30 research outputs found

    An Integrated Current-Voltage Compensator Design Method for Stable Constant Voltage and Current Source Operation of LLC Resonant Converters

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    This paper proposes a method to charge a lithium ion battery with an integrated compensator. Unlike the conventional charging method which uses separate voltage/current compensators based on a constant voltage-constant current charge profile, the proposed method uses a single compensator. The conventional method requires a complicated design process such as separate plant modeling for compensator design and the compensator tuning process in the frequency domain. Moreover, it has the disadvantage of a transient state between the mode change. However, the proposed method simplifies the complicated process and eliminates the transient response. The proposed compensator is applied to the LLC resonant converter and is designed to provide smooth and reliable performance during the entire charging process. In this paper, for the compensator design, the frequency domain models of the LLC resonant converter at the constant voltage and constant current charging mode are derived including the impedance model of the battery pack. Additionally, the worst condition of the compensator design during the entire charging process is considered. To demonstrate the effectiveness of the proposed method, the theoretical design procedure is presented in this paper, and it is verified through experimental results using a 300 W LLC converter and battery pack.This research was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20171210201100, No. 20164010200860)

    The Effect of Local Government of the Efficiency of Forest Ecosystem Management

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : μ‚°λ¦Όκ³Όν•™λΆ€ μ‚°λ¦Όν™˜κ²½ν•™μ „κ³΅, 2016. 8. μœ€μ—¬μ°½.κ΅­μ œμ‚¬νšŒμ—μ„œμ˜ 기후변화에 κ΄€ν•œ μ€‘μš”μ„±κ³Ό ν•¨κ»˜ κ΅­λ―Όλ“€μ˜ μ‚Άμ˜ μ§ˆμ— λŒ€ν•œ 관심 μ¦κ°€λ‘œ μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€μ— λŒ€ν•œ μ‚¬νšŒμ μΈ μˆ˜μš”κ°€ μ¦κ°€ν•˜κ³  μžˆλ‹€. 이에 μ§€λ°©μžμΉ˜λ‹¨μ²΄λŠ” μ‚°λ¦Ό μƒνƒœκ³„κ΄€λ¦¬λ₯Ό 효율적으둜 μˆ˜ν–‰ν•  것을 μš”κ΅¬ λ°›κ³  μžˆλ‹€. λ³Έ μ—°κ΅¬μ—μ„œλŠ” 16개 κ΄‘μ—­μžμΉ˜λ‹¨μ²΄λ₯Ό λŒ€μƒμœΌλ‘œ μžλ£Œν¬λ½λΆ„μ„λ°©λ²•μ„ μ΄μš©ν•˜μ—¬ μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€μ˜ 곡급 νš¨μœ¨μ„±μ„ λΆ„μ„ν•˜κ³ , 지역별 차이λ₯Ό μ‚΄νŽ΄λ³΄μ•˜λ‹€. μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€λ₯Ό 선행연ꡬ에 따라 4가지 λΆ„λ₯˜λ‘œ λ‚˜λˆ„κ³  그에 λ”°λ₯Έ νˆ¬μž…λ³€μˆ˜λ‘œλŠ” 인λ ₯, 산림면적, μ˜ˆμ‚°μ„ μ‚¬μš©ν•˜μ˜€κ³ , μ‚°μΆœλ³€μˆ˜λ‘œλŠ” μž„μ‚°λ¬Ό μƒμ‚°λŸ‰, νƒ„μ†Œν‘μˆ˜λŸ‰, μž„λͺ©μΆ•μ κ³Ό 혼효림 및 ν™œμ—½μˆ˜λ¦Όμ˜ λΉ„μœ¨ 그리고 νœ΄μ–‘λ¦Ό 이용자수λ₯Ό μ‚¬μš©ν•˜μ˜€λ‹€. λ˜ν•œ Tobit λͺ¨ν˜•μ„ ν™œμš©ν•˜μ—¬ 지역별 μƒλŒ€μ  νš¨μœ¨μ„±μ˜ 차이λ₯Ό μ§€μ—­ν™˜κ²½ μš”μΈμ— 따라 μ •λŸ‰μ μœΌλ‘œ λΆ„μ„ν•˜μ˜€λ‹€. λ³Έ μ—°κ΅¬μ˜ μ£Όμš” κ²°κ³ΌλŠ” λ‹€μŒκ³Ό κ°™λ‹€. 첫째, μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€λŠ” μ§€λ°©μžμΉ˜μ œλ„κ°€ μ‹€μ‹œλ˜κΈ° μ „κ³Ό μ‹€μ‹œ 된 이후에 μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€ κ³΅κΈ‰νš¨μœ¨μ„±μ˜ 차이가 μžˆλŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. λ¬Όμ§ˆκ³΅μ—¬μ„œλΉ„μŠ€μ™€ ν™˜κ²½μ‘°μ ˆμ„œλΉ„μŠ€λŠ” μ§€λ°©μžμΉ˜μ œ μ‹€μ‹œ 전인 1992-1994λ…„κ³Ό μ‹€μ‹œ 이후인 2002-2014λ…„ κΈ°κ°„μ—λŠ” νš¨μœ¨μ„±μ΄ κ°μ†Œν•˜λŠ” λ³€ν™”κ°€ λ‚˜νƒ€λ‚œ 반면, 생λͺ…μ§€μ§€μ„œλΉ„μŠ€λŠ” 동기간에 νš¨μœ¨μ„±μ΄ μ¦κ°€ν•˜μ˜€λ‹€. λ¬Όμ§ˆκ³΅μ—¬μ„œλΉ„μŠ€μ˜ κ°μ†ŒλŠ” μΈκ±΄λΉ„λŠ” μ¦κ°€ν•˜λŠ” 반면 μž„μ‚°λ¬Ό 가격은 κ°μ†Œν•˜μ—¬ μž„μ—…κ²½μŸλ ₯이 μ•½ν™”λ˜μ—ˆκΈ° λ•Œλ¬ΈμœΌλ‘œ μΆ”λ‘ λ˜λ©°, ν™˜κ²½μ‘°μ ˆμ„œλΉ„μŠ€λŠ” 지속적인 민유림의 κ°μ†Œ λ•Œλ¬ΈμΈ κ²ƒμœΌλ‘œ 보인닀. 