• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.8
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• pp.725-734
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• 2016

Recently, novel candidate waveform techniques for spectral efficiency improvement was proposed in order to satisfy key performance indicators(KPIs) of 5th generation(5G) mobile communication. Multi-carrier based universal filtered multi-carrier(UFMC) and filter bank multi-carrier(FBMC) are very famous as 5G candidate waveform techniques. Also, weighted orthogonal frequency division multiplexing (W-OFDM) that has low-complexity is receiving the spotlight slowly. In this paper, firstly, we describe a basic OFDM system. And then, we also describe UFMC, FBMC, and W-OFDM system. Next, we evaluate and analyze spectrum and BER performance of these systems under the nonlinear high power amplifier(HPA) environment. As simulation results, spectrum characteristic and BER performance of UFMC, FBMC, and W-OFDM are similar to each other. Therefore, under the nonlinear HPA environment, W-OFDM system is more advantageous because W-OFDM system uses a simple time-domain windowing technique and has similar characteristics to the others.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.6
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• pp.716-722
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• 2014

In order to design the speed controller of the DC motor system, firstly, parameters estimation of the system must be preceded. In this paper, we proposed the application of genetic algorithm(GA) optimization in estimating the parameters of DC motor. Estimated models are considered both first and second order models, and each estimated model is optimized by minimizing three different types of the evaluation function of GA. Also, GA is imported in comparison with estimation result of numerical analysis method because of its power in searching entire solution space with more probability of finding the global optimum. Data for parameter estimation is acquired from input and output signals of the actual experiment device and the butterworth filter also designs for removing noise in the signals. Finally comparison between real data of the actual device and estimated models is presented to indicate effectiveness and resolution of proposed identification method.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.3
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• pp.251-257
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• 2017

In this paper, an algorithm for detecting defects in thin-film-transistor liquid-crystal display (TFT-LCD) cell images is presented. TFT-LCD cell images typically contain periodic cell patterns that make it difficult to detect defects. We propose an efficient and powerful algorithm for eliminating the cell patterns using magnitude spectrum analysis. The first step was to obtain a spectrum for a cell image using the Fourier transform while eliminating larger coefficients using an adaptive filter. Next, an image without the cell pattern was obtained by using the inverse Fourier transform. Finally, the defect pixels were detected using the STD algorithm. The validity of the proposed method was investigated using real TFT-LCD cell images. The experimental results indicate that the proposed technique is extremely effective for detecting defects in TFT-LCD cell images.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.131-139
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• 2009

The existing vessel monitoring system using the ground surveillance radar has a difficulty in monitoring ships continuously due to the limited range of detecting ships. For resolving this problem, we carry out a research on ship detection which is to be the core technology of vessel monitoring system for ocean monitoring using SAR imagery. There are two different methods of detecting ships in SAR imagery: detection of the ship target itself and detection of the ship wake. In this paper, we mainly focus on algorithms which detect the ship itself, and also present the accuracy test after extracting positional and directional figures of the ships. After rectifying input SAR imagery using polynomial transformation, we use Wiener filter to remove speckle noises. A labeling technique and morphological filtering in conjunction with Otsu's method are used to automatically detect the ships based on the image processing domain. For ground truth data, information from a radar system is used, which allows assessing the accuracy of the proposed method. The results show that the proposed method has the high potential in automatically detecting the ships and its positional/directional figures in a fast way.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.2
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• pp.145-151
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• 2004

This paper presents a fully integrated 5 GHz band low phase noise LC tank VCO. The implemented VCO is tuned by integrated PN diodes and tuning rage is 5.01∼5.30 GHz with 0∼3 V control voltage. For improved phase noise performance, a LC filtering technique is adapted. The measured phase noise is -87.8 dBc/Hz at 100 kHz offset frequency and -111.4 dBc/Hz at 1 MHz offset frequency which is excellent performance. Moreover phase noise is improved by 5 dB after employing the LC filter. It is the first experimental result in field of InGaP/GaAs HBT VCOs. The figure of merit of the fabricated VCO with LC filter is -172.1 dBc/Hz. It is the best result among 5 GHz InGaP HBT VCOs. Moreover this work shows lower DC power consumption, higher output power and more fixed output power compared with previous 4, 5 GHz band InGaP HBT VCOs.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.12
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• pp.1091-1099
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• 2015

