• 한국인터넷방송통신학회논문지
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• 제15권1호
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• pp.171-178
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• 2015

IT 관련 교과에서 실습 교육이 그 어느 때보다 강조되고 있다. 이 글은 IT 교육을 위한 클라우드 컴퓨팅 기반의 가상 데스크탑 서비스 제공 방안과 효율적인 운영 관리 방안을 연구하였다. 구현된 가상 실습 환경 시스템을 이용하면 교과의 커리큘럼에 적합하도록 커스토마이징된 실습환경을 제공할 수 있다. 뿐만 아니라, 제안된 시스템을 이용할 경우 교과운영 사전에 미리 교과별로 프로비저닝 할 수 있다. 따라서 본 논문에서 산정한 규모와 운영 정책을 참고하여 여러 사이버대학이 공동 활용할 수 있는 클라우드 실습 포털시스템을 구축한다면 보다 효율적이고 효과적인 가상실습 교육 서비스 시스템을 구축하고 제공할 수 있을 것으로 예상된다.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.52-59
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• 2014

Operator training simulator, within a training environment designed to understand the principles and behavior of the railway system with respect to operator's entries and predefined scenario, can provide a very strong benefit in facilitating operators' handling undesired operations. This simulator consists of computer system and applications, and the purpose of applications is to generate the power and voltage and analyze the AC substation and DC railway, respectively. This paper describes a novel approach to the new techniques for AC/DC hybrid simulation for the operator training simulator in the railway system. We first propose the structure the database of railway system. Then, topology processing and power flow using a linked-list method based on the proposed database, full or decoupled newton-rapshon methods are presented. Finally, the interface between the analysis for AC substation using a newton-rapshon method and the analysis for DC railway system using a time-interval power flow method is described. We have verified and tested the developed algorithm through the extensive testing for the proposed test system. To demonstrate the validity of the developed algorithm, comparative simulations between the proposed algorithm and PSS/E for the test system were conducted.

• 한국인지과학회:학술대회논문집
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• 한국인지과학회 2010년도 춘계학술대회
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• pp.60-65
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• 2010

Language learning involves linguistic environments around the learner. So the variation in training input to which the learner is exposed has been linked to their language learning. We explore how linguistic experiences can cause differences in learning linguistic structural features, as investigate in a probabilistic graphical model. We manipulate the amounts of training input, composed of natural linguistic data from animation videos for children, from holistic (one-word expression) to compositional (two- to six-word one) gradually. The recognition and generation of sentences are a "probabilistic" constraint satisfaction process which is based on massively parallel DNA chemistry. Random sentence generation tasks succeed when networks begin with limited sentential lengths and vocabulary sizes and gradually expand with larger ones, like children's cognitive development in learning. This model supports the suggestion that variations in early linguistic environments with developmental steps may be useful for facilitating language acquisition.

• 전자공학회논문지S
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• 제35S권5호
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• pp.59-67
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• 1998

This paper propose a new vector-quantizer design method for coefficients of wavelet transformed images. In conventional wavelet transform, it is quite often to employ wavelet transformed coefficients, not containing images to be encoded, as training sequences for designing a vector-quantizer. This method has a serious drawback ; it is not known how to find a proper set of training images. This paper investigates characteristics of images that should be considered in the design of vector-quantizers for wavelet transformed images. Besides the statistical parameters such as correlation and standard deviation, edge components are shown to characterise wavelet transform images. Training sequences established in accordance with the above knowledge are used in the design of quantizers having guaranteed range of applicable images. Results of computer simulations are shown to demonstrate the effectiveness of the proposed method.

• 로봇학회논문지
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• 제14권2호
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• pp.114-121
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• 2019

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in 'tfrecords' format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.

• 정보교육학회논문지
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• 제14권1호
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• pp.53-60
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• 2010

2005년 12월에 정보통신기술교육 운영지침의 개정안이 발표되었으나 아직도 학교 현장에서는 이와 같은 개정안의 내용에 따라 제대로 교육이 실시되고 있지 않다. 그것은 개정안의 내용을 담고 있는 교과서의 내용이 충실하지 못한 것도 하나의 원인이라고 할 수 있다. 본 논문에서는 2006년 6월 이후에 출판된 초등학교 컴퓨터 교과서를 분석하였다. 분석한 결과 많은 교과서가 정보통신기술교육 운영지침 개정안의 내용을 제대로 반영하지 않은 것으로 나타났다. 특히, "정보 처리의 이해"와 "종합 활동" 영역의 내용이 상대적으로 부실한 것으로 나타났다. 따라서 초등 컴퓨터 교육의 활성화를 위해서는 교과서 검정의 충실화, 컴퓨터 교육 전문가의 참여, 상세한 교육과정의 개발, 교사 연수의 실시 등이 꼭 필요하다.

• Journal of information and communication convergence engineering
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• 제18권3호
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• pp.176-182
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• 2020

In this paper, we propose a web-based open artificial intelligence (AI) platform which provides high convenience in input data pre-processing, artificial neural network training, and the configuration of subsequent operations according to inference results. The proposed platform has the advantages of the GUI-based environment which can be easily utilized by a user without complex installation. It consists of a web server implemented with the JavaScript Node.js library and a client running the tensorflow.js library. Using the platform, many users can simultaneously create, modify and run their projects to apply AI functionality into various smart services through an open web interface. With our implementation, we show the operability of the proposed platform. By loading a web page from the server, the client can perform GUI-based operations and display the results performed by three modules: the Input Module, the Learning Module and the Output Module. We also implement two application systems using our platform, called smart cashier and smart door, which demonstrate the platform's practicality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.1118-1133
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• 2017

Computer vision-based human activity recognition (HAR) has become very famous these days due to its applications in various fields such as smart home healthcare for elderly people. A video-based activity recognition system basically has many goals such as to react based on people's behavior that allows the systems to proactively assist them with their tasks. A novel approach is proposed in this work for depth video based human activity recognition using joint-based motion features of depth body shapes and Deep Belief Network (DBN). From depth video, different body parts of human activities are segmented first by means of a trained random forest. The motion features representing the magnitude and direction of each joint in next frame are extracted. Finally, the features are applied for training a DBN to be used for recognition later. The proposed HAR approach showed superior performance over conventional approaches on private and public datasets, indicating a prominent approach for practical applications in smartly controlled environments.

• 통합자연과학논문집
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• 제4권3호
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• pp.238-242
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.

• Computers and Concrete
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• 제21권1호
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• pp.21-30
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• 2018

In this study, we analyze the behavior of concrete which contains zeolite and diatomite. In order to achieve the goal, we utilize expert system methods. The utilized methods are artificial neural network and adaptive network-based fuzzy inference systems. In this respect, we exploit seven different mixes of concrete. The concrete mixes contain zeolite, diatomite, mixture of zeolite and diatomite. All seven concrete mixes are exposed to 28, 56 and 90 days' compressive strength experiments with 63 specimens. The results of the compressive strength experiments are used as input data during the training and testing of expert system methods. In terms of artificial neural network and adaptive network-based fuzzy models, data format comprises seven input parameters, which are; the age of samples (days), amount of Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer. On the other hand, the output parameter is defined as the compressive strength of concrete. In the models, training and testing results have concluded that both expert system model yield thrilling medium to predict the compressive strength of concrete containing zeolite and diatomite.