48 research outputs found
μμ± μλ―Έ μ§κ°μμ κ³ λ± μΈμ΄ μ±λΆ μ²λ¦¬ λμ½λ©
νμλ
Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : μμ°κ³Όνλν νλκ³Όμ λκ³Όνμ 곡, 2022. 8. μ μ²κΈ°.High-level linguistic processing in the human brain remains incompletely understood and constitutes a challenging topic in speech neuroscience. While most studies focused on decoding low-level phonetic components using intracranial recordings of the human brain during speech perception, few studies have attempted to decode high-level syntactic or semantic features. If any, most of the research targeting semantic decoding is conducted with picture naming tasks, which only deal with visual language rather than spoken language.
The presenting study is focused on better characterizing the neural representations of processing spoken language perception, namely speech perception. Especially not on the lower-level language components such as phonemes or phonetics, but the higher-level components such as syntax and semantics. Since it is widely accepted that the tripartite nature of language processing consists of phonology, syntax, and semantics, a strategical method for analyzing speech perception tasks that can reject the intervention of phonetic factors was mandatory. Therefore, we conducted a question-and-answer speech task containing four questions revolving around two semantic categories (alive, body parts) with phonetically controlled words.
Intracranial neural signals were recorded during the question-and-answer speech task using electrocorticography (ECoG) electrodes for 14 epilepsy patients. Post hoc brain activity analysis was conducted for three subjects who answered correctly to every trial (144 trials in total) to ensure the analyzed data contained only brain signals collected during the correct semantic processing. The decoding results suggest that absolute and relative spectral neural feature trends occur across all participants in particular time windows. Furthermore, the spatial aspect of the neural features that yield the best decoding accuracy verifies the current biophysiological brain language model explaining the circular nature of word meaning comprehension in the left hemisphere language network.μΈκ°μ κ³ λ± μ±λΆ μΈμ΄ μ²λ¦¬μ κ΄λ ¨ν λλ νλμ ν΄λ
νλ μ°κ΅¬λ μ κ²½μΈμ΄ν λΆμΌμμλ μμ§ κΉμ΄ μ°κ΅¬λμ§ μμ λΆμΌ μ€ νλμ΄λ€. μΉ¨μ΅μ μ κ·Ήμ ν΅ν΄ μ»μ λνΌμ§ λνλ₯Ό μ΄μ©ν λλΆλΆμ μΈμ΄ λμ½λ© μ°κ΅¬λ μμλ μμ μμ€μ νμ μΈμ΄ μ±λΆμμ μ§νλμ΄ μκ³ , ν΅μ¬λ μλ―Έμ κ°μ κ³ λ± μΈμ΄ μ±λΆμ λν λμ½λ© μ°κ΅¬λ λλ¬Όλ€. λλ¬Όκ² μ§νλ κ³ λ± μΈμ΄ μ±λΆ λμ½λ© μ°κ΅¬ λν λλ€μκ° μκ°μ μΈμ΄ μ²λ¦¬λ₯Ό μ°κ΅¬ν κ²°κ³Όλ€μ΄λ©°, μ리 μΈμ΄ λμ½λ© μ°κ΅¬λ νλ λ¨κ³μ 머무λ₯΄κ³ μλ€.
λ³Έ μ°κ΅¬λ μ리 μΈμ΄ μ§κ°μμ λλ νμ±μ λΆμνμ¬ κ·Έ μ²λ¦¬ κ³Όμ μ λν μ νΈ νΉμ±μ κ·λͺ
νκ³ μ νλ€. νΉν μΈκ° μμ± μΈμ΄μ νμ κ΅¬μ± μ±λΆλ³΄λ€λ ν΅μ¬μ μλ―Έ μμ£Όμ κ³ λ± κ΅¬μ± μ±λΆμ μ²λ¦¬νλ λ°μ κ΄μ¬νλ λνμ μκ°μ , μ£Όνμμ , 곡κ°μ νΉμ±μ μ§μ€νμ¬ λΆμμ μ§ννμλ€. μΈμ΄ μ²λ¦¬μ μ£Όλ μΈ κ°μ§ μμλ βμμ (phonetics)β, βν΅μ¬ (syntactics)β, βμλ―Έ (semantics)βλΌλ μ μ κ³ λ €νμ¬, μμ μμ€μ λν νλμ ν΅μ ν μ μλ μ€ν ν¨λ¬λ€μμ ꡬμνμμΌλ©°, ꡬ체μ μΌλ‘λ λκ°μ λ€λ₯Έ μλ―Έ λ²μ£Ό (μλͺ
, μ 체)μ λν΄μ 묻λ μμμ μΌλ‘ λλ±ν λ¨μ΄κ° ν¬ν¨λ μ§λ¬Έμ λ€λ €μ€ ν μλ―Έλ₯Ό νμ
ν΄ λλ΅νλ κ³Όμ μ λνλ₯Ό κΈ°λ‘νλ μ€νμ μ§ννμλ€.
