• Journal of Families and Better Life
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• v.22 no.6 s.72
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• pp.165-175
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• 2004

The purpose of this study was to classify the household financial strategies and investigate major determinants of the household financial strategies and financial performance. The data of 3,994 households is from the Korean Labor and Income Panel Stud?. The major findings were as follows. (1) The classified household financial strategies types were Residual ($44.6\%$), Informal Institutional ($13.3\%$), Financial Assets ($16.7\%$), Real Estate ($13.4\%$), and Diversified Portfolio ($12.0\%$). (2) The criteria of classification of the household financial strategies were relative, not absolute. (3) The household financial strategy types changed largely during a short period(1999-2000). (4) In all households, the variables that affected changes in household financial strategies were education, occupation, number of children, residential location and home ownership. (5) Households that employed a diversified portfolio strategy had the greatest financial performance (2,316,000 won net gain). (6) In all households, the variables that had the greatest influence on financial performance were the number of children, assets and debts. 1'he financial performance was significantly different according to changes in the household financial strategy.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.83-90
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• 2012

In e-commerce systems that deal with a large number of items, the function of personalized recommendation is essential. Collaborative filtering that is a successful recommendation algorithm, suffers from the sparsity, cold-start, and scalability restrictions. Additionally, this work raises a new flaw of the algorithm, inconsistent performance of recommendation. This is also not measurable by the current MAE-based evaluation that does not consider the deviation of prediction error, and furthermore is performed independently of precision and recall measurement. To evaluate the collaborative filtering comprehensively, this work proposes an extended evaluation model that includes the current criteria such as MAE, Precision, Recall, deviation, and applies it to cluster-based combined collaborative filtering.

• Environmental Engineering Research
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• v.25 no.3
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• pp.418-425
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• 2020

In this study soft computing techniques including, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were investigated for the prediction of Cr(VI) transport efficiency by novel Polymer Inclusion Membranes (PIMs). Transport experiments carried out by varying parameters such as time, film thickness, carrier type, carier rate, plasticizer type, and plasticizer rate. The predictive performance of ANN and ANFIS model was evaluated by using statistical performance criteria such as Root Mean Standard Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). Moreover, Sensitivity Analysis (SA) was carried out to investigate the effect of each input on PIMs Cr(VI) removal efficiency. The proposed ANN model presented reliable and valid results, followed by ANFIS model results. RMSE and MAE values were 0.00556, 0.00163 for ANN and 0.00924, 0.00493 for ANFIS model in the prediction of Cr(VI) removal efficiency on testing data sets. The R² values were 0.973 and 0.867 on testing data sets by ANN and ANFIS, respectively. Results show that the ANN-based prediction model performed better than ANFIS. SA demonstrated that time; film thickness; carrier type and plasticizer type are major operating parameters having 33.61%, 26.85%, 21.07% and 8.917% contribution, respectively.

• ETRI Journal
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• v.37 no.4
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• pp.772-779
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• 2015

This paper presents a power-efficient hardware realization for a motion estimation technique that is based on the full-search block matching algorithm (FSBMA). The considered input is the quarter common intermediate format of digital video. The mean of absolute difference (MAD) is the distortion criteria employed for the block matching process. The conventional architecture considered for the hardware realization of FSBMA is that of the shift register-based 2-D systolic array. For this architecture, a conservative approximation technique is adapted to eliminate unnecessary MAD computations involved in the block matching process. Upon introducing the technique to the conventional architecture, the power and complexity of its implantation is reduced, while the accuracy of the motion vector extracted from the block matching process is preserved. The proposed architecture is verified for its functional specifications. A performance evaluation of the proposed architecture is carried out using parameters such as power, area, operating frequency, and efficiency.

