148 research outputs found

    The Effect of Lubricant Containing Copper Alloy Nano-Powder on a Diesel Engine

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    The characteristic of a lubricant is very important when it is used under severe running condition. Especially on diesel engines the better lubrication performance is required because of the extreme condition such as high temperature and pressure by combustion process in a cylinder. Many researches work for improving a boundary lubrication performance have been executed by using solid lubricant but this general lubricant has not been applied to engine due to the extreme condition of high temperature and pressure in a cylinder. Recently, the solid lubricant which contained a copper nickel alloy has been introduced and studied. In this study, the author investigated the effects of lubricant which contains the copper nickel alloy nano-powder on a diesel engine performance. The results were manifested to show the increase of maximum torque, and the decrease of cylinder pressure, exhaust gas temperature, scavenging air temperature, CO emission according to running time lapse at various speed and torque on a diesel engine. It was assured that the lubricant which contains the copper nickel alloy nano-powder decreases friction and wear, and simultaneously increases the sealing effect in a cylinder on diesel engines.๋ชฉ์ฐจ Abstract = iii ์ œ1์žฅ ์„œ๋ก  = 1 ์ œ2์žฅ ์œคํ™œ์œ  ๋ฐ ์ฒจ๊ฐ€์ œ = 4 2.1 ๊ธฐ๊ด€์˜ ์œคํ™œ = 4 2.1.1 ์œคํ™œ์ƒํƒœ = 5 2.1.2 ์œคํ™œ์œ ์˜ ๊ธฐ๋Šฅ = 6 2.1.3 ์œคํ™œ์œ ์˜ ์กฐ๊ฑด = 7 2.2 ์ฒจ๊ฐ€์ œ์˜ ์ข…๋ฅ˜ = 8 2.3 ์ฒจ๊ฐ€์ œ์˜ ํŠน์„ฑ = 10 2.3.1 ์ฒญ์ •๋ถ„์‚ฐ์ œ = 10 2.3.2 ์‚ฐํ™”๋ฐฉ์ง€์ œ ๋ฐ ๋งˆ๋ชจ๋ฐฉ์ง€์ œ = 12 2.3.3 ์ ๋„์ง€์ˆ˜ํ–ฅ์ƒ์ œ = 13 2.4 ๋‚˜๋…ธ๊ตฌ๋ฆฌํ•ฉ๊ธˆ์ฒจ๊ฐ€ ์œคํ™œ์ œ = 15 ์ œ3์žฅ ์‹คํ—˜์žฅ์น˜ ๋ฐ ๊ณ„์ธก์žฅ์น˜ = 17 3.1 ์‹คํ—˜์žฅ์น˜ = 17 3.1.1 ๋Œ€์ƒ๊ธฐ๊ด€ = 17 3.1.2 ๋ƒ‰๊ฐ๊ณ„ํ†ต = 21 3.2 ๊ณ„์ธก์žฅ์น˜ = 22 3.2.1 ๋™๋ ฅ์ธก์ •์žฅ์น˜ = 24 3.2.2 ๋ฐฐ๊ธฐ๊ฐ€์Šค๋ถ„์„๊ธฐ = 26 3.2.3 ์‹ค๋ฆฐ๋”๋‚ด ์••๋ ฅ๊ฒ€์ถœ์žฅ์น˜ = 28 3.2.4 ์—ฐ๋ฃŒ์†Œ๋น„์œจ ์ธก์ •์žฅ์น˜ = 30 ์ œ4์žฅ ์‹คํ—˜๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ = 31 4.1 ์ตœ๋Œ€ํ† ํฌ = 32 4.2 ์‹ค๋ฆฐ๋”๋‚ด ์—ฐ์†Œ ์ตœ๊ณ ์••๋ ฅ = 33 4.3 ์—ฐ๋ฃŒ์†Œ๋น„์œจ = 38 4.4 ๋ฐฐ๊ธฐ๊ฐ€์Šค์˜จ๋„ = 42 4.5 ๊ธ‰๊ธฐ์˜จ๋„ = 45 4.6 ๋ฐฐ๊ธฐ๋ฐฐ์ถœ๋ฌผ = 48 4.6.1 NOx์˜ ๋ฐฐ์ถœ๋†๋„ = 48 4.6.2 CO์˜ ๋ฐฐ์ถœ๋†๋„ = 51 4.6.3 Oโ‚‚์˜ ๋†๋„ = 55 4.6.4 ์Šค๋ชจํฌ์˜ ๋ฐฐ์ถœ๋†๋„ = 58 ์ œ5์žฅ ๊ฒฐ๋ก  = 61 ์ฐธ๊ณ ๋ฌธํ—Œ = 6

