• Journal of Internet Computing and Services
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• v.9 no.2
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• pp.119-129
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• 2008

The business of fortifying teachers' abilities to utilize information is in progress in various ways such as ICT(Information & Communication Technology) training for teachers, the intensification of ICT application, and training teachers who are responsible for information education of school. And teachers' ICT training is being expanded progressively. Therefore, the purposes of this study are to find out the general situation of ICT training for teachers in preparation for the rapidly changing knowledge-information society, and to investigate teachers' recognition about how the training influences on teaching & learning and on the works they are charge in. Also, we analyze the utilization rate of ICT ruining curriculum in relation to activities of teaching and learning and the works teachers' are charge in. On the basis of these, we propose improvement methods for ICT training, thus improve the fundamental quality of education.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.145-153
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• 2024

Through the 106th session of the International Maritime Organization(IMO)'s Maritime Safety Committee(MSC), a mandatory safety training requirement for all personnel transferred or accommodated for offshore industrial activities was established and adopted under the name of SOLAS Chapter XV, IP(Industrial Personnel) Code. This regulation mandates pre-boarding safety training to enable individuals to anticipate and mitigate hazardous risks in navigation and operational environments. Consequently, the IP Code includes provisions regarding the training content for industrial personnel and regulations for the refusal of master who has a full responsibility for individuals who have not completed the required training(non-qualified industrial personnel). Referred to as the IP Code, this agreement is set to enter into force in July 2024, necessitating the establishment and operation of safety education for industrial personnel boarding ships before that date. Accordingly, this paper reviews the legal requirements related to training within IP code and analyzes the details of models including training objectives, target audience, duration, and course structure of safety trainings such as STCW, OPITO, GWO training, and other delegated training related to current ships. Additionally, it aims to propose a curriculum model for IP training courses which consists of a total of 16 hours over 2 days, offered by the Korea Institute of Maritime and Fisheries Technology, including teaching objectives, duration, and course structure.

• Journal of Creative Information Culture
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• v.7 no.3
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• pp.145-155
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• 2021

In this study, a SW interactive education training program for elementary school teachers was developed in order to cultivate the SW competency of teachers who apply SW education to schools, and the effect was analyzed by applying it to the school training site. For the development of the training program, the training development direction was set based on the current SW teacher training program, and an interactive training program was opened so that training could be conducted in a non-face-to-face situation in the COVID-19 situation. The developed training program was applied to 104 elementary school teachers in Gyeonggi-do. In order to analyze the effectiveness of the interactive training program, a survey on professor efficacy and satisfaction was conducted, and positive results were confirmed in terms of professor efficacy and program satisfaction. As it is expected that various SW/AI education training for teachers will be conducted as interactive training in the future, it is judged that it is necessary to conduct an analysis study on the effect of SW/AI training training.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2037-2040
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• 2004

In this paper we introduce a new prototype based classifiers for overlapping data, where training pattern can be overlap on the feature space. The proposed classifier is based on the prototype from neural network classifier (NNC)[1] for overlap data. The method automatically chooses the initial center and two radiuses for each class. The center is used as a mean representative of training data for each class. The unclassified pattern is classified by measure distance from the class center. If the distance is in the lower (shorter radius) the unknown pattern has the high percentage of being in this class. If the distance is between the lower and upper (further radius), the pattern has the probability of being in this class or others. But if the distance is outside the upper, the pattern is not in this class. We borrow the words upper and lower from the rough set to represent the region of certainty [3]. The training algorithm to find number of cluster and their parameters (center, lower, upper) is presented. The clustering result is tested using patterns from Thai handwritten letter and the clustering result is very similar to human eyes clustering.

• The Journal of Korea Robotics Society
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• v.15 no.2
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• pp.184-189
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• 2020

This paper is a study on data augmentation for small dataset by using deep learning. In case of training a deep learning model for recognition and classification of non-mainstream objects, there is a limit to obtaining a large amount of training data. Therefore, this paper proposes a data augmentation method using perspective transform and image synthesis. In addition, it is necessary to save the object area for all training data to detect the object area. Thus, we devised a way to augment the data and save object regions at the same time. To verify the performance of the augmented data using the proposed method, an experiment was conducted to compare classification accuracy with the augmented data by the traditional method, and transfer learning was used in model learning. As experimental results, the model trained using the proposed method showed higher accuracy than the model trained using the traditional method.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1349-1360
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• 2020

In recent years, Convolutional Neural Networks (CNNs) have been successfully implemented in different tasks of computer vision. Since CNN models are the representatives of supervised learning algorithms, they demand large amount of data in order to train the classifiers. Thus, obtaining data with correct labels is imperative to attain the state-of-the-art performance of the CNN models. However, labelling datasets is quite tedious and expensive process, therefore real-life datasets often exhibit incorrect labels. Although the issue of poorly labelled datasets has been studied before, we have noticed that the methods are very complex and hard to reproduce. Therefore, in this research work, we propose Deep CleanNet - a considerably simple system that achieves competitive results when compared to the existing methods. We use K-means clustering algorithm for selecting data with correct labels and train the new dataset using a deep CNN model. The technique achieves competitive results in both training and validation stages. We conducted experiments using MNIST database of handwritten digits with 50% corrupted labels and achieved up to 10 and 20% increase in training and validation sets accuracy scores, respectively.

• ETRI Journal
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• v.39 no.5
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• pp.643-651
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• 2017

Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph-II dataset and have proven that the proposed method can be used effectively using the Adience dataset.

• ETRI Journal
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• v.36 no.3
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• pp.429-438
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• 2014

Semantic role labeling (SRL) is a task in natural-language processing with the aim of detecting predicates in the text, choosing their correct senses, identifying their associated arguments, and predicting the semantic roles of the arguments. Developing a high-performance SRL system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Constructing SRL training data for a new domain is very expensive. Therefore, domain adaptation in SRL can be regarded as an important problem. In this paper, we show that domain adaptation for SRL systems can achieve state-of-the-art performance when based on structural learning and exploiting a prior model approach. We provide experimental results with three different target domains showing that our method is effective even if training data of small size is available for the target domains. According to experimentations, our proposed method outperforms those of other research works by about 2% to 5% in F-score.

• Journal of the Korea Computer Industry Society
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• v.2 no.4
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• pp.537-546
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• 2001

Griffth's and LCCMA which are Single-user detection adaptive algorithm are proposed for mitigate MAI(multiple access interference) and the near-far problem in direct-sequence spread-spectrum CDMA system and MOE Algorithm is proposed for MMSE(Minimum Mean-Square Error). This paper pertains to three types of Blind adaptive algorithms which can upgrade system functionality without the requirements from training sequence. It goes further to compare and analyze the functionalities of the algorithms as per number of interfering users or data update rate of the users. The simulation results was that LCCMA algorithm was superior to other algorithms in both areas. Blind application enabled a more flexible network design by eliminating the necessity of training sequence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1411-1418
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• 2010

Training for using e-learning has offered various options of contents and methods to learners. Both contents with high quality and the effective operation of LMS are very important to increase effectiveness. This paper analyzed the status of LMS which was necessary to improve quality of training for learners using e-learning. Based on analysis, it collected the opinions of experts by using delphi methods. Then, it suggested the improvement of the LMS and necessary functions of LMS in Distance Education & Training Institutes.