30 research outputs found
An Integrated Current-Voltage Compensator Design Method for Stable Constant Voltage and Current Source Operation of LLC Resonant Converters
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
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μ°λ¦Όκ³ΌνλΆ μ°λ¦Όνκ²½νμ 곡, 2016. 8. μ€μ¬μ°½.κ΅μ μ¬νμμμ κΈ°νλ³νμ κ΄ν μ€μμ±κ³Ό ν¨κ» κ΅λ―Όλ€μ μΆμ μ§μ λν κ΄μ¬ μ¦κ°λ‘ μ°λ¦Ό μνκ³μλΉμ€μ λν μ¬νμ μΈ μμκ° μ¦κ°νκ³ μλ€. μ΄μ μ§λ°©μμΉλ¨μ²΄λ μ°λ¦Ό μνκ³κ΄λ¦¬λ₯Ό ν¨μ¨μ μΌλ‘ μνν κ²μ μꡬ λ°κ³ μλ€.
λ³Έ μ°κ΅¬μμλ 16κ° κ΄μμμΉλ¨μ²΄λ₯Ό λμμΌλ‘ μλ£ν¬λ½λΆμλ°©λ²μ μ΄μ©νμ¬ μ°λ¦Ό μνκ³μλΉμ€μ κ³΅κΈ ν¨μ¨μ±μ λΆμνκ³ , μ§μλ³ μ°¨μ΄λ₯Ό μ΄ν΄λ³΄μλ€. μ°λ¦Ό μνκ³μλΉμ€λ₯Ό μ νμ°κ΅¬μ λ°λΌ 4κ°μ§ λΆλ₯λ‘ λλκ³ κ·Έμ λ°λ₯Έ ν¬μ
λ³μλ‘λ μΈλ ₯, μ°λ¦Όλ©΄μ , μμ°μ μ¬μ©νμκ³ , μ°μΆλ³μλ‘λ μμ°λ¬Ό μμ°λ, νμν‘μλ, μλͺ©μΆμ κ³Ό νΌν¨λ¦Ό λ° νμ½μλ¦Όμ λΉμ¨ κ·Έλ¦¬κ³ ν΄μλ¦Ό μ΄μ©μμλ₯Ό μ¬μ©νμλ€. λν Tobit λͺ¨νμ νμ©νμ¬ μ§μλ³ μλμ ν¨μ¨μ±μ μ°¨μ΄λ₯Ό μ§μνκ²½ μμΈμ λ°λΌ μ λμ μΌλ‘ λΆμνμλ€. λ³Έ μ°κ΅¬μ μ£Όμ κ²°κ³Όλ λ€μκ³Ό κ°λ€.
첫째, μ°λ¦Ό μνκ³μλΉμ€λ μ§λ°©μμΉμ λκ° μ€μλκΈ° μ κ³Ό μ€μ λ μ΄νμ μ°λ¦Ό μνκ³μλΉμ€ 곡κΈν¨μ¨μ±μ μ°¨μ΄κ° μλ κ²μΌλ‘ λνλ¬λ€. λ¬Όμ§κ³΅μ¬μλΉμ€μ νκ²½μ‘°μ μλΉμ€λ μ§λ°©μμΉμ μ€μ μ μΈ 1992-1994λ
κ³Ό μ€μ μ΄νμΈ 2002-2014λ
κΈ°κ°μλ ν¨μ¨μ±μ΄ κ°μνλ λ³νκ° λνλ λ°λ©΄, μλͺ
μ§μ§μλΉμ€λ λκΈ°κ°μ ν¨μ¨μ±μ΄ μ¦κ°νμλ€. λ¬Όμ§κ³΅μ¬μλΉμ€μ κ°μλ μΈκ±΄λΉλ μ¦κ°νλ λ°λ©΄ μμ°λ¬Ό κ°κ²©μ κ°μνμ¬ μμ
κ²½μλ ₯μ΄ μ½νλμκΈ° λλ¬ΈμΌλ‘ μΆλ‘ λλ©°, νκ²½μ‘°μ μλΉμ€λ μ§μμ μΈ λ―Όμ λ¦Όμ κ°μ λλ¬ΈμΈ κ²μΌλ‘ 보μΈλ€. λ°λ©΄ μλͺ
μ§μ§μλΉμ€λ μ§μκ°λ₯ν μ°λ¦Όκ²½μμ λ°λ₯Έ μ λμ μ λΉμ μλͺ©μΆμ μ κΎΈμ€ν μ¦κ°λ‘ μ§λ°©μμΉμ λ μ€μ μ κ³Ό νμ μνκ³μλΉμ€ κ³΅κΈ ν¨μ¨μ±μ΄ μ¦κ°ν κ²μΌλ‘ 보μΈλ€.
