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    ๋ฌด์ธ๋น„ํ–‰์ฒด ํƒ‘์žฌ ์—ดํ™”์ƒ ๋ฐ ์‹คํ™”์ƒ ์ด๋ฏธ์ง€๋ฅผ ํ™œ์šฉํ•œ ์•ผ์ƒ๋™๋ฌผ ํƒ์ง€ ๊ฐ€๋Šฅ์„ฑ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ์กฐ๊ฒฝํ•™๊ณผ, 2022.2. ์†ก์˜๊ทผ.์•ผ์ƒ๋™๋ฌผ์˜ ํƒ์ง€์™€ ๋ชจ๋‹ˆํ„ฐ๋ง์„ ์œ„ํ•ด, ํ˜„์žฅ ์ง์ ‘ ๊ด€์ฐฐ, ํฌํš-์žฌํฌํš๊ณผ ๊ฐ™์€ ์ „ํ†ต์  ์กฐ์‚ฌ ๋ฐฉ๋ฒ•์ด ๋‹ค์–‘ํ•œ ๋ชฉ์ ์œผ๋กœ ์ˆ˜ํ–‰๋˜์–ด์™”๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•๋“ค์€ ๋งŽ์€ ์‹œ๊ฐ„๊ณผ ์ƒ๋Œ€์ ์œผ๋กœ ๋น„์‹ผ ๋น„์šฉ์ด ํ•„์š”ํ•˜๋ฉฐ, ์‹ ๋ขฐ ๊ฐ€๋Šฅํ•œ ํƒ์ง€ ๊ฒฐ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด์„  ์ˆ™๋ จ๋œ ํ˜„์žฅ ์ „๋ฌธ๊ฐ€๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๊ฒŒ๋‹ค๊ฐ€, ์ „ํ†ต์ ์ธ ํ˜„์žฅ ์กฐ์‚ฌ ๋ฐฉ๋ฒ•์€ ํ˜„์žฅ์—์„œ ์•ผ์ƒ๋™๋ฌผ์„ ๋งˆ์ฃผ์น˜๋Š” ๋“ฑ ์œ„ํ—˜ํ•œ ์ƒํ™ฉ์— ์ฒ˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ, ์นด๋ฉ”๋ผ ํŠธ๋ž˜ํ•‘, GPS ์ถ”์ , eDNA ์ƒ˜ํ”Œ๋ง๊ณผ ๊ฐ™์€ ์›๊ฒฉ ์กฐ์‚ฌ ๋ฐฉ๋ฒ•์ด ๊ธฐ์กด์˜ ์ „ํ†ต์  ์กฐ์‚ฌ๋ฐฉ๋ฒ•์„ ๋Œ€์ฒดํ•˜๋ฉฐ ๋”์šฑ ๋นˆ๋ฒˆํžˆ ์‚ฌ์šฉ๋˜๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•๋“ค์€ ์—ฌ์ „ํžˆ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ๋Œ€์ƒ์˜ ์ „์ฒด ๋ฉด์ ๊ณผ, ๊ฐœ๋ณ„ ๊ฐœ์ฒด๋ฅผ ํƒ์ง€ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ํ•œ๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด, ๋ฌด์ธ๋น„ํ–‰์ฒด (UAV, Unmanned Aerial Vehicle)๊ฐ€ ์•ผ์ƒ๋™๋ฌผ ํƒ์ง€์˜ ๋Œ€์ค‘์ ์ธ ๋„๊ตฌ๋กœ ์ž๋ฆฌ๋งค๊น€ํ•˜๊ณ  ์žˆ๋‹ค. UAV์˜ ๊ฐ€์žฅ ํฐ ์žฅ์ ์€, ์„ ๋ช…ํ•˜๊ณ  ์ด˜์ด˜ํ•œ ๊ณต๊ฐ„ ๋ฐ ์‹œ๊ฐ„ํ•ด์ƒ๋„์™€ ํ•จ๊ป˜ ์ „์ฒด ์—ฐ๊ตฌ ์ง€์—ญ์— ๋Œ€ํ•œ ๋™๋ฌผ ํƒ์ง€๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด์— ๋”ํ•ด, UAV๋ฅผ ์‚ฌ์šฉํ•จ์œผ๋กœ์จ, ์ ‘๊ทผํ•˜๊ธฐ ์–ด๋ ค์šด ์ง€์—ญ์ด๋‚˜ ์œ„ํ—˜ํ•œ ๊ณณ์— ๋Œ€ํ•œ ์กฐ์‚ฌ๊ฐ€ ๊ฐ€๋Šฅํ•ด์ง„๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ์ด์  ์™ธ์—, UAV์˜ ๋‹จ์ ๋„ ๋ช…ํ™•ํžˆ ์กด์žฌํ•œ๋‹ค. ๋Œ€์ƒ์ง€, ๋น„ํ–‰ ์†๋„ ๋ฐ ๋†’์ด ๋“ฑ๊ณผ ๊ฐ™์ด UAV๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ํ™˜๊ฒฝ์— ๋”ฐ๋ผ, ์ž‘์€ ๋™๋ฌผ, ์šธ์ฐฝํ•œ ์ˆฒ์†์— ์žˆ๋Š” ๊ฐœ์ฒด, ๋น ๋ฅด๊ฒŒ ์›€์ง์ด๋Š” ๋™๋ฌผ์„ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์ด ์ œํ•œ๋œ๋‹ค. ๋˜ํ•œ, ๊ธฐ์ƒํ™˜๊ฒฝ์— ๋”ฐ๋ผ์„œ๋„ ๋น„ํ–‰์ด ๋ถˆ๊ฐ€ํ•  ์ˆ˜ ์žˆ๊ณ , ๋ฐฐํ„ฐ๋ฆฌ ์šฉ๋Ÿ‰์œผ๋กœ ์ธํ•œ ๋น„ํ–‰์‹œ๊ฐ„์˜ ์ œํ•œ๋„ ์กด์žฌํ•œ๋‹ค. ํ•˜์ง€๋งŒ, ์ •๋ฐ€ํ•œ ํƒ์ง€๊ฐ€ ๋ถˆ๊ฐ€๋Šฅํ•˜๋”๋ผ๋„, ์ด์™€ ๊ด€๋ จ ์—ฐ๊ตฌ๊ฐ€ ๊พธ์ค€ํžˆ ์ˆ˜ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ์„ ํ–‰์—ฐ๊ตฌ๋“ค์€ ์œก์ƒ ๋ฐ ํ•ด์ƒ ํฌ์œ ๋ฅ˜, ์กฐ๋ฅ˜, ๊ทธ๋ฆฌ๊ณ  ํŒŒ์ถฉ๋ฅ˜ ๋“ฑ์„ ํƒ์ง€ํ•˜๋Š” ๋ฐ์— ์„ฑ๊ณตํ•˜์˜€๋‹ค. UAV๋ฅผ ํ†ตํ•ด ์–ป์–ด์ง€๋Š” ๊ฐ€์žฅ ๋Œ€ํ‘œ์ ์ธ ๋ฐ์ดํ„ฐ๋Š” ์‹คํ™”์ƒ ์ด๋ฏธ์ง€์ด๋‹ค. ์ด๋ฅผ ์‚ฌ์šฉํ•ด ๋จธ์‹ ๋Ÿฌ๋‹ ๋ฐ ๋”ฅ๋Ÿฌ๋‹ (ML-DL, Machine Learning and Deep Learning) ๋ฐฉ๋ฒ•์ด ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์€ ์ƒ๋Œ€์ ์œผ๋กœ ์ •ํ™•ํ•œ ํƒ์ง€ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ์ง€๋งŒ, ํŠน์ • ์ข…์„ ํƒ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋ธ์˜ ๊ฐœ๋ฐœ์„ ์œ„ํ•ด์„  ์ตœ์†Œํ•œ ์ฒœ ์žฅ์˜ ์ด๋ฏธ์ง€๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์‹คํ™”์ƒ ์ด๋ฏธ์ง€ ์™ธ์—๋„, ์—ดํ™”์ƒ ์ด๋ฏธ์ง€ ๋˜ํ•œ UAV๋ฅผ ํ†ตํ•ด ํš๋“ ๋  ์ˆ˜ ์žˆ๋‹ค. ์—ดํ™”์ƒ ์„ผ์„œ ๊ธฐ์ˆ ์˜ ๊ฐœ๋ฐœ๊ณผ ์„ผ์„œ ๊ฐ€๊ฒฉ์˜ ํ•˜๋ฝ์€ ๋งŽ์€ ์•ผ์ƒ๋™๋ฌผ ์—ฐ๊ตฌ์ž๋“ค์˜ ๊ด€์‹ฌ์„ ์‚ฌ๋กœ์žก์•˜๋‹ค. ์—ดํ™”์ƒ ์นด๋ฉ”๋ผ๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด ๋™๋ฌผ์˜ ์ฒด์˜จ๊ณผ ์ฃผ๋ณ€ํ™˜๊ฒฝ๊ณผ์˜ ์˜จ๋„ ์ฐจ์ด๋ฅผ ํ†ตํ•ด ์ •์˜จ๋™๋ฌผ์„ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค. ํ•˜์ง€๋งŒ, ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ๊ฐ€ ์‚ฌ์šฉ๋˜๋”๋ผ๋„, ์—ฌ์ „ํžˆ ML-DL ๋ฐฉ๋ฒ•์ด ๋™๋ฌผ ํƒ์ง€์— ์ฃผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•์€ UAV๋ฅผ ํ™œ์šฉํ•œ ์•ผ์ƒ๋™๋ฌผ์˜ ์‹ค์‹œ๊ฐ„ ํƒ์ง€๋ฅผ ์ œํ•œํ•œ๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์—ดํ™”์ƒ๊ณผ ์‹คํ™”์ƒ ์ด๋ฏธ์ง€๋ฅผ ํ™œ์šฉํ•œ ๋™๋ฌผ ์ž๋™ ํƒ์ง€ ๋ฐฉ๋ฒ•์˜ ๊ฐœ๋ฐœ๊ณผ, ๊ฐœ๋ฐœ๋œ ๋ฐฉ๋ฒ•์ด ์ด์ „ ๋ฐฉ๋ฒ•๋“ค์˜ ํ‰๊ท  ์ด์ƒ์˜ ์ •ํ™•๋„์™€ ํ•จ๊ป˜ ํ˜„์žฅ์—์„œ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค.For wildlife detection and monitoring, traditional methods such as direct observation and capture-recapture have been carried out for diverse purposes. However, these methods require a large amount of time, considerable expense, and field-skilled experts to obtain reliable results. Furthermore, performing a traditional field survey can result in dangerous situations, such as an encounter with wild animals. Remote monitoring methods, such as those based on camera trapping, GPS collars, and environmental DNA sampling, have been used more frequently, mostly replacing traditional survey methods, as the technologies have developed. But these methods still have limitations, such as the lack of ability to cover an entire region or detect individual targets. To overcome those limitations, the unmanned aerial vehicle (UAV) is becoming a popular tool for conducting a wildlife census. The main benefits of UAVs are able to detect animals remotely covering a wider region with clear and fine spatial and temporal resolutions. In addition, by operating UAVs investigate hard to access or dangerous areas become possible. However, besides these advantages, the limitations of UAVs clearly exist. By UAV operating environments such as study site, flying height or speed, the ability to detect small animals, targets in the dense forest, tracking fast-moving animals can be limited. And by the weather, operating UAV is unable, and the flight time is limited by the battery matters. Although detailed detection is unavailable, related researches are developing and previous studies used UAV to detect terrestrial and marine mammals, avian and reptile species. The most common type of data acquired by UAVs is RGB images. Using these images, machine-learning and deep-learning (MLโ€“DL) methods were mainly used for wildlife detection. MLโ€“DL methods provide relatively accurate results, but at least 1,000 images are required to develop a proper detection model for specific species. Instead of RGB images, thermal images can be acquired by a UAV. The development of thermal sensor technology and sensor price reduction has attracted the interest of wildlife researchers. Using a thermal camera, homeothermic animals can be detected based on the temperature difference between their bodies and the surrounding environment. Although the technology and data are new, the same MLโ€“DL methods were typically used for animal detection. These ML-DL methods limit the use of UAVs for real-time wildlife detection in the field. Therefore, this paper aims to develop an automated animal detection method with thermal and RGB image datasets and to utilize it under in situ conditions in real-time while ensuring the average-above detection ability of previous methods.Abstract I Contents IV List of Tables VII List of Figures VIII Chapter 1. Introduction 1 1.1 Research background 1 1.2 Research goals and objectives 10 1.2.1 Research goals 10 1.2.2 Research objectives 11 1.3 Theoretical background 13 1.3.1 Concept of the UAV 13 1.3.2 Concept of the thermal camera 13 Chapter 2. Methods 15 2.1 Study site 15 2.2 Data acquisition and preprocessing 16 2.2.1 Data acquisition 16 2.2.2 RGB lens distortion correction and clipping 19 2.2.3 Thermal image correction by fur color 21 2.2.4 Unnatural object removal 22 2.3 Animal detection 24 2.3.1 Sobel edge creation and contour generation 24 2.3.2 Object detection and sorting 26 Chapter 3. Results 30 3.1 Number of counted objects 31 3.2 Time costs of image types 33 Chapter 4. Discussion 36 4.1 Reference comparison 36 4.2 Instant detection 40 4.3 Supplemental usage 41 4.4 Utility of thermal sensors 42 4.5 Applications in other fields 43 Chapter 5. Conclusions 47 References 49 Appendix: Glossary 61 ์ดˆ๋ก 62์„

