이취미 물질인 2-MIB를 합성하는 원인종에 대한 정보는 담수생태계에서 이와 관련된 환경 및 경제적 문제를 해결하는 데 필수적이다. 본 연구는 북한강 수계에서 출현하는 Pseudanabaena strain을 분리·배양하고, 16S rDNA 염기서열을 이용하여 종 수준의 동정과 2-MIB 합성 유전자 탐색을 통해 이취미 물질 발생 잠재성을 분석하였다. 북한강 본류 지역 (삼봉리, 조암면, 의암호 지역)에서 분리한 Pseudanabaena strain은 총 11개로서 NHUA201911과 NHPD201909 strain을 제외하고 단일세포의 크기는 서로 유사하였다. 그러나 16S rDNA 계통분석을 통한 유전자 염기서 열의 유연관계를 분석한 결과, 분리된 strain들은 총 5개 종 (P. cinerea, P. yagii, P. mucicola, P. galeata, P. redekei)으 로 분류되었다 (40~55% 유사도). 2-MIB를 합성하는 mibC 유전자는 P. cinerea 07 strain (NHUA202007-07)와 P. yagii (NHUA202007-08), P. redekei (NHUA201911)에서만 발견 되었으며, 가스크로마토그래피 분석에 따라 실질적인 2-MIB 합성은 P. cinerea와 P. redekei 종에서 확인되었다. 본 연구 결과는 분자생물학적 수준에서 북한강 수역에서 발생하는 Pseudanabaena속 남조류의 다양도에 대한 증거를 제공하는 연구로서 북한강 수계에서 2-MIB 생산 원인종에 대한 중요한 정보를 제공한다.

2010년대 이후로 사과원내 신초와 어린 과실에서 총채벌레류의 발생과 피해가 확인되었다. 사과원내 총채벌레류에 는 대만총채벌레(Frankliniella intonsa Trybom), 파총채벌레(Thrips tabaci Lindeman), 콩어리총채벌레(Mycterothrips glycines Okamato) 등이 확인되었고, 그 중 대만총채벌레가 90% 이상을 차지하는 우점종으로 조사되었다. 본 연구는 사과원에 우점하는 대만총채벌레를 예찰하는 데 가장 효과적인 끈끈이트랩의 색상을 선정하기 위해 진행되었다. 3개 지역(군위, 안동, 영주)에 위치한 10개의 사과원에 청색, 백색, 황색의 끈끈이트랩을 한 과원당 3반복으로 설치하였 다. 총채벌레류가 주로 발생하여 사과의 어린 과실과 신초를 가해하는 5월부터 6월까지 2주 간격으로 총채벌레류 유살수를 조사하였다. 총채벌레류의 발생량이 적은 5월에는 색상별 유인효과가 크게 차이나지 않았지만, 총채벌레류의 발생량이 많은 6월에는 청색 끈끈이트랩이 효과적으로 사과원 총채벌레류를 유인하는 것으로 확인되었다. 사과원 내 총채벌레류를 예찰하고 방제하는데 청색 끈끈이트랩이 효과적으로 이용될 것이라 생각된다.

This paper presents the approach of design parameters optimization based on Taguchi method for the uniformity of outlet pressure in a plasma discharge chamber. The key issue of a plasma discharge chamber is to have the uniformity of outlet pressure which can make a high performance of surface treatment. To extend the length of a outlet from 60mm to 250mm with the uniformity, This study optimally designed the middle holes, outlet width and height, and diameter of the second chamber by using SolidWorks and flow simulation tool. Simulation results demonstrate the validity of the proposed approach.

Insect-killing fungi have high potential for controlling agriculturally harmful pests. However, their pathogenicity is slow and this is one reason for their poor acceptance as a fungal insecticide. The expression of bumblebee, Bombus ignitus, venom serine protease (VSP) by Beauveria bassiana ERL1170 induced melanization of yellow spotted longicorn beetles, Psacothea hilaris as an over-reactive immune response, and caused substantially earlier mortality in beet armyworm, Spodopetra exigua larvae when compared to the wild type. No fungal outgrowth or sporulation was observed on the melanized insects, thus suggesting a self-restriction of the dispersal of the genetically modified fungus in the environment. The research is the first use of a multi-functional bumblebee VSP to significantly increase the speed of fungal pathogenicity, while minimizing the dispersal of the fungal transformant in the environment

This research is a case study of underwater object tracking based on real-time recurrent regression networks (Re3). Re3 has the concept of generic object tracking. Because of these characteristics, it is very effective to apply this model to unclear underwater sonar images. The model also an pursues object tracking method, thus it solves the problem of calculating load that may be limited when object detection models are used, unlike the tracking models. The model is also highly intuitive, so it has excellent continuity of tracking even if the object being tracked temporarily becomes partially occluded or faded. There are 4 types of the dataset using multi-beam sonar images: including (a) dummy object floated at the testbed; (b) dummy object settled at the bottom of the sea; (c) tire object settled at the bottom of the testbed; (d) multi-objects settled at the bottom of the testbed. For this study, the experiments were conducted to obtain underwater sonar images from the sea and underwater testbed, and the validity of using noisy underwater sonar images was tested to be able to track objects robustly.

In the ground environment, mobile robot research uses sensors such as GPS and optical cameras to localize surrounding landmarks and to estimate the position of the robot. However, an underwater environment restricts the use of sensors such as optical cameras and GPS. Also, unlike the ground environment, it is difficult to make a continuous observation of landmarks for location estimation. So, in underwater research, artificial markers are installed to generate a strong and lasting landmark. When artificial markers are acquired with an underwater sonar sensor, different types of noise are caused in the underwater sonar image. This noise is one of the factors that reduces object detection performance. This paper aims to improve object detection performance through distortion and rotation augmentation of training data. Object detection is detected using a Faster R-CNN.

This paper proposes a convolutional neural network model for distinguishing areas occupied by obstacles from a LiDAR image converted from a 3D point cloud. The channels of a LiDAR image used as input consist of the distances to 3D points, the reflectivities of 3D points, and the heights of 3D points from the ground. The proposed model uses a LiDAR image as an input and outputs a result of a segmented LiDAR image. The proposed model adopts refinement modules with skip connections to segment a LiDAR image. The refinement modules with skip connections in the proposed model make it possible to construct a complex structure with a small number of parameters than a convolutional neural network model with a linear structure. Using the proposed model, it is possible to distinguish areas in a LiDAR image occupied by obstacles such as vehicles, pedestrians, and bicyclists. The proposed model can be applied to recognize surrounding obstacles and to search for safe paths.

This paper suggests the method of the spherical signature description of 3D point clouds taken from the laser range scanner on the ground vehicle. Based on the spherical signature description of each point, the extractor of significant environmental features is learned by the Deep Belief Nets for the urban structure classification. Arbitrary point among the 3D point cloud can represents its signature in its sky surface by using several neighborhood points. The unit spherical surface centered on that point can be considered to accumulate the evidence of each angular tessellation. According to a kind of point area such as wall, ground, tree, car, and so on, the results of spherical signature description look so different each other. These data can be applied into the Deep Belief Nets, which is one of the Deep Neural Networks, for learning the environmental feature extractor. With this learned feature extractor, 3D points can be classified due to its urban structures well. Experimental results prove that the proposed method based on the spherical signature description and the Deep Belief Nets is suitable for the mobile robots in terms of the classification accuracy.