양식장 부표 등과 같은 해상의 소형 장애물을 탐지하고 거리와 방위를 시각화시켜 주는 해상물체탐지시스템은 선체운동으로 인한 오차를 보정하기 위해 3축 짐벌이 장착되어 있지만, 파도 등에 의한 카메라와 해상물체의 상하운동으로 발생하는 거리오차를 보정 하지 못하는 한계가 있다. 이에 본 연구에서는 외부환경에 따른 수면의 움직임으로 발생하는 해상물체탐지시스템의 거리오차를 분석하 고, 이를 평균필터와 이동평균필터로 보정하고자 한다. 가우시안 표준정규분포를 따르는 난수를 이미지 좌표에 가감하여 불규칙파에 의 한 부표의 상승 또는 하강을 재현하였다. 이미지 좌표의 변화에 따른 계산거리, 평균필터와 이동평균필터를 통한 예측거리 그리고 레이저 거리측정기에 의한 실측거리를 비교하였다. phase 1,2에서 불규칙파에 의한 이미지 좌표의 변화로 오차율이 최대 98.5%로 증가하였지만, 이동평균필터를 사용함으로써 오차율은 16.3%로 감소하였다. 오차보정 능력은 평균필터가 더 좋았지만 거리변화에 반응하지 못하는 한계 가 있었다. 따라서 해상물체탐지시스템 거리오차 보정을 위해 이동평균필터를 사용함으로써 실시간 거리변화에 반응하고 오차율을 크게 개선할 수 있을 것으로 판단된다.

Novel ionic liquid-functionalized carbon quantum dots (IL-CDs) were prepared by hydrothermal method, and characterized with FT-IR, TEM and XPS. The IL-CDs exhibited narrower particle size distribution with more uniform dispersion and the surface potential changes from negative to positive due to the function of IL. IL-CDs could be quenched (“turned off”) after adding ascorbic acid (AA), and as an “on–off”, fluorescent probe could be established for direct analysis AA. The linear range of AA was 0.34–30.00 μg/mL and the LOD was 0.11 μg/mL. The method was successfully applied to the determination of AA in real samples with satisfactory results.

Recently, the development of computer vision with deep learning has made object detection using images applicable to diverse fields, such as medical care, manufacturing, and transportation. The manufacturing industry is saving time and money by applying computer vision technology to detect defects or issues that may occur during the manufacturing and inspection process. Annotations of collected images and their location information are required for computer vision technology. However, manually labeling large amounts of images is time-consuming, expensive, and can vary among workers, which may affect annotation quality and cause inaccurate performance. This paper proposes a process that can automatically collect annotations and location information for images using eXplainable AI, without manual annotation. If applied to the manufacturing industry, this process is thought to save the time and cost required for image annotation collection and collect relatively high-quality annotation information.

Targeting Microcystin (MC), which is most abundantly detected in the North-Han River water area, we analyzed the relationship between the MC biosynthesis gene (mcyA gene), cyanobacteria cell density, and MC concentration, derived an RNA-MC conversion formula, and derived the cyanobacteria. The concentration of MC present in cells was predicted. In the North-Han River waters, the mcyA gene was found mainly at downstream sites of the North-Han River after Muk-Hyeon Stream junction, and higher copy numbers were found on average than other sites. In the Uiam Lake waters upstream of the North-Han River, the mcyA gene copy number increased at the Kong-Ji Stream point, and after September, the mcyA gene copy number decreased throughout the North-Han River waters. The expression of the mcyA gene was concentrated in the short period of summer due to the spatio-temporal difference between upstream and downstream water bodies. The mcyA gene expression level was not only highly correlated with MC concentration, but also correlated with the cell density of Microcystis aeruginosa and Dolichospermum circinale, which are known to biosynthesize MC. Six conversion formulas derived based on the RNA-MC relationship showed statistical significance (p<0.05) and exhibited high correlation coefficients (r) of 0.9 or higher. The expression level of MC biosynthesis gene present in eRNA determines the synthesis of cyanotoxin substances in water, quickly quantifies gene activity, and can be fully utilized for early warning of MC development.