    ๋Œ€๋‡Œํ”ผ์งˆ์˜ ์‹œ๋ƒ…์Šค ๋ฐ ์‹ ๊ฒฝํšŒ๋กœ ๋ฐœ๋‹ฌ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋‡Œ๊ณผํ•™์ „๊ณต, 2019. 2. ์ตœ์„ธ์˜.The brain is the most complex organ with about 100 billion nerve cells. The "Cerebral cortex" is the largest suborgan in the brain and is structurally segmented. In addition, the cerebral cortex performs various functions such as sensation, movement, judgment, memory, information processing, and language through the compartmentalized area. For example, V1, the primary sensory cortex, processes and transmits visual information to the other cortex, while the sensory association cortex collects information from the sensory cortex and processes the intermediate information. Thus, the association cortex is related to perception-based judgment functions. What we can see while simultaneously listening and exercising is that the cerebral cortex can perform various functions at the same time. For so called multi-tasking, the cerebral cortex is developed in such a way that each cortical region forms optimized neural circuits for their specialized function. Dysfunction of the neurons and synapses or neural circuits that composed the cerebral cortex, can causes various and complicated brain diseases. For example, bipolar disorder, schizophrenia, and autism are known to be caused by dysfunction of the cerebral cortex Understanding the function and development of normal cerebral cortical neurons and neural circuits will not only broaden our understanding of the brain, but will also contribute to the effective treatment of various neuropsychiatric disorders in the future. Despite the involvement of the cerebral cortex in the regulation of various functions, the basic structure of the neuronal circuits in the cortical areas is substantially the same. The functional diversity of the cerebral cortex has been understood to be due to the diversity of neuronal circuitry coming from the outside, and it is presumed that they are optimized for the function of each region, but it is not well known whether different cerebral cortex have the same developmental mechanism. The primary sensory cortex, including the V1, undergoes the optimization process of the neural circuit through the experience of the early life in order to secure its inherent function. The specific period of this early life is generally called the 'critical period'. The visual cortex (V1) has the greatest plasticity in the critical period, and at this time, the optimization of neural circuits by experience occurs. Previous studies have shown that in the primary sensory cortex, the intra-cortical synaptic plasticity decreases or disappears after the end of the critical period, and it is known that the development of inhibitory neural circuits plays an important role for termination of critical period. On the other hand, it is important that the association cortex has the ability to collect and process information between different cerebral regions throughout the life rather than optimization of experience-dependent neural circuits. However, there is limited research into whether there is a critical period in the development of association cortex and what functional development is occurring at this time. In chapter 1, I have confirmed the structural and functional changes of two different cortical regions with have similar internal circuit structures, the primary visual cortex and the temporal association cortex. In particular, recent clinical reports have shown that patients with mood and cognitive impairment such as bipolar disorder, autism and schizophrenia show structural and functional abnormalities in cerebral cortex, particularly the prefrontal Cortex (PFC). PFC is an association cortex that by using a perceptual and emotional information provided by each cortex or subcortical regions, known to function as a value-based judgment and selection. Understanding how PFC synapses and associated neural circuits originally function will not only broaden our understanding of the brain, but will also contribute to the effective treatment of various neuropsychiatric disorders in the future. To identify correlation between behavioral abnormalities and neuronal, synaptic, and neural circuit dysfunctions, I used two mouse models with behavioral anomalies known to be associated with various PFCs Cytoplasmic FMR1-interacting protein 2 (Cyfip2) is an actin-regulatory protein that is expressed in synapses. Cyfip2+/- mice exhibit manic-like behavior in response to the mood stabilizer lithium. Manic episode is one of the main features of bipolar disorder, which characterized by increased mood, increased activity and energy, and increased sociability. Although there are clinical reports of structural and functional abnormality of PFC in patients with bipolar disorder, pathophysiological and the drug reaction mechanisms at the neuronal and synaptic levels have not been elucidated before. In chapter 2, I attempted to identify the correlation between neuronal and synaptic dysfunction and behavior in PFC through Cyfip2 gene loss mouse model showing manic-like behavior. Depression, anxiety, and social hierarchy are also related to the prefrontal area of the cerebral cortex. Cereblon (CRBN) deficient mouse model exhibits impaired behavior of depression, anxiety, and social dominance. Follow the previous report, CRBN was first identified from the patient with mild intellectual disability (ID), that mutation in CRBN genome was found. Deletion or microduplication in various regions of CRBN was associated with cognitive and behavioral disturbances. Recent studies have shown that CRBN protein forms a CRL4CRBN E3 ubiquitin ligase complex with cullin-4A (CUL4A), DNA-binding protein 1 (DDB1) and regulator of cullins 1 (ROC1). With the CRL4CRBN complex, acts as a receptor that binds to the protein to be regulated and brings them into the E3 ubiquitin ligase complex. Previous studies using CRBN deficient mice have shown that the CRBN protein modulates the secretory probability and cognitive behavior of pre-synaptic cells by regulating BK channel expression on the surface of pre-synaptic