• Journal of Bio-Environment Control
• /
• v.26 no.4
• /
• pp.424-431
• /
• 2017

This study was conducted to set the optimum nutrient solution concentration by growth stage for new strawberry cultivars 'Berrystar' and 'Jukhyang'(Fragaria ${\times}$ ananassa Duch. cvs. 'Berrystar', 'Jukhyang') grown through hydroponics to improve the quality and yield. Three different EC levels were applied to the nutrient solution. The treatment levels were 0.7, 1.0 and 1.3 times higher than the nutrient concentration standard for 'Seolhyang' based on the 'Manual for strawberry cultivation' of Rural Development Administration. Based on the results, there were no significant differences in growth of 'Berrystar' by EC level. 'Jukhyang' showed the most vigorous growth grown in 1.3 times higher nutrient concentration. While the growth of 'Berrystar' and 'Jukhyang' grown in higher EC level has leaves with more chlorophyll concentration. However the quantum yield of leaves was not affected by the treatments. On the treatment with 1.3 times higher EC level, the weight, length, width and firmness of 'Berrystar' and 'Jukhyang' were significantly high. The sugar contents of the harvest analyzed by HPLC did not differed particularly, but the percentage composition of reducing sugar and non-reducing sugar were presented differently depending on the treatments. Marketable fruit yield increased as nutrient concentration increases. However, there were no large differences by treatments. Meanwhile, 'Jukhyang' showed significant difference by nutrient concentration and had the largest yield for a treatment grown in 1.3 times higher EC level. Based on these results, it is recommended to provide the same nutrient solution concentration level to the nutrient concentration standard of 'Seolhyang' for 'Berrystar', and the 1.3 times higher level for 'Jukhyang'.

• Horticultural Science & Technology
• /
• v.33 no.5
• /
• pp.687-696
• /
• 2015

Black mini tomato 'Hei-G' fruits were harvested at different stages of maturity (immature-mature green and mature-black red) and stored at different temperatures (8, 12, and $20^{\circ}C$) to investigate the quality and lycopene content during storage. Weight loss increased dramatically at higher temperature for both harvesting stages without significant differences. Firmness of immature fruits decreased below the initial level of mature fruit (8.1N) after 5, 8, and 19 days storage, when they were stored at 20, 12, and $8^{\circ}C$, respectively. Soluble solid contents of mature fruit increased at initial storage, and were higher as compared to immature fruits before deterioration at each storage temperature. Decrease in titratable acid of mature fruits depended on storage time and temperature. However, titratable acid of immature fruits showed little change during storage, and so it did not affect flavor. Hunter a value changed greatly in immature fruit stored at high temperature. Unlike ripe tomatoes, there was no significant difference in black tomato Hunter b values of immature and mature fruit at initial and 12 days storage. However, immature fruits stored at $8^{\circ}C$ did not reach full maturity and color development and ripening. High storage temperature increased lycopene production while low storage temperature blocked lycopene development. Shelf life of the immature fruits, which was evaluated by elapsed days to conventional mature stage, was 12 and 15 days when they were stored at 20 and $12^{\circ}C$, respectively. The optimum storage temperature to maintain the quality and lycopene content of mature fruits was $12^{\circ}C$. Moreover, the shelf life of mature fruits stored at $20^{\circ}C$ could reach up to 5 days.

• Journal of Internet Computing and Services
• /
• v.14 no.6
• /
• pp.1-8
• /
• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Economic and Environmental Geology
• /
• v.49 no.6
• /
• pp.459-467
• /
• 2016

Due to the limited areal space for installation, borehole heat exchangers (BHEs) at depths deeper than 300 m are considered for geothermal heating and cooling in the urban area. The deep vertical closed-loop BHEs are unconventional due to the depth and the range of the typical installation depth is between 100 and 200 m in Korea. The BHE in the study consists of 50A (outer diameter 50 mm, SDR 11) PE U-tube pipe in a 150 mm diameter borehole with the depth of 300 m. In order to compensate the buoyancy caused by the low density of PE pipe ($0.94{\sim}0.96g/cm^3$) in the borehole filled with ground water, 10 weight band sets (4.6 kg/set) were attached to the bottom of U-tube. A thermal response test (TRT) and fundamental basic surveys on the thermophysical characteristics of the ground were conducted. Ground temperature measures around $15^{\circ}C$ from the surface to 100 m, and the geothermal gradient represents $1.9^{\circ}C/100m$ below 100 m. The TRT was conducted for 48 hours with 17.5 kW heat injection, 28.65 l/min at a circulation fluid flow rate indicates an average temperature difference $8.9^{\circ}C$ between inlet and outlet circulation fluid. The estimated thermophysical parameters are 3.0 W/mk of ground thermal conductivity and 0.104 mk/W of borehole thermal resistance. In the stepwise evaluation of TRT, the ground thermal conductivity was calculated at the standard deviation of 0.16 after the initial 13 hours. The sensitivity analysis on the borehole thermal resistance was also conducted with respect to the PE pipe diameter and the thermal conductivity of backfill material. The borehole thermal resistivity slightly decreased with the increase of the two parameters.

