• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.45
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• pp.365-374
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• 1998

Control Chart is a powerful tool to detect that process is in control or out of control. CIM can have real effect when CIM involve automated quality control. A neural network approach is used for unnatural pattern detecting of control chart. The previous moving window method uses all unnatural pattern that is detected as moving time window. Therefore, It trains a large number of unnatural pattern and takes training time long. In this paper, the proposed method tests a small number of training unnatural pattern which modifies test data without repeating time. We shows that the proposed method has differences In training time and identification rate on the previous moving windows method. As results, we reduced training time and obtain the same identification rate.

• PNF and Movement
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• v.18 no.1
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• pp.55-64
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• 2020

Purpose: The purpose of this study was to compare the strength and walking ability of chronic stroke patients following either proprioceptive neuromuscular facilitation (PNF) pattern training with pressure biofeedback units (feedback group) or PNF pattern training without pressure biofeedback units (control group). Methods: Eighteen participants with chronic stroke were recruited from a rehabilitation hospital. They were divided into two groups: a feedback group (n = 8) and a control group (n = 10). They all received 30 minutes of neurodevelopmental therapy and PNF training for 15 minutes five times a week for three weeks. Muscle strength and spatiotemporal gait parameters were measured. Muscle strength was measured by hand-held dynamometer; gait parameters were measured by the Biodex Gait trainer treadmill system. Results: After the training periods, the feedback group showed a significant improvement in hip abductor muscle strength, hip extensor muscle strength, step length of the unaffected limb, and step time of the affected limb (p<0.05). Conclusion: The results of this study showed that proprioceptive neuromuscular facilitation pattern training with pressure biofeedback units was more effective in improving hip muscle strength and walking ability than the proprioceptive neuromuscular facilitation pattern training without pressure biofeedback units. Therefore, to strengthen hip muscles and improve the walking ability of stroke patients, using pressure biofeedback units to improve trunk stability should be considered.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.11
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• pp.596-603
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• 2004

This paper proposes an expert system based on the pattern recognition method which can enhance the accuracy and effectiveness of real-time bus reconfiguration strategy for the transfer of faulted load when a main transformer fault occurs in the automated substation. The minimum distance classification method is adopted as the pattern recognition method of expert system. The training pattern set is designed MTr by MTr to minimize the searching time for target load pattern which is similar to the real-time load pattern. But the control pattern set, which is required to determine the corresponding bus reconfiguration strategy to these trained load pattern set is designed as one table by considering the efficiency of knowledge base design because its size is small. The training load pattern generator based on load level and the training load pattern generator based on load profile are designed, which are can reduce the size of each training pattern set from max L/sup (m+f)/ to the size of effective level. Here, L is the number of load level, m and f are the number of main transformers and the number of feeders. The one reduces the number of trained load pattern by setting the sawmiller patterns to a same pattern, the other reduces by considering only load pattern while the given period. And control pattern generator based on exhaustive search method with breadth-limit is designed, which generates the corresponding bus reconfiguration strategy to these trained load pattern set. The inference engine of the expert system and the substation database and knowledge base is implemented in MFC function of Visual C++ Finally, the performance and effectiveness of the proposed expert system is verified by comparing the best-first search solution and pattern recognition solution based on diversity event simulations for typical distribution substation.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.139-147
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• 2009

The memory based reasoning just stores in the memory in the form of the training pattern of the representative pattern. And it classifies through the distance calculation with the test pattern. Because it uses the techniques which stores the training pattern whole in the memory or in which it replaces training patterns with the representative pattern. Due to this, the memory in which it is a lot for the other machine learning techniques is required. And as the moreover stored training pattern increases, the time required for a classification is very much required. In this paper, We propose the EAS(Evaluation And Selection) algorithm in order to minimize memory usage and to improve classification performance. After partitioning the training space, this evaluates each partitioned space as MDL and PM method. The partitioned space in which the evaluation result is most excellent makes into the representative pattern. Remainder partitioned spaces again partitions and repeat the evaluation. We verify the performance of Proposed algorithm using benchmark data sets from UCI Machine Learning Repository.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.675-679
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• 2001

In this paper, we present a new competitive learning algorithm called Dynamic Competitive Learning (DCL). DCL is a supervised learning method that dynamically generates output neurons and initializes automatically the weight vectors from training patterns. It introduces a new parameter called LOG (Limit of Grade) to decide whether an output neuron is created or not. If the class of at least one among the LOG number of nearest output neurons is the same as the class of the present training pattern, then DCL adjusts the weight vector associated with the output neuron to learn the pattern. If the classes of all the nearest output neurons are different from the class of the training pattern, a new output neuron is created and the given training pattern is used to initialize the weight vector of the created neuron. The proposed method is significantly different from the previous competitive learning algorithms in the point that the selected neuron for learning is not limited only to the winner and the output neurons are dynamically generated during the learning process. In addition, the proposed algorithm has a small number of parameters, which are easy to be determined and applied to real-world problems. Experimental results for pattern recognition of remote sensing data and handwritten numeral data indicate the superiority of DCL in comparison to the conventional competitive learning methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.10
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• pp.410-417
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• 2006

