• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.175-182
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• 2020

This paper introduces a study to estimate the user altitude in need of rescue in an emergency. The altitude is estimated by using the barometric pressure sensor embedded in the smartphone. Compared to GPS, which is degraded in urban or indoor environments, it has the advantage of not having spatial restrictions. With the endless development of smartphone hardware, it is possible to estimate the absolute altitude using the measured value if only the bias of the embedded barometric pressure sensor is applied. The altitude information of the person in need of rescue in an emergency is a great help in reducing rescue time. Since time is tight, we propose online calibration that provides the barometric pressure sensor bias used for altitude estimation through database. Furthermore, experiments were conducted to understand the characteristics of the barometric pressure sensor, which is greatly affected by wind. At the end, the altitude estimation performance was confirmed through an actual field tests in various floors in the building.

• Korean Journal of Occupational Health Nursing
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• v.29 no.4
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• pp.342-353
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• 2020

Purpose: This study aimed to systematically review literature and conduct a meta-analysis to comprehensively identify and evaluate the effects of workplace risk assessment-based ergonomic intervention on work-related muscular-skeletal disorders in workers. Methods: We searched the Ovid-Medline, EMBASE, and Cochrane library and up to 2018 using search terms such as muscular-skeletal, disorder, impairment, work-related muscular-skeletal disorders, ergonomic, intervention, management with no language limitations; screened reference lists; and contacted experts in the field. Results: We identified 545 references and included 13 randomized controlled tests (3,368 workers). We judged nine studies to have a low risk of bias, while the other four studies have a high risk of bias. Conclusion: Ergonomic intervention based on risk assessment in the workplace did not significantly differ in terms of the intensity of pain or duration of workers in the workplace, but low-quality evidence decreased the frequency of musculoskeletal disorder pain in three to six months moderate-quality evidence and in six to nine months low-quality evidence. Besides, low-quality evidence to reduce discomfort and moderate-quality evidence to improve worker posture. Therefore, ergonomic intervention based on the assessment of risk factors in the workplace should be applied to reduce pain frequency and discomfort and improve workers posture among musculoskeletal disorders.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3233-3239
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• 2015

Background: Whether depression causes increased risk of the development of breast cancer has long been debated. We conducted an updated meta-analysis of cohort studies to assess the association between depression and risk of breast cancer. Materials and Methods: Relevant literature was searched from Medline, Embase, Web of Science (up to April 2014) as well as manual searches of reference lists of selected publications. Cohort studies on the association between depression and breast cancer were included. Data abstraction and quality assessment were conducted independently by two authors. Random-effect model was used to compute the pooled risk estimate. Visual inspection of a funnel plot, Begg rank correlation test and Egger linear regression test were used to evaluate the publication bias. Results: We identified eleven cohort studies (182,241 participants, 2,353 cases) with a follow-up duration ranging from 5 to 38 years. The pooled adjusted RR was 1.13(95% CI: 0.94 to 1.36; $I^2=67.2%$, p=0.001). The association between the risk of breast cancer and depression was consistent across subgroups. Visual inspection of funnel plot and Begg's and Egger's tests indicated no evidence of publication bias. Regarding limitations, a one-time assessment of depression with no measure of duration weakens the test of hypothesis. In addition, 8 different scales were used for the measurement of depression, potentially adding to the multiple conceptual problems concerned with the definition of depression. Conclusions: Available epidemiological evidence is insufficient to support a positive association between depression and breast cancer.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.141-148
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• 2003

This paper presents a rotating ann test for assessment of an underwater hybrid navigation system for a semi-autonomous underwater vehicle. The navigation system consists of an inertial measurement unit (IMU), an ultra-short baseline (USBL) acoustic navigation sensor and a doppler velocity log (DVL) accompanying a magnetic compass. The errors of inertial measurement units increase with time due to the bias errors of gyros and accelerometers. A navigational system model is derived to include the error model of the USBL acoustic navigation sensor and the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 25 in the order. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors and correct the state equation when the measurements are available. The rotating ann tests are conducted in the Ocean Engineering Basin of KRISO, KORDI to generate circular motion in laboratory, where the USBL system was absent in the basin. The hybrid underwater navigation system shows good tracking performance against the circular planar motion. Additionally this paper checked the effects of the sampling ratio of the navigation system and the possibility of the dead reckoning with the DVL and the magnetic compass to estimate the position of the vehicle.

• Journal of Korean Clinical Nursing Research
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• v.25 no.3
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• pp.221-236
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• 2019

Purpose: The purpose of this study was to systematically review and identify factors relevant to the positive psychological capital of clinical nurses. Methods: These was no limit on year of publication. Articles related to Korean clinical nurses were retrieved from computerized database using a manual search. A systematic review was conducted based on the PRISMA flow. The total correlational effect size (ESr) for each related factor was calculated from Fisher's Zr. Funnel plots, fail-safe numbers, and Egger regression tests were used to evaluate publication bias in meta-analysis studies. The correlational effect size of 25 studies was analyzed through meta-analysis using Comprehensive Meta-Analysis software 3.0 (CMA). Results: The review included 25 studies. In the systematic review, 14 demographic factors and 46 organizational factors were found to be influential. Eleven factors (6 demographic factors and 5 organizational factors) were appropriate for meta-analysis. The overall effect size was .26. The demographic total correlation effect size of related factors was .20 and the total effect size of organization was .46. Organizational commitment (ESr=.38) and job satisfaction (ESr=.54) were statistically positively related variables. Negative variables were burnout (ESr=-.61), turnover intention (ESr=-.41) and workplace bullying (ESr=-.33). The total effect size of the organizational factors was larger than the demographic total effect size. There was no publication bias except for demographic variables. Conclusion: Organizational factors and adjustable variables have a significant impact on positive psychological capital. The results of this study support the need for development of interventions focusing on organizational factors.

