• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5A
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• pp.861-872
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• 2006

The element free Galerkin method is extended by the local partition of unity method to model the cohesive cracks in two dimensional continuum. The shape function of a particle whose domain of influence is completely cut by a crack is enriched by the step enrichment function. If the domain of influence contains a crack tip inside, it is enriched by a branch enrichment function which does not have the LEFM stress singularity. The discrete equations are obtained directly from the standard Galerkin method since the enrichment is only for the displacement field, which satisfies the local partition of unity. Because only particles whose domains of influence are influenced by a crack are enriched, the system matrix is still sparse so that the increase of the computational cost is minimized. The condition for crack growth in dynamic problems is obtained from the material instability; when the acoustic tensor loses the positive definiteness, a cohesive crack is inserted to the point so as to change the continuum to a discontiuum. The crack speed is naturally obtained from the criterion. It is found that this method is more accurate and converges faster than the classical meshless methods which are based on the visibility concept. In this paper, several well-known static and dynamic problems were solved to verify the method.

• Journal of Biomedical Engineering Research
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• v.45 no.1
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• pp.10-19
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• 2024

The purpose of this study is to conduct a summative evaluation of the usability of a general-purpose ventilator to determine whether it can be used for its intended purpose in the intended environment by the intended user and to find possible errors in use. The importance of ventilators has increased due to the accelerated aging of the population and the impact of the pandemic. In addition, patients who require ventilators are often in critical condition, so even a small error in use can be fatal. Therefore, it is important to ensure that the ventilator has sufficient stability and can be used satisfactorily without inconvenience to the user. In this study, we conducted a usability test with 17 respiratory nurses with more than 3 years of experience using the ventilator. We analyzed the task success rate, satisfaction, and opinions of the intended users while going through a total of 17 scenarios. Satisfaction was captured through an ASQ questionnaire and subjective opinions were captured through a detailed opinion questionnaire. The results showed a high level of satisfaction with an average score of 6.3 for the use scenarios. Evaluators expressed satisfaction with the overall visibility and versatility of the features, but noted that improvements were needed for calibration tasks with low task success rates. As the calibration method is different from other equipment, it was suggested that specific explanations of the calibration method and the picture that appears when calibrating are needed, and that if relevant training is provided, the equipment can be used without problems. If the usability evaluation is not limited to securing efficiency and satisfaction from the intended users, but also continuously receives feedback from users to prepare for use in emergency environments such as pandemic situations, it will be very helpful to seize opportunities such as emergency authorization in future situations, and ultimately contribute to patient safety by reducing use errors.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.11-19
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• 2012

Air-borne pollen, biogenically created aerosol particle, influences Earth's radiative balance, visibility impairment, and human health. The importance of pollens has resulted in numerous experimental studies aimed at characterizing their dispersion and transport, as well as health effects. There is, however, limited scientific information concerning the optical properties of airborne pollen particles contributing to total ambient aerosols. In this study, for the first time, optical characteristics of pollen such as aerosol backscattering coefficient, aerosol extinction coefficient, and depolarization ratio at 532 nm and their effect to the atmospheric aerosol were studied by lidar remotes sensing technique. Dual-Lidar observations were carried out at the Gwangju Institute of Science & Technology (GIST) located in Gwagnju, Korea ($35.15^{\circ}E$, $126.53^{\circ}N$) for a spring pollen event from 5 to 7 May 2009. The pollen concentration was measured at the rooftop of Gwangju Bohoon hospital where the building is located 1.0 km apart from lidar site by using Burkard trap sampler. During intensive observation period, high pollen concentration was detected as 1360, 2696, and $1952m^{-3}$ in 5, 6, and 7 May, and increased lidar return signal below 1.5km altitude. Pollen optical depth retrieved from depolarization ratio was 0.036, 0.021, and 0.019 in 5, 6, and 7 May, respectively. Pollen particles mainly detected in daytime resulting increased aerosol optical depth and decrease of Angstrom exponent.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.4
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• pp.104-117
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• 2017

This study compares the results of landscape image analysis using text mining techniques and factor analysis for Lotte World Tower, which is the first atypical skyscraper building in Korea, and identifies landscape images of the site to determine possibilities of use. Lotte World Tower's landscape image has been extracted from text mining analysis focusing on adjectives such as 'new', 'transformational', 'unusual', 'novelty', 'impressive', and 'unique', and phrases such as in the process of change, people's active elements(caliber, outing, project, night view), media(newspaper, blog), and climate(weather, season). As a result of the factor analysis, factors affecting the landscape image of Lotte World Tower were symbolic, aesthetic, and formative. Identification, which is a morphological feature, has characteristics of scale and visibility but it is not statistically significant in preference. Rather, the psychological factors such as the symbolism with characteristics such as poison and specialty, harmony with the characteristics of the surrounding environment, and beautiful aesthetic characteristics were an influence on the landscape image. The common results of the two research methods show that psychological characteristics such as factors that can represent and represent the city affect the landscape image more greatly than the morphological and physical characteristics such as location and location of the building. In addition, the text mining technique can identify nouns and adjectives corresponding to the images that people see and feel, and confirms the relationship between the derived keywords, so that it can focus the process of forming the landscape image and further the image of the city. It would appear to be a suitable method to complement the limitation of landscape research. This study is meaningful in that it confirms the possibility that big data can be utilized in landscape analysis, which is one research field of landscape architecture, and is significant for understanding the information of a big data base and contribute to enlarging the landscape research area.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.25-32
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• 2021