• Korean Journal of Veterinary Research
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• v.44 no.4
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• pp.487-495
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• 2004

In order to compare the induced time of osteoporosis between ovariectomized and neurectomized models in ddY mice. Experimental groups were divided into Sham, ovariectomized (OVX group) and neurectomized (NX group) group. The changes of body weight, tibia weight and histomorphometry of epiphyseal regions of tibia that were generally used as criteria index in osteoporosis, were evaluated at 2 and 4 weeks after operations with other generally used index-changes of serum osteocalcin. Also, calcium and phosphorus levels in the ash tibia were demonstrated with their ratio (Ca/P ratio). From the result of this study, evidences which reflect osteoporotic states of animals such as decrease of absolute and relative tibia weight, histomorphometrical index of epiphyseal region of tibia including trabecular bone volume %, and calcium and phosphorous contents in tibia, were generally detected from 4 weeks after ovariectomy and 2 weeks after neurectomy with increase of serum osteocalcin levels. In conclusion, it is considered that more rapid and favorable osteoporosis was induced in neurectomized model compared to that of ovariectomized model.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.207-212
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• 2008

This paper evaluates software cost estimation models, and presents the most suitable model. First, we transformed a relevant model into variables to make in linear. Second, we evaluated model's performance considering how much suitable the cost data of the actual development software was. In the stage of model performance evaluation criteria, we used MMRE which is the relative error concept rather than the absolute error. Existing software cost estimation model follows Weibull, Gamma, and Rayleigh function. In this paper, Gompertz function model is suggested which is a kind of growth curve. Additionally, we verify the compatability of other different growth curves. As a result of evaluation of model's performance, Gompertz function was considered to be the most suitable for the cost estimation model.

• Mass Spectrometry Letters
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• v.10 no.3
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• pp.88-92
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• 2019

A sensitive analytical method of rhodanthpyrone A in rat plasma was developed using a liquid chromatography-tandem mass spectrometry (LC-MS/MS). Rhodanthpyrone A and rhodanthpyrone B (internal standard) in rat plasma were extracted by a liquid-liquid extraction method with ethyl acetate. This extraction method gave results in high and reproducible extraction recovery in the range of 73.75-79.90% with no interfering peaks around the peak elution time of rhodanthpyrone A and B. The standard calibration curves for rhodanthpyrone A ranged from 0.5 to 2000 ng/mL were linear with $r^2$ > 0.994 and the inter- and intra-day accuracy and precision and the stability were within acceptance criteria. Using this validated analytical method, pharmacokinetics of rhodanthpyrone A following intravenous and oral administration of rhodanthpyrone A at doses of 2 mg/kg and 30 mg/kg, respectively, were investigated. Rhodanthpyrone A in rat plasma showed multi-exponential elimination pattern with high clearance and volume of distribution values. The absolute oral bioavailability of this compound was calculated as 3.7%. Collectively, the newly developed sensitive LC-MS/MS analytical method of rhodanthpyrone A could be successfully applied to investigate the pharmacokinetic properties of this compound and would be useful for the further studies on the efficacy, toxicity, and biopharmaceutics of rhodanthpyrone A.

• Journal of Apiculture
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• v.33 no.4
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• pp.277-282
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• 2018

Ethanol extracted propolis (EEP) was mixed with honey (honeypolis) to dissolve well in water and in vitro skin irritation test was conducted. In vitro method is designed to predict and classify the skin irritation potential of a chemical by assessment of its effect on $EpiDerm^{TM}$, a reconstituted three-dimensional human epidermis model. Cytotoxicity is expressed as the reduction of mitochondrial dehydrogenase activity measured by formazan production from MTT after a 60 min exposure period. In this study under the given conditions honeypolis showed no irritant effects. Honeypolis meets acceptance criteria if: mean absolute OD 570 nm of the three negative control tissues is ${\geq}0.8$ and ${\leq}2.8$, mean relative tissue viability of the three positive control tissues is ${\leq}20%$, standard deviation of relative tissue viability obtained from each three concurrently tested tissues is ${\leq}18%$. Honeypolis is therefore classified as "non-irritant" in accordance with UN GHS "No Category".

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.623-632
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• 2021

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

• Steel and Composite Structures
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• v.39 no.4
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.