• 대한방사선기술학회지:방사선기술과학
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• 제24권2호
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• pp.19-22
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• 2001

The purpose of this study is to evaluate time course of signal enhancement on Gadomer-17 enhance MRI, and to correlate the size of enhanced area with that of the infarct area on 2'3'5'-triphenyl tetrazolium chloride(TTC) histochemical examination for the assessment of myocardial viability in reperfused Myocardial Infarction in a cat model. Tan cats(average weight: 3.8 kg) which had undergone 90 minutes of occlusion of the LAD followed by 90 minutes of reperfusion underwent MR T2-weighted imaging, and T1-weighted imaging, enhanced T1-weighted imaging. We used 1.5T Magneton Vision MRI system(Siemens, Erlangen, Germany). Signal intensities were measured in the enhanced and non-enhanced areas of enhanced T1-weighted imaging. and TTC histochemical staining the size of the abnormal signal area on each image was compared with that of the infarct area. Maximum enhancement was detected during a $40{\sim}60$ minute period with an average enhancement of $168{\pm}9.9%$ of normal myocardium. TTC staining revealed that the size of the high signal area on T2-weighted images and of the enhanced area on enhanced T1-weighted images was greater than that of the infarct area($T2=48.1%{\pm}3.7$, enhanced $T1=47.2%{\pm}2.6$, TTC $staining=38.7%{\pm}3.1$ ; p<0.05). In reperfused Myocardial Infarction in a cat model, enhanced MR imaging delineates reversibly and irreversibly damaged myocardium, with a strong enhancement and a broad temporal window. We may therefore expect that enhanced MR image is useful for demonstrating myocardial injury.

• 한국환경보건학회지
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• 제26권4호
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• pp.58-64
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• 2000

Indoor air quality tends to be the dominant contributor to personal exposure, because most people spend over 80% of their time indoors. In this study, indoor and outdoor NO$_2$ concentrations were measured and compared with simultaneously personal exposures of 21 university students in weekday and weekend. House characteristics and activity pattern were used to determine the impacts of these factors on personal exposure. Since university students spent most of their times in indoor, their NO$_2$ exposure was associated with indoor NO$_2$ level rather than outdoor NO$_2$ level both weekday and weekend in spite of different time activity. Using time-weighted average model, NO$_2$ exposures of university students were estimated by NO$_2$ measurements in indoor home, indoor school, and outdoor home levels. Estimated NO$_2$ personal exposures were significantly correlated with measured NO$_2$ personal exposures($r^2$=0.87). However, estimated personal NO$_2$ exposures by time-weighted average model were underestimated, comparing with the measured personal NO$_2$ exposure. Using multiple regression analysis, effect of personal NO$_2$ exposure for transportation was confirmed.

• International Journal of Fluid Machinery and Systems
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• 제4권1호
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• pp.172-178
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• 2011

This paper presents an optimization procedure for high-efficiency design of a mixed-flow pump. Optimization techniques based on a weighted-average surrogate model are used to optimize a vane diffuser of a mixed-flow pump. Validation of the numerical results is performed through experimental data for head, power and efficiency. Three-level full factorial design is used to generate nine design points within the design space. Three-dimensional Reynoldsaveraged Navier-Stokes equations with the shear stress transport turbulence model are discretized by using finite volume approximation and solved on hexahedral grids to evaluate the efficiency as the objective function. In order to reduce pressure loss in the vane diffuser, two variables defining the straight vane length ratio and the diffusion area ratio are selected as design variables in the present optimization. As the results of the design optimization, the efficiency at the design flow coefficient is improved by 7.05% and the off-design efficiencies are also improved in comparison with the reference design.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2008년도 춘계학술대회논문집
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• pp.420-423
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• 2008

This present work is to find optimum design of a NACA65 axial fan blade with weighted average surrogate model. The numerical analysis by Reynolds-average Navier-Stokes equations with shear stress turbulence(SST) is discretized by finite volume approximations and solved on hexahedral grids for flow analysis. The blade aerodynamic shape is modified by six design variables for the optimization. The blade profile as well as stacking line is modified to enhance blade total efficiency. Six design variables, airfoil maximum camber, maximum camber location, leading edge radius, trailing edge radius, lean angle at 50% span and lean angle at 100% span, are selected for blade profile to enhance the total efficiency. The PBA model which is basically weighted average of the basis surrogates is used to find the optimal design in the design space from the constructed response surface model for the objective function. By the optimization, the total efficiency is increased by 1.4%.

• Journal of Information Technology Applications and Management
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• 제29권3호
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• 대한산업공학회지
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• 제39권4호
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• pp.247-252
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• 2013

Korea gas corporation (KOGAS) is responsible for the whole sale of natural gas in the domestic market. It is important to forecast the daily demand of city gas for supply and demand control, and delivery management. Since there is the autoregressive characteristic in the daily gas demand, we introduce a modified autoregressive model as the first step. The daily gas demand also has a close connection with the outdoor temperature. Accordingly, our second proposed model is a temperature-based model. Those two models, however, do not meet the requirement for forecasting performances. To produce acceptable forecasting performances, we develop a weighted average model which compounds the autoregressive model and the temperature model. To examine our proposed methods, the forecasting results are provided. We confirm that our method can forecast the daily city gas demand accurately with reasonable performances.

• 대기
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• 제17권4호
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• 대기
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• 제21권2호
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• pp.209-220
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• 2011

A new consensus algorithm for the prediction of tropical cyclone track has been developed. Conventional consensus is a simple average of a few fixed models that showed the good performance in track prediction for the past few years. Meanwhile, the consensus in this study is a weighted average of a few models that may change for every individual forecast time. The models are selected as follows. The first step is to find the analogous past tropical cyclone tracks to the current track. The next step is to evaluate the model performances for those past tracks. Finally, we take the weighted average of the selected models. More weight is given to the higher performance model. This new algorithm has been named as DYTRAP (DYnamic data-base Typhoon tRAck Prediction) in the sense that the data base is used to find the analogous past tracks and the effective models for every individual track prediction case. DYTRAP has been applied to all 2009 tropical cyclone track prediction. The results outperforms those of all models as well as all the official forecasts of the typhoon centers. In order to prove the real usefulness of DYTRAP, it is necessary to apply the DYTRAP system to the real time prediction because the forecast in typhoon centers usually uses 6-hour or 12-hour-old model guidances.

• 자원ㆍ환경경제연구
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• 제12권4호
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• pp.545-557
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• 2003

양분형 조건부가치평가모형의 준모수적 추정 방법을 소위 회귀함수 1차 도함수의 밀도가중평균(density weighted average derivative or regression function) 추정을 응용하여 제안한다. 논문에서 제안된 준모수 추정량의 소표본 특성은 몬데칼로 시뮬레이션 결과를 제시함으로써 간접적으로 나타난다. 또 추정량을 동강보존을 위한 지불용의액을 조사한 조건부가치평가자료에 실제 적용함으로써 현실 적용 가능성을 보여준다.

• 대한산업공학회지
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• 제7권1호
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• pp.21-31
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• 1981

This paper develops a short term forecasting model for household electric power consumption in Seoul, which can be used for the effective planning and control of utility management. The model developed is based on exponentially weighted moving average model and incorporates monthly average temperature as an exogeneous factor so as to enhance its forecasting accuracy. The model is empirically compared with the Winters' three parameter model which is widely used in practice and the Box-Jenkins model known to be one of the most accurate short term forecasting techniques. The result indicates that the developed hybrid exponential model is better in terms of accuracy measured by average forecast error, mean squared error, and autocorrelated error.