24 research outputs found
μ μλ ΈμΈ, κ²½λμΈμ§μ₯μ , μμΈ νμ΄λ¨Έλ³μμ λ μλ°λ‘μ΄λ μΆμ , νκ΄μ± μνκ³Όλ λ 립μ μΈ ν΄λ§ μμΆμ λν νΈλͺ¨μμ€ν μΈμ μν₯ μ°κ΅¬
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μνκ³Ό μ μ κ³Όν μ 곡, 2013. 2. μ΄λμ.Objectives: To clarify whether homocysteine has any independent effect, not mediated by cerebral beta amyloid protein (AΞ²) deposition and vascular burden, on whole brain or hippocampal atrophy in elderly individuals with normal cognition, mild cognitive impairment (MCI) and Alzheimers disease (AD).
Methods: Fourteen cognitively normal, 19 MCI, and 24 AD individuals were included. All subjects received three-dimensional volumetric MRI, Pittsburgh Compound B - positron emission tomography and comprehensive clinical evaluation including vascular burden assessment for diabetes, hypertension, dyslipidemia, coronary artery disease, stroke and transient ischemic attack. Blood homocysteine, vitamin B12, and folate levels were also measured.
Results: Multiple linear regression analyses showed that plasma total homocysteine level was significantly associated with hippocampal atrophy even after controlling the degree of global cerebral AΞ² deposition and vascular burden as well as other potential confounders including age, gender, education and apolipoprotein E Ξ΅4 genotype. In contrast, plasma total homocysteine level did not show any significant association with whole brain volume.
Conclusions: Our finding of the independent negative association between plasma homocysteine and hippocampal volume suggests that homocysteine has a direct adverse effect, not mediated by cerebral AΞ² deposition and vascular burden, on the hippocampus.Abstract i
Contents iii
List of tables and figures v
Introduction 1
Methods
Subjects 3
Clinical and neuropsychological assessment 4
MRI image acquisition and analysis 5
11C-PiB PET image acquisition and analysis 7
Blood sample collection and analysis 9
Statistical analysis 9
Results
Subject characteristics 11
Simple correlations between brain volume and related variables 13
Simple correlations between homocysteine and related variables Results 14
Partial correlations between homocysteine and brain volume 15
Multiple regression analysis between homocysteine and brain volume for overall subjects 16
Multiple regression analysis between homocysteine and brain volume only for 11C-PiB negative subjects 18
Discussion 20
References 24
Abstract in Korean 32Maste
A Research on the Ecological Time Expression
OAIID:oai:osos.snu.ac.kr:snu2010-01/104/0000025799/19SEQ:19PERF_CD:SNU2010-01EVAL_ITEM_CD:104USER_ID:0000025799ADJUST_YN:NEMP_ID:A075458DEPT_CD:611CITE_RATE:0FILENAME:μνμ _μκ°_ννμ_κ΄ν_μ°κ΅¬ (1).pdfDEPT_NM:λμμΈνλΆEMAIL:[email protected]:
Research on the Ecological Time Expression
λ³Έ μ°κ΅¬λ μνμ 보μ μΈλ₯νμ 보λ₯Ό κΈ°λ°μΌλ‘ μλ€λ₯Έ μκ° μ λ¬μ μ λνλ κ²μ λͺ©μ μΌλ‘ κ½μ κ°νμκΈ°μ λ°λ₯Έ μκ³λ₯Ό μ μνκ³ μ νλ€.
