• 전력전자학회:학술대회논문집
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• 전력전자학회 2006년도 전력전자학술대회 논문집
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• pp.225-227
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• 2006

The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) first developed ADVISOR in 1994. Between 1998 and 2003 it was downloaded by more than 7,000 individuals, corporations, and universities world-wide. In early 2003 NREL initiated the commercialisation of ADVISOR through a public solicitation. AVL responded and was awarded the exclusive rights to license and distribute ADVISOR world-wide. AVL is committed to continuously enhance ADVISOR's capabilities. Provides rapid analysis of the performance and fuel economy of conventional and advanced, light and heavy-duty vehicle models as well as hybrid electric and fuel cell vehicle models. ADVISOR Simulates the Following Vehicle Characteristics. - Optimal drivetrain component sizes that provide the best fuel economy Vehicle's ablility to follow the speed trace - Amount of fuel and/or electric energy required by various vehicle concepts - Peak power and efficiency achieved by the drivetrain components - Torque and speed distribution of the engine - Average efficiency of the transmission - Gradeability of vehicles at various velocities

• 동의신경정신과학회지
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• 제21권2호
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• pp.141-155
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• 2010

Objectives : This study was performed to develop a standard instrument of oriental medical evaluation for hwa-byung. Methods : The advisor committee on this study was organized by 17 neuropsychiatry professors of oriental medical colleges. The items and structure of the instrument were quoted from the instrument of pattern identification for hwa-byung. We took consultation twice from the advisor committee and we also took additional advices by e-mail. Results : We discriminated between bian-zheng and su-zheng from the answers of the advisor committee. We got the mean weight of each symptom and sign from the answers of the advisor committee. We got the final weight from the combination of the ratio of bian-zheng to the number of all answers of the advisor committee and mean weight. Conclusions : The instrument of oriental medical evaluation for hwa-byung was developed through experts' discussion. If the validity and reliability of this instrument is confirmed through additional clinical trial, the instrument of oriental medical evaluation for hwa-byung is expected to be applied to the subsequent research.

• 지식경영연구
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• 제19권1호
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• pp.79-95
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• 2018

The purpose of this case study is to analyze the B2C security information service model using the robo-advisor, to develop various service models and to urge new companies to enter. Overseas robo-advisor service market is growing rapidly with the launch of various B2C service models beyond B2B. On the other hand, as the domestic market is dominated by B2B services and serviced just index portfolio which is nascent, it lacks products which are used for active asset management. Recently as the government announced the approval of online investment advisory service, the B2C market of domestic asset management has entered a growth phase, centered on generations familiar with IT. We propose to extend the concept of Robo-Advisor service in accordance with the financial market change. By that model, we will study the case of the algorithm of the investment masters' philosophy and contribute to the expansion of the B2C service market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4270-4284
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• 2019

This study presents a reference model (RM) and the architecture of a cognitive health advisor (CHA) that integrates information with ambient intelligence. By controlling the information using the CHA platform, the reference model can provide various ambient intelligent solutions to a user. Herein, a novel approach to a CHA RM based on evolutional cyber-physical systems is proposed. The objective of the CHA RM is to improve personal health by managing data integration from many devices as well as conduct a new feedback cycle, which includes training and consulting to improve quality of life. The RM can provide an overview of the basis for implementing concrete software architectures. The proposed RM provides a standardized clarification for developers and service designers in the design and implementation process. The CHA RM provides a new approach to developing a digital healthcare model that includes integrated systems, subsystems, and components. New features for chatbots and feedback functions set the position of the conversational interface system to improve human health by integrating information, analytics, and decisions and feedback as an advisor on the CHA platform.

• Journal of the Korean Data and Information Science Society
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• 제28권2호
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• pp.287-295
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• 2017

2016년 3월 구글 (Google)의 바둑인공지능 알파고 (AlphaGo)가 이세돌 9단과의 바둑대결에서 승리한 이후 다양한 분야에서 인공지능 사용에 대한 관심이 높아지고 있는 가운데 금융투자 분야에서도 인공지능과 투자자문 전문가의 합성어인 로보어드바이저 (Robo-Advisor)에 대한 관심이 높아지고 있다. 인공지능 (artificial intelligence)기반의 의사결정은 비용 절감은 물론 효과적인 의사결정을 가능하게 한다는 점에서 큰 장점이 있다. 본 연구에서는 기술적 분석 (technical analysis) 지표와 딥러닝 (deep learning) 모형을 결합하여 한국 코스피 지수를 예측하는 모형을 개발하고 제시한 모형들의 예측력을 비교, 분석한다. 분석 결과 기술적 분석 지표에 딥러닝 알고리즘을 결합한 모형이 주가지수 방향성 예측 문제에 응용될 수 있음을 확인하였다. 향후 본 연구에서 제안된 기술적 분석 지표와 딥러닝모형을 결합한 기법은 로보어드바이저서비스에 응용할 수 있는 일반화 가능성을 보여준다.

