• Steel and Composite Structures
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• v.44 no.1
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• pp.119-139
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• 2022

This paper aims to use the artificial intelligence approach to develop a new model for predicting the ultimate axial strength of the circular concrete-filled steel tubular (CFST) stub columns. For this, the results of 314 experimentally tested circular CFST stub columns were employed in the generation of the design model. Since the influence of the column diameter, steel tube thickness, concrete compressive strength, steel tube yield strength, and column length on the ultimate axial strengths of columns were investigated in these experimental studies, here, in the development of the design model, these variables were taken into account as input parameters. The model was developed using the backpropagation algorithm named Bayesian Regularization. The accuracy, reliability, and consistency of the developed model were evaluated statistically, and also the design formulae given in the codes (EC4, ACI, AS, AIJ, and AISC) and the previous empirical formulations proposed by other researchers were used for the validation and comparison purposes. Based on this evaluation, it can be expressed that the developed design model has a strong and reliable prediction performance with a considerably high coefficient of determination (R-squared) value of 0.9994 and a low average percent error of 4.61. Besides, the sensitivity of the developed model was also monitored in terms of dimensional properties of columns and mechanical characteristics of materials. As a consequence, it can be stated that for the design of the ultimate axial capacity of the circular CFST stub columns, a novel artificial intelligence-based design model with a good and robust prediction performance was proposed herein.

• Proceedings of the KSME Conference
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• 2001.11b
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• pp.288-293
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• 2001

A critical heat flux (CHF) prediction method using an artificial neural network (ANN) was evaluated for application to the high-heat-flux (HHF) subcooled flow boiling. The developed ANN predictions were compared with the experimental database consisting of a total of 3069 CHF data points. Also, the prediction performance by the ANN was compared with those by mechanistic models and a look up table technique. The parameter ranges of the experimental data are: $0.33{\leq}D{\leq}37.5mm$, $0.002{\leq}L{\leq}4m$, $0.37{\leq}G{\leq}134Mg/m^2s$, $0.1{\leq}P{\leq}20MPa$, $50\leq{\Delta}h_{sub,in}\leq1660kJ/kg$, and $1.1{\leq}q_{CHF}\leq276MW/m^2$. $276MW/m^2$. It was found that 91.5% of the total data points were predicted within $a{\pm}20%$ error band, which showed the best prediction performance among the existing CHF prediction methods considered.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• Journal of Korean Neurosurgical Society
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• v.64 no.2
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• pp.143-150
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• 2021

Spinal dysraphism often causes neurological impairment from direct involvement of lesions or from cord tethering. The conus medullaris and lumbosacral roots are most vulnerable. Surgical intervention such as untethering surgery is indicated to minimize or prevent further neurological deficits. Because untethering surgery itself imposes risk of neural injury, intraoperative neurophysiological monitoring (IONM) is indicated to help surgeons to be guided during surgery and to improve functional outcome. Monitoring of electromyography (EMG), motor evoked potential, and bulbocavernosus reflex (BCR) is essential modalities in IONM for untethering. Sensory evoked potential can be also employed to further interpretation. In specific, free-running EMG and triggered EMG is of most utility to identify lumbosacral roots within the field of surgery and filum terminale or non-functioning cord can be also confirmed by absence of responses at higher intensity of stimulation. The sacral nervous system should be vigilantly monitored as pathophysiology of tethered cord syndrome affects the sacral function most and earliest. BCR monitoring can be readily applicable for sacral monitoring and has been shown to be useful for prediction of postoperative sacral dysfunction. Further research is guaranteed because current IONM methodology in spinal dysraphism is still deficient of quantitative and objective evaluation and fails to directly measure the sacral autonomic nervous system.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.256-266
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• 2021

The Convolutional Neural Network (CNN) has recently made potential improvements in face verification applications. In fact, different models based on the CNN have attained commendable progress in the classification rate using a massive amount of data in an uncontrolled environment. However, the enormous computation costs and the considerable use of storage causes a noticeable problem during training. To address these challenges, we focus on relevant data trained within the CNN model by integrating a lifting method for a better tradeoff between the data size and the computational efficiency. Our approach is characterized by the advantage that it does not need any additional space to store the features. Indeed, it makes the model much faster during the training and classification steps. The experimental results on Labeled Faces in the Wild and YouTube Faces datasets confirm that the proposed CNN framework improves performance in terms of precision. Obviously, our model deliberately designs to achieve significant speedup and reduce computational complexity in deep CNNs without any accuracy loss. Compared to the existing architectures, the proposed model achieves competitive results in face recognition tasks

