18 research outputs found
ì°ì ìì ë¥ë¬ëì ìŽì©í ìê³ìŽ ë°ìŽí° ë¶ì
íìë
Œë¬ž (ë°ì¬)-- ììžëíêµ ëíì : ìì°ê³Œíëí íë곌ì ê³ì°ê³Œíì ê³µ, 2019. 2. ê°ëª
죌.ë¥ë¬ëì ìµê·Œ ëª ë
ê° ì°ì
ìíì ììŽì ê°ì¥ ê°ë ¥íê³ ì€ìì ì¬ê²šì§ë ë°©ë²ìŽë€. ì°ëŠ¬ë ì°ì
ìíì ìê³ìŽ ë°ìŽí°ì ììŽì ë¥ë¬ë 몚ëžì ë¶ì ë° ììž¡ ë±ì ì ìíìë€. 첫 ë²ì§žë¡, ìŽì ê°ì§ë¥Œ ìí ìë¡ìŽ ë¥ë¬ë 몚ëžì ê°ë°íììŒë©° ìŽë ë€ìí êžžìŽ ë¿ë§ ìëëŒ ë
žìŽìŠ, ìê° ì°šê° ìë ë°ìŽí°ììë ìì§ëìŽìê² íìí ìê³ìŽ ë°ìŽí° ë¶ìì í ì ììë€. ë ë²ì§žë¡, êžìµ ìì¥ì ížë ë륌 ììž¡íêž° ìí ë€ìí ë¥ë¬ë 몚ëžì ê°ë° ë° ìííŽë³ŽììŒë©° ìŽ ì€ ê°ì€ì¹ ìŽí
ì
ë€ížìí¬ì ê²œì° ëì ììž¡ ì íë ë¿ë§ ìëëŒ ìê°í륌 íµíŽ ì§êŽì ìŒë¡ 몚ëžì ìŽíŽ ë° ììž¡í ìŽì 륌 ë¶ìí ì ììë€.Deep learning, also called as artificial neural networks, is one of the most important and powerful subjects in industrial in recent years. Deep learning starts to show a great performance from image classification and in these days it have been applied to fields including computer vision, natural language process, speech recognition and etc. The performance is better than not only previous machine learning techniques, but also human experts in some cases. For an area with time series data, recurrent neural networks is widely used algorithm of deep learning. The aim of this theseis is to apply deep learning, especially with recurrent neural networks, for an industrial such as anomaly detection and trend prediction in financial market, with time series data . Its main contributions are (1) a new model for anomaly detection in time series data even for various length inputs, (2) various neural architectures for prediction in finance, and (3) attention networks and model analysis with attention vectors. Each experimental results of applications show better performances than previous machine learning techniques.1 Introduction
2 Deep Learning Background
2.1 Neural Networks
2.2 Various Activation Functions
2.3 Error Backpropagation
2.4 Regularization
2.4.1 Dropout
2.4.2 Batch Normalization
3 Deep Learning Models
3.1 Multi Layer Perceptron
3.2 Convolutional Neural Networks
3.3 Recurrent Neural Networks
3.4 Long Short Term Memory
3.5 Attention Networks
4 Anomaly Detection
4.1 Related Works of Anomaly Detection
4.1.1 Anomaly detection
4.1.2 t-SNE
4.1.3 Clustering
4.2 Deep Correlation Mapping
4.2.1 LSTM
4.2.2 t-SNE
4.2.3 Full Model Architecture
4.2.4 Anomaly detection using Deep Correlation Mapping
4.3 Experimental Results
4.3.1 Correlation
4.3.2 Anomaly detection using DeepCorr
4.4 Conclusion
5 Trend Prediction
5.1 Related works of Trend Prediction
5.2 Trend Prediction with Deep Learning Models
5.2.1 Dataset
5.2.2 MLP
5.2.3 1D-CNN
5.2.4 LSTM
5.2.5 Attention Networks
5.2.6 Weighted Attention Networks
5.3 Experimental Results
5.3.1 Best Lookback Days
5.3.2 Results of Various Deep Learning Models
5.3.3 Visualization Attention Vectors
5.4 Conclusion
6 Conclusion and Future Works
Abstract (in Korean)
Acknowledgement (in Korean)Docto
ììì ì íí°ë¥Œ ìŽì©í ì ì¥ì± ë°êŽ ë€ìŽì€ë
íìë