반면 생λͺ…μ§€μ§€μ„œλΉ„μŠ€λŠ” 지속가λŠ₯ν•œ μ‚°λ¦Όκ²½μ˜μ— λ”°λ₯Έ μ œλ„μ  정비와 μž„λͺ©μΆ•μ μ˜ κΎΈμ€€ν•œ μ¦κ°€λ‘œ μ§€λ°©μžμΉ˜μ œλ„ μ‹€μ‹œ μ „κ³Ό 후에 μƒνƒœκ³„μ„œλΉ„μŠ€ 곡급 νš¨μœ¨μ„±μ΄ μ¦κ°€ν•œ κ²ƒμœΌλ‘œ 보인닀. λ‘˜μ§Έ, λ„μ‹œμ§€μ—­κ³Ό λΉ„λ„μ‹œμ§€μ—­μ˜ μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€ 곡급 νš¨μœ¨μ„±μ˜ 차이가 λ‚˜νƒ€λ‚¬λ‹€. λ¬Όμ§ˆκ³΅μ—¬μ„œλΉ„μŠ€, ν™˜κ²½μ‘°μ ˆμ„œλΉ„μŠ€, 생λͺ…μ§€μ§€μ„œλΉ„μŠ€, λ¬Έν™”μ„œλΉ„μŠ€ λͺ¨λ‘μ—μ„œ μ „μ²΄μ μœΌλ‘œ λΉ„λ„μ‹œμ§€μ—­λ³΄λ‹€ λ„μ‹œμ§€μ—­μ˜ νš¨μœ¨μ„±μ΄ λ†’κ²Œ λ‚˜νƒ€λ‚¬λ‹€. μƒλŒ€μ  νš¨μœ¨μ„± μˆœμœ„μ—μ„œλŠ” λ¬Όμ§ˆκ³΅μ—¬μ„œλΉ„μŠ€, 생λͺ…μ§€μ§€μ„œλΉ„μŠ€, λ¬Έν™”μ„œλΉ„μŠ€λŠ” μ§€λ°©μžμΉ˜μ œ μ‹€μ‹œ 이전 1992-1994λ…„κ³Ό μ‹€μ‹œ 이후 2002-2004λ…„, 2012-2014년에 λ„μ‹œμ§€μ—­μ΄ νš¨μœ¨μ„±μ΄ λ†’μ•˜μœΌλ‚˜, ν™˜κ²½μ‘°μ ˆμ„œλΉ„μŠ€λŠ” μ§€μžμ²΄ μ‹€μ‹œ μ΄μ „μ—λŠ” λ„μ‹œμ§€μ—­κ³Ό λΉ„λ„μ‹œμ§€μ—­μ— νš¨μœ¨μ„±μ˜ μ°¨μ΄λ‚˜ λ‚˜νƒ€λ‚˜μ§€ μ•Šμ•˜μœΌλ‚˜ μ‹œκ°„μ΄ 흐름에 따라 λ„μ‹œμ§€μ—­μ˜ νš¨μœ¨μ„±μ΄ μƒλŒ€μ μœΌλ‘œ λ†’μ•„μ‘Œλ‹€. μ΄λŠ” λ„μ‹œλ―Όλ“€μ˜ μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€μ— λŒ€ν•œ μˆ˜μš”κ°€ 더 많기 λ•Œλ¬ΈμΈ κ²ƒμœΌλ‘œ μΆ”λ‘ λœλ‹€. μ˜ˆμ‚°λŒ€λΉ„ μƒλŒ€μ  νš¨μœ¨μ„± μˆœμœ„μ—μ„œλŠ” λ¬Έν™”μ„œλΉ„μŠ€μ—μ„œ λ„μ‹œμ§€μ—­μ˜ νš¨μœ¨μ„± μˆœμœ„κ°€ λ†’μ•˜μœΌλ©°, 인λ ₯λŒ€λΉ„ μƒλŒ€μ  νš¨μœ¨μ„± μˆœμœ„μ—μ„œλŠ” λͺ¨λ“  μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€μ—μ„œ λ„μ‹œμ§€μ—­μ΄ λΉ„λ„μ‹œμ§€μ—­λ³΄λ‹€ νš¨μœ¨μ„±μ΄ λ†’κ²Œ λ‚˜νƒ€λ‚¬λ‹€. μ…‹μ§Έ, 영ν–₯μš”μΈ λΆ„μ„μœΌλ‘œ 지역내총생산, μž„λ„λ§λ°€λ„λŠ” μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€ 곡급 νš¨μœ¨μ„±μ— 영ν–₯을 λ―Έμ³€λ‹€. 지역내총생산은 생λͺ…μ§€μ§€μ„œλΉ„μŠ€ 곡급 νš¨μœ¨μ„±μ— 긍정적(+)인 μš”μ†Œλ‘œ λ°ν˜€μ‘ŒμœΌλ©°, μ΄λŠ” μ†Œλ“μ΄ μ¦κ°€ν• μˆ˜λ‘ μ‚¬λžŒλ“€μ΄ μƒλ¬Όλ‹€μ–‘μ„±μ˜ μ€‘μš”μ„±μ„ μΈμ‹ν•˜κΈ° λ•Œλ¬ΈμΈ κ²ƒμœΌλ‘œ 보인닀. 