• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.947-960
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• 2021

In this study, we examine the AI ethics perception of university students to explore the direction of AI ethics education. For this, 83 students wrote their thoughts about 5 discussion topics on online bulletin board. We analyzed it using language networks, one of the text mining techniques. As a result, 62.5% of students spoke the future of the AI society positively. Second, if there is a self-driving car accident, 39.2% of students thought it is the vehicle owner's responsibility at the current level of autonomous driving. Third, invasion of privacy, abuse of technology, and unbalanced information acquisition were cited as dysfunctions of the development of AI. It was mentioned that ethical education for both AI users and developers is required as a way to minimize malfunctions, and institutional preparations should be carried out in parallel. Fourth, only 19.2% of students showed a positive opinion about a society where face recognition technology is universal. Finally, there was a common opinion that when collecting data including personal information, only the part with the consent should be used. Regarding the use of AI without moral standards, they emphasized the ethical literacy of both users and developers. This study is meaningful in that it provides information necessary to design the contents of artificial intelligence ethics education in liberal arts education.

• Science of Emotion and Sensibility
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• v.25 no.3
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• pp.91-106
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• 2022

This study targets four Soho fashion shopping malls that are operating successfully in the online fashion market. This study analyzed the entrepreneurship process by dividing it into three stages. The results of the case study are as follows. In the case of Company S, the founder, who had little work experience, started an e-commerce business with a sense of fashion and entrepreneurship. It is a contemporary, casual brand with competitive prices, design power, and diverse product assortment, and the business performance was achieved through data management and analysis and the diversification of distribution channels. In the case of Company B, the founder, who had little work experience, started a manufacturing and e-commerce business by leveraging their SNS network capabilities and entrepreneurial spirit. It is a contemporary fashion brand with product competitiveness of specific items and start-up characteristics, and performance was achieved through the establishment of brand identity and market expansion. Third, Company M and Company C are examples of Soho fashion shopping malls where the founders with more extensive work experience at the time of founding their respective start-ups focused on brand recognition as their core competitiveness. In the case of Company M, the apparel brand was launched with a wealth of experience and design spirit. It is a fashion designer brand that stands out for its sensibility, and the owner has achieved performance through various entrepreneurial activities that broaden the corporate horizon. Company C is a manufacturing and e-commerce brand that was started with design capabilities and an entrepreneurial spirit. It is a luxury fashion brand that focuses on emotional expression, and the outcomes, such as brand recognition and sales, were achieved through active customer management. The results of this study can be used as basic data in education for and research of Soho shopping malls and the prospective founders.

• Korean Medical Education Review
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• v.24 no.2
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• pp.141-155
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• 2022

The purpose of this study was to investigate the current status of interprofessional education (IPE) and the efforts required to promote, popularize, and implement it in Korea. The IPE status of 40 medical colleges was investigated using a survey with questions regarding the details of IPE, the future plans and necessary support required, and the reasons for not implementing IPE. Thirty-two medical colleges responded, of which 10 are implementing or have implemented IPE. Most of these colleges started IPE in 2018, and the duration of IPE was less than 9 hours. All medical colleges held classes with nursing students. As for the type of IPE, there were independent courses for IPE, one-time special lectures, or partial sessions in one course. Lectures, discussions and presentations, role playing, and high-fidelity simulations were mainly used as educational methods. The support and interest of the dean was the most important facilitating factor. No medical colleges were currently preparing to implement IPE, four colleges had planned IPE but failed to implement it, and 16 had no plans for IPE at all. All medical colleges cited scheduling or cooperation with other majors as the most significant barrier. All the colleges listed their requirements for educational materials, cases, guidelines, and teaching and learning methods for IPE from external institutions. To activate IPE, it is necessary to create an appropriate atmosphere and conditions for developing IPE competencies and a model suitable for the domestic situation. External medical education support organizations should distribute IPE development guidelines and educational materials, form a network between medical colleges with IPE experience, and make efforts to promote the importance of IPE.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.93-114
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• 2023

Information extraction can facilitate the intensive analysis of documents by providing semantic triples which consist of named entities and their relations recognized in the texts. However, most of the research so far has been carried out separately for named entity recognition and relation extraction as individual studies, and as a result, the effective performance evaluation of the entire information extraction systems was not performed properly. This paper introduces two models of end-to-end information extraction that can extract various entity names in clinical records and their relationships in the form of semantic triples, namely pipeline and joint models and compares their performances in depth. The pipeline model consists of an entity recognition sub-system based on bidirectional GRU-CRFs and a relation extraction module using multiple encoding scheme, whereas the joint model was implemented with a single bidirectional GRU-CRFs equipped with multi-head labeling method. In the experiments using i2b2/VA 2010, the performance of the pipeline model was 5.5% (F-measure) higher. In addition, through a comparative experiment with existing state-of-the-art systems using large-scale neural language models and manually constructed features, the objective performance level of the end-to-end models implemented in this paper could be identified properly.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.198-210
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• 2022

