23 research outputs found

    Effect of education on proper use of metered-dose inhaler

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    μ˜ν•™κ³Ό/석사[ν•œκΈ€] μ •λŸ‰ ν‘μž…κΈ° (Metered-dose inhaler)λŠ” κΈ°λ„μ§ˆν™˜ ν™˜μž μΉ˜λ£Œμ— μžˆμ–΄ ν•„μˆ˜ λΆˆκ°€κ²°ν•œ 치료 λ°©λ²•μœΌλ‘œ 이용되고 μžˆμœΌλ‚˜ μ‚¬μš©μ‹œ ν‘μž…κΈ°λ₯Ό λˆ„λ₯΄λŠ” μ‹œμ κ³Ό ν‘μž…μ˜ μ‹œμ μ΄ μ •ν™•νžˆ μΌμΉ˜ν•˜μ—¬μ•Ό ν•˜κ³ , ν‘μž…μ†λ„λ₯Ό 천천히 μ§€μ†μ μœΌλ‘œ ν•˜μ—¬μ•Ό ν•˜λ©°, νν™œλŸ‰μ—λ„ 영ν–₯을 λ°›λŠ” λ“± 문제점이 μžˆμ–΄μ„œ μ‚¬μš©λ²•μ— λŒ€ν•œ μ •ν™•ν•œ ꡐ윑이 ν•„μš”ν•˜λ‹€. 이에 μ €μžλ“€μ€ κ±΄κ°•ν•œ λŒ€ν•™μƒμ„ λŒ€μƒμœΌλ‘œ μ •λŸ‰ ν‘μž…κΈ°μ˜ 효율적 μ‚¬μš©μ— λŒ€ν•œ ꡐ윑효과λ₯Ό 비ꡐ 뢄석해보고, κ΅μœ‘ν•˜λŠ” κ°•μ‚¬κ°„μ˜ ꡐ윑효과의 차이λ₯Ό λΉ„κ΅ν•˜κ³ μž λ³Έ 연ꡬλ₯Ό μ‹œν–‰ν•˜μ˜€λ‹€. 1998λ…„ 6μ›”λΆ€ν„° 1999λ…„ 9μ›”κΉŒμ§€ μ—°μ„ΈλŒ€ν•™κ΅ μ˜κ³ΌλŒ€ν•™ μ„ΈλΈŒλž€μŠ€λ³‘μ›μ— μž„μƒ μ‹€μŠ΅μ„ λ‚˜μ˜¨ μ˜κ³ΌλŒ€ν•™μƒ 218λͺ…을 λŒ€μƒμœΌλ‘œ 강사에 λ”°λ₯Έ 영ν–₯이 μžˆλŠ”μ§€ μ—¬λΆ€λ₯Ό ν™•μΈν•˜κ³ μž κ°•μ‚¬λ³„λ‘œ A, B의 두 ꡰ으둜 λ‚˜λˆ„μ–΄ μ •λŸ‰ ν‘μž…κΈ° μ‚¬μš©μ˜ μ •ν™•μ„± μ—¬λΆ€λ₯Ό κ²€μ‚¬ν•˜μ˜€λ‹€. 각 κ΅°λ³„λ‘œ 1λ‹¨κ³„μ—μ„œλŠ” ν•™μƒλ“€μ—κ²Œ μ •λŸ‰ ν‘μž…κΈ°μ˜ μ„€λͺ…μ„œλ§Œμ„ 읽게 ν•œ ν›„ 검사λ₯Ό μ‹œν–‰ν•˜μ˜€κ³ , 2λ‹¨κ³„μ—μ„œλŠ” 1λ‹¨κ³„μ—μ„œ μ‹€νŒ¨ν•œ 학생듀을 λŒ€μƒμœΌλ‘œ 강사가 μ •λŸ‰ ν‘μž…κΈ°μ˜ μ‚¬μš©λ²•μ„ μ„€λͺ…ν•œ ν›„ 검사λ₯Ό μ‹œν–‰ν•˜μ˜€λ‹€. 3λ‹¨κ³„μ—μ„œλŠ” 2λ‹¨κ³„μ—μ„œ μ‹€νŒ¨ν•œ 학생듀을 λŒ€μƒμœΌλ‘œ μ •λŸ‰ ν‘μž…κΈ° μ„€λͺ…μ„œλ₯Ό 읽게 ν•œ ν›„ 강사가 ν‘μž…κΈ° μ‚¬μš©μ— λŒ€ν•΄ μ„€λͺ…κ³Ό ν•¨κ»˜ ν‘μž…κΈ° μ‚¬μš©μ˜ μ‹œλ²”μ„ 보여주고 검사λ₯Ό μ‹œν–‰ν•˜μ˜€λ‹€. μ •λŸ‰ ν‘μž…κΈ°λŠ” μ—°μŠ΅μš© Aerosol inhalation monitor (Vitalograph Ltd, Maids Moreton House, Buckingham, England) κΈ°κΈ°λ₯Ό μ‚¬μš©ν•˜μ˜€λ‹€. κ²€μ‚¬λŠ” 각각의 λ‹¨κ³„λ§ˆλ‹€μ„Έ λ²ˆμ”© μ‹œν–‰ν•˜μ˜€μœΌλ©° 이쀑 두 번 이상 μ„±κ³΅ν•˜λ©΄ μ •λŸ‰ ν‘μž…κΈ°μ˜ μ‚¬μš©λ²•μ΄ μ˜¬λ°”λ₯Έ κ²ƒμœΌλ‘œ μƒκ°ν•˜μ—¬ μ„±κ³΅μœΌλ‘œ νŒμ •ν•˜μ˜€κ³ , ν•œ 번만 μ„±κ³΅ν•˜λ©΄ μ‹€νŒ¨λ‘œ νŒμ •ν•˜μ˜€λ‹€. κ²°κ³ΌλŠ” 두 λͺ…μ˜ 강사가 κ΅μœ‘μ„ μ‹œν–‰ν•œ 각 ꡰ에 λŒ€ν•˜μ—¬ 단계별 성곡λ₯ κ³Ό λˆ„μ  성곡λ₯ μ„ 비ꡐ λΆ„μ„ν•˜μ˜€λ‹€. λ˜ν•œ, κ°•μ‚¬κ°„μ˜ 차이λ₯Ό μ•Œμ•„λ³΄κΈ° μœ„ν•΄ 검사에 강사가 κ°œμž…λ˜μ§€ μ•Šμ€ 1단계λ₯Ό μ œμ™Έν•œ 2단계와 3단계에 λŒ€ν•΄ Aκ΅°κ³Ό Bꡰ의 ν•™μƒλ“€μ˜ 검사성적을 λΉ„κ΅ν•˜μ—¬ λ³΄μ•˜λ‹€. λ˜ν•œ 두 ꡰ을 ν•©μ‚°ν•œ 전체 단계별 성곡λ₯  및 전체 λˆ„μ  성곡λ₯ μ„ 비ꡐ λΆ„μ„ν•˜μ˜€λ‹€. 