http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Nutrigenomic Functions of PPARs in Obesogenic Environments
Chung, Soonkyu,Kim, Young Jun,Yang, Soo Jin,Lee, Yunkyoung,Lee, Myoungsook Hindawi Publishing Corporation 2016 PPAR research Vol.2016 No.-
<P>Peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors that mediate the effects of several nutrients or drugs through transcriptional regulation of their target genes in obesogenic environments. This review consists of three parts. First, we summarize current knowledge regarding the role of PPARs in governing the development of white and brown/beige adipocytes from uncommitted progenitor cells. Next, we discuss the interactions of dietary bioactive molecules, such as fatty acids and phytochemicals, with PPARs for the modulation of PPAR-dependent transcriptional activities and metabolic consequences. Lastly, the effects of PPAR polymorphism on obesity and metabolic outcomes are discussed. In this review, we aim to highlight the critical role of PPARs in the modulation of adiposity and subsequent metabolic adaptation in response to dietary challenges and genetic modifications. Understanding the changes in obesogenic environments as a consequence of PPARs/nutrient interactions may help expand the field of individualized nutrition to prevent obesity and obesity-associated metabolic comorbidities. </P>
레이저 열화상 기법과 CNN 딥러닝을 이용한 용접부 표면의 자동 균열 검출 기술 개발
김치성(Chisung Kim),황순규(Soonkyu Hwang),정준연(Junyeon Chung),손훈(Hoon Sohn) 한국비파괴검사학회 2020 한국비파괴검사학회지 Vol.40 No.3
본 연구에서는 레이저 열화상 시스템과 균열 검출 알고리즘 개발을 통해 용접부에서 균열을 자동검출하는 기술을 연구하였다. 레이저 열화상 시스템은 레이저 가진으로 인해 균열부에서 발생하는 열파 집중현상을 관측하도록 구성되었다. 균열 검출 알고리즘은 (1) 온도 분포 특성을 이용한 열화상 이미지 병합으로 균열을 가시화하고, (2) 과적합을 방지하는 input 이미지 생성과 (3) CNN 딥러닝을 통해 균열부의 특징을 분석, 분류하여, (4) 원본 열화상 이미지에 균열의 위치를 Masking 한다. SUS 시험편 2개로 개발 기술을 검증하였고, 현미경과 액체침투법으로 확인한 실제 균열 정보와 비교하였다. 시험편 #1의 균열 이미지 618 개와 정상 이미지 1834개로 CNN 을 훈련시켰다. 시험편 #1과 #2의 총 9개 영역을 각 300개의 Test 이미지로 나눠 훈련된 알고리즘 성능을 검증해본 결과, 총 균열 14개 중 13개를 검출하였고, 정상 이미지 4개가 과검출되었다. 따라서 개발된 알고리즘은 용접부에서 용접의 복잡한 패턴과 구별하여 균열을 검출할 수 있다. In this study, automatic crack detection for welded surfaces was studied through the development of a laser active thermography system and a crack detection algorithm. The laser active thermography system observes thermal wave concentrations in the crack while exciting the surface of the weld. The crack detection algorithm (1) visualizes the cracks by merging the infrared (IR) images using the temperature distribution characteristics; (2) employs input image generation with a specific method to prevent overfitting; (3) analyzes and classifies the characteristics of the cracks using a deep learning convolutional neural network (CNN); and (4) marks the location of the cracks in the original IR image. The system and algorithm were verified using two SUS specimens (#1 and #2) and compared with actual crack data obtained by microscopy and penetration test. The CNN was trained with 618 images of cracks and 1834 images of intact specimen #1. For performance verification, a total of nine areas of specimens #1 and #2 were divided into 300 test images; 13 out of 14 cracks were detected while four intact images were overdetected. Thus, the developed algorithm can detect cracks in welded surfaces by distinuishing them from complex patterns of welding.
허건수(Kunsoo Huh),서문석(Munsuk Suh),김재용(Jaeyong Kim),정순규(Soonkyu Jeong),정정주(Chungchoo Chung),김일민(Ilmin Kim) 대한기계학회 2003 대한기계학회 춘추학술대회 Vol.2003 No.4
Maintaining track tension in tracked vehicles minimizes the excessive load on the tracks and prevents the<br/> peal-off of tracks from the road-wheel, and adequately guarantees the stable and improved driving of the<br/> tracked vehicles. However, the track tension cannot be easily measured due to the limitation in the sensor<br/> technology, harsh environment, etc. In this study, the track tension is estimated in real-time from the<br/> measurable signals of tracked vehicles and controlled based on a fuzzy logic controller. The proposed control<br/> system is implemented on tracked vehicles and its performance is evaluated under various driving conditions.