Curve Fitting Toolbox Of MATLAB Research Articles

Optimal Design of Formulas for a Single Degree of Freedom Tuned Mass Damper Parameter Using a Genetic Algorithm and H2 Norm.

One of the researchers' concerns in structural engineering is to control the dynamic behavior of structures efficiently. The TMD (tuned mass damper) is one of the effective methods of controlling the vibration of structures, and various numerical techniques have been proposed to find the optimal parameters of the TMD. This paper develops a new explicit formula to derive the optimal parameters of the TMD of a single degree of freedom (SDOF) structure under seismic load using a genetic algorithm (GA). In addition, the state-space model and the H2 norm function are used to identify the optimal frequency ratio and damping ratio of the TMD that minimize the overall vibration energy of the structure. The MATLAB curve fitting toolbox is used for the explicit formula proposal, and the validity of the proposed formula is verified through multidimensional performance verification technique. Finally, the TMD parameters of the SDOF structure are applied to the multi-degrees of freedom (MDOF) structure to compare and analyze with the existing research results, and the results of the explicit formula proposed in this paper are confirmed to be excellent. This paper can suggest a new direction for determining the optimal TMD parameters using a GA and can be effectively applied to vibration control problems of various structures.

Evaluation of 25-hydroxyvitamin D (25(OH)D) levels before and during the COVID-19 pandemic: A cross-sectional study and trend analysis involving 86,772 samples.

The aim of this paper was to evaluate the change in 25-hidroxyvitamin D (25(OH)D) levels before and during the COVID-19 pandemic. In this retrospective, cross-sectional and methodological study included 86,772 patients (18-75 years) samples who were admitted to the Izmir Dokuz Eylul University Hospital (latitude and longitude (Turkey): 27 E 09; 38 N 25, respectively) for various reasons and whose 25(OH)D levels were measured in the biochemistry unit between 2019-2020 and 2020-2021 (before and during the COVID-19 outbreak). A time series analysis of monthly averages for 25(OH)D was performed. For the purpose of seasonal study, the mean levels of 25(OH)D are grouped by years. Data were modeled in terms of 25(OH)D levels using the MATLAB Curve Fitting Toolbox. There was no significant difference between the sexes according to 25(OH)D levels (p>0.05). 25(OH)D levels were significantly higher in the summer months and lower in the winter months (p<0.001). When comparing the spring months, 25(OH)D levels in 2020 (18 ± 10) were found to be significantly lower than in 2019 (22 ± 12) (p<0.001); on the contrary, when examined based on the summer, autumn, and winter months, it was determined that 25(OH)D levels increased in 2020 (summer: 25 ± 13, autumn: 25 ± 14, and winter: 19 ± 10) compared to 2019 (summer: 23 ± 11, autumn: 22 ± 10, and winter: 19 ± 11) (p<0.001). In the estimates curve obtained with an error margin of 11% in the time series analysis, it was estimated that the 25(OH)D averages after the pandemic would be similar to those before the pandemic. Restrictions, partial or complete closures, and curfews can significantly affect individuals' 25(OH)D levels during the COVID-19 outbreak. There is a need for multicenter studies with larger populations covering different regions to strengthen and support our results.

Modeling pH and Temperature Effects as Climatic Hazards in Vibrio Vulnificus and Vibrio Parahaemolyticus Planktonic Growth and Biofilm Formation.

Climate-induced stressors, such as changes in temperature, salinity, and pH, contribute to the emergence of infectious diseases. These changes alter geographical constraint, resulting in increased Vibrio spread, exposure, and infection rates, thus facilitating greater Vibrio-human interactions. Multiple efforts have been developed to predict Vibrio exposure and raise awareness of health risks, but most models only use temperature and salinity as prediction factors. This study aimed to better understand the potential effects of temperature and pH on V. vulnificus and V. parahaemolyticus planktonic and biofilm growth. Vibrio strains were grown in triplicate at 25°, 30°, and 37°C in 96 well plates containing Modified Seawater Yeast Extract modified with CaCl2 at pH's ranging from 5 to 9.6. AMiGA software was used to model growth curves using Gaussian process regression. The effects of temperature and pH were evaluated using randomized complete block analysis of variance, and the growth rates of V. parahaemolyticus and V. vulnificus were modeled using the interpolation fit on the MatLab Curve Fitting Toolbox. Different optimal conditions involving temperature and pH were observed for planktonic and biofilm Vibrio growth within- and between-species. This study showed that temperature and pH factors significantly affect Vibrio planktonic growth rates and V. parahaemolyticus biofilm formation. Therefore, pH effects must be added to the Vibrio growth modeling efforts to better predict Vibrio risk in estuarine and coastal zones that can potentially experience the cooccurrence of Vibrio and harmful algal bloom outbreak events.

An application for the PID-based optimizer loop: Estimation of the annual production regression models of Malatya’s apricot

In this study, a data analysis application for the PID-based optimizer loop, which was previously proposed in a former study, is carried out. In this application, quadratic and cubic polynomial regression models were obtained for the estimation of annual apricot production by using the yearly total apricot production data of Malatya between 1991 and 2020. In addition, an average of these regression model estimations was calculated to increase estimation reliability. Annual apricot production amount was estimated by using the regression models obtained with the PID-based optimizer system between 2021-2025. The results were compared with the results obtained with the Matlab curve fitting toolbox.

Novel use of the Monte-Carlo methods to visualize singularity configurations in serial manipulators

This paper analyses the problem of the kinematic singularity of 6 DOF serial robots by extending the use of Monte-Carlo numerical methods to visualize singularity configurations. To achieve this goal, first, forward kinematics and D-H parameters have been derived for the manipulator. Second, the derived equations are used to generate and visualize a workspace that gives a good intuition of the motion shape of the manipulator. Third, the Jacobian matrix is computed using graphical methods, aiming to locate positions that cause singularity. Finally, the data obtained are processed in order to visualize the singularity and to design a trajectory free of singularity. MATLAB robotics toolbox, Symbolic toolbox, and curve fitting toolbox are the MATLAB toolboxes used in the calculations. The results of the surface and contour plots of the determinate of the Jacobian matrix behavior lead to design a manipulator’s trajectory free of singularity and show the parameters that affect the manipulator’s singularity and its behavior in the workspace.

Kinetics and Reactor Model of Biogas Production from Abattoir Waste (Cow Dung)

A laboratory scale anaerobic digester unit was setup and applied for biogas production from cow dung. Anaerobic digestion was conducted at 35°C and observed for a retention period of fifty days. The ambient and slurry temperatures, pH of slurry and amount of biogas produced were measured on a four-day interval basis. The experimental data obtained were used for kinetic studies by fitting the data to known kinetic models such as Linear, Exponential, Gaussian, Logistics and Modified Gompertz. The constants in these models were determined by linear regression using the Matlab curve fitting tool box. A reactor model for the bioreactor was also developed. The experimental results showed biogas production occurred within the mesophilic temperature range (28°C – 29°C - 36°C), measured pH values were 6.9 – 7 – 6.1 for fifty days’ production (retention) period. The volume of biogas produced was 27.7 ml/g, maximum production rate of biogas is 5.1 ml/g/day and the minimum time required for biogas production (the lag phase) was three days. The kinetic evaluation of the production data showed that the coefficient of determination (R²) were as follows, linear model: 0.9673 and 0.7808, exponential model: 0.9742 and 0.779 for the ascending and descending climbs respectively, Gaussian model: 0.9132, Logistic Growth model: 0.9979 and Modified Gompertz model: 0.999 with the logistic model gave the kinetic constant of 2.564. Thus, the Modified Gompertz model yielded high accuracy result. In addition, the reactor model developed solved with the Modified Gompertz kinetic model predicted the biogas production process accurately with cumulative biogas production of 28.13 ml/g compared to the experimental cumulative production of 27.7 ml/g with a deviation of 1.53%.

Research on Generalized Error Control Mechanism of Monocular Vision Ranging Method

Distance perception is the basis and necessary prerequisite of environment perception, attitude perception and obstacle avoidance of intelligent vehicle and unmanned vehicle. Monocular vision ranging method is one of the mainstream distance sensing methods at present. In order to improve the accuracy of monocular vision ranging, a monocular vision ranging method based on machine learning is proposed. The monocular vision ranging method studied in this paper has the advantages of high accuracy, simple training data, simple ranging formula and can be explained, but the uncontrollable generalization error is one of the disadvantages of this method. Therefore, in order to explore the relationship between generalization error and training error, according to the monocular vision ranging method based on the pinhole imaging principle and a large number of measured data, this paper uses the polynomial method in the curve fitting toolbox of MATLAB to fit the functional relationship between coordinates and image distance, so as to obtain the model parameters. Finally, the threshold value of the optimal model is 0.5%, 49 training data and 21 test data. The extreme value of the ratio of the generalization error to the threshold value of the training error is 1.68, which can be used to control the generalization error of the monocular vision ranging method.

An Artificial Potential Function For Battery Life Optimization In Car-following System

For a pure electric car-following system, if the auto-following vehicle acts in an aggressive following manner, battery life fades evidently, since overcharging or over-discharging damage the cell irreversibly. In this regard, this paper proposed an artificial potential function for battery life extension. First, the electric vehicle physical model and an empirical lithium-ion battery model have established form real-world data measurement. The physical layer models car-following dynamics and the battery model describes the energy consumption. Second, with the perceptive of the battery life in a loss-minimal, optimize manner, the controller mathematically computes the optimal acceleration/deceleration value with the Lagrange multipliers method. Then using the Matlab curve fitting tool toolbox to fusion optimal acceleration data with potential function, thus the acceleration consistent rule is realized through the consistency of an artificial potential function. Finally, the control strategy is validated through a simulation test in Matlab/Simulink, and the results show that the proposed control strategy extends battery life while keeping good tracking ability.

Optimisation of Multi-Response Surface Parameters of the Roving Twist Factor and Spinning Back Zone Draft

In order to obtain the optimal collocation of two important technological parameters in the spinning process, the multi-response surface method was used to optimise the experimental results. Through the orthogonal design of the two factors and four levels of factor encoding, 16 groups of orthogonal experiments were designed; the experimental results were fit using the curve fitting toolbox of Matlab; a regression equation of yarn quality indicators, and in addition to a three-dimensional surface chart, an optimal scheme of the roving twist factor and spinning drafting for a roving twist factor of 110 were established; a spinning back draft of 1.5 was obtained based on experimental results of the response surface analysis and variance regression analysis. Experiments on the optimal scheme were carried out to verify the practicability of the results obtained by this method. The results show that application of the multiple response surface method to the optimisation of process parameters is of practical significance. This method can be applied for the optimisation of other process parameters.

A Novel Data Driven Voltage Control Approach for Grid Connected Wind Power Plants

Due to increasing penetration of wind power plants (WPP), power grids are facing significant power quality challenges at the point of interconnection (POI). To achieve stable and robust power system operation, reactive power plays a vital role. Reactive power needed to compensate voltage fluctuations at the POI of the WPP varies with a short-circuit ratio (SCR). The reactive power capability of a WPP is also limited and largely depends on various operating conditions. In a physical system, it is difficult to find the correlation among critical parameters for voltage control. In this paper, a data-driven voltage control approach is proposed for a grid-connected WPP. Two regression models are developed through surface fitting using MATLAB curve fitting toolbox: One model based on simulation data is to determine the required reactive power for grid voltage compensation; another model based on field measurement data is to determine the reactive power characteristics of the WPP. The reactive power compensation device is capacitors in this study. Two controllers, a central WPP controller, and a capacitor controller are designed, and their effectiveness is validated through several case and sensitivity studies.

Estimation of the energy level of the wind stream for the accompanying data

In the article the matrix of Pearson correlation coefficients for wind speed, air temperature, pressure at sea level and time on meteorological data taken from 19.02.2019-27.02.2019 in Kyiv was calculated. The matrix was calculated for the size of the sample equal to 24. The significance of the coefficients according to Student's t-criterion was determined, that the value of the correlation coefficients and their significance depend on the concrete sample. It is concluded that due to the stochastic nature of the wind mass movement, the amount of solar heat received and other parameters, the significance of the correlation coefficients of the investigated values and their magnitude may vary depending on the time and day of observations. Graphs of changes in correlation coefficients between wind speed and time, between wind speed and pressure, between wind speed and temperature, between temperature and pressure, depending on the number of observations, have been constructed, the character of the change and the required minimum number of observations have been found. Near Fourier with 7-8 harmonics, graphs of functions of correlation coefficients between wind speed and time, between wind speed and pressure using the Matlab Curve Fitting Toolbox application are approximated. The number of harmonics was chosen with the best approximation of the approximated graph to the original. According to the approximated graphs, a graph of correlation coefficient correlation between the coefficient of correlation of wind speed and the coefficient of correlation of wind speed and pressure was constructed.The obtained results show that the nature of the correlation coefficients between the coefficient of correlation of wind speed and time and the correlation coefficient between pressure and wind speed allows predicting the values of these meteorological data.Ref. 19, fig. 13, tabl. 4

Measurement-Based Characteristic Curves for Voltage Stability and Control at the Point of Interconnection of Wind Power Plants

In this paper, the measurement-based characteristic curves, reactive power-voltage ( Q-V) curve and reactive power-active power-voltage (Q-P-V) curve, are developed for voltage stability and control at the point of interconnection (POI) between a wind power plant (WPP) and a utility grid. The procedure to obtain an effective Q-V curve using the supervisory control and data acquisition (SCADA) measurement data is proposed. The SCADA data used in this paper were recorded in 2016 at the POI of a 27-MW WPP currently in operation in Newfoundland and Labrador (NL), Canada. Each Q-V curve is created for a particular month using the SCADA data corresponding to a very narrowly defined active power range, so the active power is considered constant as the conventional Q-V curve concept. The MATLAB curve fitting toolbox is used to realize the most fit mathematical equations for Q-V and Q-P-V curves. The developed Q-V and Q-P-V curves can be used for the data-driven voltage control for WPPs; and the Q-V curves can also be used for the voltage stability evaluation.

A Comparative Study on Open Circuit Voltage Models for Lithium-ion Batteries

The current research of state of charge (SoC) online estimation of lithium-ion battery (LiB) in electric vehicles (EVs) mainly focuses on adopting or improving of battery models and estimation filters. However, little attention has been paid to the accuracy of various open circuit voltage (OCV) models for correcting the SoC with aid of the ampere-hour counting method. This paper presents a comprehensive comparison study on eighteen OCV models which cover the majority of models used in literature. The low-current OCV tests are conducted on the typical commercial LiFePO4/graphite (LFP) and LiNiMnCoO2/graphite (NMC) cells to obtain the experimental OCV-SoC curves at different ambient temperature and aging stages. With selected OCV and SoC points from experimental OCV-SoC curves, the parameters of each OCV model are determined by curve fitting toolbox of MATLAB 2013. Then the fitting OCV-SoC curves based on diversified OCV models are also obtained. The indicator of root-mean-square error (RMSE) between the experimental data and fitted data is selected to evaluate the adaptabilities of these OCV models for their main features, advantages, and limitations. The sensitivities of OCV models to ambient temperatures, aging stages, numbers of data points, and SoC regions are studied for both NMC and LFP cells. Furthermore, the influences of these models on SoC estimation are discussed. Through a comprehensive comparison and analysis on OCV models, some recommendations in selecting OCV models for both NMC and LFP cells are given.

Geometric Programming-Based Power Optimization and Design Automation for a Digitally Controlled Pulse Width Modulator

This paper proposes a tool for the synthesis of the design and optimization of digitally controlled pulse width modulator (PWM). There are three phases for the proposed tool. In the first phase, an accurate transistor level model for 90 nm fabrication technology is generated using MATLAB curve-fitting tool box (Dunbar in Am J Phys 24(6):464–464, 1956) and Cadence Spectre Circuit Simulator, which successfully replicates the transistor performance of Cadence 90 nm fabrication technology. In the second phase, the PWM specification is optimally decomposed among its subcomponents. The optimized design of subcomponents is accomplished via Geometric programming in the third phase. A practical design example in Cadence 90 nm fabrication technology is presented to substantiate the suggested methodology for unified design automation and power optimization.

Analytical Modeling of Magnetically Saturated Inductance by Lambert W Function

Inductance is one of the most important components in order to make energy conversion in the power converters. The dynamics of power converter are affected by inductance. Therefore, in order to obtain accurate power converter dynamic model, one needs to have accurate inductance model. An inductance value is not always constant as it depends on current. Especially in the saturation region, inductance value decreases when current increases. On the other hand, exact eddy current and hysteresis losses estimation can be obtained with an accurate inductance model. This paper proposes an improved analytical inductance model that considers nonlinear B-H curve of a core material. A nonlinear B-H function is fitted using the Matlab Curve Fitting Toolbox. This function includes magnetic flux density (B) against magnetic field strength (H) variation in the both linear and saturation regions. The magnetic circuit model is obtained using a lumped parameter method and the Lambert W function is used to solve the equation which includes nonlinear core reluctance. In conclusion, a nonlinear analytical inductance model is proposed.

Study of Energy Consumption in the Injection Molding Process

ABSTRACTA computing method for simulating the energy consumption in an injection molding process using the Moldflow MPI analysis software is proposed. Considering such factors as product structure, processing parameters, and mold structure, it was shown by orthogonal testing that the melt temperature, product diameter, product wall thickness, and gate diameter are the four largest factors impacting on the injection energy consumption. Based on the MATLAB curve fitting toolbox, the relationship between the four main factors and the injection energy consumption was established, and a calculation model for the injection energy consumption was proposed.The accuracy of the model was above 95%. Finally, by using an experimental system of injection energy consumption to research the relationship between energy consumption and actual injection power, and by verifying the calculation accuracy of injection energy consumption of the established model, the results show that the established model can be an excellent predictor of the actual electric energy required and the accuracy of it was above 85% compared with measured experimental data.

Comparative Analysis Between Cam and Cam-less Valve Actuating for Automotive System

A promising alternative of the conventional camshaft in internal combustion engines is one that replaces the camshaft with electromagnetic actuators. This so-called camless system provides great opportunities for the automotive industry. To investigate the advantage of the system against the cam system firstly, we modelled the lift profiles of both systems with novel mathematical expressions. For the camless system we modelled an Electromagnetic Valve Actuating (EMVA) system that captures a plant transfer function and a PID controller with a set-point tracking scheme. Simulation result in MATLAB/Simulink of the theoretical camless lift profile was imported into Curve Fitting (CF) Toolbox of MATLAB and the novel mathematical model was realized. Experimentally measured data for the camless lift profile were then fitted with this model and a tuned experimental model was realized. While for the cam system, the mathematical model was developed directly from experimental data via Curve Fitting Toolbox of MATLAB. Secondly, we computed volumetric efficiencies of both systems using the novel mathematical lift profiles at different engine speeds. The camless system was observed to outperform the cam system.

