• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.575-582
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• 2019

Advanced driving assist system can support safety of driver and passengers which may require vehicle dynamics states as well as road geometry. It is essential to have in real-time estimation of related variables and parameters. Among the road geometry parameters, road slope angle which can not be measured is essential parameter in pose estimation, adaptive cruise control and others on sag road. In this paper, Kalman filter based method for the estimation of the vehicle dynamics and road slope angle using a nonlinear vehicle model is proposed. It uses a combination of Kalman filter as Cascade Extended Kalman Filter. CEKF uses measured vehicle states such as yaw rate, longitudinal/lateral acceleration and velocity. Unknown vehicle parameters such as center of gravity and inertia are obtained by 2 D.O.F lateral model and experimentally. Simulation and Experimental tests conducted with commercialized vehicle dynamics model and real-car.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.277-287
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• 2024

In satellite systems, efficient communication and observation require identifying of specific signal arrival points using onboard antenna systems. When utilizing massive array antennas to estimate the angle of arrival (AOA) of signals, traditional high-performance AOA estimation algorithms such as Multiple Signal Classification (MUSIC) encounter extremely high complexity due to the numerous individual antenna elements. Although, in order to improve this computational complexity problem, the cascade AOA estimation algorithm with CAPON and beamspace-MUSIC was recently proposed, the comparison of the computational complexity of the proposed algorithm across different massive array antenna configurations has not yet been conducted. In this paper, we provide the analyzed results of the computational complexity of the proposed cascade algorithm based on various massive array antennas, and determine an optimal antenna configuration for the efficient AOA estimation in satellite systems.

• Journal of IKEEE
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• v.26 no.1
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• pp.10-18
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• 2022

Accurate SOC estimation is an important indicator of battery operation strategies, and many studies have been conducted. The simulation method which was mainly used in previous studies, is difficult to conduct real-time SOC estimation like real BMS environment. Therefore, this paper aims to implement a real-time battery SOC estimation embedded system and analyze problems that can arise during the verification process. In environment consisting of two Raspberry Pi boards, SOC estimation with the EKF uses data measured by the Simscape battery model. Considering that the operating characteristics of the battery vary depend on the temperature, the results were analyzed at various ambient temperatures. It was confirmed that accurate SOC estimation was performed even when offset fault and packet loss occurred due to communication or sensing problems. This paper proposes a guide for embedded system strategies that enable real-time SOC estimation with errors within 5%.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.

• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.2
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• pp.127-138
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• 2012

In this paper, we propose a real-time virtual re-convergence hardware platform especially to reduce the visual fatigue caused by stereoscopy. Our unique idea to reduce visual fatigue is to utilize the virtual re-convergence based on the optimized disparity-map that contains more depth information in the negative disparity area than in the positive area. Our virtual re-convergence hardware platform, which consists of image rectification, disparity estimation, depth post-processing, and virtual view control, is realized in real time with 60 fps on a single Xilinx Virtex-5 FPGA chip.

• International Journal of Contents
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• v.14 no.1
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• pp.39-44
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• 2018

Today, cars have developed into intelligent automobiles that combine advanced control equipment and IT technology to provide driving assistance and convenience to users. These vehicles provide infotainment services to the driver, but this does not improve the safety of the driver. Accordingly, V2X communication, which forms a network between a vehicle and a vehicle, between a vehicle and an infrastructure, or between a vehicle and a human, is drawing attention. Therefore, various techniques for improving channel estimation performance without changing the IEEE 802.11p standard have been proposed, but they do not satisfy the packet error rate (PER) performance required by the C-ITS service. In this paper, we analyze existing channel estimation techniques and propose a new channel estimation scheme that achieves better performance than existing techniques. It does this by applying the updated matrix for the data pilot symbol to the construct data pilot (CDP) channel estimation scheme and by further performing the interpolation process in the frequency domain. Finally, through simulations based on the IEEE 802.11p standard, we confirmed the performance of the existing channel estimation schemes and the proposed channel estimation scheme by coded PER.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.49-56
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• 2015

Korea government has established clear cost estimation standard based on a survey of e-learning contents development cost and presented 'e-Learning Contents Development Cost Estimation Guidelines' that reflect the characteristics of the e-learning industry. However, if there is no institutional support, this guideline and system fails to achieve the purposes and objectives. And it is likely to be facing a dead document. Therefore, the policy foundation is required. This study suggested the following policy; stepwise activation of cost estimation standard, enact announcement and periodically adjustment of cost estimation standard, installation and operation of cost estimation standard operational committee, conjunction with the e-learning industry survey, cultural diffusion of co-owned copyright, systematic monitoring of the e-learning contents development process, research on activating policy of cost estimation standard, conjunction with the standard contract for enhancing policy effectiveness.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.37-43
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• 2017

In this paper, we propose a method for estimation software quality in terms of software test data, and it is necessary to predict the period of time required for software test evaluation. We need a model to understand of estimation of software quality. In this paper, we propose a model to estimate the number of days for software test using the data obtained through the tester's sex, and present a model for analysing the number of errors according to six quality characteristics by software type.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.808-810
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• 2011

The background estimation algorithms had a significant impact on the performance of image processing and recognition. In this paper, background estimation algorithms were analysis of complexity and performance as preprocessing of image recognition. It was evaluated the performance of Gaussian Running Average, Mixture of Gaussian, and KDE algorithm. The simulation results show that KDE algorithm outperforms compared to the other algorithms.

• Smart Media Journal
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• v.10 no.2
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• pp.22-29
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• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.