• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.541-548
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• 2021

Recently, multispectral cameras are being actively utilized in various application fields such as agriculture, forest management, coastal environment monitoring, and so on, particularly onboard UAV's. Resultant multispectral images are typically georeferenced primarily based on the onboard GPS (Global Positioning System) and IMU (Inertial Measurement Unit)or accurate positional information of the pixels, or could be integrated with ground control points that are directly measured on the ground. However, due to the high cost of establishing GCP's prior to the georeferencing or for inaccessible areas, it is often required to derive the positions without such reference information. This study aims to provide a means to improve the georeferencing performance of a multispectral camera images without involving such ground reference points, but instead with the simultaneously onboard high resolution RGB camera. The exterior orientation parameters of the drone camera are first estimated through the bundle adjustment, and compared with the reference values derived with the GCP's. The results showed that the incorporation of the images from a high resolution RGB camera greatly improved both the exterior orientation estimation and the georeferencing of the multispectral camera. Additionally, an evaluation performed on the direction estimation from a ground point to the sensor showed that inclusion of RGB images can reduce the angle errors more by one order.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.207-215
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• 2022

This research has been focused on accessing precision and accuracy of UAV (Unmanned Aerial Vehicle)-derived 3-D surveying coordinates. To this end, a highly precise and accurate testing control network had been established by GNSS (Global Navigation Satellite Systems) campaign and its network adjustment. The coordinates of the ground control points and the check points were estimated within 1cm accuracy for 95% of the confidence level. FC330 camera mounted on DJI Phantom 4 repeatedly took aerial photos of an experimental area seven times, and then processed them by two widely used software packages. To evaluate the precision and accuracy of the aerial surveys, 3-D coordinates of the ten check points which automatically extracted by software were compared with GNSS solutions. For the 95% confidence level, the standard deviation of two software's result is within 1cm, 2cm, and 4cm for the north-south, east-west, and height direction, and RMSE (Root Mean Square Error) is within 9cm and 8cm for the horizontal, vertical component, respectively. The interest is that the standard deviation is much smaller than RMSE. The F-ratio test was performed to confirm the statistical difference between the two software processing results. For the standard deviation and RMSE of most positional components, exception of RMSE of the height, the null hypothesis of the one-tailed tests was rejected. It indicates that the result of UAV photogrammetry can be different statistically based on the processing software.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.363-369
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• 2021

High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.

• Journal of Life Science
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• v.32 no.12
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• pp.956-961
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• 2022

Plant Chloroplast have several advantages as an expression platform of biopharmaceuticals over conventional expression platforms such as mammalian cells, yeast and bacteria. First, plants do not serve as a host for mammalian infectious virus and have endotoxin like bacteria which can cause anaphylactic shock. In addition, high copy number of chloroplast genome allows for chloroplast transformants to reach the high level of expression of heterologous genes. Moreover, the integration of transgenes into specific region of chloroplast genomes makes chloroplast transformants unaffected by positional effect which can be frequently observed from nuclear transformants, resulting in loss of transgene expressions. Antimicrobial peptides (AMPs) are a kind of innate immunity which is found from bacteria to humans. Unlike conventional antibiotics, very less dosage of AMPs can have catastrophic effect on bacterial survival. Further, the repeated use of AMPs does not trigger the development of bacterial resistance. Moricin, one of the AMPs, was isolated from Bombyx mori, a silkworm moth. The C-terminal of moricin consists largely of basic amino acids, and the N-terminal has an α-helix structure. Moricin was chosen and expressed in a SUMO/SUMOase without leaving any unwanted amino acids which could potentially affect the anti-bacterial activity of the moricin. The transformation vector used in this study has already been created in this lab for the expression in both prokaryotic systems such as E. coli and chloroplast. The expressed moricin was purified using Ni columns and SUMOase, and the antibacterial activity of the purified moricin was confirmed using an agar diffusion assay.

• Journal of Platform Technology
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• v.11 no.2
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• pp.26-38
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• 2023

This paper is a study on a transport robot capable of autonomously driving to a destination using a QR code in an indoor environment. The transport robot was designed and manufactured by attaching a lidar sensor so that the robot can maintain a certain distance during movement by detecting the distance between the camera for recognizing the QR code and the left and right walls. For the location information of the delivery robot, the QR code image was enlarged with Lanczos resampling interpolation, then binarized with Otsu Algorithm, and detection and analysis were performed using the Zbar library. The QR code recognition experiment was performed while changing the size of the QR code and the traveling speed of the transport robot while the camera position of the transport robot and the height of the QR code were fixed at 192cm. When the QR code size was 9cm × 9cm The recognition rate was 99.7% and almost 100% when the traveling speed of the transport robot was less than about 0.5m/s. Based on the QR code recognition rate, an experiment was conducted on the case where the destination is only going straight and the destination is going straight and turning in the absence of obstacles for autonomous driving to the destination. When the destination was only going straight, it was possible to reach the destination quickly because there was little need for position correction. However, when the destination included a turn, the time to arrive at the destination was relatively delayed due to the need for position correction. As a result of the experiment, it was found that the delivery robot arrived at the destination relatively accurately, although a slight positional error occurred while driving, and the applicability of the QR code-based destination self-driving delivery robot was confirmed.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.190-196
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• 2023

Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.