결과의 비ꡐ뢄석은 chi-square testλ₯Ό μ΄μš©ν•˜μ—¬ νŒμ •ν•˜μ˜€κ³ , p값이 0.05미만일 λ•Œ μ˜λ―ΈμžˆλŠ” κ²ƒμœΌλ‘œ νŒμ •ν•˜μ˜€λ‹€.Aꡰ의 단계별 성곡λ₯ μ€ 1λ‹¨κ³„μ—μ„œλŠ” 24.56%, 2λ‹¨κ³„μ—μ„œλŠ” 56.98%, 3λ‹¨κ³„μ—μ„œλŠ” 75.68%μ˜€λ‹€. Aꡰ의 단계별 성곡λ₯ μ„ 비ꡐ해보면 2단계와 3단계 λͺ¨λ‘ν‘μž…κΈ° μ‚¬μš©λ²•μ˜ ꡐ윑이 νš¨κ³Όκ°€ μžˆμŒμ„ λ‚˜νƒ€λ‚΄μ—ˆλ‹€. Bꡰ의 단계별 성곡λ₯ μ€1λ‹¨κ³„μ—μ„œλŠ” 35.58%, 2λ‹¨κ³„μ—μ„œλŠ” 47.76%, 3λ‹¨κ³„μ—μ„œλŠ” 51.43%μ˜€λ‹€. 8κ΅°μ˜λ‹¨κ³„λ³„ 성곡λ₯ μ„ 비ꡐ해 보면 2단계와 3단계 λͺ¨λ‘ ν‘μž…κΈ° μ‚¬μš©λ²•μ˜ ꡐ윑이 νš¨κ³Όκ°€ μ—†λŠ” κ²ƒμœΌλ‘œ 판λͺ…λ˜μ—ˆλ‹€. λˆ„μ  성곡λ₯ μ€ Aκ΅°μ—μ„œλŠ” 1단계 24.56%, 2단계 67.54%, 3단계 92.11%μ΄μ—ˆκ³ , Bκ΅°μ—μ„œλŠ” 1단계 35.58%, 2단계 66.35%, 3단계 83.65%둜 단계별 κ΅μœ‘μ— λŒ€ν•œ 톡계적 μ˜μ˜κ°€ μžˆμ—ˆλ‹€. 두 ꡰ을 ν•©ν•œ 전체 단계별 성곡λ₯ κ³Ό 전체 λˆ„μ μ„±κ³΅λ₯ μ˜ κ²°κ³ΌλŠ” 2단계와 3단계 λͺ¨λ‘ κ΅μœ‘νš¨κ³Όκ°€ μžˆλŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. κ΅μœ‘μ„ μ‹œν–‰ν•œ 두 κ°•μ‚¬κ°„μ˜ μ°¨μ΄λŠ” 2λ‹¨κ³„μ—μ„œλŠ” 두 κ°•μ‚¬κ°„μ˜ 차이가 μ—†μ—ˆμœΌλ‚˜, 3λ‹¨κ³„μ—μ„œλŠ” 두 강사간에 μœ μ˜ν•œ 차이λ₯Ό λ³΄μ˜€λ‹€. 결둠적으둜 μ •λŸ‰ ν‘μž…κΈ°μ˜ μ •ν™•ν•œ μ‚¬μš©μ„ μœ„ν•΄μ„œλŠ” ν‘μž…κΈ°μ˜ μ„€λͺ…μ„œλ§ŒμœΌλ‘œλŠ” λΆ€μ‘±ν•˜λ©°, μ˜λ£Œμ§„μ˜ μžμ„Έν•˜κ³  μ •ν™•ν•œ μ„€λͺ…κ³Ό ν•¨κ»˜ μ‹œλ²”κ΅μœ‘μ„ λ”λΆˆμ–΄ μ‹œν–‰ν•  λ•Œ κ΅μœ‘νš¨κ³Όκ°€ μš°μˆ˜ν•˜λ‹€. λ˜ν•œ ν‘μž…κΈ° μ‚¬μš©λ²•μ„ μ„€λͺ…ν•˜λŠ” μ‚¬λžŒκ³Ό μ‹œλ²”μ„ λ³΄μ΄λŠ” μ‚¬λžŒμ— λ”°λΌμ„œλ„ μ μ ˆν•œ ν‘μž…μ˜ 성곡 μ—¬λΆ€κ°€ 차이가 λ‚˜λ―€λ‘œ μ˜λ£Œμ§„λ“€λ„ μ μ ˆν•œ ν‘μž…λ²•μ„ μˆ™μ§€ν•˜κ³  μ˜¬λ°”λ₯Έ μ‹œλ²”μ„ κ΅μœ‘λ°›μ•„ ν™˜μžλ“€μ„ κ΅μœ‘ν•˜μ—¬μ•Ό ν•  κ²ƒμœΌλ‘œ μƒκ°λœλ‹€ [영문] Metered-dose inhalers are widely used and they are necessary in the treatment of airway disease. However, correct coordination of activation and inhalation, inhaling slowly and steadily, should be required for their proper use. Vital capacity may also influence the effect of using an inhaler. As well, education is vital for the correct use of a metered-dose inhaler. By comparing and analyzing the effects, the author studied the method of education for propel use of a metered-dose inhaler in healthy college students and the differences in teaching methods between two instructors. From June 1998 to September 1999, we studied the proper use of a