Comparative Analysis of Equal and Unequal Grounding Grid Configurations by Compression Ratio and Least Square Curve Fitting Techniques

The primary aim of the power system grounding is to safeguard the person and satisfying the performance of the power systemtomaintain reliable operation.With equal conductor spacing grounding grid design,the distribution of the current in the grid is not uniform.Hence,unequal grid conductor span in which grid conductors are concentrated more at the periphery is safer to practice than equal spacing.This paper presents the comparative analysis of two novel techniques that create unequal spacing among the grid conductors:the least-square curve fitting technique and the compression ratio techniquewith equal grid configuration for both square and rectangular grids.Particle Swarm Optimization(PSO)is adopted for finding out one optimal feasible solution among many feasible solutions of equal grid configuration for both square and rectangular grids.Comparative analysis is also carried out between square and rectangular grids using the least square curve fitting technique as it results in only one unequal grid configuration.Simulation results are obtained by theMATLAB software developed.Percentage of improvement in ground potential rise,step voltage,touch voltage,and grid resistancewith variation in compression ratios are plotted.

Energy-Minimizing Curve Fitting for High-Order Surface Mesh Generation

We investigate different techniques for fitting Bézier curves to surfaces in context of high-order curvilinear mesh generation. Starting from distance-based least-squares fitting we develop an incremental algorithm, which incorporates approximations of stretch and bending energy. In the process, the algorithm reduces the energy weight in favor of accuracy, leading to an optimized set of sampling points. This energy-minimizing fitting strategy is applied to analytically defined as well as triangulated surfaces. The results confirm that the proposed method straightens and shortens the curves efficiently. Moreover the method preserves the accuracy and convergence behavior of distance-based fitting. Preliminary application to surface mesh generation shows a remarkable improvement of patch quality in high curvature regions.

Microwave method based on curve fitting method for high-precision collision frequency diagnosis

In this work,the results of plasma microwave transmission diagnosis were analyzed.According to the attenuation and phase shift of the electromagnetic wave propagating in the plasma,the electron density and collision frequency of the plasma can be diagnosed.Since part of the electromagnetic wave is reflected or diffracted when propagating in the plasma,and is not absorbed by the plasma,and this part of the attenuation is still included in the measured attenuation,the attenuation is distorted.Therefore,a curve fitting method is proposed to remove the attenuation caused by the plasma reflection,thereby improving the accuracy of the diagnosis of the collision frequency.The calibration effect of this method on plasmas with different electron densities and collision frequencies is analyzed,and a diagnostic frequency band with good calibration results is given.The curve fitting method is verified by experiment and simulation.After adopting the newly proposed method,the diagnosis accuracy of collision frequency can be increased by 30%.This method can be widely used in various types of plasma diagnosis and provides a new idea for plasma diagnosis.

Curve Fitting and Least Square Analysis to Extrapolate for the Case of COVID-19 Status in Ethiopia

On 30 January 2020 World Health Organization (WHO), declared the novel corona virus as a Public Health Emergency of International Concern (PHEIC), COVID-19 virus as an epidemic transmitted virus. It was on 31 December 2019, the WHO China Country office was informed the cases of pneumonia unknown etiology detected in Wuhan city, Hubei Province of China. Just after WHO’s declaration, Ethiopia has taken different measures to protect from this public health emergency problem. The disease is human to human transmitted virus. It comes from outside of the country, so that it opens check points in different entrance of the country. However, in 13 March 2020 the first positive case was reported from Japanese man. The virus is continuing the transmission in the public progressively more. While this research has been working, within 90 days from the first case, the country reported 2506 positive cases, 35 deaths. The research has done after collecting the first 90 days of data in Ethiopian case. Daily report announced by Ethiopian MoH is based on the test. And hence, the reported data as positive cases with COVID-19 is not actual positive case data in the country. There for, this paper has contribution for planning and taking further measure on the viruses by demonstrating the next 90 days predictive data. I use best curve fitting analysis using python function of the module polyfit algorithm to predict the trend of COVID-19 cases in Ethiopia.