cells in the hippocampus. However, depression and anxiety behaviors of Crbn KO mice are controlled independent of BK channel in hippocampal region, unlike cognitive behavior. In chapter 3, I investigated changes in the structure and function of excitatory synapses using the neuromuscular junction synapses of the Crbn mutant Drosophila. In addition, using the Crbn KO mouse which showing abnormal mood and social behavior, observed changes in neural circuit and synaptic function in the connected region of the PFC and in the area where signal is transmitted to the corresponding region These studies, which examined the development of diverse functional cortical and external neural networks and how their dysfunctions appeared in neurons and synapses of neuropsychiatric disorders, have contributed greatly to understanding the structural universality and functional diversity of the cerebral cortex. It is expected that it will be applied to understand the cortex - dependent higher brain function and brain diseases and to establish the future treatment mechanism.์šฐ๋ฆฌ์˜ ๋‡Œ๋Š” ์•ฝ ์ฒœ์–ต ๊ฐœ์˜ ์‹ ๊ฒฝ์„ธํฌ๋“ค๋กœ ๊ตฌ์„ฑ๋œ ๊ฐ€์žฅ ๋ณต์žกํ•œ ๊ธฐ๋Šฅ์„ ํ•˜๋Š” ๊ธฐ๊ด€์ด๋‹ค. ๋Œ€๋‡Œํ”ผ์งˆ์€ ์ด๋Ÿฌํ•œ ๋‡Œ๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ํ•˜์œ„๊ธฐ๊ด€ ์ค‘ ๊ฐ€์žฅ ๋„“์€ ๋ฉด์ ์„ ์ฐจ์ง€ํ•˜๋ฉฐ ๊ตฌ์กฐ์ ์œผ๋กœ ๊ตฌํšํ™”๋˜์–ด ์žˆ๋‹ค. ๋˜ํ•œ ๋Œ€๋‡Œํ”ผ์งˆ์€ ๊ตฌํšํ™”๋œ ์˜์—ญ์„ ํ†ตํ•ด ๊ฐ๊ฐ, ์šด๋™, ํŒ๋‹จ, ๊ธฐ์–ต, ์ •๋ณด์˜ ๊ฐ€๊ณต, ์–ธ์–ด ๋“ฑ ๋‹ค์–‘ํ•œ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์˜ˆ๋ฅผ ๋“ค๋ฉด, ์ผ์ฐจ ๊ฐ๊ฐํ”ผ์งˆ์ธ ์‹œ๊ฐํ”ผ์งˆ(Visual Cortex)์€ ์‹œ๊ฐ ์ •๋ณด๋ฅผ ๊ฐ€๊ณตํ•˜์—ฌ ๋‹ค๋ฅธ ํ”ผ์งˆ์— ์ „๋‹ฌํ•˜๋Š” ๋ฐ˜๋ฉด, ์—ฐํ•ฉํ”ผ์งˆ์ธ ์ธก๋‘์—ฝํ”ผ์งˆ (Temporal Association Cortex)์€ ๊ฐ๊ฐํ”ผ์งˆ๋กœ๋ถ€ํ„ฐ ๋ฐ›์€ ์ •๋ณด๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ์ค‘๊ฐ„์ •๋ณด๋ฅผ ๊ฐ€๊ณตํ•˜๋Š” ๋Œ€๋‡Œํ”ผ์งˆ ์˜์—ญ์œผ๋กœ์„œ, ์ง€๊ฐ๊ธฐ๋ฐ˜ ํŒ๋‹จ ๊ธฐ๋Šฅ์— ์—ฐ๊ด€๋˜์–ด ์žˆ๋‹ค. ์šฐ๋ฆฌ๊ฐ€ ๋ณด๋ฉด์„œ ๋™์‹œ์— ๋งํ•˜๊ณ , ๋“ค์œผ๋ฉด์„œ ์šด๋™ํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์€ ๋Œ€๋‡Œํ”ผ์งˆ์ด ๋™์‹œ์— ๋‹ค์–‘ํ•œ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋Œ€๋‡Œํ”ผ์งˆ์€ ์ด๋Ÿฌํ•œ ๋ฉ€ํ‹ฐํƒœ์Šคํ‚น (multi-tasking)์„ ์œ„ํ•ด ๊ฐ๊ฐ ๊ธฐ๋Šฅ์— ์ตœ์ ํ™”๋œ ์˜์—ญ๋“ค์ด ํ•ด๋‹น ๊ธฐ๋Šฅ์— ํŠนํ™”๋œ ์‹ ๊ฒฝํšŒ๋กœ๋ฅผ ์ด๋ฃจ๊ฒŒ๋” ๋ฐœ๋‹ฌ๋œ๋‹ค. ๋Œ€๋‡Œํ”ผ์งˆ์„ ๊ตฌ์„ฑํ•˜๋Š” ์‹ ๊ฒฝ์„ธํฌ์™€ ์‹œ๋ƒ…์Šค, ๋˜๋Š” ์‹ ๊ฒฝํšŒ๋กœ์˜ ๊ธฐ๋Šฅ์ด์ƒ์€ ๋‹ค์–‘ํ•˜๊ณ  ๋ณต์žกํ•œ ๋‡Œ์งˆํ™˜์˜ ์›์ธ์ด ๋œ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด ์กฐ์šธ์ฆ, ์กฐํ˜„๋ณ‘, ์žํ์ฆ ๋“ฑ์ด ๋Œ€๋‡Œํ”ผ์งˆ์˜ ๊ธฐ๋Šฅ ์ด์ƒ์—์„œ ๊ธฐ์ธํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋Š” ์ •์‹ ์งˆํ™˜์ด๋‹ค. ๋”ฐ๋ผ์„œ ์ •์ƒ์ ์ธ ๋Œ€๋‡Œํ”ผ์งˆ ์‹ ๊ฒฝ ๋ฐ ์‹ ๊ฒฝํšŒ๋กœ์˜ ๊ธฐ๋Šฅ๊ณผ ๋ฐœ๋‹ฌ์„ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์€ ๋‡Œ์— ๋Œ€ํ•œ ์šฐ๋ฆฌ์˜ ์ดํ•ด๋ฅผ ๋„“ํ˜€ ์ค„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์žฅ์ฐจ ๋‹ค์–‘ํ•œ ์‹ ๊ฒฝ์ •์‹ ์งˆํ™˜์˜ ํšจ๊ณผ์ ์ธ ์น˜๋ฃŒ๋ฒ•์„ ๋„์ถœํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•  ๊ฒƒ์ด๋‹ค. ๋Œ€๋‡Œํ”ผ์งˆ์ด ๋‹ค์–‘ํ•œ ๊ธฐ๋Šฅ ์กฐ์ ˆ์— ๊ด€์—ฌํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์˜์—ญ ๋‚ด ์‹ ๊ฒฝํšŒ๋กœ์˜ ๊ธฐ๋ณธ ๊ตฌ์กฐ๋Š” ์˜์—ญ ๊ตฌ๋ถ„ ์—†์ด ์ƒ๋‹นํžˆ ๋™์ผํ•˜๋‹ค. ๋Œ€๋‡Œํ”ผ์งˆ์˜ ๊ธฐ๋Šฅ์  ๋‹ค์–‘์„ฑ์€ ์™ธ๋ถ€์—์„œ ๋“ค์–ด์˜ค๋Š” ์‹ ๊ฒฝํšŒ๋กœ๊ฐ€ ๊ฐ€์ง€๋Š” ๋‹ค์–‘์„ฑ ๋•Œ๋ฌธ์œผ๋กœ ์ดํ•ด๋˜์–ด ์™”์œผ๋ฉฐ, ๊ฐ๊ฐ ์˜์—ญ์˜ ๊ธฐ๋Šฅ์— ์ตœ์ ํ™”๋˜์–ด ๋ฐœ๋‹ฌ๋  ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์ง€๋งŒ, ์ด๋“ค ์„œ๋กœ ๋‹ค๋ฅธ ๋Œ€๋‡Œํ”ผ์งˆ๊ฐ„ ๊ณผ์—ฐ ๋™์ผํ•œ ๋ฐœ๋‹ฌ๊ธฐ์ „์„ ๊ฐ€์ง€๋Š”์ง€๋Š” ์ž˜ ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š๋‹ค. ์‹œ๊ฐํ”ผ์งˆ์„ ํฌํ•จํ•œ ์ผ์ฐจ๊ฐ๊ฐํ”ผ์งˆ์€ ๊ณ ์œ ์˜ ๊ธฐ๋Šฅ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•ด ์ƒ์•  ์ดˆ๋ฐ˜์˜ ๊ฒฝํ—˜์„ ํ†ตํ•œ ์‹ ๊ฒฝํšŒ๋กœ์˜ ์ตœ์ ํ™” ๊ณผ์ •์„ ๊ฑฐ์นœ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒ์•  ์ดˆ๊ธฐ ํŠน์ •์‹œ๊ธฐ๋ฅผ ์ผ๋ฐ˜์ ์œผ๋กœ ๊ฒฐ์ •์  ์‹œ๊ธฐ(Critical Period) ๋ผ๊ณ  ํ•œ๋‹ค. ์‹œ๊ฐํ”ผ์งˆ์€ critical period์— ๊ฐ€์žฅ ํฐ ๊ฐ€์†Œ์„ฑ์„ ๊ฐ€์ง€๋ฉฐ, ์ด ์‹œ๊ธฐ์— ๊ฒฝํ—˜์— ์˜ํ•œ ์‹ ๊ฒฝํšŒ๋กœ์˜ ์ตœ์ ํ™”๊ฐ€ ์ผ์–ด๋‚œ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์„ ํ†ตํ•ด ์ผ์ฐจ๊ฐ๊ฐํ”ผ์งˆ์—์„œ๋Š” critical period๊ฐ€ ์ข…๋ฃŒ๋œ ์ดํ›„์—๋Š” ํ”ผ์งˆ ๋‚ด(intra-cortical) ์‹œ๋ƒ…์Šค์˜ ๊ฐ€์†Œ์„ฑ์ด ๊ฐ์†Œํ•˜๊ฑฐ๋‚˜ ์†Œ์‹ค๋œ๋‹ค๋Š” ๊ฒƒ์ด ๋ฐํ˜€์กŒ์œผ๋ฉฐ, ์ด์— ์–ต์ œ์„ฑ ์‹ ๊ฒฝํšŒ๋กœ์˜ ๋ฐœ๋‹ฌ์ด ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค๋Š” ๊ฒƒ์ด ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ฐ˜๋ฉด, ์—ฐํ•ฉํ”ผ์งˆ์€ ๊ฒฝํ—˜์˜์กด์  ์‹ ๊ฒฝํšŒ๋กœ์˜ ์ตœ์ ํ™”๋ณด๋‹ค๋Š”, ์„œ๋กœ ๋‹ค๋ฅธ ๋Œ€๋‡Œ ์˜์—ญ๊ฐ„์˜ ์ •๋ณด๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ๊ฐ€๊ณตํ•˜๋Š” ๊ธฐ๋Šฅ์ด ์ƒ์•  ์ „๋ฐ˜์— ๊ฑธ์ณ ์ผ์–ด๋‚˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์—ฐํ•ฉํ”ผ์งˆ์˜ ๋ฐœ๋‹ฌ์— ๊ฒฐ์ •์  ์‹œ๊ธฐ๊ฐ€ ์žˆ๋Š”์ง€, ์ด ์‹œ๊ธฐ์— ์–ด๋– ํ•œ ๊ธฐ๋Šฅ์  ๋ฐœ๋‹ฌ์ด ์ผ์–ด๋‚˜๋Š”์ง€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•˜๋‹ค. 1์žฅ์—์„œ๋Š” ์œ ์‚ฌํ•œ ๋‚ด๋ถ€ ์‹ ๊ฒฝํšŒ๋กœ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๋Š” ์„œ๋กœ ๋‹ค๋ฅธ ๋‘ ํ”ผ์งˆ ์˜์—ญ, ์ผ์ฐจ๊ฐ๊ฐํ”ผ์งˆ์ธ ์‹œ๊ฐํ”ผ์งˆ๊ณผ, ์—ฐํ•ฉํ”ผ์งˆ์ธ ์ธก๋‘์—ฝํ”ผ์งˆ์˜ ๋ฐœ๋‹ฌ์ƒ ์‹ ๊ฒฝ์„ธํฌ์˜ ๊ตฌ์กฐ ๋ฐ ๊ธฐ๋Šฅ ๋ณ€ํ™”๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๋Œ€๋‡Œ ํ”ผ์งˆ์˜ ๊ธฐ๋Šฅ์ด์ƒ์€ ๋‹ค์–‘ํ•œ ์‹ ๊ฒฝ์ •์‹ ์งˆํ™˜์„ ์•ผ๊ธฐํ•œ๋‹ค. ํŠนํžˆ ์ตœ๊ทผ์˜ ์—ฌ๋Ÿฌ ์ž„์ƒํ•™์  ๋ณด๊ณ ๋“ค์— ์˜ํ•˜๋ฉด, ์กฐํ˜„๋ณ‘, ์žํ์ฆ, ์กฐ์šธ์ฆ ๋“ฑ๊ณผ ๊ฐ™์€ ๊ธฐ๋ถ„ ๋ฐ ์ธ์ง€์žฅ์•  ํ™˜์ž๋“ค์€ ๋Œ€๋‡Œํ”ผ์งˆ ์ค‘์—์„œ๋„ ํŠนํžˆ ์ „์ „๋‘์—ฝํ”ผ์งˆ(prefrontal Cortex, PFC)์˜ ๊ตฌ์กฐ ๋ฐ ๊ธฐ๋Šฅ์ด์ƒ์„ ๋ณด์ž„์ด ์•Œ๋ ค์ ธ ์žˆ๋‹ค. PFC๋Š” ๊ฐ ํ”ผ์งˆ์—์„œ ์ œ๊ณต๋œ ์ง€๊ฐ์ •๋ณด๋ฅผ ๊ฐ์ •์ •๋ณด์™€ ํ•จ๊ป˜ ์ข…ํ•ฉํ•˜์—ฌ ๊ฐ€์น˜์— ๊ธฐ๋ฐ˜์„ ๋‘” ํŒ๋‹จ๊ณผ ์„ ํƒ์˜ ๊ธฐ๋Šฅ์„ ํ•œ๋‹ค๊ณ  ์•Œ๋ ค์ง„ ์—ฐํ•ฉํ”ผ์งˆ์˜์—ญ์ด๋‹ค. ๋”ฐ๋ผ์„œ PFC์˜ ์‹œ๋ƒ…์Šค ๋ฐ ์—ฐ๊ด€๋œ ์‹ ๊ฒฝํšŒ๋กœ๊ฐ€ ์›๋ž˜๋Š” ์–ด๋– ํ•œ ๊ธฐ๋Šฅ์„ ํ•˜๋Š”์ง€ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์€ ๋‡Œ์— ๋Œ€ํ•œ ์šฐ๋ฆฌ์˜ ์ดํ•ด๋ฅผ ๋„“ํ˜€ ์ค„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์žฅ์ฐจ ๋‹ค์–‘ํ•œ ์‹ ๊ฒฝ์ •์‹ ์งˆํ™˜์˜ ํšจ๊ณผ์ ์ธ ์น˜๋ฃŒ๋ฒ•์„ ๋„์ถœํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•  ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์ž๋Š” ๋‹ค์–‘ํ•œ PFC์™€ ์—ฐ๊ด€๋˜์–ด์žˆ๋‹ค๊ณ  ์•Œ๋ ค์ง„ ํ–‰๋™ํ•™์  ์ด์ƒ์„ ๋ณด์ด๋Š” ๋‘ ๊ฐ€์ง€ ๋งˆ์šฐ์Šค ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์‹ ๊ฒฝ์„ธํฌ์™€ ์‹œ๋ƒ…์Šค, ์‹ ๊ฒฝํšŒ๋กœ์˜ ๊ธฐ๋Šฅ์ด์ƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. Cytoplasmic FMR1-interacting protein 2(Cyfip2) ๋Š” ์‹œ๋ƒ…์Šค์— ๋ฐœํ˜„ํ•˜๋Š” actin-์กฐ์ ˆ์ž ๋‹จ๋ฐฑ์งˆ์ด๋ฉฐ, Cyfip2 ์œ ์ „์ž ์†์‹ค ๋งˆ์šฐ์Šค๋Š” ์ •์‹ ์•ˆ์ •์ œ์ธ lithium์— ๋ฐ˜์‘ํ•˜๋Š” manic-like ํ–‰๋™์„ ๋ณด์ธ๋‹ค. ์กฐ์ฆ(manic episode)์€ ์–‘๊ทน์„ฑ ์žฅ์• ์˜ ์ฃผ๋œ ํŠน์ง• ์ค‘ ํ•˜๋‚˜๋กœ, ๊ธฐ๋ถ„์ด ๊ณ ์กฐ๋˜๊ณ , ํ™œ๋™์„ฑ๊ณผ ์—๋„ˆ์ง€์˜ ์ฆ๊ฐ€์™€ ์‚ฌ๊ต์„ฑ์˜ ์ฆ๊ฐ€๋ฅผ ํŠน์ง•์œผ๋กœ ํ•˜๋Š” ์žฅ์• ์ด๋‹ค. ์–‘๊ทน์„ฑ ์žฅ์• ๋ฅผ ๊ฐ€์ง„ ํ™˜์ž๊ตฐ์—์„œ PFC์˜ ๊ตฌ์กฐ ๋ฐ ๊ธฐ๋Šฅ์ด์ƒ์„ ๋ณด์ธ๋‹ค๋Š” ์ž„์ƒํ•™์  ๋ณด๊ณ ๋“ค์ด ์žˆ์œผ๋‚˜, ์‹ ๊ฒฝ์„ธํฌ ๋ฐ ์‹œ๋ƒ…์Šค ์ˆ˜์ค€์—์„œ์˜ ๋ณ‘ํƒœ์ƒ๋ฆฌํ•™์  ๊ธฐ์ „๊ณผ ์•ฝ๋ฌผ๋ฐ˜์‘๊ธฐ์ „์— ๋Œ€ํ•ด์„œ๋Š” ๊ฑฐ์˜ ๋ฐํ˜€์ง„ ๋ฐ”๊ฐ€ ์—†๋‹ค. ์ด๋Ÿฌํ•œ ์ค‘์š”์„ฑ์— ์ฐฉ์•ˆํ•˜์—ฌ 2์žฅ์—์„œ๋Š” manic-like ํ–‰๋™์„ ๋ณด์ด๋Š” Cyfip2 ์œ ์ „์ž ์†์‹ค ๋งˆ์šฐ์Šค ๋ชจ๋ธ์„ ํ†ตํ•ด, PFC์—์„œ์˜ ์‹ ๊ฒฝ์„ธํฌ ๋ฐ ์‹œ๋ƒ…์Šค ๊ธฐ๋Šฅ์ด์ƒ๊ณผ ์ด์ƒํ–‰๋™๊ณผ์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์šฐ์šธ, ๋ถˆ์•ˆ๊ณผ ๊ฐ™์€ ๊ธฐ๋ถ„์žฅ์• ์™€ ์‚ฌํšŒ์„ฑ, social dominance ์—ญ์‹œ ๋Œ€๋‡Œํ”ผ์งˆ์˜ ์ „์ „๋‘์—ฝ ์˜์—ญ๊ณผ ์—ฐ๊ด€๋˜์–ด ์žˆ์Œ์ด ์ตœ๊ทผ ๋งŽ์€ ์—ฐ๊ตฌ๋“ค์„ ํ†ตํ•ด ๋ฐํ˜€์ง€๊ณ  ์žˆ๋‹ค. Cereblon (CRBN) ๊ฒฐ์† ๋งˆ์šฐ์Šค ๋ชจ๋ธ์€ subordinateํ•œ dominance behavior๋ฅผ ๋ณด์ด๋ฉฐ, ์šฐ์šธ, ๋ถˆ์•ˆ ์žฅ์•  ํ–‰๋™์„ ๋ณด์ธ๋‹ค. Cereblon (CRBN)์€ IQ 50~70 ์‚ฌ์ด์˜ ๊ฒฝ๋„ ์ง€์ ์žฅ์• (mild intellectual disability)๋ฅผ ๋ณด์ด๋Š” ํ™˜์ž๋“ค์—์„œ ๋Œ์—ฐ๋ณ€์ด๊ฐ€ ์žˆ์Œ์ด ์ตœ์ดˆ๋กœ ๋ฐœ๊ฒฌ๋˜์—ˆ์œผ๋ฉฐ, CRBN ์œ ์ „์ฒด ๋‹ค์–‘ํ•œ ์˜์—ญ์˜ ๊ฒฐ์†์ด๋‚˜ microduplication์ด ์ธ์ง€ ๋ฐ ํ–‰๋™์žฅ์• ์™€ ์—ฐ๊ด€์ด ์žˆ์Œ์ด ๋ณด๊ณ ๋œ ๋ฐ” ์žˆ๋‹ค. CRBN ๋‹จ๋ฐฑ์งˆ์€ DNA-binding protein 1(DDB1), cullin-4A(CUL4A), regulator of cullins 1(ROC1)๊ณผ ํ•จ๊ป˜ CRL4CRBN E3 ubiquitin ligase complex๋ฅผ ํ˜•์„ฑํ•˜๋ฉฐ ์œ„ ๋ณตํ•ฉ์ฒด์—์„œ CRBN์€ ์กฐ์ ˆ์„ ๋ฐ›์„ ๋‹จ๋ฐฑ์งˆ๊ณผ ๊ฒฐํ•ฉํ•˜์—ฌ ์ด๋“ค์„ E3 ubiquitin ligase complex๋กœ ๋ฐ๋ ค์˜ค๋Š” receptor ์—ญํ• ์„ ํ•จ์ด ๋ฐํ˜€์กŒ๋‹ค. ์ตœ๊ทผ CRBN ์œ ์ „์ž ๊ฒฐ์† ๋งˆ์šฐ์Šค๋ฅผ ์ด์šฉํ•œ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด CRBN ๋‹จ๋ฐฑ์งˆ์ด BKca channel๊ณผ AMPKฮฑ์˜ ์ธ์‚ฐํ™”๋ฅผ ํ†ตํ•ด ํ•ด๋งˆ์˜ Schaffer collateral โ€“ CA1 ์‹œ๋ƒ…์Šค์˜ ๊ธฐ๋Šฅ ๋ฐ cognitive behavior๋ฅผ ์กฐ์ ˆํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐํžŒ ๋ฐ” ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ CRBN KO ๋งˆ์šฐ์Šค์˜ ์šฐ์šธ, ๋ถˆ์•ˆํ–‰๋™ ๋ฐ social dominance behavior๋Š” ์ธ์ง€ํ–‰๋™๊ณผ๋Š” ๋‹ฌ๋ฆฌ ํ•ด๋งˆ์˜์—ญ์˜ BK channel๊ณผ ๋ฌด๊ด€ํ•˜๊ฒŒ ์กฐ์ ˆ๋œ๋‹ค. 3์žฅ์—์„œ๋Š” CRBN mutant ์ดˆํŒŒ๋ฆฌ์˜ ๊ทผ์‹ ๊ฒฝ์ ‘ํ•ฉ๋ถ€๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํฅ๋ถ„์„ฑ ์‹œ๋ƒ…์Šค์˜ ๊ตฌ์กฐ ๋ฐ ๊ธฐ๋Šฅ์˜ ๋ณ€ํ™”๋ฅผ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, Social dominance ๋ฐ ๊ธฐ๋ถ„์žฅ์•  ํ–‰๋™ ์ด์ƒ์„ ๋ณด์ด๋Š” CRBN ์œ ์ „์ž ๊ฒฐ์† ๋งˆ์šฐ์Šค๋ฅผ ์ด์šฉํ•˜์—ฌ PFC ๋ฐ ํ•ด๋‹น์˜์—ญ์— ์‹ ํ˜ธ๋ฅผ ์ฃผ๊ณ  ๋ฐ›๋Š” ์ฃผ๋ณ€์˜์—ญ๊ณผ ๊ตฌ์ถ•ํ•˜๋Š” ์‹ ๊ฒฝํšŒ๋กœ ๋ฐ ์‹œ๋ƒ…์Šค ๊ธฐ๋Šฅ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๋‹ค์–‘ํ•œ ๊ธฐ๋Šฅ์„ ํ•˜๋Š” ๋Œ€๋‡Œํ”ผ์งˆ ๋‚ด/์™ธ๋ถ€ ์‹ ๊ฒฝ๋ง์˜ ๋ฐœ๋‹ฌ๊ณผ, ๊ทธ ๊ธฐ๋Šฅ์ด์ƒ์ด ์‹ ๊ฒฝ์ •์‹ ์งˆํ™˜์˜ ์‹ ๊ฒฝ์„ธํฌ ๋ฐ ์‹œ๋ƒ…์Šค์—์„œ ์–ด๋–ป๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š”์ง€๋ฅผ ํ™•์ธํ•œ ๋ณธ ์—ฐ๊ตฌ๋“ค์€ ๋Œ€๋‡Œํ”ผ์งˆ์˜ ๊ตฌ์กฐ์  ๋ณดํŽธ์„ฑ๊ณผ ๊ธฐ๋Šฅ์  ๋‹ค์–‘์„ฑ์„ ์ดํ•ดํ•˜๋Š” ๋ฐ ํฌ๊ฒŒ ๊ธฐ์—ฌํ•  ๋ฟ ์•„๋‹ˆ๋ผ ํ”ผ์งˆ ์˜์กด์ ์ธ ๊ณ ๋“ฑ ๋‡Œ๊ธฐ๋Šฅ ๋ฐ ๋‡Œ์งˆํ™˜์„ ์ดํ•ดํ•˜์—ฌ ํ–ฅํ›„ ์น˜๋ฃŒ๊ธฐ์ „์„ ํ™•๋ฆฝํ•˜๋Š” ๋ฐ ์ฃผ์š”ํ•˜๊ฒŒ ์‘์šฉ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.General introduction 1 What is the cerebral cortex 1 Previous studies on cerebral cortical development 2 Current studies and neuropsychiatric disorder associated with cerebral cortex 3 Purposes 5 References 6 Chapter I. Functional diversity and intra-cortical circuit development of cerebral cortex 9 Abstract 9 Introduction 10 Material and Methods 12 Results 15 Discussion 25 References 27 Chapter II. Alteration of layer-specific neuronal function in prefrontal cortex of Cyfip2 mutant, mouse model of mania 30 Abstract 30 Introduction 31 Material and Methods 34 Results 36 Discussion 48 References 50 Chapter III. Loss of Cereblon induces abnormalities in Prefrontal circuit-dependent presynaptic function and behavior. 55 Abstract 55 Introduction 56 Material and Methods 59 Results 65 Discussion 84 References 86 Abstract in Korean 93Docto

    ์–‘์ž์  ๋ฐœ๊ด‘ ๋‹ค์ด์˜ค๋“œ ๊ตฌ๋™ ์•ˆ์ •์„ฑ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€,2020. 2. ๊ณฝ์ •ํ›ˆ.์ฝœ๋กœ์ด๋“œ์„ฑ ์–‘์ž์ ์€ ๊ด‘์†Œ์ž์— ์‚ฌ์šฉํ•˜๊ธฐ์— ์ ํ•ฉํ•œ ๊ด‘ํ•™์  ์ „๊ธฐ์  ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํŠนํžˆ๋‚˜ ๋†’์€ ์–‘์žํšจ์œจ, ์ข์€ ๋ฐœ๊ด‘ ํŒŒ์žฅ๋Œ€, ๋ฌด๊ธฐ ์žฌ๋ฃŒ์˜ ๋‚ด์  ์—ด์•ˆ์ •์„ฑ๊ณผ ๊ด‘์•ˆ์ •์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๊ธฐ์— ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ์˜ ๊ด‘๋ฌผ์งˆ๋กœ ์‚ฌ์šฉํ•˜๊ธฐ์— ์ ํ•ฉํ•˜๋‹ค. ๋‹ค๋…„๊ฐ„์˜ ์†Œ์žฌ์— ๋Œ€ํ•œ ๊ฐœ๋ฐœ๊ณผ ์†Œ์ž ๊ตฌ์กฐ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ์–‘์ž์  ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ๋Š” ์†Œ์ž ํŠน์„ฑ๊ณผ ๊ตฌ์กฐ์ ์œผ๋กœ ๋งŽ์€ ๋ฐœ์ „์ด ์žˆ์–ด ์™”๋‹ค. ํ˜„์žฌ๊นŒ์ง€ ๋†’์€ ํšจ์œจ๊ณผ ์ˆ˜๋ช…์— ๋„๋‹ฌํ•œ ์–‘์ž์  ๋ฐœ๊ด‘์†Œ์ž์ด์ง€๋งŒ ์•„์ง๊นŒ์ง€ ์–‘์ž์  ๋ฐœ๊ด‘์ธต์œผ๋กœ์˜ ์ „ํ•˜ ์ฃผ์ž… ๋ถˆ๊ท ํ˜• ๋ฌธ์ œ๋ฅผ ์™„์ „ํžˆ ํ•ด๊ฒฐํ•˜์ง€ ๋ชปํ•˜์˜€์œผ๋ฉฐ, ๊ตฌ๋™ ์•ˆ์ •์„ฑ์˜ ๊ฐ์†Œ ์›์ธ์„ ๋ถ„์„ํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์กฑํ•œ ์ƒํ™ฉ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ •๊ณต ์ฃผ์ž… ๋ฒ„ํผ์ธต ์‚ฝ์ž…์„ ํ†ตํ•œ ์–‘์ž์  ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ ๋‚ด๋ถ€์˜ ์ •๊ณต ํŠน์„ฑ์„ ํ–ฅ์ƒ์‹œ์ผœ ์ „ํ•˜ ์ฃผ์ž… ๋ถˆ๊ท ํ˜•์„ ํ•ด์†Œํ•˜๊ณ , ์ด ๊ตฌ์กฐ์  ํŠน์ด์„ฑ์„ ํ™œ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ๊ตฌ๋™ ์กฐ๊ฑด๋“ค์ด ๊ตฌ๋™ ์•ˆ์ •์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ œ์–ดํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋“  ์กฐ๊ฑด์„ ํ†ต์ผํ•˜๊ณ  ์ •๋Ÿ‰์ ์ธ ๋ถ„์„์„ ์ง„ํ–‰ํ•œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์–‘์ž์  ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ์˜ ๊ตฌ๋™ ์•ˆ์ •์„ฑ๊ณผ ๊ตฌ๋™ ์กฐ๊ฑด์˜ ์—ฐ๊ด€ ๊ด€๊ณ„๋ฅผ ์ง๊ด€์ ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋„์™€์ค€๋‹ค. ๋จผ์ € ํšจ๊ณผ์ ์ธ ์ •๊ณต ์ฃผ์ž…์„ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์œ ๊ธฐ๋ฌผ ์ •๊ณต์ˆ˜์†ก์ธต (CBP)๊ณผ ์ •๊ณต์ฃผ์ž…์ธต (MoOx) ์‚ฌ์ด Pinning ํšจ๊ณผ์— ์˜ํ•ด ์กด์žฌํ•˜๋Š” 0.3 eV์˜ ์ •๊ณต ์ฃผ์ž… ์žฅ๋ฒฝ์„ ์—†์• ๊ณ  ์–‘์ž์  ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ์˜ ์ „ํ•˜ ์ฃผ์ž… ๊ท ํ˜•์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๊นŠ์€ ์ตœ๊ณ ์ค€์œ„์ ์œ ๋ถ„์ž๊ถค๋„ ((HOMO, Highest occupied molecular orbital) ์—๋„ˆ์ง€ ์ค€์œ„๋ฅผ ๊ฐ€์ง€๋Š” ๋ฌผ์งˆ๋“ค (BST, HAT-CN, DPEPO, C60)์ด ์†Œ์ž ํŠน์„ฑ ๋ถ„์„์— ํ™œ์šฉ๋˜์—ˆ์œผ๋ฉฐ, ๊ทธ ์ค‘ ๊ฐ€์žฅ ๋†’์€ ํšจ์œจ ํ–ฅ์ƒ๊ณผ ์ˆ˜๋ช… ํ–ฅ์ƒ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์˜จ C60๋ฅผ ์ •๊ณต ๋ฒ„ํผ์ธต์œผ๋กœ ํ™œ์šฉํ•˜์—ฌ ์†Œ์ž์˜ ํŠน์„ฑ ํ–ฅ์ƒ์„ ์ „๊ธฐ์  ๊ทธ๋ฆฌ๊ณ  ๊ด‘ํ•™์ ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ •๊ณต์ˆ˜์†ก์ธต๊ณผ ์ •๊ณต์ฃผ์ž…์ธต ์‚ฌ์ด์˜ ์—๋„ˆ์ง€ ์žฅ๋ฒฝ๋งŒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋Š”HOD (Hole only device)๋ฅผ ์ œ์ž‘ํ•˜์—ฌ ์ €ํ•ญ ๋ถ„์„๊ณผ ์˜จ๋„ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ €ํ•ญ ๋ถ„์„์˜ ๊ฒฐ๊ณผ ์–‡์€ C60 ๊ณ„๋ฉด์ธต์„ ์‚ฝ์ž…ํ•˜์˜€์„ ๋•Œ CBP์™€ MoOx ๊ณ„๋ฉด์ €ํ•ญ์ด ๋น ๋ฅด๊ฒŒ ๊ฐ์†Œํ•˜๊ณ  ์ „ํ•˜๋Ÿ‰์„ ๋‚˜ํƒ€๋‚˜๋Š” ๊ณ„๋ฉด ์ •์ „ ์šฉ๋Ÿ‰ (Capacitance)๊ฐ€ ๋น ๋ฅด๊ฒŒ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ •๊ณต ์ฃผ์ž… ๊ณ„๋ฉด ํŠน์„ฑ ํ–ฅ์ƒ์€ ์†Œ์ž ์ „์ฒด์— ์˜ํ–ฅ์„ ๋ฏธ์ฒ˜ HOD์™€ ์–‘์ž์  ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ์˜ ํ™œ์„ฑํ™” ์—๋„ˆ์ง€ (Activation Energy, Et) ๊ฐ์†Œ์—๋„ ์˜ํ–ฅ์„ ์ฃผ์–ด ๊ฒฐ๊ณผ์ ์œผ๋กœ ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ์˜ ํšจ์œจ๊ณผ ์ˆ˜๋ช… ํ–ฅ์ƒ์œผ๋กœ ์ด์–ด์ง„๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ•œํŽธ ์–‡์€ C60 ๊ณ„๋ฉด์ธต ์‚ฝ์ž…์œผ๋กœ ์†Œ์ž์˜ ๊ตฌ๋™ ํŠน์„ฑ (์ „์••, ์ „๋ฅ˜, ์ „ํ•˜ ์ฃผ์ž… ๊ท ํ˜•)์ด ๋ณ€ํ•œ๋‹ค๋Š” ํŠน์ด์„ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์–‘์ž์  ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ์˜ ๊ตฌ๋™ ์•ˆ์ •์„ฑ ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์ง„ํ–‰๋˜๋Š” ๋™์ผ ํœ˜๋„์—์„œ์˜ ๊ตฌ๋™ ์•ˆ์ •์„ฑ ๋น„๊ต๋Š” ๊ตฌ๋™ ์ „๋ฅ˜ ๋ฐ ๊ตฌ๋™ ์ „์••์ด ๋‹ค๋ฅด๊ธฐ์— ๊ตฌ๋™ ์•ˆ์ •์„ฑ์— ๋ฏธ์น˜๋Š” ์š”์†Œ๋“ค์ด ๋งŽ์•„ ์ •๋Ÿ‰์ ์ธ ๋ถ„์„์ด ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ๋™์ผํ•œ ์ „๋ฅ˜ ์กฐ๊ฑด (30, 100, 200 mA/cm2)ํ•˜์— ์†Œ์ž๋“ค์˜ ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ, C60 ๊ณ„๋ฉด์ธต ๋‘๊ป˜๋ฅผ ๋ณ€ํ™”์‹œ์ผœ ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ๋“ค์ด ์„œ๋กœ ๋‹ค๋ฅธ ๊ตฌ๋™ ์ „์••์„ ๊ฐ€์ง€๊ฒŒ ํ•˜์—ฌ ๊ตฌ๋™ ์ „์•• ๋ฐ ์ „ํ•˜ ๋ถˆ๊ท ํ˜• ๊ฐ’์ด ๊ตฌ๋™ ์•ˆ์ •์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ดˆ๊ธฐ ๋น ๋ฅธ ํœ˜๋„ ๊ฐ์†Œ ๊ตฌ๊ฐ„์ธ Stage I์€ ๊ตฌ๋™ ์ „๋ฅ˜์™€ ์ „ํ•˜ ์ฃผ์ž… ๋ถˆ๊ท ํ˜• ๊ฐ’์— ํฌ๊ฒŒ ์˜ํ–ฅ์„ ๋ฐ›์ง€๋งŒ ๊ตฌ๋™ ์ „์••์—๋Š” ์˜ํ–ฅ์„ ๋ฐ›์ง€ ์•Š๋Š”๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์ดํ›„ ๋Š๋ฆฐ ํœ˜๋„ ๊ฐ์†Œ ๊ตฌ๊ฐ„์ธ Stage II๋Š” ๊ตฌ๋™ ์ „๋ฅ˜์™€ ์ „ํ•˜ ์ฃผ์ž… ๋ถˆ๊ท ํ˜• ๊ฐ’์— ํฐ ์˜ํ–ฅ์„ ๋ฐ›๊ณ , ํ•œํŽธ ๊ตฌ๋™ ์ „์•• ์—ญ์‹œ ๋ถ€๋ถ„์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ๊ฐœ๋ฐœ๋œ ์–‘์ž์  ๋ฐœ๊ด‘ ๋‹ค์ด์˜ค๋“œ์˜ ์†Œ์ž ๊ตฌ์กฐ์™€ ๊ตฌ๋™ ์•ˆ์ •์„ฑ ๋ถ„์„ ๊ฒฐ๊ณผ๋Š” ๊ณ ์•ˆ์ •์„ฑ ์–‘์ž์  ๋ฐœ๊ด‘๋‹ค์ด์˜ค๋“œ์˜ ์‹คํ˜„๊ณผ ์‹ค์งˆ์ ์ธ ์ƒ์šฉํ™” ์ œํ’ˆ ๊ฐœ๋ฐœ์— ํฐ ๋„์›€์ด ๋  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค.Colloidal quantum dot light-emitting diodes (QLEDs) are p-i-n junction diodes exhibiting excellence in color gamut, brightness and flexible form factors, promising their use in next-generation displays. Within the last few decades, QLEDs have demonstrated great progress in efficiency and brightness that are comparable to the performance of commercialized organic light-emitting diodes (OLEDs) are reported. To enhance performance of QLEDs, lots of attempts have been made on QD and QLED, such as modifying core/shell composition and structure, surface ligand modification, and optimization QLEDs device architectures. Through these methods, tremendous improvement in terms of photoluminescence quantum yields (PL QY) of QDs and external quantum efficiency (EQE) of QLEDs have been accomplished. However the origin of key factors that reduces operational stability of QLEDs is still miles behind. There have been studies related to enhancement of QLEDs lifetime by insertion of blocking layer to prevent acidity of hole transport layer (HTL) or improving electron-hole balance by modifying device structure, but the mechanism of optical and electrical deterioration of the devices is still insufficient. For the practical use of QLED, it is prerequisite to identify the relation between device operation conditions (Applied current density, voltage, and charge balance factors) and QLED lifetime. However lifetime analysis of the QLEDs, which have different structures (QDs or charge transport layer), leads unreliable comparison results due to the different operating conditions. In this study, we improved QLEDs lifetime and performance with enhanced hole transport property by insertion of thin fullerene (C60) as hole injection interlayer between CBP (HTL) and MoOx layer (HIL, Hole injection layer). Insertion of buffer layer which has deep highest occupied molecular orbital (HOMO) level such as C60 can eliminate pinning effect between CBP and MoOx layer. This eventually increase hole transport property in QLEDs and enhance balance of electron and hole transport rate to QD emissive layer. To clarify the relation between operational condition and lifetime of QLED, we quantitatively conduct the comparison between electrical properties of QLEDs and photophysical properties of the QD emissive layer within the devices under various operation condition. As these analytic researches are taken in the QLEDs which have nearly identical structure, the result shows intuitive understanding on the effect of operational condition in QLEDs. As a result, the QLEDs with C60 interlayer showed 10% reduced initial rapid luminance drop compared to non-C60 interlayer QLEDs which leads to 5 times increase in operational lifetime at 1000 nit. (75% lifetime (LT75) ~ 5.6 hours @ 1000 cd/m2 for non-C60 interlayer QLEDs and (LT75) ~ 36.5 hours @ 1000 cd/m2 for C60 interlayer QLEDs). Comprehensive study across spectroscopic analysis on the QD emissive layer and optoelectronic characterization on working devices under all-else-being-equal operation conditions enable us to understand the key factors that are responsible for the device degradation. The device efficiency drop at Stage I is attributed solely to the charge injection imbalance into QDs. The device efficiency loss at Stage II is also attributed mainly to the charge injection imbalance, and further exacerbated by the increase in the operation voltage. These results shows the impact of charge injection balance on the device performance, and suggest that the equalized charge injection will enable complete eradication of device degradation factors and promise prolonged operation lifetime of QLEDs. I believe that engineering at the interface between QDs and HTL will certainly enable the complete charge injection balance and a long-lived QLED.Chapter 1 1 1.1 Colloidal Quantum Dot Light-Emitting Diodes 1 1.2 Charge Balance Issues in QLEDs 9 1.3 Outline of Thesis 12 Chapter 2 14 2.1 Materials 14 2.1.1 Preparation of ZnO Nanoparticles 14 2.1.2 Synthesis of Red-color Emitting CdSe(core radius (r) = 2.0 nm)/Zn1-XCdXS(shell thickness (h) = 6.0) 15 2.1.3 Organic Materials 16 2.2 Device Fabrication and Characterization Methods 18 2.2.1 Device Fabrication 18 2.2.2 Current-Voltage-Luminance Measurement 19 2.2.3 Efficiency Calculation Methods 21 2.2.4 Measurement of Electrical Characteristics 22 2.2.5 Modeling and Simulation 23 2.2.6 Other Characterization Methods 25 Chapter 3 27 3.1 QDs based LEDs made of a Series of HIIL with Deep HOMO Energy Levels. 30 3.2 Characteristics of Electroluminescence Devices with various HIIL 33 3.3 Electrical Characteristics of HODs with HIIL analyzed by Impedance Spectroscopy 44 3.4 Electrical Characteristics of HODs with HIIL analyzed by Temperature Experiment 52 3.5 Summary 57 Chapter 4 58 4.1 Lifetime Characteristics of Electroluminescence Devices assisted by deep HOMO level HIIL 60 4.2 Characteristics of Electroluminescence and Photoluminescence of QD based LED assisted by Deep HOMO Level HIIL 64 4.3 Quantitative Assessment of Operational Stability of Electroluminescence Devices 73 4.4 Characteristics of Operational Stability of QD based LED based on All-Else-Being-Equal Conditions 76 4.5 Characteristics of Operational Stability of QD based LED with various HTL 81 4.6 Effect of Joule Heating on the Electroluminescence Devices 83 4.7 Summary 87 Chapter 5 88 Bibilography 91 Publication 98 ํ•œ๊ธ€ ์ดˆ๋ก 101Docto