λμ§Έ, λμμ§μκ³Ό λΉλμμ§μμ μ°λ¦Ό μνκ³μλΉμ€ κ³΅κΈ ν¨μ¨μ±μ μ°¨μ΄κ° λνλ¬λ€. λ¬Όμ§κ³΅μ¬μλΉμ€, νκ²½μ‘°μ μλΉμ€, μλͺ
μ§μ§μλΉμ€, λ¬ΈνμλΉμ€ λͺ¨λμμ μ 체μ μΌλ‘ λΉλμμ§μλ³΄λ€ λμμ§μμ ν¨μ¨μ±μ΄ λκ² λνλ¬λ€. μλμ ν¨μ¨μ± μμμμλ λ¬Όμ§κ³΅μ¬μλΉμ€, μλͺ
μ§μ§μλΉμ€, λ¬ΈνμλΉμ€λ μ§λ°©μμΉμ μ€μ μ΄μ 1992-1994λ
κ³Ό μ€μ μ΄ν 2002-2004λ
, 2012-2014λ
μ λμμ§μμ΄ ν¨μ¨μ±μ΄ λμμΌλ, νκ²½μ‘°μ μλΉμ€λ μ§μ체 μ€μ μ΄μ μλ λμμ§μκ³Ό λΉλμμ§μμ ν¨μ¨μ±μ μ°¨μ΄λ λνλμ§ μμμΌλ μκ°μ΄ νλ¦μ λ°λΌ λμμ§μμ ν¨μ¨μ±μ΄ μλμ μΌλ‘ λμμ‘λ€. μ΄λ λμλ―Όλ€μ μ°λ¦Ό μνκ³μλΉμ€μ λν μμκ° λ λ§κΈ° λλ¬ΈμΈ κ²μΌλ‘ μΆλ‘ λλ€. μμ°λλΉ μλμ ν¨μ¨μ± μμμμλ λ¬ΈνμλΉμ€μμ λμμ§μμ ν¨μ¨μ± μμκ° λμμΌλ©°, μΈλ ₯λλΉ μλμ ν¨μ¨μ± μμμμλ λͺ¨λ μ°λ¦Ό μνκ³μλΉμ€μμ λμμ§μμ΄ λΉλμμ§μλ³΄λ€ ν¨μ¨μ±μ΄ λκ² λνλ¬λ€.