λν μ νΈλ κ²½λ§ν μ κ·Ή μ½μ
μ (Electrocorticography, ECoG)μ ν΅ν΄ 14λͺ
μ λμ μ¦ νμλ‘λΆν° μΉ¨μ΅μ λ°©μμΌλ‘ μΈ‘μ λμλ€. λν λμ½λ© λΆμμλ νΌνμμ λλκ° μ³μ λ°©μμΌλ‘ μ²λ¦¬ν κ³ λ± μΈμ΄ μ±λΆμ΄ λ°μλ μ€νλ§μ ν¬ν¨νκΈ° μν΄μ λͺ¨λ μ€νμμ μ³μ λλ΅μ ν μΈ λͺ
μ νμλ§μ λμμΌλ‘ νμ¬ λΆμμ μ§ννμλ€. λμ½λ© λΆμ κ²°κ³Ό μΈ λͺ
μ νμμ κ±Έμ³ ν΅μ¬ λ¨μ΄ (βκ²μβ, β무μμ
λκΉβ) μμ± μ§κ° μ΄ν νΉμ μκ°λμμ νΉμ μ£Όνμλμ λνκ° μ κ·Ήλ¨μ μλ―Έλ₯Ό λμ μμ€μ μ νλ(%)λ‘ λΆλ₯νλ λ°μ μ¬μ©λ μ μλ€λ κ²μ λ°νλ€. λν μ΄λ¬ν λμ μ νλλ₯Ό κΈ°λ‘ν λνμ νΉμ±μλ λͺ¨λ νμμ κ±Έμ³ μ λμ νΉμ μλμ νΈλ λκ° κ΄μ°°λλ©°, κ΄μ°°λλ λνμ 곡κ°μ νΉμ±μ νμ¬ ν΅μ©λλ μ κ²½μΈμ΄νμ μΈμ΄ μ²λ¦¬ λͺ¨λΈμ΄ μ€λͺ
νλ μμ± μΈμ΄ μ²λ¦¬ λ°©μκ³Ό μΌλ§₯μν΅ν¨μ λ°νλ€.Abstract β
°
1. Introduction 1
2. Materials and Methods 4
3. Results 8
4. Discussion 12
References 15
List of Figures 20
Supplementary information 28
Abstract in Korean 36μ
Nikhil Pal Singh, Black is a Country: Race and the Unfinished Struggle for Democracy. Cambridge, Mass.; London: Harvard University Press, 2004. 285 pages.
1990λ
λ λ―Έκ΅μ¬νλ μΈμ’
μ λ¬Έμ μ μμ΄μ λ·κ±Έμμ§ μΉλ λͺ¨μ΅μ 보μ΄κ³€ νλ€. μλ₯Ό λ€λ©΄, μ κ·Ήμ μΈ μ°¨λ³ μ² ν μ‘°μ²κ° μμ°¨λ³μ΄λΌλ μ¬λ‘ μ΄ νμ±λλλ κ³Όκ±°μ μ΄λ ΅κ² μΌκ΅¬μ΄λΈ μ λμ μΈ μ₯μΉλ€μ΄ μ κ±°λμλ€. ννΈμμλ μΈμ’
μ°¨λ³μ μΈκΈνλ κ² μμ²΄κ° λ§μΉ μλμ°©μ€μ μΈ λ°μμΈ κ²μΌλ‘ μΉλΆλμλ€. νμΈμ μκ³ λ―Έκ΅μΈλ§μ΄ μμ λΏμ΄λΌλ μ΄ μ
μ₯μ, κ²μΌλ‘ λλ¬λλ νλ±μ λ
Όλ¦¬μλ λ€λ₯΄κ² μ¬νκ²½μ μ λΆνλ±μ μΈλ©΄ λλ μ¬νμν€λ ν¨κ³Όλ₯Ό λ³κ³ μμλ€. λν ν μ±μ μ μλ λ―Έκ΅ νμΈμ μ νμ μμ¬λ₯Ό μλ‘κ² μ‘°λͺ
νμ¬ 90λ
λλΆν° ν½λ°°ν μΈμ’
μ μλ§Ήμ λ
Όλ¦¬μ μΌμΉ¨μ κ°νλ€. λ³΄λ€ λμ μλ―Έμμμ λ―Όμ£Όμ£Όμλ₯Ό μ΄λ£©νκ³ μ λ
Έλ ₯ν΄μ¨ λ―Έκ΅ νμΈμ μ¬κ°λ λ―Έκ΅μ κ΅κ°μ΄λ
μ λΆν©νλ λ°©μμΌλ‘ μ¬ν΄μλκ³ νΈμ
λμ΄μ¨ κ²½ν₯μ΄ μλ€. κ·Έ λνμ μΈ μλ‘ 20μΈκΈ° μ€λ°μ νμΈλ―ΌκΆμ΄λμ λ€ μ μλ€. νμΈλ―ΌκΆ μ΄λμ λν 보νΈμ μΈ μμ¬μμ μ λ°λ₯΄λ©΄ μ΄λ 1950λ
λ μ€λ°μ μμλμ΄ 1960λ
λ μ€λ°κΉμ§ μ§μλ μ¬νμ΄λμΌλ‘μ¨ λ―Έκ΅ λ¨λΆμ λ¨μμλ μ
μ΅ μΈμ’
λΆλ¦¬λ₯Ό μ² ννλ κ²°κ³Όλ₯Ό λ³μκ³ λ―Έκ΅μ λ―Όμ£Όμ£Όμ μ μ μ μΉλ¦¬λ₯Ό 보μ¬μ£Όλ μ¬κ±΄μ΄λ€. λ λμκ° νμ¬ λ―Έκ΅μλ κ·Έ μ΄λ ν λ²μ μΈμ’
μ°¨λ³λ‘ μ‘΄μ¬νμ§ μμΌλ©°, λ―Έκ΅μ λͺ¨λ μλ―Όμ΄ λ³Έλμ μΆμ μ λ²λ¦¬κ³ λ―Έκ΅μΈμ΄ λμ΄ λλ±νκ² μ΄μκ°λ μ μΌλ¬΄μ΄ν κ΅κ°λΌλ κ²μ΄λ€
Linear proportional-integral control of turbulent channel ow for skin-friction reductionLinear proportional-integral control of turbulent channel ow for skin-friction reduction
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : κΈ°κ³ν곡곡νλΆ, 2015. 2. μ΅ν΄μ².In the present study, proportional (P) and proportional-integral (PI) feedback control methods are applied to a turbulent channel flow when ReΟ = 140 as a means of drag reduction. The control strategy comes from the opposition
control method proposed by Choi, Moin & Kim (J. Fluid Mech., Vol. 262, 1994, pp. 75-110) which is a proportional control method with a fixed control gain.
