44 research outputs found

    손λͺ© x-선을 ν™œμš©ν•œ λ―Έμˆ™μ•„ λŒ€μ‚¬μ„± 골 μ§ˆν™˜ 진단 λ”₯λŸ¬λ‹ λͺ¨λΈ ꡬ좕

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    ν•™μœ„λ…Όλ¬Έ(석사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : μ˜κ³ΌλŒ€ν•™ μ˜ν•™κ³Ό, 2022. 8. 김이경.Background: Metabolic bone disease (MBD) of prematurity is an important complication of prematurity and accurate diagnosis and timely intervention should be made for preterm infants. Objective: To develop a diagnostic tool for MBD of prematurity via deep learning by using wrist x-rays of preterm infants. Methods: Study enrolled preterm infants whose birth weight was less than 1500g born at Seoul National University Children’s Hospital and admitted to Neonatal Intensive Care Unit from 2010 to 2020. Demographic and clinical information as well as wrist x-rays taken between 4-8 weeks of postnatal age were collected retrospectively. Two types of regions of interests (β€˜ROI 0’ and β€˜ROI 1’) were annotated for deep learning model training. Demographic and clinical data was analyzed to determine the factors associated with MBD of prematurity, thus evaluating the representativeness of our study population. Wrist x-ray images were used to train and develop a diagnostic model via various deep learning algorithms, including AlexNet, DenseNet-121, ResNet-50, ResNext-50, VGG-19, CheXNet, and EfficientNet-b3. Results: Fourteen percent (116/814) of enrolled patients were diagnosed with MBD of prematurity between 4-8 weeks of postnatal age. Analysis of clinical information revealed that birth weight less than 1000g (82.8% vs. 37.5%, p<0.001), gestational age less than 28 weeks (75.0% vs. 29.5%, p<0.001), parenteral nutrition longer than or equal to 28 days (49.1% vs, 12.0%, p<0.001) were statistically significant risk factors of MBD of prematurity. These risk factors concurred with