반면 μž„λ„λ§ λ°€λ„λŠ” λ¬Έν™”μ„œλΉ„μŠ€μ—λŠ” 긍정적(+)인 μš”μ†Œκ°€ λ˜λ‚˜, λ¬Όμ§ˆκ³΅μ—¬μ„œλΉ„μŠ€μ™€ 생λͺ…μ§€μ§€μ„œλΉ„μŠ€μ—λŠ” 뢀정적(-)인 μš”μ†Œλ‘œ μž‘μš©ν•˜λŠ” 것을 확인할 수 μžˆμ—ˆλ‹€. μ΄λŠ” μž„λ„λ§ 밀도가 μ¦κ°€ν• μˆ˜λ‘ λͺ©μž¬ 생산을 μœ„ν•œ μˆ˜ν™•κ³Ό 재쑰림 ν™œλ™μ΄ μš©μ΄ν•΄μ Έμ„œ 숲의 생물닀양성이 κ°μ†Œν•  수 있기 λ•Œλ¬ΈμœΌλ‘œ μΆ”μ •λœλ‹€. λ˜ν•œ μš°λ¦¬λ‚˜λΌλŠ” λͺ©μž¬λ³΄λ‹€ λΉ„λͺ©μž¬ μž„μ‚°λ¬Όμ˜ κ°€μΉ˜κ°€ 높은데, μž„λ„λŠ” λͺ©μž¬μƒμ‚°μ„ μ€‘μ‹¬μœΌλ‘œ κ°œμ„€λ˜μ–΄ μžˆμ–΄, λΉ„λ„μ‹œμ˜ μž„λ„λ§ 밀도 차이와 μž„μ‚°λ¬Ό μ†ŒλΉ„μ‹œμž₯의 μœ„μΉ˜μ— 따라 이런 κ²°κ³Όκ°€ λ‚˜νƒ€λ‚œ κ²ƒμœΌλ‘œ 보인닀. 이 μ—°κ΅¬κ²°κ³ΌλŠ” 좔가적인 싀증연ꡬ가 더 ν•„μš”ν•œ κ²ƒμœΌλ‘œ 보이며, μž„λ„μ˜ 개발 μ΄μš©μ„ λ¬Όμ§ˆκ³΅μ—¬μ„œλΉ„μŠ€λ³΄λ‹€ λ¬Έν™”μ„œλΉ„μŠ€λ₯Ό κ³ λ €ν•˜μ—¬ μ΄μš©λ°©λ²•μ„ 강ꡬ해야 ν•  κ²ƒμœΌλ‘œ 보인닀.제 1 μž₯ μ„œλ‘  1 1. 연ꡬ λ°°κ²½ 1 2. 연ꡬλͺ©μ  4 3. μ—°κ΅¬μ˜ ꡬ성 4 제 2 μž₯ 이둠적 λ°°κ²½ 5 1. μƒνƒœκ³„μ„œλΉ„μŠ€ κ°œλ… 5 2. λ‘œμ»¬κ±°λ²„λ„ŒμŠ€ 9 3. κ³΅κ³΅λΆ€λ¬Έμ—μ„œμ˜ νš¨μœ¨μ„± μΈ‘μ • 11 4. 선행연ꡬ 12 4.1 DEA기법을 ν™œμš©ν•œ νš¨μœ¨μ„± 뢄석 12 4.2 μƒνƒœκ³„μ„œλΉ„μŠ€ 13 4.3 λ‘œμ»¬κ±°λ²„λ„ŒμŠ€ 14 제 3 μž₯ 연ꡬ방법 15 1. μ—°κ΅¬λŒ€μƒ 및 μžλ£Œμ†Œκ°œ 15 2. 연ꡬλͺ¨ν˜• 20 3. 뢄석방법 21 3.1 μžλ£Œν¬λ½λΆ„μ„(DEA) 21 3.2 Post-DEA λͺ¨ν˜•: AP λͺ¨ν˜• 26 3.3 Tobit νšŒκ·€λΆ„μ„ 27 제 4 μž₯ κ²°κ³Ό 및 κ³ μ°° 29 1. λΆ„μ„λ²”μœ„ 및 κΈ°μˆ ν†΅κ³„λΆ„μ„ 29 2. DEA λͺ¨ν˜•μ— μ˜ν•œ μƒλŒ€μ  νš¨μœ¨μ„± 평가 30 2.1 DEA 뢄석결과 30 2.2 지역간 μ‚°λ¦Ό μƒνƒœκ³„μ„œλΉ„μŠ€ νš¨μœ¨μ„± 상관 관계 40 3. Post-DEA에 μ˜ν•œ νš¨μœ¨μ„± 평가 48 3.1 APλͺ¨ν˜• 48 3.2 μ˜ˆμ‚° 및 인λ ₯λŒ€λΉ„ νš¨μœ¨μ„± 비ꡐ 53 3.3 Tobit λͺ¨ν˜•μ— μ˜ν•œ νš¨μœ¨μ„± 영ν–₯μš”μΈλΆ„μ„ 57 제 5 μž₯ κ²°λ‘  59 μ°Έκ³  λ¬Έν—Œ 61 뢀둝 69 Abstract 86Maste

    Cognitive Diagnostic Multistage Testing by Partitioning Attribute Hierarchy

