• Korean Journal of Environmental Biology
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• v.36 no.4
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• pp.479-487
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• 2018

The purpose of this study is to analyze photochemical activity of nursery seedlings under drought stress, using chlorophyll fluorescence reaction analysis. Young nursery seedlings of tomato (Lycopersicon esculentum Mill.) and cucumber (Cucumis sativa L.), were grown under drought stress for 8 days. Analysis of chlorophyll fluorescence reaction (OJIP) and parameters, were performed to evaluate photochemical fluctuation in nursery seedlings under drought stress. Chlorophyll fluorescence reaction analysis showed maximal recorded fluorescence (P) decreased from the 5 day after treatment in tomato seedlings, while an amount of chlorophyll fluorescence increased at the J-I step. Thus, physiological activity was reduced. In cucumber seedlings, maximal recorded fluorescence (P) and maximal variable fluorescence ($F_V$) lowered from the 4 day after treatment, and chlorophyll fluorescence intensity of J-I step increased. Chlorophyll fluorescence parameter analysis showed electron transfer efficiency of PSII and PSI were significantly inhibited with decreasing $ET2_O/RC$ and $RE1_O/RC$ from the 5 day after treatment, in tomato seedlings and from the 4 day after treatment, in cucumber seedlings. $ET2_O/RC$ and $PI_{ABS}$ significantly changed. In conclusion, 6 indices such as $F_V/F_M$, $DI_O/RC$, $ET2_O/RC$, $RE1_O/RC$, $PI_{ABS}$ and $PI_{TOTAL}ABS$ were selected for determining drought stress in nursery seedlings. Drought stress factor index (DFI) was used to evaluate whether the crop was healthy or not, under drought stress. Cucumber seedlings were less resistant to drought stress than tomato seedlings, in the process of drought stress.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.249-267
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• 2021

The use of big data accumulated along with the progress of digitalization is bringing disruptive innovation to the global agricultural industry. Recently, the government is establishing an agricultural big data platform and a support organization. However, in the domestic agricultural industry, the use of big data is insufficient except for some companies in the field of cultivation and growth. In this context, this study identifies factors affecting the intention to use big data in terms of technology, organization and environment, and also confirm the moderating effect of technical field, focusing on agricultural ventures which should be the main entities in creating innovation by using big data. Research data was obtained from 309 agricultural ventures supported by the A+ Center of FACT(Foundation of AgTech Commercialization and Transfer), and was analyzed using IBM SPSS 22.0. As a result, Among technical factors, relative advantage and compatibility were found to have a significant positive (+) effect. Among organizational factors, it was found that management support had a positive (+) effect and cost had a negative (-) effect. Among environmental factors, policy support were found to have a positive (+) effect. As a result of the verification of the moderating effect of technology field, it was found that firms other than cultivation had a moderating effect that alleviated the relationship between all variables other than relative advantage, compatibility, and competitor pressure and the intention to use big data. These results suggest the following implications. First, it is necessary to select a core business that will provide opportunities to generate new profits and improve operational efficiency to agricultural ventures through the use of big data, and to increase collaboration opportunities through policy. Second, it is necessary to provide a big data analysis solution that can overcome the difficulties of analysis due to the characteristics of the agricultural industry. Third, in small organizations such as agricultural ventures, the will of the top management to reorganize the organizational culture should be preceded by a high level of understanding on the use of big data. Fourth, it is important to discover and promote successful cases that can be benchmarked at the level of SMEs and venture companies. Fifth, it will be more effective to divide the priorities of core business and support business by agricultural venture technology sector. Finally, the limitations of this study and follow-up research tasks are presented.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.73-82
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• 2009

Due to the many advantages including low price, low power consumption, and miniaturization, the CMOS camera has been utilized in many applications, including mobile phones, the automotive industry, medical sciences and sensoring, robotic controls, and research in the security field. In particular, the 360 degree omni-directional camera when utilized in multi-camera applications has displayed issues of software nature, interface communication management, delays, and a complicated image display control. Other issues include energy management problems, and miniaturization of a multi-camera in the hardware field. Traditional CMOS camera systems are comprised of an embedded system that consists of a high-performance MCU enabling a camera to send and receive images and a multi-layer system similar to an individual control system that consists of the camera's high performance Micro Controller Unit. We proposed the SL-AVS (Small Size/Low power Around-View System) to be able to control a camera while collecting image data using a high speed synchronization technique on the foundation of a single layer low performance MCU. It is an initial model of the omni-directional camera that takes images from a 360 view drawing from several CMOS camera utilizing a 110 degree view. We then connected a single MCU with four low-power CMOS cameras and implemented controls that include synchronization, controlling, and transmit/receive functions of individual camera compared with the traditional system. The synchronization of the respective cameras were controlled and then memorized by handling each interrupt through the MCU. We were able to improve the efficiency of data transmission that minimizes re-synchronization amongst a target, the CMOS camera, and the MCU. Further, depending on the choice of users, respective or groups of images divided into 4 domains were then provided with a target. We finally analyzed and compared the performance of the developed camera system including the synchronization and time of data transfer and image data loss, etc.

