test data, and ran five cross-validated replicates for each model. The threshold indicating maximum training sensitivity plus specificity was considered as a more robust approach, so this study used it to conduct the distribution into presence(1)-absence(0) predictions and totalled up a value of 5 times for uncertainty reduction. The test data's ROC curve of endangered mammals was as follows: growing down goat(0.896), otter(0.857), flying squirrel(0.738), marten(0.725), and leopard cat(0.629). This study was divided into two groups based on habitat: the first group consisted of the goat, marten, leopard cat and flying squirrel in the forest; and the second group consisted of the otter in the river. More than 60 percent of endangered mammals' distribution probability were 56.9% in the forest and 12.7% in the river. A future study is needed to conduct other species' distribution modeling exclusive of mammals and to develop a collection method of field survey data.

• Journal of KIBIM
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• v.9 no.1
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• pp.32-41
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• 2019

As population in Sarasota and Manatee Counties, Florida in the United States is projected to increase, land use changes from land development happen continuously. The more land development means the more impervious surfaces and stormwater runoff to Sarasota Bay, which causes critical impact on the resiliency of the ecosystem. In order to decrease its impact on water quality and the ecosystem function of Sarasota Bay, it is important to assess the resilient status of communities that create negative impacts on the ecosystem. Three types of guiding principles of resiliency for Sarasota Bay watershed are suggested. To assess resiliency status, three indexes - vulnerability index, socio-economic index, and ecological index are developed and analyzed by using geographic information system for each census tract in the two counties. Since each indicator for vulnerability index, socio-economic index, and ecological index is measured with different metrics, statistical standardizing method - distance from the best and worst performers is used for this study to directly compare and combine them all to show total resilience score for each census tract. Also, the ten most and the ten least scores for the total resilience index scores are spatially distributed for better understanding which census tracts are most or least resilient. As Sarasota Watershed boundary is also overlaid, it is easy to understand how each census tract attains its resilience and how each census tract impacts to Sarasota Bay ecosystem. Based on results of the resiliency assessment several recommendations, guidelines, or policies for attaining or enhancing resiliency are suggested.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.513-525
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• 2017

In the estimation of the exploitable carrying capacity (ECC) in the Korean water of the East China Sea, two approaches, which are the ecosystem modeling method (EMM) and the holistic production method (HPM), were applied. The EMM is accomplished by Ecopath with Ecosim model using a number of ecological data and fishery catch for each species group, which was categorized by a self-organizing mapping (SOM) based on eight biological characteristics of species. In this method, the converged value during the Ecosim simulation by setting the instantaneous rate of fishing mortality (F) as zero was estimated as the ECC of each group. The HPM is to use surplus production models for estimateing ECC. The ECC estimates were 4.6 and 5.1 million mt (mmt) from EMM and HPM, respectiverly. The estimate from the EMM has a considerable uncertainty due to the lack of confidence in input ecological parameters, especially production/biomass ratio (P/B) and consumption/biomass ratio (Q/B). However, ECC from the HPM was estimated on the basis of relatively fewer assumptions and long time-series fishery data as input, so the estimate from the HPM is regarded as more reasonable estimate of ECC, although the ECC estimate could be considerd as a preliminary one. The quality of input data should be improved for the future study of the ECC to obtain more reliable estimate.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.73-75
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• 2003

게임 세계에 존재하는 수많은 보이드들의 지능적인 집단 행동을 모델링하기 위한 방법으로 플로킹 기법이 많이 사용되고 있다. 특히 생태계에 존재하는 객체들의 행동을 재현하기 위한 연구가 활발하게 진행되고 있는 실정이다. 따라서 본 연구에서는 생태계에서 흔히 볼 수 있는 먹고 먹히는 관계를 퍼지 논리를 통해 게임의 보이드 행동을 모델링하고 구현한다.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.93-101
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• 2018

A city is like a living organism that is born, grows and become extinct within an ecosystem. In recent years, more and more cities have been planned by designers rather than spontaneously growing over time. This planning means that if a city is not appropriately designed at the beginning, it is very hard to fix or adjust it later. A poor urban design inconveniences many people, and forces them to adjust to it. For this reason, it is important to design a city as optimally as possible at the design phase. One of the reasons why a city is not designed optimally is complexity. Previous urban design methods have attempted to resolve the complexity by using a top-down problem-solving method based on the experience and knowledge of the urban planner. However, such an approach does not have the organic characteristics of a bottom-up problem-solving method of an artificial life, based on the creation principle of the ecosystem. Therefore, in this study the general principle of artificial life, which can provide a solution to the bigger problems that accumulate as a result of the solutions to small units of problems, is adopted. This enables us to draw various urban design alternatives, and it proves that the alternatives, despite being drawn through a limited modeling method, have almost no differences from those designed by an expert, and its possibilities of future development has also been verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.841-854
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• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.81-92
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• 2023

The purpose of this study is to identify rural research topics, differences in research topics over time, and key mediators through the analysis of academic research trends using topic modeling. This study analyzed a total of 1,183 articles published in the Journal of Rural Planning and Rural Society over a 23-year period (2000-2022). We categorized rural research topics into 30, examined the proportion of research in each topic, and identified major changes in research topics over time. We also identified key words that mediate between research topics. The study found that, first, rural research trends can be categorized into five types (resources and utilization, area/space, people, ecosystem/environment, and tourism), with area/space being the most studied. Subtopics include rural amenities, rural disappearance/village miniaturization, and rural landscape management. Second, the research topics for each period were different. In the first period(2003-2007), the main research topics were rural amenities and Agricultural production- based climate vulnerability assessment. In the second period(2008-2012), the main research topics were Rural extinction and village depopulation, and rural landscape management, and in the third period(2013-2017), the main research topics were rural sixth industrialization and rural ecotourism. In the fourth period(2018-2022), rural development planning and rural life services(life SOC) were the main research topics. The significance of this study is that it extends the existing method of analyzing research trends and provides basic data to enhance comprehensive insights and understanding of rural research.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.161-170
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• 2023

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semimangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha - 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.