• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.541-572
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• 2016

Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.

• Journal of Korean Society for Quality Management
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• v.41 no.1
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• pp.149-162
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• 2013

Purpose: The purpose of this study was to design of a probabilistic model for optimum manpower planning in R&D department by Montecarlo simulation. Methods: We investigate the process and the requirement of manpower planning and scheduling in R&D department. The empirical distributions of necessary time and manpower for R&D projects are developed. From the empirical distributions, we can estimate a probability distribution of optimum manpower in R&D department. A simulation method of estimating the probability distribution of optimum manpower is considered. It is a useful tool for obtaining the sum, the variance and other statistics of the distributions. Results: The real industry cases are given and the properties of the model are investigated by Montecarlo Simulation. we apply the model to the research laboratory of the global company, and investigate and compensate the weak points of the model. Conclusion: The proposed model provides various and correct information such as average, variance, percentile, minimum, maximum and so on. A decision maker of a company can easily develop the future plan and the task of researchers may be allocated properly. we expect that the productivity can be improved by this study. The results of this study can be also applied to other areas including shipbuilding, construction, and consulting areas.

• Structural Engineering and Mechanics
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• v.50 no.5
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• pp.697-708
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• 2014

The intentional buckling design of micro-films has various potential applications in engineering. The buckling amplitude and critical strain of micro-films are the crucial parameters for the buckling design. In the reported studies, the film parameters were regarded as deterministic. However, the geometrical and physical parameters uncertainty of micro-films due to manufacturing becomes prominent and needs to be considered. In the present paper, the probabilistic nonlinear buckling analysis of micro-films with uncertain parameters is proposed for design accuracy and reliability. The nonlinear differential equation and its asymptotic solution for the buckling micro-film with nominal parameters are firstly established. The mean values, standard deviations and variation coefficients of the buckling amplitude and critical strain are calculated by using the probability densities of uncertain parameters such as the film span length, thickness, elastic modulus and compressive force, to reveal the effects of the film parameter uncertainty on the buckling deformation. The results obtained illustrate the probabilistic relation between buckling deformation and uncertain parameters, and are useful for accurate and reliable buckling design in terms of probability.

• Journal of Environmental Health Sciences
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• v.43 no.3
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• pp.223-232
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• 2017

Objectives: In response to increased interest in the safety of children's products, a risk management system is being prepared through exposure assessment of hazardous chemicals. To estimate exposure levels, risk assessors are using deterministic and probabilistic approaches to statistical methodology and a commercialized Monte Carlo simulation based on tools (MCTool) to efficiently support calculation of the probability density functions. This study was conducted to analyze and discuss the usage patterns and problems associated with the results of these two approaches and MCTools used in the case of probabilistic approaches by reviewing research reports related to exposure assessment for children's products. Methods: We collected six research reports on exposure and risk assessment of children's products and summarized the deterministic results and corresponding underlying distributions for exposure dose and concentration results estimated through deterministic and probabilistic approaches. We focused on mechanisms and differences in the MCTools used for decision making with probabilistic distributions to validate the simulation adequacy in detail. Results: The estimation results of exposure dose and concentration from the deterministic approaches were 0.19-3.98 times higher than the results from the probabilistic approach. For the probabilistic approach, the use of lognormal, Student's T, and Weibull distributions had the highest frequency as underlying distributions of the input parameters. However, we could not examine the reasons for the selection of each distribution because of the absence of test-statistics. In addition, there were some cases estimating the discrete probability distribution model as the underlying distribution for continuous variables, such as weight. To find the cause of abnormal simulations, we applied two MCTools used for all reports and described the improper usage routes of MCTools. Conclusions: For transparent and realistic exposure assessment, it is necessary to 1) establish standardized guidelines for the proper use of the two statistical approaches, including notes by MCTool and 2) consider the development of a new software tool with proper configurations and features specialized for risk assessment. Such guidelines and software will make exposure assessment more user-friendly, consistent, and rapid in the future.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.149-160
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• 2017

In hypothesis testing, the interpretation of a statistic obtained from the data analysis relies on a probabilistic distribution of the statistic constructed according to several statistical theories. For instance, the statistical significance of a mean difference between experimental conditions is determined according to a probabilistic distribution of the mean differences (e.g., Student's t) constructed under several theoretical assumptions for population characteristics. The present study explored the logic and advantages of random-resampling approach for analyzing event-related potentials (ERPs) where a hypothesis is tested according to the distribution of empirical statistics that is constructed based on randomly resampled dataset of real measures rather than a theoretical distribution of the statistics. To motivate ERP researchers' understanding of the random-resampling approach, the present study further introduced a specific example of data analyses where a random-permutation procedure was applied according to the random-resampling principle, as well as discussing several cautions ahead of its practical application to ERP data analyses.

• Journal of the Korean Society of Safety
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• v.25 no.3
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• pp.1-6
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• 2010

Ball bearings are one of main components that support the rotational shaft in high speed rotary machinery. So, it is very important to detect the incipient faults and fault growth of bearing since the damage and failure of bearing can cause a critical failures or accidents of machinery system. In the past, many researchers mainly performed to detect the bearing fault using traditional method such as wavelet, statistics, envelope etc in vibration signals. But study on the detection technique for bearing fault growth has a little been performed. In this paper, we verified the possibility for monitoring of fault growth and detection of fault size in bearing outer-race by using the envelope powerspectrum and probabilistic density function from measured vibration signals.

• Wind and Structures
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• v.25 no.6
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• pp.589-608
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• 2017

A novel probabilistic approach is presented for estimating the equivalent static wind loads that produce a static response of the structure, which is "equivalent" in a probabilistic sense, to the extreme dynamic responses due to the unsteady pressure random field induced by the wind. This approach has especially been developed for complex structures (such as stadium roofs) for which the unsteady pressure field is measured in a boundary layer wind tunnel with a turbulent incident flow. The proposed method deals with the non-Gaussian nature of the unsteady pressure random field and presents a model that yields a good representation of both the quasi-static part and the dynamical part of the structural responses. The proposed approach is experimentally validated with a relatively simple application and is then applied to a stadium roof structure for which experimental measurements of unsteady pressures have been performed in boundary layer wind tunnel.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.3
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• pp.300-305
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• 2013

LOOP(Loss of Offsite Power) and SBO(Station Blackout) events have been occurring in nuclear power plants should be reviewed and be controlled on important electrical equipments by professional engineer to prevent and to safety improvement from safety assessment and reliability analysis report. LOOP and SBO occasionally happened by internal or external causes. This paper contained that LOOP frequency in the United States NPPs and in the domestic NPPs have compared and analyzed data by the past lessons and probabilistic statistics. Additionally will be installed MG(Mobile Generator) according to the lessons of Fukushima nuclear accident in Japan, which CDF(Core Damage Frequency) and LOOP frequency have reconsidered. And this paper proposed to reduce reliability criteria using PSA(Probabilistic Safety Analysis).

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1127-1135
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• 2012

Association rule mining is the method to quantify the relationship between each set of items in a large database. One of the well-studied problems in data mining is exploration for association rules. There are three primary quality measures for association rule, support and confidence and lift. We generate some association rules using confidence. Confidence is the most important measure of these measures, but it is an asymmetric measure and has only positive value. Thus we can face with difficult problems in generation of association rules. In this paper we apply the similarity measures by probabilistic interestingness measure to find a solution to this problem. The comparative studies with support, two confidences, lift, and some similarity measures by probabilistic interestingness measure are shown by numerical example. As the result, we knew that the similarity measures by probabilistic interestingness measure could be seen the degree of association same as confidence. And we could confirm the direction of association because they had the sign of their values.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.