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μ§Έ, μν₯μμΈ λΆμμΌλ‘ μ§μλ΄μ΄μμ°, μλλ§λ°λλ μ°λ¦Ό μνκ³μλΉμ€ κ³΅κΈ ν¨μ¨μ±μ μν₯μ λ―Έμ³€λ€. μ§μλ΄μ΄μμ°μ μλͺ
μ§μ§μλΉμ€ κ³΅κΈ ν¨μ¨μ±μ κΈμ μ (+)μΈ μμλ‘ λ°νμ‘μΌλ©°, μ΄λ μλμ΄ μ¦κ°ν μλ‘ μ¬λλ€μ΄ μλ¬Όλ€μμ±μ μ€μμ±μ μΈμνκΈ° λλ¬ΈμΈ κ²μΌλ‘ 보μΈλ€. λ°λ©΄ μλλ§ λ°λλ λ¬ΈνμλΉμ€μλ κΈμ μ (+)μΈ μμκ° λλ, λ¬Όμ§κ³΅μ¬μλΉμ€μ μλͺ
μ§μ§μλΉμ€μλ λΆμ μ (-)μΈ μμλ‘ μμ©νλ κ²μ νμΈν μ μμλ€. μ΄λ μλλ§ λ°λκ° μ¦κ°ν μλ‘ λͺ©μ¬ μμ°μ μν μνκ³Ό μ¬μ‘°λ¦Ό νλμ΄ μ©μ΄ν΄μ Έμ μ²μ μλ¬Όλ€μμ±μ΄ κ°μν μ μκΈ° λλ¬ΈμΌλ‘ μΆμ λλ€. λν μ°λ¦¬λλΌλ λͺ©μ¬λ³΄λ€ λΉλͺ©μ¬ μμ°λ¬Όμ κ°μΉκ° λμλ°, μλλ λͺ©μ¬μμ°μ μ€μ¬μΌλ‘ κ°μ€λμ΄ μμ΄, λΉλμμ μλλ§ λ°λ μ°¨μ΄μ μμ°λ¬Ό μλΉμμ₯μ μμΉμ λ°λΌ μ΄λ° κ²°κ³Όκ° λνλ κ²μΌλ‘ 보μΈλ€. μ΄ μ°κ΅¬κ²°κ³Όλ μΆκ°μ μΈ μ€μ¦μ°κ΅¬κ° λ νμν κ²μΌλ‘ 보μ΄λ©°, μλμ κ°λ° μ΄μ©μ λ¬Όμ§κ³΅μ¬μλΉμ€λ³΄λ€ λ¬ΈνμλΉμ€λ₯Ό κ³ λ €νμ¬ μ΄μ©λ°©λ²μ κ°κ΅¬ν΄μΌ ν κ²μΌλ‘ 보μΈλ€.μ 1 μ₯ μλ‘ 1
1. μ°κ΅¬ λ°°κ²½ 1
2. μ°κ΅¬λͺ©μ 4
3. μ°κ΅¬μ κ΅¬μ± 4
μ 2 μ₯ μ΄λ‘ μ λ°°κ²½ 5
1. μνκ³μλΉμ€ κ°λ
5
2. λ‘컬거λ²λμ€ 9
3. 곡곡λΆλ¬Έμμμ ν¨μ¨μ± μΈ‘μ 11
4. μ νμ°κ΅¬ 12
4.1 DEAκΈ°λ²μ νμ©ν ν¨μ¨μ± λΆμ 12
4.2 μνκ³μλΉμ€ 13
4.3 λ‘컬거λ²λμ€ 14
μ 3 μ₯ μ°κ΅¬λ°©λ² 15
1. μ°κ΅¬λμ λ° μλ£μκ° 15
2. μ°κ΅¬λͺ¨ν 20
3. λΆμλ°©λ² 21
3.1 μλ£ν¬λ½λΆμ(DEA) 21
3.2 Post-DEA λͺ¨ν: AP λͺ¨ν 26
3.3 Tobit νκ·λΆμ 27
μ 4 μ₯ κ²°κ³Ό λ° κ³ μ°° 29
1. λΆμλ²μ λ° κΈ°μ ν΅κ³λΆμ 29
2. DEA λͺ¨νμ μν μλμ ν¨μ¨μ± νκ° 30
2.1 DEA λΆμκ²°κ³Ό 30
2.2 μ§μκ° μ°λ¦Ό μνκ³μλΉμ€ ν¨μ¨μ± μκ΄ κ΄κ³ 40
3. Post-DEAμ μν ν¨μ¨μ± νκ° 48
3.1 APλͺ¨ν 48
3.2 μμ° λ° μΈλ ₯λλΉ ν¨μ¨μ± λΉκ΅ 53
