• Journal of Technology Innovation
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• v.24 no.1
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• pp.143-167
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• 2016

The purpose of this study is to analyze changes of innovation activities and their performance in pre-IPO and post-IPO of KOSDAQ IPO listed companies in medical and pharmaceutical fields, which require high R&D investment, from 2000 to 2005 in Korea. The innovation efficiencies of the IPO companies were measured before and after three years based on the DEA model. The financial data and patent information of the listed company during total 6 years, which were 3 years before IPO and 3 years after IPO, were collected. The main results of this research are as follows. First, it took an average 12.86 years until IPO in the start-up of the IPO companies in the pharmaceutical sector, and innovation was on average more active than the IPO before. R&D investment was higher than the IPO before, and the number of the applied patent during 3 years after IPO was 16.67 which was increased from 8.43 during 3 years before IPO. In addition, the average scope of technology of the IPO companies was expanded from 11 to 22 technology fields during previous 3 year and after 3 year each, and financial growth after IPO was lower than the previous IPO. Second, the financial performance of R&D investment and the performance of patent activity were weakened in the efficiency after the IPO, and the integrated performance from the patenting activities and the R&D investment was decreased after the IPO. Finally, the efficiency of the financial performance of the patenting activity was lower than the efficiency of the financial performance of the patent and R&D investment and patent activities under the R&D investment. In particular, the inefficiency of the firms' patenting activities performance after the IPO was caused by the decreasing return to scale, according to the results of this study. This results implicate that the expansion of R&D investments through the IPO had not lead to the financial performance of the market, and that the overall inefficiency since the IPO is due to the inefficiencies at the stage for the outcome of innovation activity rather than the output obtained through the R&D investments that appear to lead the performance of the market.

• Journal of Korean Academy of Nursing Administration
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• v.7 no.3
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• pp.561-569
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• 2001

This study was designed to analyze all the papers that were in the Journal of the Korean Nursing Administration Academic Society from the first publication edition (1995) to 2000. Analyses methods are research designs, data collection methods, research subjects and key words. all papers were 145. The results of the study are as follows : First, research designs, nonexperimentals are 106 papers, experimentals are 9, qualitatives are 9 papers. Research subjects that all subjects in each paper were accepted are as follows, hospital staffs including nurses, doctors, and other employees are 115 paper(59.28%), adults including patient's family, medical, nursing students are 44 papers(22.68%), informatic systems including medical record sheets, database, and management system are 8 papers(4.12%), others including country, literature, researches are 21 papers(10.82%). Second, data collection methods that all methods in each paper were accepted that questionnaires are 93 papers(57.76%), interviews are 13 papers(8.07%), observations are 10 papers(6.21%), scale & psycholgical are 8 papers(4.97%), and self-reportings are 6 papers(3.73%), etc. Conclusion : Even though research topics are various, the fields of informatics and financial management are poor. Therefore studying for those fields and its practical implication are needed.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.5
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• pp.141-146
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• 2007

Barcodes have been widely used to implement automatic identification systems but there are various problems such as security weakness or distance restriction in scanning barcode signals in a barcode-based automatic identifcation systems. Recently researchers are gradually interested in radio frequency identification (RFID) and RFID systems have been applied to various fields than before. Especially one of RFID application fields, a bank system uses RFID tagged bankontes to prevent illegal transactions such as counterfeiting banknotes and money laundering. In this paper, we propose a RFID system for protecting location provacy of a banknote holder. In addition, our paper describes that a trust party can trace a counterfeit banknote holder to provide against emergencies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.303-304
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• 2021

In the 21st century, as we enter the Fourth Industrial Revolution, research in various fields utilizing big data is being conducted, and innovative and useful technologies are constantly emerging in the world. Among several technologies recently in the big data era, among various fields utilizing some algorithms of artificial intelligence, it shines in the field of finance and is used for pin tech, financial fraud detection and risk management, etc., and recently Even in the booming stock market, it is used for investment prediction and investment factor analysis using artificial intelligence algorithm models. In this paper, we plan to investigate various research cases and investigate trends in how they are used in the stock market through artificial intelligence that utilizes big data.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Korea Journal of Hospital Management
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• v.6 no.2
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• pp.138-155
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• 2001