The wall-normal velocity at a sensing plane above the wall is measured as a sensing parameter, and blowing/suction is provided at the wall based on the
control strategies.
The performance of the control methods is investigated by the direct measurement of the drag in a direct numerical simulation while varying the sensing plane location y+
s , the proportional gain Ξ±, and the integral gain Ξ². For the P control, as ys+ increases, the drag decreases, reaches the minimum at an optimum sensing position and increases significantly. The effects of Ξ± are also investigated. As Ξ± increases the sensing velocity fluctuations decrease as 1/(1 + Ξ±), resulting in drag decreases. With smaller Ξ±, the amount of drag reduction becomes smaller while the range of ys+ reducing drag becomes wider. With large Ξ± (Ξ± > 1), the drag increases significantly. Thus, other control strategies, such as
I control, are needed for more drag reduction. The PI control results in greater drag reduction than the P control when the sensing plane locates very close to
the wall (ys+ < 10). The sensing velocity fluctuations, considered as an error in the control, approach zero with the PI control, while they do not go to zero with the P control. From the frequency spectra of sensing velocity fluctuations, it is found that the P control reduces the fluctuations at all frequency range, furthermore the I component of the control effectively reduces the sensing velocity fluctuations at low frequency range.
The performance of the control methods is also investigated in a linearized flow model. From the frequency response of the system, it is found that the I
component of the control effectively reduces the sensing velocity fluctuations at low frequency. Furthermore, the performance of control methods is investigated by testing the ability of suppressing the transient energy growth of disturbances in the linearized flow model. The variation of the maximum transient energy growth by the control methods in a linearized flow model is very similar to the
drag variation in a turbulent channel flow. When the sensing plane locates very near the wall (ys+ 10), the maximum transient growth ratio increases significantly with the PI control. This indicates that the linearized flow model can be used as a guideline for control designs for drag reduction.Abstract
Contents
List of Figures
Nomenclature
Chapter
1 Introduction
2 Control methods
3 Flow control of turbulent channel flow
3.1 Introduction
3.2 Numerical method
3.3 Results and discussion
3.3.1 Proportional control
3.3.2 Proportional-Integral control control
3.4 Summaries
4 Flow control of Linearized flow model
4.1 Introduction
4.2 Mathematical formulations
4.2.1 Linearized Navier-Stokes equations
4.2.2 State-space representation
4.3 Transient energy growth of disturbances
4.4 Results and discussion
4.4.1 Eigenmodes of the linearized flow system
