Fescues, which are widely cultivated as grasses and forages around the world, are often naturally infected with the endophyte, Epichloë. This fungus, transmitted through seeds, imparts resistance to drying and herbivorous insects in its host without causing any external damage, thereby contributing to the adaptation of the host to the environment and maintaining a symbiosis. However, some endophytes, such as E. coenophialum synthesize ergovaline or lolitrem B, which accumulate in the plant and impart anti-mammalian properties. For example, when livestock consume excessive amounts of grass containing toxic endophytes, problems associated with neuromuscular abnormalities, such as convulsions, paralysis, high fever, decreased milk production, reproductive disorders, and even death, can occur. Therefore, pre-inoculation with non-toxic endogenous fungi or management with endophyte-free grass is important in preventing damage to livestock and producing high-quality forage. To date, the diagnosis of endophytes has been mainly performed by observation under a microscope following staining, or by performing an immune blot assay using a monoclonal antibody. Recently, the polymerase chain reaction (PCR)-based molecular diagnostic method is gaining importance in the fields of agriculture, livestock, and healthcare given the method’s advantages. These include faster results, with greater accuracy and sensitivity than those obtained using conventional diagnostic methods. For the diagnosis of endophytes, the nested PCR method is the only available option developed; however, it is limited by the fact that the level of toxic alkaloid synthesis cannot be estimated. Therefore, in this study, we aimed to develop a triplex real-time PCR diagnostic method that can determine the presence or absence of endophyte infection using DNA extracted from seeds within 1 h, while simultaneously detecting easD and LtmC genes, which are related to toxic alkaloid synthesis. This new method was then also applied to real field samples.

African swine fever (ASF) is a hemorrhagic viral disease of pigs requiring laboratory diagnosis for confirmation. Though tissue and blood samples are considered optimal for ASF diagnosis, collection of these samples can be laborious, time-consuming, and pose a risk of contaminating the environment. Here, we suggest an alternative non-invasive sampling method, hair plucking, for ASF diagnosis. ASF virus was detected in plucked hair samples from experimentally infected pigs. Although the sensitivity was inferior to whole blood, the results suggest that hair plucking can be an alternative method that can also improve animal welfare.

자율운항선박이 상용화되어 연안을 항해하기 위해서는 해상의 장애물을 탐지할 수 있어야 한다. 연안에서 가장 많이 볼 수 있 는 장애물 중의 하나는 양식장의 부표이다. 이에 본 연구에서는 YOLO 알고리즘을 이용하여 해상의 부표를 탐지하고, 카메라 영상의 기하 학적 해석을 통해 선박으로부터 떨어진 부표의 거리와 방위를 계산하여 장애물을 시각화하는 해상물체탐지시스템을 개발하였다. 1,224장 의 양식장 부표 사진으로 해양물체탐지모델을 훈련시킨 결과, 모델의 Precision은 89.0 %, Recall은 95.0 % 그리고 F1-score는 92.0 %이었다. 얻 어진 영상좌표를 이용하여 카메라로부터 떨어진 물체의 거리와 방위를 계산하기 위해 카메라 캘리브레이션을 실시하고 해상물체탐지시 스템의 성능을 검증하기 위해 Experiment A, B를 설계하였다. 해상물체탐지시스템의 성능을 검증한 결과 해상물체탐지시스템이 레이더보 다 근거리 탐지 능력이 뛰어나서 레이더와 더불어 항행보조장비로 사용이 가능할 것으로 판단된다.

본 논문은 딥러닝 알고리즘을 이용하여 딸기 영상 데이터의 병충해 존재 여부를 자동으로 검출할 수 있는 서비스 모델을 제안한다. 또한 병징에 특화된 분할 이미지 데이터 세트를 제 안하여 딥러닝 모델의 병충해 검출 성능을 향상한다. 딥러닝모델은 CNN 기반 YOLO를 선정하여 기존의 R-CNN 기반 모델의 느린 학습속도와 추론속도를 개선하였다. 병충해 검 출 모델을 학습하기 위해 일반적인 데이터 세트와 제안하는 분할 이미지 데이터 세트를 구축하였다. 딥러닝 모델이 일반 적인 학습 데이터 세트를 학습했을 때 병충해 검출률은 81.35%이며 병충해 검출 신뢰도는 73.35%이다. 반면 딥러닝 모델이 분할 이미지 학습 데이터 세트를 학습했을 때 병충해 검출률은 91.93%이며 병충해 검출 신뢰도는 83.41%이다. 따 라서 분할 이미지 데이터를 학습한 딥러닝 모델의 성능이 우 수하다는 것을 증명할 수 있었다.

