• 한국인터넷방송통신학회논문지
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• 제20권3호
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• pp.171-179
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• 2020

최근 연구 결과에 따르면, 컨볼루션 신경 회로망은 이미지 분류, 객체 검출, 이미지 생성 등의 문제에서 최고의 성능을 보여주고 있다. 비전 카메라를 사용한 결함 검사는 다른 결함 검사보다 경제적이기 때문에 공장 자동화에 있어서 아주 중요하고, 딥러닝의 지도학습은 전통 기계학습 방식의 결함 검사 성능을 월등히 뛰어넘었다. 하지만, 딥러닝의 지도학습은 엄청난 양의 데이터 주석 작업을 요구하기 때문에, 이를 실제 산업 현장에 적용하는 것은 효율적이지 않다. 따라서 본 연구는 최근 이미지 생성 과업에서 큰 성공을 보여주고 있는 변분 오토인코더와 적대적 생성 신경망을 활용하여 비지도 방식의 비정상 검출을 위한 신경망 회로 구조를 제안하였고, 이를 MNIST, 용접 결함 데이터에 적용하여 비정상 검출 성능을 검증하였다.

• 전기전자학회논문지
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• 제24권2호
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• pp.475-485
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• 2020

최근, 별도의 센서를 설치하지 않고 센서가 포함된 사용자의 기기로부터 제공되는 실시간 센싱 데이터를 가지고 새로운 센싱 서비스를 제공하는 크라우드센싱(Crowdsensing) 시스템이 주목받고 있다. 크라우드센싱 시스템에서는 사용자의 조작실수나 통신 문제로 인해 의미 없는 데이터가 제공되거나 보상을 얻기 위해 거짓 데이터를 제공할 수 있어 해당 이상 데이터의 탐지 및 제거가 크라우드센싱 서비스의 질을 결정짓는다. 이러한 이상데이터를 탐지하기 위해 제안되었던 방법들은 크라우드센싱의 빠른 변화 환경에 효율적이지 않다. 본 논문은 머신러닝 기술을 활용하여 지속적이고 빠르게 변화하는 센싱 데이터의 특징을 추출하고 적절한 알고리즘을 통해 모델링하여 이상데이터를 탐지하는 방법을 제안한다. 지도학습의 딥러닝 이진 분류 모델과 비지도학습의 오토인코더 모델을 사용하여 제안 시스템의 성능 및 실현 가능성을 보인다.

• 대한수의학회지
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• 제55권2호
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• pp.97-103
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• 2015

In this study, the Korean veterinary medical devices management system was evaluated relative to systems in the USA, EU, and Japan. Veterinary medical devices are regulated in Korea based on the Medical Appliance Act of 1997. This was initially supervised by the Ministry of Agriculture, Food and Rural Affairs and Korea Animal Health Products Association, and subsequently by the Animal and Plant Quarantine Agency (QIA) in 2000. These devices were classified approximately 1,400 categories as instruments, supplies, artificial insemination apparatus, and other categories. Each of these devices was assigned to four regulatory grades by the QIA in 2007. The ranking system for veterinary medical devices was implemented in 2014 with 820 products from 162 companies registered by that year. However, in vitro diagnostic devices (IVDDs) for animals were managed as medical devices and biological medicine. In vitro diagnostic reagents for treating infection diseases are not subjected to either a classification or grading system. Veterinary medical devices are currently exempt from good manufacturing practices (GMP) and device tracking requirements. Due to gradual growth of the domestic veterinary medical devices market since 2008, regulation of these devices should be improved with re-examination of IVDDs and GMP certification for the effective operating system.

• 한국식품영양과학회지
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• 제43권3호
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• pp.419-424
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• 2014

본 연구에서는 LC-MS/MS를 이용한 블루베리의 methanol과 ethyl acetate 추출 분획에 존재하는 대사체의 분석을 통해 효율적인 대사체 profiling의 가능성을 탐색하였다. LC-MS/MS에서 검출되는 대사체를 통계 처리한 결과, methanol 추출 분획에서는 5-O-feruloylquinic acid, malvidin hexoside, malvidin-3-arabinoside, petunidin-3-arabinoside, delphinidin hexoside, delphinidin, petunidin hexoside와 같은 안토시아닌 계열의 화합물들이 존재하였고, ethyl acetate 분획에서는 chlorogenic acid, chlorogenic acid dimer, 6,8-di-C-arabinopyranosylluteolin, luteolin과 같은 플라보노이드 계열의 화합물이 검출되었다. 본 연구는 기존 연구와 달리 대사체학 기법을 이용한 블루베리 추출물 전체 대사물질의 profiling을 시도한 최초의 연구로서 블루베리에 함유된 유용 성분의 스크리닝 등 향후 응용 연구에 유용한 기반으로 이용될 수 있을 것으로 기대된다.

