• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.140-140
• /
• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• Journal of the Korean Society of Radiology
• /
• v.83 no.6
• /
• pp.1240-1258
• /
• 2022

The lymphatic system provides a route for the spread of inflammation and malignancies. The identification of nodal stations and lymphatic pathways of tumor spread is important for tumor staging, choice of therapy, and the prediction of the prognosis of patients with malignant diseases. Because lymph node metastasis is common in primary intra-abdominal malignant tumors, its detection is essential for radiologists to understand the pattern of disease spread. Using schematic pictures and color-coded CT images, this pictorial essay describes the locations and nomenclature of the abdominal lymph nodes. Furthermore, the lymphatic drainage pathways of the upper and lower gastrointestinal tracts, liver, gallbladder, bile duct, and pancreas have been highlighted. In addition, lymph nodes belonging to the regional lymph nodes in malignant tumors arising from each organ are described, and certain cases are presented with images from patients.

• Geomechanics and Engineering
• /
• v.37 no.1
• /
• pp.65-72
• /
• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.

• Steel and Composite Structures
• /
• v.50 no.5
• /
• pp.515-529
• /
• 2024

Cold-formed steel (CFS) is a popular choice for construction due to its low cost, durability, sustainability, resistance to high environmental and seismic pressures, and ease of installation. The beam-column connections in residential and medium-rise structures are formed using self-drilling screws that connect two CFS channel sections and a gusset plate. In order to increase the moment capacity of these CFS screwed beam-column connections, stiffeners are often placed on the web area of each single channel. However, there is limited literature on studying the effects of stiffeners on the moment capacity of CFS screwed beam-column connections. Hence, this paper proposes a new test approach for determining the moment capacity of CFS screwed beam-column couplings. This study describes an experimental test programme consisting of eight novel experimental tests. The effect of stiffeners, beam thickness, and gusset plate thickness on the structural behaviour of CFS screwed beam-column connections is investigated. Besides, nonlinear elasto-plastic finite element (FE) models were developed and validated against experimental test data. It found that there was reasonable agreement in terms of moment capacity and failure mode prediction. From the experimental and numerical investigation, it found that the increase in gusset plate or beam thickness and the use of stiffeners have no significant effect on the structural behaviour, moment capacity, or rotational capacity of joints exhibiting the same collapse behaviour; however, the capacity or energy absorption capacities have increased in joints whose failure behaviour varies with increasing thickness or using stiffeners. Besides, the thickness change has little impact on the initial stiffness.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.6
• /
• pp.2641-2647
• /
• 2012

Quality and sensory characteristics such as microbial count, pH, acidity, flavor, taste, color and overall acceptance of bamboo shoot pickle cured with red pepper paste and bamboo shoot pickle cured with soy sauce paste made of Maengjong bamboo shoots were investigated during a long-term storage at different temperature (at $25^{\circ}C$, $35^{\circ}C$ and $45^{\circ}C$). Microbial contamination was not observed, and water content did not showed significant change in all samples of both pickles during the whole storage period of 30 days, regardless of storage temperature. At $25^{\circ}C$, all sensory characteristics of bamboo shoot-red pepper paste pickle did not show a significant change for 30 d. However, at $35^{\circ}C$ and $45^{\circ}C$, the flavor, taste and color of bamboo shoot-red pepper paste pickle did not change remarkably, but the overall acceptance significantly changed from the beginning of storage. Bamboo shoot-soy sauce pickle did not give a significant change in flavor, taste and overall acceptance at $25^{\circ}C$, $35^{\circ}C$ and $45^{\circ}C$. However a remarkable change in color started to be shown at 25 d in case of storage at $45^{\circ}C$. Overall acceptance and color were selected as indicating parameters for the shelf-life estimation of bamboo shoot-red pepper paste pickle and bamboo shoot-soy sauce pickle, respectively. Based on room temperature storage and delivery at $20^{\circ}C$, the shelf-life of bamboo shoot-red pepper paste pickle and bamboo shoot-soy sauce pickle were determined as 308 d (about 10 month) and 447 d (about 14 month), respectively.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.99-112
• /
• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.2
• /
• pp.93-98
• /
• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.

