scholarly journals Assessment of drip lateral design methods based on uniformity coefficient

2019 ◽  
Author(s):  
J. Ramachandran ◽  
V. Ravikumar ◽  
R. Lalitha
Keyword(s):  
Relative Error ◽  
Design Methods ◽  
Variation Method ◽  
Percentage Error ◽  
Step Method ◽  
Relative Percentage ◽  
Uniformity Coefficient ◽  
True Value ◽  
Relative Percentage Error ◽  
Discharge Method

In this paper, six drip lateral design methods were selected and a comparative assessment was done to find its practical applicability for finding accurate uniformity coefficient. Step-By-Step (SBS) method, Differential method (DM), Constant Discharge method (CDM), Variable discharge method (VDM), Outlet variation method (OVM) and Statistical method (STM) were the different methods assessed. The percentage relative error in calculating the uniformity coefficient by different methods were obtained as the difference between step-by-step method (true) value and alternate method (observed) value. These errors were tabulated. VDM and OVM method performed well with equal accuracy to SBS method at different slopes. For L=250m, DM method performed well. The STM performed good for down slope and lateral length of 250m with 6 per cent relative error. The method having lesser relative percentage error can be selected by the design engineers for designing the laterals from the relative percentage error tables.

Hyperelastic models for the description and simulation of rubber subjected to large tensile loading

2021 ◽  
Vol 2 (108) ◽  
pp. 75-85
Author(s):  
Q.H. Jebur ◽  
M.J. Jweeg ◽  
M. Al-Waily ◽  
H.Y. Ahmad ◽  
K.K. Resan
Keyword(s):  
Tensile Strength ◽  
Carbon Black ◽  
Tensile Test ◽  
Accurate Description ◽  
Percentage Error ◽  
Relative Percentage ◽  
Elastomeric Material ◽  
Tensile Data ◽  
Relative Percentage Error ◽  
Hyperelastic Models

Purpose: Rubber is widely used in tires, mechanical parts, and user goods where elasticity is necessary. Some essential features persist unsolved, primarily if they function in excessive mechanical properties. It is required to study elastomeric Rubber's performance, which is operational in high-level dynamic pressure and high tensile strength. These elastomeric aims to increase stress breaking and preserve highly pressurised tensile strength. Design/methodology/approach: The effects of carbon black polymer matrix on the tensile feature of different Rubber have been numerically investigated in this research. Rubber's material characteristics properties were measured using three different percentages (80%, 90%and 100%) of carbon black filler parts per Hundreds Rubber (pphr). Findings: This study found that the tensile strength and elongation are strengthened as the carbon black filler proportion increases by 30%. Practical implications: This research study experimental tests for Rubber within four hyperelastic models: Ogden's Model, Mooney-Rivlin Model, Neo Hooke Model, Arruda- Boyce Model obtain the parameters for the simulation of the material response using the finite element method (FEM) for comparison purposes. These four models have been extensively used in research within Rubber. The hyperelastic models have been utilised to predict the tensile test curves—the accurate description and prediction of elastomer rubber models. For four models, elastomeric material tensile data were used in the FEA package of Abaqus. The relative percentage error was calculated when predicting fitness in selecting the appropriate model—the accurate description and prediction of elastomer rubber models. For four models, elastomeric material tensile data were used in the FEA package of Abaqus. The relative percentage error was calculated when predicting fitness in selecting the appropriate model. Numerical Ogden model results have shown that the relative fitness error was the case with large strains are from 1% to 2.04%. Originality/value: In contrast, other models estimate parameters with fitting errors from 2.3% to 49.45%. The four hyperelastic models were tensile test simulations conducted to verify the efficacy of the tensile test. The results show that experimental data for the uniaxial test hyperelastic behaviour can be regenerated effectively as experiments. Ultimately, it was found that Ogden's Model demonstrates better alignment with the test data than other models.

An aptamer-based electrochemical biosensor for simple and sensitive detection of staphylococcal enterotoxin B in milk

2018 ◽  
Vol 10 (3) ◽  
pp. 365-370 ◽  
Author(s):  
Xiaohui Xiong ◽  
Xinping Shi ◽  
Yuanjian Liu ◽  
Lixia Lu ◽  
Jingjing You
Keyword(s):  
Electrochemical Biosensor ◽  
Staphylococcal Enterotoxin ◽  
Sensitive Detection ◽  
Percentage Error ◽  
Staphylococcal Enterotoxin B ◽  
Relative Percentage ◽  
Significant Difference ◽  
Relative Percentage Error ◽  
Enterotoxin B

The relative percentage error between the proposed method and ELISA ranged from −8.38 to 8.33, which indicates that there is no significant difference between the results.

