• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.453-457
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• 2006

This study predicts the modified structure of eigenvectors and eigenvalues due to the changes in the mass and the stiffness of the structure. The sensitivity method of natural frequency using partial differential are derived with respect to the physical parameter to calculate the structure modification. The method are applied to the 3 degree of freedom???slumped mass model by modeling the mass and stiffness, and then applies the method to a real crankshaft system. The position, direction of parameter change and modified value were predicted for modification. Finally the predicted value is used to investigate the magnitude of vibration and we found that the effect of modification results to reduce the level of magnitude vibration is satisfactory.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.555-561
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• 2009

The established mathematical modeling methods have limitation to know the hydraulic characteristics at the wastewater treatment plant which are complex and nonlinear systems. So, an artificial neural network (ANN) model based on hydraulic characteristics is applied for modeling wastewater quality of a full-scale wastewater treatment plant using DNR (Daewoo nutrient removal) process. ANN was trained using data which are influents (TSS, BOD, COD, TN, TP) and effluents (COD, TN, TP) components in a year, and predicted the effluent results based on the training. To raise the efficiency of prediction, inputs of ANN are added the influent and effluent information that are in yesterday and the day before yesterday. The results of training data tend to have high accuracy between real value and predicted value, but test data tend to have lower accuracy. However, the more hydraulic characteristics are considered, the results become more accuracy.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.274-283
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• 2002

This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.67-73
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• 2014

We consider the mean value function for NHPP software reliability model and time series regression model in software failure data. We estimate parameters for the proposed models from two data sets. The values of SSE and MSE is presented from two data sets. We compare the predicted number of faults with the actual two data sets using the mean value function and regression curve.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.181.3-181.3
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• 2003

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 60 PPAR-g agonists. Partial Least Squars (PLS) analysis produced good predicted models with $q^2$ value of 0.62 (SDEP=0.33, F value=93.22, $r^2$=0.92) and 0.56 (SDEP=0.47 F value=27.65, $r^2$=0.86), respectivly. The key spatial properties were detected by careful analysis of the isocontour maps.

• Journal of Biosystems Engineering
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• v.32 no.5
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• pp.309-315
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• 2007

This study was performed to develop a simulation model of circulating concurrent-flow rice dryer. The simulation model consists of drying model, tempering model and crack prediction model. The drying and tempering models were developed based on mathematical analysis, and the crack prediction model was developed by thin layer drying tests. Rice drying tests were done with three replications by use of a pilot scale dryer of holding capacity of 700 kg. Experimental values for moisture content, rice temperature, rice crack, and drying energy were compared with predicted values by simulation model. The RMSEs of predicted moisture contents were ranged from 0.5807% (d.b.) to 1.1951% (d.b.). and the coefficients of determination were 0.9688 to 0.9812. The RMSEs of predicted rice temperatures at the exit of the drying chamber were 1.83 to $3.81^{\circ}C$ and the coefficients of determination were 0.8834 to 0.9482. The results for moisture contents and rice temperatures showed very good relationships between predicted values and experimental values. The RMSEs of predicted value of crack ratio were 0.4082 to 0.7967% and the coefficients of determination were 0.8742 to 0.9547.

• Nuclear Engineering and Technology
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• v.31 no.4
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• pp.375-384
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• 1999

The credibility of CHECWORKS FAC model analysis was evaluated for plant application in a model plant chosen for demonstration. The operation condition at each pipe component was defined before the wear rate analysis by plant data base, water chemistry analysis, and network flow analysis. The predicted wear was compared with the measured wear for 57 sample components selected from 43 susceptible line groups analysed. The inspected 57 locations represent components of highest predicted wear in each line group. Both absolute value and relative ranking comparisons indicated reasonable correlations between the predicted and the measured values. Four components showed much higher measured wear rates than the predicted ones in the feed water train from main feed water pump discharge to steam generator, probably due to high hydrazine concentration operation the effect of which had not been incorporated into the CHECWORKS model. The measured wear was higher than the predicted one consistently for components with least susceptibility to FAC. It is believed that the conservatism maintained during UT data analysis dominated the measurement accuracy. A great deal of enhancement is anticipated over the current plant pipe management program when a comprehensive plant pipe management program is implemented based on the model analysis.