    ๋ฏธ์„ธ ๋ฌผ์ฒด ์ˆ˜์†ก์„ ์œ„ํ•œ ๋‹ˆํ‹ฐ๋†€ ๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๊ณผ, 2021.8. ์•ˆ์„ฑํ›ˆ.A micro-robot is an attractive tool that performs micro-scale tasks within the system by remote control. Most micro-robots are driven by an external force and its characteristic differs according to the type of the external force. Therefore, micro-robots have been developed to utilize the type of external force suitable for their respective application fields. Among various external forces, a light-driven micro-robot has superior controllability in terms of precision and regionality. Recently, lots of studies have been conducted on micro-robot for performing micro-scale tasks in bio-medical fields such as drug transport, surgery and diagnosis. Especially in micro-object transportation, since sophisticated control is required, a light-driven micro-robot which has excellent controllability is advantageous. Micro-robots for transportation so far have focused on force, speed and control, but a few of them have a function of holding objects to avoid object loss. Our micro-object transportation Ni-Ti structure robot(MTNs) not only has sufficient thrust force and speed but also has the capability of holding objects and physically separating them from external systems, thus demonstrating the advantage of excellent transport stability and controllability. It can be fabricated and controlled automatically by a vision-guided laser control system. In consideration of mass production, we designed the micro-robot so that the fabrication process has low cost in terms of time, price and labor, and can be operated by commercial equipment. The newly designed transport micro-robot, which displays holding capability and enhanced control, can be used as an actuator in lab-on-a-chip testing.๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡์€ ์›๊ฒฉ ์ œ์–ด๋ฅผ ํ†ตํ•ด ์‹œ์Šคํ…œ ๋‚ด์—์„œ ๋ฏธ์„ธ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๋„๊ตฌ๋กœ์จ, ์•ฝ๋ฌผ ์ˆ˜์†ก, ์ˆ˜์ˆ , ์ง„๋‹จ๊ณผ ๊ฐ™์€ ์ƒ๋ฌผ ์˜ํ•™ ๋ถ„์•ผ์—์„œ ๋งŽ ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡์€ ์™ธ๋ ฅ์„ ํ†ตํ•ด ์—๋„ˆ์ง€๋ฅผ ๊ณต๊ธ‰ ๋ฐ›๊ณ , ์ œ์–ด๋˜๋ฏ€๋กœ, ์ด์šฉํ•˜๋Š” ์™ธ๋ ฅ์˜ ์ข…๋ฅ˜์— ๋”ฐ๋ผ ๊ตฌ๋™ ํŠน์„ฑ์ด ๋‹ฌ๋ผ์ง„๋‹ค. ์—ฌ๋Ÿฌ ์™ธ๋ ฅ ์ค‘ ๊ด‘ ๊ตฌ๋™ํ˜• ๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡์€ ์ •๋ฐ€ํ•˜๊ณ  ๊ตญ์†Œ์ ์ธ ์ œ์–ด๊ฐ€ ๊ฐ€ ๋Šฅํ•˜๋‹ค๋Š” ์žฅ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ •๊ตํ•œ ์ œ์–ด๊ฐ€ ํ•„์š”ํ•œ ๋งˆ์ดํฌ๋กœ ๋ฌผ์ฒด ์šด๋ฐ˜ ์ž‘์—…์— ๊ด‘๊ตฌ๋™ํ˜• ๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡์ด ์ ํ•ฉํ•˜๋‹ค. ์ง€๊ธˆ๊นŒ์ง€ ์šด์†ก์šฉ ๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡์€ ํž˜, ์†๋„ ๋ฐ ์ œ์–ด์— ์ค‘์ ์„ ๋‘์—ˆ์ง€๋งŒ, ๋ฌผ์ฒด ์œ ์‹ค์„ ๋ฐฉ ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ๋ฌผ์ฒด๋ฅผ ์žก๋Š” ๊ธฐ๋Šฅ์„ ๊ฐ€์ง„ ๋กœ๋ด‡์€ ๊ฑฐ์˜ ์—†์Šต๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๊ฐ€ ๊ฐœ ๋ฐœํ•œ ๋ฏธ์„ธ ๋ฌผ์ฒด ์ˆ˜์†ก Ni-Ti ๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡์€ ์ถฉ๋ถ„ํ•œ ์ถ”์ง„๋ ฅ๊ณผ ์†๋„๋ฅผ ๊ฐ€์งˆ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์šด๋ฐ˜ ๋ชฉํ‘œ ๋ฌผ์ฒด๋ฅผ ํฌํšํ•œ ์ƒํƒœ๋กœ ์™ธ๋ถ€ ์‹œ์Šคํ…œ๊ณผ ๊ฒฉ๋ฆฌ ํ•œ ์ƒํƒœ๋กœ ์šด๋ฐ˜ํ•  ์ˆ˜ ์žˆ๋Š” ๋Šฅ๋ ฅ์„ ๊ฐ–์ถ”๊ณ  ์žˆ์–ด ์šฐ์ˆ˜ํ•œ ์ˆ˜์†ก ์•ˆ์ •์„ฑ๊ณผ ์ œ ์–ด ํŽธ์˜์„ฑ ๋“ฑ ์ด์ ์„ ๋ณด์ธ๋‹ค. ๋ณธ ๋กœ๋ด‡์€ ๋น„์ „ ์œ ๋„ ๋ ˆ์ด์ € ์ œ์–ด ์‹œ์Šคํ…œ์— ์˜ํ•ด ์ž๋™์œผ๋กœ ์ œ์ž‘์ด ๊ฐ€๋Šฅ ํ•˜๋‹ค. ์–‘์‚ฐ์„ ๊ณ ๋ คํ•˜์—ฌ ์ƒ์šฉ์žฅ๋น„ ๋งŒ์œผ๋กœ ์ œ์ž‘ ๊ณต์ •์„ ๊ตฌ์„ฑํ•˜์˜€์œผ๋ฉฐ, ์‹œ ๊ฐ„, ๊ฐ€๊ฒฉ ๊ทธ๋ฆฌ๊ณ  ๋…ธ๋™๋ ฅ ์ธก๋ฉด์—์„œ ์ €๋ ดํ•˜๋„๋ก ๋งˆ์ดํฌ๋กœ ๋กœ๋ด‡์„ ์„ค๊ณ„ํ•˜์˜€ ๋‹ค. ํฌํš ๋Šฅ๋ ฅ๊ณผ ํ–ฅ์ƒ๋œ ์ œ์–ด ๊ธฐ๋Šฅ์„ ๊ฐ€์ง„ ๋ณธ ๋กœ๋ด‡์€ ๋žฉ ์˜จ์–ด ์นฉ ํ…Œ์ŠคํŠธ ์—์„œ ์•ก์ถ”์—์ดํ„ฐ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค.Chapter 1. Introduction 1 1.1. Reviews on micro robots for bio-medical applications 1 1.2. Reviews on micro transportation 3 1.3. Reviews on micro robots using external forces 4 1.4. Reviews on light-driven Ni-Ti micro robots 5 1.5. Purpose of research 7 Chapter 2. Ni-Ti Unit 8 2.1. Actuation mechanism 8 2.2. Fabrication of Ni-Ti unit 10 Chapter 3. Fabrication process 11 3.1. Overview of fabrication process 11 3.2. Formation morphing control 13 3.2.1. Single unit control 13 3.2.2. Vision-guided laser control system 15 3.2.3. Control strategy 17 3.3. Bonding process 19 3.3.1. Adhesion applying using EHD 19 3.3.2. Adhesion applying using microstage 22 Chapter 4. Experiment and Application 23 4.1. Force measurement experiment 23 4.2. Energy efficiency comparison 25 4.3. Functionality of transportation 27 Chapter 5. Conclusion 29 Bibliography 30 Abstract in Korean 35์„