• Journal of Practical Agriculture & Fisheries Research
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• v.11 no.1
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• pp.103-111
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• 2009

This study was carried out to determine effects of long-term supply of Cordyceps militaris mycelia on cordycepin content in milk in dairy cows. Ten Holstein cows in the early stages of lactation were divided into two groups. Control group received no supplement whereas treatment group received 6% of C. militaris mycelia of their dry matter intake for 6 months. Feed intake, milk urea nitrogen and somatic cell counts were not affected by long-term supply of C. militaris mycelia for the whole period. In addition, milk yield and milk composition were not affected by long-term supply of C. militaris mycelia at any time of the periods with the exception of milk protein content and yield. The average of milk protein content and yield from the whole period was higher for C. militaris mycelia supplement group than for the control group. As expected, cordycepin in whole blood and milk was not detected in the control group. The range of cordycepin content in the treatment was 0.31~0.38µ/ml and 0.18~0.26(µ/ml for whole blood and milk, respectively. Individual variation was found to be very high and, furthermore cordycepin was undetected in some milk samples. Thus, no clear pattern could be seen in cordycepin content in milk throughout the whole period. Overall, the results of the present study suggest that the transfer efficiency of cordycepin to milk by supplementing C. militaris mycelia in dairy cows was unpredictable and low.

• Journal of Practical Agriculture & Fisheries Research
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• v.11 no.1
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• pp.113-123
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• 2009

This study was carried out to determine effects of long-term supply of Cordyceps militaris mycelia on cordycepin content in milk in dairy cows. Ten Holstein cows in the early stages of lactation were divided into two groups. Control group received no supplement whereas treatment group received 6% of C. militaris mycelia of their dry matter intake for 6 months. Feed intake, milk urea nitrogen and somatic cell counts were not affected by long-term supply of C. militaris mycelia for the whole period. In addition, milk yield and milk composition were not affected by long-term supply of C. militaris mycelia at any time of the periods with the exception of milk protein content and yield. The average of milk protein content and yield from the whole period was higher for C. militaris mycelia supplement group than for the control group. As expected, cordycepin in whole blood and milk was not detected in the control group. The range of cordycepin content in the treatment was 0.31~0.38µ/ml and 0.18~0.26(µ/ml for whole blood and milk, respectively. Individual variation was found to be very high and, furthermore cordycepin was undetected in some milk samples. Thus, no clear pattern could be seen in cordycepin content in milk throughout the whole period. Overall, the results of the present study suggest that the transfer efficiency of cordycepin to milk by supplementing C. militaris mycelia in dairy cows was unpredictable and low.

• Journal of Practical Agriculture & Fisheries Research
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• v.4 no.1
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• pp.119-127
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• 2002

This study was conducted to develop the heat exchanger by utilizing the heat energy of underground water(15℃), which might be used for cooling and heating system of the agricultural facilities. We developed the heat exchanger, parallel type plat fin tube made of Aluminum(Al 6063), which was named Aloo-Heat(No. of The registration design : 0247164, by Korean Intellectual property Office). The fin of exchanger was design of the granulated surface for minimizing fouling factor and dew forms, and also placed parallel to the tube in order to minimized the resistance of flows. 1. Aloo-heat was designed to have 0.03m for inside diameter, 0.036m for outside diameter of tube, 0.0012m for thickness of fin and 0.032m for length of plat fin. 2. t was also designed to have 1.5248m²/m for outside area of heat transfer, 0.0942m²/m for inside area contacting hot liquid, and the ratio (Ra) was 16.1869. 3. Efficiency of the fin was 93 percentage when fin length was 0.032m, and the fin thickness satisfied equation $\frac{h{\rho}}{k}$< 0.2 when it was 0.0012m. 4. According to the performance test of Aloo-heat, as the temperature and rate increased, the heating value also increased, heating value was 504kJ/h·m and 6,048kJ/h·m when it was 60℃, 10 𝑙/min and 80℃, 40 𝑙/min respectively. 5. The test of heating value was confident, because correlation value(R²) was 0.9898 for the temperature and 0.9721 for flow rate of hot liquid, respectively.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• Magazine of the Korean Society of Agricultural Engineers
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• v.16 no.1
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• pp.3225-3262
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• 1974

