• Korean Journal of Environmental Agriculture
• /
• v.22 no.4
• /
• pp.278-283
• /
• 2003

This study was carried out to examine the nitrogen removal rate of a subsurface-flow treatment wetland system which was constructed on floodplain of the Kwangju River from May to June 2001. Its dimensions were 29m in length, 9m in width and 0.65m in depth. A bottom layer of 45cm in depth was filled with crushed granite with about $15{\sim}30\;mm$ in diameter and a middle layer of 10cm in depth had pea pebbles with about 10 mm in diameter. An upper layer of 5 cm in depth contained course sand. Reeds (Phragmites australis) were transplanted on the surface of the system. They were dug out of natural wetlands and stems were cut at about 40 cm height from their bottom ends. Water of the Kwangju River flowed into it via a pipe by gravity flow and its effluent was funneled back into the river. The height of reed stems was 44.2 cm in July 2001 and 75.3cm in September 2001. The number of stems was increased from $80\;stems/m^2$ in July 2001 to $136\;stems/m^2$ in September 2001. Volume and water quality of inflow and outflow were analyzed from July 2001 through December 2001. Inflow and outflow averaged 40.0 and $39.2\;m^3/day$, respectively. Hydraulic detention time was about 1.5 days. Average nitrogen uptake by reeds was $69.31\;N\;mg/m^2/day$. Removal rate of $NO_3-N$, $NH_3-N$, T-N averaged 195.58, 53.65, and $628.44\;mg/m^2/day$, respectively. Changes of $NO_3-N$ and $NH_3-N$ abatement rates were closely related to those of wetland temperatures. The lower removal rate of nitrogen species compared with that of subsurface-flow wetlands operating in North America could be attributed to the initial stage of the system and inclusion of two cold months into the six-month monitoring period. Increase of standing density of reeds within a few years will develop both root zones suitable for the nitrification of ammonia and surface layer substrates beneficial to the denitrification of nitrates into nitrogen gases, which may lead to increment in the nitrogen retention rate.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.109-125
• /
• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.