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μ΄λ©° μκ°μ μΈ‘μ μ μΈκ°μ λ³Έμ±μ΄λ€. κΈ°μ‘΄μ μκ³λ νμμ μ΄κ³ , μΈκ°μ μ체주기μ μΌμμν λ°©μμλ λμ λκΈ° λλ¬Έμ μ μ ν κ²μ΄λΌκ³ ν μ μλ€. μ΄λ μκ° μμμ μμ μ μμΉλ₯Ό μ νκ³ , νμ¬λ₯Ό μ ννκ² μμν μ μκ² νλ©° κ³Όκ±°λ₯Ό μ λνκ³ λ―Έλλ₯Ό κ³νν μ μλλ‘ λμμ μ£ΌκΈ° λλ¬Έμ΄λ€. μ°κ΅¬μ λ²μλ κ³Όκ±°μμλΆν° νλκΉμ§ μκ³λ₯Ό ν΅μμ μΌλ‘ μ‘°μ¬νμλ€. λν λμ§νΈμκ³μ μλ λ‘κ·Έ μκ³μμ μκ°μ λͺ
μνλ λ°©μμ κ³ λ €ν΄ λ³΄μμ λ, μκ³λ₯Ό μ«μμ μμΉ¨μΌλ‘λ§ κ΅¬μ±νλ κ²λ λ
Όλ¦¬μ μΌλ‘ μ΄μμ μμΌλ©° μ‘°κΈμ κ°μ±μ μ΄κΈ°μ λμ§νΈ μκ³λΌλ λμκ³Όλ λ€λ₯΄λ€. κ·Έλμ μνμκ³λ₯Ό κΈ°λ°μΌλ‘ ν μκ° ννλ²λ νμνλ€κ³ ν μ μλ€. μλ λ‘κ·Έ μκ³λ κΈ°μ μ νμ μ»μ΄ λμ§νΈν λμκ³ , κ°μ λ°©μμΌλ‘ μλ¬Όκ³Ό κ½μ(μνν)λ³ν μ 보λ μκ°ννμ λ°©λ² μ€μ νλκ° λ μ μλ€. μΌλ°μ μΌλ‘ νμ΅νκ³ μ΄ν΄νλ κ°λ
μ μκ³λ³΄λ€ μνμ 보μ λμ§νΈκΈ°μ μ μ μ©νμ¬ κ°μ±μ μΌλ‘ νννλ μκ° μ λ¬ λ°©λ²μ μ μ νλ€. ν΄κ²°λ°©μμΌλ‘λ μ²μ²΄μ μμ°, κ·Έλ¦¬κ³ μΈκ°μ νλμ κ΄ν μ°λ ₯μ μ‘°μ¬ νμλ€. κ·Έ μ€μμλ κ½μ κ°νμκΈ°μ μΈλ₯νμ λκ²½νλμ λ³ν κ³Όμ μ κ·Έλν½ μ 보 μμμ ν¨κ» κ³ λ €νμ¬ μ°κ΅¬λ₯Ό μ§ννμλ€. μΈλ₯ν νλκ³Ό μνμ λ³νκ³Όμ λ¨κ³λ 무μν λ§μλ€. λ€μν μ λ³΄κ° μκ°μ νλ¦μ νΌλμ μ€ μ μμ§λ§, μΈν°λμ
μ μ₯μ μ μ΄λ € νΈλν° μμ μ€ν¨μΌλ‘ μνμ 보(κ½κ°ν μκΈ°)λ₯Ό μκ°λ λ³λ‘ κ΅¬μ± νμλ, μνμ μκ°μ μμ°μ€λ½κ² μ΄ν΄ ν μ μμ κ²μ΄λ€.
λμ§νΈκ½μκ³λ μΉμμ μμ ― κΈ°λ₯μ²λΌ μΈμ λ μ΄λμλ λ³Ό μ μλ€. νλΉμ μν κ³Ό μΌμ‘°λ λ±μ μΉνκ²½ κ΄μ μμλ λμ λ κ²μ΄λ©°, μΈκ°μ΄ λλΌλ μμ²΄λ¦¬λ¬ μ μμλ ν¨κ³Όμ μΌ κ²μ΄λ€. ν₯ν μ°κ΅¬κ³Όμ λ‘λ μκ³λ₯Ό μμ°¨μ μΌλ‘ ꡬμ±νλ κ²λΏλ§ μλλΌ μλ€λ₯Έ κ΄μ μμλ μκ°μ λ³Ό μ μκ³ , μλ‘μ΄ μλ―Έλ₯Ό λΆμ¬νλ λ°©μμ μ μ ν μ΄μ©ν μ°½μμ λ°μμ΄ κΉλ λμμΈμ΄ μ μ λμ΄μΌ ν κ²μ΄λ€.This research induces an effective time conveyance based on ecological information and anthropological information. The range of research is about the clock from the past to the modern times in the order of time diachronically. In addition, when considering the time in a digital clock and an analog clock, the construction of a clock with only numbers and an hour hand is not abnormal logically but an ecological expression method of time that is more emotional and sophisticated and suits the modern senses and has a standard is also needed.