• Journal of Dairy Science and Biotechnology
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• v.41 no.1
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• pp.1-8
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• 2023

Response surface methodology (RSM) is a statistical approach widely used in food processing to optimize the formulation, processing conditions, and quality of food products. The homogenization process is achieved by subjecting milk to high pressure, which breaks down fat globules and disperses fat more evenly throughout milk. This study focuses on an application of RSM including the logit transformation to predict the efficiency of milk homogenization, which can be maximized by minimizing the relative difference in fat percentage between the top part and the remainder of milk. To avoid a negative predicted value of the minimum of this proportion, the logit transformation is used to turn the proportion into the logit, whose possible values are real numbers. Then, the logit values are modeled and optimized. Subsequently, the logistic transformation is used to turn the predicted logit into the predicted proportion. From our model, the optimum condition for the maximized efficiency of milk homogenization was predicted as the combination of a homogenizer pressure of 30 MPa, a storage temperature of 10℃, and a storage period of 10 days. Additionally, with a combination of a homogenizer pressure of 30 MPa, a storage temperature of 10℃, and a storage period of 50 days, the level of milk homogenization was predicted to be acceptable, even with the problem of extrapolation taken into account.

• KIEAE Journal
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• v.4 no.4
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• pp.11-18
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• 2004

In this study, the measurement system was developed for the measurement of pollutants from building materials, and specimens were made with concrete, gypsum board, mortar and wall paper. Characteristics of VOCs and TVOC concentration and Emission Factor as a function of time were assessed, and the conclusion was drawn as follows. (1) From predicting TVOC concentration decrease of specimen 7 with the wall paper attached to the concrete, the graph may become linear by converting the value of y-axis into the log function, and the prediction equation can be expressed as $y=34906{\ast}e^{-0.0093{\ast}time}$. Moreover, chi-square value was 0.83 which is relatively high value, indicating that TVOC concentration can be properly predicted if the same materials are used indoors. (2) From predicting VOCs Emission Factor decrease of specimen 7, the prediction equation can be expressed as $EF=15111{\ast}e^{-0.0093{\ast}time}$, and chi-square value was 0.83. (3) From predicting TVOC concentration decrease of specimen 7, prediction equation can be considered to be $y=254323{\ast}(1-e^{-0.1046{\ast}time})$, and chi-square was 0.994 which is significantly high value, indicating that indoor TVOC concentration can be properly predicted if the same materials are used indoors. Furthermore, the prediction of concentration decrease using cumulative value of hourly measured concentration is considered to be more accurate than that using just hourly measured value directly. (4) From predicting Emission Factor decrease with cumulative hourly data of Emission Factor, chi-square appeared to be higher than that by just using hourly data of Emission Factor directly. Therefore, the prediction of Emission Factor with cumulative hourly data can provide more reliable prediction equation than the case by using just hourly concentration directly.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.706-712
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• 2022

Purpose: In this study, field experimental research was conducted to analyze the settlement characteristics of soft ground in the central inland region of Korea and use it in practice. Method: The design predicted values and comparative analysis were performed using the ten settlement measurement data actually measured in the field experiment. For the design prediction value, Terzaghi's one-dimensional consolidation settlement analysis was used. In the experiment, the surface subsidence plate was used for field measurement. Result: The settlement behavior of the predicted value and the actual value was generally similar, but in the settlement value, the actual settlement value showed a settlement behavior of 30% or less compared to the predicted settlement value. The rate of consolidation settlement in this study area was in the range of 9.6% to 27.0%, and the average value was 18.21%. It is analyzed that the prediction of the settlement amount of the silty soils distributed in the inland plains of the central region of Korea can be relatively overestimated. Conclusion: It is judged that precise ground investigation and detailed prediction are necessary because there is a possibility of over-design in the design for predicting the amount of settlement of the silty soils distributed in the inland plains of the central region of Korea.