renowned risk factors of MBD, suggesting that our population could represent general preterm population and our ground truth is reliable. Deep learning models developed by EfficientNet-b3 and VGG-19 using β€˜ROI 0’ appeared to show the best quality of performance demonstrated by highest F1-score (0.844 for both models) and AUROC (0.962 for EfficientNet-b3 and 0.968 for VGG-19). β€˜ROI 0’ EfficientNet-b3 model and VGG-19 model both showed sensitivity of 0.907, specificity of 0.924, positive predictive value of 0.790, negative predictive value of 0.969, and accuracy of 0.915. Conclusion: Novel deep learning models to diagnose MBD of prematurity have been developed as a result. Our models showed sensitivity of 0.907, specificity of 0.924, and accuracy of 0.915. If applied to clinical settings, it would assist clinicians, especially for those who are novice, to detect MBD more accurately and conveniently, thereby enabling timely management to treat and prevent disease progression for preterm infants.μ„œλ‘ : λ―Έμˆ™μ•„ λŒ€μ‚¬μ„± κ³¨μ§ˆν™˜μ€ λ―Έμˆ™μ•„κ°€ κ²ͺλŠ” μ€‘μš”ν•œ 합병증 쀑 ν•˜λ‚˜λ‘œ μ •ν™•ν•œ 진단 및 μ μ ˆν•œ μ‹œμ μ—μ„œμ˜ 치료적 κ°œμž…μ΄ ν•„μš”ν•œ μ§ˆν™˜μ΄λ‹€. λͺ©μ : λ³Έ μ—°κ΅¬λŠ” λ―Έμˆ™μ•„ λŒ€μ‚¬μ„± κ³¨μ§ˆν™˜μ˜ 진단을 μš©μ΄ν•˜κ²Œ ν•˜κ³ μž 손λͺ© x-ray μ˜μƒ 정보λ₯Ό λ°”νƒ•μœΌλ‘œ λ―Έμˆ™μ•„ λŒ€μ‚¬μ„± κ³¨μ§ˆν™˜ 진단 λ”₯λŸ¬λ‹ λͺ¨λΈμ„ κ΅¬μΆ•ν•˜κ³ μž ν•œλ‹€. 방법: 2010λ…„λΆ€ν„° 2020λ…„ 사이에 μ„œμšΈλŒ€ν•™κ΅ μ–΄λ¦°μ΄λ³‘μ›μ—μ„œ 1500g 미만으둜 μΆœμƒν•œ λ―Έμˆ™μ•„λ“€ 쀑 μ‹ μƒμ•„μ€‘ν™˜μžμ‹€μ— μž…μ‹€ν•œ ν™˜μžλ“€μ„ λŒ€μƒμœΌλ‘œ 연ꡬ가 μ§„ν–‰λ˜μ—ˆλ‹€. 인ꡬ학적 정보, μž„μƒ 정보, 생후 4-8μ£Ό 사이에 촬영된 손λͺ© x-ray μ˜μƒλ“€μ€ν›„ν–₯적으둜 μˆ˜μ§‘λ˜μ—ˆλ‹€. λ”₯λŸ¬λ‹ λͺ¨λΈ ν•™μŠ΅μ„ μœ„ν•΄ 두 가지 관심 μ˜μ—­ (β€˜ROI 0’과 β€˜ROI 1’)의 μ–΄λ…Έν…Œμ΄μ…˜μ΄ μ™„λ£Œλ˜μ—ˆλ‹€. μž„μƒμ •λ³΄λŠ” λ―Έμˆ™μ•„ λŒ€μ‚¬μ„± κ³¨μ§ˆν™˜κ³Ό μ—°κ΄€λœ μΈμžλ“€μ„ λΆ„μ„ν•˜κ³ μž μ‚¬μš©λ˜μ—ˆκ³ , 이λ₯Ό 톡해 연ꡬ λͺ¨μ§‘λ‹¨μ˜ λŒ€ν‘œμ„±μ„ ν™•μΈν•˜κ³ μž