This study was designed to find out the factors which influence on the financial performance of the hospital. Out of 32 provincial hospitals which were established by the government, 10 hospitals were selected as sample hospitals. Ten hospitals were divided into two groups(5 hospitals each), one of which was profit-making and the other loss-making. The criteria in selecting profit or loss-making hospitals was net profit to total revenue. The major finding of the study was as follows; 1. Whether or not a hospital had specialized in certain departments was proved to be the major factor influencing on the financial performance. Three out of five profit-making hospitals could harvest following results by operating specific departments. (1) Man powers needed for the operation of specific departments were 14.6 persons per 100 bed, which was only 1/7 of the general hospital. (2) The number of doctors has not increased in proportion to the increase of the number of beds. (3) Ratio of total revenue to MD.'s payroll expenses of the profit-making hospitals was 75.0% higher than the loss-making hospitals. (4) The average length of stay of specific department was very long(388.1 days). However, the specific departments were found to have contributed much to the financial performance because the occupancy rate of such departments was very high(94.5%). 2. The headcount per 100 bed of the profit-making hospitals was 23.9 persons(24.0%) less than the loss-making hospitals and the ratio of payroll expenses to total revenue 15.1% less. 3. Averagel revenue per specialist of the profit-making hospitals was 100 million(25.1%) more than loss-making hospitals and the ratio of total revenue to MD's payroll expenses of profit-making hospital was 75.0% higher. 4. Profit-making hospitals have introduced new systems or renovation in 36 fields, such as incentive payment system, utilization of contracted man powers, change of the payroll structure of the nurses, specialization in certain departments, etc; however, loss-making hospitals introduced only 25 new systems or renovations. These kind of renovation could not be achieved without the cooperation of the labor union and the strong will of the top management. Therefore, it could be said that the labor union of the profit-making hospitals seems to have been very cooperative compared with that of loss-making hospitals.

• Korea Real Estate Review
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• v.24 no.1
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• pp.105-120
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• 2014

This research is to examine the impacts of the on-going global financial crisis on the housing systems of welfare states. Four developed economies in the Western Europe were selected for the analysis, and the qualitative research employed in-depth interviews with scholars in the fields of housing market and social policy in order to meet the research goal. The major findings indicate that the global economic crisis embedded into the liberalization of housing finance and the inadequacy of regulatory measures caused the vicissitude of housing markets, and its scale and magnitude could be determined by the resilience of each state's housing system. While the globalization of housing finance markets rendered easy borrowing for homeownership, intensive competition for excessive lending among financial institutions backed by heavy reliance on inter-bank and overall bank triggered market volatility, and further worsened household and public debts. It's clearly evident that a housing system with varied safety nets becomes a greater cushion to bear the risks of the financial crisis and to weather the economic storm.

• Information Systems Review
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• v.25 no.4
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• pp.291-314
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• 2023

The concept of Mydata emerged with the expansion of the data economy. MyData aims to empower individuals by enhancing their right to self-determination over their personal data. The use of MyData is expected to enable the provision of innovative service in various fields. Since 2022, MyData has been introduced and actively used in the financial sector. In the future, not only financial institutions but also Bigtech and Fintech companies are expected to actively join and demonstrate rapid expansion. To ensure steady growth for MyData in the financial sector, it is necessary to assess acceptance behaviors from multiple perspectives. However, the majority of existing research solely focuses on positive acceptance. This study analyzed the impact of users' personal characteristics and innovation characteristics on both innovation resistance and acceptance resistance. The analysis revealed that personal and innovation characteristics contribute to an increase in distrust and innovation resistance in the MyData service. In addition, it has been confirmed that it can lead to actions such as delayed acceptance and refusal to accept. The results of this study offer both theoretical and practical insights into user behavior within the MyData service market.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.