• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.631-642
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• 2013

For the emerging High Efficiency Video Coding (HEVC) standard, a fast transform unit (TU) decision method is proposed. HEVC defines the TU representing a region sharing the same transformation, and it allows a residual block to be split into multiple TUs recursively to form a quadtree. By using the quadtree of TUs, HEVC supports various transform sizes from $4{\times}4$ to $32{\times}32$. The various sizes of TUs can provide a good coding efficiency, whereas this may increase encoding complexity dramatically. In the assumption that a TU with highly compacted energy is unlikely to be split, the proposed method determines TU sizes according to the position of the last non-zero transform coefficient. In the experimental results, the proposed method reduced 18% encoding run time with the negligible coding loss of 0.7% BD-rate for the Random_access case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.763-770
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• 2002

Recognition of CW Morse signals can be divided into several phases such as detection of tones and spaces, signal processing for removing noise from detected signals, decision of tones/spaces, mapping a sequence of tones and spaces into characters, error correction of a character message with textual repetition. In this paper, in order to cope with signal fading effectively we propose a signal detection method of identifying peaks in the frequency domain and present techniques for combining multiple frequency peaks and for removing residual signal components and noise. LMS adaptive method is applied for decision of tones/spaces, and initial value setting and malfunctioning conditions are analyzed. In recognition experiments, we used CW Morse signals collected by radio receivers and found that the proposed method achieves good recognition performance even in severe fading conditions.

• ETRI Journal
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• v.37 no.6
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• pp.1199-1210
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• 2015

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components - multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1819-1826
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• 2009

To enhance the automatic performance of existing predicting and planning algorithms that require a predefined probability of the states' transition, this paper proposes a multiple sequence generation system. When interacting with unknown environments, a virtual agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. We describe a sequential behavior generation method motivated from the change in the agent's state in order to help the virtual agent learn how to adapt to unknown environments. In a sequence learning process, the sensed states are grouped by a set of proposed motivation filters in order to reduce the learning computation of the large state space. In order to accomplish a goal with a high payoff, the learning agent makes a decision based on the observation of states' transitions. The proposed multiple sequence behaviors generation system increases the complexity and heightens the automatic planning of the virtual agent for interacting with the dynamic unknown environment. This model was tested in a virtual library to elucidate the process of the system.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.20 no.1
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• pp.46-55
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• 2013

Purpose: The purpose of this study was to identify university students' willingness to write advance directives and factors related to this willingness. Method: A cross-sectional survey design was used and 400 nursing students and 287 non-nursing students participated in the study. Data were collected by convenience sampling using self-report questionnaires. Data were analyzed with descriptive statistics, t-test, ANOVA, correlation, and multiple regression. Results: The mean score for willingness to write advance directives was 4.09. From the multiple regression analysis, predictors of willingness to write advance directives were found to be majoring in nursing, awareness of advance directives, preference for autonomous decision making related to the end of life care, family preference related to the end of life care, and self esteem. Conclusion: The study results indicate that various factors are related to willingness to write advance directives and nurses need to consider client characteristics, knowledge, and own values when taking care of issues on advance directives.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.951-960
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• 2021

The paper aims to explain the influence of celebrity endorser on consumers' purchase intention, and explain the influence of brand perception on purchase decision in regard to the case of OPPO F-series and singer Son Tung MTP, in the Vietnamese context. Advertisement is regarded as the fastest and most effective communication method for brands to build an association and attract their target consumers. Among various types of communication tools, the use of celebrity endorsement is considerably growing owing to the explosion of social platforms. This study employs survey data from 258 people living and working in Ho Chi Minh City, Vietnam. Then, those data were used to analyze the general hypotheses by conducting research reviews based on foregoing investigations. The results from data analysis reveal that celebrity's physical attractiveness, credibility, popularity, expertise, match-up, and multiple endorsements all have positive impacts on purchase intention. The findings from this study would suggest that brands, in general, and smart-phone brands, in particular, should not only focus on these attributes to select a suitable brand endorser, but also need to predict some potential risks when utilizing celebrity endorsement. Finally, various implications and recommendations for further studies and marketers are also discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.289-293
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• 2005

Pattern classification extracts various types of pattern information expressing objects in the real world and decides their class. The top priority of pattern classification technologies is to improve the performance of classification and, for this, many researches have tried various approaches for the last 40 years. Classification methods used in pattern classification include base classifier based on the probabilistic inference of patterns, decision tree, method based on distance function, neural network and clustering but they are not efficient in analyzing a large amount of multi-dimensional data. Thus, there are active researches on multiple classifier systems, which improve the performance of classification by combining problems using a number of mutually compensatory classifiers. The present study identifies problems in previous researches on multiple SVM classifiers, and proposes BORSE, a model that, based on 1:M policy in order to expand SVM to a multiple class classifier, regards each SVM output as a signal with non-linear pattern, trains the neural network for the pattern and combine the final results of classification performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.275-285
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• 2003

The now video coding standard H.264 employs variable block size motion compensation, multiple references, and quarter-pel motion vector accuracy. These techniques are key features to accomplish higher coding gain, however, at the same time main factors that increase overall computational complexity. Therefore, in order to apply H.264 to many applications, key techniques are requested to improve their speed. For this reason, we propose a fast motion estimation which is suited for variable block size motion communication. In addition, we propose a fast mode decision method to choose the best mode at early stage. Experimental results show the reduction of the number of SAT SATD calculations by a factor of 4.5 and 2.6 times respectively, when we compare the proposed fast motion estimation and the conventional MVFAS $T^{［8-10］}$. Besides, the number of RDcost computations is reduced by about 45%. Therefore, the proposed methods reduces significantly its computational complexity without noticeable coding loss.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.417-425
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• 2003

H.264, which is the latest video coding standard of both ITU-T(International Telecommunication Union-Telecommunication standardization sector) and MPEG(Moving Picture Experts Group), adopts new video coding tools such as variable block size motion estimation, multiple reference frames, quarter-pel motion estimation/compensation(ME/MC), 4${\times}$4 Integer DCT(Discrete Cosine Transform), and Rate-Distortion Optimization, etc. These new video coding tools provide good coding of efficiency compared with existing video coding standards as H.263, MPEG-4, etc. However, these new coding tools require the increase of encoder complexity. Therefore, in order to apply H.264 to many real applications, fast algorithms are required for H.264 coding tools. In this paper, when encoder MacroBlock(MB) mode is decided by rate-distortion optimization tool, fast mode decision algorithm by skipping variable block size ME/MC and spatial-predictive coding, which occupies most encoder complexity, is proposed. In terms of computational complexity, the proposed method runs about 4 times as far as JM(Joint Model) 42 encoder of H.264, while the PSNR(peak signal-to-noise ratio)s of the decoded images are maintained.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1C
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• pp.82-91
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• 2005

This paper introduces an efficient affine projection algorithm(APA) using iterative hyperplane projection. Among various fast converging adaptation algorithms, APA has been preferred to be employed for various applications due to its inherent effectiveness against the rank deficient problem. However, the amount of complexity of the conventional APA could not be negligible because of the accomplishment of sample matrix inversion(SMI). Moreover, the 'shifting invariance property' usually exploited in single channel case does not hold for the application of space-time decision-directed equalizer(STDE) deployed in single-input-multi-output(SIMO) systems. Thus, it is impossible to utilize the fast adaptation schemes such as fast transversal filter(FlF) having low-complexity. To accomplish such tasks, this paper introduces the low-complexity APA by employing hyperplane projection algorithm, which shows the excellent tracking capability as well as the fast convergence. In order to confirm th validity of the proposed method, its performance is evaluated under wireless SIMO channel in respect to bit error rate(BER) behavior and computational complexity.