Œë¬ž (ìì¬) -- ììžëíêµ ëíì : 공곌ëí ííì묌공íë¶(ìëì§í겜 ííìµí©êž°ì ì ê³µ), 2021. 2. ê¹ëí.The flexible and stretchable display is a key technology in developing a next-generation smart display such as skin-attachable wearable electronics. However, the uniform light-conversion efficiency and light intensity of the display during its stretching are still challenging. Here, we present a stretchable and uniform full-color quantum dot display for wearable electronics. The quantum dot color based on a low modulus elastomer efficiently converts blue light into red and green light while being stretched. Light diffusion layer containing titania and zinc oxide nanoparticles spread the photons generated by a light-emitting diode to uniformly illuminate a unit pixel of the display. The light conversion and diffusion features were demonstrated by optical characterizations. The results present the potential of the stretchable and full-color quantum-dot display for a next-generation wearable electronicsì ì° ë° ì ì¥ì± ëì€íë ìŽë íŒë¶ì ë¶ì°© í ì ìë ìšìŽë¬ëž ì ì ì¥ì¹ì ê°ì ì°šìžë ì€ë§íž ëì€íë ìŽë¥Œ ê°ë°íêž° ìí íµì¬ì ìž êž°ì ì
ëë€. ê·žë¬ë ëì€íë ìŽê° ëìŽëë ëì ê· ìŒí êŽ íšìšì ì ì§íë ê²ì ì¬ì í êž°ì ì ìŽë €ììŽ ììµëë€. ìŽ ë
Œë¬žììë ìšìŽë¬ëž ì ì ì í 구ì±ì ìí ì ì¶ì± ìê³ ê· ìŒí í ì»¬ë¬ ììì ëì€íë ìŽë¥Œ ìê°í©ëë€. ë®ì 몚ëë¬ì€ë¥Œ ê°ì§ ìëŒì€í 뚞륌 êž°ë°ìŒë¡ í ììì ì íí°ë ëìŽëë©Žì ì²ìêŽì ì ì ë° ë
¹ìêŽìŒë¡ íšê³Œì ìŒë¡ ë³íí©ëë€. íìŽíëìì ì°í ìì° ëë
ž ì
ì륌 í¬íšíë êŽ íì°ìžµì ë°êŽ ë€ìŽì€ëìì ìì± ë ë¹ì íì°ììŒ ëì€íë ìŽì ëšì íœì
ì ê· ìŒíê² ì¡°ëª
í©ëë€. êŽ ë³í ë° íì° êž°ë¥ì êŽí ì뮬ë ìŽì
ì íµíŽ ì¶ê°ì ìŒë¡ ì
ìŠëììµëë€. ìŽ ë
Œë¬žì 결곌ë ì°šìžë ìšìŽë¬ëž ì ì ì íì ìí ì ì¥ì± ììì ëì€íë ìŽì ìì©íì ëí ì ì¬ë ¥ì 볎ì¬ì€ëë€.1. Introductionââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ 1
2. QD-SEBS nanocomposites as a color filterâââââââââââââââââââââââââââââââââââââââââââââââââââââ 5
2.1 Preparation and integration of QD-SEBS nanocompositesâââââââââââââââââââââââ 5
2.2 Optical characterization of a color filterâââââââââââââââââââââââââââââââââââââââââââââââââââââ 9
3. SEBS with titania and zinc oxide nanocomposites as a light diffusion layerââââ17
3.1 Fabrication of the light diffusion layerââââââââââââââââââââââââââââââââââââââââââââââââââââââ 19
3.2 Characterization of the light diffusion layerââââââââââââââââââââââââââââââââââââââââââââââ 22
3.3 Optical simulation of the light diffusion layerâââââââââââââââââââââââââââââââââââââââââââ 26
4. Electrode backplane with a serpentine structureâââââââââââââââââââââââââââââââââââââââââââââ 27
4.1 Fabrication of the electrode backplaneââââââââââââââââââââââââââââââââââââââââââââââââââââââ 27
4.2 Mechanical characterization of the stretchable backplaneââââââââââââââââââââââââ 29
5. Experimental Sectionââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ 31
6. Referencesââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ 33Maste
Consumption of Added Sugars and Lipid Profiles in Korean Population from a Cohort Study
Objectives : The purpose of the study was to examine the relationship between added sugar consumption and dyslipidemia.
