• Korean Journal of Construction Engineering and Management
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• v.6 no.6 s.28
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• pp.133-141
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• 2005

In the resent years, the importance of VE (value engineering) and LCC (life cycle cost) analysis for apartment building construction projects has been fully recognized. Accordingly theoretical models, guidelines, and supporting software systems were developed for the value engineering and life cycle cost analysis for construction management including large building systems. However, the level of consensus on VE and LCC analysis results is still low due to the lack of reliable data on maintenance. This paper presents time dependent LCC model based value analysis method for rational investment decision making and design alternative selection for construction of apartment building. The proposed method incorporates a time dependent LCC model and a performance evaluation technique by fuzzy logic theory to properly handle the uncertainties associated with statistics data and to analyze the value of alternatives more rationally. The presented time dependent VE and LCC analysis procedure were applied to a real world project, and this case study is discussed in the paper. The model and the procedure presented in this study can greatly contribute to design value engineering alternative selection, the estimation of the life cycle cost, and the allocation of budget for apartment building construction projects.

• Spatial Information Research
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• v.11 no.2
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• pp.101-117
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• 2003

High resolution satellite image analysis has been recognized as an effective technique for monitoring local land-cover and atmospheric changes. In this study, a new high resolution map for land-cover was generated using both high-resolution IKONOS image and conventional land-use mapping. Fuzzy classification method was applied to classify land-cover, with minimum operator used as a tool for joint membership functions. In separateness analysis, the values were not great for all bands due to discrepancies in spectral reflectance by seasonal variation. The land-cover map generated in this study revealed that conifer forests and farm land in the ground and tidal flat and beach in the ocean were highly changeable. The kappa coefficient was 0.94% and the overall accuracy of classification was 95.0%, thus suggesting a overall high classification accuracy. Accuracy of classification in each class was generally over 90%, whereas low classification accuracy was obtained for classes of mixed forest, river and reservoir. This may be a result of the changes in classification, e.g. reclassification of paddy field as water area after water storage or mixed use of several classification class due to similar spectral patterns. Seasonal factors should be considered to achieve higher accuracy in classification class. In conclusion, firstly, IKONOS image are used to generated a new improved high resolution land-cover map. Secondly, IKONOS image could serve as useful complementary data for decision making when combined with GIS spatial data to produce land-use map.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.29-37
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• 2020

This study analyzed the importance of factors according to the investor's perspective through a survey of residential real estate experts using AHP and fuzzy theory. Analysis results showed that rent, profitability, traffic accessibility, commercial and infrastructure, and financial regulation are important in common. By expert group, financial and credit groups cited profitability, rent, traffic accessibility, supply and tax benefits, construction and development groups cited traffic accessibility, rent, direct access, profitability, commercial area and infrastructure, and appraisal and evaluation groups cited rent, profitability, transportation accessibility, financial regulation and supply as the most important factors. This showed that it had a preference characteristic that was associated with work. In other words, it focuses most on the financial perspective in investment characteristics, and it values convenience such as accessibility to transportation and commercial districts and infrastructure as its location characteristics. In addition, it was found that easing financial regulations in the market is important to expand investment in real estate. This study aims to help the business feasibility analysis of residential property developers and rational decision-making of general investors who are consumers, taking into account the various perspectives of the expert group.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.977-987
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• 2018

The purpose of the water distribution system is gradually changing to increase the flexibility for responding to various abnormal situations. In addition, it is essential to improve resilience through preparing emergency plans against water supply failure. The most efficient way is emergency interconnections which supply water from interconnected adjacent blocks. To operate successful interconnections, it is essential to evaluate the supply performance in spatial and temporal aspects. The spatial and temporal aspects are dominated by its interconnected pipes and interconnected reservoirs respectively. In this study, an emergency interconnection scenario where problem occurred in reservoir 1 at 0:00hr in A city, Korea. An Advanced-Pressure Driven Analysis model was used to simulate the volume and inflow volume of the interconnected reservoirs. Based on the hydraulic analysis results, a multi-dimensional evaluation of the supply performance was conducted by applying possible water supply range indicator (PWSRI) and possible water supply temporal indicator (PWSTI) which are based on fuzzy membership functions. As a result, it was possible to evaluate the supply performance on the sides of consumers in spatio-temporal aspects and to review whether established plans mitigate the damage as intended. It is expected to be used for decision making on structural and non-structural emergency plan to improve the performance of an emergency interconnection.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• Korean Journal of Environment and Ecology
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• v.33 no.4
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• pp.472-480
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• 2019

Selection of the plant material for planting to reduce fine dust should comprehensively consider the visual characteristics, such as the shape and texture of the plant leaves and form of bark, which affect the adsorption function of the plant. However, previous studies on reduction of fine dust through plants have focused on the absorption function rather than the adsorption function of plants and on foliage plants, which are indoor plants, rather than the outdoor plants. In particular, the criterion for selection of fine dust reduction species is not specific, so research on the selection criteria for plant materials for fine dust reduction in urban areas is needed. The purpose of this study is to identify the priorities of eight indicators that affect the fine dust reduction by using the fuzzy multi-criteria decision-making model (MCDM) and establish the tree selection criteria for the urban planting to reduce fine dust. For the purpose, we conducted a questionnaire survey of those who majored in fine dust-related academic fields and those with experience of researching fine dust. A result of the survey showed that the area of leaf and the tree species received the highest score as the factors that affect the fine dust reduction. They were followed by the surface roughness of leaves, tree height, growth rate, complexity of leaves, edge shape of leaves, and bark feature in that order. When selecting the species that have leaves with the coarse surface, it is better to select the trees with wooly, glossy, and waxy layers on the leaves. When considering the shape of the leaves, it is better to select the two-type or three-type leaves and palm-shaped leaves than the single-type leaves and to select the serrated leaves than the smooth edged leaves to increase the surface area for adsorbing fine dust in the air on the surface of the leaves. When considering the characteristics of the bark, it is better to select trees that have cork layers or show or are likely to show the bark loosening or cracks than to select those with lenticel or patterned barks. This study is significant in that it presents the priorities of the selection criteria of plant material based on the visual characteristics that affect the adsorption of fine dust for the planning of planting to reduce fine dust in the urban area. The results of this study can be used as basic data for the selection of trees for plantation planning in the urban area.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.