    Microchannel network hydrogel induced ischemic blood perfusion connection

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    Angiogenesis induction into damaged sites has long been an unresolved issue. Local treatment with pro-angiogenic molecules has been the most common approach. However, this approach has critical side effects including inflammatory coupling, tumorous vascular activation, and off-target circulation. Here, the concept that a structure can guide desirable biological function is applied to physically engineer three-dimensional channel networks in implant sites, without any therapeutic treatment. Microchannel networks are generated in a gelatin hydrogel to overcome the diffusion limit of nutrients and oxygen three-dimensionally. Hydrogel implantation in mouse and porcine models of hindlimb ischemia rescues severely damaged tissues by the ingrowth of neighboring host vessels with microchannel perfusion. This effect is guided by microchannel size-specific regenerative macrophage polarization with the consequent functional recovery of endothelial cells. Multiple-site implantation reveals hypoxia and neighboring vessels as major causative factors of the beneficial function. This technique may contribute to the development of therapeutics for hypoxia/inflammatory-related diseases.ope

    Utility of Diffusion and Magnetization Transfer MRI in Cervical Spondylotic Myelopathy: A Pilot Study

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    Diffusion tensor imaging (DTI) and magnetization transfer (MT) magnetic resonance imaging (MRI) can help detect spinal cord pathology, and tract-specific analysis of their parameters, such as fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD), radial diffusivity (RD) and MT ratio (MTR), can give microstructural information. We performed the tract-based acquisition of MR parameters of three major motor tracts: the lateral corticospinal (CS), rubrospinal (RuS) tract, and lateral reticulospinal (RS) tract as well as two major sensory tracts, i.e., the fasciculus cuneatus (FC) and spinal lemniscus, to detect pathologic change and find correlations with clinical items. MR parameters were extracted for each tract at three levels: the most compressed lesion level and above and below the lesion. We compared the MR parameters of eight cervical spondylotic myelopathy patients and 12 normal controls and analyzed the correlation between clinical evaluation items and MR parameters in patients. RuS and lateral RS showed worse DTI parameters at the lesion level in patients compared to the controls. Worse DTI parameters in those tracts were correlated with weaker power grasp at the lesion level. FC and lateral CS showed a correlation between higher RD and lower FA and MTR with a weaker lateral pinch below the lesion level.ope