Methods : Final study subjects consisted of 18,713 participants after the exclusion of participants with dyslipidemia or under lipid lowering medications at baseline. Added sugar levels were categorized into tertiles [men: Low <8.0 g, Middle: 8.0-21.9 g, High â¥22.0 g; women: Low <6.0 g, Middle 6.0-14.9 g, High â¥15.0 g]. Dyslipidemia was analyzed based on two of the most recent guidelines identified from the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) and the 2009 Korean Society of Lipidology and Atherosclerosis (KSLA). We used Kaplan-Meier and Cox proportional hazard models to estimate the hazard ratio (HR) with 95% confidence interval (CI) of dyslipidemia.
Results : High added sugar was associated with hypercholesteremia (HR, 1.22; 95% CI, 1.06-1.40), high LDL cholesterolemia (1.29; 1.13-1.48), and low HDL cholesterolemia (1.26; 1.10-1.44) based on the KSLA Standard in men. In women, the high added sugar was only related to the risk for hypercholesteremia (1.26; 1.07-1.49) based on the KSLA Standard. A similar trend was shown in both men and women with application of NCEP-ATP III standard.
Conclusion : In this study, an increase in added sugar consumption was associated with an increased risk of dyslipidemia in men. Additional studies assessing the association between cardiovascular and other diseases should be conducted in the future.ope
(Die)Reflexivitat der Zivilgesellschaft : Erforschung nach dem Ubergang von der Hegelschen burgerlichen Gesellschaft zur modernen Zivilgesellschaft
íìë
Œë¬ž(ìì¬)--ììžëíêµ ëíì :ì² í곌 ììì² íì ê³µ,2007.Maste
íšì¹ ê° ë¹ìšì ìŽì©íŽ ê°ì ë í볞ì ë°íìŒë¡í ì¬ëŒì§ ìŽë¯žì§ ì±ì°êž°
íìë
Œë¬ž (ìì¬)-- ììžëíêµ ëíì : ì늬곌íë¶, 2015. 2. ê°ëª
죌.Image inpainting is the art of modyng part of image or video that cannot
detectable by ordinary observer. There are two dierent classes of algorithms
for inpainting. Texture synthesis, and geometric partial dierential
equations(PDEs). We think that we don't have to make new color in image
so we only focus on nd the best matching pixels for each unknown pixels.
In this paper, we suggest a new method based on exemplar based inpainting.
