• Journal of the Korean Society of Radiology
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• v.15 no.7
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• pp.935-942
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• 2021

Involuntary movement of internal organs by respiration is a factor that greatly affects the results of radiotherapy and diagnosis. In this study, a moving phantom was fabricated to simulate the movement of an organ or a tumor according to respiration, and ¹⁸F-FDG PET/CT scan images were acquired under various respiratory simulating conditions to analyze the movement range of the tumor movement by respiration, the level of artifacts according to the size of the tumor and the maximum standardized uptake value (SUVmax). Based on Windows CE 6.0 as the operating system, using electric actuator, electric actuator positioning driver, and programmable logic controller (PLC), the position and speed control module was operated normally at a moving distance of 0-5 cm and 10, 15, and 20 reciprocations. For sphere diameters of 10, 13, 17, 22, 28, and 37 mm at a delay time of 100 minutes, 80.4%, 99.5%, 107.9%, 113.1%, 128.0%, and 124.8%, respectively were measured. When the moving distance was the same, the difference according to the respiratory rate was insignificant. When the number of breaths is 20 and the moving distance is 1 cm, 2 cm, 3 cm, and 5 cm, as the moving distance increased at the sphere diameters of 10, 13, 17, 22, 28, and 37 mm, the ability to distinguish images from smaller spheres deteriorated. When the moving distance is 5 cm compared to the still image, the maximum values of the standard intake coefficient were 18.0%, 23.7%, 29.3%, 38.4%, 49.0%, and 67.4% for sphere diameters of 10, 13, 17, 22, 28, and 37 mm, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.497-505
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• 2018

In a situation where the amount of bulky waste needs to be quantified, a three-dimensional model of the wastes can be constructed using drones. This study constructed a drone-based 3D model with a range of flight parameters and a GCPs survey, analyzed the relationship between the accuracy and time required, and derived a suitable drone application technique to estimate the amount of waste in a short time. Images of waste were photographed using the drone and auto-matching was performed to produce a model using 3D coordinates. The accuracy of the 3D model was evaluated by RMSE calculations. An analysis of the time required and the characteristics of the top 15 models with high accuracy showed that the time required for Model 1, which had the highest accuracy with an RMSE of 0.08, was 954.87 min. The RMSE of the 10th 3D model, which required the shortest time (98.27 min), was 0.15, which is not significantly different from that of the model with the highest accuracy. The most efficient flight parameters were a high overlapping ratio at a flight altitude of 150 m (60-70% overlap and 30-40% sidelap) and the minimum number of GCPs required for image matching was 10.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.2
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• pp.159-167
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• 2022

The present study assessed the micro structure and durability characteristics of ternary blended cement with different types of alkali activators. Ground granulated blast furnace slag(GGBS) and ferronickel slag(FNS) was replaced until 50 % of the weight of cement. In addition, potassuim hydroxide and sodium hydroxide were used for comparing the properties of different type of alkali activator. Ternary blended cement with alkali activators showed higher peak portlandite peak than that of OPC(Ordinary Portlande Cement) and non activated ternary blended cement. Also, there was no new hydration products in ternary blended cement or/and alkali activators. Based on the mercury intrustion porosimetry(MIP) test result, ternary blended cement increased macro pore while alkali activated ternary blended cement modified pore structure and increased microp pore as compared to OPC as control. Combination with alkali activators is desirable to enhance the compressive strength and freeze thaw resistance.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.1-10
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• 2022

As the volume of marine hazardous and noxious substances (HNSs) transported in domestic and overseas seas increases, the risk of HNS spill accidents is gradually increasing. HNS leaked into the sea causes destruction of marine ecosystems, pollution of the marine environment, and human casualties. Secondary accidents accompanied by fire and explosion are possible. Therefore, various types of HNSs must be rapidly detected, and a control strategy suitable for the characteristics of each substance must be established. In this study, the ground HNS spill experiment process and application result of detection algorithms were presented based on hyperspectral remote sensing. For this, styrene was spilled in an outdoor pool in Brest, France, and simultaneous observation was performed through a hyperspectral sensor. Pure styrene and seawater spectra were extracted by applying principal component analysis (PCA) and the N-Findr method. In addition, pixels in hyperspectral image were classified with styrene and seawater by applying spectral matching techniques such as spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), and spectral angle mapper (SAM). As a result, the SDS and SSV techniques showed good styrene detection results, and the total extent of styrene was estimated to be approximately 1.03 m². The study is expected to play a major role in marine HNS monitoring.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.245-257
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• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.