본 연구에서는 콘크리트 이미지에서 균열의 크기와 위치를 검출하는 알고리즘을 개발하였다. 균열은 총 9단계로 자 동 검출되었으며, 기본 기능은 매트랩 프로그램의 기능이었다. 5단계와 8단계에서는 균열 검출 정확도를 높이기 위해 사용자 알고리즘을 추가하였으며, 균열 영상과 비균열 영상을 각각 1,000개씩 사용하였다. 균열 이미지에서는 균열이 100% 검출됐지만 품질 측면에서 나쁘지 않은 결과를 제외하더라도 91.8%의 결과가 매우 양호했다. 또한, 균열되지 않은 이미지의 정확도도 94.7%로 매우 양호했다. 이에 본 연구에서 제시한 균열검출 알고리즘은 콘크리트 우물 균열의 위치와 크기를 검출할 수 있을 것으로 기대된다.

Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.

It is essential to provide a safe working environment for radiation workers. At a research reactor decommissioning site in Seoul (KRR1 & KRR2), radioactive waste drum disposal work is in progress. Before performing radiation work, it is necessary to determine the radioactivity of the waste drum to ensure safety. In this reason, we conducted a study to determine the detection efficiency of waste drums using the EXVol code. Determination of the full energy absorption peak efficiency (detection efficiency) is one of the important processes of the gamma-ray activation analysis. For the large voluminous gamma-ray sources like waste drum, the geometrical and attenuation effect should be considered. EXVol (Efficiency calculator for eXtended Voluminous source) code is a detection efficiency calculation code using the effective solid angle method. EXVol can calculate both coaxial and asymmetric structure. In addition, the introduction of a collimator made it possible to reduce the radiation intensity of a high radiation source. And it is possible to determine the precise detection efficiency according to the energy of a gamma ray at a specific position of the volume source. To verify the performance of the EXVol, a high resolution gamma spectroscopy system was constructed and measurement and analysis were performed. Measurements were performed on coaxial, asymmetric and collimated structures with standard point source, standard 1 L liquid volume source and HPGe detector. The measured results were compared with the calculation results of EXVol. The relative deviation of the measurement and calculation in the coaxial and asymmetric structures was 10%, and that of the collimation structure was 20%. Results can be available in analysis of waste drums’ radioactivity determination at a specific position.

With the enhancement of the spatial resolution of satellite imagery (1 m or less), the satellite image analysis has been considered as the indispensable means for remote sensing of nuclear proliferation activities in the restricted access areas such as North Korea. Notably, in the case of an open-pit uranium mine, e.g. the Pyongsan uranium mine, the mining activity can be presumed if detecting the location and extent uranium tailing piles near shafts within temporal images. Several studies have researched on the target detection for minerals of interest such as limestone and coal to evaluate the economic activities by utilizing similarity measures, e.g., a spectral angle mapper and a spectral information divergence (SID). Thus, this paper presented a systematic change detection methodology for monitoring the uranium mining activity in the Pyongsan uranium mine with a similarity measure of SID. The proposed methodology using the target detection results consists of the following five steps. The first step is to acquire stereo images of areas of interest for change detection. The second step is to preprocess the stereo images as following measures: (i) the QUick Atmospheric Correction and the image-to-image registration with ENVI and (ii) the Gram-Schmidt pansharpening. The third step is to extract spectral information for minerals of interest, i.e., uranium tailing piles, by sampling pixels within the reference image. It is based on the satellite analysis report for the Pyongsan uranium mine by CSIS, which specified the location of the uranium tailing piles. As the fourth step, the target detection for uranium tailing piles was performed through the similarity measure of SID between the extracted spectral information and the spectral reflectance of the image. In the fifth step, the change detection was processed using the multivariate alteration detection algorithm, which compares the target detection results by canonical correlation analysis. Furthermore, this paper evaluated the performance of the proposed methodology with the change detection accuracy assessment index, i.e., the area under a receiver operating characteristic curve. In conclusion, this paper suggests the systematic change detection methodology utilizing time series analysis of target detection for uranium tailing piles, which can save time and cost for humans to interpret large amounts of satellite information at the restricted access areas. As future works, the feasibility of the proposed methodology would be investigated by analyzing distribution of minerals of interest regarding nuclear proliferation at Yongbyon, which has the historical events of suspicious nuclear activities.

In this study, the multi-lane detection problem is expressed as a CNN-based regression problem, and the lane boundary coordinates are selected as outputs. In addition, we described lanes as fifth-order polynomials and distinguished the ego lane and the side lanes so that we could make the prediction lanes accurately. By eliminating the network branch arrangement and the lane boundary coordinate vector outside the image proposed by Chougule’s method, it was possible to eradicate meaningless data learning in CNN and increase the fast training and performance speed. And we confirmed that the average prediction error was small in the performance evaluation even though the proposed method compared with Chougule’s method under harsher conditions. In addition, even in a specific image with many errors, the predicted lanes did not deviate significantly, meaningful results were derived, and we confirmed robust performance.