The purposes of this thesis are to clarify experimentally the variation of ground water temperature in tube wells during the irrigation period of paddy rice, and the effect of ground water irrigation on the growth, grain yield and yield components of the rice plant, and, furthermore, when and why the plant is most liable to be damaged by ground water, and also to find out the effective ground water irrigation methods. The results obtained in this experiment are as follows; 1. The temperature of ground water in tube wells varies according to the location, year, and the depth of the well. The average temperatures of ground water in a tubewells, 6.3m, 8.0m deep are $14.5^{\circ}C$ and $13.1^{\circ}C$, respercively, during the irrigation period of paddy rice (From the middle of June to the end of September). In the former the temperature rises continuously from $12.3^{\circ}C$ to 16.4$^{\circ}C$ and in the latter from $12.4^{\circ}C$ to $13.8^{\circ}C$ during the same period. These temperatures are approximately the same value as the estimated temperatures. The temperature difference between the ground water and the surface water is approximately $11^{\circ}C$. 2. The results obtained from the analysis of the water quality of the "Seoho" reservoir and that of water from the tube well show that the pH values of the ground water and the surface water are 6.35 and 6.00, respectively, and inorganic components such as N, PO4, Na, Cl, SiO2 and Ca are contained more in the ground water than in the surface water while K, SO4, Fe and Mg are contained less in the ground water. 3. The response of growth, yield and yield components of paddy rice to ground water irrigation are as follows; (l) Using ground water irrigation during the watered rice nursery period(seeding date: 30 April, 1970), the chracteristics of a young rice plant, such as plant height, number of leaves, and number of tillers are inferior to those of young rice plants irrigated with surface water during the same period. (2) In cases where ground water and surface water are supplied separately by the gravity flow method, it is found that ground water irrigation to the rice plant delays the stage at which there is a maximum increase in the number of tillers by 6 days. (3) At the tillering stage of rice plant just after transplanting, the effect of ground water irrigation on the increase in the number of tillers is better, compared with the method of supplying surface water throughout the whole irrigation period. Conversely, the number of tillers is decreased by ground water irrigation at the reproductive stage. Plant height is extremely restrained by ground water irrigation. (4) Heading date is clearly delayed by the ground water irrigation when it is practised during the growth stages or at the reproductive stage only. (5) The heading date of rice plants is slightly delayed by irrigation with the gravity flow method as compared with the standing water method. (6) The response of yield and of yield components of rice to ground water irrigation are as follows: \circled1 When ground water irrigation is practised during the growth stages and the reproductive stage, the culm length of the rice plant is reduced by 11 percent and 8 percent, respectively, when compared with the surface water irrigation used throughout all the growth stages. \circled2 Panicle length is found to be the longest on the test plot in which ground water irrigation is practised at the tillering stage. A similar tendency as that seen in the culm length is observed on other test plots. \circled3 The number of panicles is found to be the least on the plot in which ground water irrigation is practised by the gravity flow method throughout all the growth stages of the rice plant. No significant difference is found between the other plots. \circled4 The number of spikelets per panicle at the various stages of rice growth at which_ surface or ground water is supplied by gravity flow method are as follows; surface water at all growth stages‥‥‥‥‥ 98.5. Ground water at all growth stages‥‥‥‥‥‥62.2 Ground water at the tillering stage‥‥‥‥‥ 82.6. Ground water at the reproductive stage ‥‥‥‥‥ 74.1. \circled5 Ripening percentage is about 70 percent on the test plot in which ground water irrigation is practised during all the growth stages and at the tillering stage only. However, when ground water irrigation is practised, at the reproductive stage, the ripening percentage is reduced to 50 percent. This means that 20 percent reduction in the ripening percentage by using ground water irrigation at the reproductive stage. \circled6 The weight