• 한국의학물리학회지:의학물리
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• 제30권2호
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• pp.49-58
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• 2019

Purpose: Automated analytical systems have begun to emerge as a database system that enables the scanning of medical images to be performed on computers and the construction of big data. Deep-learning artificial intelligence (AI) architectures have been developed and applied to medical images, making high-precision diagnosis possible. Materials and Methods: For diagnosis, the medical images need to be labeled and standardized. After pre-processing the data and entering them into the deep-learning architecture, the final diagnosis results can be obtained quickly and accurately. To solve the problem of overfitting because of an insufficient amount of labeled data, data augmentation is performed through rotation, using left and right flips to artificially increase the amount of data. Because various deep-learning architectures have been developed and publicized over the past few years, the results of the diagnosis can be obtained by entering a medical image. Results: Classification and regression are performed by a supervised machine-learning method and clustering and generation are performed by an unsupervised machine-learning method. When the convolutional neural network (CNN) method is applied to the deep-learning layer, feature extraction can be used to classify diseases very efficiently and thus to diagnose various diseases. Conclusions: AI, using a deep-learning architecture, has expertise in medical image analysis of the nerves, retina, lungs, digital pathology, breast, heart, abdomen, and musculo-skeletal system.

• Clinics in Shoulder and Elbow
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• 제1권2호
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• pp.198-204
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• 1998

Unstable fractures of the proximal humerus continue to be difficult problems for orthopaedic surgeons. The optimum treatment of these fractures has remained a matter of controversy. We analyzed the clinical results of open reduction and plate fixation underwent for patients of unstable fractures of proximal humerus after minimum 12 months follow up. The purpose of this study is to evaluate the efficacy of open reduction and rigid plate fixation. Twenty-two patients were managed with open reduction and plate fixation. Mean follow up duration was 20.6 months(range, 12 to 28 mon.). Because the age of patient as a maker of degree of osteoporosis was considered the key factor in the success of anatomic reconstruction, we divided into two groups according to age. Group A was comprised of 12 cases with younger than 50 yrs of age. Ten cases of older than 50 yrs of age were Group B. According to Neer's classification, five cases(22%) were two part fracture, 12 cases(64%) were three part fracture, and three cases(14%) were four part fracture. We used the Neer rating system for evaluating the results. In Group A, overall scores were 79.1. In Group B, overall scores were 76.8. Overall scores in two part fracture were 85, overall scores in three part fracture 78.4 and overall scores in three part fracture 68.3. We achieved excellent or good results in nine cases(75%) of Group A and seven cases(70%) of Group B. Also, we obtained excellent or good results in all cases of two part fracture, ten cases(71%) of three fracture and one case(33%) of four part fracture. The complications were three metal loosening, one avascular necrosis of humeral head, one severe stiff shoulder, one superficial wound infection and one ectopic ossification. The results were excellent or good in 16 cases(73%) out of 22 cases. In conclusion, rigid fixation and supervised early exercise would be a good option for unstable fracture of the proximal humerus.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.426-429
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• 2007

One of the widely used applications of remote sensing studies is environmental change detection and biodiversity conservation. The study area Hustai Mountain is situated in the transition zone between the Siberian taiga forest and Central Mongolian arid steppe. Hustai National Park carries out one of several reintroduction programs of takhi (wild horse or Equus ferus przewalskii) from various zoos in the world and it represents one of a few textbook examples of successful reintroduction of an animal extinct in the wild. In this paper we describe the results of an analysis on the change of remaining forest area over the 7-year period since Hustai Mountain was designated as a protected area for reintroduction to wild horses. Today the forested area covers approximately 5% of the Hustai National Park, mostly the north-facing slopes above 1400 m altitude. Birch (Betula platyphylla) and aspen (Populus tremula) trees are predominant in the forest. We used Landsat ETM+ images from two different years and multi temporal MODIS NDVI data. Land types were determined by supervised classification methods (Maximum Likelihood algorithm) verified with ground-truthing data and the Land Change Modeler (LCM) which was developed by Clark Labs. Forested area was classified into three different land types, namely the forest land, mountain meadow and mountain steppe. The study results illustrate that the remaining birch forest has rapidly changed to fragmented forest land and to open areas. Underlying causes for such a rapid change during the 15-year period may be manifold. However, the responsible factors appear to be the drying off and outbreak of forest pest species (such as gypsy moth or Lymantria dispar) in the area.

