• 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.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.673-684
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• 2024

STREAM, developed by the Computational Reactor Physics and Experiment laboratory (CORE) of the Ulsan National Institute of Science and Technology (UNIST), is a deterministic neutron- and photon-transport code primarily designed for light water reactor (LWR) analysis. Initially, the photon module in STREAM did not account for fluorescence and bremsstrahlung photons. This article presents recent developments regarding the integration of atomic relaxation and bremsstrahlung models into the existing photon module, thus allowing for the transport of secondary photons. The photon flux and photon heating computed with the newly incorporated models is compared to results obtained with the Monte Carlo code MCS. The incorporation of secondary photons has substantially improved the accuracy of photon flux calculations, particularly in scenarios involving strong gamma emitters. However, it is essential to note that despite the consideration of secondary photon sources, there is no noticeable improvement in the photon heating for LWR problems when compared to the photon heating obtained with the previous version of STREAM.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.235-245
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• 1998

This paper presents the fuzzy optimization model based scheduling methodology for the efficient production control of a FFS(FIexible Flow Shop) under the uncertain production environment. To develop the methodology, a fuzzy optimization technique is introduced in which the uncertain production capacity caused by the random events like the machine breakdowns or the absence of workers is modeled by fuzzy number. Since the problem is NP hard, the goal of this study is to obtain the near optimal but practical schedule in an efficient way. Thus, Lagrangian relaxation method is used to decompose the problem into a set of subproblems which are easier to solve than the original one. Also, to construct the feasible schedule, a heuristic algorithm was proposed. To evaluate the performance of the proposed method, computational experiments, based on the real factory data, are performed. Then, the results are compared with those of the other methods, the deterministic one and the existing one used in the factory, in the various performance indices. The comparison results demonstrate that the proposed method is more effective than the other methods.