• Journal of Broadcast Engineering
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• v.17 no.5
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• pp.716-724
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• 2012

In this paper, we propose a fast decision method of maximum coding depth decision and reference frame selection in HEVC. To reduce computational complexity and encoding time of HEVC, two methods are proposed. In the first method, the maximum depth of each coding unit (CU) in a largest CU (LCU) is constrained by using the maximum coding depth used by adjacent LCUs based on the assumption that the spatial correlation is very high and rate-distortion (R-D) cost. And we constrain the number of reference pictures for prediction unit (PU) performing motion estimation by using the motion information of the upper depth PU. The proposed methods reduce computational complexity of the HEVC encoder by constraining the maximum coding depth and the reference frame. We could achieve about 39% computational complexity reduction with marginal bitrate increase of 1.2% in the comparison with HM6.1 HEVC reference software.

• Food Science and Biotechnology
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• v.18 no.1
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• pp.60-65
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• 2009

Near infrared reflectance spectroscopy (NIRS) was used as a rapid and non-destructive method to determine the protein content in intact and ground seeds of pea (Pisum sativum L.) germplasms grown in Korea. A total of 115 samples were scanned in the reflectance mode of a scanning monochromator at intact seed and flour condition, and the reference values for the protein content was measured by auto-Kjeldahl system. In the developed ground and intact NIRS equations for analysis of protein, the most accurate equation were obtained at 2, 8, 6, 1 math treatment conditions with standard normal variate and detrend scatter correction method and entire spectrum (400-2,500 nm) by using modified partial least squares regression (n=78). External validation (n=34) of these NIRS equations showed significant correlation between reference values and NIRS estimated values based on the standard error of prediction (SEP), $R^2$, and the ratio of standard deviation of reference data to SEP. Therefore, these ground and intact NIRS equations can be applicable and reliable for determination of protein content in pea seeds, and non-destructive NIRS method could be used as a mass analysis technique for selection of high protein pea in breeding program and for quality control in food industry.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.485-494
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• 2011

In this paper, novel sub-space interference alignment algorithms are proposed to boost the capacity in multi-cell environment. In the case of conventional sub-space alignment, arbitrary reference vectors have been adopted as transmitting vectors at the transmitter side, and the inter-cell interference among users are eliminated by using orthogonal vectors of the chosen reference vectors at the receiver side. However, in this case, sum-rate varies using different reference vectors even though the channel values keep constant, and vice versa. Therefore, the relationship between reference vectors and channel values are analyzed in this paper, and novel interference alignment algorithms are proposed to increase multi-cell capacity. Reference vectors with similar magnitude are adopted in the proposed algorithm. Simulation results show that the proposed algorithms provide about 50 % higher sum-rate than conventional algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.284-288
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.347-350
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• Journal of Electrical Engineering and Technology
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• v.6 no.1
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• pp.48-58
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• 2011

In this paper, an attempt has been made to design the current and speed proportional and integral (PI) regulators of self-commutating current source inverter-fed induction motor drive having capacitors at the machine end and to investigate the transient performance of the same for step changes in reference speed. The mathematical model of the complete drive system is developed in closed loop, and the characteristic equations of the systems are derived using perturbation about steady-state operating point in order to develop the characteristic equations. The D-partition technique is used for finding the stable region in the parametric plane. Frequency scanning technique is used to confirm the stability region. Final selection of the regulator parameters is done by comparing the transient response of the current and speed loops for step variations in reference. The performance of the drive is observed analytically through MATLAB simulation.

• Journal of the Korean Solar Energy Society
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• v.21 no.4
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• pp.13-20
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• 2001

The purpose of this study is to analyze the effect of Super-insulation for self-sufficient house. The process of the study is presented in the following. 1) selection reference model for simulation and verification of reference model with computer simulation program(DOE2.1E and ESP-r 9.0). 2) analysis of effect according to insulation-thickness, installed insulation position, kinds of windows, rate of infiltration, Finally, the results of this study will be to provide the most reasonable method concerned with self-sufficient house.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.771-789
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• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• Women's Health Nursing
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• v.30 no.3
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• pp.216-225
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• 2024

Purpose: Selection-optimization-compensation (SOC) models have been proposed and applied to various populations to examine successful aging from a multidimensional perspective. This study aimed to develop a scale to measure SOC strategy among late middle-aged women (aged 50 to 64 years) and to test its validity and reliability. Methods: Preliminary items were developed through a literature review and interviews. Overall, 32 preliminary items were confirmed via two rounds of expert content validity analysis and a pilot survey. Data were collected from 299 late middle-aged women and analyzed using IBM SPSS/PC+ version 27.0. Construct validity, criterion validity, and reliability tests were conducted. Results: The SOC strategy scale, reflecting the characteristics of late middle-aged women and developed through exploratory factor analysis, comprised 19 items across four factors: goal-oriented selection, compensation for loss, outcome optimization, and ability-based optimization. The scale explained 66.9% of the variance in total factors, with a Cronbach's α of .95. Statistically significant correlations with the reference scale (r=.30, p<.001) were observed. Conclusion: The developed scale demonstrated high validity and reliability, thus representing a viable instrument for measuring SOC strategy among late middle-aged women. Using this scale to assess the use of SOC approaches in these women can improve our understanding of the aging process and help establish supportive programs for their aging journeys.