The southern strait of Jeju is a divergence point of the Tsushima Warm Current (TWC), and it is the starting point of the thermohaline circulation in the waters of the Korean Peninsula, affecting the size and frequency of marine disasters such as typhoons and tsunamis, and has a very important oceanographic impact, such as becoming a source of harmful organisms and radioactively contaminated water. Therefore, for an immediate response to these maritime disasters, real-time ocean observation is required. However, compared to other straits, in the case of southern Jeju, such wide area marine observations are insufficient. Therefore, in this study, surface current field of the southern strait of Jeju was calculated using High-Frequency radar (HF radar). the large surface current field is calculated, and post-processing and data improvement are carried out through APM (Antenna Pattern Measurement) and FOL (First Order Line), and comparative analysis is conducted using actual data. As a result, the correlation shows improvement of 0.4~0.7 and RMSE of about 1~19 cm/s. These high-frequency radar observation results will help solve domestic issues such as response to typhoons, verification of numerical models, utilization of wide area wave data, and ocean search and rescue in the future through the establishment of an open data network.

• Journal of Life Science
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• v.33 no.1
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• pp.34-42
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• 2023

In a signal transduction network, from the perception of stress signals to stress-responsive gene ex- pression, binding of various transcription factors to cis-acting elements in stress-responsive promoters coordinate the adaptation of plants to abiotic stresses. Among the AP2/ERF transcription factor family genes, group VII ERF genes, such as RAP2.12, RAP2.2, RAP2.3, AtERF73/HRE1, and AtERF71/ HRE2, are known to be involved in the response to hypoxia stress in Arabidopsis. In this study, we dissected the HRE1 promoter to identify hypoxia-responsive region(s). The 1,000 bp upstream promoter region of HRE1 showed increased promoter activity in Arabidopsis protoplasts and transgenic plants under hypoxia conditions. Analysis of the promoter deletion series of HRE1, including 1,000 bp, 800 bp, 600 bp, 400 bp, 200 bp, 100 bp, and 50 bp upstream promoter regions, using firefly luciferase and GUS as reporter genes indicated that the -200 to -100 region of the HRE1 promoter is responsible for the transcriptional activation of HRE1 in response to hypoxia. In addition, we identified two putative hypoxia-responsive cis-acting elements, the ERF-binding site and DOF-binding site, in the -200 to -100 region of the HRE1 promoter, suggesting that the expression of HRE1 might be regulated via the ERF transcription factor(s) and/or DOF transcription factor(s). Collectively, our results suggest that HRE1 contains hypoxia-responsive cis-acting elements in the -200 to -100 region of its promoter.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.367-374
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• 2022

This papter presents the use of the automatic differential method based on the backpropagation method to obtain the design sensitivity and its application to topology optimization considering the stress constraints. Solving topology optimization problems with stress constraints is difficult owing to singularities, the local nature of stress constraints, and nonlinearity with respect to design variables. To solve the singularity problem, the stress relaxation technique is used, and p-norm for stress constraints is applied instead of local stresses for global stress measures. To overcome the nonlinearity of the design variables in stress constraint problems, it is important to analytically obtain the exact design sensitivity. In conventional topology optimization, design sensitivity is obtained efficiently and accurately using the adjoint variable method; however, obtaining the design sensitivity analytically and additionally solving the adjoint equation is difficult. To address this problem, the design sensitivity is obtained using a backpropagation technique that is used to determine optimal weights and biases in the artificial neural network, and it is applied to the topology optimization with the stress constraints. The backpropagation technique is used in automatic differentiation and can simplify the calculation of the design sensitivity for the objectives or constraint functions without complicated analytical derivations. In addition, the backpropagation process is more computationally efficient than solving adjoint equations in sensitivity calculations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.64-79
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• 2023

This study reviewed the process of applying the Monte Carlo simulation technique to the traffic allocation problem of metropolitan subways. The analysis applied the assumption of a normal distribution in which the travel time information of the inter-station sample is the basis of the probit model. From this, the average and standard deviation are calculated by separating the traffic between stations. A plan was proposed to apply the simulation with the weights of the in-vehicle time of individual links and the walking and dispatch interval of transfer. Long-distance traffic with a low number of samples of 50 or fewer was evaluated as a way to analyze the characteristics of similar traffic. The research results were reviewed in two directions by applying them to the Seoul Metropolitan Subway Network. The travel time between single stations on the Seolleung-Seongsu route was verified by applying random sampling to the in-vehicle time and transfer time. The assumption of a normal distribution was accepted for sample sizes of more than 50 stations according to the inter-station traffic sample of the entire Seoul Metropolitan Subway. For long-distance traffic with samples numbering less than 50, the minimum distance between stations was 122Km. Therefore, it was judged that the sample deviation equality was achieved and the inter-station mean and standard deviation of the transport card data for stations at this distance could be applied.