4.4.2 Frequency response
4.4.3 Transient energy growth of disturbances
4.5 Summaries
5 Summary and Conclusion
References
Appendix
A Transient growth of disturbances in near-wall region of turbulent channel flow
A.1 Introduction
A.2 Mathematical formulations
A.3 Results
A.4 ConclusionsDocto
Local Good Governance: A Research Framework
κ΅Ώ κ±°λ²λμ€μ λν μ μΈμ οΌ λΉμμ μ£Όμ₯μ λμ΄ κ³Όμ° λ¬΄μμ΄ κ΅Ώ κ±°λ²λμ€μ΄κ³ οΌ μ΄λ»κ² κ°κ΄μ μΌλ‘ κ΅Ώ κ±°λ²λμ€μ μ±ν¨λ₯Ό κ°λ ν μ μμΌλ©°οΌ μ΄λ€ 쑰건 νμμ κ΅Ώ κ±°λ²λμ€κ° κ°λ₯νμ§μ λν 본격μ μΈ μ°κ΅¬λ λΆμ‘±νλ€.01μ λ³΄λ€ κ΅¬μ²΄μ οΌ κ²½νμ λΆμμ κΈ°μ΄ν κ΅Ώ κ±°λ²λμ€ μ°κ΅¬κ° νμν μν©μ΄λ€ λ³Έ λ
Όλ¬Έμ μ΄λ¬ν λ¬Έμ μμ νμ λ‘ν¬ κ±°λ²λμ€-μ¦ μ§λ°©μμΉ μ°¨μμ κ±°λ²λμ€-λ₯Ό λμμΌλ‘ κ΅Ώ κ±°λ²λμ€ μ°κ΅¬λ₯Ό μν λΆμνμ μ μνλ κ²μ λͺ©μ μΌλ‘ νκ³ μλ€. μ΄λ₯Ό μνμ¬ μ°μ λ‘ν¬ κ±°λ²λμ€ κ΄λ ¨ κ΅λ΄μΈ μ°κ΅¬λ€μ κ²ν νμ¬ λ‘컬 κ±°λ²λμ€μ κΈ°λ³Έμ μΈ κ°λ
κ³Ό μ£Όμ₯μ μ΄ν΄λ³΄κ³ κ΅Ώ κ±°λ²λμ€ μ§ν κ΄λ ¨κ΅λ΄ κΈ°μ‘΄ μ°κ΅¬λ€μ λΉνμ μΌλ‘ λΆμνκ³ μλ€. μ΄λ¬ν κΈ°μ‘΄ μ°κ΅¬μ λν κ²ν μ λΉνμ λΆμμ κΈ°μ΄νμ¬ λ‘컬 κ΅Ώ κ±°λ²λμ€ μ°κ΅¬λ₯Ό μν λΆμν΄οΌ κ΅Ώ κ±°λ²λμ€ μ§νοΌ κΈ°μ€οΌ κ°μ΄λλΌμΈ λ° κ°μ€μ μ μνκ³ λ³Έ λ
Όλ¬Έμ λ‘컬 κ΅Ώ κ±°λ²λμ€ λΆμνμ νκ΅μ λ‘컬 κ±°λ²λμ€ κΈ°μ‘΄ μ¬λ‘μ μ¬μ μ© ν΄λ΄μΌλ‘μ κ·Έ μ μ©μ±μ μμνκ³ κ²°λ‘ μ λ§Ίκ³ μλ€. This paper proposes a research framework for the empirical analysis of good governance, particularly at the local level. It first reviews existing literature on local governance and discusses basic conceptual issues on local good governance. It then criticizes and sheds light on the limits of empirical studies on good governance indicators. It then provides an analytic framework on local good governance and key indicators, guidelines and hypotheses to be utilized for future empirical research. The finally illustrates the utility of the framework by applying it to two successful cases of local governance in South Korea.μ΄ λ
Όλ¬Έμ 2008λ
λ μ λΆμ¬μ(κ΅μ‘μΈμ μμλΆ νμ μ°κ΅¬μ‘°μ±μ¬μ
)μΌλ‘ νκ΅νμ μ§ν₯μ¬λ¨μ μ§μμ λ°μ μ°κ΅¬λμμ(KRF-2008-321-B00003
Biochemical characterization of histone modifying enzymes
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :κ³Όνκ΅μ‘κ³Ό μλ¬Όμ 곡,2007.Maste
λΉμ ν μ ν μμ ν΄μμ μν λ±κ° κ°μ± νλ ¬ κΈ°λ² κΈ°λ°μ νλΌλ©νΈλ¦ μΆμ λͺ¨λΈ
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : κΈ°κ³ν곡곡νλΆ, 2016. 8. μ‘°λ§Ήν¨.κΈ°ν λΉμ ν λ° μ¬λ£ λΉμ ν κ±°λμ κ³ λ €ν ꡬ쑰 ν΄μμμ μ΅μ’
λ³νμ λμΆνκΈ° μν΄μλ, λ΄λ ₯ 벑ν°μ κ°μ± νλ ¬μ λ°λ³΅μ μΌλ‘ λμΆνλ©° λ³νμ κ°±μ ν΄ λμκ°λ κ³Όμ μ κ±°μ³μΌ νλ€. μ΄λ λΉμ νμ±μ κ³ λ €ν μ μ λ° λμ ν΄μμ μμ΄μ κ³μ° μκ°μ΄ κΈμ¦νλ κΉλμ΄λ€. λ°λΌμ ν¨κ³Όμ μΈ ν΄μμ μνμ¬ λ€μν μΆμ λͺ¨λΈ κΈ°λ²λ€μ΄ μ μλμ΄ μμ§λ§ κ·Έ ν¨μ¨μ±μ΄ λμ§ μλ€. 보ν΅μ μΆμ κΈ°λ²μ κ°κ° μμ€ν
νλ ¬μ ν¬κΈ°λ₯Ό μ€μ΄κ³ , μ€μ΄λ ν¬κΈ°μ μμ€ν
νλ ¬μ ν΅ν΄ ν¨μ¨μ μΌλ‘ μλ΅μ λμΆν μ μλλ‘ νλ€. μ¦, μΆμ κΈ°λ²μ μ μ©νκΈ° μν΄μλ μμ€ν
νλ ¬μ 미리 ꡬμΆν΄μΌ νλ κ²μ΄λ€. λΉμ ν ν΄μμ κ²½μ° λ³νμ λ°λΌ μμ€ν
νλ ¬μ ꡬμΆμ λ°λ³΅ν΄μΌ νλ©°, μ΄μ μμλλ μκ°μ΄ μλΉνλ€. κ·Έλ¬λ κΈ°μ‘΄μ μΆμ κΈ°λ²μ μμ€ν
μλ΅ λμΆκ³Ό κ΄λ ¨λ ν¨μ¨μ±λ§ λμ΄λ©°, νλ ¬ ꡬμΆμ μμλλ μκ°μ κ·Έλλ‘ λ¨λλ€.
