• Communications of Mathematical Education
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• v.31 no.3
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• pp.349-366
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• 2017

The 2015 revised curriculum is an integrated curriculum that reflects national and societal needs to foster creative convergent talent in the school curriculum. Along with these changes, the Ministry of Education introduced a system to change the major from 2017 to the fourth year of university. Therefore, each university should prepare to reflect the curriculum and institutional change before welcoming students who have completed the 2015 revised curriculum. The university needs to study the countermeasures for implementing the 2015 revised curriculum and expanding the period of major change when preparing the curriculum and contents of the calculus courses that freshmen take. Handong University has been studying the operation methods of new students who want to decide their major at the first grade, such as operating calculus courses at various levels and allocating appropriate proportions of calculus for preliminary examinations. This case is similar to the basic purpose of the revised curriculum in 2015, so it can suggest implications for the operation of the university calculus class after the curriculum revision. In this paper, we have analyzed the results of the recent freshman mathematics test for the recent 5 years and the students' calculus grades and compared them with the contents of the calculus curriculum operated by Handong University and the 2015 revised higher mathematics curriculum. As a result, we proposed five classes of calculus suitable for college major and it was found that the calculus curriculum should include the missing quadratic method in the 2015 revised curriculum.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.3
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• pp.229-235
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• 2019

The implementation of an in-band full-duplex wireless communication system is demonstrated in this study. In the analog/RF domain, the self-interference(SI) signal is reduced using a separate antenna for the transmitter and receiver paths, and most of the SI signal is canceled in the digital domain. A software defined radio(SDR) is used to implement the in-band full-duplex wireless communication system. The USRP X310 device uses transmitting and receiving antennas. By adjusting the gain of the transmitting and receiving ends of the SDR device, the magnitude of the SI signal entering the receiving antenna, and the size of the received signal from the outside, are both set to -64 dB. To verify the in-band full-duplex wireless communication performance, the source data is image and orthogonal frequency-division multiplexing is used for modulation. A WiFi standard frame with a carrier frequency of 2.67 GHz and bandwidth of 20 MHz is used. In the received signal, the SI signal is canceled by digital signal processing and the SI signal is attenuated by up to 34 dB. OFDM demodulation was impossible when the SI signal was not removed. However, the bit error rate is reduced to $2.63{\times}10^{-5}$ when the SI signal is attenuated by 34 dB, and no error is detected in the 100 Mbit data output as a result of passing through the Viterbi decoder.

• Journal of the HCI Society of Korea
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• v.14 no.2
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• pp.41-47
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• 2019

In this study, a smart assistive device is designed to recognize pedestrian signal and to provide audio instructions for visually impaired people in crossing streets safely. Walking alone is one of the biggest challenges to the visually impaired and it deteriorates their life quality. The proposed device has a camera attached on a pair of glasses which can detect traffic lights, recognize pedestrian signals in real-time using a machine learning algorithm on GPU board and provide audio instructions to the user. For the portability, the dimension of the device is designed to be compact and light but with sufficient battery life. The embedded processor of device is wired to the small camera which is attached on a pair of glasses. Also, on inner part of the leg of the glasses, a bone-conduction speaker is installed which can give audio instructions without blocking external sounds for safety reason. The performance of the proposed device was validated with experiments and it showed 87.0% recall and 100% precision for detecting pedestrian green light, and 94.4% recall and 97.1% precision for detecting pedestrian red light.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.553-560
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• 2020

This paper presents an algorithm for identifying and eliminating errors by seagrasses in coastal bathymetry surveying using drone and HD camera. Survey errors due to seagrasses were identified, segmentated and eliminated using a L∗a∗b color space model. Bathymetry survey using a drone and HD camera has many advantages over conventional survey methods such as ship-board acoustic sounder or manual level survey which are time consuming and expensive. However, errors caused by sea bed reflectance due to seagrasses habitat hamper the development of new surveying tool. Seagrasses are the flowering plants which start to grow in November and flourish to maximum density until April in Korea. We developed a new algorithm for identifying seagrasses habitat locations and eliminating errors due to seagrasses to get the accurate depth survey data. We tested our algorithm at Wolpo beach. Bathymetry survey data which were obtained using a drone with HD camera and calibrated to eliminate errors due to seagrasses, were compared with depth survey data obtained using ship-board multi-beam acoustic sounder. The abnormal bathymetry data which are defined as the excess of 1.5 times of a standard deviation of random errors, are composed of 8.6% of the test site of area of 200 m by 300 m. By applying the developed algorithm, 92% of abnnormal bathymetry data were successfully eliminated and 33% of RMS errors were reduced.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.73-86
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• 2021