The method is suggested by Criminisi et al. [1]. But its method takes a long
time, but also cannot give a good result for some cases. First, we analyze
deciency of Criminisi algorithm and then we oer a new algorithm. Our
algorithm uses a modied calculation for priority and then uses patch ratio
for nding the best candidates. For faster computing, we use window and
number of nonzero elements of candidates. Finally, among the candidates we
concern about gradients of the template.Abstract
1 Introduction
2 Inpainting Algorithm of Criminisi et al.
2.1 Motivaltion of the algorithm
2.1.1 Priority Calculation
2.1.2 Texture Synthesis
2.2 Criminisi Algorithm
3 Our Algorithm
3.1 Calculate priorities of exemplar blocks
3.2 Patch Ratio for Choosing Best Matching Block
3.3 Patch Energy term
4 Results
5 Conclusion
Abstract (in Korean)
Acknowledgement (in Korean)Maste
íë ê³µë¡ ì¥ê³Œ ì믌ì¬íì ì±ì°°ì±
ì€ëë ì믌ì¬íì ëí ìŽíŽë 묞íì ì¢
êµì ì ì¹ì 배겜ì ë°ëŒ ì ë§ë€ ë€ì±ë¡ë€. í¹í ë€ìí 첎í곌 íµì°°ìì ë¹ë¡¯ë ì믌ì¬í ëŽë¡ ë€ì ê°ê° ê³ ì í 맥ëœê³Œ ì§í ë€ìì ìŽíŽëìì ë ê·ž ììê° ë ë¶ëª
íŽì§ë€. ì¬ì ìŽ ê·žë¬íë¯ìŽ, ì€ëë ì믌ì¬í ê°ë
ê·ž ì첎ì ëí ê°ë
ì¬ì ·ìŽë
ì¬ì ì°êµ¬ë³Žë€ ì¬íí, ì ì¹í, ìì¬í ë±ê³Œ ê°ì ì€ìŠì ë¶ê³Œí묞ìì êŽë š ë
Œìê° ëì± íë°í ìŽì ë ê±°êž°ìì ì°Ÿì ì ìê² ë€. íì§ë§ 겜í곌íìì ì믌ì¬í ì°êµ¬ë ëë¡ ìŽë
ì¬ì ì°êµ¬ ì±ê³Œë¥Œ ì°žì¡°íê±°ë ì°êµ¬ì ì¶ë°ì ìŒë¡ì ìŽë
ì¬ì ìž ê°ë
ê·ëª
ì ëìì 구íê³€ íë€. ê·žë ë€ë©Ž ì² íì ìŽëŠìŒë¡ ìíëë ì믌ì¬íì êŽí ë
Œìë ìŽë€ ëŽì©ê³Œ íí륌 ê°ì¶ ì ììê¹? ì€ìŠí묞ì ê·žê²ê³Œ ëìŒíë€ë©Ž, ì ìš ì² íìŽëŒë ìŽëŠìŒë¡ êŽë š ì°êµ¬ë€ìŽ ìíë íìë ìì ê²ìŽë€. 죌ì ì ë°©ë² ë©Žìì ë€ë¥Žë€ë©Ž, ì² íì ì믌ì¬íì êŽë šíì¬ ë¬Žìì ìŽë»ê² í ì ìëì§ë¥Œ ë°íìŒ íë€
ìì ë€ê°í 곡í ê°ê³µì ìí ê°ìŽ ì 볎 ì°ì¶ ìê³ ëŠ¬ìŠ ì°êµ¬
íìë
Œë¬ž (ìì¬)-- ììžëíêµ ëíì : ì°ì
·조ì ê³µíë¶, 2012. 2. ì ì¢
ê³.ì 첎 곡ìžíì ì 첎 ë¶ìë³ë¡ ìì¥íì, 볌ë¡íì ë±ì ë€ìí íí륌 ëê³ ìë€. ìŽë°í 곡ìžíì ê°ê³µíêž° ìíŽì ìŽì ìë ì ì ìŒë¡ ììì
ìŒë¡ ìŽë£šìŽ ì¡ê³ ë³ëª©ê³µì ìŒë¡ ê°ì£Œëë ë¶ë¶ì ìëí íê³ ì ë§ì ë
žë ¥ìŽ ìŽë£šìŽì¡ê³ ê·ž 결곌 ì¬ê°í íìì ëë 곡ìžíì êŽí ê°ìŽ ì 볎ì°ì¶ìŽ ê°ë¥íŽì¡ë€.
íì§ë§ êž°ì¡Žì ê°ë°ë ê°ìŽ ì 볎 ì°ì¶ ìê³ ëŠ¬ìŠì ì¬ê°í ì 첎 곡ìžíìë§ ì ì©ëë íê³ì ìŽ ììë€. ìŒë°ì ìŒë¡ ì 첎 곡ìžíì ëë¶ë¶ ì¬ê°íìŒë¡ ë¶í ëì§ë§ ìŒê°íìŽë ì€ê°í ë° ì¡ê°íì íìì ëë ìì ë€ê°í íìì ì 첎 곡ìžíìŽ ê·ž ë¹ìšì ëì§ ìì§ë§ ì¡Žì¬íê³ ìë€. ìŽì 곡ìží ë¶í ê°ì ê³µì륌 ì€ìŽê³ ìëí ì°êµ¬ì ìì±ë륌 ëìŽê³ í¥í ìëí êž°ê³ì ëì
íšì ë°ëŒì ìì ë€ê°í 곡ìžíì ê°ìŽ ì 볎륌 ì°ì¶íë ìë¡ìŽ ìê³ ëŠ¬ìŠì ëí ì°êµ¬ê° íìíë€.