• Journal of Oriental Neuropsychiatry
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• v.34 no.2
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• pp.71-82
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• 2023

Objectives: The existing reductionist approach has not reached complete understanding of the cause of schizophrenia. The objective of this study was to investigate yin-yang and five-element characteristics reflected on four time pillars of birth of patients with schizophrenia through comparison with the general population in the perspective of consilience-based holistic approach. Methods: This study was conducted using a random sequential recruitment method for the general population and individuals with schizophrenia aged 18 to 64 based on the exact date and time of birth using structured questionnaires. Relative positional relations of yin-yang and five-element with day master were primarily examined. In addition, the strength of day master with a score range of 0~100 points was assessed through operational score allocation. Results: Of 591 participants, 576 (346: general population, 230: individuals with schizophrenia) were analyzed. Between-group analyses showed no significant difference in the distribution of types of day master (χ²=10.41, df=9, p=0.318). However, significant between-group differences were shown in the distribution of the strength of day master (t=2.14, p=0.032) and frequency of restraining month branch (χ²=5.23, df=1, p=0.022). In logistic regression analysis, 10-point increase on the strength of day master decreased the probability of onset of schizophrenia over the age of 30 by 29.6% (p=0.002; 95% confidence interval, 0.566~0.876). Conclusions: Findings in this study suggest that four time pillars of birth might be associated with schizophrenia through yin-yang and five-element theory and synchronicity principle, implicating the plausibility of consilience-based holistic approach in the determination of risk factors or cause of schizophrenia.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.291-298
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• 2022

Recently developed drones are inexpensive and very convenient to operate. As a result, the production and utilization of spatial information using drones are increasing. However, most drones acquire images with a low-cost global navigation satellite system (GNSS) and an inertial measurement unit (IMU). Accordingly, the accuracy of the initial location and rotation angle elements of the image is low. In addition, because these drones are small and light, they can be greatly affected by wind, making it difficult to maintain a certain overlap, which degrades the positioning accuracy. Therefore, in this study, images are taken at different times in order to analyze the positioning accuracy according to changes in certain exterior orientation parameters. To do this, image processing was performed with Pix4D Mapper and the accuracy of the results was analyzed. In order to analyze the variation of the accuracy according to the exterior orientation parameters in detail, the exterior orientation parameters of the first processing result were used as meta-data for the second processing. Subsequently, the amount of change in the exterior orientation parameters was analyzed by in a strip-by-strip manner. As a result, it was proved that the changes of the Omega and Phi values among the rotation elements were related to a decrease in the height accuracy, while changes in Kappa were linked to the horizontal accuracy.

• The Journal of Advanced Prosthodontics
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• v.15 no.6
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• pp.281-289
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• 2023

PURPOSE. This study aimed to predict the positional coordinates of incisor points from the scan data of conventional complete dentures and verify their accuracy. MATERIALS AND METHODS. The standard triangulated language (STL) data of the scanned 100 pairs of complete upper and lower dentures were imported into the computer-aided design software from which the position coordinates of the points corresponding to each landmark of the jaw were obtained. The x, y, and z coordinates of the incisor point (X_P, Y_P, and Z_P) were obtained from the maxillary and mandibular landmark coordinates using regression or calculation formulas, and the accuracy was verified to determine the deviation between the measured and predicted coordinate values. Y_P was obtained in two ways using the hamularincisive-papilla plane (HIP) and facial measurements. Multiple regression analysis was used to predict Z_P. The root mean squared error (RMSE) values were used to verify the accuracy of the X_P and Y_P. The RMSE value was obtained after crossvalidation using the remaining 30 cases of denture STL data to verify the accuracy of Z_P. RESULTS. The RMSE was 2.22 for predicting X_P. When predicting Y_P, the RMSE of the method using the HIP plane and facial measurements was 3.18 and 0.73, respectively. Cross-validation revealed the RMSE to be 1.53. CONCLUSION. Y_P and Z_P could be predicted from anatomical landmarks of the maxillary and mandibular edentulous jaw, suggesting that Y_P could be predicted with better accuracy with the addition of the position of the lower border of the upper lip.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.