• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.271-272
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• 2018

제4차 산업혁명시대에서 강조하는 SW교육은 컴퓨팅 관련 교과 뿐만 아니라 타 교과에서도 그 중요성이 대두되고 있으며, 따라서 정보과 교사가 아닌 비 정보과 교사들에게도 SW 교사 연수의 필요성이 높아지고 있다. 따라서 본 연구에서는 선행 연구들의 고찰을 통하여 비 정보과 교사를 위한 햄스터로봇을 활용한 SW 교사 연수 프로그램을 개발하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.385-386
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• 2022

2015 개정 교육과정에서는 미래 사회의 인재를 육성하기 위해 중학교에 정보 교과를 필수화하였다. 이를 지도할 정보·컴퓨터 교사를 확보하기 위해 비 정보과 교사를 대상으로 부전공연수를 실시하여 정보 교육을 할 수 있는 자격을 부여하고 있다. 이들은 일반 학습자와 다르게 교육학적 지식과 역량은 높으나, 내용 지식은 컴퓨터과학의 초보자 수준이다. 이러한 학습자의 특성을 고려하여 마이크로비트를 활용한 연수를 12차시 동안 진행하였고, 이는 비 정보과 교사의 프로그래밍 자아효능감에 긍정적인 영향을 준다는 것을 확인할 수 있었다. 그러나 본 연구는 단일집단에게 실험을 실시하였으므로 그 효과를 일반화하는 데 한계가 있다. 따라서 후속 연구에서는 비교 집단을 설정하는 실험 설계로 교육 효과를 검증할 필요가 있다.

• Journal of Korea Game Society
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• v.8 no.4
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• pp.65-73
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• 2008

In this paper, we designed and implemented edutainment platform that can be effectively applied to developmental disabilities for their education and treatment of sensibility and intelligence training. We developed embedded hardware and contents authoring tool to make multimedia contents operated on the hardware, a management tool to provide result of training, and a real-time monitoring tool for observing the state of study. The hardware is designed by considering the characteristics of developmental disabilities and provides visual, auditory and tactile sense to assist sensibility training for their attention. User-friendly and easy-to-use authoring tool enable teachers and non-specialist to make educational contents. Also the real-time monitoring tool make us to observe user's status even in the outside of classroom. The management tool stores result of training and make us to review the result for further steps. Using this edutainment platform, efficient repetitive training is possible without restriction of time and location. Also when it applied to practical education, we can recognize that our system is effective on improving the ability of attention and studying.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.69-75
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• 2022

This paper proposes a method of identifying the moment when a student with developmental disabilities needs assistance intervention in performing barista vocational training using virtual reality-based realistic contents. To this end, 21 students enrolled in a vocational training center for persons with disabilities were selected as study subjects. These students were trained to recognize the barista tools in a virtual reality environment. During the training, if students experienced difficulties and were unable to proceed further, they were asked to raise their hands or verbally request assistance. Using the collected data, two hypotheses were established based on the distance between the hand of the student and each barista tool in the virtual reality space in order to derive a criterion for judging the moment when an intervention is required. As a result of verifying the hypotheses, this study found that the cumulative distance from the hand of a student, who successfully finished the training without requiring an intervention, to the target barista tool as well as adjacent tools was significantly shorter than the cumulative distance to other barista tools.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.19-27
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• 2023

In the process of collecting social reviews, a number of noise reviews irrelevant to a given search keyword can be included in the search results. To filter out such reviews, machine learning can be used. However, if the number of reviews is insufficient for a target place to be analyzed, filtering accuracy can be degraded due to the lack of training data. To resolve this issue, we propose a supervised learning method to improve accuracy of the noise review filtering for the places with insufficient reviews. In the proposed method, training is not performed by an individual place, but by a group including several places with similar characteristics. The classifier obtained through the training can be used for the noise review filtering of an arbitrary place belonging to the group, so the problem of insufficient training data can be resolved. To verify the proposed method, a noise review filtering model was implemented using LSTM and BERT, and filtering accuracy was checked through experiments using real data collected online. The experimental results show that the accuracy of the proposed method was 92.4% on the average, and it provided 87.5% accuracy when targeting places with less than 100 reviews.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.44-54
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• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.11-20
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• 2024

Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.

• Korean Journal of Computational Design and Engineering
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• v.20 no.4
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• pp.420-430
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• 2015

Operator Training Simulators (OTSs) provide macroscopic training environment for plant operation. They are equipped with simulation systems for the emulation of remote monitoring and controlling operations. OTSs typically provide 2D block diagram-based graphic user interface (GUI) and connect to process simulation tools. However, process modeling for OTSs is a difficult task. Furthermore, conventional OTSs do not provide real plant field information since they are based on 2D human machine interface (HMI). In order to overcome the limitation of OTSs, we propose a new type of plant training system. This system has the capability required for collaborative training between operators and field workers. In addition, the system provides 3D virtual training environment such that field workers feel like they are in real plant site. For this, we designed system architecture and developed essential functions for the system. For the verification of the proposed system design, we implemented a prototype training system and performed experiments of collaborative training between one operator and two field workers with the prototype system.

• 한국공간정보시스템학회:학술대회논문집
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• 2002.03a
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• pp.45-52
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• 2002

Artificial neural networks (ANN) have been successfully used for classifying remotely sensed imagery. However, ANN still is not the preferable choice for classification over the conventional classification methodology such as the maximum likelihood classifier commonly used in the industry production environment. This can be attributed to the ANN characteristic built-in stochastic process that creates difficulties in dealing with unequally represented training classes, and its training performance speed. In this paper we examined some practical aspects of training classes when using a back propagation neural network model for remotely sensed imagery. During the classification process of remotely sensed imagery, representative training patterns for each class are collected by polygons or by using a region-growing methodology over the imagery. The number of collected training patterns for each class may vary from several pixels to thousands. This unequally populated training data may cause the significant problems some neural network empirical models such as back-propagation have experienced. We investigate the effects of training over- or under- represented training patterns in classes and propose the pattern repopulation algorithm, and an adaptive alpha adjustment (AAA) algorithm to handle unequally represented classes. We also show the performance improvement when input patterns are presented in random fashion during the back-propagation training.

• Korean Management Science Review
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• v.14 no.2
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• pp.161-184
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• 1997

This research examines the importance of IT training/education, present situation and possible suggestion for the successful training/education. The research method adopts a comparative analytical approach based on questionnaire survey responses from three work groups - managers, employees, and union representatives - drawn from five sample Korean banks. The evidence indicates that all three groups agree that IT improves banking efficiency and reduces job repetitiveness, but their job satisfaction level with IT-based work is surprisingly very low. The main reasons are mainly lack of training/education and poor user manuals. Also the research shows that most respondents would like to get further training/education to more adequately fit them for their jobs. Those from banks which invested in continuing training/education revealed more positive work attitudes and higher job satisfaction.