• 산업경영시스템학회지
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• 제21권45호
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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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• 제18권1호
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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.

• 대한전기학회논문지:전력기술부문A
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• 제53권11호
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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.

• 한국컴퓨터정보학회논문지
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• 제14권3호
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• pp.139-147
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• 2009

메모리 기반 추론 기법은 단순히 학습패턴이나 대표패턴의 형태로 메모리에 저장하며 테스트 패턴과의 거리 계산을 통하여 분류한다. 이 기법의 가장 큰 문제점은 학습 패턴 전체를 메모리에 저장하거나 학습 패턴들을 대표 패턴으로 대체하는 방법을 사용함으로 다른 기계학습 방법에 비하여 많은 메모리 공간을 필요로 하며, 저장되는 학습패턴이 증가할수록 분류에 필요한 시간도 많이 소요된다는 단점을 갖는다. 본 논문은 효율적인 메모리 사용과 분류 성능의 향상을 위한 EAS 기법을 제안하였다. 즉, 학습패턴에 대해 분할공간을 생성한 후 생성된 각 분할공간을 MDL기법과 PM기법으로 평가하였다. 그리고 평가 결과 가장 우수한 분할공간만을 취하여 대표패턴으로 삼고 나머지는 다시 분할하여 평가를 반복하는 기법이다. UCI Machine Learning Repository에서 벤치마크 데이터를 발췌한 실험 자료를 사용하여 제안한 기법의 성능과 메모리 사용량에 있어 우수함을 입증하였다.

• 한국지능시스템학회논문지
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• 제11권7호
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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.

• 대한전기학회논문지:전력기술부문A
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• 제55권10호
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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.

• 한국산학기술학회논문지
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• 제14권11호
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• pp.5730-5736
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• 2013

본 연구는 뇌졸중 환자에게 고유수용성 신경근 촉진법의 체간 패턴 훈련과 체중 이동 훈련이 하지 근 활성도와 정적 균형 능력에 어떠한 영향을 미치는지 알아보기 위하여 실시하였다. 본 연구에는 총 20명의 뇌졸중 환자가 참여하였으며, 대상자들은 무작위 추출법으로 고유수용성 신경근 촉진법의 체간 패턴 훈련군 10명과 체중 이동 훈련군 10명으로 각각 배정되었다. 모든 대상자들은 전통적 물리치료를 6주 동안 주 5회, 하루 30분씩 시행하였으며, 고유수용성 신경근 촉진법의 체간 패턴 훈련과 체중 이동 훈련을 하루에 20분씩 각각 추가적으로 실시하였다. 연구 결과 고유수용성 신경근 촉진법의 체간 패턴 훈련이 체중 이동 훈련에 비해 마비측 하지의 대퇴직근, 비복근의 활성도와 정적 균형 능력에서 유의한 차이를 보였다(p<.05). 이것으로 고유수용성 신경근 촉진법의 체간 패턴 훈련이 체중 이동 훈련에 비해 뇌졸중 환자의 회복에 효과적이었다고 할 수 있다. 따라서 고유수용성 신경근 촉진법의 체간 패턴훈련은 뇌졸중 환자에게 적용 가능한 유익한 훈련이 될 수 있을 것이다.

• The Journal of Korean Physical Therapy
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• 제21권1호
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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.

• 한국운동역학회지
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• 제34권1호
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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.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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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.