• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1217-1233
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• 2017

Effective monitoring and testing during each step are essential for document verification in research and development (R&D) projects. In software development, proper testing is required to verify it carefully and constantly because of the invisibility features of software. However, not enough studies on test processes for R&D projects have been done. Thus, in this paper, we introduce a Test Maturity Model integration (TMMi)-based software field R&D test process that offers five integrity levels and makes the process compatible for different types of projects. The Software & Systems Process Engineering Metamodel (SPEM) is used widely in the software process-modeling context, but it lacks built-in enactment capabilities, so there is no tool or process engine that enables one to execute the process models described in SPEM. Business Process Model and Notation (BPMN)-based workflow engines can be a solution for process execution, but process models described in SPEM need to be converted to BPMN models. Thus, we propose an approach to support enactment of SPEM-based process models by converting them into business processes. We show the effectiveness of our approach through converting software R&D test processes specified in SPEM in a case study.

• Journal of Applied Reliability
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• v.18 no.1
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• pp.33-39
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• 2018

Purpose: Accelerated degradation tests can be effective in assessing product reliability when degradation leading to failure can be observed. This article proposes an accelerated degradation test model for highly reliable solid state drives (SSDs). Methods: We suggest a nonlinear mixed-effects (NLME) model to degradation data for SSDs. A Monte Carlo simulation is used to estimate lifetime distribution in accelerated degradation testing data. This simulation is performed by generating random samples from the assumed NLME model. Conclusion: We apply the proposed method to degradation data collected from SSDs. The derived power model is shown to be much better at fitting the degradation data than other existing models. Finally, the Monte Carlo simulation based on the NLME model provides reasonable results in lifetime estimation.

• Journal of Korean Society of Water and Wastewater
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• v.25 no.5
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• pp.635-642
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• 2011

Hydraulic efficiency was a vital component in evaluating the disinfection capability of clearwell. Current practice evaluates these system based on the tracer test only. In this paper, CFD(Computational Fluid Dynamics) was applied on the clearwell for alternating or supplementing the tracer test. The baffle factor derived from the CFD modeling closely matched the values obtained from full scale tracer testing. And, for suggesting proper numerical model in clearwell; the turbulence model, discretization scheme, convergence criteria were investigated through separate simulation runs. The model validation was conducted by comparing the simulated data with experimental data. In the turbulence model, the realizable ${\kappa}-{\varepsilon}$ model and the standard ${\kappa}-{\varepsilon}$ model were found to be more appropriate than RNG ${\kappa}-{\varepsilon}$ model. The residuals of convergence criteria should be used as not $10^{-3}$ but $10^{-4}$ or $10^{-5}$. In discretization scheme, the difference of simulated values in 1st, 2nd, 3rd upwind scheme was found to be insignificant. Moreover, the result of this study suggest that CFD modeling can be a reliable alternative to tracer testing for evaluating the hydraulic efficiency.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.39-46
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• 1996

Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. In this paper we compare power of goodness-of-fit test statistics for testing the (null)parametric model versus the (alternative) nonparametric model.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1329-1341
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• 2006

When X and Y have independent gamma distributions, we consider the testing problem for two gamma means. We propose a solution based on a Bayesian model selection procedure to this problem in which no subjective input is considered. The reference prior is derived. Using the derived reference prior, we compute the fractional Bayes factor and the intrinsic Bayes factors. The posterior probability of each model is used as a model selection tool. Simulation study and a real data example are provided.

• Elastomers and Composites
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• v.56 no.3
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• pp.124-135
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• 2021

Evaluating service lives of rubber materials at certain temperatures requires a destructive method (typically using elongation at break). In this study, a non-destructive method based on hardness change rate was proposed for evaluating the service life of chloroprene rubber (CR). Compared to the destructive method, this non-destructive method ensures homogeneity of CR specimens and requires a small number of samples. Thermal accelerated degradation test was conducted on the CR specimens at 55, 70, 85, 100, and 125℃, and the tensile strength, elongation at break, and hardness were measured. The results of the experiment were compared to those of the accelerated life evaluation method proposed in this study. Comparing the analyzed lives in the high temperature region (70, 85, 100, and 125℃), the difference between the service lives for the destructive method (using the elongation at break) and non-destructive method (using the hardness) was approximately 0.1 year. Therefore, it was confirmed that the proposed non-destructive evaluation method based on hardness changes can evaluate the actual life of CR under thermally accelerated degradation conditions.

• Geomechanics and Engineering
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• v.20 no.3
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• ETRI Journal
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• v.30 no.1
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• pp.129-140
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• 2008

In this paper, we propose a novel ant colony optimization (ACO)-based test scheduling method for testing network-on-chip (NoC)-based systems-on-chip (SoCs), on the assumption that the test platform, including specific methods and configurations such as test packet routing, generation, and absorption, is installed. The ACO metaheuristic model, inspired by the ant's foraging behavior, can autonomously find better results by exploring more solution space. The proposed method efficiently combines the rectangle packing method with ACO and improves the scheduling results by dynamically choosing the test-access-mechanism widths for cores and changing the testing orders. The power dissipation and variable test clock mode are also considered. Experimental results using ITC'02 benchmark circuits show that the proposed algorithm can efficiently reduce overall test time. Moreover, the computation time of the algorithm is less than a few seconds in most cases.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.6
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• pp.441-452
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• 2020

In vivo animal models are limited in their ability to mimic the extremely complex systems of the human body, and there is increasing disquiet about the ethics of animal research. Many authorities in different geographical areas are considering implementing a ban on animal testing, including testing for cosmetics and pharmaceuticals. Therefore, there is a need for research into systems that can replicate the responses of laboratory animals and simulate environments similar to the human body in a laboratory. An in vitro two-dimensional cell culture model is widely used, because such a system is relatively inexpensive, easy to implement, and can gather considerable amounts of reference data. However, these models lack a real physiological extracellular environment. Recent advances in stem cell biology, tissue engineering, and microfabrication techniques have facilitated the development of various 3D cell culture models. These include multicellular spheroids, organoids, and organs-on-chips, each of which has its own advantages and limitations. Organoids are organ-specific cell clusters created by aggregating cells derived from pluripotent, adult, and cancer stem cells. Patient-derived organoids can be used as models of human disease in a culture dish. Biomimetic organ chips are models that replicate the physiological and mechanical functions of human organs. Many organoids and organ-on-a-chips have been developed for drug screening and testing, so competition for patents between countries is also intensifying. We analyzed the scientific and technological trends underlying these cutting-edge models, which are developed for use as non-animal models for testing safety and efficacy at the nonclinical stages of drug development.