ν•˜μ˜€λ‹€. μˆ˜μ§‘λœ 손λͺ© x-ray μ˜μƒμ€ λ”₯λŸ¬λ‹μ„ ν†΅ν•œ 진단 ν”„λ‘œκ·Έλž¨μ„ κ°œλ°œν•˜κΈ° μœ„ν•œ ν•™μŠ΅λ°μ΄ν„°λ‘œ μ‚¬μš©λ˜μ—ˆλ‹€. ν”„λ‘œκ·Έλž¨ κ°œλ°œμ„ μœ„ν•΄ AlexNet, DenseNet-121, ResNet-50, ResNext-50, VGG-19, CheXNet, EfficientNet-b3 λ”₯λŸ¬λ‹ architecture κ°€ μ‚¬μš©λ˜μ—ˆλ‹€. κ²°κ³Ό: λͺ¨μ§‘단 쀑 14.3% (116/814)κ°€ 생후 4-8μ£Ό 사이에 λ―Έμˆ™μ•„ λŒ€μ‚¬μ„± κ³¨μ§ˆν™˜μœΌλ‘œ μ§„λ‹¨λ˜μ—ˆλ‹€. 생후 4-8μ£Ό 이내에 ν•œ λ²ˆμ΄λΌλ„ 손λͺ© μ˜μƒμ—μ„œ λŒ€μ‚¬μ„± κ³¨μ§ˆν™˜μœΌλ‘œ μ§„λ‹¨λœ κ²½μš°μ™€ 그렇지 μ•Šμ€ 경우λ₯Ό 두 ꡰ으둜 λΉ„κ΅ν•˜μ˜€κ³ , μΆœμƒμ²΄μ€‘ 1000g 미만 (82.8% vs. 37.5%, p=0.000), μž¬νƒœμ£Όμˆ˜ 28μ£Ό 미만 (75.0% vs. 29.5%, p=0.000), μ •λ§₯μ˜μ–‘ 곡급 κΈ°κ°„ 28일 이상 (49.1% vs, 12.0%, p=0.000)이 μ§ˆν™˜μ„ κ²ͺ은 κ΅°μ—μ„œ μœ μ˜λ―Έν•˜κ²Œ 높은 λΉˆλ„μž„μ΄ ν™•μΈλ˜μ–΄, λŒ€μ‚¬μ„± κ³¨μ§ˆν™˜μ˜ μœ„ν—˜μΈμžλ‘œ ν™•μΈλ˜μ—ˆλ‹€. μ΄λŠ” 이미 잘 μ•Œλ €μ§„ λ―Έμˆ™μ•„ λŒ€μ‚¬μ„± κ³¨μ§ˆν™˜μ˜ μœ„ν—˜μΈμžμ™€ μΌμΉ˜ν•˜λ©°, 이λ₯Ό 톡해 λͺ¨μ§‘단이 일반적인 λ―Έμˆ™μ•„ 집단을 λŒ€ν‘œν•  수 μžˆμŒμ„ ν™•μΈν•˜μ˜€λ‹€. λ”λΆˆμ–΄ ν•™μŠ΅μ— μ‚¬μš©λœ ground truth의 신뒰도 λ˜ν•œ μž…μ¦ν•  수 μžˆμ—ˆλ‹€. β€˜ROI 0’을 μ΄μš©ν•˜μ—¬ EfficientNet-b3와 VGG-19λ₯Ό 톡해 κ°œλ°œν•œ 진단 λͺ¨λΈμ΄ κ°€μž₯ λ›°μ–΄λ‚œ μ„±λŠ₯을 λ‚˜νƒ€λ‚΄λ©°, μ΅œλŒ€κ°’μ˜ F1 μŠ€μ½”μ–΄ (0.844)와 AUROC κ°’ (EfficientNet-b3: 0.962, VGG-19: 0.968)을 λ³΄μ˜€λ‹€., 두 λͺ¨λΈμ˜ λ―Όκ°λ„λŠ” 0.907, νŠΉμ΄λ„λŠ” 0.924, μ–‘μ„± μ˜ˆμΈ‘λ„λŠ” 0.790, μŒμ„± μ˜ˆμΈ‘λ„λŠ” 0.969, μ •ν™•λ„λŠ” 0.915μ˜€λ‹€. κ²°λ‘ : λ³Έ 연ꡬλ₯Ό 톡해 λ―Έμˆ™μ•„ λŒ€μ‚¬μ„± κ³¨μ§ˆν™˜ 진단을 μœ„ν•œ λ”₯λŸ¬λ‹ λͺ¨λΈμ΄ κ°œλ°œλ˜μ—ˆκ³  λ―Όκ°λ„λŠ” 0.907, νŠΉμ΄λ„λŠ” 0.924, μ •ν™•λ„λŠ” 0.915이닀. ν–₯후에 μ΄λŸ¬ν•œ 진단기법이 μ‹€μ œ μž„μƒμ— μ μš©λœλ‹€λ©΄, νŠΉνžˆλ‚˜ μž„μƒκ²½λ ₯이 적은 μž„μƒμ˜μ˜ κ²½μš°μ—λ„ μ§ˆν™˜μ˜ 진단이 μ •ν™•ν•˜κ³  κ°„νŽΈν•˜κ²Œ μ΄λ£¨μ–΄μ§ˆ 수 μžˆμ„ κ²ƒμœΌλ‘œ μƒκ°ν•˜λ©°, 이λ₯Ό 톡해 치료 및 μ˜ˆλ°©μ„ μœ„ν•œ μ μ ˆν•œ κ°œμž…μ΄ κ°€λŠ₯ν•΄μ§ˆ κ²ƒμœΌλ‘œ κΈ°λŒ€ν•œλ‹€.Introduction 1 Material and methods 3 Results 8 Discussion 12 Conclusion 15 References 27 Abstract in Korean 29석

