• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.89-98
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• 2007

For browsing, searching, and manipulating video documents, an indexing technique to describe video contents is required. Until now, the indexing process is mostly carried out by specialists who manually assign a few keywords to the video contents and thereby this work becomes an expensive and time consuming task. Therefore, automatic classification of video content is necessary. We propose a fully automatic and computationally efficient method for analysis and summarization of spots news video for 5 spots news video such as soccer, golf, baseball, basketball and volleyball. First of all, spots news videos are classified as anchor-person Shots, and the other shots are classified as news reports shots. Shot classification is based on image preprocessing and color features of the anchor-person shots. We then use the dominant color of the field and motion features for analysis of sports shots, Finally, sports shots are classified into five genre type. We achieved an overall average classification accuracy of 75% on sports news videos with 241 scenes. Therefore, the proposed method can be further used to search news video for individual sports news and sports highlights.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.4
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• pp.388-395
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• 2015

The waste gate valve (WGV) for gasoline vehicles operate in a harsh high-temperature environment. Hence, WGVs are typically made of Inconel 713C, which is a type of Ni-based superalloy. Recently, the metal injection molding (MIM) process has attracted considerable attention for parts used under high-temperature conditions. In this study, an MIM analysis for the head and other parts of the WGV is conducted using a commercial CAE program Moldflow. Further, optimal manufacturing conditions are determined by analyzing flow characteristics at various injection times and locations. Moreover, to improve the accuracy of the analysis results, we compare the actual temperature of the mold during injection processing with that observed through the analysis. As the results, metal injection patterns of analysis are well in accord with these of short shot test. And the temperature variations of analysis is also very similar with those of feedstock when metal injection molding.

• The KSFM Journal of Fluid Machinery
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• v.10 no.5
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• pp.54-63
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• 2007

A barrel is a high length-to-diameter ratio cylinder that is influenced by environmental factors such as sunlight, precipitation, wind and clouds. Cross-barrel temperature differences caused by uneven heating or cooling lead to thermal deformation that degrades accuracy. Therefore, a barrel is covered by thermal shrouds to minimize the type of thermal deformation, "fall-of-shot". In this paper, an analytical and experimental study is presented to design the thermal shrouds for a gun barrel and to evaluate the thermal shroud effect. First, an analytical study on the thermal shroud effect to minimize thermal deformation of a gun barrel by sunlight and wind is performed. The coupled analysis of thermal fluid dynamics of the air flow between a barrel and thermal shrouds and thermal stresses of a barrel Is performed to clarify both the thermal shroud effect and the drift in gun muzzle orientation by thermal deformation. Second, experiments are carried out to test and evaluate the thermal shroud effect on the performance of a gun barrel. The drift in gun muzzle orientation against the solar radiation is confirmed by the experiments, and the results well agree with the analytical estimation. Third, three principal design factors that are presumed to have an effect on the performance of the thermal shrouds are also analyzed; sorts of shroud materials, wall-thickness of thermal shrouds, and distance of the gap between a barrel and thermal shrouds.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.34-40
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• 2003

The paper presents a method to detect dissolve shots of video scene change detections in an MPEG compressed domain. The proposed algorithm uses color-R DCT coefficients of Ⅰ, P-frames for a fast operation and accurate detection and a minimum decoding process in MPEG sequences. The paper presents a method to detect dissolve shot for three-dimensional visualization and analysis of Image in order to recognize easily in computer as a human detects accurately shots of scene change. First, Color-R DCT coefficients for 8*8 units are obtained and the features are summed in a row. Second, Four-step analysis are Performed for differences of the sum in the frame sequences. The experimental results showed that the algorithm has better detection performance, such as precision and recall rate, than the existing method using an average for all DC image by performing four step analysis. The algorithm has the advantage of speed, simplicity and accuracy. In addition. it requires less amount of storage.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.134-139
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• 2000

In order to understand the removal mechanism and seek the optimal conditions. KrF excimer laser ablation of Cr films on glass substrates is investigated. The surface morphology of the laser-irradiated spot is examined by SEM. The measured single-shot ablation rate is found to be about two times the result of numerical analysis based on a surface vaporization model and heat conduction theory. Surface morphology examination indicates that the Cr film is removed by the sequence of melting-surface vaporization-,melt expulsion by plasma recoil and that the outmost ripple of the diffraction pattern gives a strong effect on the morphology of molten Cr during the melting and vaporization processes. To seek the optimal process parameters for micro patterning morphological investigation is carried out experimentally on samples having different chromium film thicknesses. Optimal processing conditions are determined to enhance the accuracy and quality of thin film removal for micro patterning.

• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Communications of Mathematical Education
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• v.37 no.3
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• pp.545-567
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• 2023

This study investigates effective ChatGPT prompts for solving mathematical problems, focusing on the chapters of quadratic equations and quadratic functions. A structured prompt was designed, following a sequence of 'Role-Rule-Example Solution-Problem-Process'. In this study, an artificial intelligence model combining GPT-4, Wolfram plugin, and Advanced Data Analysis was utilized. Wolfram was used as the primary tool for calculations to reduce computational errors. When using the structured prompt, the accuracy rate for problems from nine high school mathematics textbooks on quadratic equations and quadratic functions was 91%, showing higher performance compared to zero-shot prompts. This confirmed the effectiveness of the structured prompts in solving mathematical problems. The structured prompts designed in this study can contribute to the development of intelligent information systems for personalized and customized education.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.513-524
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• 2021

Objective: To assess the noncontrast two-dimensional single-shot balanced turbo-field-echo magnetic resonance angiography (b-TFE MRA) features of the abdominal aortic aneurysm (AAA) status following endovascular aneurysm repair (EVAR) and evaluate to detect endoleaks (ELs). Materials and Methods: We examined four aortic stent-grafts in a phantom study to assess the degree of metallic artifacts. We enrolled 46 EVAR-treated patients with AAA and/or common iliac artery aneurysm who underwent both computed tomography angiography (CTA) and b-TFE MRA after EVAR. Vascular measurements on CTA and b-TFE MRA were compared, and signal intensity ratios (SIRs) of the aneurysmal sac were correlated with the size changes in the AAA after EVAR (AAA prognoses). Furthermore, we examined six feasible b-TFE MRA features for the assessment of ELs. Results: There were robust intermodality (r = 0.92-0.99) correlations and interobserver (intraclass correlation coefficient = 0.97-0.99) agreement. No significant differences were noted between SIRs and aneurysm prognoses. Moreover, "mottled high-intensity" and "creeping high-intensity with the low-band rim" were recognized as significant imaging findings suspicious for the presence of ELs (p < 0.001), whereas "no signal black spot" and "layered high-intensity area" were determined as significant for the absence of ELs (p < 0.03). Based on the two positive features, sensitivity, specificity, and accuracy for the detection of ELs were 77.3%, 91.7%, and 84.8%, respectively. Furthermore, the k values (0.40-0.88) displayed moderate-to-almost perfect agreement. Conclusion: Noncontrast MRA could be a promising imaging modality for ascertaining patient follow-up after EVAR.

• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.9-15
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• 2005

Purpose : This study attempted to investigate the effects of supply of highly concentrated $(30\%)$ oxygen on human ability of visuospatial cognition using time course data analysis of functional Magnetic Resonance Imaging (fMRI). Materials and Methods : To select an item set in the visuospatial performance test, two questionnaires with similar difficulty were developed through group testing. A group test was administered to 263 college students. Two types of questionnaire containing 20 questions were developed to measure the ability of visuospatial cognition. Eight college students (right-handed male, average age of 23.5 yrs) were examined for fMRI study. The experiment consisted of two runs of the visuospatial cognition testing, one with $21\%$ level of oxygen and the other with $30\%$ oxygen level. Each run consisted of 4 blocks, each containing control and visuospatial items. Functional brain images were taken from 37 MRI using the single-shot EPI method. Using the subtraction procedure, activated areas in the brain during visuospatial tasks were color-coded by t-score. To investigate the time course data in each activated area from brain images, 4 typical regions (cerebellum, occipital lobe, parietal lobe, and frontal lobe) were selected. Results : The average accuracy was $50.63{\pm}8.63$ and $62.50{\pm}9.64$ for $21\%\;and\;30\%$ oxygen respectively, and a statistically significant difference was found in the accuracy between the two types of oxygen (p<0.05). There were more activation areas observed at the cerebellum, occipital lobe, parietal lobe and frontal lobe with $30\%$ oxygen administration. The rate of increase in the cerebellum, occipital lobe and parietal lobe was $17\%$ and that of the frontal lobe, $50\%$. Especially, there were increase of intensity of BOLD signal at the parietal lobe with $30\%$ oxygen administration. The increase rate of the left parietal lobe was $1.4\%$ and that of the right parietal lobe, $1.7\%$. Conclusion : It is concluded that while performing visuospatial tasks, high concentrations of oxygen administration make oxygen administration sufficient, thus making neural network activate more, and the ability to perform visuospatial tasks increase.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.143-149
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• 2018

Recently, due to the large-scale damage of natural disasters caused by global climate change, a monitoring system applying remote sensing technology is being constructed in disaster areas. Among remote sensing platforms, the drone has been actively used in the private sector due to recent technological developments, and has been applied in the disaster areas owing to advantages such as timeliness and economical efficiency. This paper deals with the development of a preprocessing system that can map the drone image data in a near-real time manner as a basis for constructing the disaster monitoring system using the drones. For the research purpose, our system is based on the SURF algorithm which is one of the computer vision technologies. This system aims to performs the desired correction through the feature point matching technique between reference images and shot images. The study area is selected as the lower part of the Gahwa River and the Daecheong dam basin. The former area has many characteristic points for matching whereas the latter area has a relatively low number of difference, so it is possible to effectively test whether the system can be applied in various environments. The results show that the accuracy of the geometric correction is 0.6m and 1.7m respectively, in both areas, and the processing time is about 30 seconds per 1 scene. This indicates that the applicability of this study may be high in disaster areas requiring timeliness. However, in case of no reference image or low-level accuracy, the results entail the limit of the decreased calibration.