• 음성과학
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• 제12권4호
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• pp.31-42
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• 2005

Recently, Gaussian Mixture Model (GMM), a special form of CHMM, has been applied to speaker identification and it has proved that performance of GMM is better than CHMM. Therefore, in this paper the speaker models based on GMM and a new GMM using the modified EM algorithm are introduced and evaluated for text-independent speaker identification. Various experiments were performed to evaluate identification performance of two algorithms. As a result of the experiments, the GMM speaker model attained 94.6% identification accuracy using 40 seconds of training data and 32 mixtures and 97.8% accuracy using 80 seconds of training data and 64 mixtures. On the other hand, the new GMM speaker model achieved 95.0% identification accuracy using 40 seconds of training data and 32 mixtures and 98.2% accuracy using 80 seconds of training data and 64 mixtures. It shows that the new GMM speaker identification performance is better than the GMM speaker identification performance.

• 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.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 추계 학술발표회논문집
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• pp.13-19
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• 2003

Discriminant method using seismic signal was studied for discrimination of surface explosion. By means of the seismic spectral characteristics, multi-variate discriminant analysis was performed. Four single discriminant techniques - Pg/Lg, Lg1/Lg2, Pg1/Pg2, and Rg/Lg - based on seismic source theory were applied to explosion and earthquake training data sets. The Pg/Lg discriminant technique was most effective among the four techniques. Nevertheless, it could not perfectly discriminate the samples of the training data sets. In this study, a compound linear discriminant analysis was defined by using common characteristics of the training data sets for the single discriminants. The compound linear discriminant analysis was used for the single discriminant as an independent variable. From this analysis, all the samples of the training data sets were correctly discriminated, and the probability of misclassification was lowered to 0.7％.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2019년도 제59차 동계학술대회논문집 27권1호
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• pp.123-124
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• 2019

This paper proposes an improved deep learning method based on small data sets for animal image classification. Firstly, we use a CNN to build a training model for small data sets, and use data augmentation to expand the data samples of the training set. Secondly, using the pre-trained network on large-scale datasets, such as VGG16, the bottleneck features in the small dataset are extracted and to be stored in two NumPy files as new training datasets and test datasets. Finally, training a fully connected network with the new datasets. In this paper, we use Kaggle famous Dogs vs Cats dataset as the experimental dataset, which is a two-category classification dataset.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 춘계학술발표대회
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• pp.424-427
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• 2022

본 연구는 도메인 특성이 강한 HR 평가문장을 BERT PLM 모델을통해 4 가지 class 로 구분하는 문제를 다룬다. 다양한 PLM 모델 적용과 training data 수에 따른 모델 성능 비교를 통해 특정 도메인에 언어모델을 적용하기 위해서 필요한 기준을 확인하였다. 또한 Unlabeled 된 HR 분야 corpus 를 활용하여 BERT 모델을 post-training 한 HR-BERT 가 PLM 분석모델 정확도 향상에 미치는 결과를 탐구한다. 위와 같은 연구를 통해 HR 이 가지고 있는 가장 큰 text data 에 대한 활용 기반을 마련하고, 특수한 도메인 분야에 PLM 을 적용하기 위한 가이드를 제시하고자 한다

• 융합신호처리학회논문지
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• 제1권1호
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• pp.1-6
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• 2000

본 논문은 HMM(Hidden Markov Model)을 이 용하여 인식을 수행할 경우의 오류를 최소화 할 수 있는 후처리 과정으로 신경망을 결합시켜 HMM 단독으로 사용하였을 때 보다 높은 인식률을 얻을 수 있는 HMM과 신경망의 하이브리드 시스템을 제안한다 HMM을 이용하여 학습한 후 학습에 참여하지 않은 데이터를 인식하였을 때 오인식 데이터를 정인식으로 인식하도록 HMM의 출력으로 얻은 각 출력확률을 후처리에 사용될 신경망의 학습용으로 사용하여 신경망을 학습하여 HMM과 신경망을 결합한 하이브리드 시스템을 만든다 이와 같은 HMM과 신경망을 결합한 하이브리드 모델을 사용하여 단독 숫자음에서 실험한 결과 HMM 단독으로 사용하였을 때 보다 MLP에서는 약 $4.5\%$ RBFN에서는 약 $2\%$의 인식률 향상이 있었다. 기존의 하이브리드 시스템이 갖는 많은 학습시간이 소요되는 문제점과 실시간 음성인식시스템을 구현할 패의 학습데이터의 부족으로 인한 인식률 저하를 해결할 수 있는 방법임을 확인할 수 있었다

• 한국간호교육학회지
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• 제27권3호
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• pp.227-239
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• 2021