• Steel and Composite Structures
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• v.47 no.6
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• pp.759-779
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• 2023

The paper proposes two hybrid metaheuristic optimization and artificial neural network (ANN) methods for the close prediction of the ultimate axial compressive capacity of concentrically loaded concrete filled double skin steel tube (CFDST) columns. Two metaheuristic optimization, namely genetic algorithm (GA) and particle swarm optimization (PSO), approaches enable the dynamic training architecture underlying an ANN model by optimizing the number and sizes of hidden layers as well as the weights and biases of the neurons, simultaneously. The former is termed as GA-ANN, and the latter as PSO-ANN. These techniques utilize the gradient-based optimization with Bayesian regularization that enhances the optimization process. The proposed GA-ANN and PSO-ANN methods construct the predictive ANNs from 125 available experimental datasets and present the superior performance over standard ANNs. Both the hybrid GA-ANN and PSO-ANN methods are encoded within a user-friendly graphical interface that can reliably map out the accurate ultimate axial compressive capacity of CFDST columns with various geometry and material parameters.

• Steel and Composite Structures
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• v.46 no.2
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.13-19
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• 2010

In this paper, we study a new estimation system for the prediction of steam generator tube defects. In the previous research works, defect size estimators were independently designed for each defect types in order to estimate the defect size. As a result, the structure of estimation system is rather complex and the estimation performance gets worse if the classification performance is degraded for some reason. This paper studies a new estimation system that does not require the classification of defect types. Although the previous works are expected to achieve much better estimation performance than the proposed system since it uses the estimator specialized in each defect, the performance difference is not so large. Therefore, it is expected that the proposed estimator can be effectively used for the case where the defect type classification is imperfect.

• Biomolecules & Therapeutics
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• v.26 no.5
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• pp.439-445
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• 2018

T-type calcium channels are low voltage-activated calcium channels that evoke small and transient calcium currents. Recently, T-type calcium channels have been implicated in neurodevelopmental disorders such as autism spectrum disorder and neural tube defects. However, their function during embryonic development is largely unknown. Here, we investigated the function and expression of T-type calcium channels in embryonic neural progenitor cells (NPCs). First, we compared the expression of T-type calcium channel subtypes (CaV3.1, 3.2, and 3.3) in NPCs and differentiated neural cells (neurons and astrocytes). We detected all subtypes in neurons but not in astrocytes. In NPCs, CaV3.1 was the dominant subtype, whereas CaV3.2 was weakly expressed, and CaV3.3 was not detected. Next, we determined CaV3.1 expression levels in the cortex during early brain development. Expression levels of CaV3.1 in the embryonic period were transiently decreased during the perinatal period and increased at postnatal day 11. We then pharmacologically blocked T-type calcium channels to determine the effects in neuronal cells. The blockade of T-type calcium channels reduced cell viability, and induced apoptotic cell death in NPCs but not in differentiated astrocytes. Furthermore, blocking T-type calcium channels rapidly reduced AKT-phosphorylation (Ser473) and $GSK3{\beta}$-phosphorylation (Ser9). Our results suggest that T-type calcium channels play essential roles in maintaining NPC viability, and T-type calcium channel blockers are toxic to embryonic neural cells, and may potentially be responsible for neurodevelopmental disorders.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.6
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• pp.781-787
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• 2009

The sense and antisense digoxigenin-labeled RNA probes of four genes, Cdc25A, Cdc25B, Sox2 and Mnb, were produced by using SP6 and T7 RNA polymerases, respectively, and in vitro transcription. Expression patterns of the four genes were detected by in situ hybridization in HH (Hamburger and Hamilton) stage 10 chick embryos. In general, expression patterns of the four genes were similar. mRNA of the four genes was mostly restricted to the entire CNS (central nervous system). All were confined to an identical region, neural tube, neural groove and caudal neural plate, corresponding to the notochord or spinal cord, but there was some distinction in specific region or in concentration, for example in somites. The overlap in expression at the same developmental stage in the CNS suggests that the four genes may be functional similar or related in CNS development. Expression patterns of the four genes support specific roles of these regulators in the developing CNS.