• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.567-569
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• 2005

NARX(Nonlinear AutoRegressive with eXogenous input) neural network was used for prediction of nuclear reactor behavior which was influenced by control rods in short-term period and also by the concentration of xenon and boron in long-term period in load following operations. The developed model was designed to predict reactor power, xenon worth and axial offset with different burnup states when control rods and boron were adjusted in load following operations. Data of the Korea Next Generation PWR were collected by ONED94 code. The test results presented exhibit the capability of the NARX neural network model to capture the long term and short term dynamics of the reactor core and the developed model seems to be utilized as a handy tool for the use of a plant simulation.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1239-1241
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• 2003

This paper presents the results of Landsat-TM imagery applications for detecting spatial variations of the water environments in the Saemankeum (STLR) project areas. The simulated tidal flow patterns from a two -dimensional hydro - dynamic model and water quality data from STRL project were used for relationships with the satellite data. Unsupervised classification of the tidal water body reflects the overall flow patterns at a flooding tide. Regressive equations for water quality parameters were derived and used for supervised classifications. The results were found to be useful to synoptically evaluate the water environments during the construction stages of the STLR project.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.273-295
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• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.251-257
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• 2020

The study investigates the role of commodity prices and tax purpose recognition on bitcoin prices. Since the introduction of bitcoin in 2008, emphasis has focused on economists, policy-makers and analysts drastically increasing bitcoin's accessibility and commodity values (Dumitrescu & Firică, 2014). This study employs GARCH and EGARCH from ARCH/GARCH family on daily nature data. We measure the volatile behavior of bitcoin by employing auto-regressive conditional heteroscedasticity model with the aim to explore the relationship between major commodities and bitcoin volatility. We focus on major commodities like gold, silver, platinum, and crude oil to be regressed with bitcoin. The daily prices of commodities were retrieved from www.investing.com and bitcoin prices from www.coindesk.com for the period from 29April 2013 to 16 October 2018. Results confirmed the currency's long-term volatile behavior, which is due to its composition and market dynamics, whereas the existence of asymmetric information effect is not confirmed. Tax recognition by other countries may in future help in controlling the volatility as bitcoin is not a country-specific security. But, only silver impacts on volatility in comparison to oil prices and platinum, which is due to its similar features with gold. Eventually, bitcoin can be used for risk diversification and money making.

• Earthquakes and Structures
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• v.8 no.4
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• pp.915-934
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• 2015

Within the context of Structural Health Monitoring (SHM), it is often the case that structural systems are described by uncertainty, both with respect to their parameters and the characteristics of the input loads. For the purposes of system identification, efficient modeling procedures are of the essence for a fast and reliable computation of structural response while taking these uncertainties into account. In this work, a reduced order metamodeling framework is introduced for the challenging case of nonlinear structural systems subjected to earthquake excitation. The introduced metamodeling method is based on Nonlinear AutoRegressive models with eXogenous input (NARX), able to describe nonlinear dynamics, which are moreover characterized by random parameters utilized for the description of the uncertainty propagation. These random parameters, which include characteristics of the input excitation, are expanded onto a suitably defined finite-dimensional Polynomial Chaos (PC) basis and thus the resulting representation is fully described through a small number of deterministic coefficients of projection. The effectiveness of the proposed PC-NARX method is illustrated through its implementation on the metamodeling of a five-storey shear frame model paradigm for response in the region of plasticity, i.e., outside the commonly addressed linear elastic region. The added contribution of the introduced scheme is the ability of the proposed methodology to incorporate uncertainty into the simulation. The results demonstrate the efficiency of the proposed methodology for accurate prediction and simulation of the numerical model dynamics with a vast reduction of the required computational toll.

• Annual Conference of KIPS
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• 2024.05a
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• pp.611-614
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• 2024

최근 많은 분야에서 인공지능을 사용한 산업이 각광을 받고 있고 그중 챗-GPT 로 인하여 챗봇에 관한 관심도가 높아져 관련 연구가 많이 진행되고 있다. 특히 질문에 대한 답변을 생성해주는 분야에 대한 연구가 많이 이루어지고 있는데, 질문-답변의 데이터 셋에 대한 학습 방식보다는 질문-답변-배경지식으로 이루어진 데이터 셋에 대한 학습 방식이 많이 연구가 되고 있다. 그러다 보니 배경지식을 어떤 방식으로 모델에게 이해를 해줄 지가 모델 성능에 큰 부분 차지한다. 그리고 최근 연구에 따르면 이러한 배경지식 정보를 이해시키기 위해 잠재 변수 모델링 기법을 활용하는 것이 높은 성능을 갖는다고 하고 트랜스포머 기반 모델 중 생성 문제에서 강점을 보이는 BART(Bidirectional Auto-Regressive Transformer)[1]도 주로 활용된다고 한다. 본 논문에서는 BART 모델에 잠재 변수 모델링 기법 중 잠재 변수를 어텐션에 곱하는 방식을 이용한 모델을 통해 답변 생성 문제에 관한 해결법을 제시하고 그에 대한 결과로 배경지식 정보를 담은 답변을 보인다. 생성된 답변에 대한 평가는 기존에 사용되는 BLEU 방식과 배경지식을 고려한 방식의 BLEU 로 평가한다.