    μ‹œκ³΅κ°„ μ£Όμ˜μ§‘μ€‘μ„ κ°–λŠ” 이쀑 흐름 행동인식 신경망

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    ν•™μœ„λ…Όλ¬Έ(석사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :κ³΅κ³ΌλŒ€ν•™ 컴퓨터곡학뢀,2019. 8. μ „ν™”μˆ™.μ˜€λŠ˜λ‚  ν™œλ°œν•œ 심측 신경망 연ꡬ와 데이터 μ €μž₯ 및 처리 기술 λ°œλ‹¬λ‘œ 인해 이 미지 뿐만 μ•„λ‹ˆλΌ λΉ„λ””μ˜€μ™€ 같은 μ‹œκ°„ 흐름을 가진 λŒ€μš©λŸ‰ λ°μ΄ν„°μ—μ„œ λ‹€μ–‘ν•œ 인식 문제λ₯Ό μˆ˜ν–‰ν•˜λŠ” 연ꡬ가 λ”μš± 더 λ§Žμ€ 관심을 λ°›κ³  μžˆλ‹€. κ·Έ μ€‘μ—μ„œλ„ 이쀑 흐름 신경망은 처음으둜 신경망을 ν†΅ν•œ ν•™μŠ΅μ΄ 기쑴의 μˆ˜μž‘μ—…μœΌλ‘œ 뽑은 νŠΉμ§•λ³΄λ‹€ (hand- crafted features) 쒋은 μ„±λŠ₯을 보여쀀 μ΄ν›„λ‘œ, λΉ„λ””μ˜€ 행동 μΈμ‹μ—μ„œ μ£Όλ₯˜ μ•„ν‚€ν…μ³λ‘œ μžλ¦¬μž‘μ•˜λ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” ν•΄λ‹Ή 아킀텍쳐λ₯Ό ν™•μž₯ν•˜μ—¬ λΉ„λ””μ˜€μ—μ„œ λ™μž‘ 인식을 μœ„ν•΄ λ…λ¦½μ μœΌλ‘œ ν›ˆλ ¨λœ 이쀑 흐름 신경망에 μ‹œκ³΅κ°„ μ£Όμ˜μ§‘μ€‘μ„ μ£ΌλŠ” 아킀텍쳐λ₯Ό μ œμ•ˆν–ˆ λ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” cross attention을 톡해 기쑴의 독립적인 신경망에 μƒν˜Έ 보완적인 ν•™μŠ΅μœΌλ‘œ μ„±λŠ₯ ν–₯상을 μœ λ„ν–ˆλ‹€. HMDB-51의 ν‘œμ€€ λΉ„λ””μ˜€ 행동인식 벀치 λ§ˆν¬μ—μ„œ λ³Έ λ…Όλ¬Έμ˜ μ•„ν‚€ν…μ³μ˜ μ„±λŠ₯을 μ‹€ν—˜ν•˜μ˜€μœΌλ©°, 기쑴의 아킀텍쳐보닀 κ°œμ„ λœ μ„±λŠ₯을 얻을 수 μžˆμ—ˆλ‹€.Two-stream architecture has been mainstream since the success of [1], but two important information is processed independently and not interacted until the late fusion. We investigate a different spatio-temporal attention architecture based on two separate recognition streams (spatial and temporal), which interact with each other by cross attention. The spatial stream performs action recognition from still video frames, whilst the temporal stream is trained to recognise action from motion in the form of dense optical flow. Both streams convey their learned knowledge to the other stream in the form of attention maps. Cross attentions allow us to exploit the availability of supplemental information and enhance learning of the streams. To demonstrate the benefits of our proposed cross-stream spatio-temporal attention architecture, it has been evaluated on two standard action recognition benchmarks where it boosts the previous performance.μš” μ•½ 제 1 μž₯ μ„œλ‘  제 2 μž₯ κ΄€λ ¨ 연ꡬ 2.1 행동 μΈμ‹μ—μ„œμ˜ 이쀑 흐름 신경망 2.2 ν–‰λ™μΈμ‹μ—μ„œμ˜ 주의 집쀑(Attention) 제 3 μž₯ μ‹œκ³΅κ°„ μ£Όμ˜μ§‘μ€‘μ„ κ°–λŠ” 이쀑 흐름 행동인식 신경망 3.1 효과적인 μ£Όμ˜μ§‘μ€‘ μΆ”μΆœ 3.2 ν–‰λ™νŒ¨ν„΄ ν•™μŠ΅κ³Όμ • 제 4 μž₯ μ‹€ν—˜ 4.1 데이터셋과 κ΅¬ν˜„ 세뢀사항 4.2 μ„±λŠ₯ 비ꡐ 제 5 μž₯ κ²°λ‘  ABSTRACTMaste

    Timing of Admission to the Surgical Intensive Care Unit is Associated with in-Hospital Mortality