    Surgical Ligation of Patent Ductus Arteriosus Using the Descending Aortic Approach in Two Dogs

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    Surgical ligation is the treatment of the choice in patients with patent ductus arteriosus (PDA). This case series presents two cases of PDA, one with and one without persistent left cranial vena cava (PLCVC), treated with surgical ligation through the descending aortic approach with mini-thoracotomy. There were no specific complications during the surgical procedures. The descending aortic approach would be an alternative method for dissection of the PDA.ope

    Clinical Characteristics and Risk Factors of First-Ever Stroke in Young Adults: A Multicenter, Prospective Cohort Study

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    Stroke in young adults has catastrophic consequences and has increased in prevalence, contrary to the trends of most other diseases. This study aimed to determine the major characteristics and risk factors for stroke in younger adults compared with older adults. From the Korean Stroke Cohort for Functioning and Rehabilitation, 10,584 patients with first-ever stroke between August 2012 and March 2015 were enrolled retrospectively and divided into younger (age โ‰ค 45) and older groups (age > 45). The clinical characteristics and risk factors of stroke were compared between the younger and older groups. The younger group comprised 915 patients (8.6%). The proportion of hemorrhage strokes in the younger group (42.3%) was significantly higher than in the older group (20.0%) (p < 0.001). Obesity, current smoking, and heavy alcohol consumption were significantly more common risk factors in the younger group than in the older group for all stroke types, whereas hypertension, diabetes mellitus, hyperlipidemia, atrial fibrillation, and coronary heart disease were significantly more frequent in the older group (both p < 0.001). The major risk factors in the younger group may be lifestyle-related. Therefore, increasing awareness of lifestyle-related risk factors may be necessary to prevent stroke in young adults.ope

    Changes in Bihemispheric Structural Connectivity Following Middle Cerebral Artery Infarction

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    This study investigated the changes in the structural connectivity of the bilateral hemispheres over time following a middle cerebral artery infarction. Eighteen patients in the subacute group and nine patients in the chronic group with mild upper extremity motor impairment (Fugl-Meyer motor assessment score for the upper limb > 43) following middle cerebral artery infarction were retrospectively evaluated in this study. All the patients underwent T1-weighted and diffusion tensor imaging. Tract-based statistical analyses of fractional anisotropy were used to compare the changes in the bilateral structural connectivity with those of age-matched normal controls. The corticospinal tract pathway of the affected hemisphere, corpus callosum, and corona radiata of the unaffected hemisphere had decreased structural connectivity in the subacute group, while the motor association area and anterior corpus callosum in the bilateral frontal lobes had increased structural connectivity in the chronic group. The bilateral hemispheres were influenced even in patients with mild motor impairment following middle cerebral artery infarction, and the structural connectivity of the bilateral hemispheres changed according to the time following the stroke.ope

    Development and accuracy evaluation of field soil temperature prediction model by depth using artificial intelligence and meteorological parameters