    Axillary artery cannulation reduces early embolic stroke and mortality after open arch repair with circulatory arrest

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    OBJECTIVE: To evaluate the efficacy of axillary artery cannulation for early embolic stroke and operative mortality, we retrospectively compared the outcomes between patients with or without axillary artery cannulation during open aortic arch repair with circulatory arrest. METHODS: Between January 2004 and December 2017, 468 patients underwent open aortic arch repair with circulatory arrest using antegrade cerebral perfusion and were divided into 2 groups according to the site of arterial cannulation: the axillary artery (axillary group, n = 352) or another site (nonaxillary group, n = 116) groups. Embolic stroke was defined as a physician-diagnosed new postoperative neurologic deficit lasting more than 72 hours, generally confirmed by computed tomography or magnetic resonance imaging. RESULTS: After propensity score matching, the patients' characteristics were comparable between the groups (n = 116 in each). The incidences of acute type A dissection, aortic rupture, shock, or emergency operation were similar between groups. The incidence of early embolic stroke was significantly lower in axillary group (n = 3 [2.6%] vs n = 10 [8.6%]; P = .046). Also, 30-day mortality (n = 3 [2.6%] vs n = 10 [8.6%]; P = .046) and in-hospital mortality (n = 3 [2.6%] vs n = 11 [9.5%]; P = .027) occurred significantly lower in the axillary group. CONCLUSIONS: Axillary artery cannulation reduced the early embolic stroke and early mortality after open arch repair with circulatory arrest. Axillary artery cannulation as the arterial cannulation site during open arch repair with circulatory arrest may be helpful in preventing embolic stroke and reducing early mortality.ope

    The association between blood levels mercury and risk for obesity in a general adult population : results from the Korean national health and nutrition examination survey

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    ๋ณด๊ฑด๋Œ€ํ•™์›/์„์‚ฌObjective: The rising prevalence of overweight and obesity has been recognized as a serious, worldwide public health concern in the 21st century. Many studies have reported about risk for gain weight according to countless causes of obesity. The primary objective of this study was to estimate association between blood mercury levels and obesity in Korean adults. Methods: We analyzed cross-sectional data from 9,923 participants (4,619 men and 5,304 women) who completed the Korean National Health and Nutrition Examination Survey (KNHANES), 2007๏ผŸ2013. The population was divided into 2 groups according to body mass index (BMI) and waist circumference (WC). Blood mercury levels were analyzed using a gold amalgam collection method and categorized by interquartiles stratified by sex and occupational status(manual and non-manual workers). The study population was evaluated by Studentโ€™s t-tests, ๏ผŸ2 tests and logistic regression. Results: A multiple logistic regression analysis after adjusting for all covariates showed that blood mercury levels were significantly associated with overweight and abdominal obesity in all subjects. According to BMI criteria, the adjusted odds ratio of being in the highest blood mercury quartile was 1.92 (95% confidence interval [CI], 1.69๏ผŸ2.18) overall, 2.32 (95% CI, 1.93๏ผŸ2.80) in men, and 1.68 (95% CI, 1.42๏ผŸ1.99) in women. According to WC criteria, the adjusted odds ratio of being in the highest blood mercury quartile was 1.97 (95% CI, 1.61๏ผŸ2.41) in men and 2.01 (95% CI, 1.69๏ผŸ2.40) in women compared with the lowest quartile. Additionally, a linear trend in overweight and abdominal obesity across increasing blood mercury levels was observed by P for trend test in multiple diagnostic criteria. After stratification by occupational status, the adjusted odds ratio of being in the highest blood mercury quartile was 2.06 (95% CI, 1.69๏ผŸ2.50) overall manual worker group, 2.42 (95% CI, 1.88๏ผŸ3.13) in men manual workers, and 1.86 (95% CI, 1.39๏ผŸ2.50) in women manual workers based on BMI categorize. According to WC criteria, the adjusted odds ratio of being in the highest blood mercury quartile was 2.07 (95% CI, 1.56๏ผŸ2.74) in men and 2.37 (95% CI, 1.75๏ผŸ3.20) in women compared with the lowest quartile in manual worker group In non-manual worker group, the adjusted odds ratio of being in the highest blood mercury quartile was 1.95 (95% CI, 1.44๏ผŸ2.63) overall non-manual worker group, 3.02 (95% CI, 2.02๏ผŸ4.52) in men, and 1.54 (95% CI, 1.02๏ผŸ2.30) in women based on BMI categorize. According to WC criteria, the adjusted odds ratio of being in the highest blood mercury quartile was 1.93 (95% CI, 1.31๏ผŸ2.86) in men and 2.25 (95% CI, 1.41๏ผŸ3.59) in women compared with the lowest quartile in manual worker group Conclusion: We found meaningful associations between blood mercury level and weight gain in a dose-dependent manner. Moreover, we attempted to stratify by occupation (manual and non-manual workers), which no study has done previously. A meaningful association of blood mercury and obesity was confirmed in some of these subgroups.ope

    Metal catalytic growth and characteristics of ฮฒ-Ga2O3 nanowires by metal organic chemical vapor deposition

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    Catalytic synthesis and properties of ฮฒ-Ga2O3 nanowires grown by metal organic chemical vapor deposition are reported. Au, Ni and Cu catalysts were suitable for the growth of Ga2O3 nanowires under our experimental conditions. The Ga2O3 nanowires grown by using Au, Ni and Cu catalysts showed different growth rates and morphologies in each case. We found the Ga2O3 nanowires were grown by the Vapor-Solid (VS) process when Ni was used as a catalyst while the Vapor-Liquid-Solid (VLS) was a dominant process in case of Au and Cu catalysts. Also, we found the Ga2O3 nanowires showed different optical properties depend on catalytic metals. On the other hand, for the cases of Ti, Sn and Ag catalysts, Ga2O3 nanowires could not be obtained under the same condition of Au, Cu and Ni catalytic synthesis. We found that these results are related to the different characteristics of each metal catalyst, such as, melting points and phase diagrams with gallium metal.1. ์„œ ๋ก  1 2. ์ด ๋ก  2.1 ์‚ฐํ™”๋ฌผ ๋ฐ˜๋„์ฒด 3 2.2 ฮฒ-Ga2O3 5 2.3 ๋‚˜๋…ธ ์™€์ด์–ด 2.3.1 ๋‚˜๋…ธ ์™€์ด์–ด์˜ ํŠน์„ฑ ๋ฐ ์‘์šฉ 8 2.3.2 ๋‚˜๋…ธ ์™€์ด์–ด์˜ ์„ฑ์žฅ ๋ฐฉ๋ฒ• 9 3. ์‹คํ—˜ ๋ฐฉ๋ฒ• 3.1 MOCVD ์‹œ์Šคํ…œ 15 3.2 ๊ธฐํŒ ์ค€๋น„ 16 3.3 ๋‚˜๋…ธ ์™€์ด์–ด ์„ฑ์žฅ ๋ฐ ํŠน์„ฑ ์ธก์ • 16 4. ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 4.1 ํ˜•์ƒ ํŠน์„ฑ ๋ถ„์„ 4.1.1 Ga2O3 ๋‚˜๋…ธ ์™€์ด์–ด์˜ ์„ฑ์žฅ ์˜จ๋„ ์˜์กด์„ฑ 22 4.1.2 Ga2O3 ๋‚˜๋…ธ ์™€์ด์–ด์˜ ์†Œ์Šค ์œ ๋Ÿ‰ ์˜์กด์„ฑ 25 4.1.3 Ga2O3 ๋‚˜๋…ธ ์™€์ด์–ด์˜ ๊ธˆ์† ์ด‰๋งค ์˜์กด์„ฑ 32 4.2 X-ray diffration ๋ถ„์„ 42 4.3 Raman spectroscopy ๋ถ„์„ 44 4.4 CL ํŠน์„ฑ ๋ถ„์„ 47 5. ๊ฒฐ ๋ก  49 ๊ฐ์‚ฌ์˜ ๊ธ€ 50 ์ฐธ๊ณ ๋ฌธํ—Œ 51Maste