볞 ë
Œë¬žì êž°ì¡Žì ì¡Žì¬í ì¬ê°í 곡ìží ëìì ê°ìŽ ì 볎 ì°ì¶ ìê³ ëŠ¬ìŠì 묞ì ì ì íì
íê³ ìŽë¥Œ íŽê²°í ì ìë ìê³ ëŠ¬ìŠì ì ì©íì¬ ìë¡ê² ìì ë€ê°í 곡ìžíìì ê°ìŽ ì 볎륌 ì°ì¶í ì ìëë¡ ì°êµ¬ë¥Œ ì§ííë€. Ʞ볞ì ìŒë¡ í¬ê² ìž ê°ì§ êŽì ìì êž°ì¡Ž ê°ìŽ ì 볎 ì°ì¶ ìê³ ëŠ¬ìŠì íì¥íìë€. 첫짞ë¡, êž°ì¡Ž Manning[1980]ì Isometric Tree Mappingì ë°©ë²ì ì¬ê°í 곡멎ììë§ ì ì© ê°ë¥íì¬ 2ì°šì íë©ŽìŒë¡ ì ê°í ì ììë€. ìŽ ë¶ë¶ì íê³ì ì íŽê²°íêž° ìíŽ ë¥ì² íž[2006]ì ì°êµ¬ë¥Œ ì°žì¡°íì¬ ìë ë©ì¬ ìì±ì ì ì©í í¥ìë ìì 곡멎ì ìµì ê·Œì¬ ì ê°ë¥Œ ì§ííê³ ì ê· ê²©ì ììë¡ ííëìë ë¶ë¶ì ìë¡ê² ë¹ ì ê· ê²©ì ììë¡ ê³¡ë©Žì ì¬ìì±íì¬ ê³¡ë©Žì ê³¡ë¥ ê¹ì§ ê³ ë €í 2ì°šì ì ê° íë©Žì ì»ìë€. ë짞ë¡, ìŽë ê² ë¹ ì ê· ê²©ìë¡ ì¬ìì±í 곡멎ì ìì륌 ë° ëªšì늬(Half-Edge) ìë£êµ¬ì¡°ë¥Œ íµíì¬ ì ì¥íìë€. 곡멎ì ìì륌 íìí ì ììŽìŒ íë ê²ì í¥í ê°ìŽ ë°©í¥ì íìíŽìŒ íëë° ììŽì ë§€ì° ì€ìíë€. ê° ììì ìì¹í ê°ìŽì ììì ë³íë¥ ìëì§ì í¬êž°ì ë°©í¥ì ìììŒ ê°ìŽ ë°©í¥ì íìí ì ìëë° ìŽê²ì ê° ìììì ê°ìŽì ìŽ ìì¹íê³ ìë ìì ì 볎륌 ì ííê² ìììŒ ê°ë¥íêž° ë묞ìŽë€. ì
짞ë¡, êž°ì¡Ž ê°ìŽì ìì ê°ìŽ ë°©í¥ì 죌 굜í ë³íë¥ ìëì§ì ìì§í ë°©í¥ìŒë¡ ì§ííìëë° ìŽ ë¶ë¶ì ììŽì ê°ìŽ ë°©í¥ì ëë늌 í¹ì ë¹ì ìì ìž ìì§ììŽ ëªëª ê°ìŽì ìì ë°ê²¬ëìë€. ìŽë¬í 묞ì ì ì íŽê²°íêž° ìíŽ ìë¡ê² ë©ŽëŽ ë³íë¥ ìëì§ì 굜í ë³íë¥ ìëì§ì ë°©í¥ì 몚ë ê³ ë €íì¬ ê°ì¥ í©ëŠ¬ì ìž ê°ìŽ ë°©í¥ì íìíë ìì ë ìê³ ëŠ¬ìŠì ì ìíë€. ë§ì§ë§ìŒë¡ ìë¡ê² ê°ë°í ìì ë€ê°í 곡ìží ê°ìŽ ì 볎 ì°ì¶ ìê³ ëŠ¬ìŠì 결곌륌 ê°ìí íê³ êž°ì¡Ž ì¬ê°í 곡멎ì ì ì© ê°ë¥íìë ê°ìŽ ì 볎 ì°ì¶ ìê³ ëŠ¬ìŠê³Œì 결곌륌 ì€ì ëìŒí ì¬ê°í ì 첎 곡ìžíì ì ì©íì¬ ê·ž 결곌륌 ë¹êµíšìŒë¡ìš ê²ìŠíŽ 볎ìë€.Maste
