• KSCE Journal of Civil and Environmental Engineering Research
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• v.1 no.1
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• pp.77-90
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• 1981

This study involves 1) air quality disperson predictions and 2) a comparison of the predicted data with the actually measured ones in terms of annual sulfur dioxide concentration in the Ulsan Industial Complex. The prediction was made by utilizing the CDMQC air quality simulation computer model. The higher concentrations were observed at the Bugok Dong (Sampling Site) and the Yeochun Dong Sampling Site with the values of 44 and 46 ppb, respectively whereas the predicted values for both sites were 52 and 47 ppb, respectively. A statistical examination has revealed that the level of confidence was 90.02% from the Chi-squared test and the corelation coefficient was 0.827. It thus demonstrates that the model used for the study appears to be applicable to yield reliable predictions in terms of annual sulfur dioxide concentrations in the study area.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.5
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• pp.699-706
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• 2020

Predictive models in food microbiology are used for predicting microbial growth or death rates using mathematical and statistical tools considering the intrinsic and extrinsic factors of food. This study developed predictive growth models for Bacillus cereus on dried laver Pyropia pseudolinearis stored at different temperatures (5, 10, 15, 20, and 25℃). Primary models developed for specific growth rate (SGR), lag time (LT), and maximum population density (MPD) indicated a good fit (R²≥0.98) with the Gompertz equation. The SGR values were 0.03, 0.08, and 0.12, and the LT values were 12.64, 4.01, and 2.17 h, at the storage temperatures of 15, 20, and 25℃, respectively. Secondary models for the same parameters were determined via nonlinear regression as follows: SGR=0.0228-0.0069*T₁+0.0005*T₁²; LT=113.0685-9.6256*T₁+0.2079*T₁²; MPD=1.6630+0.4284*T₁-0.0080*T₁² (where T₁ is the storage temperature). The appropriateness of the secondary models was validated using statistical indices, such as mean squared error (MSE<0.01), bias factor (0.99≤B_f≤1.07), and accuracy factor (1.01≤A_f≤1.14). External validation was performed at three random temperatures, and the results were consistent with each other. Thus, these models may be useful for predicting the growth of B. cereus on dried laver.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1590-1597
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• 2017

Objective: This study aims to identify the relationship between odd- and branched-chain fatty acids (OBCFAs) and microbial nucleic acid bases in the rumen, and to establish a model to accurately predict microbial protein flow by using OBCFA. Methods: To develop the regression equations, data on the rumen contents of individual cows were obtained from 2 feeding experiments. In the first experiment, 3 rumen-fistulated dry dairy cows arranged in a $3{\times}3$ Latin square were fed diets of differing forage to concentration ratios (F:C). The second experiment consisted of 9 lactating Holstein dairy cows of similar body weights at the same stage of pregnancy. For each lactation stage, 3 cows with similar milk production were selected. The rumen contents were sampled at 4 time points of every two hours after morning feeding 6 h, and then to analyse the concentrations of OBCFA and microbial nucleic acid bases in the rumen samples. Results: The ruminal bacteria nucleic acid bases were significantly influenced by feeding diets of differing forge to concentration ratios and lactation stages of dairy cows (p<0.05). The concentrations of OBCFAs, especially odd-chain fatty acids and C15:0 isomers, strongly correlated with the microbial nucleic acid bases in the rumen (p<0.05). The equations of ruminal microbial nucleic acid bases established by ruminal OBCFAs contents showed a good predictive capacity, as indicated by reasonably low standard errors and high R-squared values. Conclusion: This finding suggests that the rumen OBCFA composition could be used as an internal marker of rumen microbial matter.

• Journal of the Korean Society of Costume
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• v.32
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• pp.21-30
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• 1997

Chogori the basic upper garment of korea costume occupies an important role in tra-ditional dressing and continues to be in use to the present days. Of course there has been changes in the length and line of Chogori with the flow of time based on the Ancient Yoo. This is a study of the 14 parts of Chgori based on statistical analysis by computing the practical measuements. Here the statistical analysis is a objective and quantitative of the stylistic changes in Chogori with time. In this study from the data the Mean and Standard deviation has been evaluated and periodic change is shown by graph to test the periodic change T-test Regressional analysis Index analysis has been used. The results are as follows: 1. The length of clothing has changed with time except the sleeve length. Here the length of clothing means all the other measurements ex-cept the sleeve Thus while the measurements of sleeve length has been uniquely unchanged the other measurements have influenced each other. 2. Generally the form of Chogori had the tendency towards smallness in the 19th cen-tury. But it tended to get larger in the 20th century. 3. Compared to other periods the mode of 19th and 20th century Chogori was widely ac-cepted as the Standard deviation of that period was very narrow. 4. The results seen from the regressional analysis of the Cho-sun period woman's Chogori satisfy the t-value and R-squared and thus support the regression formula presump-tion. 5. From the index analysis it is revealed that with decrease in the armhole measurement sleeve measurement and neckband; relatively same decrease in the wrist measurement; and very marked decrease in the sideline measurement.