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    Purpose The relationship between the timing of admission (work-hours or after-hours) to the intensive care unit (ICU) and mortality among surgical ICU (SICU) patients is unclear. This study aimed to investigate whether admission to SICU during after-hours was associated with in-hospital mortality. Methods This retrospective cohort study was conducted in a tertiary academic hospital. The data of 571 patients who were admitted to the SICU and whose complete medical records were available were analyzed. Work-hours were defined as 07:00 to 19:00 Monday to Friday, during which the ICU was staffed with intensivists. After-hours were defined as any other time during which the SICU was not staffed with intensivists. The primary outcome measure was in-hospital mortality according to the time of admission (work-hours or after-hours) to the SICU. Results A total of 333 patients, were admitted to the SICU during work-hours, and 238 patients after-hours. Unplanned admissions (47.1% vs. 33.3%, p < 0.001), acute physiology and chronic health evaluation II score β‰₯ 25 (23.9% vs. 11.1%, p < 0.001), the need for ventilator support (34.0% vs. 17.4%, p < 0.001), and the use of vasopressors (50.0% vs. 33.3%, p < 0.001) were significantly higher in the after-hours group compared with the work-hours group. Multivariate analyses revealed that the timing of SICU admission was an independent predictor of in-hospital mortality (odds ratio, 2.526; 95% confidence interval, 1.010-6.320; p = 0.048). Conclusion This study showed that admission to the SICU during after-hours was associated with increased in-hospital mortality.ope

    Coexistence of chronic lymphocytic thyroiditis with papillary thyroid carcinoma: clinical manifestation and prognostic outcome

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    The study aimed to identify the clinical characteristics of coexisting chronic lymphocytic thyroiditis (CLT) in papillary thyroid carcinoma (PTC) and to evaluate the influence on prognosis. A total of 1,357 patients who underwent thyroid surgery for PTC were included. The clinicopathological characteristics were identified. Patients who underwent total thyroidectomy (n = 597) were studied to evaluate the influence of coexistent CLT on prognosis. Among the total 1,357 patients, 359 (26.5%) had coexistent CLT. In the CLT group, the prevalence of females was higher than in the control group without CLT (P < 0.001). Mean tumor size and mean age in the patients with CLT were smaller than without CLT (P = 0.040, P = 0.047, respectively). Extrathyroidal extension in the patients with CLT was significantly lower than without CLT (P = 0.016). Among the subset of 597 patients, disease-free survival rate in the patients with CLT was significantly higher than without CLT (P = 0.042). However, the multivariate analysis did not reveal a negative association between CLT coexistence and recurrence. Patients with CLT display a greater female preponderance, smaller size, younger and lower extrathyroidal extension. CLT is not a significant independent negative predictive factor for recurrence, although presence of CLT indicates a reduced risk of recurrence.ope

    Exploring science learning using smartphones in science museums: Focused on the feature of scaffolding

