• Journal of Digital Convergence
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• v.13 no.5
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• pp.219-226
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• 2015

OTP(One Time Password) is for user authentication of Internet banking and users should carry their security card or OTP generator to use OTP. If they lost their security card or OTP generator, there is at risk for OTP leak. This paper suggests a new User Authentication Framework using personal health information from diverse technology of u-Health. It will cover the problem of OTP loss and illegal reproduction A User Authentication Framework is worthy of use because it uses various combinations of user's physical condition which is inconstant. This protocol is also safe from leaking information due to encryption of reliable institutes. Users don't need to bring their OTP generator or card when they use bank, shopping mall, and game site where existing OTP is used.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.05a
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• pp.101-106
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• 2000

자동화 기기 분야에서 컴퓨터의 적용 및 응용은 하드웨어 발달에 따라 매우 빠르고 민감하게 반영되어왔다. 이는 컴퓨터 하드웨어의 빠른 개발 주기에 맞춰 저 가격, 고 효율성, 높은 신뢰성, 호환성 등의 장점을 가진 PC가 현대의 컴퓨터 흐름을 주도하게 되면서 자동화 산업분야 또한 이를 적용하여 왔기 때문이다. 이에 따라, 자동화 기기 분야에서는 고 가격, 긴 개발기간 등을 필요로 했던 과거와 달리 저 가격, 짧은 개발기간, 다양한 개발환경 등을 이룰 수 있었다. 또한, 생산량 증가에만 의존하던 과거와 달리 현대에 이르러서는 시스템의 최적화, 효율의 극대화, 시스템의 안정성, 운용의 편리성, 호환성 등의 개념들이 도입되고 있는 것이다. 자동화 기기를 구성하는 요인으로는 크게 시스템의 틀을 이루는 기계부분과 이를 제어하는 제어 시스템부로 나뉠 수 있다. 제어 시스템에서는 기계부분의 동작을 제어하는 동작 제어부와 이에 관한 정보를 화면에 나타내는 GUI(Graphical User Interface)부분으로 나뉘게된다. 현재에는 이를 통합하여 하나의 하드웨어에서 제어부와 GUI를 모두 담당하는 방법이 연구 진행되고 있으나, 하드웨어를 둘로 나누거나 하나로 하여도 제어부와 GUI 사이의 통신부분은 빼놓을 수 없는 요소가 된다. 따라서, 본 논문에서는 시스템의 안정성을 위하여 두 시스템간에 송·수신되는 데이터를 추적할 수 있도록 하는 Emulate 기법을 구현 및 개발하고자 한다. 이는, 두 시스템간의 통신 데이터를 실시간으로 누적, 저장하여 사용자로 하여금 시스템의 운용상태를 분석할 수 있게 하였으며, 시스템 오류발생 시 Emulate 자료를 근거로 시스템의 운용상태를 파악할 수 있게 하였다.근 제한기능을 제공하며 각 클라이언트와 서버간의 실시간 연결 혹은 지연연결을 지원하는 독립적인 애플리케이션이다. 이러한 처방전달 메시징시스템을 구성하는 각 요소에 대해 정의하고 개념적 모델을 설계하고자 한다.에게 청구되며, 소비자에게 전송 되는 청구서는 사용자DB를 참조하여 사용자가 미리 정의한 원하는 형태로 변환되어 전달되며, 필요시 암호화 과정을 거치는 것이 가능해야 한다. 전송된 청구서는 전자우편의 경우, 암호해독이 가능한 전용 브라우저를 통해 열람 되며, 이는 다시 전용 브라우저를 통해 지불인증이 승인되어 청구 제시서버에게 전송된다. EBPP 시스템의 제어 흐름은 크게 기업이 청구 정보를 소비자에게 제시하는 흐름과 소비자의 지불 승인으로 인해 기업이 은행에 지불을 요구하는 흐름으로 구분할 수 있다. 본 논문에서는 통합 청구서버 및 정구 제시서버의 역할 및 구성 요소들에 대해 서술하고, EBPP 시스템과 연동하여야 하는 메일 서버와의 상호 작용에 대해 서술할 것이다. 본 시스템을 아직 구현이 되지 않은 관계로 시스템의 성능 등의 수치적 결과를 제시할 수 없는 상태다., 취약계층을 위한 일차의료, 의약관리), ${\circled}2$ 보건소 조직 개편 및 민간의료기관과 협력체계 확립, ${\circled}3$ 전문인력 확보 및 인력구성 조정, 그리고 ${\circled}4$ 방문보건사업의 강화 등이다., 대사(代謝)와 관계(關係)있음을 시사(示唆)해 주고 있다.ble nutrient (TDN) was highest in booting stage (59.7%); however no significant difference was foun

• Korean Journal of Construction Engineering and Management
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• v.17 no.5
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• pp.13-21
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• 2016

Insolvency of construction companies that play the role of main contractors can lead to clients' losses due to non-fulfillment of construction contracts, and it can have negative effects on the financial soundness of construction companies and suppliers. The construction industry has the cash flow financial characteristic of receiving a project and getting payment based on the progress of the construction. As such, insolvency during project progress can lead to financial losses, which is why the prediction of construction companies is so important. The prediction of insolvency of Korean construction companies are often made through the KMV model from the KMV (Kealhofer McQuown and Vasicek) Company developed in the U.S. during the early 90s, but this model is insufficient in predicting construction companies because it was developed based on credit risk assessment of general companies and banks. In addition, the predictive performance of KMV value's insolvency probability is continuously being questioned due to lack of number of analyzed companies and data. Therefore, in order to resolve such issues, the Bayesian Probabilistic Approach is to be combined with the existing insolvency predictive probability model. This is because if the Prior Probability of Bayesian statistics can be appropriately predicted, reliable Posterior Probability can be predicted through ensured conditionality on the evidence despite the lack of data. Thus, this study is to measure the Expected Default Frequency (EDF) by utilizing the Bayesian Probabilistic Approach with the existing insolvency predictive probability model and predict the accuracy by comparing the result with the EDF of the existing model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.215-235
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• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.