3.3 Tobit λͺ¨νμ μν ν¨μ¨μ± μν₯μμΈλΆμ 57
μ 5 μ₯ κ²°λ‘ 59
μ°Έκ³ λ¬Έν 61
λΆλ‘ 69
Abstract 86Maste
Cognitive Diagnostic Multistage Testing by Partitioning Attribute Hierarchy
νμλ
Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :μ¬λ²λν μνκ΅μ‘κ³Ό,2020. 2. μ μ°μ£Ό.μΈμ§μ§λ¨νκ°λ νμλ€μ μΈμ§μνκ° μ¬λ¬ κ°μ μΈμ§μμλ‘ μ΄λ£¨μ΄μ Έ μλ€κ³ λ°λΌλ³΄κ³ , κ° μΈμ§μμμ μ΅λμ 무λ₯Ό νμλ€μκ² μ§λ¨ν΄μ£Όλ κ²μ λͺ©μ μΌλ‘ νκ³ μλ€. μΈμ§μ§λ¨νκ°λ₯Ό μ€ννκΈ° μν ν΅κ³μ μΈ λͺ¨νμ μΈμ§μ§λ¨λͺ¨νμ΄λΌκ³ νλ©°, νμλ€μ μΈμ§μνλ₯Ό λ€μ°¨μμ μΌλ‘ μ§λ¨νκ² λλ λ§νΌ μΈμ§μ§λ¨λͺ¨νμλ λ§μ λͺ¨μλ€μ΄ ν¬ν¨λμ΄ μκ³ , λͺ¨μμ μ νν μΆμ μ μν΄μλ λ€μμ λ¬Ένκ³Ό ν° νλ³Έμ΄ νμμ μ΄λ€. μ΄μ μΈμ§μ§λ¨λͺ¨νμ ν¨μ¨μ±μ ν보νκΈ° μν μ°κ΅¬λ€μ΄ μ΄λ£¨μ΄μ Έ μμΌλ©°, κ·Έμ€ νλλ μΈμ§μκ³κ΅¬μ‘°λ₯Ό λμ
νλ λ°©λ²μ΄λ€. μΈμ§μκ³κ΅¬μ‘°λ₯Ό λμ
νκ² λλ©΄ μΈμ§ν¨ν΄μ΄ μκ³μ λ§λ ν¨ν΄λ€λ‘ κ·Έ κ°μκ° μ€μ΄λ€μ΄ λ³΄λ€ ν¨μ¨μ μΈ μΆμ μ΄ κ°λ₯νκ² λλ€. λν, λ¬Ένλ°μλͺ¨νκ³Ό μ£Όλ‘ κ²°ν©λλ μ»΄ν¨ν° μ μκ²μ¬ λ°©λ²μ μΈμ§μ§λ¨λͺ¨νμ μ μ©ν μΈμ§μ§λ¨ μ»΄ν¨ν° μ μκ²μ¬λ, νμλ€μ΄ λ¬Ένμ ν΄κ²°ν λλ§λ€ κ·Έ κ²°κ³Όμ λ§μΆμ΄ μ΅μ μ λ¬Ένμ μ 곡νλ λ°©μμΌλ‘ κ²μ¬μ ν¨μ¨μ±μ ν보νκΈ°λ νλ€.
ννΈ, μ»΄ν¨ν° μ μκ²μ¬λ₯Ό ꡬννκΈ° μν΄μλ μμ§μ λ¬Ένλ€μ ν¬ν¨ν λ¬Έμ μνμ΄ μ€λΉλμ΄ μμ΄μΌ νκ³ , λ κ²μ¬μ μνκ³Ό κ΄λ¦¬κ° 볡μ‘νλ€λ λ¨μ μ΄ μ‘΄μ¬νλ€. μ΄μ κ°μ μ»΄ν¨ν° μ μκ²μ¬μ λ¨μ μ 극볡νκΈ° μν΄ λ€λ¨κ³ κ²μ¬ λ°©λ²μ΄ μ μλμμΌλ, μμ§ μΈμ§μ§λ¨νκ°μ λ€λ¨κ³ κ²μ¬ λ°©λ²μ κ²°ν©ν μ°κ΅¬λ κ±°μ μ΄λ£¨μ΄μ§μ§ μμλ€. μ΄μ λ³Έ μ°κ΅¬μμλ μΈμ§μ§λ¨λͺ¨νκ³Ό λ€λ¨κ³ κ²μ¬λ₯Ό κ²°ν©νλ λ°©λ²μ μ μνκ³ , μ μλ λ°©λ²μΌλ‘ λ§λ€μ΄μ§ CD-MST-PH λͺ¨νμ μ ν¨μ±μ μ€μ λ°μ΄ν°μ λͺ¨μμ€νμ ν΅ν΄ κ²μ¦ν΄ λ³΄κ³ μ νμλ€.
