• Journal of Bio-Environment Control
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• v.19 no.4
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• pp.266-274
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• 2010

This study aim to investigate fundamentally the growth and physiological responses of tomato plants in responses to two different levels of water deficit, a weak drought stress (-25 kPa) and a severe drought stress (-100 kPa) in soil. The two levels of water deficit were maintained using a micro-irrigation system consisted of soil sensors for the real-time monitoring of soil water content and irrigation modules in a greenhouse experiment. Soil water contents were fluctuated throughout the 30 days treatment period but differed between the two treatments with the average -47 kPa in -25 kPa set treatment and the -119 kPa in -100 kPa set treatment. There were significant differences in plant height between the two different soil water statuses in plant height without differences of the number of nodes. The plants grown in the severe water-deficit treatment had greater accumulation of biomass than the plants in the weak water-deficit treatment. The severe water-deficit treatment (-119 kPa) also induced greater leaf area and leaf dry weight of the plants than the weak water-deficit treatment did, even though there was no difference in leaf area per unit dry weight. These results of growth parameters tested in this study indicate that the severe drought could cause an adaptation of tomato plants to the drought stress with the enhancement of biomass and leaf expansion without changes of leaf thickness. Greater relative water content of leaves and lower osmotic potential of sap expressed from turgid leaves were recorded in the severe water deficit treatment than in the weak water deficit treatment. This finding also postulated physiological adaptation to be better water status under drought stress. The drought imposition affected significantly on photosynthesis, water use efficiency and stomatal conductance of tomato plants. The severe water-deficit treatment increased PSII activities and water use efficiency, but decreased stomatal conductance than the weak water-deficit treatment. However, there were no differences between the two treatments in total photosynthetic capacity. Finally, there were no differences in the number and biomass of fruits. These results suggested that tomato plants have an ability to make adaptation to water deficit conditions through changes in leaf morphology, osmotic potentials, and water use efficiency as well as PSII activity. These adaptation responses should be considered in the screening of drought tolerance of tomato plants.

• Journal of Bio-Environment Control
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• v.20 no.3
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• pp.182-188
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• 2011

This research was conducted to evaluate the effect of supplemental light-emitting diode (LED) light on growth characteristics and phytochemical content of pepper (Capsicum annuum L.) seedling using LED blue (470 nm, B), red (660 nm, R), blue + red (BR), far red (740 nm, FR) and UV-B (300 nm) light treatment, and without artificial light. Photon flux of LED light was 49, 16, 40, 5.0 and $0.82{\mu}mol\;m^{-2}s^{-1}$ for B, R, BR, FR, and UV-B light, respectively, during experiment. Supplemental LED light duration was $16hr\;day^{-1}$ and UV-B light duration was 10 min. per day after sunset up to 15 days (12 days after germination) of plants age. In our research, growth characteristics and phytochemical content of pepper seedlings were greatly influenced by supplemental LED light compare to control treatment. Red light increased the number of leaves, number of nodes, leaf width and plant fresh weight by 34%, 27%, 50% and 40%, respectively. Blue light increased the leaf length by 13%, and stem length and length of inter node were increased by 17% and 34%, respectively under grown far red light. After 15 days of light treatments phytochemical concentrations of pepper plants were significantly changed. Blue light enhanced the total anthocyanin and chlorophyll concentration by 6 times and 2 times, respectively. Red light increased the total phenolic compound at least two folds meanwhile far red light reduced the ascorbic acid and antioxidant activity 31% and 66%, respectively compared to control treatment.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.444-451
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• 2022

Recently, long-term cultivation is becoming more common with the increase in tomato hydroponics. In hydroponics, it is very important to supply an appropriate nutrient solution considering the nutrient and moisture requirements of crops, in terms of productivity, resource use, and environmental conservation. Since seasonal environmental changes appear severely in long-term cultivation, it is so critical to manage irrigation control considering these changes. Therefore, this study was carried out to investigate the effect of irrigation volume on growth and yield in tomato long-term cultivation using coir substrate. The irrigation volume was adjusted at 4 levels (high, medium high, medium low and low) by different irrigation frequency. Irrigation scheduling (frequency) was controlled based on solar radiation which measured by radiation sensor installed outside the greenhouse and performed whenever accumulated solar radiation energy reached set value. Set value of integrated solar radiation was changed by the growing season. The results revealed that the higher irrigation volume caused the higher drainage rate, which could prevent the EC of drainage from rising excessively. As the cultivation period elapsed, the EC of the drainage increased. And the lower irrigation volume supplied, the more the increase in EC of the drainage. Plant length was shorter in the low irrigation volume treatment compared to the other treatments. But irrigation volume did not affect the number of nodes and fruit clusters. The number of fruit settings was not significantly affected by the irrigation volume in general, but high irrigation volume significantly decreased fruit setting and yield of the 12-15th cluster developed during low temperature period. Blossom-end rot occurred early with a high incidence rate in the low irrigation volume treatment group. The highest weight fruits was obtained from the high irrigation treatment group, while the medium high treatment group had the highest total yield. As a result of the experiment, it could be confirmed the effect of irrigation amount on the nutrient and moisture stabilization in the root zone and yield, in addition to the importance of proper irrigation control when cultivating tomato plants hydroponically using coir substrate. Therefore, it is necessary to continue the research on this topic, as it is judged that the precise irrigation control algorithm based on root zone-information applied to the integrated environmental control system, will contribute to the improvement of crop productivity as well as the development of hydroponics control techniques.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.1
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• pp.23-29
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• 1989