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :μ‚¬λ²”λŒ€ν•™ μˆ˜ν•™κ΅μœ‘κ³Ό,2020. 2. μœ μ—°μ£Ό.μΈμ§€μ§„λ‹¨ν‰κ°€λŠ” ν•™μƒλ“€μ˜ μΈμ§€μƒνƒœκ°€ μ—¬λŸ¬ 개의 μΈμ§€μš”μ†Œλ‘œ 이루어져 μžˆλ‹€κ³  바라보고, 각 μΈμ§€μš”μ†Œμ˜ μŠ΅λ“μœ λ¬΄λ₯Ό ν•™μƒλ“€μ—κ²Œ μ§„λ‹¨ν•΄μ£ΌλŠ” 것을 λͺ©μ μœΌλ‘œ ν•˜κ³  μžˆλ‹€. 인지진단평가λ₯Ό μ‹€ν–‰ν•˜κΈ° μœ„ν•œ 톡계적인 λͺ¨ν˜•μ„ 인지진단λͺ¨ν˜•μ΄λΌκ³  ν•˜λ©°, ν•™μƒλ“€μ˜ μΈμ§€μƒνƒœλ₯Ό λ‹€μ°¨μ›μ μœΌλ‘œ μ§„λ‹¨ν•˜κ²Œ λ˜λŠ” 만큼 인지진단λͺ¨ν˜•μ—λŠ” λ§Žμ€ λͺ¨μˆ˜λ“€μ΄ ν¬ν•¨λ˜μ–΄ 있고, λͺ¨μˆ˜μ˜ μ •ν™•ν•œ 좔정을 μœ„ν•΄μ„œλŠ” λ‹€μˆ˜μ˜ λ¬Έν•­κ³Ό 큰 ν‘œλ³Έμ΄ ν•„μˆ˜μ μ΄λ‹€. 이에 인지진단λͺ¨ν˜•μ˜ νš¨μœ¨μ„±μ„ ν™•λ³΄ν•˜κΈ° μœ„ν•œ 연ꡬ듀이 이루어져 μ™”μœΌλ©°, 그쀑 ν•˜λ‚˜λŠ” μΈμ§€μœ„κ³„κ΅¬μ‘°λ₯Ό λ„μž…ν•˜λŠ” 방법이닀. μΈμ§€μœ„κ³„κ΅¬μ‘°λ₯Ό λ„μž…ν•˜κ²Œ 되면 μΈμ§€νŒ¨ν„΄μ΄ μœ„κ³„μ— λ§žλŠ” νŒ¨ν„΄λ“€λ‘œ κ·Έ κ°œμˆ˜κ°€ 쀄어듀어 보닀 효율적인 좔정이 κ°€λŠ₯ν•˜κ²Œ λœλ‹€. λ˜ν•œ, λ¬Έν•­λ°˜μ‘λͺ¨ν˜•κ³Ό 주둜 κ²°ν•©λ˜λ˜ 컴퓨터 적응검사 방법을 인지진단λͺ¨ν˜•μ— μ μš©ν•œ 인지진단 컴퓨터 μ μ‘κ²€μ‚¬λŠ”, 학생듀이 문항을 ν•΄κ²°ν•  λ•Œλ§ˆλ‹€ κ·Έ 결과에 λ§žμΆ”μ–΄ 졜적의 문항을 μ œκ³΅ν•˜λŠ” λ°©μ‹μœΌλ‘œ κ²€μ‚¬μ˜ νš¨μœ¨μ„±μ„ ν™•λ³΄ν•˜κΈ°λ„ ν•œλ‹€. ν•œνŽΈ, 컴퓨터 적응검사λ₯Ό κ΅¬ν˜„ν•˜κΈ° μœ„ν•΄μ„œλŠ” μ–‘μ§ˆμ˜ 문항듀을 ν¬ν•¨ν•œ λ¬Έμ œμ€ν–‰μ΄ μ€€λΉ„λ˜μ–΄ μžˆμ–΄μ•Ό ν•˜κ³ , 또 κ²€μ‚¬μ˜ μ‹œν–‰κ³Ό 관리가 λ³΅μž‘ν•˜λ‹€λŠ” 단점이 μ‘΄μž¬ν•œλ‹€. 이와 같은 컴퓨터 μ μ‘κ²€μ‚¬μ˜ 단점을 κ·Ήλ³΅ν•˜κΈ° μœ„ν•΄ 닀단계 검사 방법이 μ œμ‹œλ˜μ—ˆμœΌλ‚˜, 아직 인지진단평가에 닀단계 검사 방법을 κ²°ν•©ν•œ μ—°κ΅¬λŠ” 거의 이루어지지 μ•Šμ•˜λ‹€. 이에 λ³Έ μ—°κ΅¬μ—μ„œλŠ” 인지진단λͺ¨ν˜•κ³Ό 닀단계 검사λ₯Ό κ²°ν•©ν•˜λŠ” 방법을 μ œμ‹œν•˜κ³ , μ œμ‹œλœ λ°©λ²•μœΌλ‘œ λ§Œλ“€μ–΄μ§„ CD-MST-PH λͺ¨ν˜•μ˜ μœ νš¨μ„±μ„ μ‹€μ œ 데이터와 λͺ¨μ˜μ‹€ν—˜μ„ 톡해 검증해 보고자 ν•˜μ˜€λ‹€. 인지진단λͺ¨ν˜•μ— 닀단계 검사 ꡬ쑰λ₯Ό λ„μž…ν•˜κΈ° μœ„ν•΄ μš°μ„  μΈμ§€μš”μ†Œλ“€μ„ μœ„κ³„ κ·Έλž˜ν”„λ‘œ ν‘œν˜„ν•˜κ³ , μΈμ§€μœ„κ³„κ·Έλž˜ν”„λ₯Ό μ˜μ—­μ— 맞게 λΆ„ν• ν•˜μ—¬ 볡수의 μΈμ§€μš”μ†Œ 그룹을 μƒμ„±ν•˜μ˜€λ‹€. 그리고 각 μΈμ§€μš”μ†Œ 그룹에 λŒ€ν•΄ μ†Œκ²€μ‚¬λ₯Ό μ œμž‘ν•˜κ³  이λ₯Ό 닀단계 검사λ₯Ό μœ„ν•œ λ‹¨μœ„μΈ λͺ¨λ“ˆλ‘œ μ‚¬μš©ν•˜μ˜€λ‹€. 