Generally, the training set of maximum $m{\times}L(m+f)$ patterns in the pattern recognition method is required for the real-time bus reconfiguration strategy when a main transformer fault occurs in the distribution substation. Accordingly, to make the application of pattern recognition method possible, the size of the training set must be reduced as efficient level. This Paper proposes a methodology which obtains the minimized training set by applying the pattern clustering method to load patterns of the main transformers and feeders during selected period and to obtain bus reconfiguration strategy based on it. The MaxMin distance clustering algorithm is adopted as the pattern clustering method. The proposed method reduces greatly the number of load patterns to be trained and obtain the satisfactory pattern matching success rate because that it generates the typical pattern clusters by appling the pattern clustering method to load patterns of the main transformers and feeders during selected period. The proposed strategy is designed and implemented in Visual C++ MFC. Finally, availability and accuracy of the proposed methodology and the design is verified from diversity simulation reviews for typical distribution substation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5730-5736
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• 2013

The study was conducted to determine the effect of trunk pattern training in proprioceptive neuromuscular facilitation(PNF) and weight-shift training on the muscle activity and static balance in patients with hemiplegia due to stroke. Twenty patients with hemiplegia due to stroke were assigned to the trunk pattern training in PNF group(n=10) or weight-shift training group(n=10). Both groups were executed conventional treatment for 5 times per week for 6 weeks 30 minutes per session. Each group performed additional training for 20 minutes. Post training, compared to the weight-shift training group, trunk pattern training in PNF group showed significantly increased on muscle activity of rectus femoris, gastrocnemius and static balance(p<.05). These results support the perceived benefits of trunk pattern training in PNF to augment on the static balance and muscle activity of stroke patients. Therefore, trunk pattern training in PNF is feasible and suitable for stroke patients.

• The Journal of Korean Physical Therapy
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• v.21 no.1
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• pp.89-96
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• 2009

Purpose: The purpose of this study was to determine the effect of modified golf swing training on gait characteristic in hemiplegic patient through Kwon 3D motion analysis system. Methods: This study has performed single subject design from September to October 2008. The subject had left hemiplegia due to CVA in December 2003. He has treated Bobath approach twice a week. In order to increase ankle dorsiflexion and knee flexion, the subject has applied modified golf swing training on the basis of Bobath approach. The measurement of gait characteristic was taken by Kwon 3D motion analysis system. Results: The results were as follows : 1) Walking velocity was increased 0.62m/sec than before the training. 2) Step length was increased 0.09m than before the training. 3) Left ankle and hip angle were increased, but left knee angle was decreased. Conclusion: It could be concluded that the activity modified golf swing training in walking pattern contributed to improve the movement quality and speed of gait.

• Korean Journal of Applied Biomechanics
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• v.34 no.1
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• pp.18-24
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• 2024

Objective: The purpose of this study was to evaluate the effects of an 8-week velocity-based training on the maximum vertical jump in elite sprinters. Method: Ten elite sprinters were participated in this study (age: 21 ± 0.97 yrs., height: 179 ± 3.54 cm, body mass: 72 ± 2.98 kg). An 8-week velocity-based power training was provided to all subjects for twice per week. Their maximum vertical jumps were measured before and after velocity-based training. A 3-dimensional motion analysis with 8 infrared cameras and 4 channels of EMG was performed in this study. A paired t-test was used for statistical verification. The significant level was set at α=.05. Results: There were no statistically significant differences were found between pre and post the training (p>.05). However, most variables included jump record, knee joint ROM, and muscle activation of rectus femoris showed increased pattern after the training. Conclusion: In this study, an 8-week velocity-based training did not showed the significant training effects. However, knee joint movement which is the key role of the vertical jump revealed positive kinematic and kinetic pattern after the training. From this founding, it is believed that velocity-based training seems positively affect the vertical jump which is the clear measurement of mechanical power of sprinter. In addition, to get more clear evidence of the training more training period would be needed.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.59-64
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• 2001

The objectives of the thesis are to propose a pattern classification method for remote sensing data using artificial neural network. First, we apply the error back propagation algorithm to classify the remote sensing data. In this case, the classification performance depends on a training data set. Using the training data set and the error back propagation algorithm, a layered neural network is trained such that the training pattern are classified with a specified accuracy. After training the neural network, some pixels are deleted from the original training data set if they are incorrectly classified and a new training data set is built up. Once training is complete, a testing data set is classified by using the trained neural network. The classification results of Landsat TM data show that this approach produces excellent results which are more realistic and noiseless compared with a conventional Bayesian method.