μΈμ§μ§λ¨λͺ¨νμ λ€λ¨κ³ κ²μ¬ ꡬ쑰λ₯Ό λμ
νκΈ° μν΄ μ°μ μΈμ§μμλ€μ μκ³ κ·Έλνλ‘ νννκ³ , μΈμ§μκ³κ·Έλνλ₯Ό μμμ λ§κ² λΆν νμ¬ λ³΅μμ μΈμ§μμ κ·Έλ£Ήμ μμ±νμλ€. κ·Έλ¦¬κ³ κ° μΈμ§μμ κ·Έλ£Ήμ λν΄ μκ²μ¬λ₯Ό μ μνκ³ μ΄λ₯Ό λ€λ¨κ³ κ²μ¬λ₯Ό μν λ¨μμΈ λͺ¨λλ‘ μ¬μ©νμλ€. κ° λ¨κ³μμ λͺ¨λμ μ ννλ μ²λλ₯Ό λ§λ€κΈ° μν΄ κΈ°μ‘΄μ μΈμ§μ§λ¨ μ»΄ν¨ν° μ μκ²μ¬μμ μ¬μ©λλ μ²λλ€μ λ³ννμκ³ , λλΆμ΄ μΈμ§μκ³κ΅¬μ‘°μ λΆν μ΄λΌλ νΉμ±μ νμ©νμ¬ μλ‘κ² μ²λλ€μ μ μνμλ€.
CD-MST-PH λͺ¨νμ μ ν¨μ±μ κ²μ¦νκΈ° μν΄μλ μ€μ λ°μ΄ν° λΆμκ³Ό λ κ°μ λͺ¨μμ€νμ μ€ννμλ€. μ€μ λ°μ΄ν° λΆμμμλ TIMSS 2003λ
8νλ
μν κ²μ¬μ λμ μμ 21λ¬Ένμ λν μ€μ μλ΅ λ°μ΄ν°λ₯Ό λΆμνμ¬, μ€κ³λ CD-MST-PH λͺ¨νμ΄ μ€μ λ‘ μ μλνκ³ , μ 체 λ¬Ένμ λͺ¨λ μ¬μ©νλ κΈ°μ‘΄μ λͺ¨νκ³Ό λΉκ΅νμ¬ μΆμ μΌμΉλλ λκ² μ μ§νλ©΄μ κ²μ¬μ κΈΈμ΄λ ν¨μ¨νμν¬ μ μμμ νμΈνμλ€.
