• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.4
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• pp.18-29
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• 2022

Five species of plants (Clerodendrum trichotomum Thunb., Angelica dahurica (Fisch. ex Hoffm.) Benth. & Hook. f. ex Franch. & Sav., Caryopteris incana (Thunb. ex Houtt.) Miq., Lonicera japonica Thunb., and Parasenecio auriculatus var. matsumurana Nakai) native to the clean area of Mt. Jiri were selected. The collection period was from May to September 2021, and the five species plants were collected in their native habitats with flowers in full bloom. The collected plants were extracted with 70% EtOH, and 17 kinds of polyphenol components were analyzed. Next, flowers, leaves, stems, and roots were separated from plants, extracted with 70% EtOH for each part and experiments were conducted on DPPH, ABTS, total polyphenols, and total flavonoids. The results are as follows. 1. It was found that there were a total of 8 kinds of polyphenols contained in 5 species of plants that are native to Mt. Jiri. Among the polyphenol components, chlorogenic acid was contained in 4 species of plants, and caffeic acid was contained in 2 species of plants. 2. As a result, the DPPH radical scavenging activity was the best in the stem of P. auriculata and the C. trichotomum was good regardless of the specific part. It was found that the activity-scavenging activity was good in the flowers of A. dahurica and the leaves of L. japonica. 3. The highest ABTS radical scavenging activity was C. trichotomum Thunb., whose EC₅₀ value was 38.73~66.28ppm. Next, the leaves and stems of L. japonica Thunb., A. dahurica and P. auriculata, and the leaves and stems of C. incana appeared in that order. 4. The highest total polyphenol content was 154.83mg GAE/g in the leaves of C. trichotomum, followed by about 130mg GAE/g in the flowers of C. trichotomum and P. auriculata. The lowest was 26.27mg GAE/g in the stems of A. dahurica.

• Journal of Practical Agriculture & Fisheries Research
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• v.4 no.1
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• pp.128-137
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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 by using the parallel type plat fin tube made of Aluminum(Al 6063), which was named Aloo-Heat(No. 0247164, offered by Korean Intellectual property Office). The trial manufactures were made from Aloo-heat which was 600mm, 700mm length respectively, and It were welded to the end "U" type in order to direct flow of the underground water. The performance test was carried out under the condition of open space and room temperature with the change of flow rate of the underground water and air. The results are as follows. 1. The trial manufactures had convection heat value from 33 to 156 W/m²℃, and It was coincided with design assumption. 2. The amount of energy transfer was increased with the increment of the area of heat transfer, the air flow, the gap of temperature inlet & outlet the underground water and the air. 3. The heat value was 6,825W when the air flow was 6,000m³/h and the gap of temperature between inlet and outlet of the underground water was 6℃, and It dropped from 25.8℃ to 23.2℃(-2.6℃ difference). The convection heat value was 88.5W/m²℃. 4. The heat value was 2.625W when the air flow was 4,000m³/h and the gap of temperature between inlet and outlet the underground water was 2℃, and It dropped from 27℃ to 22.5℃(-4.5℃ difference). The convection heat value was 33.6W/m²℃. 5. Correlation values(R²) of the testing heat values of the trial manufacture type I, II, and III were 0.9141, 0.8935, and 0.9323 respectively, and correlation values(R²) of the amount of the air flow 6,000m³/h, 5,000m³/h, 4,000m³/h were 0.9513, 0.9414, and 0.9003 respectively.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.49 no.4
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• pp.313-321
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• 2023