각 λ‹¨κ³„μ—μ„œ λͺ¨λ“ˆμ„ μ„ νƒν•˜λŠ” 척도λ₯Ό λ§Œλ“€κΈ° μœ„ν•΄ 기쑴의 인지진단 컴퓨터 μ μ‘κ²€μ‚¬μ—μ„œ μ‚¬μš©λ˜λ˜ 척도듀을 λ³€ν˜•ν•˜μ˜€κ³ , λ”λΆˆμ–΄ μΈμ§€μœ„κ³„κ΅¬μ‘°μ˜ λΆ„ν• μ΄λΌλŠ” νŠΉμ„±μ„ ν™œμš©ν•˜μ—¬ μƒˆλ‘­κ²Œ 척도듀을 μ œμ‹œν•˜μ˜€λ‹€. CD-MST-PH λͺ¨ν˜•μ˜ μœ νš¨μ„±μ„ κ²€μ¦ν•˜κΈ° μœ„ν•΄μ„œλŠ” μ‹€μ œ 데이터 뢄석과 두 개의 λͺ¨μ˜μ‹€ν—˜μ„ μ‹€ν–‰ν•˜μ˜€λ‹€. μ‹€μ œ 데이터 λΆ„μ„μ—μ„œλŠ” TIMSS 2003λ…„ 8ν•™λ…„ μˆ˜ν•™ κ²€μ‚¬μ˜ λŒ€μˆ˜ μ˜μ—­ 21문항에 λŒ€ν•œ μ‹€μ œ 응닡 데이터λ₯Ό λΆ„μ„ν•˜μ—¬, μ„€κ³„λœ CD-MST-PH λͺ¨ν˜•μ΄ μ‹€μ œλ‘œ 잘 μž‘λ™ν•˜κ³ , 전체 문항을 λͺ¨λ‘ μ‚¬μš©ν•˜λŠ” 기쑴의 λͺ¨ν˜•κ³Ό λΉ„κ΅ν•˜μ—¬ μΆ”μ • μΌμΉ˜λ„λŠ” λ†’κ²Œ μœ μ§€ν•˜λ©΄μ„œ κ²€μ‚¬μ˜ κΈΈμ΄λŠ” νš¨μœ¨ν™”μ‹œν‚¬ 수 μžˆμŒμ„ ν™•μΈν•˜μ˜€λ‹€. λͺ¨μ˜μ‹€ν—˜ 1은 μ‹€μ œ 데이터 λΆ„μ„μ˜ κ²°κ³Όλ₯Ό λ³΄μ™„ν•˜κΈ° μœ„ν•΄, μ‹€μ œ 데이터 λΆ„μ„μ—μ„œ 쓰인 μΈμ§€μœ„κ³„κ·Έλž˜ν”„μ™€ 검사문항을 ν† λŒ€λ‘œ 문항응닡을 κ°€μƒμœΌλ‘œ μƒμ„±ν•˜μ—¬ μ§„ν–‰ν•˜μ˜€λ‹€. λͺ¨μ˜μ‹€ν—˜ 2λŠ” μΈμ§€μœ„κ³„κ·Έλž˜ν”„ 및 검사문항듀을 μΈμœ„μ μœΌλ‘œ κ΅¬μ„±ν•˜μ—¬ λ‹€μ–‘ν•œ μ‹€ν—˜λ³€μΈλ“€κ³Ό ν•¨κ»˜ μ§„ν–‰ν•˜μ˜€λ‹€. κ·Έ κ²°κ³Ό μ•žμ„  λΆ„μ„μ—μ„œλŠ” 확인할 수 μ—†μ—ˆλ˜ μΆ”μ • μ •ν™•μ„±κΉŒμ§€ 뢄석할 수 μžˆμ—ˆκ³ , CD-MST-PH λͺ¨ν˜•μ΄ 기쑴의 λͺ¨ν˜•κ³Ό λΉ„κ΅ν•˜μ—¬ 정확성은 거의 κ°™μœΌλ©΄μ„œ κ²€μ‚¬μ˜ κΈΈμ΄λŠ” νš¨μœ¨ν™”μ‹œν‚¬ 수 μžˆλŠ” λͺ¨ν˜•μž„을 ν™•μΈν•˜μ˜€λ‹€. λ˜ν•œ, λ‹€μ–‘ν•œ μ‹€ν—˜λ³€μΈμ— λŒ€ν•œ CD-MST-PH λͺ¨ν˜•μ˜ λͺ¨λ“ˆ 선택 μ²™λ„λ“€μ˜ μ„±λŠ₯을 λΉ„κ΅ν•˜μ—¬, μ–΄λ–€ λͺ¨λ“ˆ 선택 척도가 더 νš¨μœ¨μ μΈμ§€ ν™•μΈν•˜μ˜€λ‹€.Cognitive diagnostic assessment (CDA) aims to identify the presence or absence of discrete and multi-dimensional skills and to diagnose each test takers. Cognitive diagnostic model (CDM) developed for the purpose of performing CDA contains more parameters than conventional test models, such as item response model, because the latent traits the model estimates have multiple dimensions. Hence, the CDM and its parameters could only be accurately estimated by using large-sized samples and items. A number of studies have been made to deal with this problem, and one of the suggested methods is to apply an attribute