• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.231-242
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• 1995

In this paper we consider kernel type estimator of the spectral density at a point in the analysis of stationary time series data. The kernel entails choice of smoothing parameter called bandwidth. A data-based bandwidth choice is proposed, and it is obtained by solving an equation similar to Sheather(1986) which relates to the probability density estimation. A Monte Carlo study is done. It reveals that the spectral density estimates using the data-based bandwidths show comparatively good performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.182-183
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• 2022

The large data volume of light field (LF) image has motivated much research on how to compress the data volume more efficiently. One of the approaches is to compress LF images after representing them in the form of pseudo video sequence. In this way, the pseudo temporal redundancy between views can be exploited by motion estimation and compensation. Based on our observation that images obtained by LF cameras have small range of disparity values between adjacent views, we propose to limit the motion search range to reduce the time complexity of motion estimation. Our experimental results show that a smaller motion search range reduces the encoding time while not affecting the bitrate of H.266/VVC much.

• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.275-280
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• 2004

In this paper, a very fast block matching scheme was proposed to reduce the computations of Block Sum Pyramid Algorithm for motion estimation in video coding. The proposed algorithm is based on Block Sum Pyramid Algorithm and Efficient Multi-level Successive Elimination Algorithm. The proposed algorithm can reduce the computations of motion estimation greatly with 100% motion estimation accuracy. The efficiency of the proposed algorithm was verified by experimental results.

• Bulletin of the Society of Naval Architects of Korea
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• v.21 no.3
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• pp.1-10
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• 1984

The computer programs of effective horsepower estimation of small fishing boat were developed, which was based on the statistical analysis of model test results. From the EHP estimation by these program and experimental model tests of practical fishing boats, the estimation accuracy was verified with maximum deviation of about 10 percent. Also, the EHP estimation accuracy was practically applied to initial design of four small fishing boats, and after the tank tests, the EHP reduction of the order of 15 to 25 percent was confirmed, as compared with existing ships. Moreover, a computer aided design procedure of fishing boat's hull form has been proposed in this study. The practical use of this procedure of fishing boat's hull form has been proposed in this study. The practical use of this procedure was demonstrated with the hull form design results of several fishing boats.

• ETRI Journal
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• v.34 no.3
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• pp.369-378
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• 2012

In this paper, we propose new adaptive search range motion estimation methods where the search ranges are constrained by the probabilities of motion vector differences and a search point sampling technique is applied to the constrained search ranges. Our new methods are based on our previous work, in which the search ranges were analytically determined by the probabilities. Since the proposed adaptive search range motion estimation methods effectively restrict the search ranges instead of search point sampling patterns, they provide a very flexible and hardware-friendly approach in motion estimation. The proposed methods were evaluated and tested with JM16.2 of the H.264/AVC video coding standard. Experiment results exhibit that with negligible degradation in PSNR, the proposed methods considerably reduce the computational complexity in comparison with the conventional methods. In particular, the combined method provides performance similar to that of the hybrid unsymmetrical-cross multi-hexagon-grid search method and outstanding merits in hardware implementation.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.46-53
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• 2015

This paper suggests a method of real time confidence interval estimation to detect abnormal states of sensor data. For real time confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, were compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarms. As the suggested method is for real time anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through real time confidence interval estimation.

• ETRI Journal
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• v.38 no.3
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• pp.579-588
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• 2016

To improve the ability to determine a vehicle's movement information even in a challenging environment, a hybrid approach called non-Gaussian square rootunscented particle filtering (nGSR-UPF) is presented. This approach combines a square root-unscented Kalman filter (SR-UKF) and a particle filter (PF) to determinate the vehicle state where measurement noises are taken as a finite Gaussian kernel mixture and are approximated using a sparse Gaussian kernel density estimation method. During an outage-free GPS period, the updated mean and covariance, computed using SR-UKF, are estimated based on a GPS observation update. During a complete GPS outage, nGSR-UPF operates in prediction mode. Indeed, because the inertial sensors used suffer from a large drift in this case, SR-UKF-based importance density is then responsible for shifting the weighted particles toward the high-likelihood regions to improve the accuracy of the vehicle state. The proposed method is compared with some existing estimation methods and the experiment results prove that nGSR-UPF is the most accurate during both outage-free and complete-outage GPS periods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.807-821
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• 1996

In this paper, we proposed a nonlinear hierarchical mtion estimation method. Generally, the conventional hierarchical motion estimation methods have been proposed for fast convergence and detection of large motions. But they have a common drawback that large error in motion estimation is propapated across motion discontinuities. This artifiact is due to the constriaint of motion continuity and the linear interpolation of motion vectors between hierarchical levels. In this paper, we propose an effective hierarchical motion estimation mechod that is robust to motion discontinuities. The proposed algorithm is based on the decomposition of the functional domain for optimizing the intra-level motion estimation functional. Also, we propose an inter-level nonlinear motion estimation equation rather than using the conventional linearprojection scheme of motion field. computer simulations with several test sequences show tht the proposed algorithm performs better than several conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.17-25
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• 2023

In this paper, we propose a model that can perform human pose estimation through a MobileViT-based model with fewer parameters and faster estimation. The based model demonstrates lightweight performance through a structure that combines features of convolutional neural networks with features of Vision Transformer. Transformer, which is a major mechanism in this study, has become more influential as its based models perform better than convolutional neural network-based models in the field of computer vision. Similarly, in the field of human pose estimation, Vision Transformer-based ViTPose maintains the best performance in all human pose estimation benchmarks such as COCO, OCHuman, and MPII. However, because Vision Transformer has a heavy model structure with a large number of parameters and requires a relatively large amount of computation, it costs users a lot to train the model. Accordingly, the based model overcame the insufficient Inductive Bias calculation problem, which requires a large amount of computation by Vision Transformer, with Local Representation through a convolutional neural network structure. Finally, the proposed model obtained a mean average precision of 0.694 on the MS COCO benchmark with 3.28 GFLOPs and 9.72 million parameters, which are 1/5 and 1/9 the number compared to ViTPose, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.11-20
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• 2016

In this paper, we propose the channel estimation scheme for Long Term Evolution-Advanced (LTE-A) downlink system. The proposed scheme uses the fast fourier transform (FFT) interpolation scheme for the user moving at a high speed. The FFT interpolation scheme converts the channel frequency response obtained from least square (LS) or minimum mean square error (MMSE) channel estimation scheme to time domain channel impulse response by taking the inverse FFT (IFFT). After windowing the channel response in the time domain, we can obtain the channel frequency response by taking the FFT. We perform the system level simulation based on 20MHz bandwidth of 3GPP LTE-A downlink system. Simulation results show that the proposed channel estimation scheme can improve signal-to-noise-plus-interference ratio (SINR), throughput, and spectral efficiency of conventional system.