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ³Όν•™κ΅μœ‘κ³Ό(지ꡬ과학전곡), 2013. 2. κΉ€μ°¬μ’….λŒ€ν‘œμ μΈ λΉ„ν˜•μ‹ ν•™μŠ΅ 기관인 κ³Όν•™λ°•λ¬Όκ΄€μ—μ„œλŠ” μ „μ‹œλ¬Όμ— λŒ€ν•œ κ΄€λžŒκ°μ˜ 이해λ₯Ό 돕기 μœ„ν•΄ 라벨, ν™œλ™μ§€, λ„μŠ¨νŠΈ, νœ΄λŒ€ μ•ˆλ‚΄κΈ°κΈ° λ“±μ˜ λ‹€μ–‘ν•œ 보쑰 κ΄€λžŒ μˆ˜λ‹¨μ„ μ œκ³΅ν•˜κ³  μžˆλ‹€. κ·Έ μ€‘μ—μ„œλ„ μ˜€λ””μ˜€ κ°€μ΄λ“œ, PDA, 슀마트폰 λ“±μ˜ νœ΄λŒ€ μ•ˆλ‚΄κΈ°κΈ°λŠ” 졜근 λ“€μ–΄ λ§Žμ€ 관심을 λ°›κ³  μžˆλ‹€. 연ꡬ에 λ”°λ₯΄λ©΄ νœ΄λŒ€ μ•ˆλ‚΄κΈ°κΈ°λ₯Ό μ΄μš©ν•œ κ΄€λžŒμ€ 깊이 있고 λ‹€μ–‘ν•œ μ’…λ₯˜μ˜ 해석을 μ œκ³΅ν•¨μœΌλ‘œμ¨ κ΄€λžŒκ°λ“€μ΄ 더 λ§Žμ€ ν•™μŠ΅ κ²½ν—˜μ„ ν•˜κ³ , 더 κΉŠμ€ μ°¨μ›μ˜ 이해와 λΉ„νŒμ  사고λ₯Ό ν•˜κ²Œ ν•΄μ€€λ‹€. λ˜ν•œ κ΄€λžŒκ° μžμ‹ μ˜ λ°°κ²½κ³Ό 더 λ§Žμ€ μ—°κ²° 고리λ₯Ό λ§Œλ“€μ–΄ μ£Όμ–΄, 개인적 ν•™μŠ΅(personal learning)을 μ΄‰μ§„μ‹œν‚¬ 수 μžˆλ‹€. λ³Έ μ—°κ΅¬μ—μ„œλŠ” μ΄λŸ¬ν•œ νœ΄λŒ€ μ•ˆλ‚΄κΈ°κΈ° 쀑 졜근 μ£Όλͺ©λ°›κ³  μžˆλŠ” μŠ€λ§ˆνŠΈν°μ— μ΄ˆμ μ„ λ§žμΆ”μ–΄ μ΄ˆγ†μ€‘λ“±ν•™μƒλ“€μ„ λŒ€μƒμœΌλ‘œ μŠ€λ§ˆνŠΈν°μ„ μ΄μš©ν•œ κ³Όν•™λ°•λ¬Όκ΄€μ—μ„œμ˜ κ³Όν•™ ν•™μŠ΅μ„ λΆ„μ„ν•˜μ˜€λ‹€.β… . μ„œλ‘  1. μ—°κ΅¬μ˜ ν•„μš”μ„± 및 λͺ©μ  2. 연ꡬ 문제 β…‘. 이둠적 λ°°κ²½ 1. λΉ„ν˜•μ‹ κ³Όν•™ν•™μŠ΅ 1) λΉ„ν˜•μ‹ ν•™μŠ΅κ³Ό λΉ„ν˜•μ‹ κ³Όν•™ν•™μŠ΅ 2) λ°•λ¬Όκ΄€μ—μ„œμ˜ λΉ„ν˜•μ‹ κ³Όν•™ν•™μŠ΅ 2. λΉ„ν˜•μ‹ κ³Όν•™κ΅μœ‘μ— λŒ€ν•œ μ‚¬νšŒλ¬Έν™”μ  μ ‘κ·Ό 1) λΉ„ν˜•μ‹ κ³Όν•™κ΅μœ‘ μ—°κ΅¬μ—μ„œμ˜ μ‚¬νšŒλ¬Έν™”μ  μ ‘κ·Όμ˜ ν•„μš”μ„± 2) λΉ„κ³ μΈ ν‚€μ˜ ν•™μŠ΅μ— λŒ€ν•œ 관점과 μŠ€μΊν΄λ”© 3. κ³Όν•™λ°•λ¬Όκ΄€μ—μ„œ μŠ€μΊν΄λ”© 맀체둜써의 슀마트폰 1) λͺ¨λ°”일 κΈ°κΈ°λ₯Ό μ΄μš©ν•œ κ³Όν•™κ΄€μ—μ„œμ˜ ν•™μŠ΅ 2) μŠ€λ§ˆνŠΈν°μ„ μ΄μš©ν•œ κ³Όν•™κ΄€μ—μ„œμ˜ ν•™μŠ΅ β…’. 연ꡬ방법 1. 전체 연ꡬ과정 2. μ„€λ¬Έ 쑰사 3. ν˜„μž₯연ꡬ β…£. 연ꡬ κ²°κ³Ό 1. κ΄€λžŒκ°λ“€μ΄ κΈ°λŒ€ν•˜λŠ” κ³Όν•™λ°•λ¬Όκ΄€μ—μ„œ 슀마트폰의 μŠ€μΊν΄λ”© 속성 2. 그룹의 νŠΉμ„±μ— 따라 λ‚˜νƒ€λ‚˜λŠ” 슀마트폰의 μŠ€μΊν΄λ”© 속성 3. 개인의 νŠΉμ„±μ— 따라 λ‚˜νƒ€λ‚˜λŠ” μŠ€μΊν΄λ”© β…€. κ²°λ‘  및 μ œμ–Έ 1. κ²°λ‘  2. μ œμ–Έ μ°Έκ³ λ¬Έν—Œ 뢀둝Maste