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋ฐ”์ด์˜ค์‹œ์Šคํ…œ๊ณตํ•™๊ณผ, 2022.2. ์ด์ค‘์šฉ.Precise agricultural technology that analyzes weather, soil environment and crop information using various advanced technologies, minimizes resource use, and maximizes productivity, needs to be applied in the field, but it is difficult to control the environment due to the influence of external weather. In the case of this, the efficiency is low compared to the cost of the equipment, so there are few cases where it is applied to the actual agricultural environment. Of these, soil is directly involved in crop growth, so it is essential to acquire soil environment data for the normal growth of crops. Because it is important information, it is necessary to acquire data according to depth. In this study, as a way to acquire soil temperature data by depth without the cost of measuring equipment, we made a model that predicts soil temperature by depth over time using artificial intelligence, and compares the predicted value derived through the model with the actual value. and, it was reviewed whether it can be applied to actual agriculture. Time unit geothermal data at three depths of 10 cm, 20 cm, and 30 cm using three machine learning models, RNN, LSTM, and GRU, and five meteorological factors: temperature, humidity, wind direction, wind speed, and insolation, which were judged to have an influence on the geothermal temperature. The optimal combination of parameters and models to accurately predict Confirmed. And, the applicability to actual agricultural activities was judged by confirming the similarity of the predicted values โ€‹โ€‹made from the two combinations evaluated as having the best performance through pattern analysis and residual analysis with the actual values. Evaluation was made through the values โ€‹โ€‹of three indicators: Root Mean Square Error (RMSE), Nash - Sutcliffe Efficiency (NSE), and Determination Coefficient (). As a result of the experiment, the combination of parameters and models using temperature, humidity, wind speed, solar radiation, average monthly temperature data and LSTM model showed the highest performance. and temperature, humidity, wind speed, insolation, soil temperature data by depth in other regions, and the parameter and model combination using the RNN model showed the second highest performance. In addition, as a result of residual analysis with the measured values, it was confirmed that the residuals were normal and the outlier rate at all depths was around 1%. Both the predicted values โ€‹โ€‹of the two datasets and model combinations selected through the experimental process were judged to show high agreement with the actual values โ€‹โ€‹through evaluation index, pattern analysis, and residual analysis, and were evaluated as applicable to actual agricultural activities.