scholarly journals Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

2020 ◽  
Vol 9 (3) ◽  
pp. 674 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Mohamed Abd El Aziz
Keyword(s):  
Relative Error ◽  
Computing Time ◽  
World Health ◽  
Coefficient Of Determination ◽  
Flower Pollination Algorithm ◽  
Percentage Error ◽  
Anfis Model ◽  
Local Optima ◽  
Inference System ◽  
Proposed Model

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

Dry Characteristics and Kinetics of Explosion Puffing Dried Crispy Winter Jujube at Different Vacuum Drying Temperature

2011 ◽  
Vol 236-238 ◽  
pp. 2505-2509
Author(s):  
Xin Yi He ◽  
Jin Fu Liu ◽  
Li Li Cheng ◽  
Bu Jiang Wang
Keyword(s):  
Moisture Diffusivity ◽  
Percentage Error ◽  
Vacuum Drying ◽  
Drying Temperature ◽  
Thin Layer Drying ◽  
Relative Percentage Error ◽  
Drying Rates ◽  
Predicted Values ◽  
Drying Curves

Drying characteristics of crispy winter jujube dried by explosion puffing drying at different vacuum drying temperature were investigated. Selection of the best model was examined by comparing the determination of coefficient (R2), root means square error (RMSE), and mean relative percentage error (P) between the experimental and predicted values. As expected, higher drying rates were obtained with higher vacuum drying temperature. The results showed that the Modified Henderson and Pabis model provided better simulation of drying curves for crispy winter jujube according to thin-layer drying theory. The effective moisture diffusivity of crispy winter jujube dried by explosion puffing drying with higher vacuum drying temperature was higher than the others.

A method for predicting the capacity of a lithium-ion battery based on deep neural network

2021 ◽  
Author(s):  
JamesChan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Lithium Ion ◽  
Chemical Conversion ◽  
Absolute Error ◽  
Production Costs ◽  
Percentage Error ◽  
Average Percentage ◽  
True Value ◽  
Battery Capacity

This paper proposes a solution to predict the capacity of the lithium-ion battery's capacity division process using deep learning methods. This solution extracts the physical observation records of part of the process steps from the chemical conversion and volumetric processes as features, and trains a Deep Neural Network (DNN) to achieve accurate prediction of battery capacity. According to the test, the average percentage absolute error (Mean Absolute Percentage Error, MAPE) of the battery capacity predicted by this model is only 0.78% compared with the true value. Combining this model with the production line can greatly reduce production time and energy consumption, and reduce battery production costs.

Desorption Isotherm of Mango Soy Fortified Yoghurt

2005 ◽  
Vol 1 (5) ◽  
Author(s):  
Pradyuman Kumar ◽  
H. N. Mishra
Keyword(s):  
Water Activity ◽  
Goodness Of Fit ◽  
Static Method ◽  
Desorption Isotherm ◽  
Percentage Error ◽  
Typical Type ◽  
Sigmoid Curve ◽  
Relative Percentage Error ◽  
Desorption Isotherms ◽  
Residual Plots

Desorption isotherms of three yoghurt samples viz. plain yoghurt, mango soy fortified yoghurt (MSFY) and MSFY containing 0.4 % gelatin stabilizer (MSFYG) were determined by gravimetric static method at 20, 30, 40 and 50 °C in the range of 0.11 – 0.81 water activity. It was found that desorption isotherm of yoghurt samples follow a typical type III sigmoid curve. Experimental data were fitted to five mathematical models i. e. modified Henderson, modified Chung Pfost, Oswin, Smith and Guggenheim-Anderson-deBoer (GAB). Equations were developed for the prediction of the GAB constants as a function of temperature and these equations were used during modeling. Standard error of estimate (SE), mean relative percentage error (P), percent root mean square (% RMS) and trend of residual plots were used to compare the goodness of fit. It was found that the GAB models were acceptable in describing equilibrium moisture content – water activity relationships for yoghurt samples over the entire experimental temperature range.

scholarly journals Studying of flow model and bed load transport in a coarse bed river: case study – Aland River, Iran

2010 ◽  
Vol 13 (4) ◽  
pp. 850-866 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Yousef Hassanzadeh ◽  
Mohammad Ali Keynejad ◽  
Mohammad Tagi Alami ◽  
Vahid Nourani ◽  
...  
Keyword(s):  
Grain Size ◽  
Unsteady Flow ◽  
Relative Error ◽  
Flow Rate ◽  
Bed Load ◽  
Main Role ◽  
Step Method ◽  
Discharge Data ◽  
Load Transport ◽  
Load Discharge