In order to investigate developmental habit and morphology of maize tillers, time and location of tiller development. number of tillers per plant, tiller angle, height and diameter of tillers and root systems of tillers were examined under field condition for maize with tillers. Materials used were mostly from Korean local lines and a few lines from other countries were also included for comparison. The time of the first tiller development was about 18 to 20 days after emergence when planted on May in Yusong. The second tiller appeared about 4 to 5 days after the first tiller appeared. The tiller number per plant varied with lines and hybrids and ranged from two to ten. The location of tiller development was usually basal nodes of the main stem. Each tiller appeared to have its own root system. The angle between tillers and main stem was variable depending upon the maizes and the tiller angle could be classified into three categories. The height of tillers was also variable and seemed to be under genetic control. The most productive tillers were found among the Korean local derivatives.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.204-211
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• 2022

To produce a high quality crop, light is an essential environmental factor in greenhouse cultivation. In the winter season, solar radiation is weak than other season. Therefore, using supplemental light during a low radiation period can increase the crop growth and yield. This study was conducted to select the economical supplemental light source for greenhouse cultivation in pepper during the low radiation period. The green pepper (Capsicum annuum 'Super Cheongyang') was transplanted on 5 September 2019. Supplemental lighting treatment was conducted from 1 January 2020 to 31 March 2020. RB LED (red and blue LED, red:blue = 7:3), W LED (white LED, R:G:B = 5:3:2), and HPS (high-pressure sodium lamp) were used as the supplemental light source. Non-treatment was used as the control. The plant height, SPAD, and number of nodes of pepper plants have no significant differences by supplemental light sources. However, the number of ramifications plants was the greatest in RB LED light source. Moreover, supplemental lighting increased photosynthesis of the pepper plant, and especially, the RB LED had the highest photosynthesis rate during supplemental lighting period. Also, the yield of pepper increased in the supplemental lighting treatment than in the control, and the RB LED had the greatest yield than other light sources. The electricity consumption was the highest in W LED and the lowest in HPS light. Through the economic analysis, the RB LED had high economic efficiency. In conclusion, these results suggest that using RB LED for supplemental light source during low radiation in pepper greenhouse increase the yield and economic feasibility.

• Korean Journal of Medicinal Crop Science
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• v.2 no.3
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• pp.181-186
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• 1994

Vegetative node and rhizome of Ligusticum chuanxiong Hort. were planted in spring and autumn and harvested after one and two years to determine the appropriate harvesting time which produced a high yield. Plant height, leaf number and branch number per plant were increased in order of rhizome planted in spring and harvested after two year(RST), rhizome planted in autumn and harvested after one year(RAO), vegetative node planted in autumn and harvested after one year(VNAO), and vegetative node planted in spring and harvested after one year(VNSO). Leaf area index and dry leaf weight in VNSO were highest on August 16, but dry weights of stem and rhizome was increased until harvesting time. The appropriate harvesting time was October 17, in RST, November 9 in RAO and VNAO, and November 13 in VNSO. Yield in autumn planting was more increased than that in spring planting and also that in RST was 443kg per 10a and increased by 2.8 times compared to RAO. However the yield in the rhizome planting was more increased by 17 percent than the vegetative node planting, the latter planting was inexpensive and economic for purchasing seed materials.

• Korean Journal of Plant Resources
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• v.31 no.5
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• pp.489-497
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• 2018

In Korea, common and Tartary buckwheat are cultivated mainly in spring and fall, however the available buckwheat varieties are still very limited. In this study, we have evaluated buckwheat germplasm for agronomic traits and compared flavonoids contents in different cultivation period and collection area. In common buckwheat, the number of days from sowing to flowering was 40 and 31 days and from sowing to maturity took 90 and 69 days in spring and fall cultivation, respectively. The number of nodes and branches were higher in spring cultivation while the hundred seed weight was higher in fall cultivation. The average flavonoids contents in common buckwheat were 0.20 mg/g dry weight (DW) and 0.40 mg/g DW in spring and fall cultivation, respectively. The highest flavonoids content was detected in Jeonnam accessions with 0.29 mg/g DW and 0.43 mg/g DW during spring and fall cultivation, respectively. The flavonoids contents were varied from 1.5 to 2.5 times according to the collection area. These results suggest that the agronomic traits and flavonoids contents were vary depending on the cultivation environment and germplasm collection area. Therefore, it is necessary to select the material by considering the characteristics of the germplasm for breeding of new varieties.

