• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.159-167
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• 2019

In the field of defense M&S, we are actively pursuing researches that interoperable multiple war game models to simulate various combat environments at the same time. Although the 'unit DB(Database)' for operating the war game models is originated from the identical data, it has been recognized that the method of expressing the attribute of the data is different and the cross reference is impossible. As a result, it makes unnecessary time and effort in establishing the same unit DB in the organizations that operate the war game model. In this study, a method of reusing the unit DB of the training war game model to the analysis war game model with similar resolution and simulated logic was applied to the actual field. For this purpose, we defined the procedure for converting the unit DB by analyzing metadata of the 'Chang-Jo21', a combat training model for corps and division, and the 'Vision21', an analysis model for corps and division operation plan. And we introduced an algorithm that can map different metadata of two unit DBs. This study was meaningful as the first attempt to map and integrate heterogeneous metadata semantically for the reuse of unit DB between different war game models in defense M&S field. Also, it provided implications for the necessity of paradigm shift that reuse of the unit DB between two different war game models is possible and the need for standardization of the unit DB metadata in the defense M&S filed.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.7-11
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• 2022

Object detection technology is one of the main research topics in the field of computer vision and has established itself as an essential base technology for implementing various vision systems. Recent DNN (Deep Neural Networks)-based algorithms achieve much higher recognition accuracy than traditional algorithms. However, it is well-known that the DNN model inference operation requires a relatively high computational power. In this paper, we analyze the inference time complexity of the state-of-the-art object detection architecture Yolov7 in various environments. Specifically, we compare and analyze the time complexity of four types of the Yolov7 model, YOLOv7-tiny, YOLOv7, YOLOv7-X, and YOLOv7-E6 when performing inference operations using CPU and GPU. Furthermore, we analyze the time complexity variation when inferring the same models using the Pytorch framework and the Onnxruntime engine.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.139-144
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• 2023

Engineers prefer deep neural networks (DNNs) for solving computer vision problems. However, DNNs pose two major problems. First, neural networks require large amounts of well-labeled data for training. Second, the covariate shift problem is common in computer vision problems. Domain adaptation has been proposed to mitigate this problem. Recent work on adversarial-learning-based unsupervised domain adaptation (UDA) has explained transferability and enabled the model to learn robust features. Despite this advantage, current methods do not guarantee the distinguishability of the latent space unless they consider class-aware information of the target domain. Furthermore, source and target examples alone cannot efficiently extract domain-invariant features from the encoded spaces. To alleviate the problems of existing UDA methods, we propose the mixup regularization in adversarial discriminative domain adaptation (ADDA) method. We validated the effectiveness and generality of the proposed method by performing experiments under three adaptation scenarios: MNIST to USPS, SVHN to MNIST, and MNIST to MNIST-M.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.250-256
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• 2015

We present a novel method to model the body posture of a worm for vision-based automatic monitoring and analysis. The worm considered in this study is a Caenorhabditis elegans (C. elegans), which is popularly used for research in biological science and engineering. We model the posture by an open chain of a few curved or rigid line segments, in contrast to previously published approaches wherein a large number of small rigid elements are connected for the modeling. Each link segment is represented by only two parameters: an arc angle and an arc length for a curved segment, or an orientation angle and a link length for a straight line segment. Links in the proposed method can be readily related using the Denavit-Hartenberg convention due to similarities to the kinematics of an articulated manipulator. Our method was tested with real worm images, and accurate results were obtained.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.442-448
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• 2011

There are many research results about a self-localization technique of mobile robot. In this paper we present a self-localization technique based on the features of ceiling vision using a fisheye lens. The features obtained by SIFT(Scale Invariant Feature Transform) can be used to be matched between the previous image and the current image and then its optimal function is derived. The fisheye lens causes some distortion on its images naturally. So it must be calibrated by some algorithm. We here propose some methods for calibration of distorted images and design of a geometric fitness model. The proposed method is applied to laboratory and aile environment. We show its feasibility at some indoor environment.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.3
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• pp.21-29
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• 2011

This paper presents a method that estimates distance and velocity of an object with reliability regardless of maneuver status of the target in stereo vision system. A stereo vision system can calculate a distance with disparity from left and right images. However, the distance estimation error may occur due to quantization error of image pixel. A sub-pixel interpolation method minimizes the quantization error and estimates accurate disparity with real value. Extended Kalman filter (EKF) was used to minimize the error covariance and estimate the object's velocity. However, divergence problem occurs due to model uncertainty when a target maneuvers highly, which makes the estimation error increase. In this paper, we propose a virtual model extended Kalman filter (VMEKF) method that minimizes the processing time and provides reliable estimation ability regardless of maneuver status. Computer simulations and experimental results in real road environments demonstrate that the proposed method gives a reliable estimation performance and reduces processing time under various maneuver status while comparing other estimation filters.

