• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4584-4599
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• 2021

In IEEE 802.11p-based wireless access in vehicular environments (WAVE) communication systems, channel estimation (CE) is one of the important issues to provide stable communication service. It is hard to apply conventional CE schemes based on data pilot to real systems, because error propagation occurs in high mobility and modulation order environments, resulting in degrading the CE performance. In this paper, we propose one data pilot using multiple receive antennas (ODPM) CE scheme based on the weighted sum using update matrix (WSUM) with time-domain averaging (TDA) to overcome this problem. Within the process of WSUM-TDA in the proposed scheme, the maximum ratio combining (MRC) technique is applied so as to create more accurate one data pilot. Moreover, a new reliability test criterion is proposed as the fashion of utilizing MRC, which makes it possible to apply selective TDA that guarantees performance improvement. In simulation results, the packet error rate (PER) performance of the proposed ODPM is compared with that of conventional CE methods and its superiority is demonstrated.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.98-102
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• 2023

This paper presents a study on how augmenting semi-synthetic image data improves the performance of human detection algorithms. In the field of object detection, securing a high-quality data set plays the most important role in training deep learning algorithms. Recently, the acquisition of real image data has become time consuming and expensive; therefore, research using synthesized data has been conducted. Synthetic data haves the advantage of being able to generate a vast amount of data and accurately label it. However, the utility of synthetic data in human detection has not yet been demonstrated. Therefore, we use You Only Look Once (YOLO), the object detection algorithm most commonly used, to experimentally analyze the effect of synthetic data augmentation on human detection performance. As a result of training YOLO using the Penn-Fudan dataset, it was shown that the YOLO network model trained on a dataset augmented with synthetic data provided high-performance results in terms of the Precision-Recall Curve and F1-Confidence Curve.

• Journal of the Korea Convergence Society
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• v.7 no.3
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• pp.149-157
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• 2016

For workers, industrial accidents exert a bad effect on the productivity, quality of life, and depress the morale. This study aimed to examine the overall influence on job performance of employees who returned to work after industrial accidents. This was a cross-sectional study using the data of 2013 and 2014 Panel Study of Worker's Compensation Insurance (PSWCI), and we performed logistic-regression analysis to analyze an affinity between general characteristics and job performance as independent variable and outcome variable, respectively. As a result, the major factor depressed the job performance were the 1 - 7 degree of disabilities and injuries with convalescence period for 6 to 9month or more than 12 months. In other words, this study shown that job performance was decreased as higher degree of disability and longer convalescence period. Job performance is the factor to identify indirectly worker's successful return to work, and it is important in follow-up of workers who returned to work after industrial accidents. Stable job performance of an industrial disaster victim is the key factor to maintain worker's comfortable and qualitative life as well as increase of productive capacity.

• ETRI Journal
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• v.37 no.2
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• pp.241-250
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• 2015

In this paper, we present novel high-speed transmission schemes for high-speed ultra-high frequency (UHF) radio-frequency identification communication. For high-speed communication, tags communicate with a reader using a high-speed Miller (HS-Miller) encoding and multiple antennas, and a reader communicates with tags using extended pulse-interval encoding (E-PIE). E-PIE can provide up to a two-fold faster data rate than conventional pulse-interval encoding. Using HS-Miller encoding and orthogonal multiplexing techniques, tags can achieve a two- to three-fold faster data rate than Miller encoding without degrading the demodulation performance at a reader. To verify the proposed transmission scheme, the MATLAB/Simulink model for high-speed backscatter based on an HS-Miller modulated subcarrier has been designed and simulated. The simulation results show that the proposed transmission scheme can achieve more than a 3 dB higher BER performance in comparison to a Miller modulated subcarrier.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.12-20
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• 1993

In a conventional adaptive echo canceller, an ADF(Adaptive Digital Filter) with TDL(Tapped-Delay Line) structure modelling the echo path uses the LMS(Least Mean Square) algorithm to compute the coefficients, and NET detector using energy comparison method prevents the ADF to update the coefficients during the periods of the NET signal presence. The convergence speed of the LMS algorithm depends on the eigenvalue spread ratio of the reference signal and NET detector using the energy comparison method yields poor detection performance if the magnitude of the NET signal is small. This paper presents a new adaptive echo canceller which uses the pre-whitening filter to improve the convergence speed of the LMS algorithm. The pre-whitening filter is realized by using a low-order lattice predictor. Also, a new NET signal detection algorithm is presented, where the start point of the NET signal is detected by computing the cross-correlation coefficient between the primary input and the ADF output while the end point is detected by using the energy comparison method. The simulation results show that the convergence speed of the proposed adaptive echo canceller is faster than that of the conventional echo canceller and the cross-correlation coefficient yields more accurate detection of the start point of the NET signal.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.6
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• pp.1-8
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• 2004

