Purpose: This study was tried to identify the effects of simulation program by applying hazard perception training on self-efficacy of patient safety, error recovery and problemsolving process in nursing students. Methods: A nonequivalent control group designed was used. The study was composed of hazard perception training and simulation program. Sixteen teams of a total of 61 nursing students participated in the simulation program using a high fidelity simulator. The collected data were analyzed by descriptive statistics, χ2-test and t-test using PASW 18.0 program. Result: There were statistically signigicant in self-efficacy of patient safety(t=2.55, p=.013), error recovery(t=2.82, p=.007), and problem-solving process(t=3.29, p=.002) in the experimental group. Conclusion: These results indicate that the simulation program by applying hazard perception training is effective in improving self-efficacy of patient safety, error recovery and problem-solving process for nursing students. Further study is recommended to confirm the long-term effects of the simulation program by applying hazard perception training.

Through the understanding of the change of productivity and the ability of error recovery according to aging and the assessment and analysis of them, we may take this research to contribute to make a design for the road-map to help set up the policy of employment for old generation. For this we have taken an experiment of the coordination tester for 100 person who are chosen randomly and analysed the collected data using SAS, which is one of widely used statistical analysis packages. The main results are as follow: ˚ledcirc The result of regression between the working speed and the length of the correction of error shows independence. (pr〉0.2029). ˚ledcirc The regression between age and working speed is statistically significant. (pr〈0.0001) ˚ledcirc The relation between age and the length of the correction of error is not significant. (pr〉0.9123).

This study aims to analyze region-specific trends in changing greenhouse gas emissions in incineration plants of local government where waste heat generated during incineration are reused for the recent five years (2009 to 2013). The greenhouse gas generated from the incineration plants is largely CO2 with a small amount of CH4 and N2O. Most of the incineration plants operated by local government produce steam with waste heat generated from incineration to produce electricity or reuse it for hot water/heating and resident convenience. And steam in some industrial complexes is supplied to companies who require it for obtaining resources for local government or incineration plants. All incineration plants, research targets of this study, are using LNG or diesel fuel as auxiliary fuel for incinerating wastes and some of the facilities are using LFG(Landfill Gas). The calculation of greenhouse gas generated during waste incineration was according to the Local Government's Greenhouse Emissions Calculation Guideline. As a result of calculation, the total amount of greenhouse gas released from all incineration plants for five years was about 3,174,000 tCO2eq. To look at it by year, the biggest amount was about 877,000 tCO2eq in 2013. To look at it by region, Gyeonggido showed the biggest amount (about 163,000 tCO2eq annually) and the greenhouse gas emissions per capita was the highest in Ulsan Metropolitan City(about 154 kCO2eq annually). As a result of greenhouse gas emissions calculation, some incineration plants showed more emissions by heat recovery than by incineration, which rather reduced the total amount of greenhouse gas emissions. For more accurate calculation of greenhouse gas emissions in the future, input data management system needs to be improved.

In this paper, a localization error recovery method based on bias estimation is provided for outdoor localization of mobile robot using different-type sensors. In the previous data integration method with DGPS, it is difficult to localize mobile robot due to multi-path phenomena of DGPS. In this paper, fault data due to multi-path phenomena can be recovered by bias estimation. The proposed data integration method uses a Kalman filter based estimator taking into account a bias estimator and a free-bias estimator. A performance evaluation is shown through an outdoor experiment using mobile robot.