• Journal of Korean Society of Environmental Engineers
• /
• v.22 no.12
• /
• pp.2115-2123
• /
• 2000

This research was designed to investigate the mathematical model and kinetics of phenol desorption from spent activated carbon. elucidating the desorption characteristics of phenol in the case of using acetone. The Freundlich isotherm constant ($k_e$) is expressed as a function of temperature: $k_e(T)=0.1exp(797.297/T)$. The Freundlich isotherm constant(n) is a weak temperature function and is rarely affected by temperature below $50^{\circ}C$. whereas it is necessary to correct the n value with respect to temperature above $100^{\circ}C$ owing to significant deviation (~5%). Based on the assumption that the surface desorption reaction of phenol is rate limiting, the desorption model was developed. Desorption reaction constant($k_d$) was determined by means of fitting the theoretical results best to experimental ones. The Arrhenius relationships for $k_d$ was expressed by: $k_d(sec^{-1})=0.0479{\cdot}exp(-3037/T)$. The model was verified by comparing the experimental ones under different reaction conditions with the theoretical results determined by the previously estimated $k_d$. Since the difference between them is with 5%, it is expected that the desorption model of this research seems to be appropriate to explain the desorption of phenol from activated carbon by acetone. According to studies of the model. regeneration time and ratio was estimated as a function of temperature under present conditions as follows: (1) regeneration time : ${\tau}_{reg}(hr)=-0.08130T_c+8.4775$. (2) regeneration ratio : ${\eta}(%)=0.2210T_c+83.745$. The regeneration time at 15, 55, and $100^{\circ}C$. respectively. was 7, 4.2, and 0.35 hours, whereas the regeneration ratio was 87. 96. and 99%. respectively. Also. studies of the model would make it possible to determine the regeneration time and ratio under other specific conditions (temperature, applied acetone volume, amount of activated carbon, and initially adsorbed phenol amount).

• Journal of Bio-Environment Control
• /
• v.26 no.4
• /
• pp.268-275
• /
• 2017

The photosynthetic rates of crops depend on growth environment factors, such as light intensity and temperature, and their photosynthetic efficiencies vary with growth stage. The objective of this study was to compare two different models expressing canopy photosynthetic rates of romaine lettuce (Lactuca sativa L., cv. Asia Heuk romaine) using three variables of light intensity, temperature, and growth stage. The canopy photosynthetic rates of the plants were measured 4, 7, 14, 21, and 28 days after transplanting at closed acrylic chambers ($1.0{\times}0.8{\times}0.5m$) using light-emitting diodes, in which indoor temperature and light intensity were designed to change from 19 to $28^{\circ}C$ and 50 to $500{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, respectively. At an initial $CO_2$ concentration of $2,000{\mu}mol{\cdot}mol^{-1}$, the canopy photosynthetic rate began to be calculated with $CO_2$ decrement over time. A simple multiplication model expressed by simply multiplying three single-variable models and a modified rectangular hyperbola model were compared. The modified rectangular hyperbola model additionally included photochemical efficiency, carboxylation conductance, and dark respiration which vary with temperature and growth stage. In validation, $R^2$ value was 0.849 in the simple multiplication model, while it increased to 0.861 in the modified rectangular hyperbola model. It was found that the modified rectangular hyperbola model was more suitable than the simple multiplication model in expressing the canopy photosynthetic rates affected by environmental factors (light Intensity and temperature) and growth factor (growth stage) in plant factory modules.

