• Journal of muscle and joint health
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• v.30 no.2
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• pp.120-128
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• 2023

Purpose: The purpose of the study was to investigate the effects of infection control knowledge and nursing work environment on infection control performance among general hospital nurses. Methods: A total of 138 nurses from four general hospitals located in G and P cities participated in this descriptive study. The collected data were analyzed using t-test, ANOVA, Pearson's correlation coefficients, and multiple regression. Results: The average mean score was 21.41±1.92 points out of 25 for infection control knowledge, 2.53±0.33 points out of 4 for nursing work environment, and 4.62±0.40 points out of 5 for infection control performance. In the final analysis, the variables that had the greatest influence on infection control performance were the nursing work environment (β=.32, p<.001), working department (β=.19, p=.014), and knowledge (β=.19, p=.016). Conclusion: This study showed that nurses with a better nursing work environment, employment in general department, and more infection control knowledge delivered superior infection control performance. Therefore, to improve infection control performance, it is necessary to enhance the nursing environment and develop practical education programs for augmenting infection control knowledge.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.563-570
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• 2004

Ubiquitous computing environment requires strong security and privacy. Access control is one of security areas. Access control on Ubiquitous computing is different from it on traditional information systems so that traditional access control models are not suitable for Ubiquitous comput-ing environment. This research defines Ubiquitous computing environment as an access control point of view, and shows requirements to consid-er for developing access control model for Ubiquitous computing environment. It also brings up three implementing types of access control on Ubiquitous computing environment.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.907-912
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• 1990

An approach to robot force control, which allows force manipulations to be realized without overshot and overdamping while in the presence of unknown environment, is given in this paper. The main idea is to use dynamic compensation for known robot parts and fuzzy compensation for unknown environment so as to improve system performance. The fuzzy compensation is implemented by using rule based fuzzy approach to identify unknown environment. The establishment of proposed control system consists of following two stages. First, similar to the resolved acceleration control method, dynamic compensation and PID control based on known robot dynamics, kinematics and estimated environment compliance is introduced. To avoid overshoot the whole control system is constructed overdamped. In the second stage, the unknown environment stiffness is estimated by using fuzzy reasoning, where the fuzzy estimation rules are obtained priori as the expression of the relationship between environment stiffness and system response. Based on simulation result, comparisons between cases with or without fuzzy identifications are given, which illustrate the improvement achieved.

• Journal of Korean Society on Water Environment
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• v.29 no.2
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• pp.212-222
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• 2013

A watershed model was constructed using Hydrological Simulation Program Fortran to quantitatively predict the stream flows at major tributaries of Nakdong River basin, Korea. The entire basin was divided into 32 segments to effectively account for spatial variations in meteorological data and land segment parameter values of each tributary. The model was calibrated at ten tributaries including main stream of the river for a three-year period (2008 to 2010). The deviation values (Dv) of runoff volumes for operational stream flow forecasting for a six month period (2012.1.2 to 2012.6.29) at the ten tributaries ranged from -38.1 to 23.6%, which is on average 7.8% higher than those of runoff volumes for model calibration (-12.5 to 8.2%). The increased prediction errors were mainly from the uncertainties of numerical weather prediction modeling; nevertheless the stream flow forecasting results presented in this study were in a good agreement with the measured data.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.1-6
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• 2008

The ubiquitous sensor network technique is widely applied to variety of information fields such as home automations, logistics, traffic controls, public administrations, health and environment monitoring and etc. It is particularly useful in the areas where energy consumption is minimal and where continuous monitoring of the surrounding environments, which generates streams of data, are required. In this study, we have designed and implemented a living environment automatic control system which collects the streams of temperature, humidity, light and noise data of a simulated house setting in real-time fashion, then controls the home environment based on the collected data according to the users favorites. In order to differentiate the proposed system from the currently existing similar system, we have demonstrated not only the feasibility of collecting data using sensor network in the controlled environment but also the ability to control the various household equipments through wireless communications.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2002.04b
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• pp.1-12
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• 2002

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2000.10a
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• pp.34-37
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• 2000

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.36-51
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• 1995

An approach to robot hybrid position/force control, which allows force manipulations to be realized without overshoot and overdamping while in the presence of unknown environment, is given in this paper. The manin idea is to used dynamic compensation for known robot parts and fuzzy compensation for unknown environment so as to improve system performance. The fuzzy compensation is implemented by using rule based fuzzy approach to identify the unknown environment. The establishment of proposed control system consists of following two stages. First, similar to the resovled acceleration control method, dynamic compensation and PD control based on known robot dynamics, kinematics and estimated environment stiffness is introduced. To avoid overshoot the whole control system is constructed with overdamping. In the second stage, the unknown environment stiffness is identified by using fuzzy reasoning, where the fuzzy compensation rules are obtained priori as the expression of the relationship betweenenvironment stiffness and system. Based on the simulation result, comparison between cases with or without fuzzy identifications are given, which illustrate the improvement achieced.

• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.4
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• pp.233-241
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• 2007

In an ubiquitous environment, for controlling user access according to environment of users, a number of access control models enforcing dynamic environment of users have been proposed. However, they do not support personalized environments of each user and have a run-time overhead of searching active roles. In this paper, we propose a new model, PE-RBAC, that extends the RBAC architecture by addition of a personalized environment component as a constraint to accommodate dynamic and mobile users. In this model, a dynamic role activation is presented by using a new role-to-environment structure instead of the conventional role hierarchy, which makes it efficient to find the active roles according to a user's personalized environment.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1300-1304
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• 2003

The following paper deals with a control problem of a mobile robot in home network environment. The home network causes the mobile robot to communicate with sensors to get the sensor measurements and to be adapted to the environment changes. To get the improved performance of control of a mobile robot in spite of the change in home network environment, we use the fuzzy inference system with multiple reward reinforcement learning. The multiple reward reinforcement learning enables the mobile robot to consider the multiple control objectives and adapt itself to the change in home network environment. Multiple reward fuzzy Q-learning method is proposed for the multiple reward reinforcement learning. Multiple Q-values are considered and max-min optimization is applied to get the improved fuzzy rule. To show the effectiveness of the proposed method, some simulation results are given, which are performed in home network environment, i.e., LAN, wireless LAN, etc.