    A Study on Indoor Positioning using 3-Dimensionalization Geomagnetic Fingerprint

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    Indoor positioning based on geomagnetism has been actively studied because of the stable signal and high resolution positioning accuracy even when the time has elapsed. Because the geomagnetic signal can vary according to changes in azimuth, large positioning errors may occur, even from the same position. Therefore, this thesis proposes a fingerprint-based indoor positioning algorithm that fuses 2-Dimensional magnetic vectors and yaw-axis correction techniques. In the proposed 3-Dimensional system, the curvature is less biased heavily by using the Ellipse Coefficient Map of the geomagnetism based on the normalized linear least squares method even when database size is reduced, and the accuracy of positioning is improved by applying the geomagnetic signal equalization method. To verify the validity of the proposed algorithm in general indoor spaces of 48m ร— 30m, the results of the proposed method are compared with results obtained existing research based on geomagnetism intensity. The results show that the positioning accuracy is improved by 62.14% and the error distance is reduced by 3.98m.|์ง€์ž๊ธฐ๊ธฐ๋ฐ˜ ์‹ค๋‚ด์œ„์น˜์ธ์‹์€ ์‹œ๊ฐ„์ด ๊ฒฝ๊ณผ๋˜๋”๋ผ๋„ ์•ˆ์ •์ ์ธ ์‹ ํ˜ธ ๋ฐ ๋†’์€ ๋ถ„ํ•ด๋Šฅ์œผ๋กœ ์ธก์œ„ ์ •ํ™•์„ฑ์ด ๋†’๊ธฐ ๋•Œ๋ฌธ์— ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋™์ผํ•œ ์œ„์น˜์—์„œ๋„ ๋ฐฉ์œ„ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ง€์ž๊ธฐ ์‹ ํ˜ธ๊ฐ€ ์ผ์ •ํ•˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ์œ„์น˜ ์˜ค์ฐจ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” Fingerprint๊ธฐ๋ฐ˜ 2์ฐจ์› ์ž๊ธฐ๋ฒกํ„ฐ ๋ฐ yaw์ถ• ๋ณด์ •์„ ์ ์šฉํ•œ ์‹ค๋‚ด์œ„์น˜์ธ์‹ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•œ 3์ฐจ์›ํ™” ์‹œ์Šคํ…œ์€ ์ •๊ทœํ™” ์„ ํ˜• ์ตœ์†Œ์ž์Šน๋ฒ•์„ ์ ์šฉํ•œ ์ง€์ž๊ธฐ์˜ Ellipse Coefficient Map์„ ์„ค๊ณ„ํ•จ์œผ๋กœ์จ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๊ฐ€ ๊ฐ„์†Œํ™”๋จ์—๋„ ๊ณก๋ฅ ํŽธํ–ฅ์ด ๊ฑฐ์˜ ์—†๊ณ  ์ง€์ž๊ธฐ ์‹ ํ˜ธ ํ‰ํ™œํ™” ๊ธฐ๋ฒ•์„ ์ ์šฉํ•จ์œผ๋กœ์จ ์œ„์น˜์ธ์‹ ์ •ํ™•๋„๋ฅผ ํ–ฅ์ƒ์‹œ์ผฐ๋‹ค. ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ 48m ร— 30m์˜ ์ผ๋ฐ˜์ ์ธ ์‹ค๋‚ด๊ณต๊ฐ„์—์„œ ๊ธฐ์กด ์ง€์ž๊ธฐ ์„ธ๊ธฐ๊ธฐ๋ฐ˜ ๋ฐฉ์‹๊ณผ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ต ๋ฐ ๋ถ„์„ํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ ์œ„์น˜ ์ธ์‹ ์ •ํ™•๋„๋Š” 62.14% ๊ฐœ์„ ํ•˜์˜€๊ณ  ์˜ค์ฐจ๊ฑฐ๋ฆฌ๋Š” 3.98m ๊ฐ์†Œํ•˜์˜€๋‹ค.Abstract โ…ณ ์ œ 1 ์žฅ ์„œ ๋ก  01 ์ œ 2 ์žฅ ๊ด€๋ จ์ด๋ก  06 2.1 ์ง€๊ตฌ์ž๊ธฐ์žฅ 06 2.2 ์ž๊ธฐ๋ฒกํ„ฐ๊ธฐ๋ฐ˜ ๋ฐฉ์œ„๊ฐ ํš๋“ 07 2.3 ์ตœ์†Œ์ž์Šน๋ฒ• 11 2.4 Fingerprint ์ธก์œ„ ๊ธฐ๋ฒ• 13 ์ œ 3 ์žฅ ์ œ์•ˆํ•œ ์‹ค๋‚ด ์œ„์น˜ ์ธ์‹ ๋ฐฉ๋ฒ• 15 3.1 ์‹œ์Šคํ…œ ๊ตฌ์กฐ 15 3.2 3์ฐจ์›ํ™” Training phase 16 3.3 3์ฐจ์›ํ™” Positioning phase 20 ์ œ 4 ์žฅ ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 23 4.1 ์‹คํ—˜ ํ™˜๊ฒฝ 23 4.2 ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ถ„์„ 27 ์ œ 5 ์žฅ ๊ฒฐ ๋ก  38 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 39Maste

    ์˜จ๋„ ๊ธฐ๋ฐ˜ EGR ์˜ˆ์ธก ๋ชจ๋ธ ๊ฐœ๋ฐœ๊ณผ EGR ์˜ˆ์ธก ๋ชจ๋ธ์„ ํฌํ•จํ•˜๋Š” ์—ฐ์†Œ์•• ๊ธฐ๋ฐ˜ ๋””์ ค ์—”์ง„ ์ œ์–ด๋ฅผ ํ†ตํ•œ ๋ฐฐ๊ธฐ๊ฐ€์Šค ์ €๊ฐ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2013. 2. ๋ฏผ๊ฒฝ๋•.๋””์ ค ์—”์ง„์€ ์ •์ƒ์ƒํƒœ์— ๋น„ํ•ด์„œ ๊ณผ๋„ ์ƒํƒœ์—์„œ ์šด์ „๋  ๋•Œ ๋” ๋งŽ์€ ์œ ํ•ด ๋ฐฐ์ถœ ๊ฐ€์Šค๋ฅผ ๋ฐœ์ƒํ•œ๋‹ค. ์ด๋Š” ๊ธ‰๊ธฐ ์‹œ์Šคํ…œ๊ณผ ์—ฐ๋ฃŒ ๋ถ„์‚ฌ ์‹œ์Šคํ…œ๊ฐ„์˜ ๋ถ€์กฐํ™”์™€ ํ„ฐ๋ณด๋ž™ (turbo-lag) ์— ์˜ํ•ด ๋ฐœ์ƒํ•œ๋‹ค. ํŠนํžˆ ๊ธ‰๊ฒฉํ•œ ๊ณผ๋„์ƒํƒœ์—์„œ๋Š” ํ„ฐ๋ณด๋ž™์œผ๋กœ ์ธํ•ด์„œ ํก๊ธฐ ์••๋ ฅ์ด ๋ฐฐ๊ธฐ ์••๋ ฅ์— ๋น„ํ•ด ๋Šฆ๊ฒŒ ๋ณ€ํ™”ํ•˜๊ฒŒ ๋˜๋ฉฐ EGR์˜ ๊ณผ์ž‰ ๊ณต๊ธ‰์ด๋‚˜ ๋ถ€์กฑ ํ˜„์ƒ์„ ์ผ์œผํ‚จ๋‹ค. ๋””์ ค์—”์ง„์˜ ์—ฐ์†Œ๋Š” EGR ์œจ์— ๋ฏผ๊ฐํ•˜๋ฏ€๋กœ ์ •ํ™•ํ•œ EGR ์œจ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค๋ฉด ๋””์ ค ์ฃผ๋ถ„์‚ฌ์‹œ๊ธฐ, EGR ๋ฐธ๋ธŒ ๊ฐœ๋„๋Ÿ‰, ์—ฐ๋ฃŒ๋Ÿ‰ ๋“ฑ์˜ ์ธ์ž๋“ฑ์˜ ์ œ์–ด๋ฅผ ํ†ตํ•ด์„œ ๋ฐฐ๊ธฐ ๊ฐ€์Šค๋ฅผ ์ €๊ฐํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹ค๋ฆฐ๋”๋กœ ๊ณต๊ธ‰๋˜๋Š” ๊ธฐ์ฒด๋‚ด์˜ EGR ์œจ์„ ๊ณ„์‚ฐ ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋ธ์„ ๊ฐœ๋ฐœ ํ•˜์˜€๋‹ค. ๋ฐ˜์‘์„ฑ์ด ๋งค์šฐ ๋น ๋ฅธ ์—ด์ „๋Œ€๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธํ„ฐ์ฟจ๋Ÿฌ ํ›„๋‹จ์˜ ์˜จ๋„, EGR ๊ฐ€์Šค์˜ ์˜จ๋„ ๋ฐ ํก๊ธฐ ๋‹ค๊ธฐ๊ด€์˜ ์˜จ๋„๋ฅผ ์ธก์ •ํ•˜์—ฌ ์—๋„ˆ์ง€ ๋ณด์กด ๋ฒ•์น™์„ ์ด์šฉํ•˜์—ฌ EGR ์œจ์„ ๊ณ„์‚ฐํ•˜์˜€๋‹ค. ๋ชจ๋ธ์˜ ์ •ํ™•์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•˜์—ฌ ํก๊ธฐ ๋‹ค๊ธฐ๊ด€ ๋‚ด์—์„œ์˜ ์—ด์ „๋‹ฌ์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ์ด ๋ชจ๋ธ์€ ์ •์ƒ์ƒํƒœ์—์„œ ๊ธฐ์กด์˜ CO2์˜ ๋†๋„๋ฅผ ํ†ตํ•ด์„œ EGR ์œจ์„ ๊ณ„์‚ฐํ•˜๋Š” ๊ธฐ์กด์˜ ๋ฐฉ์‹์„ ํ†ตํ•ด์„œ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ฐœ๋ฐœ๋œ ๋ชจ๋ธ์„ ํํšŒ๋กœ ์ œ์–ด์— ์ ์šฉํ•˜์˜€๋‹ค. ์‹ค์‹œ๊ฐ„ ์—ฐ์†Œ ํ•ด์„์„ ํ†ตํ•ด์„œ MFB50์„ feedback์œผ๋กœ ๋ฐ›์œผ๋ฉฐ ๋ชฉํ‘œ MFB50์„ ์ถ”์ข…ํ•˜๋„๋ก ์ฃผ ๋ถ„์‚ฌ ์‹œ๊ธฐ๋ฅผ ์ œ์–ดํ•˜๋„๋ก ์„ค๊ณ„๋˜์–ด ์žˆ๋Š” ์—ฐ์†Œ ์ œ์–ด๊ธฐ์— EGR ์˜ค์ฐจ ๋ณด์ƒ ๋กœ์ง์„ ์ถ”๊ฐ€ ํ•˜์˜€๋‹ค. ๋ชฉํ‘œ EGR ์œจ์— ๋น„ํ•ด ๋ชจ๋ธ๋กœ๋ถ€ํ„ฐ ๊ณ„์‚ฐ๋œ ํ˜„์žฌ์˜ EGR ์œจ์ด ์ž‘์„ ๊ฒฝ์šฐ ๋ชฉํ‘œ MFB50์„ ๋‘˜์˜ ์ฐจ์ด์— ๋น„๋ก€ํ•˜์—ฌ ์ง€๊ฐ ์‹œํ‚ด์œผ๋กœ์จ ๊ณผ๋‹คํ•˜๊ฒŒ ๋ฐฐ์ถœ ๋  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋˜๋Š” NOx๋ฅผ ์ค„ ์ผ ์ˆ˜ ์žˆ๋‹ค. ์—”์ง„์˜ ์†๋ ฅ๊ณผ ๋ถ€ํ•˜๊ฐ€ ๊ธ‰๊ฒฉํžˆ ๊ฐ์†Œํ•˜๋Š” ๊ตฌ๊ฐ„์—์„œ ์‹ค์ œ์˜ EGR์œจ์ด ๋ชฉํ‘œ EGR ์œจ์— ๋น„ํ•ด ํ˜„์ €ํžˆ ๊ฐ์†Œํ•˜๊ฒŒ ๋˜๊ณ  ์ด์— ๋”ฐ๋ผ NOx ๋ฐฐ์ถœ์ด ์ฆ๊ฐ€ ํ•œ๋‹ค. ์ด ๊ตฌ๊ฐ„์— ๋Œ€ํ•˜์—ฌ EGR ๋ชจ๋ธ์ด ํฌํ•จ๋œ ์œ„์˜ ์ œ์–ด ๋กœ์ง์„ ์ ์šฉํ•˜์˜€์„ ๊ฒฝ์šฐ NOx ์˜ ๊ณผ๋‹ค ๋ฐœ์ƒ์„ ์–ต์ œ ํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 1.1 Research background 1 1.1.1 Emission regulations 1 1.1.2 Emission characteristics in transient condition 2 1.1.3 Application of combustion control 3 1.1.4 Real-time EGR estimation 4 1.2 Objectives and expected benefits 5 Chapter 2. Experimental Apparatus 6 2.1 Overall configuration 6 2.2 Multi-cylinder Diesel engine 6 2.3 Engine test equipment 10 2.4 Exhaust gas analyzer 11 2.4.1 Exhaust gas analysis in steady state 11 2.4.2 Exhaust gas analysis in transient state 11 2.5 Combustion control system 19 2.5.1 ES1000 19 2.5.2 Combustion pressure measurement 20 2.6 Temperature measuring system 27 Chapter 3. Real-time estimation algorithm of engine operating conditions based on in-cylinder pressure 30 3.1 Overall logic description 30 3.2 Combustion analyzer 30 3.3 Combustion control logic 33 3.4 EGR correction factor for closed loop control 34 Chapter 4. EGR RATE ESTIMATION USING TEMPERATURE MEASUREMENT 35 4.1 EGR rate estimation using the energy balance equation 35 4.2 Validation the EGR model 37 4.3 EGR model considering the heat transfer 40 4.4 Analysis EGR model in ECU 44 Chapter 5. Result and Discussion 48 Chapter 6. Conclusions 55 Bibliography 56 ์ดˆ ๋ก 59Maste

