• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.74-79
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• 1996

This paper describes a methodology for the development of models of discrete event system(DES). The methodology is based on transformation of continuous state space into discrete one to homomorphically represent dynamics of continuous processes in discrete events. This paper proposes a formal structure which can couple DES models within a framework. The structure employs the DEVS formalism for the DES models. The proposed formal structure has been applied to develop a DEVS model for the human cardiovascular system. For this, the cardiac cycle is partitioned into a set of phases based on events identified through VisSim simulation in the CS of the electrical analog model. VisSim is the simulation tool of visual environment for developing continuous, discrete, and hybrid system models and performing dynamic simulation. For each phase, a CS of the electrical analog model for the cardiovascular system has been simulated by VisSim 2.0. To validate this model, first develop the DEVS model, then simulate the model in the DEVSIM++ environment. It has same simulation results for the data obtained from the CS simulation using VisSim. The comparison shows that the DEVS model represents dynamics of the human heart system at each phase of cardiac cycle.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.132-137
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• 2019

The amount of data generated from medical images is increasingly exceeding the limits of professional visual analysis, and the need for automated medical image analysis is increasing. For this reason, this study evaluated the classification and accuracy according to the presence or absence of tumor using Inception V3 deep learning model, using MRI medical images showing normal and tumor findings. As a result, the accuracy of the deep learning model was 90% for the training data set and 86% for the validation data set. The loss rate was 0.56 for the training data set and 1.28 for the validation data set. In future studies, it is necessary to secure the data of publicly available medical images to improve the performance of the deep learning model and to ensure the reliability of the evaluation, and to implement modeling by improving the accuracy of labeling through labeling classification.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3514-3522
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• 1999

While the method of information collection and visual interface technique have been researched actively on web information retrieval, a study on structured modeling for effective classification of a wide collective information leaves to be desired. In this paper, we represent information feature based on structured information model. It aims at carrying out effectively the user's retrieval environment through visualization technique with analyzing the information feature. We propose a information classification method using Facet units and we construct the object model, table model, SQL code to define the relation of the information, and represent the information feature based on a wide range of views. After users gain a better global understanding of the information feature, retrieve more easily through their information. Conventional information retrieval is user-oriented to be what user want, but proposed technique it data-oriented which helps users to understand what exist in database by showing information feature.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.4
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• pp.347-354
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• 2007

Since large portal service providers started web services for 3D city models around the world using spatial imagery, the competition has been getting intense to provide the models with the higher quality and accuracy. The building models are the most in number among the 3D city model objects, and it takes much time and money to create realistic model due to various shapes and visual appearances of building object. The aforementioned problem is the most significant limitation for the service and the update of the 3D city model of the large area. This study proposed a method of generating realistic 3D building models with quick and economical texture mapping using multiple spatial imagery such as aerial photos or satellite images after reconstructed geometric models of buildings from building layers in digital maps. Based on the experimental results, the suggested method has effectiveness for the generation of the 3D building models using various air-borne imagery and satellite imagery quickly and economically.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1055-1065
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• 2009

High-dynamic-range (HDR) rendering technology changes the range from the broad dynamic range (up to 9 log units) of a luminance, in a real-world scene, to the 8-bit dynamic range which is the common output of a display's dynamic range. One of the techniques, iCAM06 has a superior capacity for making HDR images. iCAM06 is capable of making color appearance predictions of HDR images based on CIECAM02 and incorporating spatial process models in the human visual system (HVS) for contrast enhancement. However there are several problems in the iCAM06, including obscure user controllable factors to be decided. These factors have a serious effect on the output image but users get into difficulty in that they can't find an adequate solution on how to adjust. So a suggested model gives a quantitative formulation for user controllable factors of iCAM06 to find suitable values which corresponds with different viewing conditions, and improves subjective visuality of displayed images for varying illuminations.

