scholarly journals Mathematical Modelling for Power Requirement of Power Take-Off of Rotary Tiller

2020 ◽  
pp. 46-55
Author(s):  
Vivek R. Kamat ◽  
Mukesh Jain ◽  
Hemant Poonia ◽  
Vijaya Rani ◽  
Manoj Kumar
Keyword(s):  
Mathematical Modelling ◽  
Weighted Least Squares ◽  
Least Square ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Power Requirement ◽  
Reasonable Limit ◽  
Hard Soil ◽  
The Mean ◽  
Best Fit

For better performance and durability of tractor and machinery during field operations, it is necessary to select a proper matching machine/implement. The purpose of the study was to analyse the effect on parameters affecting to power requirement of power take-off (P.T.O) for rotary tiller, development of mathematical modelling and validation of the model under field conditions. Three different regression models (multiple linear regression, weighted least squares and stepwise regression) were used to predict the P.T.O power requirement. All three developed models were observed significant at 1% level with R2 value of 0.945, 0.984 and 0.940 for three models respectively. Correlation analysis was performed and all the parameters expressed positive correlation in relation to P.T.O power requirement. Speed of operation, moisture content, depth of cut, working width, peripheral velocity, number of blades and weight of rotary tiller were shown linear relation with P.T.O power requirement. L shaped blades consumed more power than the J and C shaped blades. Hard soil consumed more power followed by medium and light soil. The Mean Absolute Percentage Error (MAPE) ranged in reasonable limit for all three models. Based on higher R2 value, weighted least square regression model was found to be the best fit model for prediction of P.T.O power requirement of rotary tiller.

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.

An Investigation of Optimum Cutting Conditions in Face Milling Semi-Solid Cast 6061 Aluminum Alloy Using Carbide Tool

2013 ◽  
Vol 747 ◽  
pp. 777-780 ◽  
Author(s):  
S. Rawangwong ◽  
Worapong Boonchouytan ◽  
J. Chatthong ◽  
R. Burapa
Keyword(s):  
Surface Roughness ◽  
Feed Rate ◽  
Face Milling ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Milling Machine ◽  
Optimum Cutting ◽  
The Mean ◽  
Main Factors ◽  
Semi Solid

The purpose of this research was to investigate the effect of the main factors of the surface roughness in semi-solid 6061 aluminum face milling. This study was conducted by using computer numerical controlled milling machine. The controlled factors were the speed, the feed rate and the depth of cut which the depth of cut was not over 1 mm. For this experiment, we used factorial designs and the result showed that the factors effected of surface roughness was the feed rate and the speed while the depth of cut did not effect with the surface roughness. Furthermore, the surface roughness was likely to reduce when the speed was 4,200 rpm and the feed rates was 1,300 mm/min. The result of the research led to the linear equation measurement value which was Ra = 0.186 - 0.000034 Speed + 0.000047 Feed rate. The equation formula should be used with the speed in the range of 3,200-4,200 rpm, feed rate in the range of 1,300-1,800 mm/min and the depth of cut not over 1 mm. The equation was used to confirm the research results, it was found that the mean absolute percentage error of the surface roughness obtained from the predictive comparing to the value of the experiment was 4.12%, which was less than the specified error and it was acceptable.

scholarly journals Sorption isotherms for oat flakes (Avena sativa L.)

2014 ◽  
Vol 32 (1) ◽  
pp. 52-58 ◽  
Author(s):  
José Edgar Zapata M. ◽  
Oscar Albeiro Quintero C. ◽  
Luis Danilo Porras B.
Keyword(s):  
Sorption Isotherms ◽  
Moisture Sorption ◽  
Storage Conditions ◽  
Percentage Error ◽  
Relative Air Humidity ◽  
Moisture Sorption Isotherms ◽  
Relative Percentage Error ◽  
The Mean ◽  
Best Fit

Moisture sorption isotherms of oat flakes were determined at temperatures of 5, 25 and 37°C, using a gravimetric technique in an aw range of between 0.107 and 0.855. These curves were modeled using six equations commonly applied in food. The quality of the fit was assessed with the regression coefficient (r2) and the mean relative percentage error (MRPE). The best fit were obtained with the Caurie model with r2 of 0.996, 0.901 and 0.870, and MRPE of 7.190, 17.878 and 16.206, at 5, 25 and 37°C, respectively. The equilibrium moisture presented a dependence on temperature in the studied aw range, as did the security moisture (XS). These results suggest that the recommended storage conditions of oat flakes include: a relative air humidity of 50% between 5 and 25°C and of 38% up to 37°C.

