• 전기학회논문지
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• 제58권3호
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• pp.621-626
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• 2009

This paper presents a multiple branch predictor using perceptrons. The key idea is to apply neural networks to the multiple branch predictor. We describe our design and evaluate it with the SPEC 2000 integer benchmarks. Our predictor achieves increased accuracy than the Bi-Mode and the YAGS multiple branch predictor with the same hardware cost.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.803-804
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• 2006

This paper presents a multiple branch predictor with perceptrons. We describe our design and evaluate it with the SPEC 2000 benchmarks. Our predictor achieves increased accuracy than the previous multiple branch predictors.

• 전기학회논문지
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• 제58권7호
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• pp.1443-1449
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• 2009

This paper presents a profiling model of a wide-window superscalar microprocessor using multiple branch prediction. The key idea is to apply statistical profiling technique to the superscalar microprocessor with a wide instruction window and a multiple branch predictor. The statistical profiling data are used to obtain a synthetical instruction trace, and the consecutive multiple branch prediction rates are utilized for running trace-driven simulation on the synthesized instruction trace. We describe our design and evaluate it with the SPEC 2000 integer benchmarks. Our performance model can achieve accuracy of 8.5 % on the average.

• 한국정보과학회논문지:시스템및이론
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• 제33권5호
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• pp.297-305
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• 2006

마이크로 프로세서 구조의 성능을 분석할 때, 트레이스 구동형 모의실험이 광범위하게 수행되고 있으나, 시간과 공간을 많이 차지하기 때문에 최근에 이르러 통계적 모의실험이 그 대안으로 떠오르고 있다. 기존의 통계적 모의실험이 단일 분기 예측법에 대하여 연구가 수행된 것과 달리, 본 논문에서는 다중 분기 예측법을 이용하는 고성능 수퍼스칼라 프로세서에 대한 통계적 프로화일링 모델을 제안하였다. 이때, 다중 분기 예측법은 최근 들어 유망한 기법으로 대두되고 있는 퍼셉트론 분기 예측법을 기반으로 하였다. 이것을 위하여 SPEC 2000 벤치마크 프로그램의 특성을 통계적 프로화일링 기법으로 모델링하고, 여기서 얻은 통계적 프로화일을 바탕으로 벤치마크 트레이스를 합성하여 모의실험을 수행하였다. 그 결과, 제안하는 방식으로 다중 분기 예측을 이용하는 수퍼스칼라 프로세서에서도 비교적 높은 정확도를 얻을 수 있었다.

• Asian Pacific Journal of Cancer Prevention
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• 제13권1호
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• pp.49-56
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• 2012

Objective: This study was an attempt to identify associations between health behavior, such as smoking, alcohol consumption, healthy diet, and physical activity, and psychosocial factors. Methods: This crosssectional study was conducted among 1,500 participants aged between 30 and 69 years, selected from a population-based database in October 2009 through multiple-stratified random sampling. Information was collected about the participants' smoking and drinking habits, dietary behavior, level of physical activity, stress, coping strategies, impulsiveness, personality, social support, sense of coherence, self-efficacy, health communication, and sociodemographics. Results: Agreeableness, as a personality trait, was negatively associated with smoking and a healthy diet, while extraversion was positively associated with drinking. The tendency to consume a healthy diet decreased in individuals with perceived higher stress, whereas it increased in individuals who had access to greater social support. Self-efficacy was found to be a strong predictor of all health behaviors. Provider-patient communication and physical environment were important factors in promoting positive healthy behavior, such as consumption of a healthy diet and taking regular exercise. Conclusions: Psychosocial factors influence individuals' smoking and drinking habits, dietary intake, and exercise patterns.

• 대한물리의학회지
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• 제15권4호
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• pp.67-74
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• 2020

PURPOSE: The progression of the center of pressure (COP) velocity of the stance phase may have important roles for predicting gait speed in older adults with cognitive decline. This study was conducted to identify the correlation between gait speed and the velocity of COP progression during the stance phase in older adults with cognitive decline. METHODS: Forty adults aged 65 years or older (twenty participants without cognitive decline, 20 participants with cognitive decline) were recruited. The COP progression velocity was measured using an F-scan pressure-sensitive insole system. The stance phase was divided into four sub-stages. (loading response, mid-stance, terminal stance, and pre-swing). Gait speed, double support phase, and cadence were also measured. Correlations and multiple regression analyses were performed. RESULTS: Gait speed was associated with the COP progression velocity in midstance (r = .719, p < .05), cadence (r = .719, p < .05) and the COP progression velocity in loading response velocity (r = .515, p < .05) in older adults with cognitive decline. However, no correlation was found in older adults without cognitive decline. In multiple regression analysis using gait speed as a dependent variable, the COP progression velocity in midstance and cadence were significant predictors of gait speed, with the COP progression velocity being the most significant predictor. CONCLUSION: The COP progression velocity is an important factor for predicting gait speed in older adults with cognitive decline, suggesting that the cognitive function influences gait speed and the velocity of COP progression.

• Korean Circulation Journal
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• 제48권11호
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• pp.1002-1011
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• 2018

Background and Objectives: The prognostic impact of left axis deviation (LAD) on clinical outcomes in acute heart failure syndrome (AHFS) with left bundle branch block (LBBB) is unknown. The aim of this study was to determine the prognostic significance of axis deviation in acute heart failure patients with LBBB. Methods: Between March 2011 and February 2014, 292 consecutive AHFS patients with LBBB were recruited from 10 tertiary university hospitals. They were divided into groups with no LAD (n=189) or with LAD (n=103) groups according to QRS axis <-30 degree. The primary outcome was all-cause mortality. Results: The median follow-up duration was 24 months. On multivariate analysis, the rate of all-cause death did not significantly differ between the normal axis and LAD groups (39.7% vs. 46.6%, adjusted hazard ratio, 1.01; 95% confidence interval, 0.66, 1.53; p=0.97). However, on the multiple linear regression analysis to evaluate the predictors of the left ventricular ejection fraction (LVEF), presence of LAD significantly predicted a worse LVEF (adjusted beta, -3.25; 95% confidence interval, -5.82, -0.67; p=0.01). Right ventricle (RV) dilatation was defined as at least 2 of 3 electrocardiographic criteria (late R in lead aVR, low voltages in limb leads, and R/S ratio <1 in lead V5) and was more frequent in the LAD group than in the normal axis group (p<0.001). Conclusions: Among the AHFS with LBBB patients, LAD did not predict mortality, but it could be used as a significant predictor of worse LVEF and RV dilatation (Trial registry at KorAHF registry, ClinicalTrial.gov, NCT01389843).

• Journal of Ecology and Environment
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• 제48권2호
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• pp.196-206
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• 2024

Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over- or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country's measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R² and adjusted [adj]-R²), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R² ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH₁₀), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R² varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH₁₀ as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed. AGB_Stem = exp {-1.8296 + 0.4814 natural logarithm (Ln) (ρD²H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH₃₀)} AGB_Branch = exp {-131.6 + 15.0013 Ln (ρD²H) + 13.176 Ln (CW) + 21.8506 Ln (DSH₃₀)} AGB_Foliage = exp {-0.9496 + 0.5282 Ln (DSH₃₀) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)} AGB_Total = exp {-1.8245 + 1.4358 Ln (DSH₃₀) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.