• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.479-490
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• 2003

CBD is a software development approach based on reusable component and supports easy modification and evolution of software. For the success of this approach, a component must be developed with high cohesion and low coupling. In this paper, we propose the two types of clustering analysis technique based on affinity between use-cases and classes and propose component identification method applying to this technique. We also propose component reference model and CBD methodology framework and perform a ease study to demonstrate how the affinity-based clustering technique is used in component identification method. Component identification method contains three tasks such as component extraction, component specification and component architecting. This method uses object-oriented concept for identifying component, which improves traceability from analysis to implementation and can automatically extract component. This method reflects the low coupling-high cohesion principle for good modularization about reusable component.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.760-772
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• 2004

Component Based Development(CBD) Methodology is widely used in software development lifecycle to improve software quality. The Component Based Development(CBD) results to improve software reusability and reduce development term and cost. For this reason, lots of Enterprises are trying to make their processes to components. But, there has been few quality assurance or reusability testing action to those components. Most software component users can not know how their components are reusable and what extent their components satisfy to th eir quality requirements. For this reason, this paper suggests that software components can be measured their reusability by metrics proposed by this paper. We propose that in measuring software component reusability, there are direct metrics and indirect metrics. The results made by direct metrics are suggested to measure indirect metrics, so results to obtain reusability metrics.

• Investigative Magnetic Resonance Imaging
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• v.6 no.2
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• pp.137-146
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• 2002

Purpose : This paper introduces an improved classification approach which adopts a deterministic relaxation method and an agglomerative clustering technique for the classification of MRI using neural network. The proposed approach can solve the problems of convergency to local optima and computational burden caused by a large number of input patterns when a neural network is used for image classification. Materials and methods : Application of Hopfield neural network has been solving various optimization problems. However, major problem of mapping an image classification problem into a neural network is that network is opt to converge to local optima and its convergency toward the global solution with a standard stochastic relaxation spends much time. Therefore, to avoid local solutions and to achieve fast convergency toward a global optimization, we adopt MFA to a Hopfield network during the classification. MFA replaces the stochastic nature of simulated annealing method with a set of deterministic update rules that act on the average value of the variable. By minimizing averages, it is possible to converge to an equilibrium state considerably faster than standard simulated annealing method. Moreover, the proposed agglomerative clustering algorithm which determines the underlying clusters of the image provides initial input values of Hopfield neural network. Results : The proposed approach which uses agglomerative clustering and deterministic relaxation approach resolves the problem of local optimization and achieves fast convergency toward a global optimization when a neural network is used for MRI classification. Conclusion : In this paper, we introduce a new paradigm to classify MRI using clustering analysis and deterministic relaxation for neural network to improve the classification results.