• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2521-2525
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• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• Journal of information and communication convergence engineering
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• 제4권2호
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• pp.58-61
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• 2006

A key issue toward mobile multimedia communications is to create technologies for broadband signal transmission that can support high quality services. Such a broadband mobile communications system should be able to overcome severe distortion caused by timevarying multi-path fading channel, while providing high spectral efficiency and low power consumption. For these reasons, an adaptive suboptimum decision feedback equalizer (DFE) for the single-carrier shortburst transmissions system is considered as one of the feasible solutions. For the performance improvement of the system with the short-burst format including the short training sequence, in this paper, the multiple-training least mean square (MTLMS) based DFE scheme with soft decision feedback is proposed and its performance is investigated in mobile wireless channels throughout computer simulation.

• 한국컴퓨터정보학회논문지
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• 제17권5호
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• pp.147-155
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• 2012

미국 경찰 역사상 3세기 동안 경찰 교육도 상당히 개선되었음을 보여주고 있다. 경찰학교 탄생과 더불어 일반 국민이 경찰에 거는 기대도 커졌으며 경찰이 보여주는 능력도 성장했다. 1960년대 이후 현장 훈련 과정, 기관 내 훈련 등을 개발 운영해오고 있다. 최근 몇 년 동안 여러 지역의 경찰학교 훈련에서 대학식 접근법을 택하고 있는데 특히 COP식 경찰 업무 수행과 전문화를 이루도록 하는데 목표를 두고 있다. 우리나라는 관련법규의 미정비, 비체계적인 교육과정, 학교교육과 실습교육의 Switch System의 한계, 열악한 교육여건 및 환경 등이 문제점으로 대두되고 있어 개선을 위한 방안은 첫째, 교육훈련 법규를 통합, 정비 둘째, 교과과정을 체계적으로 개편 셋째, 교육훈련효과의 극대화를 위하여 Switch System을 정착 넷째, 교육여건 및 교육환경을 개선시켜야 한다.

• 융합신호처리학회논문지
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• 제23권2호
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• pp.70-75
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• 2022

각종 디지털 기기의 보급으로 현대사회는 기기들이 일상화되었다. 더욱이 COVID-19라는 팬데믹 동안 실내에 머무르면서 기기 사용량의 증가와 온라인 학습의 증가로 눈 피로도에 따른 어린이들의 근시 증가, 젊은 노안 증가, 안구 건조증과 같은 증상이 증가하고 있고, 이제는 눈 건강에 대한 관심이 예전과 다르다. 눈 건강에 대한 처방은 여러 가지가 있지만 본 논문에서는 VR 콘텐츠를 활용한 시기능 강화 훈련 방법을 제안하고자 한다. 시기능 강화 훈련에 관한 기존의 교구에 의한 아날로그 방법들을 디지털 콘텐츠로 기획 및 제작하였으며, 일반 시기능 훈련센터에서 교구를 가지고 진행하는 다양한 방법 중 콘텐츠화가 가능한 7가지의 방법을 선택하여 VR 기반의 훈련콘텐츠로 개발하였다. 각 콘텐츠의 훈련 과정에서 사용자에게 훈련의 참여에 대한 피드백을 주기 위해 VR 기기에 아이트래킹을 적용하여 훈련 과정에 대한 관리와 집중도를 분석할 수 있도록 제안하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권11호
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• pp.2784-2799
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• 2012

Biological data have been increased exponentially in recent years, and analyzing these data using data mining tools has become one of the major issues in the bioinformatics research community. This paper focuses on the protein construction process in higher organisms where the deoxyribonucleic acid, or DNA, sequence is filtered. In the process, "unmeaningful" DNA sub-sequences (called introns) are removed, and their meaningful counterparts (called exons) are retained. Accurate recognition of the boundaries between these two classes of sub-sequences, however, is known to be a difficult problem. Conventional approaches for recognizing these boundaries have sought for solely enhancing machine learning techniques, while inherent nature of the data themselves has been overlooked. In this paper we present an approach which makes use of the data attributes inherent to species in order to increase the accuracy of the boundary recognition. For experimentation, we have taken the data sets for four different species from the University of California Santa Cruz (UCSC) data repository, divided the data sets based on the species types, then trained a preprocessed version of the data sets on neural network(NN)-based and support vector machine(SVM)-based classifiers. As a result, we have observed that each species has its own specific features related to the splice sites, and that it implies there are related distances among species. To conclude, dividing the training data set based on species would increase the accuracy of predicting splicing junction and propose new insight to the biological research.

• 한국정보기술학회 영문논문지
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• 제8권2호
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• pp.119-127
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• 2018

Training a very large deep neural network can be painfully slow and prone to overfitting. Many researches have done for overcoming the problem. In this paper, a combination of early stopping and ADAM based deep neural network was presented. This form of deep network is useful for handling the big data because it automatically stop the training before overfitting occurs. Also generalization ability is better than pure deep neural network model.

• 창의정보문화연구
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• 제7권3호
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• pp.145-155
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• 2021

본 연구에서는 SW 교육을 학교에 적용하는 교사들의 SW 역량을 함양하기 위해 초등 교원 SW 쌍방향 교육 연수 프로그램을 개발하고, 학교 연수 현장에 적용하여 그 효과를 분석하였다. 연수 프로그램 개발을 위해 현재 진행되는 SW 교원연수 프로그램을 바탕으로 연수 개발 방향을 설정하고, 코로나-19 상황에서 비대면으로 연수를 진행할 수 있도록 쌍방향 연수 프로그램을 개설하였다. 개발된 연수 프로그램을 경기도 초등 교원 104명을 대상으로 적용하였다. 쌍방향 연수 프로그램의 효과성을 분석하기 위해 교수 효능감과 만족도 조사를 실시하였으며, 교수 효능감과 프로그램 만족도 부분에서 긍정적인 결과를 확인하였다. 앞으로 교원을 대상으로 하는 다양한 SW·AI 교육 연수들이 쌍방향 연수로 진행될 것으로 예상하는 만큼, SW·AI 교육 연수의 효과에 대한 분석 연구의 시행이 필요할 것으로 판단된다.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• 한국컴퓨터정보학회논문지
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• 제21권9호
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• pp.19-25
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• 2016

This paper is to recognize and play tab chords from guitar musical sheets. The musical chord area of an input image is segmented by changing the image in saturation and applying the Grabcut algorithm. Based on a template matching, our approach detects tab starting sections on a segmented musical area. The virtual block method is introduced to search blanks over chord lines and extract tab fret segments, which doesn't cause the computation loss to remove tab lines. In the experimental tests, the prototype based classification outperforms Bayesian method and the nearest neighbor rule with the whole set of training data and its performance is similar to that of the support vector machine. The experimental result shows that the prediction rate is about 99.0% and the number of selected prototypes is below 3.0%.

• ETRI Journal
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• 제32권5호
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• pp.810-818
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• 2010

This paper presents a new framework for integrating untranscribed spoken content into the acoustic training of an automatic speech recognition system. Untranscribed spoken content plays a very important role for under-resourced languages because the production of manually transcribed speech databases still represents a very expensive and time-consuming task. We proposed two new methods as part of the training framework. The first method focuses on combining initial acoustic models using a data-driven metric. The second method proposes an improved acoustic training procedure based on unsupervised transcriptions, in which word endings were modified by broad phonetic classes. The training framework was applied to baseline acoustic models using untranscribed spoken content from parliamentary debates. We include three types of acoustic models in the evaluation: baseline, reference content, and framework content models. The best overall result of 18.02% word error rate was achieved with the third type. This result demonstrates statistically significant improvement over the baseline and reference acoustic models.