scholarly journals An Analysis of Time-Series Models for Age-Specific Mortality Rates in India

2020 ◽  
Vol 5 (8) ◽  
pp. 1133-1140
Author(s):  
Aritra Sen ◽  
Shalmoli Dutta
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Current Trend ◽  
Mortality Rates ◽  
Percentage Error ◽  
Time Series Models ◽  
Annual Data ◽  
Registration System ◽  
The Mean ◽  
Best Fit

Mortality is a continuous force of attrition, tending to reduce the population, a prime negative force in the balance of vital processes (Bhasin and Nag, 2004). Sample Registration System (SRS) serves as the only source of annual data on vital events on a full scale from 1969-70 in India. Few studies have examined the trends and patterns of mortality across time and regions in India (Preston and Bhat, 1984). The Under 5 Mortality Rates (U5MR) can be seen to decrease by more than half from 1970 to 2017 but in contrast little is known about the mortality patterns of the older children (5-9) and young adolescents (10-14), and not many studies have been done on their changing trends (Masquelier et al., 2018). Using the annual data for the 5-14 age, the trend of decline in the mortality patterns is studied from 1970 to 2013. The linear trend in the time series plot suggests analysis using time series models AR(p), MA(q), ARMA(p,q), Box- Jenkins ARIMA(p,d,q) and Random Walk with drift models to get the best fit to the trend of the data. The order of the time series models have been calculated by studying the ACF, PACF plots and the coefficients have been derived using the Yule-Walker equation matrix. An in-sample forecast of the years 2014-17 are taken. The Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) as a measure of accuracy is used to determine the best fit model. ARIMA(3,1,1) produced lower values making it the best-fit model. Out-of-sample forecasting was done for 2018-2025. The forecast value shows that at the current trend, India would have 0.03 deaths per 1000 population in the 5-14 age group in 2025 showing that the government’s policies and health care interventions towards realization of the MDG4 goal is working positively.

Direct Assessment of Effective Renal Plasma Flow from Renoscintigraphy with a Gamma Camera and an On-Line Computer

1981 ◽  
Vol 20 (06) ◽  
pp. 274-278
Author(s):  
J. Liniecki ◽  
J. Bialobrzeski ◽  
Ewa Mlodkowska ◽  
M. J. Surma
Keyword(s):  
Count Rate ◽  
Renal Plasma Flow ◽  
Early Period ◽  
Least Square Method ◽  
Least Square ◽  
Uptake Coefficient ◽  
Positive Linear Correlation ◽  
On Line ◽  
Best Fit ◽  
Plasma Activity

A concept of a kidney uptake coefficient (UC) of 131I-o-hippurate was developed by analogy from the corresponding kidney clearance of blood plasma in the early period after injection of the hippurate. The UC for each kidney was defined as the count-rate over its ROI at a time shorter than the peak in the renoscintigraphic curve divided by the integral of the count-rate curve over the "blood"-ROI. A procedure for normalization of both curves against each other was also developed. The total kidney clearance of the hippurate was determined from the function of plasma activity concentration vs. time after a single injection; the determinations were made at 5, 10, 15, 20, 30, 45, 60, 75 and 90 min after intravenous administration of 131I-o-hippurate and the best-fit curve was obtained by means of the least-square method. When the UC was related to the absolute value of the clearance a positive linear correlation was found (r = 0.922, ρ > 0.99). Using this regression equation the clearance could be estimated in reverse from the uptake coefficient calculated solely on the basis of the renoscintigraphic curves without blood sampling. The errors of the estimate are compatible with the requirement of a fast appraisal of renal function for purposes of clinical diagknosis.