• Tuberculosis and Respiratory Diseases
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• v.39 no.6
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• pp.453-473
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• 1992

Background: National survey was performed to estimate the incidence of sarcoidosis in Korea. The clinical data of confirmed cases were analysed for the practice of primary care physicians and pulmonary specialists. Methods: The period of study was from January 1991 to December 1992. Data were retrospectively collected by correspondence with physicians in departments of internal medicine, dermatology, ophthalmology and neurology of the hospitals having more than 100 beds using returning postcards. In confirmed and suspicious cases of sardoidosis, case record chart for clinical and laboratory findings were obtained in detail. Results: 1) Postcards were sent to 523 departments in 213 hospitals. Internal medicine composed 41%, dermatology 20%, ophthalmology 20% and neurology 19%. 2) Postcards were returned from 241 departments (replying rates was 48%). 3) There were 113 confirmed cases from 50 departments and 10 cases. The cases were composed from internal medicine (81%), dermatology (13%), ophthalmology (3%) and neurology (3%). 78 confirmed cases were analysed, which were composed from department of internal medicine (92%), dermatology (5%), and neurology (3%). 4) The time span for analysed cases was 1980 to 1992. one case was analysed in 1980 and the number gradually increased to 18 cases in 1991. 5) The majority of patients (84.4%) were in the age group of 20 to 49 years. 6) The ratio of male to female was 1 : 1.5. 7) The most common chief complains were respiratory symptoms, dermatologic symptoms, generalized discomforts, visual changes, arthralgia, abdominal pains, and swallowing difficulties in order. 16% of the patients were asymptomatic. 8) Mean duration between symptom onset and diagnosis was 2 months. 9) The most common symptoms were respiratory, general, dermatologic, ophthalmologic, neurologic and cardiac origin in order. 10) Hemoglobin, hematocrits and platelet were in normal range. 58% of the patients had lymphopenia measuring less than 30% of white cell count. The ratio of CD4 to CD8 lymphocytes was $1.73{\pm}1.16$ with range of 0.43 to 4.62. ESR was elevated in 43% of the cases. 11) Blood chemistry was normal in most cases. Serum angiotensin converting enzyme (S-ACE) was $66.8{\pm}58.6\;U/L$ with the range of 8.79 to 265 U /L. Proteinuria of more than 150 mg was found in 42. 9% of the patients. 12) Serum IgG was elevated in 43.5%, IgA in 45.5%, IgM in 59.1% and IgE in 46.7%. The levels of complement C3 and C4 were in the normal range. Anti-nuclear antibody was detected in 11% of the cases. Kweim test was performed in 3 cases, and in all cases the result was positive. 13) FVC was decreased in 17.3%, FEV1 in 11.5%, FEV1/FVC in 10%, TLC in 15.2%, and DLco in 64.7%. 14) PaO2 was decreased below 90 mmHg in 48.6% and PaCO2 was increased above 45 mmHg in 5.7%. 15) The percentage of macrophages in BAL fluid was $51.4{\pm}19.2%$, lymphocytes $44.4{\pm}21.1%$, and the ratio of CD4 to CD8 lymphocytes was $3.41{\pm}2.07$. 16) There was no difference in laboratory findings between male and female. 17) Hilar enlargement on chest PA was present in 87.9% (bilaterally in 78.8% and unilaterally in 9.1%). 18) According to Siltzbach's classification, stage 0 was 5%, stage 158.3%, stage 228.3%, and stage 38.3%. 19) Hilart enlargement on chest CT was present in 92.6% (bilaterally 76.4% and unilaterally in 16.2%). 20) HRCT was done in 16 cases. The most common findings were nodules, interlobular thickening, focal patchy infiltrations in order. Two cases was normal finding. 21) Other radiologic examinations showed bone change in one case and splenomegaly in two cases. 22) Gallium scan was done in 12 cases. Radioactivity was increased in hilar and mediastinal lymph nodes in 8 cases and in parenchyme in 2 cases. 23) The pathologic diagnosis was commonly performed by transbrochial lung biopsy (TBLB, 47.3%), skin and mediastinal lymph nodes biopsy (34.5%), peripheral lymph nodes biopsy (23.6%), open lung biopsy (18.2%) and bronchial biopsy in order. 24) The most common findings in pathology were non·caseating granuloma (100%), multi-nucleated giant cell (47.3%), hyalinized acellular scar (34.5%), reticulin fibrin network (20%), inclusion body (10.9%), necrosis (9.1%), and lymphangitic distribution of granuloma (1.8%) in order. Conclusion: Clinical, laboratory, radiologic and pathologic findings were summarized. This collected data will assist in finding a test for detection and staging of sarcoidosis in Korea in near future.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.