• Korean Journal of Plant Resources
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• v.37 no.4
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• pp.423-430
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• 2024

Cymbidium (C.) spp. 'Wedding Day' was developed by the National Institute of Horticultural and Herbal Science, Rural Development Administration in 2021. This cultivar was derived from the artificial cross between C. 'Boksam Holiday' and C. 'Persicolor' in 2008. After the crossing, 112 seedlings were obtained through in vitro germination and transferred to the green house. Based on their vegetative and flowering characteristics, two lines were selected through the first selection. To confirm the stability and uniformity of the two lines, the first and second trials were conducted from 2017 to 2021. As a result, the final line with the code 'C0844-29' was selected as 'Wongyuo F1-78'. After evaluating consumer preference, the line was named as 'Wedding Day'. This hybrid is medium sized cultivar with long vase life of cut flowers and more than 11 white flowers per stalk. It has 63.2 cm of erect stalks which are suitable for cut flower. The leaves are 65.8 cm long and located lower than the flowers, providing appropriate shape for viewing the flowers. It starts flowering from late January. It has sufficient proliferation ability to enable mass proliferation for commercial use (Registration No. 9523).

• 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.

• Archives of design research
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• v.18 no.4 s.62
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• pp.277-286
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• 2005

The purpose of this study is to ascertain the design element in traditional palaces of Korea, China and Japan. It takes threesteps to proceed this study. Firstly, it needs to be established the analysis framework from the documents. In second step, the design elements - the form, the material, the pattern and the color - should be collected and investigated through the observation of the actual traditional palaces the Changduckung, the Forbidden City, the Nijo castle. The third step is the analysis of the results of the investigation of the design elements from step two. To sum up similarities and dissimilarities among the design element in traditional palaces of Korea, China and Japan is as the following It is to be noticed that the mainly common characteristics of the artistic design are 'naturalism', 'harmonious ideas' and 'confucianism'. But the representation style of the design element is differed from the country. : The typical features of China are symmetry, glassy surface by artificial process, the meandered curve, the magnificent pattern and the constrable color. In Japan, the mathematical asymmetry, made-up rough surface by artificial skill, decorativepattern with abbreviation and achromatic color are important feature of the design element. While the major features of Korean design element are asymmetrical balance with nature, rough surface by natural process, moderate pattern and harmonious color.

• Journal of Chest Surgery
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• v.12 no.4
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• pp.383-394
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• 1979

Treatment of valvular heart disease with valve replacement has been one of the most popular procedures in cardiac surgery recently. Although, first effort was directed toward the prosthetic valve, it soon became popular that bioprosthesis, the valvular xenograft, was prefered in the majority cases. Valvular xenograft has some superiority to the artificial prosthetic valve in some points of thromboembolism and hemolytic anemia, and it also has some inferiority of durability, immunologic reaction and resistance to Infection. Tremendous efforts were made to cover the inferiority with several methods of collection, preservation, and valve mounting of the porcine valve or pericardium of the calf, and also with surgical technique of the valvular xenograft replacement. Auther has collected 320 porcine aortic valves immediately after slaughter, and aortic cusps were coapted with cotton balls in the Valsalva sinuses to protect valve deformity after immersion in the Hanks' solution, and oxidation, cross-linking and reduction procedures were completed after the proposal of Carpentier in 1972. Well preserved aortic valves were suture mounted in the hand-made tissue valve frame of 19, 21, and 23 mm J.d., and also in the prosthetic vascular segment of 19 mm Ld. with 4-0 nylon sutures after careful trimming of the aortic valves. Completed valves were evaluated with bacteriologic culture, pressure tolerance test with tolerane gauge, valve durability test in the saline glycerine mixed solution with tolerance test machine in the speed of 300 rpm, and again with pathologic changes to obtain following results: 1. Bacteriologic culture of the valve tissue in five different preservation method for two weeks revealed excellent and satisfactory result in view of sterilization including 0.65% glutaraldehyde preservation group for one week bacteriologic culture except one tissue with Citobacter freundii in 75% ethanol preserved group. 2. Pressure tolerance test was done with an apparatus composed of V-connected manometer and pressure applicator. Tolerable limit of pressure was recorded when central leaking jet of saline was observed. Average pressure tolerated in each group was 168 mmHg in glutaraldehyde, 128 mmHg in formaldehyde, 92 mmHg in Dakin's solution, 48 mmHg in ethylene oxide gas, and 26 mmHg in ethanol preserved group in relation to the control group of Ringer's 90 mmHg respectively. 3. Prolonged durability test was performed in the group of frame mounted xenograft tissue valve with 300 up-and-down motion tolerance test machine/min. There were no specific valve deformity or wearing in both 19, 21, and 23 mm valves at the end of 3 months (actually 15 months), and another 3 months durability test revealed minimal valve leakage during pressure tolerance test due to contraction deformity of the non-coronary cusp at the end of 6 months (actually 30 months) in the largest 23 mm group. 4. Histopathologic observation was focussed in three view points, endothelial cell lining, collagen and elastic fiber destructions in each preservation methods and long durable valvular tolerance test group. Endothel ial cell lining and collagen fiber were well preserved in the glutaraldehyde and formaldehyde treated group with minimal destruction of elastic fiber. In long durable tolerance test group revealed complete destruction of the endothelial cell lining with minimal destruction of the collagen and elastic fiber in 3 month and 6 month group in relation to the time and severity. In conclusion, porcine xenograft treated after the proposal of Carpentier in 1972 and preserved in the glutaraldehyde solution was the best method of collection, preservation and valve mounting. Pressure tolerance and valve motion tolerance test, also, revealed most satisfactory results in the glutaraldehyde preserved group.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.2
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• pp.17-24
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• 2023