Arm-Root Curve Fitting Based on Body Surface Feature Points for Young Male

This paper is aiming to obtain an arm-root curve function performing the human arm-root size and shape realistically. A gypsum replica of upper arm for young male was made and scanned for extracting the 3D coordinates of 4 feature points of shoulder point, the anterior/posterior armpit point and the axillary point describing the real arm-root shape under the normalized definitions, and the 5 landmarks were confirmed additionally for improving the fitting precision. Then, the wholly and piecewise fitting of arm-root curve with 9 feature points and mark points in total were generated respectively based on least square polynomial fitting method. Comparing to the wholly fitting, the piecewise fitted function segmented by the line between anterior and posterior axillary points showed a high fitting degree of arm-root morphology with R-square of 1, the length difference between fitted curve and gypsum curve is 0.003 cm within error range. And it provided a basic curve model with standard feature points to simulate arm-root morphology realistically by curve fitting for accurate body measurement extraction.

Predicting of Land Surface Temperature Distribution in Freetown City, Sierra Leone by Using Polynomial Curve Fitting Model

Global warming has attracted much concern about the worldwide organization, civil society groups, researchers, and so forth because the worldwide surface temperature has been expanding. This investigation intends to assess and compare the ability of a combination of land cover indices to predict the future distribution of land surface temperatures in Freetown using the Polynomial model analysis. Landsat satellite images of 1988, 1998, 2000, 2010, and 2018 of the Freetown Metropolitan zone were utilized for analysis. The investigation had adopted two land covers indices, Modification of normalized difference water index and Urban Index (UI) (e.g., MNDWI and UI) and applied a multi regression equation for forecasting the future LST. The stimulation results propose that the development will be accompanied by surface temperature increases, especially in Freetown’s western urban area. The temperature prevailing in the west of the metropolitan area may increase in the city somewhere in the range from 1988 to 2018. Additionally, the results of the LST prediction show that the model is perfect. Our discoveries can be represented as a helpful device for policymakers and community awareness by giving a scientific basis for sustainable urban planning and management.

Curve Fitting in Analyzing the Menstrual Irregularities afterImplantation of Norplant

This paper presents the menstrual disorders produced by implantation of Norplantin women during a course of five-year usage. Total 1000 women were recruited. Themain side thects consisted of changes in bleeding patterns, including: menostaxis, hxpomenorrhoea, spotting,frequent menstruation, lnfrequent menstruation, amenorrhoeaand menorrhagia. Authors of this paper suggested that all the above-mentioned men-strual disorders could be explained by mathematical methods. Except for the incidenceof menorrhagia which revealed a negative linear correlation with the implantation timeinterval (correiation coefficient is - 0. 98), all the other six patterns of the abnormalbleeding changes could be fitted by the hyperbola model, because their incidencesreached a very high value in 3~ 6 months after implantation, then sharply dropped toa lower value at the end of one year, whence in turn were reduced slowly but progres-sively to the the end of the 5 th year. The results of all F tests were statistically signWicant (P< 0.01). The advantages of this curve fitting method are: 1, to show the re-lationships between the mismenstruational rates and time afer implantation of Norplant at a theoretical level; 2, to make a direct image; 3, tosmooth the values of the ob-servation and decrease the sampling variation; 4, to estimate the incidences of mismen-struation at a given time; 5, to tell women the changing rule of mismenstruations anddeveloping prospect before and after the implantation in order to increase their confi-dence and meanwhile to increase the conlinuous using rate.

Determination of Material Parameters of EVA Foam under Uniaxial Compressive Testing Using Hyperelastic Models

The objective of this research was to determine the mechanical parameter from EVA foam and also investigate its behavior by using Blatz-Ko,Neo-Hookean,Mooney model and experimental test.The physical characteristic of EVA foam was also evaluated by scanning electron microscopy(SEM).The results show that Blatz-Ko and Neo-Hookean model can fit the curve at 5%and 8%strain,respectively.The Mooney model can fit the curve at 50%strain.The modulus of rigidity evaluated from Mooney model is 0.0814±0.0027 MPa.The structure of EVA foam from SEM image shows that EVA structure is a closed cell with homogeneous porous structure.From the result,it is found that Mooney model can adjust the data better than other models.This model can be applied for mechanical response prediction of EVA foam and also for reference value in engineering application.