๊ฐ์ข… ์ฒจ๋‹จ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ ๊ธฐ์ƒ, ํ† ์–‘ ํ™˜๊ฒฝ ๋ฐ ์ž‘๋ฌผ ์ •๋ณด๋ฅผ ๋ถ„์„ํ•˜๊ณ  ์ž์›์˜ ์‚ฌ์šฉ์„ ์ตœ์†Œํ™”ํ•˜๋ฉฐ ์ƒ์‚ฐ์„ฑ์„ ๊ทน๋Œ€ํ™”ํ•˜๋Š” ์ •๋ฐ€๋†์—… ๊ธฐ์ˆ ์ด ๋…ธ์ง€์—์„œ๋„ ์ ์šฉ๋˜์–ด์•ผ ํ•  ํ•„์š”๊ฐ€ ์žˆ์ง€๋งŒ ์™ธ๋ถ€ ๊ธฐ์ƒ์˜ ์˜ํ–ฅ์„ ๋งŽ์ด ๋ฐ›์•„ ํ™˜๊ฒฝ์ œ์–ด๊ฐ€ ์–ด๋ ค์šด ๋…ธ์ง€์˜ ๊ฒฝ์šฐ ๋ฐœ์ƒ๋˜๋Š” ์žฅ๋น„์˜ ๋น„์šฉ์— ๋น„ํ•ด ํšจ์œจ์„ฑ์ด ๋‚ฎ์•„ ์‹ค์ œ ๋†์—…ํ™˜๊ฒฝ์— ์ ์šฉ๋˜๋Š” ์‚ฌ๋ก€๊ฐ€ ์ ๋‹ค. ์ด ์ค‘ ํ† ์–‘์€ ๋†์ž‘๋ฌผ ์ƒ์žฅ์— ์ง์ ‘์ ์œผ๋กœ ๊ด€์—ฌํ•˜๋ฏ€๋กœ ์ž‘๋ฌผ์˜ ์ •์ƒ์  ์ƒ์žฅ์„ ์œ„ํ•ด ํ† ์–‘ ํ™˜๊ฒฝ ๋ฐ์ดํ„ฐ๋ฅผ ์Šต๋“ํ•˜๋Š” ๊ฒƒ์€ ํ•„์ˆ˜์ ์ด๋ฉฐ, ํ† ์–‘์˜จ๋„ ๋ฐ์ดํ„ฐ์˜ ๊ฒฝ์šฐ ๋น„๋ฃŒ ์‹œ๋น„, ๊ด€์ˆ˜, ๋†์ž‘์—… ์ผ์ • ๋“ฑ ๋†์—…ํ™œ๋™์˜ ์—ฌ๋Ÿฌ ์˜์‚ฌ๊ฒฐ์ •์„ ํ•˜๋Š”๋ฐ ํ•„์š”ํ•œ ์ค‘์š”ํ•œ ์ •๋ณด์ด๊ธฐ ๋•Œ๋ฌธ์— ๊นŠ์ด์— ๋”ฐ๋ฅธ ๋ฐ์ดํ„ฐ๋ฅผ ์Šต๋“ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณ„์ธก์žฅ์น˜์— ๋ฐœ์ƒ๋˜๋Š” ๋น„์šฉ ์—†์ด ๊นŠ์ด๋ณ„ ํ† ์–‘์˜จ๋„ ๋ฐ์ดํ„ฐ๋ฅผ ์Šต๋“ํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉํŽธ์œผ๋กœ ์ธ๊ณต์ง€๋Šฅ์„ ํ™œ์šฉํ•˜์—ฌ ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ๊นŠ์ด๋ณ„ ํ† ์–‘์˜จ๋„๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ชจ๋ธ์„ ๋งŒ๋“ค๊ณ  ๋ชจ๋ธ์„ ํ†ตํ•ด ๋„์ถœ๋œ ์˜ˆ์ธก๊ฐ’์„ ์‹ค์ธก๊ฐ’๊ณผ ๋น„๊ตํ•˜์—ฌ ์‹ค์ œ ๋†์—…์— ์ ์šฉ์ด ๊ฐ€๋Šฅํ•œ์ง€ ๊ฒ€ํ† ํ•˜์˜€๋‹ค. RNN, LSTM, GRU ์„ธ ๊ฐœ์˜ ๋จธ์‹ ๋Ÿฌ๋‹ ๋ชจ๋ธ๊ณผ ์ง€์˜จ์— ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋ผ ํŒ๋‹จ๋˜์—ˆ๋˜ ๊ธฐ์˜จ, ์Šต๋„, ํ’ํ–ฅ, ํ’์†, ์ผ์‚ฌ๋Ÿ‰ ๋‹ค์„ฏ ๊ฐ€์ง€ ๊ธฐ์ƒ์ธ์ž๋ฅผ ํ™œ์šฉํ•˜์—ฌ 10cm, 20cm, 30cm ์„ธ ๊ณณ์˜ ๊นŠ์ด์—์„œ์˜ ์‹œ๊ฐ„ ๋‹จ์œ„ ์ง€์˜จ ๋ฐ์ดํ„ฐ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์˜ˆ์ธกํ•˜๋Š” ์ตœ์ ์˜ ๋งค๊ฐœ๋ณ€์ˆ˜์™€ ๋ชจ๋ธ ์กฐํ•ฉ์„ ์„ ์ •ํ•˜์˜€๊ณ , ์‹œ๊ฐ„์— ๋”ฐ๋ผ ์ฃผ๊ธฐ์„ฑ์„ ๊ฐ–๋Š” ์›”ํ‰๊ท ๊ธฐ์˜จ, ์‹ค์‹œ๊ฐ„ ํƒœ์–‘๊ณ ๋„, ํƒ€์ง€์—ญ ๊นŠ์ด๋ณ„ ํ† ์–‘์˜จ๋„ ๋ฐ์ดํ„ฐ ์„ธ ๊ฐ€์ง€ ์ฃผ๊ธฐ์„ฑ ์ธ์ž๋ฅผ ์ถ”๊ฐ€ํ•˜์—ฌ ์ •ํ™•๋„ ์ƒ์Šน์— ๊ธฐ์—ฌํ•˜๋Š”์ง€ ๊ทธ ์—ฌ๋ถ€๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ฐ€์žฅ ์„ฑ๋Šฅ์ด ์ข‹๋‹ค๊ณ  ํ‰๊ฐ€๋œ ๋‘ ๊ฐœ ์กฐํ•ฉ์œผ๋กœ๋ถ€ํ„ฐ ๋งŒ๋“ค์–ด์ง„ ์˜ˆ์ธก๊ฐ’์„ ์‹ค์ธก๊ฐ’๊ณผ์˜ ํŒจํ„ด๋ถ„์„๊ณผ ์ž”์ฐจ๋ถ„์„์„ ํ†ตํ•ด ์œ ์‚ฌ์„ฑ์„ ํ™•์ธํ•˜์—ฌ ์‹ค์ œ ๋†์—…ํ™œ๋™์— ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ํŒ๋‹จํ•˜์˜€๋‹ค. ํ‰๊ฐ€๋Š” Root Mean Square Error(RMSE), Nash - Sutcliffe Efficiency(NSE), Determination