• Journal of Information Processing Systems
• /
• v.8 no.2
• /
• pp.213-240
• /
• 2012

Safety critical systems, real time systems, and event-based systems have a complex set of events and their own interdependency, which makes them difficult to test ma Safety critic Safety critical systems, real time systems, and event-based systems have a complex set of events and their own interdependency, which makes them difficult to test manually. In order to cut down on costs, save time, and increase reliability, the model based testing approach is the best solution. Such an approach does not require applications or codes prior to generating test cases, so it leads to the early detection of faults, which helps in reducing the development time. Several model-based testing approaches have used different UML models but very few works have been reported to show the generation of test cases that use events. Test cases that use events are an apt choice for these types of systems. However, these works have considered events that happen at a user interface level in a system while other events that happen in a system are not considered. Such works have limited applications in testing the GUI of a system. In this paper, a novel model-based testing approach is presented using business events, state events, and control events that have been captured directly from requirement specifications. The proposed approach documents events in event templates and then builds an event-flow model and a fault model for a system. Test coverage criterion and an algorithm are designed using these models to generate event sequence based test scenarios and test cases. Unlike other event based approaches, our approach is able to detect the proposed faults in a system. A prototype tool is developed to automate and evaluate the applicability of the entire process. Results have shown that the proposed approach and supportive tool is able to successfully derive test scenarios and test cases from the requirement specifications of safety critical systems, real time systems, and event based systems.

• Journal of Powder Materials
• /
• v.14 no.6
• /
• pp.362-366
• /
• 2007

Magnesium and magnesium alloys are promising materials for light weight and high strength applications. In order to obtain homogeneous and high quality products in powder compaction and powder forging processes, it is very important to control density and density distributions in powder compacts. In this study, a model for densification of metallic powder is proposed for pure magnesium. The mode] considers the effect of powder characteristics using a pressure-dependent critical density yield criterion. Also with the new model, it was possible to obtain reasonable physical properties of pure magnesium powder using cold iso-state pressing. The proposed densification model was implemented into the finite element method code. The finite element analysis was applied to simulating die compaction of pure magnesium powders in order to investigate the density and effective strain distributions at room temperature.

• Nuclear Engineering and Technology
• /
• v.51 no.3
• /
• pp.723-730
• /
• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Biomaterials and Biomechanics in Bioengineering
• /
• v.2 no.3
• /
• pp.127-141
• /
• 2015

Silver-based systems activated by low intensity direct current continue to be investigated as an alternative antimicrobial for infection prophylaxis and treatment. However there has been limited research on the quantitative characterization of the antimicrobial efficacy of such systems. The objective of this study was to develop a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model providing the quantitative relationship between the critical system parameters and the degree of antimicrobial efficacy. First, time-kill curves were experimentally established for a strain of Staphylococcus aureus in a nutrientrich fluid environment over 48 hours. Based on these curves, a modified PK/PD model was developed with two components: a growing silver-susceptible bacterial population and a depreciating bactericidal process. The test of goodness-of-fit showed that the model was robust and had good predictability ($R^2>0.7$). The model demonstrated that the current intensity was positively correlated to the initial killing rate and the bactericidal fatigue rate of the system while the anode surface area was negatively correlated to the fatigue rate. The model also allowed the determination of the effective range of these two parameters within which the system has significant antimicrobial efficacy. In conclusion, the modified PK/PD model successfully described bacterial growth and killing kinetics when the bacteria were exposed to the electrically activated silver-titanium implant system. This modeling approach as well as the model itself can also potentially contribute to the development of optimal design strategies for other similar antimicrobial systems.

• Structural Engineering and Mechanics
• /
• v.42 no.2
• /
• pp.153-174
• /
• 2012

Micromechanical facture models can be used to predict ductile fracture in steel structures. In order to calibrate the parameters in the micromechanical models for the largely used Q345 steel in China, uniaxial tensile tests, smooth notched tensile tests, cyclic notched bar tests, scanning electron microscope tests and finite element analyses were conducted in this paper. The test specimens were made from base metal, deposit metal and heat affected zone of Q345 steel to investigate crack initiation in welded steel connections. The calibrated parameters for the three different locations of Q345 steel were compared with that of the other seven varieties of structural steels. It indicates that the toughness index parameters in the stress modified critical strain (SMCS) model and the void growth model (VGM) are connected with ductility of the material but have no correlation with the yield strength, ultimate strength or the ratio of ultimate strength to yield strength. While the damage degraded parameters in the degraded significant plastic strain (DSPS) model and the cyclic void growth model (CVGM) and the characteristic length parameter are irrelevant with any properties of the material. The results of this paper can be applied to predict ductile fracture in welded steel connections.