Quantitative Assessment of Right Ventricular Systolic Function by the Analysis of Right Ventricular Contrast Time-intensity Curve

Summary: To study reliability and reliable indices of quantitative assessment of right ventricular systolic function by time-intensity curve (TIC) with right ventricular contrast, 5 % sonicated human albumin was injected intravenously at a does of 0.08 ml/kg into 10 dogs at baseline status and cardiac insufficiency. Apical four-chamber view was observed for washin and washout of contrast agent from right ventricle. The parameters of TIC were obtained by curve fitting. The differences of parameters were analyzed in different states of cardiac functions. Among the parameters derived from TIC, the time constant (k) was decreased significantly with decline of cardiac function (P<0.001). But half-time of decent of peak intensity (HT) and mean-transit-time (MTT) of washout were increased significantly (P<0.001). The k was strongly related to cardiac output of right ventricle (CO) and ejection fraction (EF) of left ventricle and fractional shortening (FS) of left ventricle. Right ventricular systolic function could be assessed reliably by the parameters derived from TIC with right ventricular contrast echocardiography. The k, HT and MTT are reliable indices for quantitative assessment of right ventricular systolic function.

Edge detection and mathematic fitting for corneal surface with Matlab software

AIM：To select the optimal edge detection methods to identify the corneal surface,and compare three fitting curve equations with Matlab software. METHODS：Fifteen subjects were recruited. The corneal images from optical coherence tomography（OCT）were imported into Matlab software. Five edge detection methods（Canny,Log,Prewitt,Roberts,Sobel）were used to identify the corneal surface. Then two manual identifying methods（ginput and getpts）were applied to identify the edge coordinates respectively. The differences among these methods were compared. Binomial curve（y=Ax2＋Bx＋C）,Polynomial curve [p（x）=p1xn＋p2x（n-1）＋....＋pnx＋pn＋1] and Conic section（Ax2＋Bxy＋Cy2＋Dx＋Ey＋F=0）were used for curve fitting the corneal surface respectively. The relative merits among three fitting curves were analyzed. Finally,the eccentricity（e）obtained by corneal topography and conic section were compared with paired t-test. RESULTS：Five edge detection algorithms all had continuous coordinates which indicated the edge of the corneal surface. The ordinates of manual identifying were close to the inside of the actual edges. Binomial curve was greatly affected by tilt angle. Polynomial curve was lack of geometrical properties and unstable. Conic section could calculate the tilted symmetry axis,eccentricity,circle center,etc. There were no significant differences between ＇e＇ values by corneal topography and conic section（t=0.9143,P=0.3760 〉0.05）.CONCLUSION：It is feasible to simulate the corneal surface with mathematical curve with Matlab software. Edge detection has better repeatability and higher efficiency. The manual identifying approach is an indispensable complement for detection. Polynomial and conic section are both the alternative methods for corneal curve fitting. Conic curve was the optimal choice based on the specific geometrical properties.

Analysis-Aware Modelling of Spacial Curve for Isogeometric Analysis of Timoshenko Beam

Geometric fitting based on discrete points to establish curve structures is an important problem in numerical modeling.The purpose of this paper is to investigate the geometric fitting method for curved beam structure from points,and to get high-quality parametric model for isogeometric analysis.ATimoshenko beam element is established for an initially curved spacial beam with arbitrary curvature.The approximation and interpolation methods to get parametric models of curves from given points are examined,and three strategies of parameterization,meaning the equally spaced method,the chord length method and the centripetal method are considered.The influences of the different geometric approximation algorithms on the precision of isogeometric analysis are examined.The static analysis and the modal analysis with the established parametric models are carried out.Three examples with different complexities,the quarter arc curved beam,the Tschirnhausen beam and the Archimedes spiral beam are examined.The results show that for the geometric approximation the interpolation method performs good and maintains high precision.The fitting algorithms are able to provide parametric models for isogeometric analysis of spacial beam with Timoshenko model.The equally spaced method and centripetal method perform better than the chord length method for the algorithm to carry out the parameterization for the sampling points.