A post-processing technique for the measurement signal of a solenoid-type sensor is introduced. The solenoid-type sensor nondestructively evaluates an external tendon of prestressed concrete using the total flux leakage (TFL) method. The TFL solenoid sensor consists of primary and secondary coils. AC electricity, with the shape of a sinusoidal function, is input in the primary coil. The signal proportional to the differential of the input is induced in the secondary coil. Because the amplitude of the induced signal is proportional to the cross-sectional area of the tendon, sectional loss of the tendon caused by ruptures or corrosion can be identified by the induced signal. Therefore, it is important to extract amplitude information from the measurement signal of the TFL sensor. Previously, the amplitude was extracted using local maxima, which is the simplest way to obtain amplitude information. However, because the sampling rate is dramatically decreased by amplitude extraction using the local maxima, the previous method places many restrictions on the direction of TFL sensor development, such as applying additional signal processing and/or artificial intelligence. Meanwhile, the proposed method uses amplitude demodulation to obtain the signal amplitude from the TFL sensor, and the sampling rate of the amplitude information is same to the raw TFL sensor data. The proposed method using amplitude demodulation provides ample freedom for development by eliminating restrictions on the first coil input frequency of the TFL sensor and the speed of applying the sensor to external tension. It also maintains a high measurement sampling rate, providing advantages for utilizing additional signal processing or artificial intelligence. The proposed method was validated through experiments, and the advantages were verified through comparison with the previous method. For example, in this study the amplitudes extracted by amplitude demodulation provided a sampling rate 100 times greater than those of the previous method. There may be differences depending on the given situation and specific equipment settings; however, in most cases, extracting amplitude information using amplitude demodulation yields more satisfactory results than previous methods.

• Journal of Chest Surgery
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• v.35 no.7
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• pp.517-522
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• 2002

Background: Congenital heart surgery may lead to myocardial swelling and hemodynamic instability. Delayed sternal closure may be beneficial in this setting. The purpose of this study was to assess mortality and mediastinal infection rate associated with delayed sternal closure after congenital heart surgery and to evaluate the risk factors which affect mortality and mediastinal infection rate. Material and Method: We retrospectively reviewed 40 patients who underwent delayed sternal closure after repair of congenital heart disease at Yonsei Cardiovascular Hospital, from January 1994 to May 2001. In these patients, we assessed the mortality and mediastinal infection rate, and evaluated their risk factors including operation time, bypass time, aortic cross clamp time, duration to sternal closure and postoperative artificial ventilation time. Mediastinal infection was defined to have positive culture in mediastinum. Result: Hemodynamic instability was the most common indication for delayed sternal closure(n=36) and other indications included postoperative bleeding(n=2) and conduit compression(n=2). The median age at operation was $14.4{\pm}33.4$months old(range, 2days-12years). The patients with postoperative bleeding and conduit compression were much older than the others. The sternum was left open for $4.5{\pm}3.4$ days(range, 1-20days). Overall mortality was 25%(10/40) and mediastinal infection occured in 24.3%(9/37) (3 patients were excluded in mediastinal infection for early death). In risk factor analyses, only aortic cross clamp time had statistical significance for mortality in univariate analyses. However, multivariate analyses revealed that there were no significant predictors for risk of mortality and mediastinal infection. Conclusion: Delayed sternal closure after repair of congenital cardiac disease had relatively high mortality and mediastinal infection rate. But, in patients with hemodynamic instability, postoperative bleeding and conduit compression after repair of congenital cardiac disease, delayed sternal closure may be an effective life saving method.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.484-495
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• 2020

This study is a mixed surfactant (MimicLipid-MSM1000) that forms the same structure as that of the stratum corneum, sucrose distearate, polyglyceryl-2 dioleate, fermented squalane, ergosterol, 10-hydroxystearic acid, mixture consisting of was synthesized. When using 2~5 wt% of this mixed surfactant, it was possible to make an artificial skin mimetic that forms a multi-layer lamellar structure of 5~30 layers. An emulsion was prepared using this mixed surfactant, and a multi-layered lamellar phase was formed and analyzed mechanically. The appearance of this surfactant was a light brown hard wax, the hydrophilic lipophilic balance (HLB) was 12.53, the critical parameter value was 0.987, and the acid value was 0.13. Stability according to pH change was also stable in acidic (3.8), neutral (7.2) and alkaline (10.8). The particle size of the liquid crystal was found to be the most stable maltese cross lamellar crystalline droplet at 5~25mm. The size of the emulsified particles according to the change in the speed of the homo agitator is 2500 rpm (17.9mm±2.6mm), 3500rpm (12.5mm±2.1mm), 4500rpm (6.2mm±1.8mm) particles were formed. Liquid crystal forming particles were observed through a polarization microscope, and the formation structure of the liquid crystal was precisely analyzed with a scanning electron microscope (cryo-SEM). As an application field, it is expected that it will be widely applicable to the development of various prescriptions, such as various skin care cosmetics, makeup care cosmetics, and scalp protection cosmetics, by using a skin-mimicking surfactant.