• Journal of the Korean Institute of Gas
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• v.3 no.2 s.7
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• pp.17-23
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• 1999

The wire and/or wireless data communication system for anti-disaster system of natural gas governor station was developed. In oder to prevent accidents of governor station, the operator was replaced by RTU(Remote Terminal Unit) which gather and transmit safety situation of governor station. The database and MMI(Man Machine Interface) were also developed to analyze the situation of governor station. The data communication between server and RTU was designed to switch automatically from wire to wireless communication and vice versa when one of them failed communication. We also have developed the patrol car management system which was applied GPS(Global Position System)/GIS(Geometric Information System), and the earthquake detection/transmission system which was adopted three dimension acceleration sensor. When a earthquake may occur, the earthquake detection/transmission system monitors data such as PGA(Peak Ground Acceleration), Sl(Spectrum Intensity) and orders the emergency shutoff valve close immediately.

• Journal of the Korean Society of Radiology
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• v.7 no.2
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• pp.145-149
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• 2013

Helical Tomotherapy is an innovative means of delivering intensity modulated radiation therapy (IMRT) using a device that merges features of a linear accelerator and helical computed tomography (CT) scanner. Hereat, during helical tomotherapy process, megavoltage computed tomography (MVCT) image are usually used for guiding the precise set-up of patient before/after treatment delivery. But which would certainly increase the total dose for patients, this study was to investigate the imaging dose of MVCT using the cylindrical "Cheese" phantom on a tomotherapy machine. A set of cylindrical "Cheese" phantom was adopted for scanning with respectively pitch value (1, 2, 3 mm) with same number slice (10 slice), same length (approximately 9 cm) and phantom set-ups on the couch of tomotherapy system. The average MVCT imaging dose were measured using A1SL ion chamber inserted in the phantom with preset geometry. The MVCT scanning average dose for the cylindrical "Cheese" phantom was 2.24 cGy, 1.02 cGy, 0.81 cGy during respectively pitch value (pitch 1, 2, 3 mm) with same number slice (10 slice), and same length's average dose was 2.47 cGy, 1.28 cGy, 0.88 cGy respectively (pitch 1, 2, 3 mm). Two major parameters, the assigned pitch numbers and scanning length, where the most important impacts to the dose variation. The MVCT dose was inversely proportional to the CT pitch value. The results may provide a reliable guidance for proper planning design of the scanning region, which is valuable to help minimize the extra dose to patient. Questionnaires were distributed to Radiology departments at hospitals with 300 sickbeds throughout the Pohang region of North Gyeongsang Province concerning awareness and performance levels of infection control. The investigation included measurements of the pollution levels of imaging equipment and assistive apparatuses in order to prepare a plan for the activation of prevention and management of hospital infections. The survey was designed to question respondents in regards to personal data, infection management prevention education, and infection management guidelines.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.