• KCI Concrete Journal
• /
• v.13 no.1
• /
• pp.19-25
• /
• 2001

Based on the orthotropic hypoelasticity formulation, a constitutive material model of concrete taking account of triaxial stress state is presented. In this model, the ultimate strength surface of concrete in triaxial stress space is described by the Hsieh's four-parameter surface. On the other hand, the different ultimate strength surface of concrete in strain space is proposed in order to account for increasing ductility in high confinement pressure. Compressive ascending and descending behavior of concrete is considered. Concrete cracking behavior is considered as a smeared crack model, and after cracking, the tensile strain-softening behavior and the shear mechanism of cracked concrete are considered. The proposed constitutive model of concrete is compared with some results obtained from tests under the states of uniaxial, biaxial, and triaxial stresses. In triaxial compressive tests, the peak compressive stress from the predicted results agrees well with the experimental results, and ductility response under high confining pressure matches well the experimental result. The reinforcing bars embedded in concrete are considered as an isoparametric line element which could be easily incorporated into the isoparametric solid element of concrete, and the average stress - average strain relationship of the bar embedded in concrete is considered. From numerical examples for a reinforced concrete simple beam and a structural beam type member, the stress state of concrete in the vicinity of talc critical region is investigated.

• Wind and Structures
• /
• v.27 no.3
• /
• pp.163-173
• /
• 2018

The design wind pressure for low-rise buildings in the ASCE 7-10 is defined by procedures that are categorized into the Main Wind Force-Resisting System (MWFRS) and the Components and Cladding (C&C). Some of these procedures were originally developed based on steel portal frames of industrial buildings, while the residential structures are a completely different structural system, most of which are designed as low-rise light-frame wood constructions. The purpose of this study is to discuss the rationality (or irrationality) of the extension of the wind loads calculated by the ASCE 7-10 to the light-frame wood residential buildings that represent the most vulnerable structures under extreme wind conditions. To serve this purpose, the same approach as used in the development of Chapter 28 of the ASCE 7-10 that envelops peak responses is adopted in the present study. Database-assisted design (DAD) methodology is used by applying the dynamic wind loads from Louisiana State University (LSU) database on a typical residential building model to assess the applicability of the standard by comparing the induced responses. Rather than the postulated critical member demands on the industrial building such as the bending moments at the knee, the maximum values at the critical points for wood frame buildings under wind loads are used as indicators for the comparison. Then, the critical members are identified through these indicators in terms of the displacement or the uplift force at connections and roof envelope. As a result, some situations for each of the ASCE 7 procedures yielding unconservative wind loads on the typical low-rise residential building are identified.

• Journal of the Korean Geotechnical Society
• /
• v.19 no.3
• /
• pp.93-104
• /
• 2003

The Disturbed State Concept (DSC) model, with simplified unloading/reloading formulation, is implemented in a nonlinear dynamic finite element program fur porous media named DSC_DYN2D. In this research, the DSC constitutive model is utilized using the HiSS model for relative intact (RI) part and the critical state model for the fully adjusted (FA) part in the material. The general formulation for implementation is developed. The cyclic loading tests from the field load test data on a pile segment were numerically simulated using the finite element program DSC_DYN2D and compared with field measurements and those from the previous analysis with the HiSS model. The DSC predictions show improved agreement with the field behavior of the pile compared to those from the HiSS model. Overall, the computer procedure with the DSC model allows improved and realistic simulation of the complex dynamic soil-structure interaction problems.

• Nuclear Engineering and Technology
• /
• v.52 no.10
• /
• pp.2196-2203
• /
• 2020

A dryout mechanism model for rectangular narrow channels at high pressure conditions is developed by assuming that the Kelvin-Helmholtz instability triggered the occurrence of dryout. This model combines the advantages of theoretical analysis and empirical correlation. The unknown coefficients in the theoretical derivation are supported by the experimental data. Meanwhile, the decisive restriction of the experimental conditions on the applicability of the empirical correlation is avoided. The expression of vapor phase velocity at the time of dryout is derived, and the empirical correlation of liquid film thickness is introduced. Since the CHF value obtained from the liquid film thickness should be the same as the value obtained from the Kelvin-Helmholtz critical stability under the same condition, the convergent CHF value is obtained by iteratively calculating. Comparing with the experimental data under the pressure of 6.89-13.79 MPa, the average error of the model is -15.4% with the 95% confidence interval [-20.5%, -10.4%]. And the pressure has a decisive influence on the prediction accuracy of this model. Compared with the existing dryout code, the calculation speed of this model is faster, and the calculation accuracy is improved. This model, with great portability, could be applied to different objects and working conditions by changing the expression of the vapor phase velocity when the dryout phenomenon is triggered and the calculation formula of the liquid film.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.165-165
• /
• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.