• Journal of the Society of Disaster Information
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• v.15 no.1
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• pp.84-97
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• 2019

Purpose: The consolidation settlement of soft ground is dependent on the distribution of pore water pressure which is also affected by hydraulic conductivities (boundary condition) of layers, thickness of clayey soil layer and surcharge. Results: However, the current consolidation analyses are mostly based on Terzaghi's consolidation theory that assumes the initial pore water pressure ratio with depth to be constant. In this study, numerical analysis are carried out to investigate the variation of pore water pressure dissipation with depth and thickness of clayey soil layer, time, surcharge as well as drainage conditions. Conclusion: Comparative study with Terzaghi's consolidation theory is also conducted. The result shows that Terzaghi's consolidation theory should be used with caution unless it is ideally corresponded to the isochrone.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.567-581
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• 2005

Geographical Customer Relationship Management (gCRM) is an integrated solution of Geographic Information System (GIS) and Customer Relationship Management (CRM). In gCRM, GIS is used to show multi-dimensional analytical results of customer information geographically. When customer information is geographically presented, more valuable information appears. In this research we briefly introduce gCRM and show real examples of customer segmentation applied to company.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.586-591
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• 2007

지식 관리 시스템, 정보 검색 시스템, 그리고 전자 도서관 시스템 등의 문서를 다루는 시스템에서는 문서의 구조화 및 문서의 저장이 필요하다. 문서에 담겨있는 정보를 추출하기 위해 가장 우선시되어야 하는 것은 키워드의 선별이다. 기존 연구에서 가장 널리 사용된 알고리즘은 단어의 사용 빈도를 체크하는 TF(Term Frequency)와 IDF(Inverted Document Frequency)를 활용하는 TF-IDF 방법이다. 그러나 TF-IDF 방법은 문서의 의미를 반영하지 못하는 한계가 존재한다. 이를 보완하기 위하여 본 연구에서는 세 가지 방법을 활용한다. 첫 번째는 문헌 속에서의 단어의 위치 및 서론, 결론 등의 특정 부분에 사용된 단어의 활용도를 체크하는 문헌구조적 기법이고, 두 번째는 강조 표현, 비교 표현 등의 특정 사용 문구를 통제 어휘로 지정하여 활용하는 방법이다. 마지막으로 어휘의 사전적 의미를 분석하여 이를 메타데이터로 활용하는 방법인 언어학적 기법이 해당된다. 이를 통하여 키워드 추출 과정에서 문서의 의미 분석도 수행하여 키워드 추출의 효율을 높일 수 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.132-151
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• 2022

Vehicle localization is one of the core technologies for autonomous driving. Image-based localization provides location information efficiently, and various related studies have been conducted. However, the image-based localization methods using feature points or lane information has a limitation that positioning accuracy may be greatly affected by road and driving environments. In this study, we propose a line segment matching framework for accurate vehicle localization. The proposed framework consists of four steps: line segment extraction, merging, overlap area detection, and MSLD-based segment matching. The proposed framework stably performed line segment matching at a sufficient level for vehicle positioning regardless of vehicle speed, driving method, and surrounding environment.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.245-254
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• 2022

Proteins are the basic unit of all life activities, and understanding them is essential for studying life phenomena. Since the emergence of the machine learning methodology using artificial neural networks, many researchers have tried to predict the function of proteins using only protein sequences. Many combinations of deep learning models have been reported to academia, but the methods are different and there is no formal methodology, and they are tailored to different data, so there has never been a direct comparative analysis of which algorithms are more suitable for handling protein data. In this paper, the single model performance of each algorithm was compared and evaluated based on accuracy and speed by applying the same data to CNN, LSTM, and GRU models, which are the most frequently used representative algorithms in the convergence research field of predicting protein functions, and the final evaluation scale is presented as Micro Precision, Recall, and F1-score. The combined models CNN-LSTM and CNN-GRU models also were evaluated in the same way. Through this study, it was confirmed that the performance of LSTM as a single model is good in simple classification problems, overlapping CNN was suitable as a single model in complex classification problems, and the CNN-LSTM was relatively better as a combination model.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.2
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• pp.179-199
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• 2019

In this paper, we propose optimal methodologies for classifying patent literature by examining various feature extraction methods, machine learning and deep learning models, and provide optimal performance through experiments. We compared the traditional BoW method and a distributed representation method (word embedding vector) as a feature extraction, and compared the morphological analysis and multi gram as the method of constructing the document collection. In addition, classification performance was verified using traditional machine learning model and deep learning model. Experimental results show that the best performance is achieved when we apply the deep learning model with distributed representation and morphological analysis based feature extraction. In Section, Class and Subclass classification experiments, We improved the performance by 5.71%, 18.84% and 21.53%, respectively, compared with traditional classification methods.

