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Do, Khac Phong,Nguyen, Ba Tung,Nguyen, Xuan Thanh,Bui, Quang Hung,Tran, Nguyen Le,Nguyen, Thi Nhat Thanh,Vuong, Van Quynh,Nguyen, Huy Lai,Le, Thanh Ha Korea Information Processing Society 2015 Journal of information processing systems Vol.11 No.4
This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.
( Khac Phong Do ),( Ba Tung Nguyen ),( Xuan Thanh Nguyen ),( Quang Hung Bui ),( Nguyen Le Tran ),( Thi Nhat Thanh Nguyen ),( Van Quynh Vuong ),( Huy Lai Nguyen ),( Thanh Ha Le ) 한국정보처리학회 2015 Journal of information processing systems Vol.11 No.4
This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.
An Cong Tran,Lai Thi Ho,Hai Thanh Nguyen 대한전자공학회 2022 IEIE Transactions on Smart Processing & Computing Vol.11 No.5
Information extraction automatically obtains structured information from unstructured or semi-structured machine-readable documents. The extraction steps consist mainly of classifying words (tagging). The output can be stored as key-value pairs in a computer-friendly file format, and then stored in a database for later reference. Information extraction from receipts or invoices is a difficult task because the tagging step should not be done solely on machine-readable words. Also, we obtain layout information or positions of words relative to other words in the invoices or receipts. This study deployed optical character recognition solutions for the Vietnamese language (VietOCR) combining a graph convolutional network (GCN) to extract information from 731 Vietnamese invoices issued by several stores. First, we collected invoice images captured with smartphones from supermarkets in Vietnam. Then, with those images we proceeded with text detection and recognition, then feature processing. The dataset was classified into two parts for training and testing, and we executed classification tasks with two GCNs. Experimental results revealed that our proposed method reached 99.50%, 98.52%, 98.52%, and 98.52% for accuracy, recall, precision, and F1-score, respectively. This work is expected to prove useful for information extraction from image-based documents.
Thanh Van PHAM,Van Luan NGUYEN,Thi Lai NGUYEN,Thi Thu PHAN 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.9
This research was conducted to check the impact of factors related to the small and medium-sized enterprises (SME) on the economic growth in the Southeast region of Vietnam, over the years from 1996–2019. This paper applies a combination of FEM, DKSE, GMM, and RIDGE-FEM regression methods to estimate the influence of independent variables on the economic growth of the whole Southeast region with the panel data collected from GSO; and applying the OLS regression model for each province. The study finds that all variables have a statistically significant positive impact on the economic growth of the study area. Accordingly, the importance of the variables is in the following order: (1) the proportion of workers by professional and technical qualification (SMEH), (2) the number of vocational training schools (LnTSCH), and educational level of workers (LnSchool), (3) the number of SME enterprises (LnSME); (4) The average number of years in the schooling of employees in the enterprise (LnSchool); (5) Enterprise capital (LnCAP); and (6) the average number of employees of SME (LnSMER). The research results also show that factors related to the quality of labor resources have a more positive influence on growth than both the labor size and financial capital of SMEs.