• Archives of Plastic Surgery
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• v.43 no.2
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• pp.181-188
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• 2016

Background Major problems with cervicoplasty by direct skin excision include the subjective nature of skin markings preoperatively and the confusing array of procedures offered. This technique incorporates curved incisions, resulting in a wave-like scar, which is why the procedure is called a "wave-plasty". Methods This prospective study includes 37 patients who underwent wave-plasty procedures from 2004 to 2015. Skin pinching technique was used to mark the anterior neck preoperatively in a reproducible fashion. Intra-operatively, redundant skin was excised, along with excess fat when necessary, and closed to form a wave-shaped scar. Patients were asked to follow up at 1 week, 6 weeks, and 6 months after surgery. Results The mean operation time was 70.8 minutes. The majority (81.3%) was satisfied with their progress. On a scale of 1 to 10 (1 being the worst, and 10 being the best), the scars were objectively graded on average 5.5 when viewed from the front and 7.3 when seen from the side 6 months after surgery. Complications consisted of one partial wound dehiscence (2.3%), one incidence of hypertrophic scarring (2.3%), and two cases of under-resection requiring revision (5.4%). Conclusions In select patients, surgical rejuvenation of the neck may be obtained through wave-like incisions to remove redundant cervical skin when other options are not available. The technique is reproducible, easily teachable and carries low morbidity and high patient satisfaction in carefully chosen patients.

• Korean Journal of Cognitive Science
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• v.19 no.1
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• pp.75-88
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• 2008

Visual scene contains lots of redundant information. To process this redundant information without increasing brain's volume, human visual system may summarize incoming information. If similar but different information are given to visual system, visual system extracts statistical properties of the information. One example of the statistical representation is representation of mean size. The mean representation is accurate and durable. The process of mean representation is suggested to be parallel. However, previous studies on the mean representation mostly used behavioral methods. The purpose of this study was to investigate which neural regions extracted the mean size of a set of circles using fMRI method. According to previous studies, BOLD signal of certain areas that were in charge of cousin stimuli decreased when the same stimuli presented repetitively. We used this paradigm and found that BOLD signal of right occipital area was decreased when same mean site was presented repeatedly. This results suggest that right occipital area is the locus of mean representation of visual stimuli.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.857-864
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• 2006

This paper describes a design of low-power/small-area arithmetic circuits which are vector processing unit powering nit, divider unit and square-root unit for mobile 3D graphic accelerator. To achieve area-efficient and low-power implementation that is an essential consideration for mobile environment, the fixed-point f[mat of 16.16 is adopted instead of conventional floating-point format. The vector processing unit is designed using redundant binary(RB) arithmetic. As a result, it can operate 30% faster and obtained gate count reduction of 10%, compared to the conventional methods which consist of four multipliers and three adders. The powering nit, divider unit and square-root nit are based on logarithm number system. The binary-to-logarithm converter is designed using combinational logic based on six-region approximation method. So, the powering mit, divider unit and square-root unit reduce gate count when compared with lookup table implementation.

• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.243-251
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• 2006

In multi-document summarization by means of salient sentence extraction, it is important to remove redundant information. In the removal process, the similarities and differences of sentences are considered. In this paper, we propose a method for multi-document summarization which extracts salient sentences without having redundant sentences by way of cohesive term clustering method that utilizes co-occurrence Information. In the cohesive term clustering method, we assume that each term does not exist independently, but rather it is related to each other in meanings. To find the relations between terms, we cluster sentences according to topics and use the co-occurrence information oi terms in the same topic. We conduct experimental tests with the DUC(Document Understanding Conferences) data. In the tests, our method shows better performance of summarization than other summarization methods which use term co-occurrence information based on term cohesion of document or sentence unit, and simple statistical information.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.56-60
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• 2016

Peptide identification in tandem mass spectrometry is usually done by searching the spectra against target databases consisting of reference protein sequences. To control false discovery rates for high-confidence peptide identification, spectra are also searched against decoy databases constructed by permuting reference protein sequences. In this case, a peptide of the same sequence could be included in both the target and the decoy databases or multiple entries of a same peptide could exist in the decoy database. These phenomena make the protein identification problem complicated. Thus, it is important to minimize the number of such redundant peptides for accurate protein identification. In this regard, we examined two popular methods for decoy database generation: 'pseudo-shuffling' and 'pseudo-reversing'. We experimented with target databases of varying sizes and investigated the effect of the maximum number of missed cleavage sites allowed in a peptide (MC), which is one of the parameters for target and decoy database generation. In our experiments, the level of redundancy in decoy databases was proportional to the target database size and the value of MC, due to the increase in the number of short peptides (7 to 10 AA). Moreover, 'pseudo-reversing' always generated decoy databases with lower levels of redundancy compared to 'pseudo-shuffling'.