metered-dose inhaler in medical students participating in clinical practice of Severance hospital. Students were randomized into one of two teaching groups (group A, B). In the first step, students were tested for correct use of the metered-dose inhaler after reading the instructions only for using the inhaler. In the second step, students from both groups, who failed the first step, received verbal instruction from an instructor. After the teaching session, the students were retested. In the third step, instructor explained and demonstrated the correct technique o( inhaler use for students who failed the second step and the students were again retested. The Metered-dose inhaler used an placebo aerosol inhalation monitor (Vitalograph Ltd, Maids Moreton House, Buckingham, England). Three attempts were made for each step. Each student classified as a proper user was required to have correct technique on at least two of the three attempts. The cumulative success rate and the success rate of each step were analyzed and compared for each group. The differences between the two instructors in the second and third steps were compared. The differences between groups were tested using chi-square analysis. A p value of 0.05 or less was considered to be significant. The success rate of group A according to each step was as follows : step 1, 24.56% ; step 2, 56.98% ; and step 3, 75.68%. This result revealed that inhaler use instruction was effective in group A. The success rate of group B according to each step was as follows : step 1, 35.58% ; step 2, 47.76% ; and step 3, 51.43%. This result revealed that Instruction of inhaler use was not effective in group B. The causes of this result in group B were as follows : first, the success rate of step 1 was too high ; second, the instructor's method of inhaler use in group B was incorrect ; and third, the teaching method of the instructor was improper. The cumulative success rate in group A was as follows : step 1,24.56% ; step 2, 67.54% ; and step 3, 92.11%. Cumulative success rate in group B was as follows : step 1, 35.58% ; step 2, 