• Korean Security Journal
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• no.61
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• pp.353-376
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• 2019

The study aims to analyze the factors affecting citizens' willingness to cooperate in police activities and to verify the intermediation effects of citizens' trust in police in these relationships. In order to achieve the purpose of such research, we selected factors that are expected to affect cooperation in police activities and police confidence by conducting a survey of 502 adult men and women living in Seoul and utilized them for analysis. The results from the analysis are summarized as follows. First, procedural fairness was confirmed as an influence factor on the cooperative history of police activities. In other words, it is necessary to have police activities conducted more fairly and citizens recognize them in order to induce cooperation from citizens, as the fairer police activities are shown to increase the willingness of citizens to cooperate. Second, it was confirmed that the level of citizen awareness of patrol activities and level of disorder in the community affected cooperation in police activities through the medium of police trust. It can be said that the more positively citizens evaluate police patrol activities, the more police confidence increases and this trust leads to citizens' cooperation in police activities. In addition, as the disorderly environment in the community improves, citizens' trust in the police increases, leading to civic cooperation in police activities. Based on this, the police trust was able to confirm that it had a fully operational effect. Third, if the procedural fairness of police activities increases, citizens trust the police, and this police trust leads to cooperation in police activities. In addition, it was found that procedural fairness had a direct impact on citizens' cooperation in police activities, so it was able to confirm that there was a partial selling effect.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.194-202
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• 2019

Reliability analysis of the components frequently starts with the data that manufacturer provides. If enough failure data are collected from the field operations, the reliability should be recomputed and updated on the basis of the field failure data. However, when the failure time record for a component contains only a few observations, all statistical methodologies are limited. In this case, where the failure records for multiple number of identical components are available, a valid alternative is combining all the data from each component into one data set with enough sample size and utilizing the useful information in the censored data. The ROK Navy has been operating multiple Patrol Killer Guided missiles (PKGs) for several years. The Korea Multi-Function Control Console (KMFCC) is one of key components in PKG combat system. The maintenance record for the KMFCC contains less than ten failure observations and a censored datum. This paper proposes a Bayesian approach with a Dirichlet mixture model to estimate failure time density for KMFCC. Trends test for each component record indicated that null hypothesis, that failure occurrence is renewal process, is not rejected. Since the KMFCCs have been functioning under different operating environment, the failure time distribution may be a composition of a number of unknown distributions, i.e. a mixture distribution, rather than a single distribution. The Dirichlet mixture model was coded as probabilistic programming in Python using PyMC3. Then Markov Chain Monte Carlo (MCMC) sampling technique employed in PyMC3 probabilistically estimated the parameters' posterior distribution through the Dirichlet mixture model. The simulation results revealed that the mixture models provide superior fits to the combined data set over single models.