• Journal of Internet Computing and Services
• /
• v.23 no.2
• /
• pp.29-35
• /
• 2022

UV rays have beneficial or harmful effects on the human body depending on the degree of exposure. An accurate UV information is required for proper exposure to UV rays per individual. The UV rays' information is provided by the Korea Meteorological Administration as one component of daily weather information in Korea. However, it does not provide an accurate UVI at the user's location based on the region's Ultraviolet index. Some operate measuring instrument to obtain an accurate UVI, but it would be costly and inconvenient. Studies which assumed the UVI through environmental factors such as solar radiation and amount of cloud have been introduced, but those studies also could not provide service to individual. Therefore, this paper proposes a deep learning model to calculate UVI using solar object information and sunlight characteristics to provide an accurate UVI at individual location. After selecting the factors, which were considered as highly correlated with UVI such as location and size and illuminance of sun and which were obtained through the analysis of sky images and solar characteristics data, a data set for DNN model was constructed. A DNN model that calculates the UVI was finally realized by entering the solar object information and sunlight characteristics extracted through Mask R-CNN. In consideration of the domestic UVI recommendation standards, it was possible to accurately calculate UVI within the range of MAE 0.26 compared to the standard equipment in the performance evaluation for days with UVI above and below 8.

• Journal of Environmental Impact Assessment
• /
• v.31 no.1
• /
• pp.63-74
• /
• 2022

In orderto understand the urban thermal conditions, many studies have been conducted to estimate the thermal fluxes. Currently sensible heat fluxes are estimated through various methods, but studies about comparing the differences between each method are very insufficient. Therefore, this study try to estimate the sensible heat flux of the same area by two representative estimation methods and compare their results to confirm the significance and limitation between methods. As a result of the study, the heat balance methods has a great advantage in terms of resolution but it can not consider the anthropogenic heat flux, so sensible heat flux can be underestimated in urban areas. When estimating based on physical equation, anthropogenic heat flux can be considered and the error is relatively small, it has a limitations in time and space resolutons. The two methods showed the largest difference in industiral areas where anthropogenic heat fluxes are high, with an average of 135 W/m² and a maximum of 400 W/m². On the other hand, the green and water have a very small difference with and average of 20 W/m². The results between two methods show significant differences in urban areas, it is necessary to select a suitable method for each research purpose.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.10
• /
• pp.11-17
• /
• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• Nuclear Engineering and Technology
• /
• v.55 no.1
• /
• pp.339-352
• /
• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Asia Marketing Journal
• /
• v.13 no.3
• /
• pp.265-274
• /
• 2011

The objective of this case study is to analyze how effectively KIA K5, which is a leading mid-size car brand, has positioned itself into the mid-size car market. Before KIA launched the K5, Sonata and SM5 were the leading brands in the mid-size car market. They had loyal customers who like their similar images. As many competitors keep launching new brands or new designs into the car industry, Sonata and SM5 were pressured to introduce new versions. But, the YF Sonata and the New SM5 failed to catch up with the new trends in the market. Whilst YF Sonata was perceived as too innovative, the New SM5 was treated as an old car by the target customers of the mid-size car. While the two leading brands struggled to attract customers, KIA K5 found a new market space by identifying and focusing on the lucrative replace and up-grade demand segment and filling the gap between the current product category values and the emerging mid-size car category values. The K5 found the right values that customers need and successfully articulated the values to the customers. This case study illustrates that a successful positioning strategy can be effectively employed to attract customers in the saturated car manufacturing industry. This case can be summarized as the successful positioning strategy of KIA K5 is comprised of four primary pillars: design innovation, market analysis, STP (segmentation, targeting, and positioning), and launch strategy. The KIA K5 case study provides valuable insights and implications for many other companies that are planning to find a proper positioning strategy for their own business.

• Information Systems Review
• /
• v.23 no.4
• /
• pp.45-67
• /
• 2021

As the transaction volume of the C2C second-hand market is growing, the number of frauds, which intend to earn unfair gains by sending products different from specified ones or not sending them to buyers, is also increasing. This study explores the model that can identify frauds in the online C2C second-hand market by examining the postings for transactions. For this goal, this study collected 145,536 field data from actual C2C second-hand market. Then, the model is built with the characteristics from postings such as the topic and the linguistic characteristics of the product description, and the characteristics of products, postings, sellers, and transactions. The constructed model is then trained by the machine learning algorithm XGBoost. The final analysis results show that fraudulent postings have less information, which is also less specific, fewer nouns and images, a higher ratio of the number and white space, and a shorter length than genuine postings do. Also, while the genuine postings are focused on the product information for nouns, delivery information for verbs, and actions for adjectives, the fraudulent postings did not show those characteristics. This study shows that the various features can be extracted from postings written in C2C second-hand transactions and be used to construct an effective model for frauds. The proposed model can be also considered and applied for the other C2C platforms. Overall, the model proposed in this study can be expected to have positive effects on suppressing and preventing fraudulent behavior in online C2C markets.