• 한국농공학회논문집
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• 제46권7호
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• pp.3-12
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• 2004

The need to predict the rate of soil erosion, both under existing conditions and those expected to occur following soil conservation practice, has been led to the development of various models. In this study Morgan model especially developed for field-sized areas on hill slopes was applied to assess the rate of soil erosion using RS/GIS environment in the Dukchun river basin, one of two tributaries flowing into Jinyang lake. In order to run the model, land cover mapping was made by the supervised classification method with Landsat TM satellite image data, the digital soil map was generated from scanning and screen digitizing from the hard copy of soil maps, digital elevation map (DEM) in order to generate the slope map was made by the digital map (DM) produced by National Geographic Information Institute (NGII). Almost all model parameters were generated to the multiple raster data layers, and the map calculation was made by the raster based GIS software, IL WIS which was developed by ITC, the Netherlands. Model results show that the annual soil loss rates are 5.2, 18.4, 30.3, 58.2 and 60.2 ton/ha/year in forest, paddy fields, built-up area, bare soil, and upland fields respectively. The estimated rates seemed to be high under the normal climatic conditions because of exaggerated land slopes due to DEM generation using 100 m contour interval. However, the results were worthwhile to estimate soil loss in hilly areas and the more precise result could be expected when the more accurate slope data is available.

• 디지털융복합연구
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• 제11권11호
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• pp.409-414
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• 2013

Naive Bayesian classifiers 네이브 베이지안 분류기는 샘플 데이터로부터 쉽게 구현될 수 있는 강력하고도 많이 사용되는 형식의 분류기다. 그러나 강한 조건부 독립성으로 인하여 효율이 저하되는 분류 결과를 초래한다. 일반적으로 네이브 베이지안 분류기는 연속성을 가진 특징 데이터의 우도를 처리하기 위해 가우시안 분산을 사용한다. 속성들의 확률밀도는 항상 가우시안 분산에 적합한 것만은 아니다. 또 다른 형식의 분류기는 지도학습을 통해 퍼지 규칙과 퍼지집합을 학습할 수 있는 퍼지신경망이다. 퍼지신경망과 네이브 베이지안 분류기간에는 구조적 유사성을 가지고 있기 때문에 퍼지신경망으로 학습된 분산 그래프를 네이브 베이지안 분류기에 적용하고자 하는 방안이 본 연구의 목적이다. 따라서 네이브 베이지안 분류기에 가우시안 분산 그래프를 사용한 결과와 퍼지 분산 그래프를 사용한 결과를 비교하였다. 이를 위해 leukemia와 colon의 DNA 마이크로어레이 데이터를 적용하여 분류하였다. 네이브 베이지안 분류기에 퍼지 분산 그래프를 사용한 결과 가우시안 분산 그래프를 사용한 결과보다 더 신뢰성이 있음을 보여주었다.

• 대한원격탐사학회지
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• 제32권1호
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• pp.39-48
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• 2016

Yangon Mega City is densely populated and most urbanization area of Myanmar. Rapid urbanization is the main causes of Land Use and Land Cover (LULC) change and they impact on Land Surface Temperature (LST). The objectives of this study were to investigate on the LST with respect to LULC of Yangon Mega City. For this research, Landsat satellite images of 1996, 2006 and 2014 of Yangon Area were used. Supervised classification with the region of interest and calculated change detection. Ground check points used 348 points for accuracy assessment. The overall accuracy indicated 89.94 percent. The result of this paper, the vegetation area decreased from $1061.08sq\;km^2$ (24.5%) in 1996 to $483.53sq\;km^2$ (11.2%) in 2014 and built up area clearly increased from $485.33sq\;km^2$ (11.2%) in 1996 to $1435.72sq\;km^2$ (33.1%) in 2014. Although the land surface temperature was higher in built up area and bare land, lower value in cultivated land, vegetation and water area. The results of the image processing pointed out that land surface temperature increased from $23^{\circ}C$, $26^{\circ}C$ and $27^{\circ}C$ to $36^{\circ}C$, $42^{\circ}C$ and $43.3^{\circ}C$ for three periods. The findings of this paper revealed a notable changes of land use and land cover and land surface temperature for the future heat management of sustainable urban planning for Yangon Mega city. The relationship of regression experienced between LULC and LST can be found gradually stronger from 0.8323 in 1996, 0.8929 in 2006 and 0.9424 in 2014 respectively.