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

• Korean Journal of Construction Engineering and Management
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• v.17 no.1
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• pp.3-9
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• 2016

This study proposes a approximate cost estimation system based on Excel with VBA using weighted CBR(Case Based Reasoning). One characteristic of this system is that it generates the sheet automatically as many as the number of similar case and new estimation when it performs a case learning and a new estimate and cell formula is automatically entered into each sheet. User can be free to compose a combination of attribute factors because they can select up to ten attribute factors. This paper presents an applicability of estimation model for estimating the soil remediation cost when it use a landfarming method. When compared to a estimation model by using average unit cost and optimum multiple regression, this model shows a better result. This study was aimed at landfarming method, but it is expected that a cost estimation model using CBR will be more likely to apply in soil remediation technologies which various remediation technologies and pollutant species exist.

• Korean Journal of Construction Engineering and Management
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• v.21 no.1
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• pp.50-60
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• 2020

Estimates of project costs in the early stages of a construction project have a significant impact on the operator's decision-making in important matters, such as the site's decision or the construction period. However, it is difficult to carry out the initial stage with confidence because information such as design books and specifications is not available. In previous studies, case-based reasoning was used to predict initial construction costs, and genetic algorithms were used to calculate the weight of the inquiry phase among them. However, some say that it is difficult to perform better than the current year because existing genetic algorithms are calculated in random numbers. To overcome these limitations, correlation numbers using correlation analysis rather than random numbers are reflected in the genetic algorithm by method of local search, and weights are calculated using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using the weights calculated and validated with the data. As a result, it was found that the hybrid GA-CBR applied with local search performed better than the existing GA-CBR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.5
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• pp.1325-1341
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• 1998

A conventional CBR channel is now capable of delivering several digitally compressed video programs due to recent advances in video compression, such as MPEG-2, and digital transmission technology. This paper presents a joint video coding scheme that is to maintain a constant sum of bit rates for all the programs but to allow the variable bit rate for individual program in the transimission environment mentioned above. Thus advantages of VBR video compression can be obtained. This paper contributes in two aspects. First, a rate-distortion estimation method for MPEG-2 video is proposed, which enavle us predict the amount of bits and the distortion generated from an encoded picture at a given quantization step size and vice versa. The most attractive features of the proposed rate-distortion estimation method are its accuracy and a computational complexity low enough to be applied to real-time video coding applications. Second, this paper presents an efficient and accurate joint rate control scheme using the rate-distortion estimation results and verifies its performance with experiments. The experimental results show that our coding scheme gives a significant gain even though a small number of video programs are coded jointly. For example, a stable picture quality is maintained among the video programs as well as within a program, and additional extra programs can be transmitted over the same channel bandwidth if the proposed joint video coding scheme is used.

• Journal of Korean Society of Steel Construction
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• v.23 no.5
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• pp.557-568
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• 2011

To effectively come up with and secure a national budget, it is very important to estimate the reasonable construction cost of each step in public construction projects. In this study, the approximate construction cost of a steel box girder bridge in the early stages of the project, on which available information is limited, was proposed using case-based reasoning. In addition, construction cost estimation models were used for existing sample design models, and the accuracy of the estimation model for the presented cost was verified. The analysis results showed that the error rate was comparatively stable. Therefore, it is expected that construction cost estimation will be effectively suggested in the country's budget preparation.

• Korean Journal of Construction Engineering and Management
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• v.14 no.6
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• pp.14-21
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• 2013

While a detailed cost estimation utilizes the comprehensive quantity information obtained from a complete drawing and specification, an approximate cost estimation heavily relies on the subjective decisions. This phenomenon is because the approximate estimation is usually conducted in early stage of the construction phase where the drawing and specification are not available. This study developed a roadway construction cost estimation system which enables users to estimate the approximated cost of the project in early stage. The system identifies the variation of the construction costs due to the road route change using a cased based reasoning(CBR) method. Overall the system is expected to improves the accuracy and effectiveness of the estimation process, because it would provide users with increased accessability and consistent results in early stage of the project which leaded to the increased work transparency.

• International Journal of Highway Engineering
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• v.10 no.4
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• pp.103-116
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• 2008

In this study, in-situ testing method, Dynamic Cone Penetration Test(DCPT) was presented to establish a new compaction control criteria with using mechanical property like elastic modulus instead of unit weight for field compaction control. Soil chamber tests and in-situ tests were carried out to confirm DCPT tests can predict the designed elastic modulus after field compaction, and correlation analysis among the DCPT, CBR and resilient modulus of sub grade were performed. Also, DCPT test spacing criteria in the construction site was proposed from the literature review. In the result of laboratory tests, Livneh's equation was the best in correlation between PR of DCPT and CBR, George and Pradesh's equation was the best in the predicted resilient modulus. In the resilient modulus using FWD, Gudishala's equation estimates little larger than predicted resilient modulus and Chen's equation estimates little smaller. And KICT's equation estimates the modulus smaller than predicted resilient modulus. But using the results of laboratory resilient modulus tests considering the deviatoric and confining stress from the moving vehicle, the KICT's equation was the best. In the results of In-situ DCPT tests, the variation of PR can occur according to size distribution of penetrate points. So DCPT test spacing was proposed to reduce the difference of PR. Also it was shows that average PR was different according to subgrade materials although the subgrade was satisfied the degree of compaction. Especially large sized materials show smaller PR, and it is also found that field water contents have influence a lot of degree of compaction but a little on the average PR of the DCPT tests.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.41 no.1
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• pp.85-91
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• 2005

The existing fisheries managers ignored the fishery environment changes, have built competitively vessels of efficiency superiority with related corporations, and have captured indisciminately fisheries resourecs. The economical engine horsepower selection model for fishing vessels shorter than 24m is developed. Also, the economical engine horsepower selection model system is verified by the existing vessels, and the optimum vessel scale and engine horsepower classified by the type of fishery and scale are proposed.

• Journal of the Korean Geotechnical Society
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• v.17 no.4
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• pp.331-339
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• 2001

인산석고(phosphogypsum)는 비료생산과정에서 발생되는 산업부산물로, 주요구성성분은 이수석고(CaSO$_4$.2$H_2O$)의 성분이 대부분이고 여기에 인(P$_2$O$_2$), 철(F)와 유기물질 등으로 이루어져 있다. 전 세계적으로 인산석고 발생량은 현재 1억8천만톤이고 향후 약 3억8천만톤 발생될 것으로 추정되며, 국내에는 약 157만톤이 발생되고 있다. 본 연구는 성토재료로 인산석고를 활용할 경우 이에 대한 물리적 환경적 특성을 분석하였다. 이를 위하여 인산석고와 화강토 혼합비율에 따른 입도분석, 비중시험, 다짐시험, CBR시험, pH시험 및 용출시험 등 일련의 실험을 실시하였다. 실험결과, 인산석고 혼합재료를 성토재료로 활용할 경우 환경적으로 유해한 영향이 적으며, 산업부산물의 자원화 차원에서 대체 성토재료로 활용이 가능한 것으로 나타났다.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.32-40
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• 2003

In this study, we develop the Genetic Fuzzy System(GFS) to estimate the link traveling speed. Based on the genetic algorithm, we can get the fuzzy rules and membership functions that reflect more accurate correlation between traffic data and speed. From the fact that there exist missing links that lack traffic data, we added a Case Base Reasoning(CBR) to GFS to support estimating the speed of missing links. The case base stores the fuzzy rules and membership functions as its instances. As cases are accumulated, the case base comes to offer appropriate cases to missing links. Experiments show that the proposed GFS provides the more accurate estimation of link traveling speed than existing methods.