hierarchy to CDM. The method of attribute hierarchy specifies the hierarchical relationships among the attributes, and the possible number of attribute patterns are reduced. Another way to improve the efficiency of CDM is to adopt a computerized adaptive testing (CAT) method which tries to provide best fitting item for each test takers. Cognitive diagnostic computerized adaptive testing (CD-CAT) provides an optimal item for estimating the latent attributes of the examinee using the previous item responses. However, there are some difficulties in implementing the CAT. A large item bank consisted of fine quality items is necessary for the CAT and it is inconvenient for the test administrators to conduct tests or manage results. Multi-stage testing (MST) method can solve this practical problems, but few attempts have been made to combine multi-stage testing methods with CDM. This study proposes CD-MST-PH and investigates the properties of the proposed model by analyzing real response data and simulation data. In order to apply the multi-stage testing method in CDM, the attribute hierarchy was represented as a directed graph and was partitioned into groups of attributes. Testlets corresponding to each attribute group were created and used as modules in a multi-stage structure. The module selection methods were generated by applying the item selection methods used in CAT, and were newly invented by using the structure of partitioned attribute hierarchy. To examine the accuracy and efficiency of the CD-MST-PH, an analysis using real response data and two simulations were performed. For the real data analysis, the response data of the TIMSS 2003 8th grade mathematics test were explored and it confirmed that CD-MST-PH uses fewer items