λͺ¨μμ€ν 1μ μ€μ λ°μ΄ν° λΆμμ κ²°κ³Όλ₯Ό 보μνκΈ° μν΄, μ€μ λ°μ΄ν° λΆμμμ μ°μΈ μΈμ§μκ³κ·Έλνμ κ²μ¬λ¬Ένμ ν λλ‘ λ¬Ένμλ΅μ κ°μμΌλ‘ μμ±νμ¬ μ§ννμλ€. λͺ¨μμ€ν 2λ μΈμ§μκ³κ·Έλν λ° κ²μ¬λ¬Ένλ€μ μΈμμ μΌλ‘ ꡬμ±νμ¬ λ€μν μ€νλ³μΈλ€κ³Ό ν¨κ» μ§ννμλ€. κ·Έ κ²°κ³Ό μμ λΆμμμλ νμΈν μ μμλ μΆμ μ νμ±κΉμ§ λΆμν μ μμκ³ , CD-MST-PH λͺ¨νμ΄ κΈ°μ‘΄μ λͺ¨νκ³Ό λΉκ΅νμ¬ μ νμ±μ κ±°μ κ°μΌλ©΄μ κ²μ¬μ κΈΈμ΄λ ν¨μ¨νμν¬ μ μλ λͺ¨νμμ νμΈνμλ€. λν, λ€μν μ€νλ³μΈμ λν CD-MST-PH λͺ¨νμ λͺ¨λ μ ν μ²λλ€μ μ±λ₯μ λΉκ΅νμ¬, μ΄λ€ λͺ¨λ μ ν μ²λκ° λ ν¨μ¨μ μΈμ§ νμΈνμλ€.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. μλ‘ 1
1. μ°κ΅¬μ νμμ± 1
2. μ°κ΅¬μ λͺ©μ λ° μ°κ΅¬λ¬Έμ 2
II. μ΄λ‘ μ λ°°κ²½ 3
1. μ ν΅μ μΈ κ²μ¬ μ΄λ‘ 3
1.1. λ¬Ένλ°μμ΄λ‘ 3
1.2. μ»΄ν¨ν° μ μκ²μ¬ 5
1.3. λ€λ¨κ³ κ²μ¬ 6
2. μΈμ§μ§λ¨λͺ¨ν 9
2.1. μΈμ§ν¨ν΄κ³Ό Qνλ ¬ 9
2.2. DINA, DINO λͺ¨ν 9
2.3 μΈμ§μκ³κ΅¬μ‘° 10
2.4. μΈμ§ν¨ν΄μ μΆμ 12
3. μΈμ§μ§λ¨ μ»΄ν¨ν° μ μκ²μ¬ 15
3.1. μΈμ§μ§λ¨ μ»΄ν¨ν° μ μκ²μ¬ 15
3.2. μΈμ§μ§λ¨ λ€λ¨κ³ κ²μ¬ 18
III. μ°κ΅¬λ°©λ² 19
1. λͺ¨ν μ€κ³ 19
1.1. μΈμ§μκ³κ΅¬μ‘° 19
1.2. λͺ¨λ μμ± 20
1.3. νλ₯ κ°±μ 21
1.4. λͺ¨λ μ ν 22
1.5. κ²μ¬ μ’
λ£ 26
1.6. μΈμ§ ν¨ν΄ μΆμ 27
2. μ°κ΅¬μ μ°¨ 28
2.1. μ€μ λ°μ΄ν° λΆμ 28
2.2. λͺ¨μμ€ν 1 34
2.3. λͺ¨μμ€ν 2 36
IV. μ°κ΅¬κ²°κ³Ό 40
1. μ€μ λ°μ΄ν° λΆμ κ²°κ³Ό 40
1.1. μΈμ§ν¨ν΄ λ° κ²μ¬ κΈΈμ΄ λΆμ 41
1.2. κ²μ¬ λ¨κ³ λ° κ²½λ‘ λΆμ 43
2. λͺ¨μμ€ν 1 κ²°κ³Ό 45
2.1. κ²μ¬ κΈΈμ΄ λΆμ 45
2.2. μΆμ μ νλ λΆμ 46
2.3. λ¨κ³λ³ λΆμ 47
3. λͺ¨μμ€ν 2 κ²°κ³Ό 50
3.1. μΆμ μ νλ λΆμ 50
3.2. κ²μ¬ κΈΈμ΄ λΆμ 52
3.3. λͺ¨λ μ ν λΉμ¨ λΆμ 55
V. κ²°λ‘ 72
1. μμ½ 72
2. μ°κ΅¬μ νκ³ λ° μ μΈ 74
μ°Έκ³ λ¬Έν 76
Abstract 80Maste