• Structural Engineering and Mechanics
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• v.71 no.4
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• pp.407-416
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• 2019

Novel steel fibre reinforced ultra-lightweight cement composite (ULCC) with compressive strength of 87.3MPa and density of $1649kg/m^3$ was developed for the flat slabs in civil buildings. This paper investigated structural behaviours of ULCC flat slabs according to a 4-specimen test program under concentrated loading and some reported test results. The investigated governing parameters on the structural behaviours of the ULCC slabs include volume fraction of the steel fibre and the patch loading area. The test results revealed that ULCC flat slabs with and without flexure reinforcement failed in different failure mode, and an increase in volume fraction of the steel fibre and loading area led to an increase in flexural resistance for the ULCC slabs without flexural reinforcement. Based on the experiment results, the analytical models were developed and also validated. The validations showed that the analytical models developed in this paper could predict the ultimate strength of the ULCC flat slabs with and without flexure reinforcement reasonably well.

• International Journal of Stem Cells
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• v.17 no.2
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• pp.158-181
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• 2024

This study offers a comprehensive overview of brain organoids for researchers. It combines expert opinions with technical summaries on organoid definitions, characteristics, culture methods, and quality control. This approach aims to enhance the utilization of brain organoids in research. Brain organoids, as three-dimensional human cell models mimicking the nervous system, hold immense promise for studying the human brain. They offer advantages over traditional methods, replicating anatomical structures, physiological features, and complex neuronal networks. Additionally, brain organoids can model nervous system development and interactions between cell types and the microenvironment. By providing a foundation for utilizing the most human-relevant tissue models, this work empowers researchers to overcome limitations of two-dimensional cultures and conduct advanced disease modeling research.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.2
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• pp.117-124
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• 2005

This study tries to develop the models of measuring the level of product availability accommodated for features of specific customers dividing customers into VIP customers and general customers. Functions of costs that the models are composed of are cost of holding safety stock and cost of lost opportunities. The existing model of measuring the level of product availability which focused on cost of holding safety stock for VIP customers should be reinforced by considering cost of lost opportunities caused by general customers' quitting trades with a company. This study tries to present realistic solutions for problems in making decisions related to the total inventory. This study concludes that the model of the level of product availability meeting general customers' needs is more efficient according to increasing of a latent demand of the general customers who quit trades with a company and the cost of lost opportunities.

• Nuclear Engineering and Technology
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• v.31 no.2
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• pp.157-171
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• 1999

In this work, an automatic software verification method for Nuclear Power Plant (NPP) protection system is developed. This method utilizes Colored Petri Net (CPN) for system modeling and Prototype Verification System (PVS) for mathematical verification. In order to help flow-through from modeling by CPN to mathematical proof by PVS, an information extractor from CPN models has been developed in this work. In order to convert the extracted information to the PVS specification language, a translator also has been developed. ML that is a higher-order functional language programs the information extractor and translator. This combined method has been applied to a protection system function of Wolsong NPP SDS2(Steam Generator Low Level Trip). As a result of this application, we could prove completeness and consistency of the requirement logically. Through this work, in short, an axiom or lemma based-analysis method for CPN models is newly suggested in order to complement CPN analysis methods and a guideline for the use of formal methods is proposed in order to apply them to NPP Software Verification and Validation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.111-115
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• 2001

A good business performance measurement system is an effective tool io sustained growth in profits. Although interest in creating performance measurement models is widespread, a well-designed system is rare. To be successful in today's competitive environment, a performance measurement system should incorporate strategic success factors and contain financial and non-financial measuring index to carry out strategic management. In the 1990s, Kaplan & Norton introduced a concept called the Balanced Scorecard. The Balanced Scorecard supplements traditional financial measures with criteria that measured performance from three additional perspectives - those perspectives of customers, internal business processes, and learning and growth. This paper presents five measuring index criteria for each perspective. To calculate the relative priority for These measuring index, we investigate weights investigated by interviews with management consultant. Then, AHP method is employed for calculating priority weight. Our evaluation model may be referred to as the Balanced Analytic Hierarchical Performance Model(BAHPM) in the sense that the analytic hierarchical scheme, along with the AHP, is applied. The BAHPM is the first kind of analytical model to cover a wide variety of measures. In comparison with previous evaluation models, our model shows strengths in structural flexibility, ease of incorporating feedback, group evaluation capacity, participation promotion, sensitivity analysis, and computational simplicity. A prototype based on the BAHPM can be applied to various industry sectors.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.163-173
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• 1997

Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.

• Journal of the Korean Society for Advanced Composite Structures
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• v.5 no.1
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• pp.28-36
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• 2014

This study describes the development of innovative connections between steel beams and concrete-filled tube columns that utilize a combination of low-carbon steel and super-elastic shape memory alloy components. The intent is to combine the recentering behavior provided by the shape memory alloys to reduce building damage and residual drift after a major earthquake with the excellent energy dissipation of the low-carbon steel. The analysis and design of structures requires that simple yet accurate models for the connection behavior be developed. The development of a simplified 2D spring connection model for cyclic loads from advanced 3D FE monotonic studies is described. The implementation of those models into non-linear frame analyses indicates hat the recentering systems will provide substantial benefits for smaller earthquakes and superior performance to all-welded moment frames for large earthquakes.

• Korean Journal of Materials Research
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• v.20 no.9
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• pp.478-482
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• 2010

A series of molecular dynamic (MD), finite element (FE) and ab initio simulations are carried out to establish suitable modeling schemes for the continuum-based analysis of aluminum matrix nanocomposites reinforced with carbon nanotubes (CNTs). From a comparison of the MD with FE models and inferences based on bond structures and electron distributions, we propose that the effective thickness of a CNT wall for its continuum representation should be related to the graphitic inter-planar spacing of 3.4${\AA}$. We also show that shell element representation of a CNT structure in the FE models properly simulated the carbon-carbon covalent bonding and long-range interactions in terms of the load-displacement behaviors. Estimation of the effective interfacial elastic properties by ab initio simulations showed that the in-plane interfacial bond strength is negligibly weaker than the normal counterpart due to the nature of the weak secondary bonding at the CNT-Al interface. Therefore, we suggest that a third-phase solid element representation of the CNT-Al interface in nanocomposites is not physically meaningful and that spring or bar element representation of the weak interfacial bonding would be more appropriate as in the cases of polymer matrix counterparts. The possibility of treating the interface as a simply contacted phase boundary is also discussed.

• Smart Structures and Systems
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• v.26 no.1
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.