Coefficient() ์„ธ ๊ฐ€์ง€ ์ง€ํ‘œ์˜ ๊ฐ’์„ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ ๊ธฐ์˜จ, ์Šต๋„, ํ’์†, ์ผ์‚ฌ๋Ÿ‰, ์›”ํ‰๊ท ๊ธฐ์˜จ ๋ฐ์ดํ„ฐ์™€ LSTM ๋ชจ๋ธ์„ ํ™œ์šฉํ•œ ๋งค๊ฐœ๋ณ€์ˆ˜์™€ ๋ชจ๋ธ ์กฐํ•ฉ์ด ๊ฐ€์žฅ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์˜€๊ณ , ๊ธฐ์˜จ, ์Šต๋„, ํ’์†, ์ผ์‚ฌ๋Ÿ‰, ํƒ€์ง€์—ญ์˜ ๊นŠ์ด๋ณ„ ํ† ์–‘์˜จ๋„ ๋ฐ์ดํ„ฐ์™€ RNN ๋ชจ๋ธ์„ ํ™œ์šฉํ•œ ๋งค๊ฐœ๋ณ€์ˆ˜์™€ ๋ชจ๋ธ ์กฐํ•ฉ์ด ๋‹ค์Œ์œผ๋กœ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์˜€์œผ๋ฉฐ, ์‹ค์ธก๊ฐ’๊ณผ์˜ ์ž”์ฐจ๋ถ„์„ ๊ฒฐ๊ณผ ์ž”์ฐจ์˜ ์ •๊ทœ์„ฑ์ด ์žˆ์œผ๋ฉฐ ๋ชจ๋“  ๊นŠ์ด์—์„œ์˜ ์ด์ƒ์น˜์œจ์ด 1% ๋‚ด์™ธ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ณผ์ •์„ ํ†ตํ•ด ์„ ๋ณ„๋œ ๋‘ ๋ฐ์ดํ„ฐ์„ธํŠธ, ๋ชจ๋ธ ์กฐํ•ฉ์˜ ์˜ˆ์ธก๊ฐ’ ๋ชจ๋‘ ํ‰๊ฐ€ ์ง€ํ‘œ, ํŒจํ„ด๋ถ„์„, ์ž”์ฐจ๋ถ„์„์„ ํ†ตํ•ด ์‹ค์ธก๊ฐ’๊ณผ ๋†’์€ ์ผ์น˜์„ฑ์„ ๋ณด์ธ๋‹ค๊ณ  ํŒ๋‹จ๋˜๋ฉฐ, ์‹ค์ œ ๋†์—…ํ™œ๋™์— ์ ์šฉ ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ํ‰๊ฐ€ํ•˜์˜€๋‹ค.1. ์„œ๋ก  1 1.1. ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 1 1.2. ๋ฌธ์ œ์ ๊ณผ ํ•„์š”์„ฑ 4 1.3. ์—ฐ๊ตฌ๋ชฉ์  5 2. ๋ฌธํ—Œ์กฐ์‚ฌ 6 2.1. ์šฉ์–ด์™€ ๊ด€๋ จ ์ด๋ก  6 2.1.1 ๊ธฐ์ƒ์ธ์ž ๋งค๊ฐœ๋ณ€์ˆ˜ 6 2.1.1.1. ๊ธฐ์˜จ 6 2.1.1.2. ์Šต๋„ 6 2.1.1.3. ํ’ํ–ฅ ๋ฐ ํ’์† 7 2.1.1.4. ์ผ์‚ฌ๋Ÿ‰ 8 2.1.2. ์ฃผ๊ธฐ์„ฑ์„ ๋‚˜ํƒ€๋‚ด๋Š” ๋งค๊ฐœ๋ณ€์ˆ˜ 8 2.1.2.1. ์ „๊ตญ ์›”ํ‰๊ท ๊ธฐ์˜จ 9 2.1.2.2. ํƒœ์–‘๊ณ ๋„ 10 2.2. ์ง€์˜จ์˜ ํŠน์„ฑ 11 2.3. ์ง€์˜จ์ด ์ž‘๋ฌผ์ƒ์œก ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 16 2.4. ๋…ธ์ง€ ์Šค๋งˆํŠธํŒœ์—์„œ์˜ ํ™˜๊ฒฝ๋ชจ๋‹ˆํ„ฐ๋ง ์—ฐ๊ตฌ 18 2.5. ๋…ธ์ง€์—์„œ ๊นŠ์ด๋ณ„ ์ง€์˜จ๋ฐ์ดํ„ฐ ์˜ˆ์ธก ์—ฐ๊ตฌ 21 2.6. ์‹œ๊ณ„์—ด๋ฐ์ดํ„ฐ ์˜ˆ์ธก์„ ์œ„ํ•œ ์—ฐ๊ตฌ 24 3. ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 26 3.1. ๊ณต๊ณต๊ธฐ์ƒ์ž๋ฃŒ ํ™•๋ณด 28 3.1.1. ๊ณต๊ณต๊ธฐ์ƒ๋Œ€ ๋ฐ์ดํ„ฐ 28 3.1.2. ํ•™์Šต์šฉ ๋ฐ์ดํ„ฐ์˜ ์ „์ฒ˜๋ฆฌ 29 3.2. ์ง€์˜จ์˜ˆ์ธก ํ‰๊ฐ€์šฉ ์ง€์˜จ๋ฐ์ดํ„ฐ 30 3.2.1. ์™ธ๋ถ€๊ธฐ์ƒ๋Œ€ ๋ฐ ํ† ์–‘์„ผ์„œ 30 3.3. ๋งค๊ฐœ๋ณ€์ˆ˜ ์„ ์ • 33 3.4. ์ธ๊ณต์ง€๋Šฅ ๋ชจ๋ธ ์„ ์ •๊ณผ ํ•™์Šต 34 3.5. ๊นŠ์ด๋ณ„ ์ง€์˜จ ์˜ˆ์ธก ์ ˆ์ฐจ ๋ฐ ํ‰๊ฐ€ ๋ฐฉ๋ฒ• 37 4. ๊ฒฐ๊ณผ 38 4.1. ๊ธฐ์ƒ์ธ์ž ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•œ ์ตœ์ ์˜ ๋งค๊ฐœ๋ณ€์ˆ˜ ์„ ํƒ 38 4.1.1. ์†Œ๊ฑฐ๋ฅผ ์ด์šฉํ•œ ๊ธฐ์ƒ์ธ์ž ์˜ํ–ฅ๋ ฅ ์ˆœ์„œ ํ‰๊ฐ€ 38 4.1.2. ์ „์ง„ ์„ ํƒ๋ฒ•์„ ํ†ตํ•œ ๊ธฐ์ƒ์ธ์ž ์ตœ์ ์˜ ๋งค๊ฐœ๋ณ€์ˆ˜ ์กฐํ•ฉ ์„ ํƒ 51 4.2. ์ฃผ๊ธฐ์„ฑ๋ณ€์ˆ˜ 62 4.3. ๋ฐ์ดํ„ฐ ๋น„๊ต ๋ฐ ๋ถ„์„ 74 4.3.1. ์ถ”์„ธ ๋น„๊ต 74 4.3.1.1. 10cm 75 4.3.1.2. 20cm 80 4.3.1.3. 30cm 85 4.3.2. ์ž”์ฐจ ๋ถ„์„ 90 4.3.2.1. Q-Q Plot์„ ์ด์šฉํ•œ ์ •๊ทœ์„ฑ ์ง„๋‹จ 90 4.3.2.2. ํ‘œ์ค€ํ™” ์ž”์ฐจ๋ฅผ ์ด์šฉํ•œ ์ด์ƒ์น˜ ๊ฒ€์ถœ 91 4.3.2.2.1. ์˜ˆ์ธก๊ฐ’A 92 4.3.2.2.2. ์˜ˆ์ธก๊ฐ’B 95 5. ๊ฒฐ๋ก  97 6. ์ฐธ๊ณ ๋ฌธํ—Œ 100์„

