• 한국컴퓨터정보학회논문지
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• 제27권4호
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• pp.153-160
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• 2022

본 논문에서는 홈트레이닝에 참여하여 체력을 단련하고 있는 경찰관련학과 학생들의 자기결정성과 홈트레이닝 만족도와 재이용과의 관계를 알아보고자 하였다. 이를 위해 부산, 경남지역 D, S, K대학교 경찰행정학과, 경찰무도학과 재학생중 홈트레이닝에 참여한 경험이 있는 학생 349명을 대상으로 조사하였다. SPSSWIN VER 25+, AMOS 20.0 프로그램을 이용하여 자기결정성, 운동만족, 재이용 요인간의 관계를 검증한 결과, 첫째, 홈트레이닝 참여자의 자기결정성 하위요인인 자율성, 유능감, 관계성은 홈트레이닝 만족도에 긍정적인 영향을 미쳤다. 둘째, 홈트레이닝 참여자의 운동만족은 홈트레이닝 재이용에 긍정적인 영향을 미쳤다.

• 아동학회지
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• 제16권1호
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• pp.79-88
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• 1995

The purpose of this study was to investigate whether the experience of computer programming in Logo geometry advances the development of young children's concepts and/or representation of Euclidian spatial relations, particularly their concepts of the vertical-horizontal. Papert's claim of the positive effects of Logo programming experiences on young children's cognitive development was tested using the Piagetian Water Level Test (PWLT), the Free Hand Drawing Test (FHDT), and the Computer Drawing Test (CDT). Forty-four subjects were drawn from 2nd graders attending a public elementary school in Ithaca. The subjects were divided into 2 groups: a treatment group (TG) with Logo training for 10 weeks and a control group (CG) without Logo training. Our results showed that TG did not make any significant improvement on PWLT. In contrast, TG outperformed CG on FHDT. We suggested several possible explanations for this contradiction.

• 음성과학
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• 제14권1호
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• pp.163-174
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• 2007

All observation has uncertainty due to noise or channel characteristics. This uncertainty should be counted in the modeling of observation. In this paper we propose a modified optimization object function of a GMM training considering inexact observation. The object function is modified by introducing the concept of observation confidence as a weighting factor of probabilities. The optimization of the proposed criterion is solved using a common EM algorithm. To verify the proposed method we apply it to the speaker recognition domain. The experimental results of text-independent speaker identification with VidTimit DB show that the error rate is reduced from 14.8% to 11.7% by the modified GMM training.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.53-60
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• 2003

생명과학 관련 문서에서의 이벤트 추출은 관련 연구자들의 연구에 많은 도움을 줄 수 있다. 기존의 연구에서는 주로 이벤트 동사에 대해 패턴을 정의한 후에 정의된 패턴에 의해서만 이벤트를 추출하고자하였다. 그러나 모든 패턴을 수동으로 정의하는 것은 너무 많은 비용이 들기 때문에 패턴을 자동 추출 또는 확장하는 방법이 필요하다. 또한 학습을 하기 위해서는 상당수의 학습 말뭉치가 있어야 하는데 그것 또한 충분하지 않은 실정이다. 본 논문에서는 초기 패턴에 의해 생성된 소량의 정답 이벤트로부터 학습한 후 공기정보와 패턴정보를 이용한 Co-training방법으로 패턴 확장 및 이벤트 추출을 시도하였다. 실험 결과, 이벤트 동사의 패턴 정보가 유용한 정보라는 것을 확인할 수 있었고, 후보 이벤트 내의 개체간 공기정보와 문법관계정보 또한 매우 중요한 정보라는 것을 새롭게 보일 수 있었다. GENIA 말뭉치에서 162개의 이벤트 동사에 대해 실험한 결과, 88.02%의 정확률, 79.25%의 재현율을 얻었다.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1910-1914
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• 2015

Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.

• The Journal of the Acoustical Society of Korea
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• 제16권1E호
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• pp.64-74
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• 1997

In many decision making systems, rule-based approaches are used to solve complex problems in the areas of pattern analysis and computer vision. In this paper, we present methods for generating fuzzy IF-THEN rules automatically from training data for pattern classification and high-level computer vision. The rules are generated by construction minimal approximate fuzzy aggregation networks and then training the networks using gradient descent methods. The training data that represent features are treated as linguistic variables that appear in the antecedent clauses of the rules. Methods to generate the corresponding linguistic labels(values) and their membership functions are presented. In addition, an inference procedure is employed to deduce conclusions from information presented to our rule-base. Two experimental results involving synthetic and real are given.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 한국컴퓨터산업교육학회 2003년도 제4회 종합학술대회 논문집
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• pp.119-126
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• 2003

Computer simulation has used to a area of military training from about several years ago. War game model(or computer simulation) endow a military man with field training such as combat experience without operating combat strength or capabilities. To samely construct simulation environment against actual combat environment is to well construct DB to operate war game model, associate among federates on network. Thus, we construct virtual combat environment enabling to efficiently manage network traffic among federates(or active nodes) on active network that construct virtual military training space such as urgent combat field needed to rapidly transfer combat information including image and video.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2019년도 하계학술대회
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• pp.102-105
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• 2019

In the classical single-image super-resolution (SISR) reconstruction method using convolutional neural networks, the extracted features are not fully utilized, and the training time is too long. Aiming at the above problems, we proposed an improved SISR method based on a residual network. Our proposed method uses a feature fusion technology based on improved residual blocks. The advantage of this method is the ability to fully and effectively utilize the features extracted from the shallow layers. In addition, we can see that the feature fusion can adaptively preserve the information from current and previous residual blocks and stabilize the training for deeper network. And we use the global residual learning to make network training easier. The experimental results show that the proposed method gets better performance than classic reconstruction methods.

• Asia Marketing Journal
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• 제19권3호
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• pp.75-98
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• 2017

Our study is trying to investigate the perceived training effectiveness by applying the theory of planned behavior (TPB) and Technological Acceptance Model (TAM) and intend to examine the effects of individual differences on perceived training effectiveness and performance of individuals. The main purpose is to evaluate the perceived training effectiveness, and role of individual differences in terms of learning. The results of this study supported all the hypothesis that participants with higher level of creative self-efficacy, intrinsic motivation, creativity and emotional intelligence (EI) will have greater inclinations to learn. Results showed that perceive training effectiveness is positively related to training transfer and training transfer increase the performance of individuals. Study results significantly agree with the theory of planned behavior (TPB) which was applied to measure the perceived training effectiveness and suggest trainee's perception of usefulness, ease and benefits enhance learning dimensions of participants that make any program effective. The study has highlighted a number of issues that influence the perceived training effectiveness.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.