• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1377-1385
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• 2016

Conventional X-band marine radar has been used as one of the effective tools for collecting and retrieving ocean surface information parameters for three decades. Several wave information extracting algorithms have been designed in such a way that they can be utilized for efficiently estimating sea surface wave parameters such as current velocities, wave direction, significant wave heights in VTS (Vessel Traffic Service). However, their performances are still restricted. For the purpose of overcoming the performance limits, in this paper, first the conventional algorithms are analyzed and their performances are compared, and then a new control algorithm is proposed. Furthermore, we try to improve the estimation performances of typical wave parameters including wave directions and significant wave heights by introducing linear regression model in the process of computing wave information extraction. Through several simulations with the X-band radar images, it is shown that the proposed method is very effective in estimating the wave information compared to the real measured buoy data.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.88-93
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• 2020

The IEEE 802.11p-based wireless access in vehicular environments (WAVE) [1] communication is a method used exclusively for wireless communication on the road. This technique enables information sharing not only among moving vehicles but also between vehicles and infrastructure [2]. As part of WAVE communication, data is transmitted to and from vehicles in motion; in this case, it is difficult to determine the channel accurately in an outdoor environment owing to the Doppler shift [3]. This paper proposes a new channel estimation scheme for enhancing the reception performance of the IEEE 802.11p-based WAVE system. The proposed technique obtains the initial channel value by estimating the least square in the time domain by inserting a pilot signal for channel estimation into the IEEE 802.11p virtual subcarrier. Subsequently, a least mean square algorithm is applied to the initial channel value to update the estimated channel value. The simulation results obtained using the proposed channel estimation technique confirm its remarkable efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2102-2123
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• 2016

This paper considers a channel estimation scheme for millimeter-wave multiuser multiple-input multiple-output systems. According to the proposed method, parts of the beams are selected and the channel parameters are estimated according to the sparsity of channels and the orthogonality of the beams. Since the beams for each channel become distinct and the signal power increases with the increased number of antennas, the proposed approach is able to achieve good estimation performance. As a result, the sum rate can be increased in comparison with traditional approaches, and channels can be estimated with fewer pilot symbols. Numerical results verify that the proposed approach outperforms traditional approaches in cases with large numbers of antennas.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.4
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• pp.84-94
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• 2023

The wave height and period regression curve is widely used to estimate the design wave period. In this study, the parameters of the curves are estimated, compared, and evaluated using the linear, robust linear, and nonlinear regression methods, respectively. The data used in the design wave height estimation are the annual maxima (AM) wave height and period data sets divided by typhoon and non-typhoon conditions, provided by the Ministry of Oceans and Fisheries (2019). The estimation parameters show significant differences in the local coastal waters and the estimation methods. The estimation parameters based on the Suh et al. (2008, 2010) method show the apparent bias, under-estimation in the intercept (scale) parameter, and over-estimation in the slope (exponent) parameter, respectively.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.1
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• pp.113-119
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• 2023

In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.25-30
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• 2017

Millimeter wave (mmWave) bands are expected to improve date rate of 5G systems due to the wide available bandwidth. While severe path loss in those bands has impeded the utilization, short wavelength enables a large number of antennas packed in a compact form, which can mitigate the path loss. However, estimating the channel with a conventional scheme requires a huge training overhead, hence an efficient estimation scheme operating with a small overhead needs to be developed. The sparsity of mmWave channels caused by the limited scatterers can be exploited to reduce the overhead by utilizing compressed sensing. In this paper, we introduce compressed sensing techniques for mmWave channel estimation. First, we formulate wideband channel estimation into a sparse recovery problem. We also analyze the characteristics of random measurement matrix constructed using quantized phase shifters in terms of mutual incoherence.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.5
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• pp.733-738
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• 2013

Orthogonal Frequency Division Multiplexing (OFDM) based IEEE 802.11 a/g systems which are widely used in wireless LAN carry out channel estimation in one time per packet since the systems use only preamble. Whereas, midamble based channel estimation supports continuous channel estimation by tracking the channel state information periodically. Using IEEE 802.11p Wireless Access in Vehicular Environments (WAVE) system, we analyze the performance of the proposed system via practical measurements. Based on these results, practical issues on midamble based channel estimation are investigated.

• International Journal of Contents
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• v.15 no.4
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• pp.27-35
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• 2019

In this paper, we propose an advanced Minimum Mean Square Error (MMSE) channel estimation method for IEEE 802.11p/Wireless Access in Vehicular Environments (WAVE) systems. To improve the performance of MMSE method, we apply the Weighted Sum using Update Matrix (WSUM) scheme to the step of calculating the instantaneously estimated channel and then, a time domain selectively averaging method is applied after the WSUM scheme. Based on that, the accuracy of instantaneously estimated channel increases and then, the accuracy of auto covariance matrix also increases. Consequently, we can achieve the performance gain over the conventional MMSE method. Through simulations based on the IEEE 802.11p standard, it is confirmed that the proposed scheme can outperform the existing channel estimation schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3050-3065
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• 2016

Neural networks (NNs) are extensively used in applications requiring signal classification and regression analysis. In this paper, a NN based threshold selection algorithm for 60 GHz millimeter wave (MMW) time of arrival (TOA) estimation using an energy detector (ED) is proposed which is based on the skewness, kurtosis, and curl of the received energy block values. The best normalized threshold for a given signal-to-noise ratio (SNR) is determined, and the influence of the integration period and channel on the performance is investigated. Results are presented which show that the proposed NN based algorithm provides superior precision and better robustness than other ED based algorithms over a wide range of SNR values. Further, it is independent of the integration period and channel model.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.3
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• pp.401-409
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• 2023

Oscillating water column (OWC) type wave power generator system is a power generation system that uses wave energy, a sustainable and renewable energy source. Irregular cycles and wave heights act as factors that make it difficult to secure generation efficiency of the wave power generator system. Recently, research for improving power generation efficiency is being conducted by applying digital twin technology to OWC type wave energy converter system. However, digital twin using sensor data can predict erroneous performance due to uncertainty in the sensor data. Therefore, this study proposes an uncertainty analysis method for sensor data which is used in digital twin to secure the reliability of digital twin prediction results. Uncertainty quantification considering sensor data characteristics and future uncertainty information according to uncertainty propagation were derived mathematically, and confidence interval estimation was performed based on the proposed method.