• PNF and Movement
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• 제21권1호
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• pp.119-128
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• 2023

Purpose: Obstacle crossing training is being used to improve the walking ability of stroke patients, but studies on which method is more effective when performing obstacle crossing training with an unaffected limb lead (OCT-ULL) and an affected limb lead (OCT-ALL) are not well known. As such, this study aims to compare the intervention effects of obstacle crossing training using unaffected limb leads (OCT-ULL) and obstacle crossing training using affected limb leads (OCT-ALL). Methods: In total, 25 patients with chronic stroke were studied and assigned randomly to the obstacle crossing training with unaffected limb leads (OCT-ULL) group or the obstacle crossing training with affected limb leads (OCT-ALL) group. A lower extremity strength test, balance and gait test, and fall efficacy test were conducted as preliminary tests, and all patients participated in the intervention for 30 minutes a day, five days a week for four weeks, and the same preliminary tests were conducted post-intervention. Results: Compared with the OCT-ALL group, the OCT-ULL group showed a significant improvement in the strength of the affected hip abductor muscle and in balance and gait, as well as in fall efficacy (p<.05). Conclusion: This study suggested that applying the OCT-ULL training method in the obstacle crossing training of stroke patients is more effective for improving balance and gait functions than OCT-ALL.

• 기술사
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• 제22권3호
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• pp.5-13
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• 1989

A major problem of stopwatch time study is how to do for the accurate and consistent performance rating, which is one of the critical variables to determine the accuracy of work measurement and should be still dependent upon time observer's judgement. Therefore the time observer's ability for the performance rating is very important, and must be improved by correct training method and procedure. This paper developed a new benchmark and benchmark-observation method for the effective performance rating training of assembling and machining operations. The trainees' ability in the accuracy and consistency of the performance rating ,improved significantly after being trained by subject method. The percentage improvement in rating accuracy and consistency values was 34.7% and 49% respectively. In addition, benchmark-practice method for the performance rating training is not significant, so it is proofed that the skill of a certain operation is not important for the improvement of the rating ability.

• 말소리와 음성과학
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• 제6권2호
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• pp.29-34
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• 2014

In the Vector Taylor Series (VTS)-based noisy speech recognition methods, Hidden Markov Models (HMM) are usually trained with clean speech. However, better performance is expected by training the HMM with noisy speech. In a previous study, we could find that Minimum Mean Square Error (MMSE) estimation of the training noisy speech in the log-spectrum domain produce improved recognition results, but since the proposed algorithm was done in the log-spectrum domain, it could not be used for the HMM adaptation. In this paper, we modify the previous algorithm to derive a novel mathematical relation between test and training noisy speech in the cepstrum domain and the mean and covariance of the Multi-condition TRaining (MTR) trained noisy speech HMM are adapted. In the noisy speech recognition experiments on the Aurora 2 database, the proposed method produced 10.6% of relative improvement in Word Error Rates (WERs) over the MTR method while the previous MMSE estimation of the training noisy speech produced 4.3% of relative improvement, which shows the superiority of the proposed method.

• 한국환경교육학회지:환경교육
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• 제14권2호
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• pp.68-75
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• 2001

The purpose of study is to provide fundamental data for the improvement of the teacher inservice training for environmental education through analysis of current inservice training programmes. The subject of analysis are documents on training programmes which was conducted after 2000 by 10 training organizations. Based on the results of this study, inservice training programmes is classified with 6 organizations which consist of education training institute, education & scientific research institute, national · public organizations, colleges of an attached organizations, civil organizations, teacher research society. The strategies for improvement of proposed in this study can be summarized as follows: First,'60 hours training programmes for general competencies improvement of environmental teacher' have to reconsider about scarcity areas to analysis of programmes. Second, this training programmes need to establish in training programmes of nothing region for increase in training opportunity of teachers. Third,'the core training programmes'is continued to be complementing about this programmes and need to establish about training programmes of teaching method of environmental education, environmentally value and attitude, etc

• 전자공학회논문지B
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• 제31B권7호
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• pp.170-178
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• 1994

The nearest neighbor classification rule is widely used because it is not only simple but the error rate is asymptotically less than twice Bayes theoretical minimum error. But the method basically use the whole training patterns as the reference vectors. so that both storage and classification time increase as the number of training patterns increases. LVQ(Learning Vector Quantization) resolved this problem by training the reference vectors instead of just storing the whole training patterns. But it is a heuristic algorithm which has no theoretic background there is no terminating condition and it requires a lot of iterations to get to meaningful result. This paper is to propose a new training method of the reference vectors. which minimize the given error function. The nearest neighbor network,the network version of the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule and the reference vectors are represented by the weights between the nodes. The network is trained to minimize the error function with respect to the weights by the steepest descent method. The learning algorithm is derived and it is shown that the proposed method can adjust more reference vectors than LVQ in each iteration. Experiment showed that the proposed method requires less iterations and the error rate is smaller than that of LVQ2.