    λ‚˜λ₯Ό ν–‰λ³΅ν•˜κ²Œ λ§Œλ“œλŠ” μ‚¬λžŒμ€ λˆ„κ΅¬μΈκ°€

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : 심리학과, 2013. 2. 졜인철.μ‚¬νšŒμ  μ˜μ—­μ—μ„œμ˜ μ ‘κ·Ό λͺ©ν‘œ(approach goal)와 νšŒν”Ό λͺ©ν‘œ(avoidance goal)λŠ” 행볡에 μ€‘μš”ν•œ 영ν–₯을 λ―ΈμΉœλ‹€. λ°”λžŒμ§ν•œ κ²°κ³Όλ₯Ό μΆ”κ΅¬ν•˜λŠ” 것에 μ΄ˆμ μ„ λ‘λŠ” μ ‘κ·Ό λͺ©ν‘œλŠ” 행볡에 긍정적인 영ν–₯을 λ―ΈμΉ˜μ§€λ§Œ, 뢀정적인 κ²°κ³Όλ₯Ό ν”Όν•˜λŠ” 것에 μ΄ˆμ μ„ λ‘λŠ” νšŒν”Ό λͺ©ν‘œλŠ” 행볡에 뢀정적인 영ν–₯을 μ€€λ‹€(Elliot et al., 2006). μ΄λŸ¬ν•œ λͺ©ν‘œμ˜ μ€‘μš”ν•œ νŠΉμ§• 쀑 ν•˜λ‚˜λŠ” μ€‘μš”ν•œ 타인(significant others)에 μ˜ν•΄μ„œ 유발될 수 μžˆλ‹€λŠ” 것이닀. κ·ΈλŸ¬λ‚˜ κΈ°μ‘΄ μ—°κ΅¬μ—μ„œλŠ” μ ‘κ·Ό λͺ©ν‘œμ™€ νšŒν”Ό λͺ©ν‘œμ™€ μ‚¬νšŒμ  κ²°κ³Ό(social outcome)의 κ΄€κ³„λŠ” ν™œλ°œνžˆ λ‹€λ€„μ™”μ§€λ§Œ 타인에 μ˜ν•΄ μœ λ„λ˜λŠ” μ ‘κ·Ό λͺ©ν‘œμ™€ νšŒν”Ό λͺ©ν‘œ, ν–‰λ³΅μ˜ 관계에 λŒ€ν•΄ 관심을 보인 μ—°κ΅¬λŠ” 거의 μ—†μ—ˆλ‹€. λ³Έ μ—°κ΅¬λŠ” μ‚¬λžŒλ“€μ„ ν–‰λ³΅ν•˜κ²Œ λ§Œλ“œλŠ” 타인은 μ ‘κ·Ό λͺ©ν‘œλ₯Ό μœ λ„ν•˜λŠ” μ‚¬λžŒμ΄κ³ , λ‚˜μ•„κ°€ ν–‰λ³΅ν•œ κ΄€κ³„λž€ μ ‘κ·Ό λͺ©ν‘œκ°€ μœ λ„λ˜λŠ” κ΄€κ³„μž„μ„ 밝히고자 ν•œλ‹€. 이λ₯Ό μœ„ν•΄ μ„Έ 가지 연ꡬλ₯Ό μˆ˜ν–‰ν•œ κ²°κ³Ό, μ‚¬λžŒλ“€μ€ μžμ‹ μ„ ν–‰λ³΅ν•˜κ²Œ λ§Œλ“œλŠ” μ‚¬λžŒμ„ λ– μ˜¬λ¦΄ λ•Œ μ ‘κ·Ό λͺ©ν‘œλ₯Ό νšŒν”Ό λͺ©ν‘œλ³΄λ‹€ 더 많이 λ– μ˜¬λ ΈμœΌλ©°(연ꡬ 1), μžμ‹ μ„ 더 ν–‰λ³΅ν•˜κ²Œ λ§Œλ“œλŠ” μ‚¬λžŒμ„ λ– μ˜¬λ¦΄ λ•Œ μƒλŒ€μ μœΌλ‘œ 덜 ν–‰λ³΅ν•˜κ²Œ λ§Œλ“œλŠ” μ‚¬λžŒμ„ λ– μ˜¬λ¦΄ λ•Œλ³΄λ‹€ μ ‘κ·Ό λͺ©ν‘œλ₯Ό 더 많이 λ– μ˜¬λ Έλ‹€(연ꡬ 2). λ˜ν•œ μ ‘κ·Ό λͺ©ν‘œλ₯Ό νšŒν”Ό λͺ©ν‘œλ³΄λ‹€ 더 많이 λ– μ˜¬λ¦΄μˆ˜λ‘ 관계 λ§Œμ‘±λ„κ°€ 더 λ†’μ•˜κ³ , μ΄λŠ” 7κ°œμ›” ν›„μ˜ 관계 λ§Œμ‘±λ„μ—λ„ 영ν–₯을 μ£Όμ—ˆλ‹€(연ꡬ 3). λ³Έ 연ꡬλ₯Ό 톡해 μ‚¬λžŒλ“€μ„ ν–‰λ³΅ν•˜κ²Œ ν•˜λŠ” 타인은 μ ‘κ·Ό λͺ©ν‘œλ₯Ό μœ λ„ν•˜λŠ” μ‚¬λžŒμž„μ„ 확인할 수 μžˆμ„ 뿐만 μ•„λ‹ˆλΌ, ν–‰λ³΅ν•œ κ΄€κ³„μ—μ„œ μ‚¬λžŒλ“€μ€ μ ‘κ·Ό λͺ©ν‘œλ₯Ό 더 많이 λ– μ˜¬λ¦°λ‹€λŠ” 것을 확인할 수 μžˆμ—ˆλ‹€.μ„œ λ‘  1 연ꡬ 1 12 방법 13 κ²°κ³Ό 및 λ…Όμ˜ 16 연ꡬ 2 21 방법 22 κ²°κ³Ό 및 λ…Όμ˜ 24 연ꡬ 3 27 방법 28 κ²°κ³Ό 및 λ…Όμ˜ 30 μ’…ν•© λ…Όμ˜ 35 μ°Έκ³ λ¬Έν—Œ 43 뢀둝 49 Abstract 52Maste