본 연구는 딥러닝을 위한 비선형 변환 접근법을 사용하여 Single-lap joint의 접착 영역을 조사하기 위한 진동 응답 기반 탐지 시스템 을 제시한다. 산업 혹은 공학 분야에서 분해가 쉽지 않은 구조 내에 보이지 않는 부분의 상태와 접착된 구조의 접착 부위 상태를 알기 어려운 문제가 있다. 이러한 문제를 해결하기 위해 본 연구는 비선형 변환을 이용하여 기준 시편의 진동 응답으로 다양한 시편의 접착 면적을 조사하는 탐지 방법을 제안한다. 이 연구에서는 CNN 기반 딥러닝으로 진동 특성을 파악하기 위해 비선형 변환을 적용한 주파 수 응답 함수를 사용했고 분류를 위해 가상의 스펙트로그램을 사용했다. 또한, 제시된 방법을 검증하기 위해 알루미늄, 탄소섬유복합 재 그리고 초고분자량 폴리에틸렌 시편에 대한 진동 실험, 분석적 해, 유한요소해석을 수행했다.

We have been conducting a exoplanet search survey using Bohyunsan Observatory Echelle Spectrograph (BOES) for the last 18 years. We present the detection of exoplanet candidate in orbit around HD 18438 from high-precision radial velocity (RV) mesurements. The target was already reported in 2018 (Bang et al. 2018). They conclude that the RV variations with a period of 719 days are likely to be caused by the pulsations because the Lomb-Scargle periodogram of HIPPARCOS photometric and Hα EW variations for HD 18438 show peaks with periods close to that of RV variations and there were no correlations between bisectors and RV measurements. However, the data were not sufficient to reach a firm conclusion. We obtained more RV data for four years. The longer time baseline yields a more accurate determination with a revised period of 803 ± 5 days and the planetary origin of RV variations with a minimum planetary companion mass of 21 ± 1MJup. Our current estimate of the stellar parameters for HD 18438 makes it currently the largest star with a planetary companion.

We successfully synthesize water-dispersible CTAB-capped CdSe@ZnS quantum dots with the crystal size of the CdSe quantum dots controlled from green to orange colors. The quenching effect of Fe(DTC)3 is very efficient to turn off the emission light of quantum dots at four molar ratios of the CdSe quantum dots, that is, the effective covering the surface of quantum dots with Fe(DTC)3. However, the reaction with Fe(DTC)3 for more than 24 h is required to completely realize the quenching effect. The highly quenched quantum dots efficiently detect nitric oxide at nano-molar concentration of 110nM of NO with 34% of recovery of emission light intensity. We suggest that Fe(DTC)3-hybridized CdSe@ZnS quantum dots are an excellent fluorescence resonance energy transfer probe for the detection of nitric oxide in biological systems.

Wild birds, especially aquatic birds, are the natural reservoir of avian influenza virus (AIV), and many kinds of water body can be contaminated with feces of these birds. Seasonally, AIVs can be dissolved in the environmental water from the feces of the infected birds, and this water can be a target for viral detection and identification. In this study, we employed and tested three different filters for concentrating AIV, and it was shown that high concentration factor in terms of viral density could be achieved with viral samples diluted with natural water. Wild bird fecal samples containing low pathogenicity H5 AIVs were successfully concentrated with the adsorption and elution method using mixed cellulose esters membrane; the recovery rate of virus was 35.5 % and the concentration factor was about 50 on average. For the larger volume of water sample, we proved that an inline disposable filter with high surface area, 300 cm2, has a comparable concentration factor to the adsorption and elution method and the filter could be used in the field conveniently by being plugged into peristaltic pump. These validated methods for water sampling may be used as a supplementary for virological surveillance on wild migratory birds or during the epidemiological investigation on the environment near affected premises by AIV.