• Advances in environmental research
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• v.4 no.4
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• pp.233-246
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• 2015

High amounts of air pollution in crowded urban areas are always considered as one of the major environmental challenges especially in developing countries. Despite the errors in air pollution prediction, the forecasting of future data helps air quality management make decisions promptly and properly. We studied the air quality of the Aqdasiyeh location in Tehran using factor analysis and the Box-Jenkins time series methods. The Air Quality Control Company (AQCC) of the Municipality of Tehran monitors seven daily air quality parameters, including carbon monoxide (CO), Nitrogen Monoxide (NO), Nitrogen dioxide ($NO_2$), $NO_x$, ozone ($O_3$), particulate matter ($PM_{10}$) and sulfur dioxide ($SO_2$). We applied the AQCC data for our study. According to the results of the factor analysis, the air quality parameters were divided into two factors. The first factor included CO, $NO_2$, NO, $NO_x$, and $O_3$, and the second was $SO_2$ and $PM_{10}$. Subsequently, the Box- Jenkins time series was applied to the two mentioned factors. The results of the statistical testing and comparison of the factor data with the predicted data indicated Auto Regressive Integrated Moving Average (0, 0, 1) was appropriate for the first factor, and ARIMA (1, 0, 1) was proper for the second one. The coefficient of determination between the factor data and the predicted data for both models were 0.98 and 0.983 which may indicate the accuracy of the models. The application of these methods could be beneficial for the reduction of developing numbers of mathematical modeling.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.457-460
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• 2021

대화 시스템은 인공지능과 사람이 자연어로 의사 소통을 하는 시스템으로 크게 목적 지향 대화와 일상대화 시스템으로 연구되고 있다. 목적 지향 대화 시스템의 경우 날씨 확인, 호텔 및 항공권 예약, 일정 관리 등의 사용자가 생활에 필요한 도메인들로 이루어져 있으며 각 도메인 별로 목적에 따른 시나리오들이 존재한다. 이러한 대화는 사용자에게 명확한 발화을 제공할 수 있으나 자연스러움은 떨어진다. 일상 대화의 경우 다양한 도메인이 존재하며, 시나리오가 존재하지 않기 때문에 사용자에게 자연스러운 발화를 제공할 수 있다. 또한 일상 대화의 경우 검색 기반이나 생성 기반으로 시스템이 개발되고 있다. 검색 기반의 경우 발화 쌍에 대한 데이터베이스가 필요하지만, 생성 기반의 경우 이러한 데이터베이스가 없이 모델의 Language Modeling (LM)으로 부터 생성된 발화에 의존한다. 따라서 모델의 성능에 따라 발화의 품질이 달라진다. 최근에는 사전학습 모델이 자연어처리 작업에서 높은 성능을 보이고 있으며, 일상 대화 도메인에서도 역시 높은 성능을 보이고 있다. 일상 대화에서 가장 높은 성능을 보이고 있는 사전학습 모델은 Auto Regressive 기반 생성모델이고, 한국어에서는 대표적으로 KoGPT2가 존재한다. 그러나, KoGPT2의 경우 문어체 데이터만 학습되어 있기 때문에 대화체에서는 낮은 성능을 보이고 있다. 본 논문에서는 대화체에서 높은 성능을 보이는 한국어 기반 KoDialoGPT2를 개발하였고, 기존의 KoGPT2보다 높은 성능을 보였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.349-356
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• 2018

This paper attempts to develop a connection model based on FE analysis that can be applied to the evaluation of earthquake fragility of Steel Storage Racks lacking research in Korea. In order to accomplish this goal, shaking table tests, modal tests, and various member tests (8 case, push-over test) for structural members have been conducted to understand the behavior of steel storage racks. Based on the experimental results, detailed modeling of the joints was conducted using the NX-Nastran program in order to develop a connection model for Steel storage racks to be applied to the seismic vulnerability assessment. Especially, surface to surface contact element and spring element are applied to simulate the connection between the column member and the beam member connected by the simple latch method. Spring element model developed and applied ARX (Auto Regressive eXogenous) based mathematical model. The simulation results based on the FE model showed excellent reliability with a mutual error rate of less than 8% when compared with the member test results. As a result, it was confirmed that the FE model based connection model developed in the study can be applied to the analytical model for the seismic vulnerability assessment of Steel storage racks.

• Journal of Korea Water Resources Association
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• v.39 no.12 s.173
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• pp.997-1012
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• 2006

We have presented a nonparametric stochastic approach for the SOI(Southern Oscillation Index) series that used nonlinear methodology called Nonlinear AutoRegressive(NAR) based on conditional kernel density function and CAFPE(Corrected Asymptotic Final Prediction Error) lag selection. The fitted linear AR model represents heteroscedasticity, and besides, a BDS(Brock - Dechert - Sheinkman) statistics is rejected. Hence, we applied NAR model to the SOI series. We can identify the lags 1, 2 and 4 are appropriate one, and estimated conditional mean function. There is no autocorrelation of residuals in the Portmanteau Test. However, the null hypothesis of normality and no heteroscedasticity is rejected in the Jarque-Bera Test and ARCH-LM Test, respectively. Moreover, the lag selection for conditional standard deviation function with CAFPE provides lags 3, 8 and 9. As the results of conditional standard deviation analysis, all I.I.D assumptions of the residuals are accepted. Particularly, the BDS statistics is accepted at the 95% and 99% significance level. Finally, we split the SOI set into a sample for estimating themodel and a sample for out-of-sample prediction, that is, we conduct the one-step ahead forecasts for the last 97 values (15%). The NAR model shows a MSEP of 0.5464 that is 7% lower than those of the linear model. Hence, the relevance of the NAR model may be proved in these results, and the nonparametric NAR model is encouraging rather than a linear one to reflect the nonlinearity of SOI series.