A fault recognition method based on clustering linear regression

Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clustering linear regression is proposed.Firstly,Hough transform is used to detect the line segment of the enhanced image obtained by the coherence cube algorithm.Secondly,the endpoint of the line segment detected by Hough transform is taken as the key point,and the adaptive clustering linear regression algorithm is used to cluster the key points adaptively according to the lin-ear relationship between them.Finally,a fault is generated from each category of key points based on least squares curve fitting method to realize fault identification.To verify the feasibility and pro-gressiveness of the proposed method,it is compared with the traditional method and the latest meth-od on the actual seismic data through experiments,and the effectiveness of the proposed method is verified by the experimental results on the actual seismic data.

Design of a new kind of chemical experiment container with virtual reality fusion

Background At present,the teaching of experiments in primary and secondary schools is affected by cost and security factors.Existing research on virtual experiment platforms has alleviated these problems.However,the lack of real experimental equipment and use of a single channel to understand user intentions weaken these platforms operationally and degrade the naturalness of interactions.Methods To solve these problems,we propose an intelligent experimental container structure and a situational awareness algorithm,both of which are verified and applied to a chemical experiment involving virtual-real fusion.First,the acquired images are denoised in the visual channel using the maximum diffuse reflection chroma to remove overexposure.Second,container situational awareness is realized by segmenting the image liquid level and establishing a relation-fitting model.Then,strategies for constructing complete behaviors and making priority comparisons among behaviors are adopted for information complementarity and independence,respectively.A multichannel intentional understanding model and an inter-active paradigm that integrates vision,hearing,and touch are proposed.Results The experimental results show that the accuracy of the intelligent container situation awareness proposed in this paper reaches 99%,and the accuracy of the proposed intention understanding algorithm reaches 94.7%.The test shows that the intelligent experimental system based on the new interaction paradigm also has better performance and a more realistic sense of operation experience in terms of experimental efficiency.Conclusion The results indicate that the proposed experimental container and algorithm can achieve a natural level of human-computer interaction in a virtual chemical experiment platform,enhance the user′s sense of operation,and achieve high levels of user satisfaction.

Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics

Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed challenging.Spatially Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary correspondingly.This research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting model.The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here.There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels.The ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative SBEFCM.In terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.

Detection of green apples in natural scenes based on saliency theory and Gaussian curve fitting

Green apple targets are difficult to identify for having similar color with backgrounds such as leaves.The primary goal of this study was to detect green apples in natural scenes by applying saliency detection and Gaussian curve fitting algorithm.Firstly,the image was represented as a close-loop graph with superpixels as nodes.These nodes were ranked based on the similarity to background and foreground queries to generate the final saliency map.Secondly,Gaussian curve fitting was carried out to fit the V-component in YUV color space in salient areas,and a threshold was selected to binarize the image.To verify the validity of the proposed algorithm,55 images were selected and compared with the common used image segmentation algorithms such as k-means clustering algorithm and FCM(Fuzzy C-means clustering algorithm).Four parameters including recognition ratio,FPR(false positive rate),FNR(false negative rate)and FDR(false detection rate)were used to evaluate the results,which were 91.84%,1.36%,8.16%and 4.22%,respectively.The results indicated that it was effective and feasible to apply this method to the detection of green apples in nature scenes.