    Aesthetics of the Grotesque and Its Ontological Meanings

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    When we confront grotesque objects, we are struck with unpleasant feelings, and then we generally find out their essential feature is integration of disparate things. Overwhelmed with the feelings and unintelligibility, we cannot represent them as anything. Consequently, they are outside our conceptual understanding: we always fail when we attempt to reduce them to something familar to our way of understanding. It is because our cognitive operation to represent them is nothing more than forcible operation to keep them within territories of our understanding. Therefore, there remain only two ways to manage to think the grotesque: one is to conceive them as ridiculous examples of sub-culture, and the other is to consider them as exceptions to history of human thoughts. However, it is obvious that we cannot explain why aesthetics of the grotesque have been maintained and developed throughout the whole passage of art history. Why has the grotesque continued despite of that kind of ceaseless aesthetic persecution? This is the primary question of this paper and I want to show what significance we can draw from aesthetics of the grotesque. In this paper, I attempt to establish the ontology on which aesthetic phenomenon called the grotesque can stand. In Gilles Deleuze's speculations on aesthetics, we can find out impersonal anonymity is playing key role in the thinker's articulation of aesthetic. I take this concept into my conceptualization of the grotesque because the concept is similar to dogmas of aesthetics of the grotesque. From his own theory about aesthetics, Deleuze constructed philosophy of difference, which provides the theoretical ground for his unique criticism on legacy of traditional Western metaphysics. With understanding about Deleuze's metaphysical discussion, I can contrive ontologically systematic analysis of the grotesque. Along the development of my discussion, aesthetics of the grotesque is established in the context of history of thought for the first time. To achieve this goal, I review historical evaluations of the grotesque, and then, explain the status occupied by the grotesque in system of human understanding. Through the passage of this kind of examination, I avoid rushing into clear definition of the grotesque, but I choose to analyze the sentiment of unpleasantness delivered to human receptivity at the sight of grotesque objects. In this way, I can fairly reveal aesthetic meanings of the grotesque because the effects of the grotesque can keep their possibilities

    전봉건 μ‹œλ‘ μ— μžˆμ–΄μ„œ μ‹œμ˜ ν˜„λŒ€μ„±

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