• Journal of Korea Technology Innovation Society
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• v.22 no.3
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• pp.340-361
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• 2019

This study analyzes the technological structure of artificial intelligence (AI) and technological capabilities of AI companies based on patent information. 2589 AI patents registered in USPTO from 2007 to 2017 were collected and analyzed by the Latent Dirichlet Allocation (LDA) to derive 20 AI technology topics. Analysis of technology development trends by AI technology reveals that visual understanding, data analysis, motion control, and machine learning are growing, while language understanding and speech technology are sluggish. In addition, we also investigated leading companies in each sub-field of AI as well as core competencies of global IT companies. The findings of this study are expected to be fruitfully used for formulation and implementation of technology strategy of AI companies.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2656-2661
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• 2023

The project of China initiative Accelerator Driven Subcritical (CiADS) system has been started to construct in southeast China's Guangdong province since 2019, which is expected to be checked and accepted in the year 2025. In order to make the students in University of Chinese Academy of Sciences (UCAS) better understand the main characteristic and the operation condition in the subcritical nuclear facility, the training platform for CiADS has been developed based on the Cave Automatic Virtual Environment (CAVE) in the Institute of Modern Physics Chinese Academy of Sciences (IMPCAS). The CAVE platform is a kind of non-head mounted virtual reality display system, which can provide the immersive experience and the alternative training platform to substitute the dangerous operation experiments with strong radioactivity. In this paper, the CAVE platform for the training scenarios in CiADS system has been presented with real-time simulation feature, where the required devices to generate the auditory and visual senses with the interactive mode have been detailed. Moreover, the three dimensional modeling database has been created for the different operation conditions, which can bring more freedom for the teachers to generate the appropriate training courses for the students. All the user-friendly features will offer a deep realistic impression to the students for the purpose of getting the required knowledge and experience without the large costs in the traditional experimental nuclear reactor.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.157-157
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• 2022

Recent decades have seen all over the world increasing drought in some regions and increasing flood in others. Climate change has been alarming in many regions resulting in degradation and diminution of available freshwater. The effect of global warming and overpopulation associated with increasing irrigated farming and valuable agricultural lands could be particularly disastrous for coastal areas like the one of Benin. The coastal region of Benin is under a heavy demographic pressure and was in the last decades the object of important urban developments. The present study aims to roughly study the general effect of climate change (Sea Level Rise: SLR) and groundwater pumping on Seawater intrusion (SWI) in Benin's coastal region. To reach the main goal of our study, the region aquifer system was built in numerical model using SEAWAT engine from Visual MODFLOW. The model is built and calibrated from 2016 to 2020 in SEAWAT, and using WinPEST the model parameters were optimized for a better performance. The optimized parameters are used for seawater intrusion intensity evaluation in the coastal region of Benin The simulation of the hydraulic head in the calibration period, showed groundwater head drawdown across the area with an average of 1.92m which is observed on the field by groundwater level depletion in hand dug wells mainly in the south of the study area. SWI area increased with a difference of 2.59km² between the start and end time of the modeling period. By considering SLR due to global warming, the model was stimulated to predict SWI area in 2050. IPCC scenario IS92a simulated SLR in the coastal region of Benin and the average rise is estimated at 20cm by 2050. Using the average rise, the model is run for SWI area estimation in 2050. SWI area in 2050 increased by an average of 10.34% (21.04 km²); this is expected to keep increasing as population grows and SLR.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• Journal of Dental Anesthesia and Pain Medicine
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• v.23 no.6
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• pp.347-355
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• 2023

Background: Eye movement desensitization and reprocessing (EMDR) therapy has been reported to be very efficacious for treating post-traumatic stress disorder (PTSD) and other anxiety-related conditions. However, a review of the literature reveals the sparse use of this therapy in the field of pediatric dentistry. This study aimed to evaluate anxiety trends in pediatric dental patients during local anesthesia and extraction with and without EMDR therapy. Methods: Children in the age range of 8-12 years who required dental extractions were assigned randomly into two groups: an EMDR group (group 1) and a routine behavior management therapy group (group 2; receiving more traditional interventions such as tender love and care behavioral modeling, and distraction). Anxiety scores were recorded at four levels using the visual facial anxiety scale (VFAS) preoperatively, after therapy, after the administration of local anesthesia (LA), and after extraction. Results: Reduced anxiety was observed after the delivery of EMDR therapy, after LA administration, and post-extraction in the EMDR group compared to pre-operative anxiety scores of anxiety (P < 0.001; unpaired Student's t and Mann-Whitney U tests). In the control group, mild reductions in anxiety after routine behavior management therapy were observed, accompanied by spikes in anxiety levels after LA and extractions. Conclusion: EMDR therapy was found to be valuable for reducing anxiety among pediatric dental patients during tooth extraction procedures.