• ETRI Journal
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• v.38 no.6
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• pp.1064-1073
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• 2016

An adaptive speech streaming method to improve the perceived speech quality of a software-based multipoint control unit (SW-based MCU) over IP networks is proposed. First, the proposed method predicts whether the speech packet to be transmitted is lost. To this end, the proposed method learns the pattern of packet losses in the IP network, and then predicts the loss of the packet to be transmitted over that IP network. The proposed method classifies the speech signal into different classes of silence, unvoiced, speech onset, or voiced frame. Based on the results of packet loss prediction and speech classification, the proposed method determines the proper amount and bitrate of redundant speech data (RSD) that are sent with primary speech data (PSD) in order to assist the speech decoder to restore the speech signals of lost packets. Specifically, when a packet is predicted to be lost, the amount and bitrate of the RSD must be increased through a reduction in the bitrate of the PSD. The effectiveness of the proposed method for learning the packet loss pattern and assigning a different speech coding rate is then demonstrated using a support vector machine and adaptive multirate-narrowband, respectively. The results show that as compared with conventional methods that restore lost speech signals, the proposed method remarkably improves the perceived speech quality of an SW-based MCU under various packet loss conditions in an IP network.

• Journal of Digital Policy
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• v.2 no.3
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• pp.1-7
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• 2023

In this paper, I proposes a method for performing single image super resolution by separating texture-spatial domains and then classifying features based on detailed information. In CNN (Convolutional Neural Network) based super resolution, the complex procedures and generation of redundant feature information in feature estimation process for enhancing details can lead to quality degradation in super resolution. The proposed method reduced procedural complexity and minimizes generation of redundant feature information by splitting input image into two channels: texture and spatial. In texture channel, a feature refinement process with step-wise skip connections is applied for detail restoration, while in spatial channel, a method is introduced to preserve the structural features of the image. Experimental results using proposed method demonstrate improved performance in terms of PSNR and SSIM evaluations compared to existing super resolution methods, confirmed the enhancement in quality.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• Journal of Power Electronics
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• v.17 no.3
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• pp.653-663
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• 2017

Multilevel active neutral-point-clamped (ANPC) converter combines the advantages of three-level ANPC converter and multilevel flying capacitor (FC) converter. However, multilevel ANPC converter often suffers from capacitor voltage balancing problems. In order to solve the capacitor voltage balancing problems for five-level ANPC converter, phase-shifted pulse width modulation (PS-PWM) is used, which generally provides natural voltage balancing ability. However, the natural voltage balancing ability depends on the load conditions and converter parameters. In order to eliminate voltage deviations under steady-state and dynamic conditions, the active voltage-balancing control (AVBC) methods of floating capacitors and dc-link capacitors based on PS-PWM are proposed. First, the neutral-point current is regulated to balance the neutral-point voltage by injecting zero-sequence voltage. After that, the duty cycles of the redundant switch combinations are adjusted to balance the floating-capacitor voltages by introducing moderating variables for each of the phases. Finally, the effectiveness of the proposed AVBC methods is verified by experimental results.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2371-2376
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• 2021

As the γ-ray energy increases, a reconstructed image becomes noisy and blurred due to the penetration of the γ-ray through the coded mask. Therefore, the thickness of the coded mask was increased for high energy regions, resulting in severely decreased the performance of the detection efficiency due to self-collimation by the mask. In order to overcome the limitation, a modified uniformly redundant array γ-ray imaging system using dual anti-mask method was developed, and its performance was compared and evaluated in high-energy radiation region. In the dual anti-mask method, the two shadow images, including the subtraction of background events, can simultaneously contribute to the reconstructed image. Moreover, the reconstructed images using each shadow image were integrated using a hybrid update maximum likelihood expectation maximization (h-MLEM). Using the quantitative evaluation method, the performance of the dual anti-mask method was compared with the previously developed collimation methods. As the shadow image which was subtracted the background events leads to a higher-quality reconstructed image, the reconstructed image of the dual anti-mask method showed high performance among the three collimation methods. Finally, the quantitative evaluation method proves that the performance of the dual anti-mask method was better than that of the previously reconstruction methods.