A study on paradox of body mass index and waist hip ratio
ìí ë° ì§ë³êŽëŠ¬í곌/ìì¬[íêž]ìŽ ì°êµ¬ë 20ìž ìŽìì ëšë
37,425ëª
ì ëììŒë¡ 첎ì€, í€ë¥Œ ìŽì©í ìŒë°ì ìž ë¹ë§ì ì§íë¡ì BMIì í늬 ëë , ëë¶ ëë 륌 ìŽì©í ë³µë¶ ë¹ë§ì ì§íë¡ì WHR륌 ìŽì©íì¬ BMIì WHRì ë¶ìŒì¹ë¡ ì€ëª
ëë íëŒë
ì€ì ì°ë ¹ë³ ì ë³ë¥ (ê³ íì, ë¹ëšë³, ê³ ìœë ì€í
ë¡€íìŠ)ì ììë³Žê³ ìŽê²ìŽ BMI, WHRì êŽë šë ì§íì ì ë³ë¥ ì 죌ë ìí¥ì ììë³Žê³ ì íìë€.
ì°êµ¬ëììë BMIì WHRì ë°ëŒ 몚ë 9귞룹ìŒë¡ ë¶ë¥íìë€. ìŽë íëŒë
ì€ 1ì ë®ì BMIì ëì WHR, íëŒë
ì€ 2ë ëì BMIì ë®ì WHRë¡ ì ìíìë€.
1. íëŒë
ì€ 1ì ëšììì 3.1%, ì¬ììì 2.8%륌 ì°šì§íììŒë©° íëŒë
ì€ 2ë ëšë
ìì ê°ê° 3.3%, 2%륌 ì°šì§íë ê²ìŒë¡ ëíë¬ë€. ëí íëŒë
ì€ 1ì ì°ë ¹ìŽ ìŠê°íšì ë°ëŒ êžê²©í ìŠê°íë ê²ìŒë¡ ëíë¬ìŒë íëŒë
ì€ 2ë ì°ë ¹ìŽ ìŠê°íšì ë°ëŒ ê°ìíë ê²ìŒë¡ ëíë¬ë€.
2. BMIì WHRì ì¡°í©ì ë°ë¥ž ê³ íìì ì ë³ë¥ ì ëšìììë WHRì êŽê³ììŽ BMI tertileìŽ ê°ì¥ ëì 귞룹ìì ëìê³ ì¬ìììë BMI ì WHR tertileìŽ ëìì ëì 귞룹ìì ëê² ëíë¬ë€. BMIì WHRì ì¡°í©ì ë°ë¥ž ë¹ëšë³ì ì ë³ë¥ ì ëšìììë WHR tertileìŽ ê°ì¥ ëì 귞룹ìì ëìê³ ì¬ìììë BMI ì WHR tertileìŽ íšê» ëì 귞룹ìì ëê² ëíë¬ë€. BMIì WHRì ì¡°í©ì ë°ë¥ž ê³ ìœë ì€í
ë¡€íìŠì ì ë³ë¥ ì ëšë
몚ëìì BMI ì WHR tertileìŽ íšê» ëì 귞룹ìì ëê² ëíë¬ë€.
3. ë¹ë§ì§íìž BMI, WHRì êŽë šë ì§ë³ì ì ë³ë¥ ì BMIì WHRì íëŒë
ì€ë¥Œ ì ìžíì§ ìì 겜ì°ì ë¹íŽ íëŒë
ì€ë¥Œ ì ìží ê²œì° ìŠê° íë 겜í¥ì 볎ìë€. ìŽë BMI, WHRì êŽë šë ì§ë³ì ì ë³ë¥ ì íëŒë
ì€ë¥Œ ì ìžíì§ ìì ê²œì° ë®ê² ì¶ì ë ì ììì ëíëžë€.
