• 방송공학회논문지
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• 제17권5호
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• pp.716-724
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• 2012

본 논문에서는 HEVC(High Efficiency Video Coding) 부호화 속도 향상을 위한 최대 부호화깊이 및 참조영상 고속결정 방법을 제안한다. 본 논문에서는 계산 복잡도 감소와 속도향상을 위하여 크게 두 가지 방법을 제안한다. 첫 번째 방법에서는 LCU(Largest Coding Unit)내 각 CU(Coding Unit)의 최대 부호화 깊이를 제한하며, 이때 공간적인 상관성을 기반으로 주변 LCU에서 사용된 최대 부호화 깊이와 율-왜곡 비용을 이용한다. 두 번째 방법에서는 각 CU의 다양한 PU(Prediction Unit) 중, 화면간 예측을 수행하는 PU에 대해서 참조영상을 제한하며, 이때 상위 깊이 PU의 움직임 정보를 이용한다. 제안하는 방법은 항상 최대 깊이까지 부호화를 수행하는 것을 적응적으로 제한하고, 상당한 복잡도를 요구하는 움직임 예측을 수행하는 PU의 참조영상 수를 제한함으로써 계산 복잡도를 감소시킬 수 있으며, 기존의 HEVC 참조 소프트웨어인 HM6.1 대비 약 1.2% 정도의 비트율이 증가하면서 약 39%의 복잡도 감소 효과를 얻을 수 있었다.

• Food Science and Biotechnology
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• 제18권1호
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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.

• 한국통신학회논문지
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• 제36권5A호
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• pp.485-494
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• 2011

본 논문에서는 다중 셀 환경에서 전체 생 용량을 향상시키기 위하여 부분공간 간섭 정렬 (Sub-space Interference Alignment) 기법을 위한 새로운 레퍼런스 벡터 설정 알고리즘을 제안한다. 송신단에서 임의의 동일한 레퍼런스 벡터를 사용하여 전송 벡터를 생성하고, 수신단에서 이와 직교하는 벡터를 사용하여 정렬된 간섭 신호를 제거하는 기존의 부분공간 간섭 정렬 기법의 경우, 사용되는 레퍼런스 벡터 및 채널 상황에 따라 전체 시스템 용량이 달라지는 문제점을 가지고 있다. 이에 본 논문에서는 레퍼런스 벡터와 채널의 변화에 의해 전체 시스템의 합용량이 변화하는 문제점 및 레퍼런스 벡터 원소들의 크기 분산이 작아질수록 합용량이 향상되는 경향을 보임을 분석한다. 이러한 분석을 바탕으로 새로운 레퍼런스 벡터 설정 방법으로 레퍼런스 벡터 원소들의 크기 분산을 고려하여 설정하는 알고리즘을 제안하며, 모의실험을 통해 기존 알고리즘과 비교하여 제안된 알고리즘이 평균적으로 약 50% 정도 향상된 합용량을 나타냄을 확인한다.

• 제어로봇시스템학회논문지
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• 제12권3호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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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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• 제6권1호
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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.

• 한국태양에너지학회 논문집
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• 제21권4호
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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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• 제7권11호
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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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• 제13권2호
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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.

• 여성건강간호학회지
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• 제30권3호
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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.