This paper describes a mathematical model which solves the 1D unsteady flow over a mobile bed. The model is based on the Richtmyer second-order explicit scheme. Comparison of the model results with the experimental flume data for alluvial steady flow (aggradation due to overloading) and unsteady flow shows that, by using the two-step method of Richtmyer, one can solve the equations, governing the phenomenon, in a coupled method with the desired accuracy. Firstly, the Badalan reach located at the Aland River is considered. Variations of flow rate, water level and bed level profiles due to flood hydrographs are assessed. Secondly, bed load discharge data were collected from the Aland River and a variety of bed load discharge formulae were compared with measured data. Results show that, by using the grain size of the bed surface layer to predict the bed load discharge, a larger relative error will occur compared to the other two cases and a proper choice of grain size has the main role in reduction of the relative error of bed load discharge estimation in gravel bed rivers. The applicability of formulae varies depending on flow rate, and should be split into low and high flow transport formulae.

scholarly journals On the Optimization of Regression-Based Spectral Reconstruction

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5586
Author(s):  
Yi-Tun Lin ◽  
Graham D. Finlayson
Keyword(s):  
Relative Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Worst Case ◽  
Regression Methods ◽  
Spectral Reconstruction ◽  
Ill Posed ◽  
The Mean ◽  
Error Metric ◽  
Regularization Strategy

Spectral reconstruction (SR) algorithms attempt to recover hyperspectral information from RGB camera responses. Recently, the most common metric for evaluating the performance of SR algorithms is the Mean Relative Absolute Error (MRAE)—an ℓ1 relative error (also known as percentage error). Unsurprisingly, the leading algorithms based on Deep Neural Networks (DNN) are trained and tested using the MRAE metric. In contrast, the much simpler regression-based methods (which actually can work tolerably well) are trained to optimize a generic Root Mean Square Error (RMSE) and then tested in MRAE. Another issue with the regression methods is—because in SR the linear systems are large and ill-posed—that they are necessarily solved using regularization. However, hitherto the regularization has been applied at a spectrum level, whereas in MRAE the errors are measured per wavelength (i.e., per spectral channel) and then averaged. The two aims of this paper are, first, to reformulate the simple regressions so that they minimize a relative error metric in training—we formulate both ℓ2 and ℓ1 relative error variants where the latter is MRAE—and, second, we adopt a per-channel regularization strategy. Together, our modifications to how the regressions are formulated and solved leads to up to a 14% increment in mean performance and up to 17% in worst-case performance (measured with MRAE). Importantly, our best result narrows the gap between the regression approaches and the leading DNN model to around 8% in mean accuracy.

scholarly journals Designing dolls according to Youkai stories and Shinto beliefs in Eastern culture

2021 ◽  
Author(s):  
◽  
Yanxiao Liu
Keyword(s):  
Modern Society ◽  
Design Methods ◽  
Eastern Culture ◽  
Participant Engagement ◽  
True Value ◽  
Spirit World ◽  
New Vision ◽  
Meaningful Relationships ◽  
The Relationship

<p>Contrary to the contemporary views on the function and complex mastery skills of an object, the Eastern world puts more emphasis on the value of the object is in its inner spirit. This view is based on Shinto beliefs, where everything is spiritual and valuable. My project undertakes a case study of the relationship between humans and things. This is done by building on the uses of Shinto beliefs to design an object that initiates a narrative. More specifically dolls. Thus, invites a relationship and engages the belief that objects have souls.  The dolls which I designed are an intersection of the spirit world and reality. By providing a process that facilitates the traditional Youkai story base on Shinto beliefs and how it has developed in modern society. In promoting participant engagement through design methods and processes, this project discovered a new vision of forming meaningful relationships between humans and objects empowers the true value of an object. This project visualizes participant experiences created an exploration of a narrative that contains the spirit.</p>

Modeling of Fixed Joints Stiffness Based on Modified Least-Square

2014 ◽  
Vol 536-537 ◽  
pp. 1365-1368
Author(s):  
Ming De Duan ◽  
Hao Liang Feng ◽  
Kang Hua Liu ◽  
Jun Yong Lu
Keyword(s):  
Experimental Data ◽  
Regression Analysis ◽  
Relative Error ◽  
Machine Tools ◽  
Mean Absolute Percentage Error ◽  
Least Square ◽  
Percentage Error ◽  
Regression Equations ◽  
Absolute Percentage Error ◽  
The Mean

According to experimental data, the model of fixed Joints stiffness in machine tools was built by least square of relative error. The new regression equations were obtained by regression analysis. Compared to the original equations with Gaussian least-square, the relative error of new regression equations is within 3.5%, which reduces by 12.5% and the mean absolute percentage error (MAPE) decreases by 18.0%, 12.4%and 19.0% respectively.

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