• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.323-326
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• 2016

High Efficiency Video Coding (HEVC) adopts intra transform skip mode, in which a residual block is directly quantized in the pixel domain without transforming the block into the frequency domain. Intra transform skip mode provides a significant coding gain for screen content. However, when intra-prediction errors are not transformed, the errors are often correlated along the intra-prediction direction. This paper introduces a residual differential pulse code modulation (DPCM) method for the intra-predicted and transform-skipped blocks to remove redundancy. The proposed method performs pixel-by-pixel residual prediction along the intra-prediction direction to reduce the dynamic range of intra-prediction errors. Experimental results show that the transform skip mode's Bjøntegaard delta rate (BD-rate) is improved by 12.8% for vertically intra-predicted blocks. Overall, the proposed method shows an average 1.2% reduction in BD-rate, relative to HEVC, with negligible computational complexity.

• Journal of Korean Medical classics
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• v.32 no.1
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• pp.61-74
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• 2019

Objectives : The purpose of this study is to help select an appropriate word embedding method when analyzing East Asian traditional medicine texts as data. Methods : Based on prescription data that imply traditional methods in traditional East Asian medicine, we have examined 4 count-based word embedding and 2 prediction-based word embedding methods. In order to intuitively compare these word embedding methods, we proposed a "prescription generating game" and compared its results with those from the application of the 6 methods. Results : When the adjacent vectors are extracted, the count-based word embedding method derives the main herbs that are frequently used in conjunction with each other. On the other hand, in the prediction-based word embedding method, the synonyms of the herbs were derived. Conclusions : Counting based word embedding methods seems to be more effective than prediction-based word embedding methods in analyzing the use of domesticated herbs. Among count-based word embedding methods, the TF-vector method tends to exaggerate the frequency effect, and hence the TF-IDF vector or co-word vector may be a more reasonable choice. Also, the t-score vector may be recommended in search for unusual information that could not be found in frequency. On the other hand, prediction-based embedding seems to be effective when deriving the bases of similar meanings in context.

• Proceedings of the Korean Nuclear Society Conference
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• 1999.05a
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• pp.159-159
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• 1999

Application of the macro layer dryout model has been performed to predict CHF at inclined plates. For the identification of the detachment frequency of coalesced bubble, experiments have been performed with high-speed motion analyzer and bubble behaviors at inclined plates have been investigated. Based on the observed bubble behaviors, the detachment frequency of the coalesced bubble is measured and linear relations between detachment frequency and heat flux have been developed. In the case of 60$^{\circ}$ and 90$^{\circ}$ inclined plate, the detachment frequency decreases with the increase of heat flux. However, opposite trend has been identified in $30^{\circ}$ in-clined plate: the detachment frequency increases with the increase of heat flux. Using the cor- relation of macro layer thickness suggested by Haramura & Katto and the extrapolation of the identified linear relations, CHFs at different conditions have been predicted. According to the prediction results, CHF values are well predictable.

• 한국연소학회:학술대회논문집
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• 2004.11a
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• pp.152-160
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• 2004

Studies to investigate the influence on hydraulic acoustic wave were conducted using pressure swirl atomizer under making frequency range from 0 to 60Hz using water as a propellant. Pressure oscillation from hydraulic sources gives a strong influences on atomization and mixing processes. The ability to drive these low frequency pressure oscillations makes spray characteristics changeable. The effect of pressure perturbation and its spray characteristics showed that low injector pressure with pressure pulsation gives more significantly than high injector pressure with pressure perturbation in SMD, spray cone angle, breakup length. Moreover, this data could be used for prediction of low combustion instability getting G factor.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.489-498
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• 2007

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as Association Rule, Frequency Matrix, and Rule Induction, this study suggests an integrative prediction model. The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set, it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as Association Rule, Frequency Matrix, and Rule Induction.

• Journal of Biomedical Engineering Research
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• v.30 no.1
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• pp.94-102
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• 2009

In this paper, we propose a hearing impairment simulator considering reduced frequency selectivity of the hearing impaired, and verify it's performance through experiments. The reduced frequency selectivity was embodied by spectral smearing using linear prediction coding(LPC). The experiments are composed of 4 kinds of tests; pure tone test, speech reception threshold(SRT) test, and word recognition score(WRS) test without spectral smearing and with spectral smearing. The experiments of the hearing impairment simulator were performed with 9 subjects who have normal hearing. The amount of spectral smearing was controlled by LPC order. The percentile score of WRS test without smearing is $89.78{\pm}2.420%$. The scores of WRS with 24th LPC order and with 8th LPC order are $88.00{\pm}3.556%$ and $83.78{\pm}2.123%$ respectively. It is verified that WRS score is lowered by decreasing LPC order. This is a reasonable result considering that spectral smearing is getting heavier according to decreasing LPC order. It is confirmed that spectral smearing using LPC simulates the reduced frequency selectivity of the hearing impaired and affects the clearness of speech reception.

