• 재무관리연구
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• 제6권1호
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• pp.31-62
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• 1989

본 연구에서는 변동환율을 취하고 있는 small open economy 가 대외경제에 얼마나 긴밀하게 연결되어 있는가에 따라 그 경제의 환율이 어떻게 달리 반응하는가를 살폈다. 그 경제주체들은 대단히 효율적으로 필요한 정보를 활용해서 미래를 예측한다. 즉 이성적 기대(rational erpectation)를 갖는다고 가정한다. Small open economy의 환율을 분석한 기존의 모델들 대부분이 어떤 특정한 국제경제여건에 국한하여서 가능하면 단순한 모델들을 설정했기 때문에 그 결론들이 얼마나 광범위하게 다른 경우에도 유효한지 쉽게 알 수가 없다. 본 연구에선 점차적으로 현실적인 요소들을 모델에 가미해가고, 현대의 고도정보처리능력에 가장 적합한 기대형태인 이성적 기대를 다룸으로써 변동환율의 움직임에 어떤 일반성이 있을 수 있는가를 공부했다. 구체적으로, 제2장에는 국내경제가 국제경제와 구매력동등가설 (purchasing power parity)과 이자율동등가성(uncovered interest parity)로 긴밀히 연결되어 있는 경우를 살피고, 제3장에는 구매력동등가설은 적용되지 않고 이자율동등가설만 국내경제에 유효한 경우를 살폈다. 2장과 3장에서 국내투자자가 투자할 수 있는 금융자산은 통화와 국내.외 채권이며 주식시장은 고려되지 않았다. 4장에서는 상당히 자립적이고 현실적인 요소가 많이 반영된 경제를 분석하였다. 즉 국내경제가 국제경제와 구매동등가설이나 이자율동등가설로 직접적으로 연결되지 아니한 상황에서 주식을 포함한 모든 금융자산이 투자대상자산으로 가능하고, 또 환율의 변동이 재화의 국내가격에 반영되는데 어느 정도의 시차가 요구되는 경우를 살펴 보았다. 여기서는 내재변수의 수가 많은 관계로 numerical simulation을 이용했다. 본 연구의 결론 일부로서 첫째, 자국경제의 통화가 팽창되는 경우, 그 경제의 국제경제유착 정도에 상관없이 자국통화의 평가절하를 곧 유발하였다. 재정팽창의 경우에는 통화팽창의 경우처럼 환율의 방향에 대한 일반적인 결론을 얻지 못 했다. 둘째, 환율의 움직임에 대해 최근의 자산가격 모델(asset model)들은 과러 전통적인 Keynesian모델들과는 다른 설명을 하고 있는데 본 연구에서는 단기적으로 금융시장에서 자산의 수급일치가 균형조건으로 고려되고, 장기적으로 는 경상수지일치가 균형조건으로 포함되었다. 그 결과 장기균형을 예측하는 경제주체들의 기대가 현재환율의 움직임에 큰 영향력을 미침으로써, 전통적인 Keynesian모델들의 단기예측 유효성을 무시할 수 없음을 보였다. 세째, 개방된 경제에서 변동환율의 초기과민반응(overshooting)이 그것이 미칠 수 있는 왜곡된 signal효과 등으로 인해 상당히 염려. 논의되고 있는데, 본 연구 4장의 경제는 상당히 자립적이고 자국통화로 표시된 채권이 국제적으로 수용되지 않음에도 불구하고, 경제주체들이 이성적기대를 견지하는 한, 환율의 초기과민반응은 쉽게 관찰될 수 있었다. 넷째, 환율의 변동이 재화의 국내통화가격에 반영되는데 시차를 인정한 경우, 경제주체들이 이성적기대를 갖는한, 시차도입 후 뚜렷이 다른 경제양상을 보이지 않았다.

• 대한토목학회논문집
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• 제14권2호
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• pp.307-316
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• 1994

기상 자료 미계측 지점의 단기 강우 예보 모형을 개발하였다. 본 연구 모형은 강우 모의 모형, 기상학적 동질성, 그리고 기상 변수 예측 및 추정에 관한 몇 가지 가정을 전제로 하였으며 강우의 예보에는 칼만 필터 기법을 사용하였다. 기존 모형의 방정식은 수운적 크기 분포(HSD)가 강우 강도에 종속이므로 강우량에 대하여 비선형이다. 본 연구 모형의 방정식은 HSD를 구름층 저류량의 함수로 구성함으로써 강우량에 대하여 비선형이다. 본 연구 모형의 방정식은 HSD를 구름층 저류량의 함수로 구성함으로써 강우량에 대하여 선형화되었다. 또한 기상 입력 변수는 경험 모형에 의하여 예측되었다. 본 연구 모형을 대청댐 유형의 호우 사상에 적용하였다. 그 결과 예보 및 실측 강우 강도간의 평균 자승 오차는 0.30~1.01 mm/hr이었다. 이 결과로 미루어 볼 때, 본 연구 모형에 수반된 가정은 합리적이며 본 연구 모형은 기상 자료 미계측 지점에서 강우를 단기 예보하는데 유용하다고 판단된다.