λ±κ° κ°μ± νλ ¬ κΈ°λ²(Stiffness Evaluation)μ νμ©νλ©΄ λΉμ ν μμ€ν
νλ ¬ ꡬμΆμ ν¨μ¨μ±μ λμΌ μ μλ€. ν΄λΉ κΈ°λ²μ λΉμ ν μμ€ν
μ λ΄λ ₯ 벑ν°λ₯Ό λ€νν¨μλ₯Ό νμ©ν λ±κ° λͺ¨λΈλ‘ λνλΈλ€. μΌλ¨ ν΄λΉ λ€νν¨μμ κ³μλ€μ λμΆν΄ λμΌλ©΄, λ³νμ κ°±μ μ μ¦κ°μ μΌλ‘ κ³ λ €νμ¬ λ΄λ ₯ 벑ν°μ κ°μ± νλ ¬μ κ³μ°ν μ μμΌλ―λ‘ νλ ¬ ꡬμΆμ μμλλ μκ°μ ν¬κ² μ€μΌ μ μλ€. λ¬Έμ λ λ±κ° λͺ¨λΈ ꡬμΆμ μμλλ μκ°μ΄ μμ€ν
ν¬κΈ°μ 3μΉμ λΉλ‘νμ¬ λμ΄λκΈ° λλ¬Έμ, ν¨μ¨μ μ΄μ§ μλ€λ κ²μ΄λ€.
λ³Έ μ°κ΅¬μμλ μ ν μμμ μ°κ²°μ±μ κ³ λ €νμ¬ κ°μ ν μλ‘μ΄ λ±κ° κ°μ± νλ ¬ κΈ°λ²(SEECStiffness Evaluation based on Element Connectivity)μ μ μνλ€. μ ν μμμ μ°κ²°μ±μ μ΄μ©νλ©΄, λ€νν¨μμ κ΅¬μ± λ° ν΄λΉ κ³μλ€μ λμΆμ κ΅λΆμ λ° λ³λ ¬μ μΌλ‘ μ§νν μ μλ€. μ ν©μ§κ΅λΆν΄λ²(PODProper Orthogonal Decomposition)μ νμ©ν μΆμ λͺ¨λΈ λν μ°λλμ΄ ν¨μ¨μ±μ λμΈλ€. λ±κ° κ°μ± νλ ¬ κΈ°λ²μ κ³μλ€μ μμ μ°κ²°μ±μ κ³ λ €νμ¬ ν¨κ³Όμ μΌλ‘ μ°μΆν λ€, μΆμ κΈ°λ²μ μ°λνλ©΄ μ νλ λ° ν¨μ¨μ±μ λμμ ν보ν μ μλ€. μ΄ μ μ κΈ°λ²μ SEECROMμΌλ‘ μΉνλ€. SEECROMμ κ° μ ν μμμ νΉμ±μ νμ©νμ¬ κ΅λΆμ μΌλ‘ ꡬμ±λλ€λ νΉμ±μ κ°κΈ° λλ¬Έμ, νλΌλ©νΈλ¦ κΈ°λ²μ μ μ©μ΄ μ©μ΄ν μ₯μ λ κ°λλ€. μ μ κΈ°λ²μ ν¨μ¨ λ° μ νμ±μ κΈ°ν λΉμ νμ±μ κ°μ§λ μ ꡬ쑰물μ λμ ν΄μκ³Ό μ΄νμ± μ¬λ£λ₯Ό κ°μ§λ λΉμ ν ꡬ쑰μ μ μ /λμ ν΄μμ μ μ©νμ¬ κ²μ¦νμλ€.
μ μ κΈ°λ²μ μ μ° λ€λ¬Όμ²΄ λμνμλ μ μ©ν μ μλ€. 보ν΅μ μ μ° λ€λ¬Όμ²΄ λμνμ μλ μ μ μ’νκ³λ₯Ό νμ©νμ¬ ν΄μνλ©°, κ·Έ κ²½μ° μμ€ν
νλ ¬μ λΉμ νμ±μ΄ κ°μ± νλ ¬μ΄ μλ μ§λ νλ ¬μ λνλλ€. κ΄μ±λ ₯κ³Ό κ΄λ ¨νμ¬ μΆκ°λλ νλ ¬λ κ³ λ €νμ¬μΌ νκΈ° λλ¬Έμ, λμ ꡬ쑰 ν΄μμ κΈ°λ°μΌλ‘ κ°λ°λ μΆμ κΈ°λ²μ μ μ° λ€λ¬Όμ²΄ λμν λ¬Έμ μ κ·Έλλ‘ νμ©νκΈ°μλ μ΄λ €μμ΄ μλ€. μ λ μ μ μ’νκ³λ₯Ό κΈ°λ°μΌλ‘ νλ ANCF(Absolute Nodal Coordinate Formulation) κΈ°λ²μ κ²½μ°, μ ν μμ κΈ°λ²μ μ μ©ν μ§λ°° λ°©μ μμ΄ λΉμ ν λμ ꡬ쑰 ν΄μ λͺ¨λΈκ³Ό μ μ¬ν νμμ κ°λλ€. λ°λΌμ ANCF κΈ°λ²μ νμ©νλ©΄ ꡬ쑰 ν΄μμ κΈ°λ°μΌλ‘ κ°λ°λ SEECROMμ μ μ° λ€λ¬Όμ²΄ λμνμ μ μ©ν μ μλ€. μ μ κΈ°λ²κ³Ό νλΌλ©νΈλ¦ κΈ°λ²μ 4μ 기ꡬλ₯Ό ν¬ν¨ν μ£Όμν μ μ° λ€λ¬Όμ²΄ λμν μμ λ€μ μ μ©νκ³ κ·Έ ν¨μ¨μ±κ³Ό μ νμ±μ κ²μ¦ν μ μμλ€.
νλΌλ©νΈλ¦ κΈ°λ²μ΄ μ μ©λ SEECROMμ λΉμ ν μμ€ν
ꡬ쑰μ μ΅μ μ€κ³ λ¬Έμ μλ μ μ©μ΄ μ©μ΄νλ€. νμ¬ λΉμ ν μμ€ν
μ μΆμ κΈ°λ²μ λν μ°κ΅¬κ° μ§ν μ€μΈ λ¨κ³λΌ, μ΄λ₯Ό μ΅μ μ€κ³ λ¬Έμ μ μ μ©νλ μ°κ΅¬λ μμ§ νλ°νμ§ μμ λΆμΌμ΄λ€. SEECROMμ λ€μν μ’
λ₯μ λΉμ ν μμ€ν
μ ν¨μ¨μ μΌλ‘ μΆμνλ©°, νλΌλ©νΈλ¦ κΈ°λ²μ μ μ© λν μ μ©νκΈ°μ μμΌλ‘ λ€μν μ΅μ μ€κ³ λ¬Έμ λ₯Ό ν¨κ³Όμ μΌλ‘ λ€λ£° μ μμ κ²μ΄λΌ μμλλ€. λ³Έ μ°κ΅¬μμλ λΉμ ν μ¬λ£ κ±°λμ κ°λ ꡬ쑰 μμ€ν
κ³Ό 4μ 기ꡬμ μ μ° λ€λ¬Όμ²΄ λμν μμ€ν
μ ꡬ쑰 μ€κ³ λ¬Έμ λ₯Ό μμ λ‘ λ€μ΄ μ μ κΈ°λ²μ ν¨κ³Όλ₯Ό κ²μ¦νμλ€.
μ μ κΈ°λ²μ μμ λ° μ μ μ κΈ°λ°μΌλ‘ μνλλ λ€μν λΉμ ν λ¬Έμ μ μ μ©μ΄ κ°λ₯ν κ²μ΄λ€. λΉμ νμ±μ κ°μ§λ λν μμ€ν
, λ€λ¬Όλ¦¬ μ°μ± μμ€ν
, μμ© νλ‘κ·Έλ¨μ νμ©ν ν¨μ¨μ μΈ ν΄μ λ° μ€κ³λ‘μ νμ₯μ΄ κΈ°λλλ€.To solve material/geometric nonlinear structural systems, iterative evaluation of internal forces and tangent stiffness matrices is required. This increases the computation time for nonlinear static/dynamic analysis. Although various reduced-order modeling techniques have been proposed to effectively solve nonlinear structural analysis problems, problems arise in the reduction of the system matrices. Since most reduction methods only reduce the system matrix after the stiffness and mass matrix construction process, the construction itself proceeds in full domain. In most cases of nonlinear analysis, the construction of system matrix takes a large amount of computation time, comparable to the computation time of the solving process. Although this problem can be tackled with STiffness Evaluation Procedure (STEP), which uses polynomial formulations to describe nonlinear internal forces, the construction time of the reduced model increases rapidly with the cubic power of the system size.
In this paper, Stiffness Evaluation method based on Element Connectivity (SEEC) is proposed. The element connectivity of the finite element models is used to evaluate the nonlinear stiffness coefficients. The proposed method minimizes the effect of the system size when the computational model is constructed. In addition, the Reduced Order Modeling (ROM) technique using Proper Orthogonal Decomposition (POD) is applied to enhance the efficiency of the SEEC method, which is referred to as SEECROM. This enables effective analysis and design of large-scale problems. Moreover, SEECROM is easily characterized by design parameters. The parameterization is readily achieved with element-wise nature of the proposed method. SEECROM is successfully demonstrated for structural dynamic analysis of geometrically nonlinear shell structures under the perturbation of external loads. SEECROM-Parameterization is also successfully demonstrated for static and dynamic analysis of hyperelastic materials that have material and geometric nonlinearities.
In the case of flexible multibody systems, nonlinearities are caused by the rigid motions of the structure rather than the deformation of the flexible parts. Since the approaches to the multibody dynamics are different from the structural dynamics, the reduction methods which have been developed for the structural analyses cannot be utilized in direct manner. However, this can be achieved with the aid of Absolute Nodal Coordinate Formulation (ANCF), which takes the analogous format to the governing finite element formulation of structural dynamics. SEECROM method is combined to ANCF to form an efficient reduced model of the flexible multibody system. A number of examples are provided for the verification of the proposed reduction method and its parameterization.
