• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1347-1350
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• 2005

A modified Time-Ratio Gray Scale AMOLED drive technique is described in which the frame period is split into two half-frames, each of which is divided into binary weighted sub-frames and driven in the conventional time-ratio manner. The proposed technique improves aperture ratio by reducing TFT sizes in pixel circuits.

• International Journal of Concrete Structures and Materials
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• v.7 no.3
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• pp.239-249
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• 2013

In the present scenario to fulfill the demands of sustainable construction, concrete made with multi-blended cement system of OPC and different mineral admixtures, is the judicious choice for the construction industry. Silica fume (SF) and fly ash (FA) are the most commonly used mineral admixtures in ternary blend cement systems. Synergy between the contributions of both on the mechanical properties of the concrete is an important factor. This study reports the effect of replacement of OPC by fly ash (20, 30, 40 and 50 % replacement of OPC) and/or silica fume (7 and 10 %) on the mechanical properties of concrete like compressive strength and split tensile strength, with three different w/b ratio of 0.3, 0.4 and 0.45. The results indicate that, as the total replacement level of OPC in concrete using ternary blend of OPC + FA + SF increases, the strength with respect to control mix increases up to certain replacement level and thereafter decreases. If the cement content of control mixes at each w/b ratio is kept constant, then as w/b ratio decreases, higher percentage of OPC can be replaced with FA + SF to get 28 days strength comparable to the control mix. A new method was proposed to find the efficiency factor of SF and FA individually in ternary blend cement system, based on principle of modified Bolomey's equation for predicting compressive strength of concrete using binary blend cement system. Efficiency factor for SF and FA were always higher in ternary blend cement system than their respective binary blend cement system. Split tensile strength of concrete using binary and ternary cement system were higher than OPC for a given compressive strength level.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.459-473
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• 2004

It has well known that an exhaustive search algorithm suggested by Breiman et. a1.(1984) has a trend to select the variable having relatively many possible splits as an splitting rule. We propose an algorithm to overcome this variable selection bias problem and then construct unbiased regression trees based on the algorithm. The proposed algorithm runs two steps of selecting a split variable and determining a split rule for binary split based on the split variable. Simulation studies were performed to compare the proposed algorithm with Breiman et a1.(1984)'s CART(Classification and Regression Tree) in terms of degree of variable selection bias, variable selection power, and MSE(Mean Squared Error). Also, we illustrate the proposed algorithm with real data sets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.7
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• pp.559-565
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• 2014

In this paper, we propose an unambiguous correlation function for time-multiplexed binary offset carrier (TMBOC) signal tracking. Specifically, considering that the TMBOC modulation transmits two kinds of sine-phased BOC signals in time domain alternatively, we generate sub-correlation functions for each of the BOC signals by using split sine-phased BOC signals, and then, obtain a correlation function with no side-peak by recombining the sub-correlation functions. From numerical results, we confirm that the proposed correlation function offers an improved tracking error standard deviation performance than the TMBOC autocorrelation function.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.293-298
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• 2002

We propose an improved version of Kim (2000) to the effect that in principle we may deal with a graphical model of any size. Kim (2000) proposed a method of estimating parameters for a model of categorical variables which is too large to handle as a single model. We applied the proposed method to a simulated data of 158 binary variables.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.885-894
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• 2001

Decision tree methods is the one of data mining techniques. Classification trees are used to predict a class label. When a tree grows, the conventional splitting criteria use the weighted average of the left and the right child nodes for measuring the node impurity. In this paper, new splitting criteria for classification trees are proposed which improve the interpretablity of trees comparing to the conventional methods. The criteria search only for interesting subsets of the data, as opposed to modeling all of the data equally well. As a result, the tree is very unbalanced but extremely interpretable.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.291-299
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• 2008

This paper discusses model building for repeated binary response data with different time-dependent covariates each occasion. Since repeated measurements data are having correlated structure, weighed least squares(WLS) methodology is applied. Repeated measures designs are usually having different sizes of experimental units like split-plot designs. However repeated measures designs differ from split-plot designs in that the levels of one or more factors cannot be randomly assigned to one or more of the sizes of experimental units in the experiment. In this case, the levels of time cannot be assigned at random to the time intervals. Because of this nonrandom assignment, the errors corresponding to the respective experimental units may have a covariance matrix. So, the estimates of effects included in a suggested logit model are obtained by using covariance structures.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.361-368
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• 2022

Quaternary tree plus multi-type tree (QT+MTT) structure was adopted in the Versatile Video Coding (VVC) standard as a block partitioning tool. QT+MTT provides excellent coding gain; however, it has huge encoding complexity due to the flexibility of the binary tree (BT) and ternary tree (TT) splits. This paper proposes a fast inter coding unit (CU) partitioning algorithm for BT and TT split types based on prediction accuracy functions using the mean of the absolute error (MAE). The MAE-based decision model was established to achieve a consistent time-saving encoding with stable coding loss for a practical low complexity VVC encoder. Experimental results under random access test configuration showed that the proposed algorithm achieved the encoding time saving from 24.0% to 31.7% with increasing luminance Bjontegaard delta (BD) rate from 1.0% to 2.1%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.355-358
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• 2010

As CMOS technology continues to scale down, signal processing is favorably done in the digital domain, which requires Analog-to-Digital (A/D) Converter to be integrated on-chip. This paper presents a design methodology of 12-bit 1-MS/s Rail-to-Rail fully differential SAR ADC using Deep N-well Switch based on binary search algorithm. Proposed A/D Converter has the following architecture and techniques. Firstly, chip size and power consumption is reduced due to split capacitor array architecture and charge recycling method. Secondly, fully differential architecture is used to reduce noise between the digital part and converters. Finally, to reduce the mismatch effect and noise error, the circuit is designed to be available for Rail-to-Rail input range using simple Deep N-well switch. The A/D Converter fabricated in a TSMC 0.18um 1P6M CMOS technology and has a Signal-to-Noise-and-Distortion-Ratio(SNDR) of 69 dB and Free-Dynamic-Range (SFDR) of 73 dB. The occupied active area is $0.6mm^2$.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.506-515
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• 2023

This study is to find a method to learn engine sound after the start-up of a diesel generator installed in nuclear power plant with an unsupervised deep learning algorithm (CNN autoencoder) and a new method to predict the failure of a diesel generator using it. In order to learn the sound of a diesel generator with a deep learning algorithm, sound data recorded before and after the start-up of two diesel generators was used. The sound data of 20 min and 2 h were cut into 7 s, and the split sound was converted into a spectrogram image. 1200 and 7200 spectrogram images were created from sound data of 20 min and 2 h, respectively. Using two different deep learning algorithms (CNN autoencoder and binary classification), it was investigated whether the diesel generator post-start sounds were learned as normal. It was possible to accurately determine the post-start sounds as normal and the pre-start sounds as abnormal. It was also confirmed that the deep learning algorithm could detect the virtual abnormal sounds created by mixing the unusual sounds with the post-start sounds. This study showed that the unsupervised anomaly detection algorithm has a good accuracy increased about 3% with comparing to the binary classification algorithm.