• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.809-815
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• 2023

This study was conducted to find out the correlation between meat quality and muscle fat ratio in pork part meat (pork belly and shoulder butt) using CT (computed tomography) imaging technique. After 24 hours from slaughter, pork loin and belly were individually prepared from the left semiconductors of 26 pigs for CT measurement. The image obtained from CT scans was checked through the picture archiving and communications system (PACS). The volume of muscle and fat in the pork belly and shoulder butt of cross-sectional images taken by CT was estimated using Vitrea workstation version 7. This assemblage was further processed through Vitrea post-processing software to automatically calculate the volumes (Fig. 1). The volumes were measured in milliliters (mL). In addition to volume calculation, a three-dimensional reconstruction of the organ under consideration was generated. Pearson's correlation coefficient was analyzed to evaluate the relationship by region (pork belly, pork shoulder butt), and statistical processing was performed using GraphPad Prism 8. The muscle-fat ratios of pork belly taken by CT was 1 : 0.86, while that of pork shoulder butt was 1 : 0.37. As a result of CT analysis of the correlation coefficient between pork belly and shoulder butt compared to the muscle-fat ratio, the correlation coefficient was 0.5679 (R² = 0.3295, p < 0.01). CT imaging provided very good estimates of muscle contents in cuts and in the whole carcass.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.208-217
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• 2024

There is so much research effort for developing and implementing deep learning-based surveillance systems to manage health and safety issues in construction sites. Especially, the development of deep learning-based object detection in various environmental changes has been progressing because those affect decreasing searching performance of the model. Among the various environmental variables, the accuracy of the object detection model is significantly dropped under low illuminance, and consistent object detection accuracy cannot be secured even the model is trained using low-light images. Accordingly, there is a need of low-light enhancement to keep the performance under low illuminance. Therefore, this paper conducts a comparative study of various deep learning-based low-light image enhancement models (GLADNet, KinD, LLFlow, Zero-DCE) using the acquired construction site image data. The low-light enhanced image was visually verified, and it was quantitatively analyzed by adopting image quality evaluation metrics such as PSNR, SSIM, Delta-E. As a result of the experiment, the low-light image enhancement performance of GLADNet showed excellent results in quantitative and qualitative evaluation, and it was analyzed to be suitable as a low-light image enhancement model. If the low-light image enhancement technique is applied as an image preprocessing to the deep learning-based object detection model in the future, it is expected to secure consistent object detection performance in a low-light environment.

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.272-289
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• 2020

Current advances in CT techniques allow thorough evaluation of the beating heart. The strengths of cardiac CT relative to echocardiography and magnetic resonance imaging are its high availability in most institutions, rapid production of high-quality images, and outstanding delineation of the anatomy. For many normal variants and pathologic conditions, such as thrombi, masses, and congenital abnormalities of the left atrium, CT findings are sufficient to make a presumptive diagnosis. Assessments of the left atrium and left atrial appendage are particularly important for the management of atrial fibrillation, as various catheter-based procedures are aimed at the mechanical and electrical isolation of these structures. CT offers information crucial to a successful catheter-based procedure or surgery. Therefore, a comprehensive review of the geometry (shape, size, and relative position), along with various CT imaging features of pathologic states, should be provided in radiology reports to be of clinical value.

• Journal of the Korean Society of Radiology
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• v.82 no.5
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• pp.1033-1052
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• 2021

Bladder cancer is a relatively common cancer type, with a high recurrence rate, that can be often encountered in the imaging study. Accurate diagnosis and staging have a significant impact on determining treatment and evaluating prognosis. Bladder cancer has been evaluated by transurethral resection of bladder tumor for clinical staging and treatment, but it is often understaged when compared with final pathologic result by radical cystectomy. If the location, size, presence of muscle invasion, lymph node metastasis, distant metastasis, and presence of upper urinary tract cancer can be accurately diagnosed and evaluated in an imaging study, it can be treated and managed more appropriately. For an accurate diagnosis, radiologists who evaluate the images must be aware of the characteristics of bladder cancer as well as its types, imaging techniques, and limitations of imaging studies. Recent developments in MRI with functional imaging have improved the quality of bladder imaging and the evaluation of cancer. In addition, the Vesical Imaging Reporting and Data System was published to objectively assess the possibility for muscle invasion of cancer. Radiologists need to know the types of bladder cancer treatment and how to evaluate the changes after treatment. In this article, the characteristics of bladder urothelial carcinoma, various imaging studies, and findings are reviewed.

• Composites Research
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• v.37 no.3
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• pp.219-225
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• 2024

It is essential to develop a light-weight, high-performance structure for the deployable reflector antenna, which is the payload of a reconnaissance satellite, considering launch and orbital operation performance. Among them, the composite main reflector is a key component that constitutes a deployable reflector antenna. In particular, the development of a high-performance main reflector is required to acquire high-quality satellite images after agile attitude control maneuvers during satellite missions. To develop main reflector, the initial design of the main reflector was confirmed considering the structural performance according to the laminate stacking design and material properties of the composite main reflector that constitutes the deployable reflector antenna. Based on the initial design, four types of composite main reflectors were manufactured with the variable for manufacturing process. As variables for manufacturing process, the curing process of the composite structure, the application of adhesive film between the carbon fiber composite sheet and the honeycomb core, and the venting path inside the sandwich composite were selected. After manufacture main reflector, weight measurement, non-destructive testing(NDT), surface error measurement, and modal test were performed on the four types of main reflectors produced. By selecting a manufacturing process that does not apply adhesive film and includes venting path, for a composite main reflector with light weight and structural performance, we developed and verified a main reflector that can be applied to the SAR(Synthetic Aperture Rader) satellite.