UFMC(Universal-Filtered Multi-carrier) and FBMC(Filter Bank Multi-carrier) system are receiving attention as candidate waveforms for 5G mobile communication system. But, these systems have high PAPR(Peak to Average Power Ratio) problem because these systems use a number of subcarrier. In this paper, we propose DFT(Discrete Fourier Transform) spreading based DFT-s(spreading)-UFMC system and DFT-s-FBMC system in order to overcome the PAPR drawback. In order to evaluate PAPR performance of the proposed systems, we design and simulate OFDM(Orthogonal Frequency Division Multiplexing), UFMC, FBMC, DFT-s-OFDM, DFT-s-UFMC, DFT-s-FBMC system. As simulation results, each PAPR performance of DFT-s-OFDM system, DFT-s-UFMC system, and DFT-s-FBMC system rise by 2.7 dB, 2.8 dB, and 1.1 dB respectively by DFT spreading technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1697-1703
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• 2020

Sensor nodes that directly collect data from the surrounding environment have many constraints, such as power supply and memory size, thus efficient use of resources is required. In this paper, in a sensor node that receives location data of a vehicle on a lane, the data reception period is changed by the target's speed estimated by the Kalman filter and distance weight. For a slower speed of the vehicle, the longer data reception interval of the sensor node can reduce the processing time performed in the entire sensor network. The proposed method was verified through a traffic simulator implemented as MATLAB, and the results achieved that the processing time was reduced in the entire sensor network using the proposed method compared to the baseline method that receives all data from the vehicle.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.383-394
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• 2018

In this paper, we investigate image and video compression techniques based on deep learning which are actively studied recently. The deep learning based image compression technique inputs an image to be compressed in the deep neural network and extracts the latent vector recurrently or all at once and encodes it. In order to increase the image compression efficiency, the neural network is learned so that the encoded latent vector can be expressed with fewer bits while the quality of the reconstructed image is enhanced. These techniques can produce images of superior quality, especially at low bit rates compared to conventional image compression techniques. On the other hand, deep learning based video compression technology takes an approach to improve performance of the coding tools employed for existing video codecs rather than directly input and process the video to be compressed. The deep neural network technologies introduced in this paper replace the in-loop filter of the latest video codec or are used as an additional post-processing filter to improve the compression efficiency by improving the quality of the reconstructed image. Likewise, deep neural network techniques applied to intra prediction and encoding are used together with the existing intra prediction tool to improve the compression efficiency by increasing the prediction accuracy or adding a new intra coding process.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.355-362
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• 2012

Remaining useful life(RUL) prediction of a system is important in the prognostics field since it is directly linked with safety and maintenance scheduling. In the physics-based prognostics, accurately estimated model parameters can predict the remaining useful life exactly. It, however, is not a simple task to estimate the model parameters because most real system have multivariate model parameters, also they are correlated each other. This paper presents representative methods to estimate model parameters in the physics-based prognostics and discusses the difference between three methods; the particle filter method(PF), the overall Bayesian method(OBM), and the sequential Bayesian method(SBM). The three methods are based on the same theoretical background, the Bayesian estimation technique, but the methods are distinguished from each other in the sampling methods or uncertainty analysis process. Therefore, a simple physical model as an easy task and the Paris model for crack growth problem are used to discuss the difference between the three methods, and the performance of each method evaluated by using established prognostics metrics is compared.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4968-4986
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• 2017

In this paper, we primarily address the difficulty of automatic generation of a plausible depth map from a single image in an unstructured environment. The aim is to extrapolate a depth map with a more correct, rich, and distinct depth order, which is both quantitatively accurate as well as visually pleasing. Our technique, which is fundamentally based on a preexisting DepthTransfer algorithm, transfers depth information at the level of superpixels. This occurs within a framework that replaces a pixel basis with one of instance-based learning. A vital superpixels feature enhancing matching precision is posterior incorporation of predictive semantic labels into the depth extraction procedure. Finally, a modified Cross Bilateral Filter is leveraged to augment the final depth field. For training and evaluation, experiments were conducted using the Make3D Range Image Dataset and vividly demonstrate that this depth estimation method outperforms state-of-the-art methods for the correlation coefficient metric, mean log10 error and root mean squared error, and achieves comparable performance for the average relative error metric in both efficacy and computational efficiency. This approach can be utilized to automatically convert 2D images into stereo for 3D visualization, producing anaglyph images that are visually superior in realism and simultaneously more immersive.