• Healthcare Informatics Research
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• 제24권4호
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• pp.309-316
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• 2018

Objectives: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. Methods: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. Results: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. Conclusions: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

• 시큐리티연구
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• 제6호
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• pp.147-165
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• 2003

Since Electronic Security System is introduced in Korea in 1981 by foreign technology, Security market has been increasing considerably during short period, and It performs it's security roles well in place of security guards. As electronic and communication technology is highly developed, Electronic Security System and security market structure is changing naturally. Especially high-tech mobile communication technology will change the method of Electronic Security business. Also the pattens of residence and life style, such as the trend toward nuclear family and single life could effect security market. In recent year, new business models that apply the mobile phone and internet is appeared. Although Electronic Security System is changed by the changes of technology, It is very difficult to change the basic elements, such as sensing, alarm signal transfering, and response. The rate of increase of Electronic Security market is expected to matain it's increase pace for the time being. But the development of new system for new protectes such as childeren, old person, vehicle rather than immovable facility is necessary to prepare for the continuous competition.

• 한국IT서비스학회지
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• 제12권2호
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• pp.85-101
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• 2013

The wide spread of smartphones and the growth of the number of internet users are reshaping the SNS (Social Network Service). Merging into other service areas and creating new business models with high profits, SNS is no more a service simply providing personal connections. Now SNS has positioned itself as a service platform. SNG (Social Network Game) is a new outcome utilizing SNS as a game platform and showing a rapid growth rate in a short period of time. The number of SNG users is expected to increase steadily. In this study, we examine whether SNG user motivations lead to flow experience and future intention to use. For that purpose, we conducted a survey with smartphone users. The results indicate that flow experience functions as a mediator and user motivations indirectly affect intention to use through flow experience. This paper concludes with discussions on findings and suggestions for future research.

• 대한기계학회논문집
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• 제16권12호
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• pp.2385-2394
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• 1992

본 연구에서는 1차원, 2상 모델중 K.K.Kuo의 2상 유체역학 모델을 사용하여 유동을 수치해석하였다.

• 전기전자학회논문지
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• 제24권4호
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• pp.969-974
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• 2020

Research into hyper parameter optimization (HPO) has recently revived with interest in models containing many hyper parameters, such as deep neural networks. In this paper, we introduce the most widely used HPO methods, such as grid search, random search, and Bayesian optimization, and investigate their characteristics through experiments. The MNIST data set is used to compare results in experiments to find the best method that can be used to achieve higher accuracy in a relatively short time simulation. The learning rate and weight decay have been chosen for this experiment because these are the commonly used parameters in this kind of experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.216-238
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• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• 한국농공학회논문집
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• 제62권4호
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• pp.99-110
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• 2020

The objective of this study is to analyze the trend of changes in the water circulation rates under climate change by adopting the concept of WCR defined by the Ministry of Environment. With the need for sound water circulation recovery, the MOE proposed the idea of WCR as (1-direct flow/precipitation). The guideline for calculating WCR suggests the SCS method, which is only suitable for short term rainfall events. However, climate change, which affects WCR significantly, is a global phenomenon and happens gradually over a long period. Therefore, long-term trends in WCRs should also be considered when analyzing changes in WCR due to climate change. RCP (Representative Concentration Pathway) 4.5 and 8.5 scenarios were used to simulate future runoff. SWAT (Soil and Water Assessment Tool) was run under the future daily data from GCMs (General Circulation Models) after the calibration. In 2085s, monthly WCR decreased by 4.2-9.9% and 3.3-8.7% in April and October. However, the WCR in the winter increased as the precipitation during the winter decreased compared to the baseline. In the aspect of yearly WCR, the value showed a decrease in most GCMs in the mid-long future. In particular, in the case of the RCP 8.5 scenario, the WCR reduced 2-3 times rapidly than the RCP 4.5 scenario. The WCR of 2055s did not significantly differ from the 2025s, but the value declined by 0.6-2.8% at 2085s.

• 운동영양학회지
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• 제25권1호
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• pp.35-41
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• 2021

[Purpose] This pilot study aimed to develop a regression model to estimate the excess post-exercise oxygen consumption (EPOC) of Korean adults using various easy-to-measure dependent variables. [Methods] The EPOC and dependent variables for its estimation (e.g., sex, age, height, weight, body mass index, fat-free mass [FFM], fat mass, % body fat, and heart rate_sum [HR_sum]) were measured in 75 healthy adults (31 males, 44 females). Statistical analysis was performed to develop an EPOC estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and HR_sum were important variables in the EPOC regression models of various exercise types. The explanatory power and standard errors of estimates (SEE) for EPOC of each exercise type were as follows: the continuous exercise (CEx) regression model was 86.3% (R²) and 85.9% (adjusted R²), and the mean SEE was 11.73 kcal, interval exercise (IEx) regression model was 83.1% (R²) and 82.6% (adjusted R²), while the mean SEE was 13.68 kcal, and the accumulation of short-duration exercise (AEx) regression models was 91.3% (R²) and 91.0% (adjusted R²), while the mean SEE was 27.71 kcal. There was no significant difference between the measured EPOC using a metabolic gas analyzer and the predicted EPOC for each exercise type. [Conclusion] This pilot study developed a regression model to estimate EPOC in healthy Korean adults. The regression model was as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum).