• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1391-1396
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• 2012

It is introduced an approach to model for numerical analysis of a sound absorbing material that has different absorbing coefficient according to frequency. For modeling of a sound absorbing material, we tried to model by a traditional modeling method. But it had large differences on frequency domain, especially a capacitance component due to increasing of frequency. We approach to model a sound absorbing material by the Debye polarization technique with non-linear least square method. At first, we estimated parameters form a polyurethane with thickness 25 mm, then we could model a polyurethane with thickness 50 mm using same parameters. Therefor, we could find that the Debye polarization is an useful way to model sound absorbing materials.

• Management Science and Financial Engineering
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• v.14 no.2
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• pp.89-104
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• 2008

Growth curves including Bass, Logistic and Gompertz functions are widely used in forecasting the market demand. Nonlinear least square method is often adopted for estimating the model parameters but it is difficult to set up the starting value for each parameter. If a wrong starting point is selected, the result may lead to erroneous forecasts. This paper proposes a method of selecting starting values for model parameters in estimating some growth curves by nonlinear least square method through grid search and transformation into linear regression model. Resealing the market data using the national economic index makes it possible to figure out the range of parameters and to utilize the grid search method. Application to some real data is also included, where the performance of our method is demonstrated.

• Journal of environmental and Sanitary engineering
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• v.23 no.4
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• pp.83-93
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• 2008

The non-linear least squares model(NLSM) has long been the standard technique used by hydrologists for constructing rating curves. The reasons for its adaptation are vague, and its appropriateness as a method of describing discharge measurement uncertainty has not been well investigated. It is shown in this paper that the classical method of NLSM can model only a very limited class of variance heterogeneity. Furthermore, this lack of flexibility often leads to unaccounted heteroscedasticity, resulting in dubious values for the rating curve parameters and estimated discharge. By introducing a heteroscedastic maximum likelihood model(HMLM), the variance heterogeneity is treated more generally. The maximum likelihood model stabilises the variance better than the NLSM approach, and thus is a more robust and appropriate way to fit a rating curve to a set of discharge measurements.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.4
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• pp.321-331
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• 2011

The present work is aimed to analyze the creep characteristics of a turbojet engine turbine blade using the theta projection method. The theta projection method has been widely used due to its advantages and flexibility. For the creep characteristic analysis of the turbine blade, tests are performed considering the operating conditions and the non-linear material properties. Results from the creep test are fitted using the four theta model. The predicted proprieties using the four theta model are compared with the prediction model and creep test results. To obtain an optimum value of the four theta parameters in non-linear square method, a number of computing processes in the non-linear least square method were carried out to obtain full creep curves. Results using the theta model has more than 0.95 value of $R^2$. The results between the experimental values and predicted four theta model has about 90.0% accuracy. The theta projection method can be utilized for a design purpose to predict the creep behavior.

• Journal of applied mathematics & informatics
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• v.6 no.3
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• pp.757-770
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• 1999

We analyse in this paper the possibility of using preconditioning techniques as for square non-singular systems, also in the case of inconsistent least-squares problems. We find conditions in which the minimal norm solution of the preconditioned least-wquares problem equals that of the original prblem. We also find conditions such that thd Kaczmarz-Extendid algorithm with relaxation parameters (analysed by the author in [4]), cna be adapted to the preconditioned least-squares problem. In the last section of the paper we present numerical experiments, with two variants of preconditioning, applied to an inconsistent linear least-squares model probelm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.138-143
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• 2006

In this paper, we have presented a Sequential Agglomerative Hierarchical Nested (SAHN) algorithm-based data clustering method in fuzzy inference system to achieve optimal performance of fuzzy model. SAHN-based algorithm is used to give possible range of number of clusters with cluster centers for the system identification. The axes of membership functions of this fuzzy model are optimized by using cluster centers obtained from clustering method and the consequence parameters of the fuzzy model are identified by standard least square method. Finally, in this paper, we have observed our model's output performance using the Box and Jenkins's gas furnace data and Sugeno's non-linear process data.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.71-79
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• 2021

This study investigates the capital structure policy among Indonesian public companies. Previous studies suggest that capital structure policy could follow either static or dynamic behavior. The sample data used in this study was companies in the manufacturing sector, divided into three sub-sectors: the basic and chemical industry, miscellaneous industry, and the consumer goods industry. This study uses panel data from 2010 to 2018, with the Generalized Least Square (GLS) method and compared whether the fixed effect model is better than the common effect model. The results show that the dynamic and non-linear model tests can explain the capital structure determinants than the static and linear models. The dynamic model shows that the capital structure of a certain year is influenced by the capital structure of the previous year. The findings indicate that the company performs some adjustments in its capital structure policy by referring to the previous debt ratio, which implies support to the trade-off theory (TOT). The study also shows that profitability, tangible assets, size, and age explain the variation of capital structure policy. The patterns on the dynamic and non-linear confirm that capital structure runs in a nonlinear pattern, based on the sector, company condition, and the dynamic environment.

• Journal of KSNVE
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• v.9 no.5
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• pp.891-898
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• 1999

The purpose of Superconducting Magnetic Bearing Flywheel Energy Storage System (SMB-FESS) is to store unused nighttime electricity until it is needed during daytime. An analytical model of the SMB-FESS is necessary to identify the system behavior. At first, we have to model the superconducting magnetic bearing. Modeling the SMB is same as estimating the bearing parameter. The theoretical modal parameter is calculated through the equation of motion and the experimental modal parameter is estimated through the impact testing (modal testing). The bearing parameter is searched by using the non-linear least square method until the theoretical result corresponds to the experimental result. The suggested modeling method is verified by comparing experimental and analytical frequency response function.

• Journal of KSNVE
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• v.10 no.5
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• pp.886-891
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• 2000

In this paper, an iterative array shape estimation technique is presented, which is based on the use of the least squares polynomial fitting to the data from heading sensors. The estimated polynomial shape model is then used for calculating the hydrophone positions on the assumption that the arc distances between sensors are constant. In order to verify the applicability of the proposed algorithm, numerical simulations are performed using two types of non-linear array shapes. In addition the noise effects of heading sensors on the array shape estimation results and the performance of beamformer are also investigated.

• Asian-Australasian Journal of Animal Sciences
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• v.9 no.4
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• pp.439-447
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• 1996

Monthly test day production for 12,020 records, were collected from six of the largest specialized dairy farms located in central region of the Kingdom of Saudi Arabia. The records described lactating cows in four parities and two seasons of calving. Monthly test day records were fitted using Wood's model $At{{^b}{_e}}^{-ct}$ with multiple and additive error term. Linear and non-linear regression models were used to find the estimates of the parameters necessary to draw the lactation curves. The shape of the lactation curves of different parities showed that third lactation has the heighest peak (43.08 kg) for linear regression model and (42.08 kg) for non-linear regression model. Fourth lactation has the lowest peak (24.00kg) for linear regression model and (25.64 kg) for non-linear regression models. Cows of second and third lactations reached the peak at 58 day for both linear and non-linear regression models. Cows of first lactation were more persistent and had late peak at 68 and 67 days for both models respectively. While, third lactation cows were lower persistent and had early peak at 58 day for both models. Cows calved at winter months have higher starting values (A), higher ascending slope (b) and higher decending slope (c). Least square means of milk yield of the first four parities and for overall data were 6,653, 7,659, 7,482, 6,988 and 7,614 kg respectively. The corresponding lactation period were 358, 367, 350, 363 and 364 days respectively.