• Journal of Korean Public Health Nursing
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• v.31 no.1
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• pp.135-148
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• 2017

Purpose: This study was conducted to predict the effects of behavioral intention on pap screening tests in unmarried college women using a path model. Methods: The study subjects were 216 university students and data were collected through self-report questionnaires including knowledge, attitude, subjective norms, perceived behavioral control, behavioral intentions to take the Pap test, and health responsibility. Results: Knowledge regarding the Pap test was moderate. The factors of knowledge, attitude, and perceived behavioral control negatively influenced the behavioral intention of the Pap test. However, the factors of subjective norms and health responsibility positively influenced behavioral intention. Conclusion: Subjective norms are the most importance factor to increase the intentions of the Pap test among unmarried college women. It is also necessary to eliminate barriers to undergoing pap testing, as well as to provide nursing intervention to obtain correct knowledge and a positive attitude regarding the Pap screening test.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.1
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• pp.7-14
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• 2004

Machining error is defined the normal distance between designed surface and actual tool path with tool deflection. This is inevitably caused by the tool deflection, tool wear, thermal effect and machine tool errors and so on. Among these factors, tool deflection is usually known as the most significant factor of machining error. Tool deflection problem is analyzed using Instantaneous horizontal cutting forces. The high quality and precision of machining products are required in finishing. In order to achieve these purposes, it is necessary work that decrease the machining error. This paper presents a study on the machining error caused by the tool deflection in ball end milling of 2 dimensional surface. Tool deflection model and simple machining error prediction model are described. This model is checked the validity with machining experiments of 2 dimensional surface. These results may be used to decrease machining error and tool path decision.

• International Journal of High-Rise Buildings
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• v.1 no.3
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• pp.229-235
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• 2012

In this paper the progressive collapse potential of building structures designed for real construction projects were evaluated based on arbitrary column removal scenario using various alternate path methods specified in the GSA guidelines. The analysis model structures are a 22-story reinforced concrete moment frames with core wall building and a 44-story interior concrete core and exterior steel diagrid structure. The progressive collapse resisting capacities of the model structures were evaluated using the linear static, nonlinear static, and nonlinear dynamic analyses. The linear static analysis results showed that progressive collapse occurred in the 22-story model structure when an interior column was removed. However the structure turned out to be safe according to the nonlinear static and dynamic analyses. Similar results were observed in the 44-story diagrid structure. Based on the analysis results, it was concluded that, compared with nonlinear analysis procedures, the linear static method is conservative in the prediction of progressive collapse resisting capacity of building structure based on arbitrary column removal scenario.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.12
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• pp.25-33
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• 2003

A burr has been defined as undesirable projection of material formed as a result of plastic flow from a cutting or shearing operation. It is unavoidable in all kinds of machining operation. As a result, burr makes troubles on manufacturing process due to deburring cost, quality of products and productivity. In this study, the primary interest is about exit burr. The burr formation mechanism in each type of burr is classified. Data bases are developed to predict burr formation result. In the milling operation, we develop an algorithm to analyze the burr formation mechanism by the geometrical analysis on the multi featured workpiece with multi cutting path. The algorithm includes three steps, i. e., the feature identification, the cutting condition identification, and the analysis on exit burr formation. We can predict which portion of workpiece would have the exit burr in advance so that we can manage to find a way to minimize the exit burr formation in an actual cutting. Also, this algorithm can be implemented in a commercial CAM package so that we can simulate the NC code to review the burr formation in advance.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.30-34
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• 1998

Path Planning is one of the important fields in robot technologies. Local path planning may be done in on-line modes while recognizing an environment of robot by itself. In dynamic environments to obtain fluent information for environments vision system as a sensing equipment is a one of the most necessary devices for safe and effective guidance of robots. If there is a predictor that tells what future sensing outputs will be, robot can respond to anticipated environmental changes in advance. The tracking of obstacles has a deep relationship to the prediction for safe navigation. We tried to deal with active contours, that is snakes, to find out the possibilities of stable tracking of objects in image plane. Snakes are defined based on energy functions, and can be deformed to a certain contour form which would converge to the minimum energy states by the forces produced from energy differences. By using point algorithm we could have more speedy convergence time because the Brent's method gives the solution to find the local minima fast. The snake algorithm may be applied to sequential image frames to track objects in the images by these characteristics of speedy convergence and robust edge detection ability.

• Journal of Korean Academy of Nursing Administration
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• v.14 no.2
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• pp.107-117
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• 2008

Purpose: The aim of this study was to testify effectiveness and adaptability of the job characteristics model in nursing organization. Methods: The subjects of this study were 250 nurses who were working in the 2 general hospitals located in Metropolitan city area. The data were collected by self-reporting questionnaires. The data were analyzed using descriptive statistics and path analysis. Results: The modified path model revealed a highly fitness of the data in the overall fitness indexes. The prediction power of modified model was from 44% to 58%, which was very high. The highest predict factors of organizational commitment were identified meaning of empowerment and feedback of job characteristics. The highest predict factors of job satisfaction were identified impact of empowerment and autonomy of job characteristics. Conclusion: With these findings, it was suggested that the nursing job-redesign plan focused on nursing feedback and autonomy among the job characteristics was needed to increase the nurse’ empowerment as well as nursing organizational effectiveness.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1A
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• pp.7-16
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• 2003

Handover is very important to support the mobility of user because wireless ATM networks have smaller cell size such as micro/pico cell for broadband mobile multimedia service In this paper, we propose dynamic path reservation handover scheme for fast inter-MSC (Mobile Switching Center) handovers To reduce the handover delay for connection re-routing, the proposed scheme reserves virtual channels from nearest common node to neighbor MSC in advance Especially, our handover scheme predicts the number of inter-MSC handover calls at each period by the prediction algorithm and reserve virtual channels The simulation and analysis results show that our scheme reduce handover complexity and has higher reservation channel utilization, compared with DVCT scheme.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.874-881
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• 2010

A automatic flight control system(AFCS) of UAS needs to control its flight path along target path exactly as adjusts flight coefficient itself depending on static or dynamic changes of airplane's features such as type, size or weight. In this paper, we propose system which tunes control gain autonomously depending on change of airplane's feature in flight as adding MLM(Machine Learning Module) on AFCS. MLM is designed with Linear Regression algorithm and Reinforcement Learning and it includes EvM(Evaluation Module) which evaluates learned control gain from MLM and verified system. This system is tested on beaver FDC simulator and we present its analysed result.

• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.