• Physical Therapy Korea
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• v.2 no.2
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• pp.98-107
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• 1995

Physical therapists use assumptions about motor control in every aspect of their work in treating stroke patients. An understanding of the recovery process after stroke, some neural mechanism of recovery and therapeutic model is critical factor for physical therapist to evaluate and obtain a higher final stage of recovery. The purpose of this article was to review the recovery process after stroke, some neural mechanism of recovery, the role of rehabilitation in the process of recovery, therapeutic model and its limitation. This article will help understanding of recovery process. evaluation, and treatment of the stroke patients. Each therapeutic method consists of a different set of assumptions and they are not completely independent of one another. Therefore specializing in any techniques of physical therapy will not be enough to treat stroke, so we are in need of integrated approach and objective measurement instrument to adequately evaluate and treat stroke patients.

• Transactions of Materials Processing
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• v.13 no.8
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• pp.731-737
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• 2004

Mechanical properties such as Young's modulus and hardness of thin film in coated steel are difficult to determine by nano-indentation from the conventional analysis using the load-displacement curve. Therefore, an analysis of the nano-indentation loading-unloading curve was used to determine the Young's modulus, hardness. A new method is recently being developed for elastic-plastic properties of materials from nano-indentation. Elastic modulus of the thin films shows relatively small influence whereas yield strength is found to have significant effect on measured data. The load-displacement curves of material tested with a Berkovich indenter and nano-indentation continuous stiffness method is used to measure the modulus and hardness through thin films, and then these are computed using the analysis procedure. The developed neural networks apply also to obtain reliable mechanical properties.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.95-97
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• 2004

This paper estimates Adaptive Resonance Theory 1(ART1) as a vigilance parameter of pattern clustering algorithm. Inherent characteristics of the model are analyzed. In particular the vigilance parameter ${\rho}$ and its role in classification of patterns is examined. Our estimates show that the vigilance parameter as designed originally does not necessarily increase the number of categories with its value but can decrease also. This is against the claim of solving the stability-plasticity dilemma. However, we have proposed a modified vigilance parameter estimate criterion which takes into account the problem of subset and superset patterns and stably categorizes arbitrarily many input patterns in one list presentation when the vigilance parameter is closer to one.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.457-458
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• 2007

Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer poor performance of learning. To archive good clustering performance, we develop a hybrid neural network model. It is the combination of Multi-Layer Perceptron (MLP) and Adaptive Resonance Theory 2 (ART2). It inherits two distinct advantages of stability and plasticity from ART2. Meanwhile, by combining the merits of MLP, it improves the performance for clustering. Experiment results show that our model can be used for clustering with promising performance.

• Journal of Korean Elementary Science Education
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• v.26 no.1
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• pp.49-62
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• 2007

The higher cognitive functions of the human brain including teaming are hypothesized to be selectively distributed across large-scale neural networks interconnected to the cortical and subcortical areas. Recently, advances in functional imaging have made it possible to visualize the brain areas activated by certain cognitive activities in vivo. Neural substrates for teaming and motivation have also begun to be revealed. Functional magnetic resonance imaging (fMRI) provides a non-invasive indirect mapping of cerebral activity, based on the blood- oxygen level dependent (BOLD) contrast which is based on the localized hemodynamic changes following neural activities in certain areas of the brain. The fMRI method is now becoming an essential tool used to define the neuro-functional mechanisms of higher brain functions such as memory, language, attention, learning, plasticity and emotion. Further research in the field of education will accelerate the verification of the effects on loaming or help in the selection of model teaching strategies. Thus, the purpose of this study was to review brain study methods using fMRI in science education. In conclusion, a number of possible strategies using fMRI for the study of elementary science education were suggested.

• Transactions of Materials Processing
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• v.29 no.2
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• pp.76-88
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• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.

• Transactions of Materials Processing
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• v.29 no.4
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Transactions of Materials Processing
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• v.24 no.3
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• pp.167-175
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• 2015

In the current study, the design methodology has been developed to produce shoe mold for a suitable walking shoes of the general Korean using ANN (Artificial Neural Network). To design the suitable and comfortable shoes for the Korean, the shapes of foots were measured for 513 people. In this research, the foot length, breadth and ankle were considered as design parameters. In order to find the optimal foot shape for the average value of design parameters, the average value of design parameters and the other measurements were used as input and output to the ANN. After training, the various foot measurements were predicted by ANN. Base on the ANN results, the walking shoes were manufactured by considering these measurements and designing a shoe mold. From the results, the proposed method could give a more systematic and feasible means for manufacturing walking shoes with greater usefulness and better generality.

• Transactions of Materials Processing
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• v.27 no.6
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• pp.331-338
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• 2018

Electrohydraulic forming (EHF) process is a high speed forming process that utilizes the electric energy discharge in fluid-filled chamber to deform a sheet material. This process is completed in a very short time of less than 1ms. Therefore, finite element analysis is essential to observe the deformation mechanism of the material in detail. In addition, to perform the numerical simulation of EHF, the material properties obtained from the high-speed status, not quasi static conditions, should be applied. In this study, to obtain the parameters in the constitutive equation of Al 6061-T6 at high strain rate condition, a surrogate model using an artificial neural network (ANN) technique was employed. Using the results of the numerical simulation with free-bulging die in LS-DYNA, the surrogate model was constructed by ANN technique. By comparing the z-displacement with respect to the x-axis position in the experiment with the z-displacement in the ANN model, the parameters for the smallest error are obtained. Finally, the acquired parameters were validated by comparing the results of the finite element analysis, the ANN model and the experiment.

• Transactions of Materials Processing
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• v.32 no.5
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• pp.276-286
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• 2023

The field of sheet metal forming using press technology has become essential in modern mass production systems. Draw bead is often used to enhance formability. However, optimal draw bead design often requires excessive time and cost due to iterative experimentation and sometimes results in some defects. Given these challenges, there is a need to enhance formability by introducing segmented blank holders without draw beads. In this paper, the feasibility of a localized holding strategy using segmented blank holders is evaluated without the use of draw beads. The possibility for improving the formability was evaluated by utilizing a combination of the forming limit diagram and the wrinkle pattern-based defect indicators. Artificial neural networks were used for predicting defect indicators corresponding to arbitrary input holding forces and the NSGA-II optimization algorithm is used to find optimum blank holder forces yielding better defect indicators than the original process with drawbeads. Using optimum holding forces obtained from the proposed procedure, the stamping process with the segmented blank holders can yield better formability than the conventional process with drawbeads.