Platycodon grandiflorus (P. grandiflorus) flower is a perennial plant belonging to the family Campanulaceae and has many excellent pharmacological effects, so it has been used as a medicinal ingredient since ancient times. In addition, anthocyanin is a purple or blue natural pigment contained in plant flowers and fruits, and is known as a powerful antioxidant. The purpose of this study was to confirm the dermatological functionality of P. grandiflorus flower extract and the value of the bluish anthocyanin contained in flowers as a cosmetic material as a natural pigment. Firstly, 50% ethanol and 80% ethanol were added to the P. grandiflorus flower and extracted under reflux for 4 h at 25, 60, and 80 ℃, and the pH of each treatment group was similar. Based on the anthocyanin content and chromaticity (E^*ab), 50% ethanol 60 ℃ extraction conditions showing the color development most similar to the natural color of the P. grandifloras flower were selected, and a sample was prepared by concentrating and lyophilizing. The analysis results showed that the total phenol, total flavonoid, and total anthocyanin contents were in the ranges of 23 ㎍/mL, 16 ㎍/mL, and 0.17 ㎍/mL, respectively. The P. grandiflorus flower extract suppressed the production of nitric oxide (NO) and interleukin-6 (IL-6) in lipopolysaccharide (LPS) induced RAW264.7 cells. Furthermore, the P. grandiflorus flower extract showed wound healing effects through the promotion of skin cell migration in TNF-α stimulated human keratinocytes. The stability of anthocyanin and extract color was studied during a storage period of 50 days at various temperatures (4 ℃, 25 ℃, and 45 ℃). Color values (L, a, and b) of the P. grandiflorus flower extract changed over 50 days, whereas the bluish-purple color of the extract was stabilized using 5% maltodextrin. These results suggest that P. grandiflorus flower extract may be useful as a natural cosmetic pigment.

• Food Science and Preservation
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• v.30 no.1
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• pp.88-97
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• 2023

As livestock consumption in Korea has been gradually increasing, the quality of the final products has been improved to meet this increased demand. In particular, maintaining the water holding capacity (WHC) and minimizing the drip loss during the thawing of frozen meat are of utmost importance. This study investigated the physicochemical properties of frozen pork subjected to thawing under different conditions: at room temperature (20℃, under air), at a low temperature (4℃ refrigerator, under air), under water (20℃, under water in a vacuum bag), under microwave (microwave-thawing, 260 W), and under low-pressure tumbling (20℃, 0.015 bar, tumbling). The shortest thawing time for frozen pork was recorded upon low-pressure tumbling thus indicating a fast heat transfer. The lowest drip loss (0.2%) and highest WHC (94.5%) were also recorded under this condition. A significantly higher drip loss was observed upon microwave- (1.0%) and water-thawing (1.2%), which resulted in the lowest WHC in microwave thawing (87.2%). The highest total count of aerobic bacteria and coliform group were observed upon room temp thawing while the low pressure tumbling and thawing resulted in the lowest aerobic bacteria (1.90 log CFU/g) and coliform (0.78 log CFU/g) count. Consequently, thawing by low pressure tumbling afforded the best food quality.

• Magazine of the Korean Society of Agricultural Engineers
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• v.11 no.1
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• pp.1534-1548
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• 1969

This experiment was conducted, making use of the 'NONG-RIM6' arecommended variety of rice for the year of 1968. Main purposes of the experiment are to explore possibilities of; a) ways and means of saving irringation water and, b) overcoming drought at the same time so that an increased yield in rice could be resulted in. Specifically, it was tried to determine the effects of the Rotation irrigation method combined with differentiated thickness of lining upon the growth and yield of rice. Some of the major findings are summarized in the following. 1) The different thicknesses show a significant relationship with the weight of 1,000 grains. In the case of 9cm lined plot, the grain weight is 23.5grams, the heaviest. Next in order is 3cm lined plot, 6cm lined plot, control plot, and wheat straw lined-plot. 2) In rice yield, it is found that there is a considerably moderate significant relationship with both the different thickness of lining and the number of irrigation, as shown in the table. 3) There is little or no difference among different plots in terms of a) physical and chemical properties of soil, b) quality of irrigation water, c) climatic conditions, and rainfalls. 4) It is found that there is a significant relationship between differences in the method of rotation irrigation and the number of ears per hill. The plot irrigated at an interval of 7 days shows 17.4 ears and plot irrigated at an interval of 6 days, 16.3 5) In vinyl-treated plots, it is shown that both yield and component elements are greatest in the case of the plot ith whole of $3cm/m^2$ Next in order are the plot with a hole of $2cm/m^2$ the plot with a hole of $1cm/m^2$ In the case of the plot with no hole it is found that both yield and component elements are decreased as compared to the control plot. 6) The irrigation water reqirement is measured for the actual irrigation days of 72 which are the number subtracted the days of rainfall of 30 from the total irrigation days of 102. It is found that the irrigation water requirement for the uncontrol plot is 1,590mm as compared to 876mm(44.9% saved) for the 9cm-lined plot, 959mm(39.7% saved) for the 6cm-lined plot 1,010mm(36% saved) for the 3cm-lined plot and 1,082mm(32% saved) for the wheat straw lined plot. In the case of the Rotation irrigation method it is found that the water requirement for the plot irrigated at an interval of 8 days is 538mm(65% saved), as compared to 617mm(61.6% saved) for plot irrigated at an interval of 7 day 672mm(57.7% saved) for plot irrigated at an interval of 6day, 746mm(53.0% saved) for the plot irrigated at an interval of 5 days, 890mm 44.0% saved) for the plot irrigated at an interval of 4 days, and 975mm(38.6% saved) for the plot irrigated at an interval of 3 days. 7) The rate of evapotranspiration is found 2.8 around the end of month of July, as compared to 2.6 at the begining of August 3.4 around the end of August and 2.6 at the begining of August 3.4 around the end of August and 2.6 at the begining of September. 8) It is found that the saturation quantity of 30mm per day is decreased to 20mm per day though the use of vinyl covering. 9) The husking rate shows 75 per cent which is considered better.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• 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.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of Korean Society of Forest Science
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• v.29 no.1
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• pp.1-19
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• 1976