Purpose: This study used an exploratory sequential approach (mixed methods) design to explore essential meaning through comparing and analyzing the experiences of nursing students in virtual simulation practice and high fidelity simulation practice education in parallel. Methods: The study participants were 20 nursing students, and data were collected through focus group meetings from July 17 to August 5, 2020, and via online quantitative data from November 10 to November 15, 2020. The qualitative data were analyzed using Giorgi's phenomenological method, and the quantitative data were analyzed using descriptive statistics, the Mann-Whitney U test, Kruskal-Wallis H test analysis of variance and Spearman's ρ correlation. Results: The comparison between the two simulation training experiences was shown in five contextual structures, as follows: (1) reflection of the clinical field, (2) thinking theorem vs. thinking expansion, (3) individual-centered learning vs. team-centered learning, (4) attitudes toward participating in practical training, (5) metacognition of personal competency as a prospective nurse, and (6) revisiting the method of practice training. There was a positive correlation between satisfaction with the practice and the clinical judgment ability of high fidelity simulation, which was statistically significant (r=.47, p=.036). Conclusion: Comparing the experiences between virtual simulation practice training and high fidelity simulation practice training, which has increased in demand due to the Coronavirus Disease-2019 pandemic, is meaningful as it provides practical data for introspection and reflection on in-campus clinical education.

• Asian Pacific Journal of Cancer Prevention
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• 제15권9호
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• pp.3885-3890
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• 2014

Background: This study aimed to determine nursing student knowledge, behavior and beliefs for breast cancer and breast self-examination receiving courses with a traditional lecturing method (TLM) and the Six Thinking Hats method (STHM). Materials and Methods: The population of the study included a total of 69 second year nursing students, 34 of whom received courses with traditional lecturing and 35 of whom received training with the STHM, an active learning approach. The data of the study were collected pre-training and 15 days and 3 months post-training. The data collection tools were a questionnaire form questioning socio-demographic features, and breast cancer and breast self-examination (BSE) knowledge and the Champion's Health Belief Model Scale. The tests used in data analysis were chi-square, independent samples t-test and paired t-test. Results: The mean knowledge score following traditional lecturing method increased from $9.32{\pm}1.82$ to $14.41{\pm}1.94$ (P<0.001) and it increased from $9.20{\pm}2.33$ to $14.73{\pm}2.91$ after training with the Six Thinking Hats Method (P<0.001). It was determined that there was a significant increase in pre and post-training perceptions of perceived confidence in both groups. There was a statistically significant difference between pre-training, and 15 days and 3 months post-training frequency of BSE in the students trained according to STHM (p<0.05). On the other hand, there was a statistically significant difference between pre-training and 3 months post-training frequency of BSE in the students trained according to TLM. Conclusions: In both training groups, the knowledge of breast cancer and BSE, and the perception of confidence increased similarly. In order to raise nursing student awareness in breast cancer, either of the traditional lecturing method or the Six Thinking Hats Method can be chosen according to the suitability of the teaching material and resources.

• 한국방재학회 논문집
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• 제2권4호
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• pp.123-129
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• 2002

본 연구에서는 3층 신경망 모형에 의해 충주호의 유입량을 예측한 결과들을 이용하여 신경망 모형의 저수지 유입량 예측 특성을 분석하였다. 신경망 모형의 적절한 입력층 및 은닉층 뉴런 개수, 학습회수를 제시하였으며, 학습 첨두유량 크기가 예측된 첨두유량보다 작을 경우 예측 값이 과소평가되는 특징을 확인하였다. 또한 뉴런 개수, 학습회수가 과다할 경우 발생 가능한 과적합 현상을 확인하였으며, 정확한 예측을 위해 필요한 최소 학습자료 기간도 제시하였다. 결과적으로 충주호의 경우 $8{\sim}10$개의 뉴런 개수 및 $1500{\sim}3000$회의 학습회수를 이용한 신경망 모형이 적합한 것으로, 학습자료 기간 수는 최소한 600개 이상의 자료를 적용하여야 정확한 예측이 가능한 것으로 결과되었다.

• 한국융합학회논문지
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• 제8권2호
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• pp.27-33
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• 2017

인터넷을 통해 사용자에게 클라우드 컴퓨팅 서비스를 효율적으로 제공하기 위해서는 데이터 센터에 가상화와 분산 컴퓨팅 기술을 기반으로 하여 IT 자원을 구성해야 한다. 본 논문은 폭넓은 분야에서 새로운 훈련 데이터가 언제라도 추가될 수 있고, 또한 언제라도 훈련 데이터에 새로운 속성이 추가될 수 있다는 문제에 특별히 초점을 맞춘다. 이러한 경우, 기존 속성 집합들을 가지는 훈련 데이터로 생성된 규칙은 쓸모없게 된다. 더구나 새롭게 추가된 데이터나 속성을 가지는 새로운 데이터는 기존 규칙과 결합될 수 없다. 본 논문은 이와 같은 경우를 자연스럽게 처리할 수 있는 보다 진보된 새 추론 엔진을 제안한다. 이 방법에서 기존의 데이터로 부터 생성된 규칙은 개선된 규칙을 생성하기 위한 새로운 데이터 집합과 결합될 수 있다.