• Structural Engineering and Mechanics
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• v.89 no.4
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• pp.399-409
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• 2024

Assessment of existing concrete bridges is a challenge for owners. It has greater economic impact when compared to designing new bridges. When using conventional linear analyses, judgment of the engineer is required to understand the behavior of redundant structures after the first element in the structural system reaches its ultimate capacity. The alternative is to use a predictive tool such as advanced nonlinear finite element analyses (ANFEA) to assess the overall structural behavior. This paper proposes a new reliability framework for the assessment of existing bridge structures using ANFEA. A general framework defined in previous works, accounting for material uncertainties and concrete model performance, is adapted to the context of the assessment of existing bridges. A "shifted" reliability problem is defined under the assumption of quasi-deterministic dead load effects. The overall exercise is viewed as a progressive pushover analysis up to structural failure, where the actual safety index is compared at each event to a target reliability index.

• Advances in aircraft and spacecraft science
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• v.10 no.6
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• pp.521-543
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• 2023

Reynolds-averaged Navier-Stokes (RANS) models are extensively employed in industrial settings for the purpose of simulating intricate fluid flows. However, these models are subject to certain limitations. Notably, disparities persist in the Reynolds stresses when comparing the RANS model with high-fidelity data obtained from Direct Numerical Simulation (DNS) or experimental measurements. In this work we propose an approach to mitigate these discrepancies while retaining the favorable attributes of the Menter Shear Stress Transport (SST) model, such as its significantly lower computational expense compared to DNS simulations. This strategy entails incorporating an explicit algebraic model and employing a neural network to correct the turbulent characteristic time. The imposition of realizability constraints is investigated through the introduction of penalization terms. The assimilated Reynolds stress model demonstrates good predictive performance in both in-sample and out-of-sample flow configurations. This suggests that the model can effectively capture the turbulent characteristics of the flow and produce physically realistic predictions.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• Journal of Korean Neurosurgical Society
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• v.53 no.3
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• pp.167-173
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• 2013

Objective : There are many cases in which degenerative changes are prevalent in both the cervical and lumbar spine, and the relation between both spinal degenerative findings of MRI is controversial. The authors analyzed the prevalence of abnormal findings on MRI, and suggested a model to explain the relationship between cervical and lumbar disc in asymptomatic Korean subjects. Methods : We performed 3 T MRI sagittal scans on 102 asymptomatic subjects (50 men and 52 women) who visited our hospital between the ages of 14 and 82 years (mean age 46.3 years). Scores pertaining to herniation (HN), annular fissure (AF), and nucleus degeneration (ND) were analyzed. The total scores for the cervical and lumbar spine were analyzed using correlation coefficients and multiple linear regression with various predictive parameters, including weight, height, sex, age, smoking, occupation, and sedentary fashion. Results : The correlation coefficients of HN, AF, and ND were 0.44, 0.50, and 0.59, respectively. We made the best model for relationship by using multiple linear regression. Conclusion : The results of the current study showed that there was a close relationship between the cervical score (CS) and lumbar score (LS). In addition, the correlation between CS and LS, as well as the LS value itself, can be altered by other explanatory variables. Although not absolute, there was also a linear relationship between degenerative changes of the cervical and lumbar spine. Based on these results, it can be inferred that degenerative changes of the lumbar spine will be useful in predicting the degree of cervical spine degeneration in an actual clinical setting.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.304-309
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• 2002

We propose the computation reduction method of real root method that is mainly used in the CELP (Code Excited Linear Prediction) vocoder. The real root method is that if polynomial equations have the real roots, we are able to find those and transform them into LSP. However, this method takes much time to compute, because the root searching is processed sequentially in frequency region. In this paper, to reduce the computation time of real root, we compare the real root method with two methods. In first method, we use the mal scale of searching frequency region that is linear below 1 kHz and logarithmic above. In second method, The searching frequency region and searching interval are ordered by each coefficient's distribution. In order to compare real root method with proposed methods, we measured the following two. First, we compared the position of transformed LSP (Line Spectrum Pairs) parameters in the proposed methods with these of real root method. Second, we measured how long computation time is reduced. The experimental results of both methods that the searching time was reduced by about 47% in average without the change of LSP parameters.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.547-557
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• 2017

The health-related quality of life data are commonly skewed and bounded with spike at the perfect health status, and the variance tended to be heteroscedastic. In this study, we have developed a prediction model for EQ-5D using linear regression model, beta regression model, and extended beta regression model with mean and precision submodel, and also compared the predictive accuracy. The extended beta regression model allows to model skewness and differences in dispersion related to covariates. Although the extended beta regression model has higher prediction accuracy than the linear regression model, the overlapped confidence intervals suggested that the extended beta regression model was superior to the linear regression model. However, the expended beta regression model could explain the heteroscedasticity and predict within the bounded range. Therefore, the expended beta regression model are appropriate for fitting the health-related quality of life data such as EQ-5D.