For the application to the optimization of nonlinear structures, it is vital that reduced order models be efficiently parameterized for the design parameters. SEECROM-Parameterization fits easily to the optimization problems concerning nonlinear systems. To show the validity of the proposed methodology, two sample optimization problems are subjected to a static structural system with a hyperelastic material and a multibody dynamic system.1. INTRODUCTION 1
2. STIFFNESS EVALUATION 6
2.1. Stiffness Evaluation Methods 6
2.2. Stiffness Evaluation Methods with Reduced-Order Modeling 9
3. STIFFNESS EVALUATION BASED ON ELEMENT CONNECTIVITY 11
3.1. Determination of Displacement Combination 11
3.2. Evaluation of Stiffness Coefficients 14
3.2.1. Sampling 14
3.2.2. Minimum Norm Least Squares 16
3.3. Computation of Tangent Stiffness 20
4. REDUCED ORDER MODELING USING SEEC 25
4.1. Proper Orthogonal Decomposition 26
4.2. Proper Orthogonal Decomposition with SEEC 27
4.3. Application of SEECROM 28
5. PARAMETERIZATION BASED ON SEECROM 39
5.1. Parameterization Strategy of SEECROM 39
5.2. Application of SEECROM-Parameterization 42
5.2.1. Static Analysis of Neo-Hookean Hyperelastic Material 43
5.2.2. Dynamic Analysis of Neo-Hookean Hyperelastic Material 44
6. APPLICATION TO MULTIBODY DYNAMICS 53
6.1. Motivation 53
6.2. ANCF Formulations 57
6.3. Reduced-Order Modeling of ANCF Model with SEECROM 60
6.3.1. Application of SEECROM to ANCF Beam Elements 60
6.3.2. Application of SEECROM 61
6.4. Parameterization of ANCF model with SEECROM 66
6.4.1. Parameterization Strategy of SEECROM 66
6.4.2. Application of SEECROM-Parameterization 67
7. APPLICATION TO OPTIMIZATION PROCESS 95
7.1. Motivation 95
7.2. Optimization of nonlinear system 98
7.3. SEECROM-Parameterization for optimization of nonlinear system 100
7.3.1. Optimization procedure using SEECROM-Parameterization 100
7.3.2. Numerical examples 105
8. CONCLUSIONS 133
REFERENCES 135
APPENDIX A 139
κ΅λ¬Έ μμ½ 145Docto
Emily Dickinsons Changing Poetics Eui
λ³Έκ³ λ μλ°λ¦¬ λν¨μ¨μ μ‘νμκ³ μ°κ΅¬μ μ±κ³Όλ₯Ό μ κ²νκ³ κ·Έ μ±κ³Όλ₯Ό κΈ°λ°μΌλ‘ λν¨μ¨μ μνμ μλ‘κ² μ‘°λͺ
νλ€. κΈ°μ‘΄μ λν¨μ¨ μ°κ΅¬μλ€μ λν¨μ¨μ μκ³ λ¬Άμμ μ΅κ³ μ μ±μ·¨λ‘ νκ°νλ€. κ·Έλ¬λ μ‘νμκ³ μ°κ΅¬λ λν¨μ¨μ μ°½μκΈ°λ²μ΄ μ΅μν μΈ λ¨κ³λ₯Ό κ±°μΉλ©° λ³ννμμ μ€μ¦μ μΌλ‘ λμΆνλ€. μ΄λ₯Ό κ·Όκ±°λ‘ λν¨μ¨μ μνλ λ³ννμμ μ§μν μ μμΌλ, μ‘νμκ³ μ°κ΅¬μλ€μ μ€μ¦μ λ°©λ²λ‘ μ μΉμ°μ³ μκ³ μ λ¬Όμ§μ±κ³Ό μμ μλ―Έλ₯Ό μ°λνλ €λ λ
Έλ ₯μ΄ λΆμ‘±νλ€. λν¨μ¨μ μνμ μμ§μ΄λ κ²μ΄λΌλ μ μ νμ λ³Έκ³ λ ꡬ체μ μΈ μ¬λ‘λ₯Ό λΆμνμ¬ μ‘νμκ³ μ λ¬Όμ§μ νΉμ±μ΄ μμ μλ―Έλ₯Ό κ°ννκ³ λλ‘λ λ³μ§νλ λ°©μμ μΆμ νλ€. μ°μ λν¨μ¨μ΄ μκ³ λ₯Ό λ¬Άλ νμλ₯Ό ν¬κΈ°ν κ³ΌλκΈ°μ μ¦νλ μμμμ μ‘°λͺ
νλ€. κ°λ³ μλ μνμ€λ‘λΆν° νλ €λ¨μΌλ‘μ¨ μλ‘μ΄ λ―Ένμ κ°λ₯μ±μ λͺ¨μνκ² λλ€. κ·Έ λ€μ λ¨κ³μμ λν¨μ¨μ μΌμμμ λ°μνλ μ’
μμ‘°κ°μ μμμ μ μλλ°, μ΄ μκ³ μ λν κΈ°μ‘΄μ νκ°λ λ§€μ° λΆμ μ μ΄λ€. κ·Έλ¬λ λ³Έκ³ λ μ΄ μκΈ°μ λν¨μ¨ μκ³ μμ μμ±μ λν μμμ μΈ κ±°λΆλ₯Ό μ½μ΄λ΄κ³ , νκΈ° μνμ νλ§₯λ½νμ λν μκ°ν μ νμΌλ‘ ν΄μνλ€. ν΄μ²΄ν νΈμ§λ΄ν¬μ λν¨μ¨μ΄ λ¨κΈ΄ κΈμ 100λ
μ΄ μ§λ μ€λλ μλ νΈμ§λ΄ν¬μ λͺ¨μ΅μΌλ‘ λ
μ μμ λͺ¨μ΅μ λλ¬λμΌλ‘μ¨ λν¨μ¨μ λ―Ένμ μ λ΅μ μλ ₯μ λ°©μ¦νλ€. κ²°λ‘ μ μΌλ‘ λ³Έ μ°κ΅¬κ° μ£Όμ₯νλ λ°λ κΈ°μ‘΄ λ
λ²μ νκΈ°ν΄μΌ νλ€λ κ²λ μλκ³ λͺ¨λ λν¨μ¨ νμκ° μμΉ΄μ΄λΈλ‘ ν₯ν΄μΌ νλ€λ κ²λ μλλ€. λ μ§μμ΄ νλ ₯ν λ λΉλ‘μ μ€μ¦μ μ¦κ±°κ° μ μλ―Έν ν΄μμΌλ‘ μ΄μ΄μ Έ λν¨μ¨μ μμΈκ³λ₯Ό μ‘°λͺ
νλ ν΅ν©μ μΈ μμ
μ΄ κ°λ₯ν κ²μ΄λ€.This essay challenges the idea that Emily Dickinsons fascicles represent a highpoint in her poetic creativity. Scholars who have studied Dickinsons manuscripts have uncovered the full trajectory of Dickinsons changing poetics. After producing forty fascicles, Dickinson gradually stopped binding the sheets of stationery on which she had transcribed her poems. Understanding this as a conscious aesthetic decision sheds new light on Dickinsons post-fascicle writing. The unbound bifolum sheets of the middle period reveal Dickinsons acute self-consciousness about her decision not to bind her poems. Poems from this period show that Dickinson was fascinated by the aesthetic possibilities and challenges presented by not binding her poems. In the final decade of her life, Dickinson dispensed with formal stationery altogether, electing to write on scrap pieces of paper. This phase of her writing is understudied because the critical consensus until recent years has been that these scraps are not quite poems. By examining two envelope poems, this essay reveals that Dickinson worked against the possibilities of abstraction and transference by refusing to finish her poems. In this final stage, her experimentation extended to include the very material on which she wrote. This essay concludes that the findings of Dickinson manuscript studies need to be embraced by scholars to appreciate the full scope of Dickinsons creative practice and to gain a contextualized understanding of individual poems