• Korean Journal of Radiology
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• v.21 no.11
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• pp.1256-1264
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• 2020

Objective: Lung segmentation using volumetric quantitative computed tomography (CT) analysis may help predict outcomes of patients with coronavirus disease (COVID-19). The aim of this study was to investigate the relationship between CT volumetric quantitative analysis and prognosis in patients with COVID-19. Materials and Methods: CT images from patients diagnosed with COVID-19 from February 18 to April 15, 2020 were retrospectively analyzed. CT with a negative finding, failure of quantitative analysis, or poor image quality was excluded. CT volumetric quantitative analysis was performed by automated volumetric methods. Patients were stratified into two risk groups according to CURB-65: mild (score of 0-1) and severe (2-5) pneumonia. Outcomes were evaluated according to the critical event-free survival (CEFS). The critical events were defined as mechanical ventilator care, ICU admission, or death. Multivariable Cox proportional hazards analyses were used to evaluate the relationship between the variables and prognosis. Results: Eighty-two patients (mean age, 63.1 ± 14.5 years; 42 females) were included. In the total cohort, male sex (hazard ratio [HR], 9.264; 95% confidence interval [CI], 2.021-42.457; p = 0.004), C-reactive protein (CRP) (HR, 1.080 per mg/dL; 95% CI, 1.010-1.156; p = 0.025), and COVID-affected lung proportion (CALP) (HR, 1.067 per percentage; 95% CI, 1.033-1.101; p < 0.001) were significantly associated with CEFS. CRP (HR, 1.164 per mg/dL; 95% CI, 1.006-1.347; p = 0.041) was independently associated with CEFS in the mild pneumonia group (n = 54). Normally aerated lung proportion (NALP) (HR, 0.872 per percentage; 95% CI, 0.794-0.957; p = 0.004) and NALP volume (NALPV) (HR, 1.002 per mL; 95% CI, 1.000-1.004; p = 0.019) were associated with a lower risk of critical events in the severe pneumonia group (n = 28). Conclusion: CRP in the mild pneumonia group; NALP and NALPV in the severe pneumonia group; and sex, CRP, and CALP in the total cohort were independently associated with CEFS in patients with COVID-19.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.335-347
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• 2024

Open-vocabulary 3D point cloud instance segmentation (OV-3DIS) is a challenging visual task to segment a 3D scene point cloud into object instances of both base and novel classes. In this paper, we propose a novel model Open3DME for OV-3DIS to address important design issues and overcome limitations of the existing approaches. First, in order to improve the quality of class-agnostic 3D masks, our model makes use of T3DIS, an advanced Transformer-based 3D point cloud instance segmentation model, as mask proposal module. Second, in order to obtain semantically text-aligned visual features of each point cloud segment, our model extracts both 2D and 3D features from the point cloud and the corresponding multi-view RGB images by using pretrained CLIP and OpenSeg encoders respectively. Last, to effectively make use of both 2D and 3D visual features of each point cloud segment during label assignment, our model adopts a unique feature ensemble method. To validate our model, we conducted both quantitative and qualitative experiments on ScanNet-V2 benchmark dataset, demonstrating significant performance gains.

• Korean Journal of Agricultural Science
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• v.51 no.2
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• pp.187-192
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• 2024

This research aimed to investigate the relationship between meat quality and muscle-to-fat ratio in specific cuts of pork (pork belly and Boston butt) utilizing magnetic resonance imaging (MRI). Twenty-eight pigs were selected, and 24 hours post-slaughter, pork belly and Boston butt samples were individually extracted from the left half carcass for MRI assessment. The MRI scans were reviewed using the Picture Archiving and Communications System. Muscle and fat volumes in the pork belly and Boston butt from the cross-sectional images captured by MRI were estimated using Vitrea workstation version 7. Subsequently, these data were processed using Vitrea post-processing software to automatically determine the volumes, measured in milliliters (mL). Additionally, a three-dimensional reconstruction of the organ being studied was generated. The relationship between regions (pork belly and Boston butt) was assessed using Pearson's correlation coefficient, and statistical analysis was conducted using Graph Pad Prism 8. The muscle-to-fat ratio determined by MRI for pork belly was 1 : 0.64, whereas for Boston butt it was 1 : 0.35. Results of comparing the muscle-fat ratio, the correlation coefficient between pork belly and Boston butt was found to be 0.6127 (R² = 0.3754, p < 0.001) based on MRI analysis. As a result of measuring the muscle-to-fat ratio using MRI as a non-destructive approach, there was a positive correlation between the muscle-to-fat ratios of pork belly and Boston butt.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.319-341
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• 2024

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.827-832
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• 2024

The AI program Sora is a video production model that can be used innovatively and is the starting point of a major paradigm shift in video planning and production in the future. In this paper, through consideration of the characteristics, application, and process of the AI video production program, the characteristics of the AI design video production method were understood, and the production algorithm was considered. The detailed consideration and characteristics of the work creation process for the video graphic AI video generation program that will be intensified every year were examined. Next, the method of generating a customized video with a text prompt and the process of innovative production results different from the previous production method were considered. In addition, the design direction through the generation of AI images was studied through the review of the strengths and weaknesses of the image details of the recently announced AI music video results. By considering the security of the AI generation video Sora and looking at the internal process of the actual AI process, it will be possible to present indicators for the future direction of AI video model production and education along with the direction of the design designer and education system. In the text and conclusion, we analyzed the strengths and weaknesses and future status of OpenAI Sora image, concluded how to apply the Sora model's capabilities, limitations, quality, and human creativity, and presented problems and alternatives through examples of the Sora model's capabilities and limitations to increase human creativity.