• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1568-1572
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• 2007

지역빈도해석은 우리나라와 같이 자료 기간이 짧은 경우 지점빈도해석보다 더 정확한 확률강우량을 산정할 수 있는 기법이다. 지역빈도해석을 통한 확률강우량 산정 결과는 수문학적으로 동질한 지역의 구분 결과에 따라 달라진다. 지역을 구분할 때에는 강우에 영향을 미치는 다양한 변수들이 사용될 수 있다. 변수의 유형과 개수가 지역 구분의 효율성을 좌우하기 때문에 활용 가능한 모든 변수들의 정보를 요약할 수 있는 변수들을 선택하는 것이 지역 구분의 효율성 면에서 유리하다고 할 수 있다. 이런 면에서 지역 구분의 효율성을 증대시킬 목적으로 다변량 분석 기법이 활용될 수 있다. 본 연구에서는 주성분 분석, 요인 분석, Procrustes analysis와 같은 다변량 분석 기법을 활용하여 42개의 강우 관련 변수들을 33개의 변수로 줄일 수 있었다. 분석 결과 변수 개수 감소로 인한 정보 손실은 크지 않은 것으로 나타났다. 따라서 이러한 기법에 의한 변수 차원의 축소는 지역 구분의 효율성 향상에 기여할 수 있는 것으로 판단된다. 선정된 변수들을 바탕으로 군집해석을 수행하여 지역을 구분하였고, L-모멘트에 근거한 이질성척도(H)를 활용하여 구분된 지역의 동질성을 검토하였다. 또한 L-모멘트에 근거한 적합성 척도(Z)를 적용하여 구분된 지역에 적합한 확률분포형을 선정하였고, 선정된 적정 확률분포형을 바탕으로 각 지역에 대한 성장 곡선(growth curve)을 유도하였다.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.267-273
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• 2020

With the strong growth of social networking sites such as Facebook in recent years, the potential of exploiting customers on Facebook is increasing. Presently, trading activities on Facebook is rapidly developing. Therefore, businesses have become increasingly competitive when selling products on Facebook, so as to retain customers as well as to satisfy customer, which is of paramount importance. This study was conducted to assess the factors affecting the satisfaction of individual customers in Vietnam when buying goods on Facebook. This study uses multivariate analysis techniques (Confirmatory Factor Analysis, Structural Equation Modeling) to determine the factors affecting customer satisfaction when buying goods on Facebook. Research results from 268 individual customers in Vietnam indicated trust and convenience are the two important factors related to customer satisfaction when buying goods on Facebook. Customer satisfaction is the result of consumer experience throughout the different stages of purchase. The more the shopping experience, the more the customers are satisfied when buying products. The price and products do not affect customer satisfaction (prices are easy to compare and products are easily understood on the Internet; hence, these two factors are not considered as determinants of customer satisfaction). Furthermore, this study provides recommendations to improve customer satisfaction.

• Korean Journal of Food Science and Technology
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• v.28 no.2
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• pp.298-304
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• 1996

Thirty-nine samples of roasted sesame seed oils were sensorially evaluated in terms of nutty odor, burnt odor and overall desirability, and their volatile compounds quantitatively analysed using direct sampling capillary GLC. Five volatile compounds were appeared to be significant for the sensory Properties of sesame oils through the multivariate analytical techniques such as stepwise discriminant analysis. canonical discriminant analysis, discriminant analysis and principal component analysis. The most important compounds were 2,5-dimethylpyrazine and 2-methylpyrazine which could be effectively used as chemical indicators related to nutty and burnt odor of sesame oils, respectively. The sesame oils which have represented a good grade of overall desirability have been always kept $35.82{\sim}4.43$ ppm of 2,5-dimethylpyrazine and also $28.90{\sim}6.35$ ppm of 2-methylpyrazine.

• Journal of the Ergonomics Society of Korea
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• v.30 no.5
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• pp.683-688
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• 2011

Objective: The aim of this study is to develop a distributed representative human model(DRHM) generation and analysis system. Background: DRHMs are used for a product with multiple-size categories such as clothing and shoes. It is not easy for a product designer to explore an optimal sizing system by applying various distributed methods because of their complexity and time demand. Method: Studies related to DRHM generation were reviewed and the RHM generation interfaces of three digital human model simulation systems(Jack$^{(R)}$, RAMSIS$^{(R)}$, and CATIA Human$^{(R)}$) were reviewed. Results: DRHM generation steps are implemented by providing sophisticated interfaces which offer various statistical techniques and visualization methods with ease. Conclusion: The DRHM system can analyze the multivariate accommodation percentage of a sizing system, provide body sizes of generated DRHMs, and visualize generated grids and DRHMs. Application: The DRHM generation and analysis system can be of great use to determine an optimal sizing system for a multiple-size product by comparing various sizing system candidates.