    ์กฐ์„  ์ดˆ๊ธฐ ๅ‘Š่บซ ่ฟฝๅฅช ๋ฐ ้‚„็ตฆ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ฒ•๊ณผ๋Œ€ํ•™ ๋ฒ•ํ•™๊ณผ, 2018. 2. ์ •๊ธ์‹.์กฐ์„ ์‹œ๋Œ€ ๋ฌธ๋ฌด๊ด€์›์—๊ฒŒ ํ’ˆ๊ณ„์— ๋”ฐ๋ผ ์ˆ˜์—ฌํ•œ ์ž„๋ช…์žฅ์ธ ๅ‘Š่บซ์€, ่ฟฝๅฅชใ†้‚„็ตฆ ๋“ฑ์˜ ์ฒ˜๋ถ„์„ ํ†ตํ•ด ์ฃ„๋ฅผ ์ง€์€ ๊ด€์›์„ ์ง•๊ณ„ํ•˜์—ฌ ์ž๊ฒฉ์„ ๋ฐ•ํƒˆํ•˜๊ฑฐ๋‚˜, ์‚ฌ๋ฉดํ•˜์—ฌ ํšŒ๋ณต์‹œํ‚ค๋Š” ๊ณผ์ •๊ณผ ์ƒ๋‹นํ•œ ๊ด€๋ จ์ด ์žˆ๋‹ค. ๅ‘Š่บซ์˜ ์œ ๋ž˜๋Š” ๅ”ไปฃ๊นŒ์ง€ ๊ฑฐ์Šฌ๋Ÿฌ ์‚ดํŽด๋ณผ ์ˆ˜ ์žˆ๋Š”๋ฐ, ๅ”ไปฃ์˜ ๋ฒ•์ „์ธ ใ€Šๅ”ๅพ‹็–่ญฐใ€‹ใ†ใ€Šๅ”ๅ…ญๅ…ธใ€‹์—์„œ ์ด๋ฏธ ๅ‘Š่บซ์„ ๊ฑฐ๋‘๋Š” ๊ทœ์ •์ด ๋ฐœ๊ฒฌ๋œ๋‹ค. ๆ˜Žไปฃ์—๋Š” ใ€Šๅคงๆ˜Žๅพ‹ใ€‹ ใ€ˆๅไพ‹ใ€‰์˜ [ๆ–‡ๆญฆๅฎ˜็Šฏ็ง็ฝช] ๆข๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ็ง็ฝช์— ๋Œ€ํ•ด ๅ‘Š่บซ์„ ๅทฎ็ญ‰็š„์œผ๋กœ ๊ฑฐ๋‘๋„๋ก ํ•˜์˜€์œผ๋ฉฐ, ์ด ์ฒด๊ณ„๋Š” ์กฐ์„ ์˜ ใ€Š็ถ“ๅœ‹ๅคงๅ…ธใ€‹์˜ ใ€ˆๅˆ‘ๅ…ธใ€‰ [ๆŽจๆ–ท] ๆข๋กœ ์ด์–ด์กŒ๋‹ค. ์‹ค๋ก์—์„œ์˜ ่ฟฝๅฅชใ†้‚„็ตฆ ์‚ฌ๋ก€๋ฅผ ๋ถ„์„ํ•˜๋ฉด, ๅ‘Š่บซ์„ ๊ฑฐ๋‘๋Š” ์ฒ˜๋ถ„์€ ๋ณธ๋ž˜์˜ ํ˜•๋ฒŒ์— ๋Œ€ํ•œ ้™„ๅŠ ๅˆ‘์œผ๋กœ์„œ์˜ ์„ฑ๊ฒฉ๊ณผ ๊ด€์›์˜ ์ž๊ฒฉ์„ ๋ฐ•ํƒˆํ•˜๊ฑฐ๋‚˜ ๊ฐ•๋“ฑํ•˜๋Š” ์ง•๊ณ„๋ฒŒใ†๋ช…์˜ˆํ˜•์˜ ์„ฑ๊ฒฉ์„ ๋ชจ๋‘ ๊ฐ–์ถ”๊ณ  ์žˆ์—ˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ์ง•๊ณ„๋ฒŒ์˜ ๊ฒฝ์šฐ ๅ‘Š่บซ์ด ๊ฑฐ๋‘ฌ์ง„ ๊ด€์›์˜ ์ง๋ฌด ์ˆ˜ํ–‰๊ณผ ๊ด€๋ จํ•˜์—ฌ, ๋ช…์˜ˆํ˜•์€ ํŠนํžˆ ๆญป่€…์— ๋Œ€ํ•œ ๅ‘Š่บซ ่ฟฝๅฅช ๋ฐ ้‚„็ตฆ์˜ ๋ฌธ์ œ์™€ ๊ด€๋ จํ•˜์—ฌ ๊ทธ ํŠน์„ฑ์„ ์ถ”์ถœํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ด€์›์˜ ๋ฒ”์ฃ„ ๋ฐ ้ž้•์— ๋Œ€ํ•ด ์ด๋ค„์ง„ ๅ‘Š่บซ ่ฟฝๅฅช์„ ์œ ํ˜•ํ™”ํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ํƒœ์ข…์กฐ์—๋Š” ๋…ธ๋น„๋ณ€์ •๋„๊ฐ์„ ํ†ตํ•ด ๋…ธ๋น„ ์‹ ๋ถ„ ๋ฐ ์†Œ์œ ๊ด€๊ณ„๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋Š”๋ฐ, ๊ทธ ๊ฒฐ๊ณผ ๅฅดๅฉข่จŸไบ‹์™€ ๊ด€๋ จํ•˜์—ฌ ๊ด€์›๋“ค์ด ์ €์ง€๋ฅธ ๋ถ€์ •ํ–‰์œ„ ๋ฐ ่ชคๆฑบ์„ ์ง•๋ฒŒํ•˜๊ธฐ ์œ„ํ•ด ๅ‘Š่บซ ่ฟฝๅฅช ๋“ฑ์„ ํฌํ•จํ•œ ์ฒ˜๋ฒŒ ๊ธฐ์ค€์ด ๋งˆ๋ จ๋˜์—ˆ๋‹ค. ํ•œํŽธ ่ด“ๆฑš็ฝช๋Š” ๋ฐฑ์„ฑ์„ ์นจํƒˆํ•˜๋Š” ้‡็ฝช๋กœ ๋ฌด๊ฒ๊ฒŒ ์ฒ˜๋ฒŒ๋˜์—ˆ์œผ๋ฉฐ, ๊ทธ ์™€์ค‘์— ๅ‘Š่บซ์„ ่ด“็‰ฉ์˜ ์ •๋„์— ๋”ฐ๋ผ ๊ฑฐ๋‘์–ด ์ง•๊ณ„ํ•˜์˜€๋‹ค. ์œ ๊ต ์ด๋…์ด ์ฃผ์ถ•์„ ์ด๋ฃจ์—ˆ๋˜ ์กฐ์„  ์‚ฌํšŒ์˜ ํŠน์„ฑ์ƒ ไธๅญใ†ไธๅฟ ๊ณผ ๊ฐ™์€ ็ถฑๅธธ็ฝช์— ๋Œ€ํ•ด์„œ๋Š” ํŠน๋ณ„ํžˆ ์ฒ˜๋ฒŒํ•˜๋ฉด์„œ ๅ‘Š่บซ์„ ๊ฑฐ๋‘์—ˆ๋Š”๋ฐ, ๊ด€์›ใ†์ข…์นœ์˜ ๏คขๅˆ‘ ๋“ฑ์œผ๋กœ ์ธํ•œ ์‚ด์ธ์˜ ๊ฒฝ์šฐ์—๋„ ์œ ๊ต์‚ฌํšŒ์˜ ์ˆ˜์ง์  ์œ„๊ณ„์งˆ์„œ๋ฅผ ๋ฐ˜์˜ํ•˜์—ฌ ๅ‘Š่บซ์„ ๊ฑฐ๋‘๋Š” ์„ ์—์„œ ๊ทธ์น˜๊ธฐ๋„ ํ•˜์˜€๋‹ค. ่ชฃๅ‘Š ์—ญ์‹œ ๅ‘Š่บซ ่ฟฝๅฅช์˜ ์œ ํ˜•์œผ๋กœ ๋“ฑ์žฅํ•œ๋‹ค. ํ•œํŽธ ็ง็ฝช์— ๊ตญํ•œํ•˜์—ฌ ๅ‘Š่บซ์„ ๊ฑฐ๋‘˜ ๊ฒƒ์„ ์ฒœ๋ช…ํ•œ ๋ฐ” ์žˆ์ง€๋งŒ, ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์กฐ์„  ์ „๊ธฐ์—๋Š” ๊ณต๋ฌด์ƒ ๊ณผ์‹ค ๋“ฑ์˜ ๅ…ฌ็ฝช์— ๋Œ€ํ•ด์„œ๋„ ๅ‘Š่บซ์„ ๊ฑฐ๋‘” ์‚ฌ๋ก€๋ฅผ ์ข…์ข… ๋ฐœ๊ฒฌํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” ใ€Šๅคงๆ˜Žๅพ‹ใ€‹ ์ฒด๊ณ„๊ฐ€ ๊ตญ์ดˆ๋ถ€ํ„ฐ ์™„์ „ํžˆ ์กฐ์„  ์‚ฌํšŒ์— ํ™•๋ฆฝ๋˜์ง€ ์•Š๊ณ , ์ ์ง„์ ์œผ๋กœ ์ •์ฐฉ๋˜๋Š” ๊ณผ๋„๊ธฐ์  ๊ณผ์ •์— ์žˆ์—ˆ์Œ์„ ๋‚˜ํƒ€๋‚ด๋Š” ์ฆ๊ฑฐ์ด๊ธฐ๋„ ํ•˜๋‹ค. ์›์น™์ ์œผ๋กœ๋Š” ๅ‘Š่บซ ่ฟฝๅฅช ๋Œ€์ƒ์ด์ง€๋งŒ ๋ฉด์ฑ…๋˜๋Š” ์˜ˆ์™ธ๋„ ์กด์žฌํ•˜์˜€๋‹ค. ๊ณต์‹ ใ†์ข…์นœ ๋“ฑ์˜ ์‹ ๋ถ„์— ์žˆ๊ฑฐ๋‚˜, ้ž้•๊ฐ€ ์‚ฌ๋ฉด ์‹œํ–‰ ์ด์ „์— ์žˆ์–ด ่ตฆๅฎฅ๊ฐ€ ์ ์šฉ๋˜๋Š” ๋“ฑ์˜ ๊ฒฝ์šฐ์—๋Š” ๅ‘Š่บซ ่ฟฝๅฅช์„ ๋ฉดํ•˜๊ธฐ๋„ ํ•˜์˜€๋‹ค. ํ•œํŽธ ๅ‘Š่บซ ่ฟฝๅฅช์˜ ํŠน์ˆ˜ํ•œ ์‚ฌ๋ก€๋กœ๋Š” ๊ด€์›์˜ ๋‚จํŽธ์ธ ๊ด€์›์˜ ๅ‘Š่บซ์— ์ข…์†๋˜์–ด ๊ฑฐ๋‘ฌ์ง„ ๊ด€์› ๋ถ€์ธ์˜ ็ˆต็‰’, ๋ณ€๋ฐฉ์— ๊ทผ๋ฌดํ•˜์—ฌ ็ฝฒ็ถ“์— ํ•„์š”ํ•œ ๅ‘Š่บซ์„ ์ œ๋•Œ ์ œ์ถœํ•˜์ง€ ๋ชปํ•œ ่ปๅฃซ์˜ ๅ‘Š่บซ์„ ๊ฑฐ๋‘๋Š” ๋ฌธ์ œ๋„ ์‚ดํŽด๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๅ‘Š่บซ ้‚„็ตฆ์€ ํฌ๊ฒŒ ์ •๊ธฐ์  ้‚„็ตฆ๊ณผ ์‚ฌ๋ฉด์— ์˜ํ•œ ้‚„็ตฆ์œผ๋กœ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹ค๋ก์—์„œ๋Š” ๅ‘Š่บซ์„ ่ฟฝๅฅช๋‹นํ•œ ์ธ์›์˜ ๋ช…๋‹จ์„ ์ •๋ฆฌํ•˜์—ฌ ์ฃผ๊ธฐ์ ์œผ๋กœ ๋ณด๊ณ ํ•  ๊ฒƒ์„ ์ง€์‹œํ•œ ๊ธฐ๋ก์„ ์ฐพ์•„๋ณผ ์ˆ˜ ์žˆ๋Š”๋ฐ, ์ด๋ฅผ ํ†ตํ•ด ์ •๋ก€ํ™”๋œ ๅ‘Š่บซ ้‚„็ตฆ ์กฐ์น˜๊ฐ€ ์žˆ์—ˆ์Œ์„ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ผ๋ถ€ ์˜ˆ์™ธ์‚ฌ๋ก€์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๅ‘Š่บซ์ด ่ฟฝๅฅช๋œ ๊ด€์›์€ 2๋…„๋’ค์— ๆ•็”จํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€์œผ๋ฏ€๋กœ ้‚„็ตฆ ์—ญ์‹œ ์›์น™์ ์œผ๋กœ๋Š” ๊ทธ์— ์ค€ํ•˜์—ฌ ์ด๋ฃจ์–ด์กŒ์„ ๊ฒƒ์œผ๋กœ ์ถ”์ •ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ ์™•์‹ค์˜ ๊ฐ์ข… ๊ฒฝ์กฐ์‚ฌ ๋ฐ ์ž์—ฐ์žฌํ•ด๋กœ ์ธํ•œ ๋Œ€์‚ฌ๋ฉด์˜ ์ผํ™˜์œผ๋กœ ๅ‘Š่บซ์„ ๋Œ€๊ฑฐ ้‚„็ตฆํ•˜๊ธฐ๋„ ํ•˜์˜€์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๋น„์ •๊ธฐ์ ์ธ ๅ‘Š่บซ ้‚„็ตฆ์ด ้‚„็ตฆ ์ˆ˜ํ˜œ์ž์˜ ์ƒ๋‹น์ˆ˜๋ฅผ ์ฐจ์ง€ํ•˜์˜€๋‹ค. ๊ทธ ๋ฐ–์—๋„ ์‹ค๋ฌด์  ํŒ๋‹จ์— ๋”ฐ๋ผ ๋‹จ๊ธฐ๊ฐ„์— ๅ‘Š่บซ์„ ๋Œ๋ ค์ฃผ๊ธฐ๋„ ํ•˜์˜€๋‹ค. ์‚ฌ๋ฉด์œผ๋กœ ์ธํ•œ ๋Œ€๊ทœ๋ชจ ๅ‘Š่บซ ้‚„็ตฆ์˜ ๋นˆ๋„์™€ ์ธ์›์ด ๅœ‹ๅˆ๋ถ€ํ„ฐ ์„ฑ์ข…์กฐ๊นŒ์ง€ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฐ€์šด๋ฐ, ่ด“ๆฑšใ†็ถฑๅธธ็ฝช์™€ ๊ฐ™์€ ้‡็ฝช๋ฅผ ๅ‘Š่บซ ้‚„็ตฆ์˜ ๋ฐฐ์ œ์‚ฌ์œ ๋กœ ์‚ผ๋Š” ๋ฌธ์ œ๋ฅผ ๋‘๊ณ  ๋…ผ์˜๊ฐ€ ์ด๋ค„์ง€๊ธฐ๋„ ํ–ˆ๋‹ค. ํ•œํŽธ ๅ‘Š่บซ ้‚„็ตฆ ๊ทธ ์ž์ฒด์— ๋Œ€ํ•œ ์˜๋ฌธ ์ œ๊ธฐ๋„ ์ด์–ด์กŒ๋‹ค. ์ „ํ†ต์ ์ธ ็ฝ็•ฐ่ง€ ๋ฐ ๆคๅˆ‘ ๊ฐœ๋…์— ๋”ฐ๋ผ ๅ‘Š่บซ ้‚„็ตฆ ๋“ฑ ์ฃ„์ธ์— ๋Œ€ํ•ด ๊ด€๋Œ€ํ•œ ์ฒ˜๋ถ„์„ ๋‚ด๋ ค์•ผ ํ•œ๋‹ค๋Š” ์˜๊ฒฌ์ด ์ฃผ๋ฅ˜๋ฅผ ์ฐจ์ง€ํ–ˆ์Œ์—๋„, ๊ทธ๋Ÿฌํ•œ ์ฒ˜๋ถ„์ด ์ฒ ์ €ํ•œ ๋ฒ• ์ง‘ํ–‰์„ ๊ฐ€๋กœ๋ง‰์•„ ์ฒ˜๋ฒŒ ํšจ๊ณผ๋ฅผ ๊ฐํ‡ด์‹œํ‚จ๋‹ค๋Š” ๋ฐ˜๋ก  ์—ญ์‹œ ์ œ๊ธฐ๋˜์—ˆ๋‹ค. ์ด๋Š” ์กฐ์„  ์ดˆ๊ธฐ ๅ‘Š่บซ ้‚„็ตฆ์ด ์ •์น˜์  ์•ˆ์ •์„ฑ ๋ฐ ํ™”ํ•ฉ ๋„๋ชจ๋ผ๋Š” ์ •์น˜์ ใ†ํ˜„์‹ค์ ์ธ ํ•„์š”์„ฑ๊ณผ, ์—„์ •ํ•œ ๋ฒ•์งˆ์„œ ๊ตฌํ˜„์ด๋ผ๋Š” ๋ช…๋ถ„๋ก ์˜ ์ง€์†์ ์ธ ๊ธด์žฅ์ƒํƒœ ํ•˜์—์„œ ์ด๋ฃจ์–ด์กŒ์Œ์„ ๋“œ๋Ÿฌ๋‚ธ๋‹ค.์ œ1์žฅ ๅบ่ซ– 1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ์˜์˜ 1 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 7 ์ œ2์žฅ ๅ‘Š่บซ์˜ ์˜์˜์™€ ๋ฒ•์  ์„ฑ๊ฒฉ 9 ์ œ1์ ˆ ๅ‘Š่บซ์˜ ์˜์˜์™€ ์—ฐํ˜ 9 1. ๅ‘Š่บซ์˜ ์˜์˜ 9 2. ๅ”ใ†ๆ˜Ž์˜ ๅ‘Š่บซ ๊ด€๋ จ ๊ทœ์ • 13 3. ์กฐ์„ ์˜ ๅ‘Š่บซ ๊ด€๋ จ ๊ทœ์ • 19 ์ œ2์ ˆ ๅ‘Š่บซ์˜ ่ฟฝๅฅช๊ณผ ้‚„็ตฆ์˜ ์„ฑ๊ฒฉ 25 1. ๋ถ€๊ฐ€ํ˜•์  ์„ฑ๊ฒฉ 25 2. ์ง•๊ณ„๋ฒŒ์  ์„ฑ๊ฒฉ 26 3. ๋ช…์˜ˆํ˜•์  ์„ฑ๊ฒฉ 30 ์ œ3์ ˆ ๅ‘Š่บซ ่ฟฝๅฅช๊ณผ ้‚„็ตฆ์˜ ์ ˆ์ฐจ 36 ์ œ3์žฅ ๅ‘Š่บซ์˜ ่ฟฝๅฅช 41 ์ œ1์ ˆ ๅ‘Š่บซ ่ฟฝๅฅช์˜ ์œ ํ˜• 41 1. ๋…ธ๋น„ใ†ํ† ์ง€ ๊ด€๋ จ ๋ฒ”์ฃ„ 41 2. ่ด“ๆฑš็ฝช 46 3. ็ถฑๅธธ็ฝช ๋ฐ ๅ„’ๆ•Žๅ€ซ็† ้•่ƒŒ 49 4. ๅฎ˜ๅ“กใ†ๅฎ—่ฆช ๋“ฑ์˜ ๆฎบไบบ 54 5. ่ชฃๅ‘Š 57 6. ์ง๋ฌด์ƒ ๊ณผ์‹คใ†ํƒœ๋งŒใ†๊ธฐ๊ฐ•ํ•ด์ด ๋“ฑ 59 7. ๊ธฐํƒ€ 63 ์ œ2์ ˆ ๅ‘Š่บซ ่ฟฝๅฅช์˜ ์˜ˆ์™ธ์‚ฌ๋ก€ 65 1. ์‹ ๋ถ„์— ๋”ฐ๋ฅธ ๅ…่ฒฌ 65 2. ์‚ฌ๋ฉด ์ด์ „ ็Šฏ็ฝช์— ๋Œ€ํ•œ ๅ…่ฒฌ 69 ์ œ3์ ˆ ๅ‘Š่บซ ่ฟฝๅฅช์˜ ํŠน์ˆ˜์‚ฌ๋ก€ 73 1. ๊ด€์› ๅคซไบบ์˜ ็ˆต็‰’ ่ฟฝๅฅช 73 2. ็ฝฒ็ถ“์˜ ๆœชๅ‚™์— ๋”ฐ๋ฅธ ๅ‘Š่บซ ่ฟฝๅฅช 75 ์ œ4์ ˆ ๅ‘Š่บซ ่ฟฝๅฅช์˜ ์ถ”์ด์™€ ๋ถ„์„ 78 ์ œ4์žฅ ๅ‘Š่บซ์˜ ้‚„็ตฆ 83 ์ œ1์ ˆ ๅ‘Š่บซ ้‚„็ตฆ์˜ ์œ ํ˜• 83 1. ์ •๊ธฐ์  ้‚„็ตฆ๊ณผ ์œ ํšจ๊ธฐ๊ฐ„ ๋ฌธ์ œ 83 2. ์‚ฌ๋ฉด์— ์˜ํ•œ ้‚„็ตฆ 86 1) ็Ž‹ๅฎค ไธ€ๅ“ก์˜ ๆ…ถๅผ”ไบ‹ 88 2) ์ž์—ฐ์žฌํ•ด์— ๋”ฐ๋ฅธ ๋ฏผ์‹ฌ ์ˆ˜์Šต์ฑ… 90 3) ์ •์น˜์  ๊ณ ๋ ค์— ๋”ฐ๋ฅธ ้‚„็ตฆ 93 4) ์‹ค๋ฌด์  ๊ณ ๋ ค์— ๋”ฐ๋ฅธ ้‚„็ตฆ 95 ์ œ2์ ˆ ๅ‘Š่บซ ้‚„็ตฆ์— ๋Œ€ํ•œ ๋…ผ์˜ 98 1. ๅ‘Š่บซ ้‚„็ตฆ ๋ฐฐ์ œ์‚ฌ์œ ์˜ ๋ณ€๋™ 98 2. ่ด“ๆฑšใ†็ถฑๅธธ็ฝช์— ๋Œ€ํ•œ ๅ‘Š่บซ ้‚„็ตฆ ๋ฐฐ์ œ ๋…ผ์˜ 104 3. ๅ‘Š่บซ ้‚„็ตฆ์˜ ํ•„์š”์„ฑ์— ๋Œ€ํ•œ ๋‹น๋Œ€์˜ ์ธ์‹๊ณผ ็•ฐ๏ฅ 107 ์ œ3์ ˆ ๅ‘Š่บซ ้‚„็ตฆ์˜ ์ถ”์ด์™€ ๋ถ„์„ 111 ์ œ4์žฅ ๊ฒฐ๋ก  104 ์ฐธ๊ณ ๋ฌธํ—Œ 117 ๋ถ€๋ก 123 Abstract 139Maste
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