scholarly journals An Effective Method to Accurately Extract the Parameters of Single Diode Model of Solar Cells

Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2615
Author(s):  
Zhaoxu Song ◽  
Kun Fang ◽  
Xiaofang Sun ◽  
Ying Liang ◽  
Wei Lin ◽  
...  
Keyword(s):  
Experimental Data ◽  
Solar Cells ◽  
Root Mean Square Error ◽  
Iterative Method ◽  
Error Analysis ◽  
Accurate Method ◽  
Extraction Process ◽  
Model Parameters ◽  
Mean Square ◽  
Calculation Process

A non-iterative method is presented to accurately extract the five parameters of single diode model of solar cells in this paper. This method overcomes the problems of complexity and accuracy by simplifying the calculation process. Key parts of the equation are to be adjusted dynamically so that the desired five parameters can be obtained from the I–V curve. Then, the I-V and P-V characteristic curves of solar cells are used to compare the effectiveness of this method with other methods. Furthermore, the root mean square error analysis shows that this method is more applicable than other methods. Finally, the I-V and P-V characteristics simulated by using the extracted parameters in this method are compared and discussed with the experimental data of solar cells under different conditions. In fact, this extraction process can be regarded as an effective and accurate method to estimate solar cells’ single diode model parameters.

Fast Iterative Solving Method of Various Types of Fuzzy Relational Equations and its Application to Image Reconstruction

2001 ◽  
Vol 5 (2) ◽  
pp. 90-98 ◽  
Author(s):  
Hajime Nobuhara ◽  
◽  
Yasufumi Takama ◽  
Kaoru Hirota
Keyword(s):  
Image Reconstruction ◽  
Root Mean Square Error ◽  
Iterative Method ◽  
Computation Time ◽  
Compression Rate ◽  
Reconstruction Method ◽  
Mean Square ◽  
Comparison Process ◽  
Fuzzy Relational Equations ◽  
Image Reconstruction Method

A fast iterative solving method of various types of fuzzy relational equations is proposed. This method is derived by eliminating a redundant comparison process in the conventional iterative solving method (Pedrycz, 1983). The proposed method is applied to image reconstruction, and confirmed that the computation time is decreased to 1/39 - 1/45 with the compression rate of 0.0625. Furthermore, in order to make any initial solution converge on a reconstructed image with good quality, a new cost function is proposed. Under the condition that the compression rate is 0.0625, it is confirmed that the root mean square error of the proposed method decreases to 24.00% and 86.03% compared with those of the conventional iterative method and a non iterative image reconstruction method (Nobuhara, 2001), respectively.

The drag of a compressible turbulent boundary layer on a smooth flat plate with and without heat transfer

1964 ◽  
Vol 18 (1) ◽  
pp. 117-143 ◽  
Author(s):  
D. B. Spalding ◽  
S. W. Chi
Keyword(s):  
Experimental Data ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mixing Length ◽  
Mean Square ◽  
Temperature Ratio ◽  
Wide Range ◽  
Mixing Length Theory ◽  
Meansquare Error

The theoretical treatments given by earlier authors are classified, reviewed and where necessary extended; then the predictions of twenty of these theories are evaluated and compared with all available experimental data, the root-meansquare error being computed for each theory. The theory of van Driest-II gives the lowest root-mean-square error (11.0%).A new calculation procedure is developed from the postulate that a unique relation exists betweencfFcandRFRwherecfis the drag coefficient,Ris the Reynolds number, andFcandFRare functions of Mach number and temperature ratio alone. The experimental data are found to be too scanty for bothFcandFRto be deduced empirically, soFcis calculated by means of mixing-length theory andFRis found semi-empirically. Tables and charts of values ofFcandFRare presented for a wide range ofMGandTS/TG. When compared with all experimental data, the predictions of the new procedure give a root-mean-square error of 9.9%.

scholarly journals Drying Characteristics of Abelmoschus esculentus Thin Piece on Drying in a Fabricated Tunnel Dryer

2019 ◽  
pp. 1-5
Author(s):  
Dinesh Kumar ◽  
L. P. Singh ◽  
A. K. Singh
Keyword(s):  
Experimental Data ◽  
Root Mean Square Error ◽  
Abelmoschus Esculentus ◽  
Mean Bias Error ◽  
Bias Error ◽  
Drying Rate ◽  
Mean Square ◽  
Drying Time ◽  
Drying Characteristics ◽  
Tunnel Dryer

For the storage of crops, various drying processes are used. It should be synthesized on the basis of drying time, product quality such as colour texture and the taste of the product when uses. To study the drying characteristics of Abelmoschus esculentus (bhindi) thin piece was performed for a temperature range of 38°C and 88°C and velocity of air is fixed at 1.1 m/s in the fabricated tunnel. For the investigation of drying, characteristics experiment was performed, the result was found, the drying rate was falling. The sample studied at 38°C was much good in colour texture and aroma than sample studied at 58°C to 88°C. The experimental data were used on different models proposed, by equating the determination coefficient , Mean Bias Error (MBE), decreased  and Root Mean Square Error (RMSE) measured along with investigated moisture ratio.

scholarly journals PROGNOSTIC EVALUATION OF THE IMPACT OF RESTRICTED HORIZON VISIBILITY ON THE ACCURACY OF POSITION (COORDINATES) OBTAINED WITH GNSS BASED ON EMPIRICAL DATA