• Environmental Engineering Research
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• v.25 no.4
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• pp.479-487
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• 2020

Current assumptions are used in the formulation of pseudo-first (PFO) and second-order (PSO) models to describe the kinetic data of filtration based on ideal operating conditions. This paper presents a new model developed with pseudo n^th order and based on real assumption. A comparison was performed between PFO, PSO and the new model to highlight their performance and the optimisation of the pseudo-order equation, using MATLAB software. Adsorption characteristic of bovine serum albumin adsorption on the track-etched membrane are used as a medium based on protein filtration data were extracted from the literature for different concentrations to demonstrate the comparison between PFO/PSO and the new model. The pseudo first and second-order kinetic models were applied to test the experimental data and they did not provide reasonable values. The results show that the predicted values are consistent with experimental values giving a good correlation coefficient R² = 0.997 and a minimum root mean squared error RMSE = 0.0171. Indeed, the experimental results follow the new model and the optimal pseudo equation order n = 1.115, the most suitable curves for the new model. As a result, we used different experimental adsorption data from the literature to examine and check the applicability and validity of the model.

• International Journal of Highway Engineering
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• v.19 no.2
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• pp.97-102
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• 2017

PURPOSES : Emulsified asphalt is critical for road construction. The objective of applying asphalt emulsion as an adhesive is to prevent the phenomenon of debonding between the upper and lower layers. The quantity and veriety of bituminous material can be varied according to the type of pavement and site conditions. The objective of this study is to reveal the optimum application rates of the emulsified asphalt materials by types of tack-coats using Interface Shear Strength(ISS). METHODS : In the research, emulsified asphalt was paved on the surface of the divided mixture. The specimens of paving asphalt emulsion were utilized to evaluate the bond strength of tack-coat materials. In the evaluation process, NCHRP Report 712 was utilized to investigate the Interface Shear Strength, which reflects the bond capacity of asphalt emulsion. Then, the optimum residual application rates by tack-coat types were determined using regression analysis. RESULTS :As a consequence of squared R values investigated from 0.7 to 1 as part of the regression analysis, the tendency of predicted ISS values was compared with the results. The optimum residual application rates of AP-3, RS(C)-4, QRS-4, and BD-Coat were determined to be $0.78{\ell}/m^2$, $0.51{\ell}/m^2$, $0.53{\ell}/m^2$, and $0.73{\ell}/m^2$, respectively, utilizing 4th regression analysis. CONCLUSIONS :Based on the result of this study, it was not feasible to conclude whether higher residual application of tack-coat material leads to improved bond capacity. Rather, the shearing strength varies depending on the type of pavement.

• Steel and Composite Structures
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• v.19 no.6
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• pp.1549-1560
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• 2015

This paper presents the characterization of fatigue crack in the A283 Grade C steel using the MMM method by identifying the effects of magnetic flux leakage towards the crack growth rate, da/dN, and crack length. The previous and current research on the relation between MMM parameters and fatigue crack effect is still unclear and requires specific analysis to validate that. This method is considered to be a passive magnetic method among other Non-Destructive Testing (NDT) methods. The tension-tension fatigue test was conducted with a testing frequency of 10 Hz with 4 kN loaded, meanwhile the MMM response signals were captured using a MMM instrument. A correlation between the crack growth rate and magnetic flux leakage produces a sigmoid shape curve with a constant values which present the gradient, m value is in the ranges of 1.4357 to 4.0506, and the y-intercept, log C in the ranges of $4{\times}10^{-7}$ to 0.0303. Moreover, a linear relation was obtained between the crack length and magnetic flux leakage which present the R-Squared values is at 0.830 to 0.978. Therefore, MMM method has their own capability to investigate and characterize the fatigue crack effects as a main source of fracture mechanism for ferrous-based materials.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.499-506
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• 2015

The friction velocity is a quantity with the dimensions of velocity defined by the friction stress and density of a wall surface at near wall of flow condition. Also, the friction velocity is the hydraulic parameter describing shear force at the bottom flow. Moreover, it is a very important factor in designing open channel and essential to determine the mixing coefficient in the main flow direction. The estimation of the friction velocity are such as methods using channel slope, linear law of the mean velocity at viscous sub-layer and direct measurement of wall shear stress, etc. In the present study, we propose a friction velocity equation that has been optimized by combining the concept of entropy, which is used in stochastic method, and to verify the proposed equation, the experimental data measured by Song was used. The R squared for friction velocities between proposed equation and friction velocity formula analyzed 0.999 to 1.000 in a very good agreement with each equation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• Advances in environmental research
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• v.5 no.3
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• pp.153-167
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• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.