• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.12
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• pp.1699-1707
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• 2003

Coriolis mass flowmeter(CMF), which can measure the mass flow directly, is getting rapid attention for the industrial and custody transfer purpose. In order to study the characteristics and the applicability of CMF, it is tested with the national flow standard system. Two types of sensing tube, U-type and straight type, are employed in the test. Water, spindle oil and viscosity Standard Reference Material whose viscosities are 1, 20 and, 67 $\textrm{mm}^2$/s, respectively, are studied. It is shown that the linearity of CMF is getting deteriorated as the fluid viscosity increases, which is due to the zero drift and the relaxation time of the fluid. To test its applicability in the case of high pressured gas, it is calibrated using compressed air, It shows 1∼l.6 % deviations compared to the calibration results using water. It concludes that the fluid velocity in CMF should be lower than the sonic velocity. In addition, the effects of the vibration from the pipeline and pump on CMF as well as the long term stability are studied.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.699-712
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• 2016

Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.250-253
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• 2014

방송 서비스를 안정적으로 제공하기 위해서는 가시청시간율을 안정적으로 확보하는 것이 중요하다. 이를 위해서는 수신단에서 ToV SNR 부근에서의 추가적인 margin을 확보하는 기술이 요구된다. 기존 방송 시스템은 안테나를 하나만 사용하는 수신 환경을 가정하고 있으므로, 본 논문에서는 하나의 안테나로부터 수신된 신호를 서로 다른 equalizer 기법 2가지를 동시에 적용하여 마치 2개의 수신 안테나부터 신호를 수신한 효과를 얻었고, 그 출력을 weight combining 하여 최종 SNR 이득을 높이는 기술을 제안하였다. 특히, equalizer 기법은 기존에 성능이 우수하다고 알려져 있는 MMSE-DFE 기술과 최근 큰 주목을 받고 있는 compressed Sensing 기반 sparse-DFE 기술을 동시에 사용하였다. Simulation을 통해서 MMSE-DFE 또는 sparse-DFE를 단독으로 사용하는 것보다 두 기법을 가중치 결합을 통해서 사용함으로써 가시청시간율이 크게 향상되는 것을 확인하였다.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.360-369
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• 2017

In this paper, a new iterative algorithm for reconstructing block sparse signals, called block backtracking-based adaptive orthogonal matching pursuit (BBAOMP) method, is proposed. Compared with existing methods, the BBAOMP method can bring some flexibility between computational complexity and reconstruction property by using the backtracking step. Another outstanding advantage of BBAOMP algorithm is that it can be done without another information of signal sparsity. Several experiments illustrate that the BBAOMP algorithm occupies certain superiority in terms of probability of exact reconstruction and running time.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.673-676
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• 1998

Future land remote sensing satellite systems will kikely be constrained in terms of communication band-width. To alleviate this limitation, the data must be compressed. Image data obtained from satellite exhibit a high degree of spatial and spectral correlations that must be properly exploited. In this paper we propose multispectral image data compression using wavelet transform and selective predicted vector quantization. Th eproposed method is based on accuratly predicting other band from reference band and reducing bit rate through threshold map. we can achieve better compression effeciency than conventional methods.

• ETRI Journal
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• v.37 no.6
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• pp.1251-1258
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• 2015

We investigate an image recovery method for sparse-view computed tomography (CT) using an iterative shrinkage algorithm based on a second-order approach. The two-step iterative shrinkage-thresholding (TwIST) algorithm including a total variation regularization technique is elucidated to be more robust than other first-order methods; it enables a perfect restoration of an original image even if given only a few projection views of a parallel-beam geometry. We find that the incoherency of a projection system matrix in CT geometry sufficiently satisfies the exact reconstruction principle even when the matrix itself has a large condition number. Image reconstruction from fan-beam CT can be well carried out, but the retrieval performance is very low when compared to a parallel-beam geometry. This is considered to be due to the matrix complexity of the projection geometry. We also evaluate the image retrieval performance of the TwIST algorithm -sing measured projection data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.657-667
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• 2019

This paper studies synthetic aperture radar (SAR) imaging problem which the scatterers are often distributed in block sparse pattern. To exploiting the sparse geometrical feature, a Kronecker constrained SAR imaging algorithm is proposed by combining the block sparse characteristics with the multiway sparse reconstruction framework with Tucker modeling. We validate the proposed algorithm via real data and it shows that the our algorithm can achieve better accuracy and convergence than the reference methods even in the demanding environment. Meanwhile, the complexity is smaller than that of the existing methods. The simulation experiments confirmed the effectiveness of the algorithm as well.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.232-239
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• 2023

Aiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multi-description ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multi-description ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point's position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.

• Current Optics and Photonics
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• v.7 no.6
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• pp.655-664
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• 2023

Ghost imaging (GI) technology is developing rapidly, but there are inevitably some limitations such as the influence of atmospheric turbulence. In this paper, we study a ghost imaging system in atmospheric turbulence and use a gamma-gamma (GG) model to simulate the medium to strong range of turbulence distribution. With a compressed sensing (CS) algorithm and generative adversarial network (GAN), the image can be restored well. We analyze the performance of correlation imaging, the influence of atmospheric turbulence and the restoration algorithm's effects. The restored image's peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) increased to 21.9 dB and 0.67 dB, respectively. This proves that deep learning (DL) methods can restore a distorted image well, and it has specific significance for computational imaging in noisy and fuzzy environments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.492-505
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• 2022

This study is a study on the results of improving the logic to effectively transmit and decode compressed data in real time by improving the encoding and decoding performance of BL-beta codes that can be used for lossless real-time transmission of IoT sensing data. The encoding process of BL-beta code includes log function, exponential function, division and square root operation, etc., which have relatively high computational burden. To improve them, using bit operation, binary number pattern analysis, and initial value setting of Newton-Raphson method using bit pattern, a new regularity that can quickly encode and decode data into BL-beta code was discovered, and by applying this, the encoding speed of the algorithm was improved by an average of 24.8% and the decoding speed by an average of 5.3% compared to previous study.