66.35% ; and step 3, 83.65%. This result showed that the education in all of the second and third steps was effective. The difference between two instructors was not significant in the second step, but was significant in the third step. In conclusion, the instructions for using the inhaler were in sufficient and the leaching and demonstration of healthcare professionals were more effective for proper use of a metered-dose inhaler The success rate of inhaler use was different according to verbal instruction and demonstration. As well, the education of healthcare professionals is necessary for patients' proper use of inhalers.restrictio

    Inverse Reinforcement Learning Control for Trajectory Tracking for a Quadrotor UAV

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : 기계항곡곡학뢀, 2014. 2. κΉ€ν˜„μ§„.The main purpose of this thesis is to imitate the demonstrations of a quadrotor UAV flown by an expert pilot. First, we collect a data set of several demonstrations by an expert for a certain task which we want to learn. We extract a representative trajectory from the dataset. Hidden Markov model (HMM) and dynamic time warping (DTW) are used for obtaining the trajectory. We extract the sequence of state and input data. But a direct use of the input data can cause the danger in stability. For that reason, a controller is required. We design a reinforcement learning controller with reward function of linear quadratic form. To track the extracted trajectory well, an inverse reinforcement learning algorithm is suggested. Using particle swarm optimization (PSO), the reward function that minimizes the trajectory tracking error is learned. With the simulation and experiment applied to a quadrotor UAV, the successful imitation result is presented.1. Introduction 2. Quadrotor dynamics 3. Reinforcement learning controller 4. Inverse reinforcement learning control 5. Simulation 6. Experiment 7. ConclusionMaste

    ALE-CIP법을 μ΄μš©ν•œ μžμœ ν‘œλ©΄ μœ λ™μ˜ CFD 해석

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :기계항곡곡학뢀,2006.Maste
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