The primary purpose of this paper is to explore the advanced models that predict entrepreneurial innovativeness most accurately. For the first time in the field of entrepreneurship research, it presents a model that predicts entrepreneurial innovativeness based on machine learning corresponding to data scientific approaches. It uses 22,099 the Global Entrepreneurship Monitor (GEM) data from 62 countries to build predictive models. Based on the data set consisting of 27 explanatory variables, it builds predictive models that are traditional statistical methods such as multiple regression analysis and machine learning models such as regression tree, random forest, XG boost, and artificial neural networks. Then, it compares the performance of each model. It uses indicators such as root mean square error (RMSE), mean analysis error (MAE) and correlation to evaluate the performance of the model. The analysis of result is that all five machine learning models perform better than traditional methods, while the best predictive performance model was XG boost. In predicting it through XG boost, the variables with high contribution are entrepreneurial opportunities and cross-term variables of market expansion, which indicates that the type of entrepreneur who wants to acquire opportunities in new markets exhibits high innovativeness.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.81-94
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• 2020

Based on innovative technologies and new start-up ideas, startups have been actively researched on the entrepreneurship needed to strengthen the organization's capabilities and generate results in the start-up process. This study studied the entrepreneurial orientation on the performance of startups in the Philippines and the financial and non-financial performance of enterprises. This study carried out not only the entrepreneurial orientation to the performance of Filipino startups but also the role of social capital as parameters in the performance of enterprises. The empirical research was completed for 93 Philippine startups and the suitability of the research model was evaluated with a PLS-based structural equation model. The results of the study first confirmed that the enterprise orientation of Philippine startups has a positive impact on both financial and non-financial performance of the enterprises. Second, the entrepreneurial orientation of Philippine startups has been shown to have a positive effect on both the structural, cognitive and relational dimensions of social capital. Third, it was found that the relevant dimensions of social capital mediated both the corporate orientation and the relationship between the financial and non-financial performance of the entity. Entrepreneurial orientation has been confirmed to be directly or indirectly affecting the performance of startups through social capital. These findings reaffirmed that entrepreneurial orientation is still a valid important factor in developing countries as well as in countries such as Korea and the United States. Based on this study, we have identified the need for research from a more integrated perspective, such as the concept of strategic orientation. Finally, practical implications were presented to reflect the findings analyzed.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.3
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• pp.101-109
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• 2021

Wave observations using a marine X-band radar are conducted by analyzing the backscattered radar signal from sea surfaces. Wave parameters are extracted using Modulation Transfer Function obtained from 3D wave number and frequency spectra which are calculated by 3D FFT of time series of sea surface images (42 images per minute). The accuracy of estimation of the significant wave height is, therefore, critically dependent on the quality of radar images. Wave observations during Typhoon Maysak and Haishen in the summer of 2020 show large errors in the estimation of the significant wave heights. It is because of the deteriorated radar images due to raindrops falling on the sea surface. This paper presents the algorithm developed to increase the accuracy of wave heights estimation from radar images by adopting convolution neural network(CNN) which automatically classify radar images into rain and non-rain cases. Then, an algorithm for deriving the Hs is proposed by creating different ANN models and selectively applying them according to the rain or non-rain cases. The developed algorithm applied to heavy rain cases during typhoons and showed critically improved results.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.217-242
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• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.159-174
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• 2023

In this paper, we proposed a methodology to effectively determine the selection and prioritization of new business and innovation projects using AI technology. AI technology is a technology that can upgrade the business of companies in various industries and increase the added value of the entire industry. However, there are various constraints and difficulties in the decision-making process of selecting and implementing AI projects in the enterprise. In this paper, we propose a new methodology for prioritizing AI projects using Morphology, AHP, and TOPSIS. The proposed methodology helps prioritize AI projects by simultaneously considering the technical feasibility of AI technology and real-world user requirements. In this study, we applied the proposal methodology to a real enterprise that wanted to prioritize multiple AI projects in the HR field and evaluated the results. The results confirm the practical applicability of the methodology and suggest ways to use it to help companies make decisions about AI projects. The significance of the methodology proposed in this study is that it is a framework for prioritizing multiple AI projects considered by a company in the most reasonable way by considering both business and technical factors at the same time.

• Information Systems Review
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• v.23 no.3
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• pp.97-125
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• 2021

Blockchain is a core technology to solve personal information leakage and data management issues, which are limitations of existing Genomic Sequencing services. Due to continuous cost reduction and deregulation, the market size of Genomic Sequencing has been increasing, also the potential of services is expected to increase when Blockchain's security and connectivity are combined. We created our research model by combining the Technology Acceptance Model (TAM) and the Innovation Resistance Theory also analyzed the factors affecting the acceptance intention and innovation resistance of the Blockchain Based Genomic Sequencing Information Platform. A survey was conducted on 150 potential users of Blockchain and Genomic Sequencing services. The analysis was conducted by setting the four Blockchain variables: Security, transparency, availability, and diversity). Also, we set the Perceived Usefulness, Perceived risk, and Perceived Complexity for Technology Acceptance and Innovation Resistance variables and analyzed the effect of the characteristics of the Blockchain on acceptance intention and innovation resistance through these variables. Through this analysis, key variables that need to be considered important to reduce resistance and increase acceptance intention could be identified. This study presents innovation factors that should be considered in companies preparing a new Blockchain Based Genomic Sequencing Information Platform.