2020 ◽  
Vol 46 (2) ◽  
pp. 67-74
Author(s):  
Oleksandr Yanchuk ◽  
Roman Shulgan
Keyword(s):  
Experimental Data ◽  
Root Mean Square Error ◽  
Empirical Data ◽  
Regression Equation ◽  
Base Line ◽  
Mean Square ◽  
Initial Value ◽  
Prognostic Evaluation ◽  
Final Point ◽  
The Impact

In this research, the practical check of regression equation to calculate the prognostic root mean square error (RMSE) of the final point position of the base line in relation to the initial one has been executed. For the investigation, experimental data from three satellite receivers within two days on 5 points have been used. According to the received results, the regression equation to calculate the RMSE of spatial, planned and height position of the final point of the base line in relation to the initial value has been made. These equations allow executing the prognostic evaluation of accuracy for conducting satellite calculations based on data about available obstacles. The dependencies received for the duration of observations sessions for 1 hour, the vectors with the length of 4 km, and the coefficient value of openness from 5.17 to 10.31 have been presented.

scholarly journals A Note on the Reproducibility of Chaos Simulation

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 953
Author(s):  
Thalita E. Nazaré ◽  
Erivelton G. Nepomuceno ◽  
Samir A. M. Martins ◽  
Denis N. Butusov
Keyword(s):  
Experimental Data ◽  
Root Mean Square Error ◽  
Computer Simulations ◽  
Chaotic Systems ◽  
Mean Square ◽  
Finite Precision ◽  
Prediction Horizon ◽  
Code Sharing ◽  
Simulation Results

An evergreen scientific feature is the ability for scientific works to be reproduced. Since chaotic systems are so hard to understand analytically, numerical simulations assume a key role in their investigation. Such simulations have been considered as reproducible in many works. However, few studies have focused on the effects of the finite precision of computers on the simulation reproducibility of chaotic systems; moreover, code sharing and details on how to reproduce simulation results are not present in many investigations. In this work, a case study of reproducibility is presented in the simulation of a chaotic jerk circuit, using the software LTspice. We also employ the OSF platform to share the project associated with this paper. Tests performed with LTspice XVII on four different computers show the difficulties of simulation reproducibility by this software. We compare these results with experimental data using a normalised root mean square error in order to identify the computer with the highest prediction horizon. We also calculate the entropy of the signals to check differences among computer simulations and the practical experiment. The methodology developed is efficient in identifying the computer with better performance, which allows applying it to other cases in the literature. This investigation is fully described and available on the OSF platform.

Analysis of S-box in Image Encryption Using Root Mean Square Error Method

2012 ◽  
Vol 67 (6-7) ◽  
pp. 327-332 ◽  
Author(s):  
Iqtadar Hussain ◽  
Tariq Shah ◽  
Muhammad Asif Gondal ◽  
Hasan Mahmood
Keyword(s):  
Statistical Analysis ◽  
Root Mean Square Error ◽  
Error Analysis ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Image Encryption ◽  
Visual Analysis ◽  
Original Data ◽  
Mean Square ◽  
The Difference

The use of substitution boxes (S-boxes) in encryption applications has proven to be an effective nonlinear component in creating confusion and randomness. The S-box is evolving and many variants appear in literature, which include advanced encryption standard (AES) S-box, affine power affine (APA) S-box, Skipjack S-box, Gray S-box, Lui J S-box, residue prime number S-box, Xyi S-box, and S8 S-box. These S-boxes have algebraic and statistical properties which distinguish them from each other in terms of encryption strength. In some circumstances, the parameters from algebraic and statistical analysis yield results which do not provide clear evidence in distinguishing an S-box for an application to a particular set of data. In image encryption applications, the use of S-boxes needs special care because the visual analysis and perception of a viewer can sometimes identify artifacts embedded in the image. In addition to existing algebraic and statistical analysis already used for image encryption applications, we propose an application of root mean square error technique, which further elaborates the results and enables the analyst to vividly distinguish between the performances of various S-boxes. While the use of the root mean square error analysis in statistics has proven to be effective in determining the difference in original data and the processed data, its use in image encryption has shown promising results in estimating the strength of the encryption method. In this paper, we show the application of the root mean square error analysis to S-box image encryption. The parameters from this analysis are used in determining the strength of S-boxes

Parameter Determination for Chloride and Tritium Transport in Undisturbed Lysimeters during Steady Flow

1992 ◽  
Vol 23 (2) ◽  
pp. 89-104 ◽  
Author(s):  
Ole H. Jacobsen ◽  
Feike J. Leij ◽  
Martinus Th. van Genuchten
Keyword(s):  
Experimental Data ◽  
Unsaturated Flow ◽  
Transport Model ◽  
Time Moment ◽  
Breakthrough Curves ◽  
Coarse Sand ◽  
Retardation Factor ◽  
Parameter Determination ◽  
Model Parameters ◽  
Water Fraction

Breakthrough curves of Cl and 3H2O were obtained during steady unsaturated flow in five lysimeters containing an undisturbed coarse sand (Orthic Haplohumod). The experimental data were analyzed in terms of the classical two-parameter convection-dispersion equation and a four-parameter two-region type physical nonequilibrium solute transport model. Model parameters were obtained by both curve fitting and time moment analysis. The four-parameter model provided a much better fit to the data for three soil columns, but performed only slightly better for the two remaining columns. The retardation factor for Cl was about 10 % less than for 3H2O, indicating some anion exclusion. For the four-parameter model the average immobile water fraction was 0.14 and the Peclet numbers of the mobile region varied between 50 and 200. Time moments analysis proved to be a useful tool for quantifying the break through curve (BTC) although the moments were found to be sensitive to experimental scattering in the measured data at larger times. Also, fitted parameters described the experimental data better than moment generated parameter values.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

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