• Journal of Korean Society of Water and Wastewater
• /
• v.35 no.6
• /
• pp.437-454
• /
• 2021

This study investgates Korean water technology through the water market perspective and analyses its competitiveness. Based on the water technology classification, water technology competitiveness is analysed through the technological influence index and market dominance index which are based on the extracted water technology patents from the US, Europe, Korea, and Japan for the last decade. As a result, the Korean water technology patents were lack in influence and competitiveness in global market considering the large volume of patents. There are two most tech-influential industries in Korea; manufacturing industry consisting pipes, sterilization, disinfection, and advanced water purification equipment, and construction industry including seawater desalination and water resource development. Due to the domestic usage of the patents, the Korean water technology patents scored low in global market PFS(Patent Family Size) index compared to their CPP(Cites Per Patent) index. The study is meaningful in a way that the analysis on Korean water technology competitiveness using water technology classification system and patent analysis was conducted based on the perspective of the global water market.

• Journal of Korea Water Resources Association
• /
• v.48 no.11
• /
• pp.945-955
• /
• 2015

The technology of the SWG (Smart Water Grid) as one of most important national projects results in significant assignment that is closely associated with systematic management and effective operation. The individual component technics are required to establish directory and classification for the purpose of effectively managing their information related to research and development (R&D). The national science technology (S&T) standard classification tree which results in the representative example has been established with an intention to manage R&D information, human resource, and budget. It has been also revised every five years and then used in the various fields related to the evaluation, administration, and prediction of the national R&D projects. In addition, the standard classification system for R&D projects has been widely used in the UNESCO (United Nations Educational, Scientific and Cultural Organization) and EU (European Union) since the Frascati Manual was established in the Organization for Economic Cooperation and Development (OECD). Therefore, it is necessary for SWG techniques to develop the standard S&T classification tree for research management and evaluation. For this, it is essential to draw the core techniques for the SWG, which are incorporated with IT (Information Technology), NT (Nano Technology), and BT (Biology Technology).

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.09b
• /
• pp.1138-1140
• /
• 2005

• Korean Journal of Remote Sensing
• /
• v.35 no.4
• /
• pp.561-571
• /
• 2019

In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image.After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.204-206
• /
• 2003

In order to utilize remote sensed images effectively, a lot of image classification methods are suggested for many years. But, the accuracy of traditional methods based on pixel-based classification is not high in general. In this study, object oriented classification based on image segmentation is used to classify Landsat images. A necessary prerequisite for object oriented image classification is successful image segmentation. Object oriented image classification, which is based on fuzzy logic, allows the integration of a broad spectrum of different object features, such as spectral values , shape and texture. Landsat images are divided into urban, agriculture, forest, grassland, wetland, barren and water in sochon-gun, Chungcheongnam-do using object oriented classification algorithms in this paper. Preliminary results will help to perform an automatic image classification in the future.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.2 no.1
• /
• pp.29-37
• /
• 1999

Land cover classification is an essential basic information in natural environment management; however, land cover classification studies in Korea have not yet been proceeded to a sufficient level. At the present, only a limited number of the precedent studies that only cover definite city area has been conducted. Furthermore, there is almost no research conducted on the land cover classification schemes that could accurately classify the Korea's land cover conditions. This study primarily focuses on the land cover classification scheme which carries the most urgent priority in order to classify and to map out the Korean land cover conditions. In order to develop the most suitable land cover classification scheme, many foreign land cover classification cases and projects that are being carried out were reviewed in depth. The land cover classification scheme this study proposes comprises 3 levels : The first level consists of 7 different classes; the second level consists of 22 different classes; and the third level is made up of 50 classes. The land cover classification map will serve many important roles in natural environment management, such as the conjecture of natural habitats and estimation of oxygen production or carbon dioxide absorption capability of a forest. In water pollution modelling, the land cover classification data can be used to estimate and locate non-point sources of water pollution. If applied to a watershed, modelling it will allow to estimate the total amount of pollution from non-point sources of pollution in the water shed. The land cover classification data will also be good as a barometer data that determines defusion of air pollutants in air pollution modelling.

• The Journal of the Korea Contents Association
• /
• v.9 no.8
• /
• pp.1-8
• /
• 2009

With the development of IT convergence technology and the construction of master plan for the four rivers restoration of the government, the importance of the eco-friendly water pollution management is being spotlighted. In this paper, we proposed the effective water pollution management using the reservoir tank automatic classification for improving the water quality and on-line managing efforts of ceo-friendly reservoir tanks. The proposed method defined the seven factors of water pollution evaluation and managed the water pollution according to hydrogen ion concentration(pH), chemical oxygen demand(COD), suspend solid(SS), dissolved oxygen(DO), count of coliform group(MPN), total phosphorus(T-P), and total nitrogen(T-N) using the sensors. We measured the values for the seven factors from the reservoir tank and normalized to ranging from 1 to 9. To evaluate the performance of the water pollution management using the reservoir tank automatic classification, we conducted F-measure so as to verify usefulness. This evaluation found that the difference of satisfaction by the traditional system was statistically meaningful.

• Spatial Information Research
• /
• v.23 no.2
• /
• pp.31-38
• /
• 2015

Remote sensing data is used to increasing efficiency on benthic cover type survey. Satellite and aerial imagery has variance of reflectance by water column effect even if bottom is consisted with same cover type and condition. This study tried to analyze advances of surveying extent and accuracy through water column correction of CASI-1500 hyperspectral image. Study area is coast of Gangneung city, South Korea where benthic environment is rapidly changing with bleaching of coral reef. Water column correction coefficient was estimated using regression models between water reflectance ($R_W$) and depth for sand bottom then the coefficients were applied to whole image. The results shows that expanded interpretable depth from 6-7m to 15m and decreased variation of reflectance by depth. Additionally, water column corrected reflectance image shows 13%p increased accuracy on benthic cover type classification.

• Ecology and Resilient Infrastructure
• /
• v.9 no.4
• /
• pp.218-227
• /
• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.403-413
• /
• 2022

Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.