Comparative Analysis Between &amp;lt;i&amp;gt;Cam&amp;lt;/i&amp;gt; and &amp;lt;i&amp;gt;Cam-less&amp;lt;/i&amp;gt; Valve Actuating for Automotive System

A promising alternative of the conventional camshaft in <i>internal combustion</i> engines is one that replaces the camshaft with electromagnetic actuators. This so-called <i>camless</i> system provides great opportunities for the automotive industry. To investigate the advantage of the system against the cam system firstly, we modelled the lift profiles of both systems with novel mathematical expressions. For the <i>camless</i> system we modelled an Electromagnetic Valve Actuating (EMVA) system that captures a plant transfer function and a PID controller with a set-point tracking scheme. Simulation result in MATLAB/Simulink of the theoretical <i>camless</i> lift profile was imported into Curve Fitting (CF) Toolbox of MATLAB and the novel mathematical model was realized. Experimentally measured data for the <i>camless</i> lift profile were then fitted with this model and a <i>tuned</i> experimental model was realized. While for the cam system, the mathematical model was developed directly from experimental data via Curve Fitting Toolbox of MATLAB. Secondly, we computed volumetric efficiencies of both systems using the novel mathematical lift profiles at different engine speeds. The <i>camless</i> system was observed to outperform the cam system.

Analytical determination of the electromechanical system starting process efficiency index with regard to the distributed nature of input products consumption

Currently, an approach based on the lumped-parameter resource consumption model is used to determine the starting process efficiency index. This is due to the simplicity of analytical expressions for the efficiency index. In actual practice, the resource consumption of the starting process is time-phased in nature, which may lead to biased estimates of the efficiency index and inaccurate operation of the optimization system. In the paper, the actual form of the signal of the cost estimate of input products over time was determined using mathematical modeling of the controlled start system. It is proposed to use a fractional rational function to approximate these signals. The parameters of this approximation at different values of the control action were obtained using the Matlab Curve Fitting Toolbox. The analytical expressions for determining the resource consumption, potential effect and efficiency index of the starting process were obtained on the basis of the proposed approximation. It was found that the position of the maximum efficiency index on the x-axis, obtained when using the lumped-parameter and distributed-parameter starting operation models varies slightly (within the 5 % margin of error). This will reasonably facilitate hardware implementation of optimal control systems of starting processes through the application of the lumped-parameter starting operation model.

Gastrointestinal Side Effects of the Radioiodine Therapy for the Patients with Differentiated Thyroid Carcinoma Two Days after Prescription.

Iodine-131 (I-131) therapy is one of the conventional approaches in the treatment of patients with differentiated thyroid carcinoma (DTC). The radioiodine agents also accumulate in the other organs that cause pain and damage to the patients. Radioiodine therapy is associated with various gastrointestinal (GI) toxicities. In this study, GI side effects of the radioiodine therapy were investigated. GI toxicities of the radioiodine therapy were studied in 137 patients with histologically proven DTC in Jun-Nov 2014. All the patients were treated by radioiodine agents in the research institute of Shariati Hospital, Tehran, Iran. The patients were examined 48 h after prescription (before discharge) and their GI side effects were registered. Correlation of the age, gender, administered dose, administered dose per body weight as the independent factors, and GI side effects were analyzed using the Pearson correlation test with Statistical Package for the Social Sciences (SPSS) version 20. Regression coefficients and linearity of the variable were investigated by MATLAB software. Line fitting was performed using MATLAB curve-fitting toolbox. From the subjects, 38 patients had GI complaints (30.4%). Significant factors influencing GI side effects were dose per body weight and administered doses. There was no significant correlation between age and gender as the independent parameters and GI complaints. The most prevalent GI side effect was nausea that occurs in 26.4% of the patients. From the results, it could be concluded that the GI side effects could be prevented by administering a safe radioiodine dose value less than 5,550 MBq.