This study has been made to make an observation regarding present status of the coal mine props which is desperately needed for coal production, despite of great shortage of the timber resources in this country, and investigate the effects of diffusion process on the decay resistances of the mine props as applied preservatives of Malenit and chromated zinc chloride. The results are as follows. 1. Present status of the coal mine props Total demand of coal mine props in the year of 1975 was approximately 456 thousand cubic meters. The main species used for mine props are conifer (mainly Pinus densiflora) and hardwood (mainly Quercus). Portions between them are half and half. With non fixed specification, wide varieties of timber in size and form are used. And volume of wood used per ton-of coal production shows also wide range from 0.017 cubic meter to 0.03 cubic meter. 2. Decay resistance test a) The oven dry weight decreased between untreated specimen and treated specimen has not shown any significantly, although it has shown some differences in average values between them. It may be caused by the shorter length of the test. b) The strength of compression test between untreated specimen and treated specimen has also shown the same results as shown in case of weight decrease. Reasons assumed are the same. c) The amounts of the extractives in one percent of sodium hydroxide (NaOH) between untreated and treated specimen have shown the large value in case of untreated specimen than that of treated. 3. The economical benifit between untreated and treated wood when applied in field has seen better in long term base in case of treated wood, although the primary cost of treated wood add a little bit more cost than that of the untreated wood.

• Radiation Oncology Journal
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• v.27 no.4
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• pp.201-209
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• 2009

Purpose: To investigate the care patterns for radiation therapy and to determine inter-hospital differences for patients with laryngeal carcinoma in Korea. Materials and Methods: A total of 237 cases of laryngeal carcinoma (glottis, 144; supraglottis, 93) assembled from 23 hospitals, who underwent irradiation in the year of 1998 and 1999, were retrospectively analyzed to investigate inter-hospital differences with respect to radiotherapy treatment. We grouped the 23 hospitals based on the number of new patients annually irradiated in 1998; and designated them as group A (${\geq}$900 patients), group B (${\geq}$400 patients and <900 patients), and group C (<400 patients). Results: The median age of the 237 patients was 62 years (range, 25 to 88 years), of which 216 were male and 21 were female. The clinical stages were distributed as follows: for glottis cancer, I; 61.8%, II; 21.5%, III; 4.2%, IVa; 11.1%, IVb; 1.4%, and in supraglottic cancer, I; 4.3%, II; 19.4%, III; 28.0%, IVa; 43.0%, IVb; 5.4%, respectively. Some differences were observed among the 3 groups with respect to the dose calculation method, radiation energy, field arrangement, and use of an immobilization device. No significant difference among 3 hospital groups was observed with respect to treatment modality, irradiation volume, and median total dose delivered to the primary site. Conclusion: This study revealed that radiotherapy process and patterns of care are relatively uniform in laryngeal cancer patients in Korean hospitals, and we hope this nationwide data can be used as a basis for the standardization of radiotherapy for the treatment of laryngeal cancer.