• Korean Journal of Construction Engineering and Management
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• v.8 no.6
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• pp.178-187
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• 2007

As the government has enforced recently the policies on the distribution of the housing, the construction cost of multi-family housing projects has increasingly become very sensitive and political issue. However, it is difficult to predict the construction cost in planning and design phase of the project because the Bill of Quantity of the multi-family housing projects was composed of breakdown structure based on each work package. To predict the construction cost in planning and design phase for multi-family housing projects in more effective and reasonable way, this study developed the cost breakdown structure based on spaces using Delphi method. The Cost Breakdown Structure (CBS) based on spaces for multi-family housing projects basically consists of three parts: (i) Building part; (ii) Non-building part; and (iii) Additional part. The characteristics of spaces in multi-family housing projects are fully taken into consideration. Then these three parts were subdivided into work packages in terms of work tasks. Additionally, the usefulness and effectiveness of Space CBS in this paper were validated by analyzing the BOQs of several collected sample projects and matching with Space CBS afterwards.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.460-463
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• 2007

기존 공간 분류 시스템은 애매모호한 데이터나 불완전한 데이터, 결손 데이터의 처리에는 취약하다는 단점을 가지고 있다. 수치 형태의 애매모호성을 효과적으로 처리하기 위해 신경망을 이용할 수 있다. 그러나, 신경망을 이용한 공간 데이터 분류 방법은 불완전한 데이터나 결손 데이터들을 무시하지 않고 처리 할 수 있으나, 다양한 수치형태를 가지는 공간 데이터들로 인해 네트워크 구조의 복잡도가 증가하고 학습성능이 저하된다는 문제점을 야기한다. 본 논문에서는 이러한 문제점을 해결하기 위해서 퍼지 신경망을 적용한 새로운 공간 분류시스템을 제안하고 구현하였다. 실험 결과 기존의 방법에 비해 좋은 성능을 보임을 확인하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.195-200
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• 1997

영상을 분류한다거나 물체를 인식하는 방법들은 대부분 흑백 영상에 대한 것이다. 그 이유는 기존의 분류 방법에 어떻게 컬러 정보를 결합시킬 것인가 하는 문제를 쉽게 해결하지 못하거나 처리하는데 훨씬 많은 시간이 소요되기 때문이다. 본 연구에서는 컬러 영상들을 분류하기 위하여 기존의 고유 백터를 컬러 공간에 이용할 수 있는 방법을 제안하고, 이 고유 백터를 이용하여 컬러 얼굴 영상에 대한 분류 실험을 통해 여러 가지 특징에 대한 고유 백터를 영상 분류에 이용할 수 있음을 보였다.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.643-652
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• 2006

Decision trees are mainly used for the classification and prediction in data mining. The distribution of spatial data and relationships with their neighborhoods are very important when conducting classification for spatial data mining in the real world. Spatial decision trees in previous works have been designed for reflecting spatial data characteristic by rating Euclidean distance. But it only explains the distance of objects in spatial dimension so that it is hard to represent the distribution of spatial data and their relationships. This paper proposes a decision tree based on spatial entropy that represents the distribution of spatial data with the dispersion and dissimilarity. The dispersion presents the distribution of spatial objects within the belonged class. And dissimilarity indicates the distribution and its relationship with other classes. The rate of dispersion by dissimilarity presents that how related spatial distribution and classified data with non-spatial attributes we. Our experiment evaluates accuracy and building time of a decision tree as compared to previous methods. We achieve an improvement in performance by about 18%, 11%, respectively.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1516-1522
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• 2007

We had proposed the FPA(Fixed Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. The algorithm worked not bad in many area, but it lead to some overhead for memory usage and lengthy computation in the multi classes area. We propose an Repetitive FPA algorithm which repetitively partitioning pattern space in the multi classes area. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.290-299
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• 2008

We propose the query optimization technique using query classification in hybrid spatial DBMS. In our approach, user queries should to be classified into three types: memory query, disk query, and hybrid query. Specialty, In the hybrid query processing, the query predicate is divided by comparison between materialized view creating conditions and user query conditions. Then, the deductions of the classified queries' cost formula are used for the query optimization. The optimization is mainly done by the selection algorithm of the smallest cost data access path. Our approach improves the performance of hybrid spatial DBMS than traditional disk-based DBMS by $20%{\sim}50%$.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.229-233
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• 2004

원격탐사 자료를 이용하여 조간대 표층퇴적상의 분류가 가능하다면 현장조사와 상호 보완적으로 사용될 수 있다. 공간해상도 30 m 급의 Landsat 위성 자료를 이용하여 0.0625 mm 입자 기준에 의한 조간대의 표층 퇴적상 분류를 한 연구가 몇 차례보고 된 바 있지만, 스펙트럴 값만으로 퇴적상을 분류하기 위해서는 몇 가지 문제점이 따른다. 첫째는 점이적으로 변하는 조간대에서 입도와 위성자료 공간해상도와의 스케일 차이로 인한 mixed pixel(mixel)을 어떻게 분류할 것인가 이고 두 번째는 입도 요인 이외의 조간대 환경요인을 어떻게 고려해야 되느냐 하는 것이다. mixel에 대한 대안으로 4 m 공간해상도의 IKONOS 영상을 이용하였으며, 입도 이외의 다른 환경 요인은 조간대 지형과 조류로 (tidal channel)를 파악하여 퇴적상의 특성과 비교하였다. IKONOS를 이용한 조간대 퇴적상 분류 결과는 현장 조사 자료와 잘 일치하였으며 지형적으로 높고 조류로가 발달한 부분에 이질 퇴적상이 위치하는 것을 알 수 있었다. 이 연구는 IKONOS와 같은 공간해상도를 갖는 KOMPSAT II 위성이 2004년 진수되어 서해조간대 지역에 대해 다시기의 많은 영상을 확보할 수 있다면 조간대의 지형변화와 생태계 변화 등의 조간대 모니터링 연구에 활용되어 연안의 종합적이고 효율적인 관리에 활용될 수 있을 것으로 생각된다.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.57-69
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• 2001

This study propose a image processing system of unsupervised analysis. This system integrates low-level segmentation and high-level classification. The segmentation and classification are conducted respectively with and without spatial constraints on merging by a hierarchical clustering procedure. The clustering utilizes the local mutually closest neighbors and multi-window operation of a pyramid-like structure. The proposed system has been evaluated using simulated images and applied for the LANDSATETM+ image collected from Youngin-Nungpyung area on the Korean Peninsula.