There are various types of clocks. The analog clock was digitalized with the power of technology, and the (ecological) change information of plants and flowers is one of the methods for time expression. A time conveyance method that expresses emotion by applying digital technology to ecological information than an analog clock of the concept that is generally learned and understood is suggested. The history of the activities of heavenly bodies, nature and humans was examined as a solution. Among those, the blossom time of flowers and the progress of change in the anthropological agricultural activities were considered with graphic information elements and the research was conducted.The stages of anthropological activities and ecological changes are countless. A digital flower clock can be seen universally like the widget function on the Web. The role and the amount of sunshine will be helpful for the eco-friendly viewpoint and will be effective in the adaptation of the human biorhythm. the research subject of a design with a creative idea that successively constructs a clock and allows the examination of time from an exotic viewpoint that uses a method to grant a proper new meaning is presented in this study.OAIID:oai:osos.snu.ac.kr:snu2010-01/102/0000025799/1SEQ:1PERF_CD:SNU2010-01EVAL_ITEM_CD:102USER_ID:0000025799ADJUST_YN:NEMP_ID:A075458DEPT_CD:611CITE_RATE:0FILENAME:μνμ _μκ°_ννμ_κ΄ν_μ°κ΅¬.pdfDEPT_NM:λμμΈνλΆEMAIL:[email protected]_YN:NCONFIRM:
Performance and study of J. S. Bach Partita No.2 in c minor, BWV 826, C. Debussy Pour le Piano, L. 95, F. Liszt Sonata in b minor, S. 178
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μμ
κ³Ό, 2011.2. λ¬Έμ΅μ£Ό.Maste
ν°λ λ°νν¨ν΄ μ€κ³ μλν νλ‘κ·Έλ¨μ κ°λ°μ κ΄ν μ°κ΅¬
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :μμ곡νκ³Ό,1998.Maste
κ³ λ μ΄μ€λΌμ μ ν΅μ μ¬ν΄μμ κ΄ν μ°κ΅¬ : γμλκΈ°γμ κΈ°λ‘μ μ€μ¬μΌλ‘
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Όλ¬Έ(μμ¬) --μμΈλνκ΅ λνμ :μ’
κ΅νκ³Ό,2008. 8.Maste
μ΄λ€λΆκ΄ μμμ endmember μΆμΆμ μν iterative error analysis μκ³ λ¦¬μ¦μμμ μ€μ°¨ μΈ‘μ λ°©λ² λΉκ΅
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μλμ§μμ€ν
곡νλΆ, 2015. 2. λ°νλ.This study represents the effect of methods for estimating error in iterative error analysis (IEA) algorithm. Three different methods are presented for estimating error in each iteration: the L2-norm, which is used in the original IEA, the L1-norm and the spectral angle. For comparing the effect of applying those error metrics and evaluating the performance of each algorithm, two hyperspectral datasets, simulated and real hyperspectral images, were used. The results of endmember extraction with those algorithms were compared to spectral library by spectral distance and the 2-D plane projection of datasets.
The results with the simulated image indicated that the spectral angle based IEA algorithm performed better than other two IEAs, the original and the L1-norm based algorithm. The spectral angle based IEA selected correct locations of endmember pixels while other two algorithms worked poorly. In addition, the spectral angle IEA produced stable results with various signal-to-noise ratio (SNR). In particular, the algorithm with the spectral angle showed a robustness when the data was highly affected by changes in pixel brightness. The 2-D projection illustrated illumination insensitivity of the spectral angle based IEA algorithm.
The experiment with the real hyperspectral data displayed similar results to those of the simulated image. Even though big difference was not detected, the spectral angle based IEA produced slightly better results of endmember spectra. The 2-D projection plane of extracted endmembers also showed that the spectral angle based algorithm produced reliable results when the image contains topographically complex regions.
The result of this study suggest that the IEA algorithms with different error metrics produce different results and the algorithm based on the spectral angle error is a reliable approach to extract endmembers from the image of topographically complex region.Chapter 1 Introduction 1
Chapter 2 Theoretical Backgrounds 5
2.1 Linear Mixing Model 5
2.2 Linear Spectral Unmixing 7
2.3 Spectral Error Metrics 8
2.4 Comparison of Spectral Error Metrics 10
Chapter 3 The Modified IEA algorithms 13
3.1 Original IEA algorithm 13
3.2 IEA algorithm based on the Spectral Angle Error 15
3.3 IEA algorithm based on the L1-norm Error 15
Chapter 4 Description of Datasets 17
4.1 Simulated Hyperspectral Image 17
4.2 Real Hyperpsectral Image 20
Chapter 5 Results and Discussion 23
5.1 Evaluation with Simulated Hyperspectral Image 23
5.1.1 Results of Extracted Endmembers 23
5.1.2 Comparison of Endmembers on 2-D Plane 31
5.2 Evaluation with Real hyperpsectral image 34
5.2.1 Results of Extracted Endmembers 34
5.2.2 Comparison of Endmembers on 2-D Plane 42
Chapter 6 Conclusions 46
References 48
Abstract 52Maste
Comparison Study on Multi Logit and Stepwise Classification
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : ν΅κ³νκ³Ό, 2012. 2. κΉμ°μ² .Multi class classification is an important topic in real world problems. The most popular strategy in doing multi class classification is classifying all at once based on posterior probability or distance metric. Discriminant analysis, k-nearest neighbors, neural network and multi logit regression are belong to this strategy.
Friedman(1996) suggested the a new intuitive approach for the multi class problems: solve each of the two-class problems and combine all the results of pairwise decisions to form a multiclass classifier. Trevor Hastie and Robert Tibshirani(1998) developed this strategy and applied it to many other areas in their studies. Linear discriminants, K nearest neighbors and support vector machine were used as classifiers. In this paper, we construct pairwise classifiers for multi class problems by using binary logit regressions and compare the results with those of classical multinomial logit model.
We use the forest cover type data from US Forest Service inventory information and predict forest cover types from cartographic variables.Maste