Statistical Description of Debris Particle Size Distribution in Electrical Discharge Machining

Gap debris as discharge product is closely related to machining process in electrical discharge machining（EDM）. A lot of recent researches have focused on the relationship among debris size, surfaces texture, remove rate, and machining stability. The study on statistical distribution of debris size contributes to the research, but it is still superficial currently. In order to obtain the distribution law of the debris particle size, laser particle size analyzer（LPSA） combined with scanning electron microscope（SEM） is used to analyze the EDM debris size. Firstly, the heating dried method is applied to obtain the debris particles. Secondly, the measuring range of LPSA is determined as 0.5–100 μm by SEM observation, and the frequency distribution histogram and the cumulative frequency distribution scattergram of debris size are obtained by using LPSA. Thirdly, according to the distribution characteristic of the frequency distribution histogram, the statistical distribution functions of lognormal, exponentially modified Gaussian（EMG）, Gamma and Weibull are chosen to achieve curve fitting of the histogram. At last, the distribute law of the debris size is obtained by fitting results. Experiments with different discharge parameters are carried out on an EDM machine designed by the authors themselves, and the machining conditions are tool electrode of red-copper material, workpiece of ANSI 1045 material and working fluid of de-ionized water. The experimental results indicate that the debris sizes of all experiment sample truly obey the Weibull distribution. The obtained distribution law is significantly important for all the models established based on the debris particle size.

The Trace Elements are Bounded by Organic Functional Groups in Coal:A Studying Result Based on FTIR Analysis

The relationship between trace elements in coal and organic functional groups of coal, also some of aromatic structure, was investigated by using curve fitting of infrared spectra. Cluster analysis was also performed according to the degree of affinity of organic groups to the trace elements. The results show that there is a possibility that trace elements, especially LREE, were bound to peripheral organic functional groups of middle rank coal macromolecule. The most possible functional group that binds trace element is the hydroxyl, and to the less degree, the asymmetric -CH3 and 〉CH2 stretching, -CH3 stretching, etc. The degree of affinity of trace elements to different functional groups varies. The tendency obeys the natural structural changing law of trace elements-- the periodic law. The deviation of some trace elements from this regular trend is attributed to the deviation of intrinsic ＂confusion degree＂ （conventional molar entropy） of the matter system of coal basin, which is affected by the inner and outer factors during the evolution.

Statistic Analysis of the Annual Extreme Wave Parameters for the Coast of China

According to the need of project design for offshore engineering and coastal engineering, this paper statistically analyses the annual extreme data of waves acquired at 19 observation stations along the coast of China. Five kinds of distribution curves are adopted: Pearson III (P-III), Log-Extreme I (LE), Log-Normal(LN), Weibull(W) and Exponential Γ(EΓ) to check the adaptability to the long-term distribution of annual extreme of wave in the China Sea areas. The New Curve Fitting Method (NFIT) method and Probability Weighted Moments (PWM) method are used to estimate the distribution parameters and thereby to derive the design wave parameters with different return periods at 19 observation stations. The test results show that by combining EΓ distribution and NFIT parameter estimation and optimum seeking by computer, the design wave parameters can be estimated with high accuracy, high speed and high efficiency, and the randomness of the estimated results can be avoided.

Zhichan decoction induces differentiation of dopaminergic neurons in Parkinson's disease rats after neural stem cell transplantation

The goal of this study was to increase the dopamine content and reduce dopaminergic metabolites in the brain of Parkinson’s disease rats. Using high-performance liquid chromatography, we found that dopamine and dopaminergic metabolite（dihydroxyphenylacetic acid and homovanillic acid） content in the midbrain of Parkinson’s disease rats was increased after neural stem cell transplantation ＋ Zhichan decoction, compared with neural stem cell transplantation alone. Our genetic algorithm results show that dihydroxyphenylacetic acid and homovanillic acid levels achieve global optimization. Neural stem cell transplantation ＋ Zhichan decoction increased dihydroxyphenylacetic acid levels up to 10-fold, while transplantation alone resulted in a 3-fold increment. Homovanillic acid levels showed no apparent change. Our experimental findings show that after neural stem cell transplantation in Parkinson’s disease rats, Zhichan decoction can promote differentiation of neural stem cells into dopaminergic neurons.

Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.