• 한국융합학회논문지
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• 제10권9호
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• pp.199-207
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• 2019

우리나라 주식시장에서 애널리스트들이 발표하는 주가 전망 자료를 입력변수로 활용한 로보어드바이저 포트폴리오의 수익성이 있는지를 분석하고자 하였다. 포트폴리오 구성을 위한 표본 주식은 업종을 대표하는 8개의 우량주이며, 분석 기간은 2003년부터 2019년까지의 17년 자료이다. 표본 주식에 대한 주가와 애널리스트 주가 전망 자료를 결합하는 블랙리터만모형을 통해 로보어드바이저 포트폴리오를 추천하고 벤치마크 대비 수익성을 비교하였다. 실증 분석 결과, 애널리스트들의 주가 전망 자료를 결합한 로보어드바이저 알고리즘의 수익성은 벤치마크 포트폴리오보다 연평균 1% 이상의 초과 수익을 시현하였다. 투자자들의 비판적 시각에도 불구하고 개별 종목에 대한 투자가 아닌 상대적 투자 비중을 구하는 로보어드바이저 관점에서는 애널리스트들의 주가 전망 자료가 경제적 가치를 보유하고 있음을 밝혔다. 향후 연구에서는 애널리스트들의 주가 전망 영향력이 대형주보다 더 클 것으로 예측되는 중 소형주를 포함한 로보어드바이저 포트폴리오의 수익성을 분석할 필요가 있다.

• The Journal of Asian Finance, Economics and Business
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• 제7권7호
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• pp.123-129
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• 2020

The objective of this study is to examine the differentiated influence of sell-side advisors and buy-side advisors on mergers and acquisitions (M&A). Unlike prior studies on M&A advisors, the study addresses different roles of target and acquirer advisors, and explores their influences on the cumulative abnormal returns (CAR) and acquisition premiums with an empirical analysis of longitudinal data of M&As conducted by Japanese listed firms except financial companies from 1995 to 2012. M&A data were obtained from the Securities Data Corporation's (SDC) database, and the individual firm data were collected from the Nikkei Economic Electronic Databank System (NEEDS), which provides a wide range of corporate information including financial status, operational performance, and strategy. Using a sample of 452 cases for the CAR and 498 cases for the analysis of acquisition premiums, the empirical results support the hypotheses of the target advisor's positive association with CAR and acquirer advisor's positive association with acquisition premiums. The findings of this study indicate the target advisor's positive contribution to the success of acquisition process and performance, and acquirer advisor's negative influence on the deal progress. The study provides theoretical implications on M&A research and practical insights into the investment banking industry.

• 한국IT서비스학회지
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• 제15권4호
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• pp.1-24
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• 2016

With the innovation of information technology, non-face-to-face robo advisor with high accessibility and convenience is spreading. The current robot advisor recommends appropriate investment products after understanding the investment propensity based on the structured data entered directly or indirectly by individuals. However, it is an inconvenient and obtrusive way for financial consumers to inquire or input their own subjective propensity to invest. Hence, this study proposes a way to deduce the propensity to invest in unstructured data that customers voluntarily exposed during consultation or online. Since prediction performance based on unstructured document differs according to the characteristics of text, in this study, classification algorithm optimized for the characteristic of text left by financial consumers is selected by performing prediction performance evaluation of various learning discrimination algorithms and proposed an intelligent method that automatically recommends investment products. User tests were given to MBA students. After showing the recommended investment and list of investment products, satisfaction was asked. Financial consumers' satisfaction was measured by dividing them into investment propensity and recommendation goods. The results suggest that the users high satisfaction with investment products recommended by the method proposed in this paper. The results showed that it can be applies to non-face-to-face robo advisor.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제30권2호
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• pp.225-249
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• 2016

본 연구는 수학교사가 수학학습 상담을 하면서 학습상담자로서의 전문성을 어떻게 키워 나가는지에 대해 자기연구(Self-study)의 방법을 활용한 사례 연구이다. 이를 위하여 수학 교사의 수학 학습 상담의 과정과 상담의 내용, 수학학습 상담자로서의 교사의 자기 성찰의 내용과 전문성 신장 과정에 대한 자기관찰을 실시하였다. 교사는 수학 학습 상담에 맞는 상담 모형을 개발하여 총 5회의 상담을 실시하였으며, 각 회기의 수학 학습 상담 과정의 내용에 대해 서술하였다. 상담 과정에서 연구자는 상담 자료 분석과 자신의 상담 내용을 토대로 자기 성찰 일지를 작성하였고, 이를 바탕으로 자신의 상담 과정을 반성하였다. 또한 상담전문가와의 면담을 통해 상담 과정에서 필요한 교사의 역할과 개선할 점에 대한 조언을 얻어 상담에서 필요한 부분을 개선해 나가는 과정을 거쳤다. 위의 두 과정을 거쳐 연구자는 수학 학습 상담자로서 자신의 강점과 약점에 대해 파악하여 강점은 강화하고 약점을 개선해 나가면서 상담에 필요한 능력을 기르고, 학생을 종합적으로 이해하는 능력을 갖추게 되었다. 자기연구 과정을 통해 교사는 스스로 변화되는 모습을 겪으며 학생과 함께 변화하고 수학 학습 상담에 필요한 실천적인 지식을 습득하였다.

• 한국IT서비스학회지
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• 제19권2호
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.