• Tunnel and Underground Space
• /
• v.34 no.3
• /
• pp.208-217
• /
• 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
• /
• v.82 no.6
• /
• pp.1413-1440
• /
• 2021

On MRI, abnormal signals of the intervertebral disc, destruction of the upper and lower vertebral body endplate around the disc, and bone marrow edema around the endplate are considered typical findings of infectious spondylitis. These findings can also appear in various non-infectious spinal diseases, such as degenerative changes, acute Schmorl's node, spondyloarthropathy, synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO), chronic recurrent multifocal osteomyelitis, and calcium pyrophosphate dihydrate crystal deposition disease. The imaging findings of infectious spondylitis that can be differentiated from these non-infectious spinal diseases on MRI are high signal intensity and abscess of the disc space, an abscess in the paraspinal soft tissue, and the loss of the linear low signal intensity on T1-weighted images of the bony endplate. However, these differentiation points do not always apply since there are many similarities in the imaging findings of infectious and non-infectious diseases. Therefore, for an accurate diagnosis, it is important to know the imaging characteristics related to the pathophysiology of not only infectious spondylitis but also non-infectious spinal diseases, which requires differentiation from infection.

• Journal of Trauma and Injury
• /
• v.37 no.1
• /
• pp.37-47
• /
• 2024

Purpose: This study aimed at developing a novel tube thoracostomy technique using the sternum, a fixed anatomical structure, as an indicator to reduce the possibility of incorrect chest tube positioning and complications in patients with chest trauma. Methods: This retrospective study analyzed the data of 184 patients with chest trauma who were aged ≥18 years, visited a single regional trauma center in Korea between April and June 2022, and underwent chest computed tomography (CT) with their arms down. The conventional gold standard, 5th intercostal space (ICS) method, was compared to the lower 1/2, 1/3, and 1/4 of the sternum method by analyzing CT images. Results: When virtual tube thoracostomy routes were drawn at the mid-axillary line at the 5th ICS level, 150 patients (81.5%) on the right side and 179 patients (97.3%) on the left did not pass the diaphragm. However, at the lower 1/2 of the sternum level, 171 patients (92.9%, P<0.001) on the right and 182 patients (98.9%, P= 0.250) on the left did not pass the diaphragm. At the 5th ICS level, 129 patients (70.1%) on the right and 156 patients (84.8%) on the left were located in the safety zone and did not pass the diaphragm. Alternatively, at the lower 1/2, 1/3, and 1/4 of the sternum level, 139 (75.5%, P=0.185), 49 (26.6%, P<0.001), and 10 (5.4%, P<0.001), respectively, on the right, and 146 (79.3%, P=0.041), 69 (37.5%, P<0.001), and 16 (8.7%, P<0.001) on the left were located in the safety zone and did not pass the diaphragm. Compared to the conventional 5th ICS method, the sternum 1/2 method had a safety zone prediction sensitivity of 90.0% to 90.7%, and 97.3% to 100% sensitivity for not passing the diaphragm. Conclusions: Using the sternum length as a tube thoracostomy indicator might be feasible.

• Journal of Bio-Environment Control
• /
• v.33 no.1
• /
• pp.63-70
• /
• 2024

This study explored computer vision methods using the OpenCV open-source library to characterize the phenotypes of various horticultural crops. In the case of tomatoes, image color was examined to assess ripeness, while support vector machine (SVM) and histogram of oriented gradients (HOG) methods effectively identified ripe tomatoes. For sweet pepper, we visualized the color distribution and used the Gaussian mixture model for clustering to analyze its post-harvest color characteristics. For the quality assessment of netted melons, the LAB (lightness, a, b) color space, binary images, and depth mapping were used to measure the net patterns of the melon. In addition, a combination of depth and color data proved successful in identifying flowers of different sizes and distances in cucumber greenhouses. This study highlights the effectiveness of these computer vision strategies in monitoring the growth and development, ripening, and quality assessment of fruits and vegetables. For broader applications in agriculture, future researchers and developers should enhance these techniques with plant physiological indicators to promote their adoption in both research and practical agricultural settings.