• Journal of Distribution Science
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• v.19 no.10
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• pp.17-22
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• 2021

Purpose: Covid-19 has caused an unprecedented situation for the tourism industry with slumping demand during the outbreak and many uncertainties about tourist behavior in the post-pandemic. This study is aimed to discover the distribution in the behavior of tourists in Vietnam, whose government has taken serious and early actions towards the health crisis and among the earliest to reopen the economy. Research design, data, and methodology: We adopted a mixed-method approach - combining qualitative interviews with quantitative research using a questionnaire survey. Through the form of the online survey through social networking channels: Facebook, Gmail. The study received 261 valid responses for analysis. Multivariate analysis techniques were used: descriptive statistics, exploratory factor analysis (EFA). Results: From the data and result of EFA, the result showed that the distribution of tourist behavior could be grouped into four main factors, including (1) the general impacts, (2) travel-related behaviors; (3) attitudes and preferences regarding modes of tours and destinations; (4) awareness of safety and hygiene. Conclusions: These results highlighted the importance of the theory of perceived risks in explaining the travelers' prudent decisions. In addition, this study provides practical implications for policymakers and various stakeholders of Vietnam's tourism industry in formulating the recovery strategy.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.221-229
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• 2021

This study aims to forecast the exchange rate of the Chinese Yuan against the US Dollar by a combination of different models as proposed by Poon and Granger (2003) during the Covid-19 pandemic. For this purpose, we include three uni-variate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Chinese Yuan against the US dollar by two combination criteria i.e. var-cor and equal weightage. After finding out the individual accuracy, the models are then assessed through equal weightage and var-cor methods. Our results suggest that Naïve outperforms all individual & combination of time series models. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models except the Naïve model, with the lowest MAPE value of 0764. The results suggesting that the Chinese Yuan exchange rate against the US Dollar is dependent upon the recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting which commensurate with the literature.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.339-347
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• 2021

This study aims to forecast the exchange rate by a combination of different models as proposed by Poon and Granger (2003). For this purpose, we include three univariate time series models, i.e., ARIMA, Naïve, Exponential smoothing, and one multivariate model, i.e., NARDL. This is the first of its kind endeavor to combine univariate models along with NARDL to the best of our knowledge. Utilizing monthly data from January 2011 to December 2020, we predict the Pakistani Rupee against the US dollar by a combination of different forecasting techniques. The observations from M1 2020 to M12 2020 are held back for in-sample forecasting. The models are then assessed through equal weightage and var-cor methods. Our results suggest that NARDL outperforms all individual time series models in terms of forecasting the exchange rate. Similarly, the combination of NARDL and Naïve model again outperformed all of the individual as well as combined models with the lowest MAPE value of 0.612 suggesting that the Pakistani Rupee exchange rate against the US Dollar is dependent upon the macro-economic fundamentals and recent observations of the time series. Further evidence shows that the combination of models plays a vital role in forecasting, as stated by Poon and Granger (2003).

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.387-397
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• 2021

The object of this article is to assess the factors affecting the behavioral intention of young retail customers to use digital banking services in Vietnam. In this article, multivariate data analysis techniques including Cronbach's Alpha, Exploratory factor analysis (EFA), Confirmatory factor analysis (CFA), Structure equation model (SEM), and Bootstrapping are used to analyze the data collected from 525 young respondents under the age of 35 who are using or having opportunities to experience digital banking services. The people taking part in the survey were mainly University students with incomes of most of them under VND 5 million. The result from the analysis illustrates that (1) perceived ease of use positively affects intention to use, (2) social influence positively impacts intention to use, and (3) customer support has a positive impact on the intention of young people to use digital banking services. While technology self-efficacy, convenience, and perceived security were found to have an impact on intention to use services in former studies, the influences of these factors on intention to use digital banking services are found insignificant in this research. From these results, the author provides implications for commercial banks to increase the intention to use digital banking of young people in Vietnam.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1630-1643
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• 2023

The correct situation awareness (SA) of operators is important for managing nuclear power plants (NPPs), particularly in accident-related situations. Among the three levels of SA suggested by Ensley, Level 3 SA (i.e., projection of the future status of the situation) is challenging because of the complexity of NPPs as well as the uncertainty of accidents. Hence, several prediction methods using artificial intelligence techniques have been proposed to assist operators in accident prediction. However, these methods only predict short-term plant status (e.g., the status after a few minutes) and do not provide information regarding the uncertainty associated with the prediction. This paper proposes an algorithm that can predict the multivariate and long-term behavior of plant parameters for 2 h with 120 steps and provide the uncertainty of the prediction. The algorithm applies bidirectional long short-term memory and an attention mechanism, which enable the algorithm to predict the precise long-term trends of the parameters with high prediction accuracy. A conditional variational autoencoder was used to provide uncertainty information about the network prediction. The algorithm was trained, optimized, and validated using a compact nuclear simulator for a Westinghouse 900 MWe NPP.