• The Journal of Society for e-Business Studies
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• v.16 no.3
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• pp.191-209
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• 2011

In a buyer-seller transaction process, 'value for money,' a measure of quality-price-ratio, is one of the most important criteria for buyers' purchasing decisions. The purpose of this paper is to suggest a method which helps online shoppers choose the best of several sellers offering homogeneous goods. We suggest FDH (free disposal hull) model, an applied model of data envelopment analysis (DEA), for online buyer-seller transactions and verify it with the data from an Internet comparison shopping site. For this purpose, we analyze consumer choice behaviors by examining how consumers respond to different sale conditions such as price, brand, or delivery time. Then, we implement a seller recommendation system to support buyers' purchasing decisions. We expect our FDH model to provide valuable information for rational buyers who want to pay the least price for high quality products/services and to be used in implementing automated evaluation processes in micro transactions. Moreover, we expect that our results can be utilized for sellers' benchmarking strategies which help sellers be more competitive by showing them how to attract buyers.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.237-244
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• 2006

시맨틱 웹 관련연구가 증가함에 따라 하나의 관련분야로 규칙기반 시스템 동의 지능적인 웹 환경에 대한 기대 역시 커지고 있다. 하지만 규칙기반 시스템을 활용하기에는 아직도 규칙습득이 많은 제약이 되고 있다. 규칙습득은 웹으로부터 필요한 규칙을 습득하는 일련의 방법인데, 이러한 규칙을 습득하기 위해서는 규칙구성요소를 먼저 식별해야만 한다. 그러나 이러한 규칙을 식별하는 작업은 대부분 지식관리자의 수작업에 의해 이루어지고 있다. 본 연구의 목적은 웹으로부터 규칙구성요소 식별을 최대한 자동화하고 지식관리자의 수작업을 최소화함으로써 그 부담을 줄여 주는 데 있다. 이러한 방법으로는 온톨로지를 근간으로 하여 웹 페이지와의 문자열 비교, 이러한 비교의 한계를 극복하기 위한 확장등의 방법이 있다. 첫 번째 방법은 온툴로지 기반으로 규칙식별 할 웹 페이지와 비교를 통해 지식관리자의 규칙식별 과정을 최대한 자동화하여 주는 것이다. 여기서 만약 현재 규칙을 식별하고자 하는 웹 사이트와 유사한 시스템의 규칙들을 활용하여 일반화 된 온툴로지가 구축되었다면, 이 온톨로지를 기반으로 규칙을 식별하고자 하는 웹사이트와의 비교를 통해 규칙구성요소를 자동화하여 추출 할 수 있다. 이러한 온툴로지를 기반으로 규칙을 식별하기 위해서는 문자열 비교 기법을 사용하게 된다. 하지만 단순한 문자열 비교 기법만으로는 규칙을 식별하는 데에 자연어 처리에 대한 한계가 있다. 이를 극복하기 위해 다음의 두 번째 방법을 사용하고자 한다. 두 번째 방법은 정형화되지 않은 정보들을 확장하여 사용하는 것이다. 우선 찾고자 하는 단어들의 원형을 찾기 위한 스테밍 알고리즘 기법, WordNet을 이용하여 동의어 유의어등으로 확장을 하는 WordNet Expansion 기법, 의미 유사도를 측정하기 위한 방법인 Semantic Similarity Measure 등을 단계적으로 수행하여 자동화되고 정확한 규칙식별을 하고자 한다. 이러한 방법들의 조합으로 인하여 규칙구성요소 추출이 되지 않을 후보 단어들의 수를 줄여서 보다 더 정확하고, 지능적인 규칙구성요소 추출 방법론을 제시하고 구현하여 지식관리자의 규칙습득에 대한 부담을 줄여 주고자 한다.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.28-36
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• 2017

After the genome projects of the 90s, a vast number of gene studies have been stored in online databases. By using these databases, several biological relationships can be inferred. In this study, we proposed a method to infer disease-gene relationships using title and body in biomedical text. The title was used to extract hub genes from data in the literature; whereas, the body of the literature was used to extract sub genes that are related to hub genes. Through these steps, we were able to construct a local gene-network for each report in the literature. By integrating the local gene-networks, we then constructed a global gene-network. Subsequent analyses of the global gene-network allowed inference of disease-related genes with high rank. We validated the proposed method by comparing with previous methods. The results indicated that the proposed method is a meaningful approach to infer disease-related genes.

• Journal of the Korean Geographical Society
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• v.46 no.3
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• pp.366-381
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• 2011

A competitive location problem in discrete space is computationally difficult to solve in general because of its combinatorial feature. In this paper, we address an alternative method for solving competitive location problems in discrete space, particularly employing deterministic allocation. The key point of the suggested method is to reducing the number of predefined potential facility sites associated with the size of problem by utilizing geometric concepts. The suggested method was applied to the existing broadband marketplace with increasing competition as an application. Specifically, we compared computational results and spatial configurations of two different sized problems: the problem with the original potential sites over the study area and the problem with the reduced potential sites extracted by a GIS-based geometric algorithm. The results show that the competitive location model with the reduced potential sites can be solved more efficiently, while both problems presented the same optimal locations maximizing customer capture.