The objective of this study is convergence acceleration of multigrid Navier-Stokes solvers. This study has been performed to enhance the performance of preconditioned multi-stage time stepping method which is a popular smoother for the multigrid Navier-Stokes solvers. Comparative study on the convergence characteristics of the ADI and DDADI preconditioners has been conducted. It is shown that the DDADI preconditioner has better performance than the ADI by numerical tests on the 2-D compressible turbulent flows past airfoils. The Spalart-Allmaras turbulent model and the Baldwin-Lomax turbulent model have been compared with the multigrid calculations.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.413-422
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• 2012

In recent years, a number of missions have been planned and conducted worldwide on the planets such as Mars, which involves the unmanned robotic exploration with the use of rover. The rover is an important system for unmanned planetary exploration, performing the locomotion and sample collection and analysis at the exploration target of the planetary surface designated by the operator. This study investigates the development of mobility system for the rover ground model necessary to the planetary surface exploration for the benefit of future planetary exploration mission in Korea. First, the requirements for the rover mobility system are summarized and a new mechanism is proposed for a stable performance on rough terrain which consists of the passive suspension system with 8 wheeled double 4-bar linkage (DFBL), followed by the performance evaluation for the mechanism of the mobility system based on the shape design and simulation. The proposed mobility system DFBL was compared with the Rocker-Bogie suspension system of US space agency National Aeronautics and Space Administration and 8 wheeled mobility system CRAB8 developed in Switzerland, using the simulation to demonstrate the superiority with respect to the stability of locomotion. On the basis of the simulation results, a general system configuration was proposed and designed for the rover manufacture.

• Bulletin of the Korean Chemical Society
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• v.34 no.1
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• pp.79-88
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• 2013

$LiFePO_4$ is a promising active material (AM) suitable for use in high performance lithium-ion batteries used in automotive applications that require high current capabilities and a high degree of safety and reliability. In this study, an optimization of the electrode design parameters was performed to produce high capacity lithium-ion batteries based on $LiFePO_4$/graphite electrodes. The electrode thickness and porosity (AM density) are the two most important design parameters influencing the cell capacity. We quantified the effects of cathode thickness and porosity ($LiFePO_4$ electrode) on cell performance using a detailed one-dimensional electrochemical model. In addition, the effects of those parameters were experimentally studied through various coin cell tests. Based on the numerical and experimental results, the optimal ranges for the electrode thickness and porosity were determined to maximize the cell capacity of the $LiFePO_4$/graphite lithium-ion batteries.

• The Journal of Information Technology
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• v.8 no.3
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• pp.105-115
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• 2005

The CMA and MMA blind equalization algorithm has the inevitable large residual error caused by mismatching between the symbol constellation at a steady state after convergence. Stop-and-Go algorithm has a very superior residual error characteristics at a steady state but a relatively slow convergence characteristics. In this paper, we propose a SAG-Flagged MMA as a new adaptive blind equalization algorithm with a Stop-and-Go flag which follows a flagged MMA in update scheme of tap weights as appling the flag obtaining from Stop-and-Go algorithm to MMA. Using computer simulation, it is confirmed that the proposed algorithm has an enhancing performance from the viewpoint of residual ISI, residual error and convergence speed in comparison with MMA and Stop-and-Go algorithm. Algorithm has a new error function using the decided original constellation instead of the reduced constellation. By computer simulation, it is confirmed that the proposed algorithm has the performance superiority in terms of residual ISI and convergence speed compared with the adaptive blind equalization algorithm of CMA family, Constant Modulus Algorithm with Carrier Phase Recovery and Modified CMA(MCMA).

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1643-1652
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• 2022

Most of the existing machine reading research has used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms as networks. Among them, RNN was slow in training, and Question Answering Network (QANet) was announced to improve training speed. QANet is a model composed of CNN and self-attention. CNN extracts semantic and syntactic information well from the local corpus, but there is a limit to extracting the corresponding information from the global corpus. Graph Convolutional Networks (GCN) extracts semantic and syntactic information relatively well from the global corpus. In this paper, to take advantage of this strength of GCN, we propose I-QANet, which changed the CNN of QANet to GCN. The proposed model performed 1.2 times faster than the baseline in the Stanford Question Answering Dataset (SQuAD) dataset and showed 0.2% higher performance in Exact Match (EM) and 0.7% higher in F1. Furthermore, in the Korean Question Answering Dataset (KorQuAD) dataset consisting only of Korean, the learning time was 1.1 times faster than the baseline, and the EM and F1 performance were also 0.9% and 0.7% higher, respectively.