• Journal of Digital Convergence
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• v.16 no.11
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• pp.221-230
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• 2018

Most of researchers and business futurists agree that traditional organizational designs are inadequate for coping with today's turbulent and increasingly networked world. Executives in small firms find that their organizations must tap into an extended network of partners to achieve the scale and power needed to succeed in industries dominated by large, global firms. As they attempt to build lean yet agile businesses, these executives are finding that they no longer rely on gut instinct alone. Neither can they simply copy organizational model that worked in the past. They must understand how organizational design choices influence operational efficiency and flexibility and, even more important, how to best align the organization with the environment and the strategy chosen to quickly and effectively sense and respond to opportunities and threats This research examines the capabilities required to build businesses that can survive and prosper in today's fast-faced and uncertain environment. The insights presented in this research have emerged from over 30 years of work with hundreds of executives and entrepreneurs as they struggled to build businesses that could cope with the demands of a rapidly changing, networked global economy. The insights from this research suggest that IT is an important enabler for developing the best capabilities required for success.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.161-171
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• 2019

Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.561-566
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• 2021

Schools are highly feared to spread widely in the event of an infectious disease, and systematic management and prompt response are needed as it can undermine students' health and learning rights. This study was conducted to identify the current status of infectious diseases common to elementary, middle and high school students and to provide basic data to protect students and faculty from the threat of infectious diseases and maintain normal school functions. Sejong City was selected for investigation. The three major infectious diseases are influenza, chickenpox and aquarium, all of which are classified as acute viral infectious diseases and have fast propagation speed and strong propagation power, which can have fatal consequences for students living in groups. The research data were analyzed using the 2019 infectious disease report data from the Education Ministry's Education Administration Information Network (NEIS), and the current status data reported by elementary, middle and high schools nationwide were analyzed. The research method was to compare the current status of infectious diseases across the country and Sejong City, compare the status of issuance by each school level, compare the status of infectious diseases by item, and analyze the status of infectious diseases by time. The results of the survey on the status of the three major infectious diseases are expected to be used as basic data for managing infectious diseases not only in Sejong City but also in the nation, so that they can be used to establish measures to manage student infectious diseases in the future.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.149-156
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• 2021

This study started from the question of what we think about big data as the term "big data" was used and discussed in our daily lives in the era of the 4th industrial revolution. For the analysis, the final 30 Q samples were selected based on prior research related to big data, and 23 respondents were secured for Q analysis, and the following results were derived. First, the explanatory power of each type was 34.30% for , 8.03% for , 7.21% for , and 6.24% for , showing a total of 55.69%. Second, the Q sample emphasized by respondents by each type shows various occupational distributions in , and for 'big data', it is 'digital' and future'. So they were named 「Digital Type」. In , the distribution of 'social workers' was high, and for 'big data', 'future', 'collaboration', 'welfare', 'local residents', and 'defense' were emphasized. It was named 「welfare type」. In , the job distribution of respondents appeared evenly, and it was named as 「Convergence Type」. Because it emphasized statements such as 'convergence', 'digital', 'future', and 'sports'. is composed of association officials, sports instructors, and graduate students, and was named 「Artificial Intelligence Type」, because it emphasizes 'artificial intelligence', 'new paradigm', 'network', and 'sports'. In the age of knowledge industrialization and knowledge informatization that followed industrialization and informatization, how to process and utilize the numerous data accumulated over the years is an important task. Right now, in sports, more than anything else, it is necessary to continuously seek ways to utilize and activate accumulated big data.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.222-230
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• 2022

Recently, Korea's cultural content industry is developing, and behind the growing recognition around the world is the real-time sharing service of global network users due to the development of science and technology. In particular, in the case of YouTube, its propagation power is fast and powerful in that everyone, not limited users, can become potential video providers. As more than 80% of mobile phone users are using YouTube in Korea, YouTube's information means that psychological factors of users are reflected. For example, information such as the number of video views, likes, and comments of a channel with a specific personality shows a measure of the channel's personality interest. This is highly related to the fact that information such as the frequency of keyword search on portal sites is closely related to the stock market economically and psychologically. Therefore, in this study, YouTube information from a representative entertainment company is collected through a crawling algorithm and analyzed for the causal relationship with major variables related to stock prices. This study is considered meaningful in that it conducted research by combining cultural content, IT, and financial fields in accordance with the era of the fourth industry.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.326-334
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• 2022

In this paper, we propose speech enhancement algorithm as a pre-processing for robust speech recognition in noisy environments. Auxiliary-function-based Independent Vector Analysis (AuxIVA) is performed with weighted covariance matrix using time-varying variances with scaling factor from target masks representing time-frequency contributions of target speech. The mask estimates can be obtained using Neural Network (NN) pre-trained for speech extraction or diffuseness using Coherence-to-Diffuse power Ratio (CDR) to find the direct sounds component of a target speech. In addition, outputs for omni-directional noise are closely chained by sharing the time-varying variances similarly to independent subspace analysis or IVA. The speech extraction method based on AuxIVA is also performed in Independent Low-Rank Matrix Analysis (ILRMA) framework by extending the Non-negative Matrix Factorization (NMF) for noise outputs to Non-negative Tensor Factorization (NTF) to maintain the inter-channel dependency in noise output channels. Experimental results on the CHiME-4 datasets demonstrate the effectiveness of the presented algorithms.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.936-939
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• 2022

As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.

• Journal of Christian Education in Korea
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• v.68
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• pp.41-76
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• 2021

This paper begins with the question of whether "on-tact" Christian education, which has become the most new-normal phenomenon since Corona 19, will remain as a decisive form of Christian education even in the post-Corona era. In order to answer that question, this study explored whether on-tact Christian education has its own domain of experience and educational elements that cannot be replaced by face-to-face education, specifically focusing on "types of on-tact Christian Education", "discussion of digital church" and "digital epistemology". Through research on "types of onn-tact Christian education," it confirmed that, when viewed on the basis of 'participation' or 'communication', on-tact Christian education has an independent field of experience and educational elements. Through contemplation on "digital ecclesiology", it found that on-tact education is the decisive channel for Christian education to reach digital generation. It also found a new metaphor from the "network" concept for the public church and the Kingdom of God. This paper also found that we experience the perception of the body that is expanded through the combination between the body and technology in the digital world, and that this is a unique epistemology that occurs only in the digital world. Based on the above points, it affirmed that on-tact Christian education is not simply a means of supplementing face-to-face education in the era of COVID-19, but is a Christian education that has an independent field of experience and educational power that face-to-face education cannot replace. Thus it foresees that on-tact Christian education will continue to expand as a center and form of Christian education even in the post-corona era.

• 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.

• 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.