• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.217-224
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• 2024

This paper proposes an efficient dimension reduction method (DRM) that considers the nonlinearity of the performance functions in reliability-based design optimization (RBDO). The dimension reduction method evaluates the reliability more accurately than the first-order reliability method (FORM) using integration quadrature points and weights. However, its efficiency is hindered as the number of quadrature points increases owing to the need for an additional evaluation of the performance function. In this study, we assessed the nonlinearity of the performance function in RBDO and proposed criteria for determining the number of quadrature points based on the degree of nonlinearity. This approach suggests adjusting the number of quadrature points during each iteration of the RBDO process while maintaining the accuracy of theDRM while improving the computational efficiency. The nonlinearity of the performance function was evaluated using the angle between the vectors used in the maximum probable target point (MPTP) search. Numerical tests were conducted to determine the appropriate number of quadrature points according to the degree of nonlinearity. Through a 2D numerical example, it is confirmed that the proposed method improves the efficiency while maintaining the accuracy of the dimension reduction method or Monte Carlo Simulation (MCS).

• The Mathematical Education
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• v.63 no.2
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• pp.165-186
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• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.

• The Korean Journal of Food And Nutrition
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• v.15 no.2
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• pp.112-118
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• 2002

Sporulation in the fission yeast Schizosaccharomyces pombe has been regarded as an important model of cellular development and differentiation. S. pombe cells proliferate by mitosis and binary fission on growth medium. Deprivation of nutrients especially nitrogen sources, causes the cessation of mitosis and initiates sexual reproduction by matting between two sexually compatible cell types. Meiosis is then followed in a diploid cell in the absence of nitrogen source. DNA fragment complemented with the mutations of sporulation gene was isolated from the S. pombe gene library constructed in the vector, pDB 248' and designated as pDB(spo5)1. We futher analyzed six recombinant plasmids, pDB(spo5)2, pDB(spo5)3, pDB(spo5)4, pDB(spo5)5, pDB (spo5)6, pDB(spo5)7 and found each of these plasmids is able to rescue the spo5-2, spo5-3, spo5-4, spo5-5, spo5-6, spo5-7 mutations, respectively. Mapping of the integrated plasmid into the homologous site of the S. pombe chromosomes demonstrated that pDB(spo5)1, and pDB(spu5)Rl contained the spo5 gene. Transcripts of spo5 gene were analyzed by Northern hybridization. Two transcripts of 3.2 kb and 2.5kb were detected with 5kb Hind Ⅲ fragment containing a part of the spo5 gene as a probe. The small mRNA(2.5kb) appeared only when a wild-type strain was cultured in the absence of nitrogen source in which condition the large mRNA (3.2kb) was produced constitutively. Appearance of a 2.5kb spo5-mRNA depends upon the function of the meil, mei2 and mei3 genes.

• Journal of Plant Biotechnology
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• v.43 no.4
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• pp.450-456
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• 2016

Rhizoctonia leaf blight (large patch) has become a serious problem in Korean lawn grass, which is extremely hard to treat and develops mostly from the roots of lawn grass to wither it away. Rhizoctonia leaf blight (large patch) is caused by Rhizoctonia solani AG2-2 (IV). To develop zoysia japonica with strong disease tolerance against this pathogenic bacterium, ${\beta}-1,3-glucanase$ was cloned from zoysia japonica, which is one of the PR-Proteins known to play a critical role in plant defense reaction. ${\beta}-1,3-glucanase$ is known to be generated within the cells when plant tissues have a hypersensitive reaction due to virus or bacterium infection and secreted outside the cells to play mainly the function of resistance against pathogenic bacteria in the space between the cells. This study utilized the commonly preserved part in the sequence of corn, wheat, barley, and rice which had been researched for their disease tolerance among the ${\beta}-1,3-glucanase$ monocotyledonous plants. Based on the part, degenerate PCR was performed to find out the sequence and full-length cDNA was cloned. E.coli over-expression was conducted in this study to mass purify target protein and implement in vitro activation measurement and antibacterial test. In addition, to interpret the functions of ZjGlu1 gene, each gene-incorporating plant transformation vectors were produced to make lawn grass transformant. Based on ZjGlu1 protein, antibacterial activity test was conducted on 9 strains. As a result, R. cerealis, F. culmorum, R.solani AG-1 (1B), and T. atroviride were found to have antibacterial activity. The gene-specific expression amount in each organ showed no huge difference in the organs based upon the transformant and against 18s gene expression amount.

• Journal of the Korean Society of Hazard Mitigation
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• v.2 no.1 s.4
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• pp.135-141
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• 2002

Both non-linear damping values of the deep and shallow crustal materials and seismic source parameters are found from the observed near-field seismic ground motions at the South-eastern Korean Peninsula. The non-linear numerical algorithm applied in this study is Levenberg-Marquadet method. All the 25 sets of horizontal ground motions (east-west and north-south components at each seismic station) from 3 events (micro to macro scale) were used for the analysis of damping values and source parameters. The non-linear damping values of the deep and shallow crustal materials were found to be more similar to those of the region of the Western United States. The seismic source parameters found from this study also showed that the resultant stress drop values are relatively low compared to those of the Western United Sates. Consequently, comparisons of the various seismic parameters from this study and those of the United States Seismo-tectonic data suggest that the seismo-tectonic characteristics of the South eastern Korean Peninsula is more similar to those of the Western U.S.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.32 no.1
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• pp.98-104
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• 1999

To investigate the relationships between ocean environmental characteristics, the time-series data of temperature and salinity observed at a station near at Hanlim set net in 1995 and 1996 are analyzed, and the results are as follow ; 1. In hanlim set net, the diurnal range of temperature and salinity variation in summer is very large and the amplitude of short-period fluctuation of temperature and salinity is very large. That is, not only the water of the middle and bottom layers (low temperature and high salinity) but also the coalstal water (high temperature and low salinity) appears alternatively depending on the current direction 2. from the result of mooring for 22 days in Hanlim set net, the mean speed and direction of tidal current in neap tide were 9.1 cm/sec and south westward in ebb time, and 11.6 cm/sec and north or northeastward in flood time, respectively. The highest speed of the current was 15cm/sec in ebb time, and 22.6 cm/sec in flood time. The mean speed and direction of tidal current in spring tide were 10.4 cm/sec, and southwestward in ebb time, and 12.3 cm/sec, and north or northestward in flood time, respectively. The highest speed of the current was 19.4 cm/sec in ebb time, and 20 cm/sec in flood time respectively. The mean speed of the current in flood time was larger than that in ebb time. The velocity vector along the major axis of semidiurnal tide ($M_2$) component was 1.5 times larger than that of diurnal tide ($K_1$), The major directions of two compornants were northwestward and east-southeastward and residiual current were 3.25 cm/sec and northwestward-directed. Result of TGPS Buoy tracer for 3 days between Biyang-Do and Chgui-Do showed that the mean speed was 1.6 knot in ebb time and 1.3 knot in flood time. Direction of tidal was southwestward in ebb time and northeastward in flood time respectively. The maximum current speed was 4.8 knot in ebb time and 3.7 knot in flood time respectively. The mean speed and direction of tidal in of offshore were 1.7 knot and northwestward in flood time. The residual current appeared 0.3 knot northeastward.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.