• Computers and Concrete
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• v.21 no.4
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• Advances in concrete construction
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• v.10 no.6
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• pp.479-488
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• 2020

The experimental and numerical works were carried out on high performance fiber reinforced concrete (HPFRC) with w/cm ratios ranging from 0.25 to 0.40, fiber volume fraction (Vf)=0-1.5% and 10% silica fume replacement. Improvements in compressive and flexural strengths obtained for HPFRC are moderate and significant, respectively, Empirical equations developed for the compressive strength and flexural strength of HPFRC as a function of fiber volume fraction. A relation between flexural strength and compressive strength of HPFRC with R=0.78 was developed. Due to the complex mix proportions and non-linear relationship between the mix proportions and properties, models with reliable predictive capabilities are not developed and also research on HPFRC was empirical. In this paper due to the inadequacy of present method, a back propagation-neural network (BP-NN) was employed to estimate the 28-day compressive strength of HPFRC mixes. BP-NN model was built to implement the highly non-linear relationship between the mix proportions and their properties. This paper describes the data sets collected, training of ANNs and comparison of the experimental results obtained for various mixtures. On statistical analyses of collected data, a multiple linear regression (MLR) model with R2=0.78 was developed for the prediction of compressive strength of HPFRC mixes, and average absolute error (AAE) obtained is 6.5%. On validation of the data sets by NNs, the error range was within 2% of the actual values. ANN model has given the significant degree of accuracy and reliability compared to the MLR model. ANN approach can be effectively used to estimate the 28-day compressive strength of fibrous concrete mixes and is practical.

• Journal of Chest Surgery
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• v.54 no.6
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• pp.466-472
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• 2021

Background: Major intraoperative hemorrhage reportedly predicts unfavorable survival outcomes following surgical resection for esophageal carcinoma (EC). However, the factors predicting the amount of blood lost during thoracoscopic esophagectomy have yet to be sufficiently studied. We sought to identify risk factors for excessive blood loss during video-assisted thoracoscopic surgery (VATS) for EC. Methods: Using simple and multiple linear regression models, we performed retrospective analyses of the associations between clinicopathological/surgical factors and estimated hemorrhagic volume in 168 consecutive patients who underwent VATS-type esophagectomy for EC. Results: The median blood loss amount was 225 mL (interquartile range, 126-380 mL). Abdominal laparotomy (p<0.001), thoracic duct resection (p=0.014), and division of the azygos arch (p<0.001) were significantly related to high volumes of blood loss. Body mass index and operative duration, as continuous variables, were also correlated positively with blood loss volume in simple linear regression. The multiple linear regression analysis identified prolonged operative duration (p<0.001), open laparotomy approach (p=0.003), azygos arch division (p=0.005), and high body mass index (p=0.014) as independent predictors of higher hemorrhage amounts during VATS esophagectomy. Conclusion: As well as body mass index, operation-related factors such as operative duration, open laparotomy, and division of the azygos arch were independently predictive of estimated blood loss during VATS esophagectomy for EC. Laparoscopic abdominal procedures and azygos arch preservation might be minimally invasive options that would potentially reduce intraoperative hemorrhage, although oncological radicality remains an important consideration.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.19-26
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• 2024

In recent years, an increasing number of experimental studies have shown that the practical application of mature active control systems requires consideration of robustness criteria in the design process, including the reduction of tracking errors, operational resistance to external disturbances, and measurement noise, as well as robustness and stability. Good uncertainty prediction is thus proposed to solve problems caused by poor parameter selection and to remove the effects of dynamic coupling between degrees of freedom (DOF) in nonlinear systems. To overcome the stability problem, this study develops an advanced adaptive predictive fuzzy controller, which not only solves the programming problem of determining system stability but also uses the law of linear matrix inequality (LMI) to modify the fuzzy problem. The following parameters are used to manipulate the fuzzy controller of the robotic system to improve its control performance. The simulations for system uncertainty in the controller design emphasized the use of acceleration feedback for practical reasons. The simulation results also show that the proposed H∞ controller has excellent performance and reliability, and the effectiveness of the LMI-based method is also recognized. Therefore, this dynamic control method is suitable for seismic protection of civil buildings. The objectives of this document are access to adequate, safe, and affordable housing and basic services, promotion of inclusive and sustainable urbanization, implementation of sustainable disaster-resilient construction, sustainable planning, and sustainable management of human settlements. Simulation results of linear and non-linear structures demonstrate the ability of this method to identify structures and their changes due to damage. Therefore, with the continuous development of artificial intelligence and fuzzy theory, it seems that this goal will be achieved in the near future.