Experimental and Numerical Investigation into Residual Stress During Turning Operation for Stainless Steel AISI 316

2020 ◽  
Vol 38 (12A) ◽  
pp. 1862-1870
Author(s):  
Safa M. Lafta ◽  
Maan A. Tawfiq
Keyword(s):  
Stainless Steel ◽  
Residual Stresses ◽  
Orthogonal Cutting ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Main Role ◽  
Turning Operation ◽  
Aisi 316

RS (residual stresses) represent the main role in the performance of structures and machined parts. The main objective of this paper is to investigate the effect of feed rate with constant cutting speed and depth of cut on residual stresses in orthogonal cutting, using Tungsten carbide cutting tools when machining AISI 316 in turning operation. AISI 316 stainless steel was selected in experiments since it is used in many important industries such as chemical, petrochemical industries, power generation, electrical engineering, food and beverage industry. Four feed rates were selected (0.228, 0.16, 0.08 and 0.065) mm/rev when cutting speed is constant 71 mm/min and depth of cutting 2 mm. The experimental results of residual stresses were (-15.75, 12.84, 64.9, 37.74) MPa and the numerical results of residual stresses were (-15, 12, 59, and 37) MPa. The best value of residual stresses is (-15.75 and -15) MPa when it is in a compressive way. The results showed that the percentage error between numerical by using (ABAQUS/ CAE ver. 2017) and experimental work measured by X-ray diffraction is range (2-15) %.

scholarly journals Behavior Modeling for a Beacon-Based Indoor Location System

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4839
Author(s):  
Aritz Bilbao-Jayo ◽  
Aitor Almeida ◽  
Ilaria Sergi ◽  
Teodoro Montanaro ◽  
Luca Fasano ◽  
...  
Keyword(s):  
Behavior Modeling ◽  
Percentage Error ◽  
Location Prediction ◽  
Indoor Environments ◽  
Prediction System ◽  
Indoor Location ◽  
The Mean ◽  
Locating System ◽  
Monitoring Devices ◽  
Do So

In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.

scholarly journals Happiness, quality of working life, and job satisfaction among nurses working in emergency departments in Iran

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Somayeh Javanmardnejad ◽  
Razieh Bandari ◽  
Majideh Heravi-Karimooi ◽  
Nahid Rejeh ◽  
Hamid Sharif Nia ◽  
...  
Keyword(s):  
Job Satisfaction ◽  
Emergency Departments ◽  
Economic Status ◽  
Linear Regression Analysis ◽  
Working Life ◽  
Least Square ◽  
Life Questionnaire ◽  
Quality Of Working Life ◽  
The Mean

Abstract Background Nurses have a vital role in the healthcare system. One of the basic steps to increase their happiness is to recognize factors such as job satisfaction and quality of working life. Therefore, the goal of the present study was to examine the relationship between happiness and quality of working life and job satisfaction among nursing personnel. Methods This descriptive study was carried out on 270 hospital nurses who worked in emergency departments in Iran. Nurses were recruited through the census method. Data collection instruments included the Oxford Happiness Inventory (OHI), the Quality of Work Life Questionnaire (QWL), and the Job Satisfaction Questionnaire (JSQ). Data were explored using descriptive statistics, and stepwise multiple linear regression analysis. Results The mean age of participants was 30.1 ± 6.26 years. The mean happiness score was 38.5 ± 16.22, the mean Quality of Working Life (QWL) score was 84.3 ± 17.62, and the mean job satisfaction score was found to be 45.5 ± 13.57); corresponding to moderate levels of attributes. The results obtained from the ordinary least-square (OLS) regression indicated that happiness significantly was associated with economic status and satisfaction with closure (R2: 0.38). Conclusion Overall the current study found that nurses who work in emergency departments did not feel happy. Additionally, the findings suggest that their happiness were associated with their economic status, and closure over their duties.

Bayesian Nonparametric Approach to Average Annual Daily Traffic Estimation for Bridges

2021 ◽  
pp. 036119812199459
Author(s):  
Grace Ashley ◽  
Nii Attoh-Okine
Keyword(s):  
Performance Indicators ◽  
Federal Funding ◽  
Percentage Error ◽  
Bayesian Nonparametric ◽  
Transportation Services ◽  
Nonparametric Approach ◽  
Nonparametric Models ◽  
Bayesian Nonparametric Models ◽  
Average Annual Daily Traffic ◽  
The Mean

Every year, the U.S. government provides several billions of dollars in the form of federal funding for transportation services in the U.S.A. Decision making with regard to the use of these funds largely depends on performance indicators like average annual daily traffic (AADT). In this paper, Bayesian nonparametric models are developed through machine learning for the estimation of AADT on bridges. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian nonparametric models is also assessed. The predictions produced using the Bayesian nonparametric approach are then compared with predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the mean absolute percentage error are subsequently employed in model evaluation. Based on the results, the best methods for AADT forecasting for the selected bridges are recommended.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

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