• Journal of vocational education research
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• v.29 no.3
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• pp.41-60
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• 2010

The purpose of this study was to investigate the relationships between teachers' characteristics and schools' peculiarities and to analyze relevant variables which influence the organization and implementation of the curriculum in specialized industry high schools. Subject were 414 teachers in specialized industry high school. The main result of this study are as follows. First, according to teachers' sex distinction, position, teaching experience, career of industry-related companies, subtle stress difference were found in establishment of education programs, relevance of educational activity, propriety of equipment. Second, in case of schools' subject of establishment, dormitory, specialized types, number of class, type of the sex of recruitment, subtle difference were found in connectivity of graders and subjects, relevance of the organization of education activity, substantial curriculum, push ahead with specialized high school. Third, measures to support development of specialized curriculum arrange sub-items in order of frequency. The way we understand things on vocational education must be settled without delay.

• Journal of vocational education research
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• v.29 no.3
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• pp.61-83
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• 2010

The purpose of this study was to analyze high schools' general and special subject required to successfully complete same stream curriculum which is identical to their major from agricultural high school, and to offer basic data on strategies of organizing agricultural high schools' curriculum for improving universities' major basic competencies. Using purposeful sampling technique, the professors of 116 universities professors in 8 agricultural university were analyzed through the survey research. The result was as follows. first, it appeared that for successful completion of major subjects of the same stream university, the basic science subject such as biology and chemistry has high relation with major basic ability, however math and physics are related highly in agricultural machine and agricultural civil engineering department, economics and math are in agricultural produce distribution department. Second, the basic ability such as linguistic competence and foreign language ability are essential to complete major subject. Third, if we look into relation of agriculture and life science industry stream specialized subject with major basic competencies, we can find considerable similarity between major field of university and subject name of specialized high school. Fourth, the main opinion is that basic concept and principle, laws of nature are should be main contents which is able to be practical, however experiment and practice is in food processing department, and academic theory is in biotechnology department.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.268-277
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• 2019

Flight test systems should monitor various conditions in real time during flight tests and take safety measures in an emergency. The importance of ensuring test safety increases in more complicated and wider test environments. Also, due to the transition of wartime operational authority, many guided missile systems must be developed simultaneously. Early deployment and budget reduction by shortening the development and T&E periods are also necessary. Consequently, the risk of flight tests under the circumstance of inefficient test resources is increasing. To address this deficiency, a flight test system model using SysML was proposed in this study. The method of designing and verifying the test system is based on the agile shift left testing methodology of advanced T&E labs and utilizing a system reference model in the aerospace field. Through modeling and simulation analysis, early identification and correction of faults resulting from inconsistent test requirements can mitigate the risk of delays during the T&E phase of flight tests. Also, because the flight test system model was constructed using SysML, it can be applied to test various guided missile systems.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.115-122
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• 2019

For Korean phoneme recognition, Hidden Markov-Gaussian Mixture model(HMM-GMM) or hybrid models which combine artificial neural network with HMM have been mainly used. However, current approach has limitations in that such models require force-aligned corpus training data that is manually annotated by experts. Recently, researchers used neural network based phoneme recognition model which combines recurrent neural network(RNN)-based structure with connectionist temporal classification(CTC) algorithm to overcome the problem of obtaining manually annotated training data. Yet, in terms of implementation, these RNN-based models have another difficulty in that the amount of data gets larger as the structure gets more sophisticated. This problem of large data size is particularly problematic in the Korean language, which lacks refined corpora. In this study, we introduce CTC algorithm that does not require force-alignment to create a Korean phoneme recognition model. Specifically, the phoneme recognition model is based on convolutional neural network(CNN) which requires relatively small amount of data and can be trained faster when compared to RNN based models. We present the results from two different experiments and a resulting best performing phoneme recognition model which distinguishes 49 Korean phonemes. The best performing phoneme recognition model combines CNN with 3hop Bidirectional LSTM with the final Phoneme Error Rate(PER) at 3.26. The PER is a considerable improvement compared to existing Korean phoneme recognition models that report PER ranging from 10 to 12.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.31-40
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• 2021

Blockchain technology is also actively studied in the real estate transaction field, and real estate transactions have various ways. In this paper, we propose a model that simplifies the authentication procedure of auction systems using Ethereum's Ether to solve the problem of offline court auctions. The proposed model is written in Ethereum's Solidity language, the court registers the sale date and the sale date with the DApp browser, and the bidder accesses the address of the individual's wallet created through Metamask's private key. The bidder then selects the desired sale and enters the bid price amount to participate in the auction. The bidder's record of the highest bid price for the sale he wants is written on the Ethereum test network as a smart contract. and creates a block. Finally, smart contracts written on the network are distributed by the court auction manager to all nodes in the blockchain network, and each node in the blockchain network can be viewed and contract verified. As a result of analyzing the smart contracts of the proposed model and the performance of the system, there are fees incurred due to the creation and use of Ether on platforms using Ethereum, and participation. Ether's changes in value affect the price of the sale, resulting in inconsistent fees in smart contracts each time. However, in future work, we issue our own tokens to solve the market volatility problem and commission problem with the value change of Ether, and refine complex court auction systems.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.169-178
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• 2022

Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean, a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing (NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer, a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance in NLP. The experimental results show that our model achieves 98.24 F1-Score.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.315-324
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• 2022

In this paper, we propose KoEPT, a Transformer-based generative model for automatic math word problems solving. A math word problem written in human language which describes everyday situations in a mathematical form. Math word problem solving requires an artificial intelligence model to understand the implied logic within the problem. Therefore, it is being studied variously across the world to improve the language understanding ability of artificial intelligence. In the case of the Korean language, studies so far have mainly attempted to solve problems by classifying them into templates, but there is a limitation in that these techniques are difficult to apply to datasets with high classification difficulty. To solve this problem, this paper used the KoEPT model which uses 'expression' tokens and pointer networks. To measure the performance of this model, the classification difficulty scores of IL, CC, and ALG514, which are existing Korean mathematical sentence problem datasets, were measured, and then the performance of KoEPT was evaluated using 5-fold cross-validation. For the Korean datasets used for evaluation, KoEPT obtained the state-of-the-art(SOTA) performance with 99.1% in CC, which is comparable to the existing SOTA performance, and 89.3% and 80.5% in IL and ALG514, respectively. In addition, as a result of evaluation, KoEPT showed a relatively improved performance for datasets with high classification difficulty. Through an ablation study, we uncovered that the use of the 'expression' tokens and pointer networks contributed to KoEPT's state of being less affected by classification difficulty while obtaining good performance.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.419-430
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• 2023

Currently, NFTs have no dominant application except for the proof of ownership for digital content, and it also have small liquidity problem, which makes their price difficult to predict. Real estate usually has very high barriers to investment due to its high pricing. Real estate can be converted into NFTs and also divided into small value fungible tokens (FTs), and it can increase the the volume of the investor community due to more liquidity and better accessibility. In this document, we implement and design a system that allows ordinary users can invest on high priced real estate utilizing Black Litterman (BL) model-based Portfolio investment interface. To this end, we target a set of real estates pegged as collateral and issue NFT for the collateral using blockchain. We use oracle to get the current real estate information and to monitor varying real estate prices. After tokenizing real estate into NFTs, we divide the NFTs into easily accessible price FTs, thereby, we can lower prices and provide large liquidity with price volatility limited. In addition, we also implemented BL based asset portfolio interface for effective portfolio composition for investing in multiple of real estates with small investments. Using BL model, investors can fix the asset portfolio. We implemented the whole system using Solidity smart contracts on Flask web framework with public data portals as oracle interfaces.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.42-50
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• 2023

The development of technology has achieved digital innovation throughout the media industry, including production techniques and editing technologies, and has brought diversity in the form of consumer viewing through the OTT service and streaming era. The convergence of big data and deep learning networks automatically generated text in format such as news articles, novels, and scripts, but there were insufficient studies that reflected the author's intention and generated story with contextually smooth. In this paper, we describe the flow of pictures in the storyboard with image caption generation techniques, and the automatic generation of story-tailored scenarios through language models. Image caption using CNN and Attention Mechanism, we generate sentences describing pictures on the storyboard, and input the generated sentences into the artificial intelligence natural language processing model KoGPT-2 in order to automatically generate scenarios that meet the planning intention. Through this paper, the author's intention and story customized scenarios are created in large quantities to alleviate the pain of content creation, and artificial intelligence participates in the overall process of digital content production to activate media intelligence.

• Journal of The Korean Association For Science Education
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• v.26 no.2
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• pp.239-245
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• 2006

The purposes of this study were to develop and implement a teaching strategy that reinforced teacher-student interaction for middle school first grade science and investigate the impact of the teaching strategy on student comprehension of scientific concepts, logical thinking ability, and science-related attitudes. 200 students attending a co-ed middle school located in Gyeonggi province were selected for the study and divided into an experiment and control group each consisting of 100 pupils. The teaching strategy reinforcing teacher-student interaction was applied to the experiment group, while conventional teaching, teacher-led lecturing, was carried out on the control group. To accomplish reinforced teacher-student interaction teacher asked students diversified questions and gave delayed feedbacks that deliberately focused on thought provocation. Results showed that the developed teaching strategy was effective in improving the students' logical thinking ability(p<.01). However, no significant differences were found in student comprehension of scientific concepts and science-related attitudes between the experimental and control group(p<.05). It was determined that more sound research is needed to develop and apply a lesson plan based on the teaching strategy used in this study.