Purification and Characterization of Ξ²-Glucosidase from Seeds of Pumpkin (Cucurbita moschata)
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : λμλͺ
곡νλΆ(μνμλͺ
곡νμ 곡), 2013. 2. μ₯νμ.The objective of this study was purification and characterization of Ξ²-glucosidase (EC 3.2.1.21), catalyzing the hydrolysis of Ξ²-glucosidic bonds, from Pumpkin seed (Cucurbita moschata). The Ξ²-glucosidase was purified by fast protein liquid chromatography using Hitrap DEAE-sepharose FF, Hitrap Q-sepharose XL, and HiPrep 16/60 Sephacryl S-100 Hiresolution column. The 8.24-fold purified enzyme had a specific activity of 16.62x10^(-2) unit/mg protein against p-nitrophenyl-Ξ²-D-glucopyranoside (pNPG). Sodium dodecyl sulfate-polyacrylamide gel electrophoretic (SDS-PAGE) analysis showed Ξ²-glucosidase from pumpkin seed was dimeric structure with molecular mass of 48.1 kDa, the sum of 28.8 and 19.3 kDa. The molecular mass estimated by SDS-PAGE was analogous with that of 42.8 kDa estimated by gel permeation chromatography using Sephacryl S-100 Hiresolution column. The optimum temperature and pH of the enzyme were 70Β°C and pH 4.0, respectively. The enzyme was stable in the range of pH 2.0 to 10.0 and under 60Β°C, respectively. The Km, Vmax, and kcat of Ξ²-glucosidase employing p-nitrophenyl-Ξ²-D-glucopyranoside (pNPG) as substrate were 2.22 mM, 0.078 unit/mg protein, and 13.29 min^(-1), respectively. From the result of zymogram analysis, the enzyme had the activity in the condition not that subunits were departed but that subunits were associated. It was composed of 364 amino acids, and the secondary structure of Ξ²-glucosidase from seeds of pumpkin (Cucurbita moschata) consists of Ξ±-helix (26.10%), antiparallel Ξ²-sheet (20.17%), parallel Ξ²-sheet (8.22%), Ξ²-turn (18.16%), and random coil (27.34%) in the stable condition and it was affected by temperature and pH. There is a possibility that the enzyme have the characteristics of pH reversible inactivation according to the study of the effect on the enzyme activity and stability.
The purified Ξ²-glucosidase could cleave Ξ²-glucosidic bonds in phytochemicals and enhance the bioavailability of phytochemicals. Moreover, it was found that pumpkin seed-derived Ξ²-glucosidase has exceptional stability at wide range of pH in pre-experiment. Therefore the purified Ξ²-glucosidase could be applied to the food industry and pharmaceutical industry.1. Introduction 1
2. Materials and Methods 5
2-1. Materials 5
2-2. Preparation of crude enzyme 6
2-3. Purification using fast protein liquid chromatography 7
2-4. Analysis of Ξ²-glucosidase activity and protein concentration 9
2-5. Polyacrylamide gel electrophoresis 11
2-6. Determine of molecular mass 12
2-7. Effects of temperature and pH on the enzyme 12
2-8. Enzyme kinetics 13
2-9. Zymography 14
2-10. Amino acid analysis 14
2-11. Circular dichroism 15
3. Results and Discussion 17
3-1. Purification of Ξ²-glucosidase seeds of pumpkin (Cucurbita moschata) 17
3-2. Characterization of Ξ²-glucosidase from seeds of pumpkin (Cucurbita moschata) 22
3-2-1. Determination of molecular mass 22
3-2-2. Effects of pH and temperature on the enzyme 24
3-2-3. Enzyme kinetics 30
3-3. Effects of interaction between subunits on the enzyme activity 32
3-4. Structural characteristics of Ξ²-glucosidase from seeds of pumpkin (Cucurbita moschata) 35
4. Conclusion 42
5. References 44
κ΅λ¬Έμ΄λ‘ 47Maste
"It ain`t yo` fault, huck" : the limits of intention in adventures of Huckleberry Finn
Thesis (master`s)--μμΈλνκ΅ λνμ :μμ΄μλ¬Ένκ³Ό λ¬Ένμ 곡,2003.Maste