• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.40.1-40.1
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• 2015

Off-axis reflective optical systems have attractive advantages relative to their on-axis or refractive counterparts, for example, zero chromatic aberration, no obstruction, and a wide field of view. For the efficient operation of off-axis reflective system, the surface accuracy of freeform mirrors should be higher than the order of wavelengths at which the reflective optical systems operate. Especially for applications in shorter wavelength regions, such as visible and ultraviolet, higher surface accuracy of freeform mirrors is required to minimize the light scattering. In this work, we propose the error compensation algorithm (ECA) for the correction of wavefront errors on freeform mirrors. The ECA converts a form error pattern into polynomial expression by fitting a least square method. The error pattern is measured by using an ultra-high accurate 3-D profilometer (UA3P, Panasonic Corp.). The measured data are fitted by two fitting models: Sag (Delta Z) data model and form (Z) data model. To evaluate fitting accuracy of these models, we compared the fitted error patterns with the measured error pattern.

• Smart Structures and Systems
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• v.11 no.4
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• pp.365-386
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• 2013

Monitoring systems currently applied to concrete bridges include strain gauges, inclinometers, accelerometers and displacement transducers. In general, vertical displacements are one of the parameters that more often need to be assessed because their information reflects the overall response of the bridge span. However, the implementation of systems to continuously and directly observe vertical displacements is known to be difficult. On the other hand, strain gauges and inclinometers are easier to install, but their measurements provide no more than indirect information regarding the bridge deflection. In this context, taking advantage of the information collected through strain gauges and inclinometers, and the processing capabilities of current computers, a procedure to evaluate bridge girder deflections based on polynomial functions is presented. The procedure has been implemented in an existing software system - MENSUSMONITOR -, improving the flexibility in the data handling and enabling faster data processing by means of real time visualization capabilities. Benefiting from these features, a comprehensive analysis aiming at assessing the suitability of polynomial functions as an approximate solution for deflection curves, is presented. The effect of boundary conditions and the influence of the order of the polynomial functions on the accuracy of results are discussed. Some recommendations for further instrumentation plans are provided based on the results of the present analysis. This work is supported throughout by monitoring data collected from a laboratory beam model and two full-scale bridges.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• Smart Structures and Systems
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• v.17 no.2
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• pp.231-256
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• 2016

This study concerns the derivation of optimum tuning formulas for a passive Tuned Mass Damper (TMD) device, for the case of benchmark ideal excitations acting on a single-degree-of-freedom (SDOF) damped primary structure. The free TMD parameters are tuned first through a non-linear gradient-based optimisation algorithm, for the case of harmonic or white noise excitations, acting either as force on the SDOF primary structure or as base acceleration. The achieved optimum TMD parameters are successively interpolated according to appropriate analytical fitting proposals, by non-linear least squares, in order to produce simple and effective TMD tuning formulas. In particular, two fitting models are presented. The main proposal is composed of a simple polynomial relationship, refined within the fitting process, and constitutes the optimum choice. A second model refers to proper modifications of literature formulas for the case of an undamped primary structure. The results in terms of final (interpolated) optimum TMD parameters and of device effectiveness in reducing the structural dynamic response are finally displayed and discussed in detail, showing the wide and ready-to-use validity of the proposed optimisation procedure and achieved tuning formulas. Several post-tuning trials have been carried out as well on SDOF and MDOF shear-type frame buildings, by confirming the effective benefit provided by the proposed optimum TMD.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1471-1481
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• 2014

This paper presents the new power dissipation model of individual switching device in a high-level modular multilevel converter (MMC), which can be mostly used in voltage sourced converter (VSC) based high-voltage direct current (HVDC) system and flexible AC transmission system (FACTS). Also, the voltage balancing method based on sorting algorithm is newly proposed to advance the MMC functionalities by effectively adjusting switching variations of the sub-module (SM). The proposed power dissipation model does not fully calculate the average power dissipation for numerous switching devices in an arm module. Instead, it estimates the power dissipation of every switching element based on the inherent operational principle of SM in MMC. In other words, the power dissipation is computed in every single switching event by using the polynomial curve fitting model with minimum computational efforts and high accuracy, which are required to manage the large number of SMs. After estimating the value of power dissipation, the thermal condition of every switching element is considered in the case of external disturbance. Then, the arm modeling for high-level MMC and its control scheme is implemented with the electromagnetic transient simulation program. Finally, the case study for applying to the MMC based HVDC system is carried out to select the appropriate insulated-gate bipolar transistor (IGBT) module in a steady-state, as well as to estimate the proper thermal condition of every switching element in a transient state.

• 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.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1802-1811
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• 2016

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.1
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• pp.100-113
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• 2017

Snap rolling during hard turning and instability during emergency rising are important features of submarine operation. Hydrodynamics modeling using a high incidence flow angle is required to predict these phenomena. In the present study, a quasi-steady dynamics model of a submarine suitable for high-incidence-angle maneuvering applications is developed. To determine the hydrodynamic coefficients of the model, static tests, dynamic tests, and control surface tests were conducted in a towing tank and wind tunnel. The towing tank test is conducted utilizing a Reynolds number of $3.12{\times}10^6$, and the wind tunnel test is performed utilizing a Reynolds number of $5.11{\times}10^6$. In addition, least squares, golden section search, and surface fitting using polynomial models were used to analyze the experimental results. The obtained coefficients are presented in tabular form and can be used for various purposes such as hard turning simulation, emergency rising simulation, and controller design.

• Animal Bioscience
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• v.37 no.8
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• pp.1333-1344
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• 2024

Objective: Litter size and piglet loss at birth significantly impact piglet production and are closely associated with sow parity. Understanding how these traits vary across different parities is crucial for effective herd management. This study investigates the patterns of the number of born alive piglets (NBA), number of piglet losses (NPL), and the proportion of piglet losses (PPL) at birth in Landrace sows under tropical conditions. Additionally, it aims to identify the most suitable model for describing these patterns. Methods: A dataset comprising 2,322 consecutive reproductive records from 258 Landrace sows, spanning parities from 1 to 9, was analyzed. Modeling approaches including 2nd and 3rd degree polynomial models, the Wood gamma function, and a longitudinal model were applied at the individual level to predict NBA, NPL, and PPL. The choice of the best-fitting model was determined based on the lowest mean and standard deviation of the difference between predicted and actual values, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Results: Sow parity significantly influenced NBA, NPL, and PPL (p<0.0001). NBA increased until the 4th parity and then declined. In contrast, NPL and PPL decreased until the 2nd parity and then steadily increased until the 8th parity. The 2nd and 3rd degree polynomials, and longitudinal models showed no significant differences in predicting NBA, NPL, and PPL (p>0.05). The 3rd degree polynomial model had the lowest prediction standard deviation and yielded the smallest AIC and BIC. Conclusion: The 3rd degree polynomial model offers the most suitable description of NBA, NPL, and PPL patterns. It holds promise for applications in genetic evaluations to enhance litter size and reduce piglet loss at birth in sows. These findings highlight the importance of accounting for sow parity effects in swine breeding programs, particularly in tropical conditions, to optimize piglet production and sow performance.