• The Korean Journal of Pain
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• v.34 no.2
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• pp.139-155
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• 2021

The quality of subgroup analyses (SGAs) in chronic non-cancer pain trials is uncertain. The purpose of this study was to address this issue. We conducted a comprehensive search in MEDLINE and EMBASE from January 2012 to September 2018 to identify eligible trials. Two pairs of reviewers assessed the quality of the SGAs and the credibility of subgroup claims using the 10 criteria developed by Sun et al. in 2012. The associations between the quality of the SGAs and the studies' characteristics including risk of bias, funding sources, sample size, and the latest impact factor, were assessed using multivariable logistic regression. Our search retrieved 3,401 articles of which 66 were eligible. The total number of SGAs was 177 of which 52 (29.4%) made a subgroup claim. Of these, only 15 (8.5%) were evaluated as being of high quality. Among the 30 SGAs that claimed subgroup effects using an appropriate method of performing interaction tests, the credibility of only 5 were assessed as high. None of the subgroup claims met all the credibility criteria. No significant association was found between the quality of SGAs and the studies' characteristics. The quality of the SGAs performed in chronic pain trials was poor. To enhance the quality of SGAs, scholars should consider the developed criteria when designing and conducting trials, particularly those which need to be specified a priori.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.167-181
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• 2024

Utilization of a driving simulator in the development of autonomous driving technology allows us to perform various tests effectively in criticial environments, thereby reducing the development cost and efforts. However, there exists a serious drawback that the driving simulator has a big difference from the real environment, so a problem occurs when the autonomous driving algorithm developed using the driving simulator is applied directly to the real vehicle system. This is defined as so-called Sim2Real problem and can be classified into scenarios, sensor modeling, and vehicle dynamics. This Paper presensts on a method to solve the Sim2Real problem in autonomous driving simulator focusing on IMU sensor. In order to reduce the difference between emulated virtual IMU sensor real IMU sensor, IMU sensor emulation techniques through precision error modeling of IMU sensor are introduced. The error model of IMU sensors takes into account bias, scale factor, misalignmnet, and random walk by IMU sensor grades.

• Journal of the Korean Geotechnical Society
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• v.23 no.12
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• pp.61-73
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• 2007

As a part of Load and Resistance Factor Design(LRFD) code development in Korea, in this paper an intensive reliability analysis was performed to evaluate reliability levels of the two static bearing capacity methods for driven steel pipe piles adopted in Korean Standards for Structure Foundations by the representative reliability methods of First Order Reliability Method(FORM) and Monte Carlo Simulation(MCS). The resistance bias factors for the two static design methods were evaluated by comparing the representative measured bearing capacities with the design values. In determination of the representative bearing capacities of driven steel pipe piles, the 58 data sets of static load tests and soil property tests were collected and analyzed. The static bearing capacity formula and the Meyerhof method using N values were applied to the calculation of the expected design bearing capacity of the piles. The two representative reliability methods(FORM, MCS) based computer programs were developed to facilitate the reliability analysis in this study. Mean Value First Order Second Moment(MVFOSM) approach that provides a simple closed-form solution and two advanced methods of FORM and MCS were used to conduct the intensive reliability analysis using the resistance bias factor statistics obtained, and the results were then compared. In addition, a parametric study was conducted to identify the sensibility and the influence of the random variables on the reliability analysis under consideration.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.367-377
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• 2008

As part of study to develop LRFD (Load and Resistance Factor Design) codes for foundation structures in Korea, resistance factors for static bearing capacity of driven steel pipe piles were calibrated in the framework of reliability theory. The 57 data sets of static load tests and soil property tests conducted in the whole domestic area were collected and these load test piles were sorted into two cases: SPT N at pile tip less than 50, SPT N at pile tip equal to or more than 50. The static bearing capacity formula and the Meyerhof method using N values were applied to calculate the expected design bearing capacities of the piles. The resistance bias factors were evaluated for the two static design methods by comparing the representative measured bearing capacities with the expected design values. Reliability analysis was performed by two types of advanced methods: the First Order Reliability Method (FORM), and the Monte Carlo Simulation (MCS) method using resistance bias factor statistics. The target reliability indices are selected as 2.0 and 2.33 for group pile case and 2.5 for single pile case, in consideration of the reliability level of the current design practice, redundancy of pile group, acceptable risk level, construction quality control, and significance of individual structure. Resistance factors of driven steel pipe piles were recommended based on the results derived from the First Order Reliability Method and the Monte Carlo Simulation method.

• Management & Information Systems Review
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• v.37 no.2
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• pp.209-233
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• 2018

This study investigates the question of how political and economic factors may affect the export of renewable energy technologies. The relationships are tested using panel data for 19 OECD member countries over the period 1992-2012. Before establishing the empirical model, the current study checks the characteristics of the panel data, which includes various panel framework analyses, such as tests for the presence of normality, structural breaks, first-order autocorrelation, heteroscedasticity, cross-sectional dependence, panel unit-root. From the panel framework analyses, a dynamic panel model is established to test the relationship between the variables examined in this study. In order to reduce the bias of the estimation of the dynamic panel model and obtain efficient parameters, this study uses the bias-corrected least square dummy variable(LSDVC) estimator to estimate the empirical model. The results of this study show that governmental policies expressed as coercive pressure and market size positively affect the export growth of renewable energy technologies. However, public pressure and traditional energy industry have no significant effects on export performance. Policy implications are presented based on the results of this study.