• 전자공학회논문지
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• 제50권10호
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• pp.142-149
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• 2013

일반적으로 영상의 색은 RGB 카메라 시스템의 red, green, blue 채널들을 사용하여 재현된다. 하지만 세 채널들의 정보만으로 실제 장면의 분광 반사율을 추정하는데 한계가 있다. 이 때문에 RGB 카메라 시스템은 색을 정확하게 재현하지 못한다. 이 한계를 극복하고 정확한 색을 재현하기 위해 다채널 카메라 시스템을 사용하여 분광 반사율을 추정하는 연구들이 활발히 진행되고 있다. 최근 분광 유사도를 사용하여 카메라 응답에 따라 기존 모집단에서 유사 모집단을 적응적으로 구성하는 분광 반사율 추정법이 소개되었다. 하지만 이 방법에는 평균 거리와 최대 거리 기반의 분광 유사도가 적용되었기 때문에 유사 모집단의 정확도가 저하된다. 본 논문에서는 유사 모집단의 정확도를 향상시키기 위해 상관 계수 기반의 분광 유사도가 적용된 분광 반사율 추정법을 제안하였다. 먼저 기존 모집단과 위너(Wiener) 추정법을 통해 획득된 분광 반사율 간의 상관 계수를 계산한다. 다음으로 상관 계수에 따라 기존 모집단에서 유사 모집단을 구성한다. 마지막으로 유사 모집단이 적용된 위너 추정법을 수행하여 분광 반사율을 추정한다. 제안된 방법과 이전의 방법들의 성능을 평가하기 위해 실험 결과를 비교하였다. 그 결과, 제안한 방법이 제일 우수한 성능을 나타내었다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권2호
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• pp.45-50
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• 2020

A Language model with neural networks commonly trained with likelihood loss. Such that the model can learn the sequence of human text. State-of-the-art results achieved in various language generation tasks, e.g., text summarization, dialogue response generation, and text generation, by utilizing the language model's next token output probabilities. Monotonous and boring outputs are a well-known problem of this model, yet only a few solutions proposed to address this problem. Several decoding techniques proposed to suppress repetitive tokens. Unlikelihood training approached this problem by penalizing candidate tokens probabilities if the tokens already seen in previous steps. While the method successfully showed a less repetitive generated token, the method has a large memory consumption because of the training need a big vocabulary size. We effectively reduced memory footprint by encoding words as sequences of subword units. Finally, we report competitive results with token level unlikelihood training in several automatic evaluations compared to the previous work.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 추계 학술대회 발표논문집
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• pp.159-162
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• 2005

Identification is the process automatically identify who is speaking on the basis of information obtained from speech waves. In training phase, each speaker models are trained using each speaker's speech data. GMMs (Gaussian Mixture Models), which have been successfully applied to speaker modeling in text-independent speaker identification, are not efficient in insufficient training data environment. This paper proposes speaker modeling method using MLLR (Maximum Likelihood Linear Regression) method which is used for speaker adaptation in speech recognition. We make SD-like model using MLLR adaptation method instead of speaker dependent model (SD). Proposed system outperforms the GMMs in small training data environment.

• 대한통합의학회지
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• 제1권2호
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• pp.13-22
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• 2013

Purpose : The purpose of the research is that get a cut above clinical practice effect through satisfaction of clinical training, practical training, content, oversight of training and evaluation system. Clinical training consists of part of university in Gwang Ju and Jeon nam. Method : The target of training student was studying at physiotherapy a tree or four-year-course collage in Gwang ju and Jean nam. Data collection period is from 21 November 2012 to 1 February. We explained how to do a means of collecting data and get students consent fill in questionnaire. Data collection prossed by using spss 10.1 program also independent proofs, descriptive statistics, crosstabulation, regression analysis and frequency analysis. Results : The subjects average age is 24 in general characteristic. A school system of subjects was a tree-year-course students. They were 58people(39.1%). A school system of subjects was a four-year-course students. They were 90people(60.9%).The male was 72(48.6%) and the female was 76(51.4%). We researched to know about satisfaction of clinical training, practical training, content, environment of practical establishment, trainee manage and evaluation method. All-round satisfaction of clinical training average was 1.90 Satisfaction of clinical training period and content average was 1.83Satisfaction of environment of practical establishment average was 1.88 Satisfaction of clinical training establishments' trainee manage and evaluation average was 1.94 Conclusion : It is important that student can get specific their future and can do at clinical throught clinical training after their graduation improving satisfaction of clinical training would give to impact a physical therapist reserve.

• 한국콘텐츠학회논문지
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• 제12권4호
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• pp.263-272
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• 2012

본 연구는 공무원 교육훈련정책의 상대적 중요도와 우선순위 분석을 통해 공무원교육훈련정책의 방향성을 모색해보고자 전문가들을 대상으로 AHP 방법론을 적용한 실증적 분석을 실시하였다. 연구결과를 요약해 보면 다음과 같다. 첫째, 측정영역별 평가요소에 대한 상대적 우선순위를 보면, 교육훈련운영시스템, 교육훈련프로그램, 교육인프라, 교육훈련평가관리 중에서 교육훈련운영시스템이 가장 중요한 평가요소로 분석되었다. 둘째, 평가항목의 관점에서 보면, 교육훈련프로그램에서는 Acting Learning 교육프로그램, 교육훈련운영시스템에서는 교육훈련기관 예산확충, 교육훈련평가관리에서는 교육훈련과 인사제도 연계, 교육인프라에서는 교수요원 확보 등이 상대적으로 가장 중요한 우선순위로 평가되었다. 이러한 분석결과는 공무원 교육훈련정책을 경험적으로 설명하는데 기여할 것으로 판단된다.