ê²°ë¡ ì ìŒë¡ íêµìžìì ë¹ë§ë륌 ëíëŒ ë BMIì WHR 몚ëê° ì§íë¡ ì¬ì©ëìŽìŒ íë€. í¹í íêµìžìì ì°ë ¹ì ë°ëŒ ìŠê° 겜í¥ì 볎ìŽë íëŒë
ì€ 1(BMIê° ìê³ WHRê° í° ê²œì°)ì ë¹ë§ê³Œ êŽë šë ì§ë³ì ì ë³ë¥ ì íì
í ë ë°ëì ê³ ë €ëìŽìŒ í ê²ìŒë¡ ìê°ëë€.
[ì묞]Some individuals have shown the paradox phenomenon that BMI, WHR are not correlated. The purpose of this study was to assess the combination associations of BMI, WHR and with obesity- related disease(Hypertension, Diabetes Mellitus, Hypercholesterolemia). This study is a cross-sectional study and its subjects are 37,425 Korean( 20 years and over ). Subjects are classified into 9 Groups by BMI, WHR; Paradox 1( lowest tertiles of BMI and highest tertiles WHR), Paradox 2( highest tertiles of BMI and lo1. Paradox 1 in the lowest tertiles of BMI and highest tertiles WHR was 3.1%, 2.8% range in men and women ,Paradox 2 in the highest tertiles of BMI and lowest tertiles WHR was 3.3%, 2.0% range in men and women respectively. Paradox 1 rapidly increased by age but Paradox 2 declined by age.
2. Prevalence ratio of Hypertension by the combination association of BMI and WHR was strongly associated with BMI(general obesity) in men and was associated with both BMI and WHR in women.
Prevalence ratio of Diabetes mellitus by the combination association of BMI and WHR was strongly associated with WHR(abdominal obesity) in men and associated with both BMI and WHR in women.
Prevalence ratio of hypercholesterolemia by the combination association of BMI and WHR was strongly associated with all of the measures of obesity in all groups.
3. When paradox was excluded, prevalence of obesity related disease was increased compared with all other groups. If paradox was included prevalence of obesity-related diseases would be underestimated.
In conclusion, BMI and WHR have to be considered in obesity related indices together. Especially, it is important to use the above parameters together when one identifies the prevalence of obesity related diseases in paradox 1 increasing pattern by age.
west tertiles WHR).ope
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Œë¬ž (ìì¬) -- ììžëíêµ ëíì : ê³µíì 묞ëíì ìì©ê³µí곌, 2021. 2. ìì±í.A machine tool spindle has been increasingly used in robots together with the development of the robot industry. While the mobility of machines has been improved, a heavy spindle is a burdening factor for machine design. This limits the range of spindle application. Besides that, carbon composite has long been used in some weight-sensitive industries, but it has not been actively used in the machine tool field. Carbon composite has superior material properties in terms of weight and thermal characteristics. With the adoption of carbon composite in a conventional spindle, this study attempted weight lightening and improvement of shaft thermal behaviour. The purpose