• Journal of Biosystems Engineering
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• v.36 no.1
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• pp.48-57
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• 2011

The computer vision system was designed and constructed to measure the weight of a dairy cattle. Its development involved the functions of image capture, image preprocessing, image algorithm, and control integrated into one program. The experiments were conducted with the model dairy cattle and the real dairy cattle by two ways. First experiment with the model dairy cattle was conducted by using the indoor vision experimental system, which was built to measure the model dairy cattle in the laboratory. Second experiment with real dairy cattle was conducted by using the outdoor vision experimental system, which was built for measuring 229 heads of cows in the cattle facilities. This vision system proved to a reliable system by conducting their performance test with 15 heads of real cow in the cattle facilities. Indirect weight measuring with four methods were conducted by using the image processing system, which was the same system for measuring of body parameters. Error value of transform equation using chest girth was 30%. This error was seen as the cause of accumulated error by manually measurement. So it was not appropriate to estimate cow weight by using the transform equation, which was calculated from pixel values of the chest girth. Measurement of cow weight by multiple regression equation from top and side view images has relatively less error value, 5%. When cow weight was measured indirectly by image surface area from the pixel of top and side view images, maximum error value was 11.7%. When measured cow weight by image volume, maximum error weight was 57 kg. Generally, weight error was within 30 kg but maximum error 10.7%. Volume transform method, out of 4 measuring weight methods, was minimum error weight 21.8 kg.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.21-29
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• 2003

It is very important to reduce the computational time in estimating the parameters of vision control algorithm for robot's position control in real time. Unfortunately, the batch estimation commonly used requires too murk computational time because it is iteration method. So, the batch estimation has difficulty for robot's position control in real time. On the other hand, the Extended Kalman Filtering(EKF) has many advantages to calculate the parameters of vision system in that it is a simple and efficient recursive procedures. Thus, this study is to develop the EKF algorithm for the robot's vision control in real time. The vision system model used in this study involves six parameters to account for the inner(orientation, focal length etc) and outer (the relative location between robot and camera) parameters of camera. Then, EKF has been first applied to estimate these parameters, and then with these estimated parameters, also to estimate the robot's joint angles used for robot's operation. finally, the practicality of vision control scheme based on the EKF has been experimentally verified by performing the robot's position control.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.93-100
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• 2021

Satellite communication for high altitude platform stations (HAPS) and geostationary orbit (GEO) systems suffers from sand and dust (SANDU) storms in desert and arid regions. The focus of this paper is to propose common relations between HAPS and GEO for the atmospheric impairments affecting the satellite communication networks operating above Ku-band crossing the propagation path. A double phase three-dimensional relationship for HAPS and GEO systems is then presented. The comparison model present the analysis of atmospheric attenuation with specific focus on sand and dust based on particular size, visibility, adding gas effects for different frequency, and propagation angle to provide systems' operations with a predicted vision of satellite parameters' values. Thus, the proposed system provides wide range of selecting applicable parameters, under different weather conditions, in order to achieve better SNR for satellite communication.

• The Journal of Korean Academic Society of Nursing Education
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• v.22 no.2
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• pp.171-181
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• 2016

Purpose: This study examined the effects of nursing professionalism, academic failure tolerance and social self-efficacy on college life satisfaction among nursing students. Methods: Data were collected between September 1 and October 16, 2015 via a self-reported questionnaire from 170 nursing students using convenient sampling methods. The survey included questions about nursing professionalism, academic failure tolerance, social self-efficacy, and college life satisfaction. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and hierarchical multiple regression with IBM SPSS/WIN 20.0. Results: Establishment vision about nursing science (${\beta}=.27$, p=.006), academic failure tolerance (${\beta}=.17$, p=.031) and social self-efficacy (${\beta}=.19$, p=.012) of nursing students were identified as significant predictors of college life satisfaction, after adjusting for establishment vision about nursing science and satisfaction in nursing science. This model explained 21.0% of the college life satisfaction in nursing students (F=6.38, p<.001). Conclusion: These results suggest that academic failure tolerance and social self-efficacy were significant factors influencing the college life satisfaction of nursing students. Also, as a strategy for improving the college life satisfaction of nursing students, it is necessary to develop programs that can help to establish apparent vision and to improve satisfaction in nursing science.