• Genomics & Informatics
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• v.11 no.2
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• pp.93-96
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• 2013

The prediction of externally visible characteristics from DNA has been studied for forensic genetics over the last few years. Externally visible characteristics include hair, skin, and eye color, height, and facial morphology, which have high heritability. Recent studies using genome-wide association analysis have identified genes and variations that correlate with human visible phenotypes and developed phenotype prediction programs. However, most prediction models were constructed and validated based on genotype and phenotype information on Europeans. Therefore, we need to validate prediction models in diverse ethnic populations. In this study, we selected potentially useful variations for forensic science that are associated with hair and eye color, iris pattern, and facial morphology, based on previous studies, and analyzed their frequencies in 1,920 Koreans. Among 20 single nucleotide polymorphisms (SNPs), 10 SNPs were polymorphic, 6 SNPs were very rare (minor allele frequency < 0.005), and 4 SNPs were monomorphic in the Korean population. Even though the usability of these SNPs should be verified by an association study in Koreans, this study provides 10 potential SNP markers for forensic science for externally visible characteristics in the Korean population.

• Wind and Structures
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• v.38 no.6
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• pp.461-475
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• 2024

A reliable wind speed forecasting method is crucial for the applications in wind engineering. In this study, the generalized S-transform (GST) is innovatively applied for wind speed forecasting to uncover the time-frequency characteristics in the non-stationary wind speed data. The improved grey wolf optimizer (IGWO) is employed to optimize the adjustable parameters of GST to obtain the best time-frequency resolution. Then a hybrid method based on IGWO-optimized GST is proposed to validate the effectiveness and superiority for multi-step non-stationary wind speed forecasting. The historical wind speed is chosen as the first input feature, while the dynamic time-frequency characteristics obtained by IGWO-optimized GST are chosen as the second input feature. Comparative experiment with six competitors is conducted to demonstrate the best performance of the proposed method in terms of prediction accuracy and stability. The superiority of the GST compared to other time-frequency analysis methods is also discussed by another experiment. It can be concluded that the introduction of IGWO-optimized GST can deeply exploit the time-frequency characteristics and effectively improving the prediction accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.475-490
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• 2010

Cognitive Radio is an advanced enabling technology for the efficient utilization of vacant spectrum due to its ability to sense the spectrum environment. It is important to determine accurate spectrum utilization of the primary system in a cognitive radio environment. In order to define the spectrum utilization state, many CR systems use what is known as the quiet period (QP) method. However, even when using a QP, interference can occur. This causes reduced system throughput and contrary to the basic condition of cognitive radio. In order to reduce the interference time, a frequency-hopping algorithm is proposed here. Additionally, to complement the loss of throughput in the FH, a HMM-based channel prediction algorithm and a channel allocation algorithm is proposed. Simulations were conducted while varying several parameters. The findings show that the proposed algorithm outperforms conventional channel allocation algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.2
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• pp.55-62
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• 2009

The power consumption of a high-end microprocessor increases very rapidly. High power consumption will lead to a rapid increase in the chip temperature as well. If the temperature reaches beyond a certain level, chip operation becomes either slow or unreliable. Therefore various approaches for Dynamic Thermal Management (DTM) have been proposed. In this paper, we propose a learning based temperature prediction scheme for a multi-core system. In this approach, from repeatedly executing an application, we learn the thermal patterns of the chip, and we control the temperature in advance through DTM. When the predicted temperature may go beyond a threshold value, we reduce the temperature by decreasing the operation frequencies of the corresponding core. We implement our temperature prediction on an Intel's Quad-Core system which has integrated digital thermal sensors. A Dynamic Frequency System (DFS) technique is implemented to have four frequency steps on a Linux kernel. We carried out experiments using Phoronix Test Suite benchmarks for Linux. The peak temperature has been reduced by on average $5^{\circ}C{\sim}7^{\circ}C$. The overall average temperature reduced from $72^{\circ}C$ to $65^{\circ}C$.