• The Korean Journal of Nuclear Medicine
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• v.32 no.3
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• pp.225-237
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• 1998

Purpose: We investigated the statistical methods to compose the functional brain map of human working memory and the principal factors that have an effect on the methods for localization. Materials and Methods: Repeated PET scans with successive four tasks, which consist of one control and three different activation tasks, were performed on six right-handed normal volunteers for 2 minutes after bolus injections of 925 MBq $H_2^{15}O$ at the intervals of 30 minutes. Image data were analyzed using SPM96 (Statistical Parametric Mapping) implemented with Matlab (Mathworks Inc., U.S.A.). Images from the same subject were spatially registered and were normalized using linear and nonlinear transformation methods. Significant difference between control and each activation state was estimated at every voxel based on the general linear model. Differences of global counts were removed using analysis of covariance (ANCOVA) with global activity as covariate. Using the mean and variance for each condition which was adjusted using ANCOVA, t-statistics was performed on every voxel To interpret the results more easily, t-values were transformed to the standard Gaussian distribution (Z-score). Results: All the subjects carried out the activation and control tests successfully. Average rate of correct answers was 95%. The numbers of activated blobs were 4 for verbal memory I, 9 for verbal memory II, 9 for visual memory, and 6 for conjunctive activation of these three tasks. The verbal working memory activates predominantly left-sided structures, and the visual memory activates the right hemisphere. Conclusion: We conclude that rCBF PET imaging and statistical parametric mapping method were useful in the localization of the brain regions for verbal and visual working memory.

• Journal of the Korean Society of Radiology
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• v.85 no.3
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• pp.566-578
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• 2024

Purpose This study investigated whether the respiratory phase during pleural puncture in CT-guided percutaneous transthoracic needle biopsy (PTNB) affects complications. Materials and Methods We conducted a retrospective review of 477 lung biopsy CT scans performed during free breathing. The respiratory phases during pleural puncture were determined based on the table position of the targeted nodule using CT scans obtained during free breathing. We compared the rates of complications among the inspiratory, mid-, and expiratory respiratory phases. Logistic regression analysis was performed to control confounding factors associated with pneumothorax. Results Among the 477 procedures, pleural puncture was performed during the expiratory phase in 227 (47.6%), during the mid-phase in 108 (22.6%), and during the inspiratory phase in 142 (29.8%). The incidence of pneumothorax was significantly lower in the expiratory puncture group (40/227, 17.6%; p = 0.035) and significantly higher in the mid-phase puncture group (31/108, 28.7%; p = 0.048). After controlling for confounding factors, expiratory-phase puncture was found to be an independent protective factor against pneumothorax (odds ratio = 0.571; 95% confidence interval = 0.360-0.906; p = 0.017). Conclusion Our findings suggest that pleural puncture during the expiratory phase may reduce the risk of pneumothorax during image guided PTNB.

• Journal of the East Asian Society of Dietary Life
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• v.19 no.6
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• pp.891-908
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• 2009

This study was performed to investigate the dietary habits, body weight satisfaction and eating disorder tendencies of female university students in Kyungnam province. Anthropometric measurements, dietary habits, body weight satisfaction, food preferences, disordered eating, and nutrient intakes were assessed in 132 female students at Kaya University. The results were analyzed with $x^2$- and ANOVA tests using the SPSS package program. The average age of the subjects was 20.1 years and average body mass index (BMI) were $21.1\;kg/m^2$. According to BMI, the percentages of students who were underweight, normal weight and overweight by BMI were 21.2, 55.3, and 23.5, respectively. Duration of exercise was significantly different by BMI. Index scores for a mini dietary assessment were significantly higher in the normal and overweight groups than in the underweight group. Scores for sweet, salty, and meat preferences were highest and scores for bean and vegetable preferences were lowest, in the underweight group. Satisfaction for present body weight was lowest, and self-perception of body image and weight control experiences were highest in the overweight group. All subjects in the overweight group wanted to be slim, and those in the normal and underweight groups, preferred to be more slim despite their current body weight being in the normal or below normal range. The percentage of the subjects who were at risk for eating disorders (based on scores from the Eating Attitude Test-26, EAT-$26{\geqq}20$) was 11.3%. Scores for EAT-26 were higher in the overweight group than in the underweight and normal weight groups. Nutrient intakes were not different among the groups. Therefore, dietary habits, taste and food preferences, satisfaction for present body weight, and disordered eating were significantly different according to BMI. These results suggest that overweight female university students need help correcting disordered eating, and nutrition counseling should be established to aid desirable weight control methods. Those who are underweight and normal weight need help establishing proper perceptions of their normal body weight and body image as well as nutrition counseling for health.