of this study is to see how carbon composite can be applied in a high-speed spindle and how it differs from conventional spindle design. A carbon composite-steel hybrid motorised spindle was manufactured, which has added carbon composite based on conventional spindle structure. The hybrid spindle was measured and compared with an equivalent specification conventional steel spindle, in terms of weight and shaft displacement under the same conditions. Its general performance was also tested. The results revealed that carbon composite made the spindle lighter by 23.7 to 32.1%, and shaft thermal stabilisation time significantly reduced by 41.3% as well as shaft displacement amount by 24.5%. This result implies that the use of carbon composite in high-speed spindle design enables weight saving, which delivers less load on machines and brings wider usage of the spindle.ì€íë€ì êž°ì¡Ž CNC ì¥ë¹ì 죌ì¶ìŒë¡ ì¬ì©ëìŽ ììŒë, ìµê·Œ ë¡ëŽì°ì
ì ë°ì ì ë°ëŒ ì€íë€ ìì ë¡ëŽì ì§ì ì¬ì©ìŽ ëìŽëê³ ìë€. ì¥ë¹ì ìŽëì±ìŽ 컀ì§ë ë°ë©Ž ì€íë€ì 묎ê²ë ì¥ë¹ì ì€ê³ì ë¶ëŽìŽ ëë ììë¡ ì€íë€ì ì ì©ë²ì륌 ì ííë ë¶ë¶ìŽë€. íìë³µí©ì¬ì ê²œì° ê²œëíê° ì€ìí ì¬ë¬ ì°ì
ìì ì€ë«ëì ì¬ì©ëìŽ ììŒë ê³µìêž°ê³ ë¶ìŒììë ë¹êµì íë°í ì¬ì©ëì§ ììë€. í¹í ë€ë¥ž ë¶í곌 ì ìŽìŽ ë§ì 구조륌 ê°ì§ ìŒë° ì€íë€ì ê²œì° ë§€ì° ì ì ì°êµ¬ë§ ìŽë£šìŽì ž ìë€. íìë³µí©ì¬ë êž°ì¡Ž ìì¬ë³Žë€ 묎ê²ì ìŽ ìž¡ë©Žìì ì°ìí ì¬ë£ì ì±ì§ì ê°ì§ê³ ìë€. ë°ëŒì, êž°ì¡Ž ì€íë€ì íìë³µí©ì¬ì ì ì©ì íµíŽ ì€íë€ì 겜ëíì ì€ííž ìŽ í¹ì± ê°ì ì ìëíê³ ì íìë€. 볞 ì°êµ¬ë íìë³µí©ì¬ê° ê³ ì ì€íë€ì ìŽë»ê² ì ì©ë ì ìëì§ ììë³Žê³ , ê·žê²ìŽ êž°ì¡Žì ì€íë€ê³Œ ìŽë€ ë€ë¥ž í¹ì±ì ë§ëëì§ë¥Œ ììë³Žê³ ì íë€. íìë³µí©ì¬-ì€íž íìŽëžëŠ¬ë ëªší° ì€íë€ìŽ êž°ì¡Ž ì€íë€ì 구조ì íìë³µí©ì¬ë¥Œ ëíì¬ ì€ê³, ì ìëìë€. íìë³µí©ì¬-ì€íž íìŽëžëŠ¬ë ì€íë€ì ì¬ë¬ í
ì€ížë¥Œ ê±°ì¹ê³ ëêž ì±ë¥ì êž°ì¡Ž ì€íž ì€íë€ê³Œ 묎ê², ì€ííž ë³ìŽì ìž¡ë©Žìì ëìŒ ì¡°ê±Žíì ë¹êµëìë€. ì€í결곌ë íìë³µí©ì¬ê° ì€íë€ì ìœ 23.7-32.1% ê°ë ê°ë³ê² ë§ë€ììŒë©°, ì€ííž ë³ìŽëì 24.5% ê°ìì ëë¶ìŽ ì€ííž ìŽ ë³ìŽ ìì í ìê°ìŽ 41.3% ê°ë íì í ì€ì ê²ì íìží ì ììë€. ìŽë¬í 결곌ë ê³ ì ì€íë€ ì€ê³ìì íìë³µí©ì¬ì ì¬ì©ìŽ ì€íë€ì 겜ëí륌 ê°ë¥ìŒ íšìŒë¡ìš ì¥ë¹ì ë¹êµì ì ì ë¶í륌 ì£Œê³ ì€íë€ì ì ì©ë²ì륌 ëí ì€ ì ììì ì믞íë€.Abstract i
I. Introduction 1
1.1 Background 1
1.2 Objectives and scope 2
1.3 Report organisation 3
II. Literature Review 4
III. Methodology 6
3.1 Research design 6
3.2 Spindle design 9
3.3 Carbon composite design 12
3.4 Measurement parameters 15
3.5 Measurement setup and test conditions 17
IV. Results 21
4.1 Weight reduction 21
4.2 Shaft thermal displacement and stabilisation 23
4.3 Shaft temperature 33
4.4 Other characteristics 37
4.4.1 Speed accuracy 37
4.4.2 Vibration 39
4.4.3 Spindle temperature 41
V. Implications 43
5.1 Interpretation of results 43
5.2 Limitations 44
VI. Conclusion 46
6.1 Summary and conclusion 46
6.2 Future directions 47
References 48
Abstract (Korean) 49Maste
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