• The Korean Journal of Food And Nutrition
• /
• v.23 no.3
• /
• pp.428-433
• /
• 2010

Tomato were stored at different temperatures($10^{\circ}C$, $20^{\circ}C$, room temperature and $30^{\circ}C$) for 5 days. During the storage period, Brix, pH, color, texture, vitamin C, lycopene were analyzed. Brix and pH had a little change. Texture force of tomato decreased with storage time and we could see a softening for tomato stored at $30^{\circ}C$ for 1 day. Addtional, the $L^*$(lightness) and $b^*$(yellowness) decreased and $a^*$(redness) increased with storage time. Addtionally, content of vitamin C increased up to 9.08 mg/100 g~17.82 mg/100 g after 5 days storage according storage temperature, whereas content of lycopene increased up to 3.81 mg/kg~34.56 mg/kg after 5 days storage according storage temperature. Optimal mature temperature for tomato was room temperature.

• Food Science and Preservation
• /
• v.21 no.2
• /
• pp.239-245
• /
• 2014

This study was performed to provide fundamental information regarding the quality change of buckwheat soksungjang (BWS) during its storage. BWS was divided into three different containers (pot, plastic, and glass) and was stored at three different temperatures (5, 15, and $25^{\circ}C$), and the changes in pH, acidity, amino-type nitrogen, total bacterial count, and chromaticity were examined during the storage period. The pH (0 day, pH 4.37) and acidity (0 day, 2.93% acidity) of the samples, except at the 15 and $25^{\circ}C$ pots, did not show any significant change during storage, but 98 days after storage, the pH values of the 15 and $25^{\circ}C$ pots were pH 5.6 and 7.4, and their acidity values were 1.85 and 0.71%, respectively. At 98 days, the amino-type nitrogen of the $25^{\circ}C$ plastic sample had slightly increased to $0.75{\pm}0.01%$, and that of the $25^{\circ}C$ pot had drastically risen to $0.92{\pm}0.01%$. It was also shown that little change in the total bacterial count was found during the experiment period in every sample. The chromaticity results confirmed that the L (lightness), a (redness), and b (yellowness) values of the $25^{\circ}C$ pot sample showed relatively large changes during storage compared to the other samples. These results suggest that the desirable storage temperature of BWS is in the range of $5-15^{\circ}C$, and that a glass container is the most suitable container for BWS as it can reduce the quality alteration during storage.

• Korean Journal of Remote Sensing
• /
• v.40 no.4
• /
• pp.387-396
• /
• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Horticultural Science & Technology
• /
• v.29 no.5
• /
• pp.420-432
• /
• 2011

Population dynamics of greenhouse whitefly, Trialeurodes vaporariorum (Westwood), were modeled and simulated to compare the temperature effects of air and tomato leaf inside greenhouse using DYMEX model simulator (pre-programed module based simulation program developed by CSIRO, Australia). The DYMEX model simulator consisted of temperature dependent development and oviposition modules. The normalized cumulative frequency distributions of the developmental period for immature and oviposition frequency rate and survival rate for adult of greenhouse whitefly were fitted to two-parameter Weibull function. Leaf temperature on reversed side of cherry tomato leafs (Lycopersicon esculentum cv. Koko) was monitored according to three tomato plant positions (top, > 1.6 m above the ground level; middle, 0.9 - 1.2 m; bottom, 0.3 - 0.5 m) using an infrared temperature gun. Air temperature was monitored at same three positions using a Hobo self-contained temperature logger. The leaf temperatures from three plant positions were described as a function of the air temperatures with 3-parameter exponential and sigmoidal models. Data sets of observed air temperature and predicted leaf temperatures were prepared, and incorporated into the DYMEX simulator to compare the effects of air and leaf temperature on population dynamics of greenhouse whitefly. The number of greenhouse whitefly immatures was counted by visual inspection in three tomato plant positions to verify the performance of DYMEX simulation in cherry tomato greenhouse where air and leaf temperatures were monitored. The egg stage of greenhouse whitefly was not counted due to its small size. A significant positive correlation between the observed and the predicted numbers of immature and adults were found when the leaf temperatures were incorporated into DYMEX simulation, but no significant correlation was observed with the air temperatures. This study demonstrated that the population dynamics of greenhouse whitefly was affected greatly by the leaf temperatures, rather than air temperatures, and thus the leaf surface temperature should be considered for management of greenhouse whitefly in cherry tomato grown in greenhouses.