of 1,000 kernels is found to show a similar tendency as in the case of ripening percentage i. e. the ground water irrigation during all the growth stages and at the reproductive stage results in a decreased weight of the 1,000 kernels. \circled7 The yield of brown rice from the various treatments are as follows; Gravity flow; Surface water at all growth stages‥‥‥‥‥‥514kg/10a. Ground water at all growth stages‥‥‥‥‥‥428kg/10a. Ground water at the reproductive stage‥‥‥‥‥‥430kg/10a. Standing water; Surface water at all growh stages‥‥‥‥‥‥556kg/10a. Ground water at all growth stages‥‥‥‥‥‥441kg/10a. Ground water at the reproductive stage‥‥‥‥‥‥450kg/10a. The above figures show that ground water irrigation by the gravity flow and by the standing water method during all the growth stages resulted in an 18 percent and a 21 percent decrease in the yield of brown rice, respectively, when compared with surface water irrigation. Also ground water irrigation by gravity flow and by standing water resulted in respective decreases in yield of 16 percent and 19 percent, compared with the surface irrigation method. 4. Results obtained from the experiments on the improvement of ground water irrigation efficiency to paddy rice are as follows; (1) When the standing water irrigation with surface water is practised, the daily average water temperature in a paddy field is 25.2$^{\circ}C$, but, when the gravity flow method is practised with the same irrigation water, the daily average water temperature is 24.5$^{\circ}C$. This means that the former is 0.7$^{\circ}C$ higher than the latter. On the other hand, when ground water is used, the daily water temperatures in a paddy field are respectively 21.$0^{\circ}C$ and 19.3$^{\circ}C$ by practising standing water and the gravity flow method. It can be seen that the former is approximately 1.$0^{\circ}C$ higher than the latter. (2) When the non-water-logged cultivation is practised, the yield of brown rice is 516.3kg/10a, while the yield of brown rice from ground water irrigation plot throughout the whole irrigation period and surface water irrigation plot are 446.3kg/10a and 556.4kg/10a, respectivelely. This means that there is no significant difference in yields between surface water irrigation practice and non-water-logged cultivation, and also means that non-water-logged cultivation results in a 12.6 percent increase in yield compared with the yield from the ground water irrigation plot. (3) The black and white coloring on the inside surface of the water warming ponds has no substantial effect on the temperature of the water. The average daily water temperatures of the various water warming ponds, having different depths, are expressed as Y=aX+b, while the daily average water temperatures at various depths in a water warming pond are expressed as Y=a(b)x (where Y: the daily average water temperature, a,b: constants depending on the type of water warming pond, X; water depth). As the depth of water warning pond is increased, the diurnal difference of the highest and the lowest water temperature is decreased, and also, the time at which the highest water temperature occurs, is delayed. (4) The degree of warming by using a polyethylene tube, 100m in length and 10cm in diameter, is 4~9$^{\circ}C$. Heat exchange rate of a polyethylene tube is 1.5 times higher than that or a water warming channel. The following equation expresses the water warming mechanism of a polyethylene tube where distance from the tube inlet, time in day and several climatic factors are given: {{{{ theta omega (dwt)= { a}_{0 } (1-e- { x} over { PHI v })+ { 2} atop { SUM from { { n}=1} { { a}_{n } } over { SQRT { 1+ {( n omega PHI) }^{2 } } } } LEFT { sin(n omega t+ { b}_{n }+ { tan}^{-1 }n omega PHI )-e- { x} over { PHI v }sin(n omega LEFT ( t- { x} over {v } RIGHT ) + { b}_{n }+ { tan}^{-1 }n omega PHI ) RIGHT } +e- { x} over { PHI v } theta i}}}}{{{{ { theta }_{$\infty$ }(t)= { { alpha theta }_{a }+ { theta }_{ w'} +(S- { B}_{s } ) { U}_{w } } over { beta } , PHI = { { cpDU}_{ omega } } over {4 beta } }}}} where $\theta$$\omega$; discharged water temperature($^{\circ}C$) $\theta$a; air temperature ($^{\circ}C$) $\theta$$\omega$';ponded water temperature($^{\circ}C$) s ; net solar radiation(ly/min) t ; time(tadian) x; tube length(cm) D; diameter(cm) ao,an,bn;constants determined from $\theta$$\omega$(t) varitation. cp; heat capacity of water(cal/$^{\circ}C$ ㎥) U,Ua; overall heat transfer coefficient(cal/$^{\circ}C$ $\textrm{cm}^2$ min-1) $\omega$;1 velocity of water in a polyethylene tube(cm/min) Bs ; heat exchange rate between water and soil(ly/min)