and produces similar results comparing with the conventional model. Simulation 1 was conducted to complement the real data analysis. The same attribute hierarchy and test items were adopted as were used in the real data analysis, and response data for each item was artificially generated. Simulation 2 was performed using artificially generated attribute hierarchies and test items. The accuracy of the CD-MST-PH, which was unobtainable in the real data analysis, was obtained from the two simulations. The results of the simulations show that the CD-MST-PH can improve efficiency while maintaining accuracy compared to the conventional model. In addition, the efficiency of each module selection method is examined and compared under various experimental variables.I. μ„œλ‘  1 1. μ—°κ΅¬μ˜ ν•„μš”μ„± 1 2. μ—°κ΅¬μ˜ λͺ©μ  및 μ—°κ΅¬λ¬Έμ œ 2 II. 이둠적 λ°°κ²½ 3 1. 전톡적인 검사 이둠 3 1.1. λ¬Έν•­λ°˜μ‘μ΄λ‘  3 1.2. 컴퓨터 적응검사 5 1.3. 닀단계 검사 6 2. 인지진단λͺ¨ν˜• 9 2.1. μΈμ§€νŒ¨ν„΄κ³Ό Qν–‰λ ¬ 9 2.2. DINA, DINO λͺ¨ν˜• 9 2.3 μΈμ§€μœ„κ³„κ΅¬μ‘° 10 2.4. μΈμ§€νŒ¨ν„΄μ˜ μΆ”μ • 12 3. 인지진단 컴퓨터 적응검사 15 3.1. 인지진단 컴퓨터 적응검사 15 3.2. 인지진단 닀단계 검사 18 III. 연ꡬ방법 19 1. λͺ¨ν˜• 섀계 19 1.1. μΈμ§€μœ„κ³„κ΅¬μ‘° 19 1.2. λͺ¨λ“ˆ 생성 20 1.3. ν™•λ₯  κ°±μ‹  21 1.4. λͺ¨λ“ˆ 선택 22 1.5. 검사 μ’…λ£Œ 26 1.6. 인지 νŒ¨ν„΄ μΆ”μ • 27 2. μ—°κ΅¬μ ˆμ°¨ 28 2.1. μ‹€μ œ 데이터 뢄석 28 2.2. λͺ¨μ˜μ‹€ν—˜ 1 34 2.3. λͺ¨μ˜μ‹€ν—˜ 2 36 IV. 연ꡬ결과 40 1. μ‹€μ œ 데이터 뢄석 κ²°κ³Ό 40 1.1. μΈμ§€νŒ¨ν„΄ 및 검사 길이 뢄석 41 1.2. 검사 단계 및 경둜 뢄석 43 2. λͺ¨μ˜μ‹€ν—˜ 1 κ²°κ³Ό 45 2.1. 검사 길이 뢄석 45 2.2. μΆ”μ • 정확도 뢄석 46 2.3. 단계별 뢄석 47 3. λͺ¨μ˜μ‹€ν—˜ 2 κ²°κ³Ό 50 3.1. μΆ”μ • 정확도 뢄석 50 3.2. 검사 길이 뢄석 52 3.3. λͺ¨λ“ˆ 선택 λΉ„μœ¨ 뢄석 55 V. κ²°λ‘  72 1. μš”μ•½ 72 2. μ—°κ΅¬μ˜ ν•œκ³„ 및 μ œμ–Έ 74 μ°Έκ³ λ¬Έν—Œ 76 Abstract 80Maste
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