    Brain Connectivity Affecting Gait Function After Unilateral Supratentorial Stroke

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    Gait dysfunction is a leading cause of long-term disability after stroke. The mechanisms underlying recovery of gait function are unknown. We retrospectively evaluated the association between structural connectivity and gait function in 127 patients with unilateral supratentorial stroke (>1 month after stroke). All patients underwent T1-weighted, diffusion tensor imaging and functional ambulation categorization. Voxel-wise linear regression analyses of the images were conducted using fractional anisotropy, mean diffusivity, and mode of anisotropy mapping as dependent variables, while the functional ambulation category was used as an independent variable with age and days after stroke as covariates. The functional ambulation category was positively associated with increased fractional anisotropy in the lesioned cortico-ponto-cerebellar system, corona radiata of the non-lesioned corticospinal tract pathway, bilateral medial lemniscus in the brainstem, and the corpus callosum. The functional ambulation category was also positively associated with increased mode of anisotropy in the lesioned posterior corpus callosum. In conclusion, structural connectivity associated with motor coordination and feedback affects gait function after stroke. Diffusion tensor imaging for evaluating structural connectivity can help to predict gait recovery and target rehabilitation goals after stroke.ope

    3D Bioprinted Artificial Trachea with Epithelial Cells and Chondrogenic-Differentiated Bone Marrow-Derived Mesenchymal Stem Cells

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    Tracheal resection has limited applicability. Although various tracheal replacement strategies were performed using artificial prosthesis, synthetic stents and tissue transplantation, the best method in tracheal reconstruction remains to be identified. Recent advances in tissue engineering enabled 3D bioprinting using various biocompatible materials including living cells, thereby making the product clinically applicable. Moreover, clinical interest in mesenchymal stem cell has dramatically increased. Here, rabbit bone marrow-derived mesenchymal stem cells (bMSC) and rabbit respiratory epithelial cells were cultured. The chondrogenic differentiation level of bMSC cultured in regular media (MSC) and that in chondrogenic media (d-MSC) were compared. Dual cell-containing artificial trachea were manufactured using a 3D bioprinting method with epithelial cells and undifferentiated bMSC (MSC group, n = 6) or with epithelial cells and chondrogenic-differentiated bMSC (d-MSC group, n = 6). d-MSC showed a relatively higher level of glycosaminoglycan (GAG) accumulation and chondrogenic marker gene expression than MSC in vitro. Neo-epithelialization and neo-vascularization were observed in all groups in vivo but neo-cartilage formation was only noted in d-MSC. The epithelial cells in the 3D bioprinted artificial trachea were effective in respiratory epithelium regeneration. Chondrogenic-differentiated bMSC had more neo-cartilage formation potential in a short period. Nevertheless, the cartilage formation was observed only in a localized area.ope
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