• Genomics & Informatics
• /
• v.16 no.1
• /
• pp.10-13
• /
• 2018

Until now microsatellite (MS) have been a popular choice of markers for parentage verification. Recently many countries have moved or are in process of moving from MS markers to single nucleotide polymorphism (SNP) markers for parentage testing. FAO-ISAG has also come up with a panel of 200 SNPs to replace the use of MS markers in parentage verification. However, in many countries most of the animals were genotyped by MS markers till now and the sudden shift to SNP markers will render the data of those animals useless. As National Institute of Animal Science in South Korea plans to move from standard ISAG recommended MS markers to SNPs, it faces the dilemma of exclusion of old animals that were genotyped by MS markers. Thus to facilitate this shift from MS to SNPs, such that the existing animals with MS data could still be used for parentage verification, this study was performed. In the current study we performed imputation of MS markers from the SNPs in the 500-kb region of the MS marker on either side. This method will provide an easy option for the labs to combine the data from the old and the current set of animals. It will be a cost efficient replacement of genotyping with the additional markers. We used 1,480 Hanwoo animals with both the MS data and SNP data to impute in the validation animals. We also compared the imputation accuracy between BovineSNP50 and BovineHD BeadChip. In our study the genotype concordance of 40% and 43% was observed in the BovineSNP50 and BovineHD BeadChip respectively.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.39 no.3
• /
• pp.319-325
• /
• 2015

High-strength steel has replaced mild steel as the material of choice for truck decks or frames, owing to the growing demand for lightweight vehicles. Although studies on the weld fatigue characteristics of mild steel are available, studies on high-strength steels have been seldom conducted. In this study, firstly, we surveyed a chosen number of approaches and selected the Radaj method, which uses the notch factor approach, as the one suitable for evaluating the fatigue life of commercial vehicles. Secondly, we obtained the S-N curves of HARDOX and ATOS60 steel welds, and the F-N curves of the T-weld and overlapped-weld structures. Thirdly, we acquired a general S-N curve of welded structures made of high-strength steel from the F-N curve, using the notch factor approach. Fourthly, we extracted the weld fatigue characteristics of high-strength steel and incorporated the results in the database of a commercial fatigue program. Finally, we compared the results of the fatigue test and the CAE prediction of the example case, which demonstrated sufficiently good agreement.

• Journal of the korean academy of Pediatric Dentistry
• /
• v.35 no.4
• /
• pp.718-724
• /
• 2008

Tooth impaction is a frequently observed eruption anomaly in pediatric dental practice. Young patients with impacted or unerupted teeth have more prediction for dentigerous cyst formation. Dentigerous cyst presents radiographic features, unilocular or multilocular radioluscency. Cysts occur most frequently in the premolar region except third molar. Dentigerous cysts can grow to a considerable size, and large cysts may be associated with a painless expansion of the bone in the involved area. Extensive lesions may result in facial asymmetry, osseous destruction, root resorption of proximal teeth and displacement of associated tooth. The nature of the causative tooth influences the type of surgical treatment required for the dentigerous cyst. If the cyst is associated with a supernumerary or wisdom tooth, complete enucleation of the cyst along with extraction of tooth may be the first treatment choice. Otherwise, preservation of the associated teeth should be considered to prevent a young patient from psychological and mental trauma because of the loss of tooth. We should consider the degree of tooth displacement, osseous destruction and growth pattern of oromaxillofacial area when planning treatment. Thus a proper and logical treatment planning can help a proper growth and development of oromaxillofacial area and can save the patient from a psychological and mental trauma. This report describes 4 cases of the management of impacted premolars and molars associated with dentigerous cysts in children.