In line with future changes in the marine environment, Aids to Navigation has been used in various fields and their use is increasing. The term "Aids to Navigation" means an aid to navigation prescribed by Ordinance of the Ministry of Oceans and Fisheries which shows navigating ships the position and direction of the ships, position of obstacles, etc. through lights, shapes, colors, sound, radio waves, etc. Also now the use of Aids to Navigation is transforming into a means of identifying and recording the marine weather environment by mounting various sensors and cameras. However, Aids to Navigation are mainly lost due to collisions with ships, and in particular, safety accidents occur because of poor observation visibility due to sea fog. The inflow of sea fog poses risks to ports and sea transportation, and it is not easy to predict sea fog because of the large difference in the possibility of occurrence depending on time and region. In addition, it is difficult to manage individually due to the features of Aids to Navigation distributed throughout the sea. To solve this problem, this paper aims to identify the marine weather environment by estimating sea fog level approximately with images taken by cameras mounted on Aids to Navigation and to resolve safety accidents caused by weather. Instead of optical and temperature sensors that are difficult to install and expensive to measure sea fog level, sea fog level is measured through the use of general images of cameras mounted on Aids to Navigation. Furthermore, as a prior study for real-time sea fog level estimation in various seas, the sea fog level criteria are presented using the Haze Model and Dark Channel Prior. A specific threshold value is set in the image through Dark Channel Prior(DCP), and based on this, the number of pixels without sea fog is found in the entire image to estimate the sea fog level. Experimental results demonstrate the possibility of estimating the sea fog level using synthetic haze image dataset and real haze image dataset.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

• Progress in Medical Physics
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• v.23 no.4
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• pp.261-268
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• 2012

Breast cancer is the second leading cause of women cancer death in Korea. The key for reducing disease mortality is early detection. Although digital mammography (DM) has been credited as one of the major reasons for the early detection to decrease in breast cancer mortality observed in the last 20 years, DM is far from perfect for several limitations. Digital breast tomosynthesis (DBT) is expected to overcome some inherent limitations of conventional mammography caused by overlapping of normal tissue and pathological tissue during the standard 2D projections for the improved lesion margin visibility and early breast cancer detection. In this study, we compared a DM system and DBT system acquired with different thickness of breast phantom. We acquired breast phantom data with same average glandular dose (AGD) from 1 mGy to 4 mGy under same experimental condition. The contrast, micro-calcification measurement accuracy and observer study were conducted with breast phantom images. As a result, the higher accuracy of lesion detection with DBT system compared to DM system was demonstrated in this study. Furthermore, the pain of patients caused by severe compression can be reduced with DBT system. In conclusion, the results indicated that DBT system play an important role in breast cancer detection.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.5
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• pp.77-90
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• 2016

This study is to examine the importance of site selection and service quality in franchise business as food service franchise became one of the fastest-growing service industries today. The chief finding of this study is as follows: First, a survey in locational and service quality factors affecting food service franchise shows that responders are more concerned with hygiene and visibility of the store than proximity and transportation advantages which reflects low statistical significance, thus the distance did not seem to be a big problem for the responders in the context that they mostly visit nearby food franchise. Second, the examination of the influence by the service quality factors and customer satisfaction shows significant positive relation with customer response, speed and accuracy, and accuracy factors which reveals that the responders prefer prompt response and swift judgment toward the customer's needs and expectations, professional knowledge services to the credibility factors in which little correlation with the customer satisfaction were found. Third, the examination of the influence by the service quality factors, locational factors, and re-visit reveals that customer response and specialty showed statistically significant correlation with intention of WOM (Word of Mouth) and revisit, which suggests that swift judgment and response toward the customer's needs and expectations, professional knowledge services is of great importance to both customer satisfaction and revisit. The study on the aspects of locational and service quality factors affecting franchise industry's customer satisfaction was conducted as above, an investigation in both factors' influence on the customer satisfaction was made, and based on the results of the analysis, this research seeks an optimal operation strategy of a franchise business. Food service franchise are relatively very competent to business adminstration and reaction capability to consumption changes due to the already established market, and there are stores springing up everywhere inspired by the founders who are too confident of their success in the franchise business. However, it is necessary for the franchise beginners to figure out a zone oriented, regular customer oriented business strategy than just complying with the head office manual. Owing to an increasing trend of opening medium to large sized stores and investments in the wake of converting to multiple business type Korean food franchise, there is growing need to set up new concept of store development and operational management strategy in order to overcome the excessive competition and limited sales volume of the old-fashioned small sized, small capital franchise stores. Furthermore, as most business category of food service franchise serve very similar menus, from a product differentiation point of view, it is required to map out flexible sales concept including the adoption of competitive and low-price strategy. In conclusion, as is shown in the analytical research, the customers' optimal choice fluctuate over their preferences like customer convenience and circumstances rather than insisting on specific brand, thus it will be necessary for the franchise stores to draw up aggressive strategy and planning in running food service franchise to maximize their profits.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.344-359
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• 2022

Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.