• Journal of Information Technology Services
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• v.8 no.2
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• pp.173-187
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• 2009

CASE tools are complex software products offering many different features. Systems professionals have evaluated various CASE products from a feature and attribute basis. Each product has a different mix of strengths and weaknesses as perceived by the end user. Specific CASE tools support different steps of the applications development process as well as varying methodologies. In this paper we develop a method for evaluating CASE tools. The model has an analytic hierarchy process for evaluating CASE tools in terms of functionality, management efficiency, and support ability of provider, and a data envelopment analysis for overall evaluation considering cost and AHP results. We applied the developed model to a real world case study.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.2 s.257
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• pp.260-268
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• 2007

Demand for the use of 3D CAD DMU systems over the Internet environment has been increased. However, transmission of commercial 3D kernels has delayed the communication effectiveness due to the kernel size. Light weight CAD geometric kernel design methodology is required for rapid transmission in the distributed environment. In this paper, an assembly data structure suitable for the top-down and bottom-up assembly models has been constructed. Part features are stored without a hierarchy so that they are created and saved in no particular order. In particular, this paper proposes a new assembly representation model, called multi-level assembly representation (MAR), for the PDM based assembly DMU system. Since the geometric kernel retains assembly hierarchy and topological information, it is applied to the web-viewer for the PDM based DMU system. Effectiveness of the proposed geometric kernel is confirmed through various case studies.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.127-128
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• 2012

Recently, Construction industry has been trying to reduce environmental loads reflecting the global trend 'Green Growth'. Internal and External countries are provided 'green building certification', 'relevant law/regulations', 'guideline to life cycle', however, construction phase has been overlooked though environmental loads occurred intensively in this phase. Therefore, this study intend to deduct components reflected the guideline in construction phase and assess them quantitatively. The basis data is collected through survey targeting construction managers and related researchers and analyze these data using Analytic Hierarchy Process.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.131-141
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• 2024

The aging population and worsening lifestyle habits have increased the risk of chronic diseases. This has heightened the importance of preventive healthcare, particularly through personalized health management services based on individual health data. Despite this, the domestic digital healthcare industry remains underdeveloped. Given the need for acceptance from both consumers and providers, this study uses the Analytic Hierarchy Process (AHP) to identify success factors for health management service platforms. AHP evaluates the relative importance of various factors to aid decision-making. Results show that providers prioritize data analysis and platform design, laws and regulations, and data standardization, while consumers prioritize system stability, laws and regulations, and system security. These findings highlight the need for strategies to bridge the expectation gap to effectively promote health management service platforms.

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.77-93
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• 2020

The literature has reported that hierarchical classification methods generally outperform the flat classification methods for a multi-class document classification problem. Unlike the literature that has constructed a class hierarchy, this paper evaluates the performance of hierarchical and flat classification methods under a situation where the class hierarchy is predefined. We conducted numerical evaluations for two data sets; research papers on climate change adaptation technologies in water sector and 20NewsGroup open data set. The evaluation results show that the hierarchical classification method outperforms the flat classification methods under a certain condition, which differs from the literature. The performance of hierarchical classification method over flat classification method depends on class similarities at levels in the class structure. More importantly, the hierarchical classification method works better when the upper level similarity is less that the lower level similarity.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.137-152
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• 2023

Data is indispensable for digital transformation of agriculture with the development of innovative information and communication technology (ICT). In order to devise and prioritize strategies for enhancing data competitiveness in the agricultural sector, we employed an Analytic Hierarchy Process (AHP) analysis. Drawing from existing research on data competitiveness indicators, we developed a three-tier decision-making structure reflecting unique characteristics of the agricultural sector such as farmers'awareness of the data industry or awareness of agriculture among data workers. AHP survey was administered to experts from both agricultural and non-agricultural sectors with a high understanding of data. The overall composite importance, derived from the respondents, was rated in the following order: 'Employment Support', 'Data Standardization', 'R&D Support', 'Start-up Ecosystem Support', 'Relaxation of Regulations', 'Legislation', and 'Data Analytics and Utilization Technology'. In the case of experts in the agricultural sector, 'Employment Support' was ranked as the top priorities, and 'Legislation', 'Undergrad and Grad Education', and 'In-house Training' were also regarded as highly important. On the other hand, experts in the non-agricultural sector perceived 'Data Standardization' and 'Relaxation of Regulations' as the top two priorities, and 'Data Center' and 'Open Public Data' were also highly rated.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• International Journal of Quality Innovation
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• v.7 no.2
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• pp.141-156
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• 2006

This study describes a novel algorithm for optimizing the quality yield of silicon wafer slicing. 12 inch wafer slicing is the most difficult in terms of semiconductor manufacturing yield. As silicon wafer slicing directly impacts production costs, semiconductor manufacturers are especially concerned with increasing and maintaining the yield, as well as identifying why yields decline. The criteria for establishing the proposed algorithm are derived from a literature review and interviews with a group of experts in semiconductor manufacturing. The modified Delphi method is then adopted to analyze those results. The proposed algorithm also incorporates the analytic hierarchy process (AHP) to determine the weights of evaluation. Additionally, the proposed algorithm can select the evaluation outcomes to identify the worst machine of precision. Finally, results of the exponential weighted moving average (EWMA) control chart demonstrate the feasibility of the proposed AHP-based algorithm in effectively selecting the evaluation outcomes and evaluating the precision of the worst performing machines. So, through collect data (the quality and quantity) to judge the result by AHP, it is the key to help the engineer can find out the manufacturing process yield quickly effectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.3
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• pp.63-68
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• 2007

The LRA(Logistic Regression Analysis) conducts a quantitative analysis by collecting a lot of samples and the AHP(Analytic Hierarchy Program) makes use of expert decision influenced by subjective judgment to a certain degree. This study is to suggest a combination method in mapping landslide hazard by giving equal weight for the result of LRA and AHP. Topographic factors(slope, aspect, elevation), soil dram, soil depth and land use were adopted to classify landslide hazard areas. The three methods(LRA, AHP, the combined approach) was applied to a $520km^2$ region located in the middle of South Korea which have occurred 39 landslides during 1999 and 2003. The suggested method showed 58.9% matching rate for the real landslide sites comparing with the classified areas of high-risk landslide While LRA and AHP Showed 46.1% and 48.7% matching rates respectively. Further studies are recommended to find the optimal combining weight of LRA and AHP with more landslide data.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1417-1424
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• 2018

In order to improve the performance of image classifications using Convolutional Neural Networks (CNN), applying a category hierarchy to the classification can be a useful idea. However, the visual separation of object categories is very different according to the upper and lower category levels and highly uneven in image classifications. Therefore, it is doubtable whether the use of category hierarchies for classification is effective in CNN. In this paper, we have clarified whether the image classification using category hierarchies improves classification performance, and found at which level of hierarchy classification is more effective. For experiments we divided the image classification task according to the upper and lower category levels and assigned image data to each CNN model. We identified and compared the results of three classification models and analyzed them. Through the experiments, we could confirm that classification effectiveness was not improved by reduction of number of categories in a classification model. And we found that only with the re-training method in the last network layer, the performance of lower category classification was not improved although that of higher category classification was improved.