• Industrial Engineering and Management Systems
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• 제11권2호
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• pp.148-154
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• 2012

Micro Electric Vehicles are considered as a solution for reducing $CO_2$ emissions, however, it is difficult to evaluate its impact in a local community when it has been introduced. In this study, we evaluated how to spread the Micro EV within the community, using the utility derived from a multinomial logit model, and analyze the effect on $CO_2$ emissions. The householder's utility model is based on an investigation about Kiryu citizen's activities of shopping, transportation methods, etc. Using the geographic information system, we get the distances of each householder and the stores, and estimate a multinomial logit model about the combination choices of shopping stores and transportation method.

• 정보관리학회지
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• 제28권1호
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• pp.123-144
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• 2011

본 연구는 학문의 지적 구조를 분석하는 새로운 분석기법인 저자프로파일링분석과 전통적인 분석기법인 저자동시인용분석을 비교하여 분석함으로써 국내 연구환경에 맞는 지적 구조 분석 방법을 제안하는 데 목적을 두고 있다. 이를 위하여 본 연구에서는 인용색인을 이용하지 않고 학문의 지적 구조를 분석할 수 있는 텍스트마이닝을 이용한 저자프로파일링분석을 통하여 새로운 지적 구조 방법의 유용성을 확인하 고자 하였다. 분석대상 학술지는 "대한건축학회 논문집 - 계획계"를 대상으로 하였다.

• 한국빅데이터학회지
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• 제7권1호
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• pp.213-224
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• 2022

물류와 유통에서 장바구니 분석(MBA: Market Basket Analysis)은 주요 판매 상품 간의 연관성을 분석하고, 내부 운영 효율성을 높이기 위한 중요한 수단으로 활용된다. 특히, 장바구니 분석의 결과는 상품 구매예측, 상품 추천 및 매장의 상품 전시 구조 등 의사결정 과정에 중요한 참고자료로 활용된다. 최근 전자상거래의 발전으로 하나의 유통 및 물류 기업이 취급하는 품목의 수가 급격하게 증가하면서 기존의 분석기법인 Apriori와 FP-Grwoth 등의 방법은 계산량의 기하급수적 증가로 인한 속도저하와 실제 비즈니스에 적용하기 위한 중요한 연관규칙을 살피기에는 한계가 있다. 본 연구에서는 이러한 한계를 극복하기 위해, 상품의 최상위 분류체계인 Main-Category 수준에서는 상품의 판매량을 함께 고려할 수 있는 utility item set mining 기법을 활용하여 주로 함께 판매된 상품군을 우선 선별하였다. 그 후, sub-category 수준에서는 FP-Growth를 활용하여 함께 판매되는 상품 유형을 식별하였다. 이렇게 순차적 레이어 필터링 기법을 활용하여 불필요한 연산을 줄일 수 있어 현실적으로 활용가능한 결과를 제시할 수 있다.

• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1999년도 가을 학술발표논문집 Vol.26 No.2 (2)
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• pp.551-553
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• 1999

This paper explores the utility of a new classifier fusion approach to discrimination. Multiple classifier fusion, a popular approach in the field of pattern recognition, uses estimates of each individual classifier's local accuracy on training data sets. In this paper we investigate the effectiveness of fusion methods compared to individual algorithms, including the artificial neural network and k-nearest neighbor techniques. Moreover, we propose an efficient meta-classifier architecture based on an approximation of the posterior Bayes probabilities for learning the oracle.

• 터널과지하공간
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• 제13권1호
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• pp.1-5
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• 2003

The United States Antarctic program (USAP) through its principal Support Contractor Raytheon polar Services Co. (RPSC), has recently finished a 3 years projects, almost 936m length of underground utility tunnels at Amundsen-Scott station. It accommodates the piping that conveys fresh water from current well sites, as well as waste water to repositories in abandoned wells. The under snow tunnels allow year-round access for system operations and maintenance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1204-1227
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• 2020

The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs.

• 한국산학기술학회논문지
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• 제17권4호
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• pp.521-528
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• 2016

개인용 컴퓨터 및 각종 모바일 기기의 이용 증가로 인해 많은 분야에서 다양한 형태의 웹기반 서비스들이 널리 활용되고 있다. 이에 따라 해당 분야에서 개인 맞춤형 서비스를 지원하기 위한 사용자 이용 로그 분석 등에 대한 연구가 활발히 진행되고 있으며, 특히 사용자 로그 데이터를 구성하는 구성요소의 중요성 차별화에 기반한 분석 기법들이 활발히 연구되었다. 본 논문에서는 웹 클릭 스트림에서 유용하게 적용될 수 있는 고유용 과거 정보 탐색 기법을 제시한다. 해당 기법을 통해 기존의 웹 클릭 스트림 분석 기법에서는 쉽게 탐색하지 못했던 정보인 타겟 마케팅 등에 유용하게 활용될 수 있는 중요 정보를 쉽게 탐색할 수 있다. 본 논문의 연구 결과는 IoT 환경 및 생물정보 분석 등과 같이 데이터 스트림 형태로 정보를 발생시키는 다양한 컴퓨터 응용 분야에도 활용될 수 있을 것이다.

• 지능정보연구
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• 제3권1호
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• pp.31-45
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• 1997

To date, research on data mining has focused primarily on individual techniques to su, pp.rt knowledge discovery. However, the integration of elementary learning techniques offers a promising strategy for challenging a, pp.ications such as forecasting nonlinear processes. This paper explores the utility of an integrated a, pp.oach which utilizes a second-order learning process. The a, pp.oach is compared against individual techniques relating to a neural network, case based reasoning, and induction. In the interest of concreteness, the concepts are presented through a case study involving the prediction of network traffic for video-on-demand.

• Industrial Engineering and Management Systems
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• 제12권3호
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• pp.234-243
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• 2013

In order to point out the best utility of a product (or a service), marketers need to clearly understand and measure the preference of the consumers. Among numerous marketing analysis techniques, the conjoint analysis is one of the popular tools for market research. One of the issues with this tool is the lack of feedback for the respondents. This paper proposes personal stepwise conjoint analysis based on an interactive Web-questionnaire allowing respondents to receive a diagnosis of their evaluation and giving the possibility to reconsider their evaluation. To validate our proposal, experimentation with forty-two respondents is also demonstrated. Experimental results, potential modifications and improvements are detailed in this paper.