• Construction Engineering and Management
• /
• v.18 no.3
• /
• pp.23-27
• /
• 2017

• Korean Journal of Construction Engineering and Management
• /
• v.14 no.3
• /
• pp.42-52
• /
• 2013

The aim of this study is to present a prediction model of construction cost for a bridge that has a high reliability using historical data from the planning phase based on a CBR (Case-Based Reasoning) method in order to overcome limitations of existing construction cost prediction methods, which is linearly estimated. To do this, a reasoning model of bridge construction cost by a spreadsheet template was suggested using complexly both CBR and GA (Genetic Algorithm). Besides, this study performed a case study to verify the suggested cost reasoning model for bridge construction projects. Measuring efficiency for a result of the case study was 8.69% on average. Since accuracy of the suggested prediction cost is relatively high compared to the other analysis methods for a prediction of construction cost, reliability of the suggested model was secured. In the case that information for detailed specifications of each bridge type in an initial design phase is difficult to be collected, the suggested model is able to predict the bridge construction cost within the minimized measuring efficiency with only the representative specifications for bridges as an improved correction method. Therefore, it is expected that the model will be used to estimate a reasonable construction cost for a bridge project.

• The Journal of Information Systems
• /
• v.18 no.3
• /
• pp.277-297
• /
• 2009

물류 프로세스내의 상황결정은 전문적인 물류지원 연구의 중요한 목표이다. CBR(Case Based Reasoning)는 기존의 사건이나 경험으로 현재 발생한 문제의 해결책을 발견하기 위한 기술이다. CBR의 주요 역할은 현재 사건에 있는 문제의 상태를 인식하며 이 사건과 유사한 기존 사건 중의 하나를 통하여 현재 사건의 해결책을 추론함으로써 기존 시스템을 업데이트하는 것이다. 이러한 과정에서 가장 중요한 이슈는 유용한 사례베이스를 구축하는 것이다. 온토로지를 이용하여 상황을 모델화하면, 여러 개체들이 협업하에서 상황에 대한 인식을 공유할 수 있게 된다. 본 논문에서는 CBR 사례베이스 구축을 위한 참조로서 물류 온토로지를 디자인하였다.

• Journal of KIISE:Databases
• /
• v.28 no.3
• /
• pp.334-343
• /
• 2001

Most of currently used videos have variable bit rate(VBR) characteristics. Since the display rate of VBR videos compared to CBR videos vary with time, it is not proper to configure resources of the VBR video server using the method proposed for the CBR video server. In this paper we propose an optimal resource configuration method for the VBR video server which is based on the probability model. The proposed method decides the amount of disk and memory, and the disk access cycle of the video server with the lowest hardware cost, while preserving the throughput of the video server. In addition, we show the usefulness of the method through the various experiments.

• Journal of KIISE:Computer Systems and Theory
• /
• v.34 no.7
• /
• pp.282-287
• /
• 2007

Information society is dramatically developing in the various areas of finance, trade, medical service, energy, and education using information system. Evaluation for risk analysis should be done before security management for information system and security risk analysis is the best method to safely prevent it from occurrence, solving weaknesses of information security service. In this paper, Modeling it did the evaluation-base CBD function it will be able to establish the evaluation plan of optimum. Evaluation-based CBD(case-based reasoning) functions manages a security risk analysis evaluation at project unit. it evaluate the evaluation instance for beginning of history degree of existing. It seeks the evaluation instance which is similar and Result security risk analysis evaluation of optimum about under using planning.

• Korean Journal of Construction Engineering and Management
• /
• v.11 no.4
• /
• pp.22-31
• /
• 2010

Because the estimated cost at early stage has great influence on decisions of project owner, the importance of early cost estimation is increasing. However, it depends on experience and knowledge of the estimator mainly due to shortage of information. Those tendency developed into case-based reasoning(CBR) method which solves new problems by adapting previous solution to similar past problems. The performance of CBR model is affected by attribute weight, so that its accurate determination is necessary. Previous research utilizes mathematical method or subjective judgement of estimator. In order to improve the problem of previous research, this suggests CBR schematic cost estimation method using genetic algorithm to determine attribute weight. The cost model employs nearest neighbor retrieval for selecting past case. And it estimates the cost of new cases based on cost information of extracted cases. As the result of validation for 17 testing cases, 3.57% of error rate is calculated. This rate is superior to accuracy rate proposed by AACE and the method to determine attribute weight using multiple regression analysis and feature counting. The CBR cost estimation method improve the accuracy by introducing genetic algorithm for attribute weight. Moreover, this makes user understand the problem-solving process easier than other artificial intelligence method, and find solution within short time through case retrieval algorithm.

• Korean Journal of Construction Engineering and Management
• /
• v.7 no.2 s.30
• /
• pp.138-147
• /
• 2006

The objective of this research is to explore a more precise forecasting method by applying Case-based Reasoning (CBR). The newly suggested method in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting, 1) the choice of the numbers of referring projects and 2) the better selection among three levels ? which include a 20-work package level, a 7-major work package level, and a total sum level analysis, were investigated in detail. It is concluded that selecting similar projects at $12{\sim}19%$ out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• Journal of Digital Contents Society
• /
• v.13 no.2
• /
• pp.213-224
• /
• 2012

Creative writing process begins with memory that contains general experience of the human. In the past creative writing was regarded as exclusive ability of the human. But today, thanks to digital technology digital story creation programs are being developed. This study compares and analyzes the story creation programs, the and the , that imitate a process of interaction between human's long term memory and creative writing. The tried to create probable story by emphasizing character's goal in building case database and retrieving cases. On the other hand, the tried to assist writer's ideation by emphasizing violating motif in building case database and retrieving cases. Hereafter, use of digital media in creating story is expected to accelerate. In this prospect, this study hope to help a development of story creation program in the future.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.43-57
• /
• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.10C
• /
• pp.945-950
• /
• 2002

In this paper the performance evaluations on Asynchronous Transfer Mode(ATM) access node are performed in the ATM access network which consists of access node and channel. The performance factors of access node are Cell Loss Ratio(CLR), buffer capacity and delay time. Both the ATM cell-scale queueing model and burst-scale queueing model are considered as the traffic model of access node for various traffic types such as Constant Bit Rate(CBR), Variable Bit Rate(VBR) and random traffic in the ATM access networks. Based on these situations, the relation of CLR, buffer capacity and delay time is analyzed in the ATM access node.