Prediction of field dependent-rheological properties of magnetorheological grease using extreme learning machine method

Magnetorheological grease is seen as a promising material for replacing the magnetorheological fluid owing to its higher stability and the lesser production of leakage. As such, it is important that the rheological properties of the magnetorheological grease as a function of a composition are conducted in the modeling studies of a magnetorheological grease model so that its optimum properties, as well as the time and cost reduction in the development process, can be achieved. Therefore, this article had proposed a machine learning method–based simulation model via the extreme learning machine and backpropagation artificial neural network methods for characterizing and predicting the relationship of the magnetorheological grease rheological properties with shear rate, magnetic field, and its compositional elements. The results were then evaluated and compared with a constitutive equation known as the state transition equation. Apart from the shear stress results, where it had demonstrated the extreme learning machine models as having a better performance than the other methods with R2 more than 0.950 in the training and testing data, the predicted rheological variables such as shear stress, yield stress, and apparent viscosity were also proven to have an agreeable accuracy with the experimental data

Field dependent-shear stress prediction of magnetorheological fluid using an optimum extreme learning machine model

Extreme learning machine (ELM) application to model the shear stress of magnetorheological (MR) fluids has superiority over the existing methods, such as Herschel-Bulkley. Although the shear stress has been successfully predicted, the hidden node numbers are too high reaching up to 10,000 that will hinder the application of the models. Furthermore, the existing works have tried to determine the hidden node number only by trial and error method. Therefore, this paper aims to reduce the hidden node number by employing the particle swarm optimization (PSO) considering the accuracy and the hidden node numbers. The ELM based-shear stress model was firstly defined by treating the magnetic field and shear rate as the inputs and shear stress as output. The objective function optimization method was then formulated to minimize the normalized error and the hidden node numbers. Finally, the proposed methods were tested at various ELM activation functions and samples. The results have shown that the platform has successfully reduced the hidden node numbers from 10,000 to 571 while maintaining the error of less than 1%. In summary, the proposed objective function for PSO optimization has successfully built the optimum shear stress model automatically

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

In this work, a new constitutive model of a magneto-rheological fluid (MRF) actuator is proposed using an extreme learning machine (ELM) technique to enhance the prediction accuracy of the field-dependent actuating force. After briefly reviewing existing rheological constitutive models of MRF actuator, ELM algorithm is formulated using a single-hidden layer feed-forward neural network. In this formulation, both the magnetic field and measured shear rates are used as inputs variables, while the shear stress predicted from the ELM training is used as an output variable. Subsequently, in order to validate the effectiveness of the proposed model, the target defined as the error between the prediction and measured data is set. Then, the fitness of the training and prediction performances is evaluated using a normalized root mean square error (NRMSE) method. It is shown that the shear stress estimation based on the ELM model using sinusoidal activation function is more accurate than conventional rheological constitutive models such as Herschel-Bulkley model. It is also demonstrated that the proposed model is capable of predicting the field-dependent yield stress which is defined as an actuating force of the MRF actuator without causing significant errors

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

In this work, a new constitutive model of a magneto-rheological fluid (MRF) actuator is proposed using an extreme learning machine (ELM) technique to enhance the prediction accuracy of the field-dependent actuating force. After briefly reviewing existing rheological constitutive models of MRF actuator, ELM algorithm is formulated using a single-hidden layer feed-forward neural network. In this formulation, both the magnetic field and measured shear rates are used as inputs variables, while the shear stress predicted from the ELM training is used as an output variable. Subsequently, in order to validate the effectiveness of the proposed model, the target defined as the error between the prediction and measured data is set. Then, the fitness of the training and prediction performances is evaluated using a normalized root mean square error (NRMSE) method. It is shown that the shear stress estimation based on the ELM model using sinusoidal activation function is more accurate than conventional rheological constitutive models such as Herschel-Bulkley model. It is also demonstrated that the proposed model is capable of predicting the field-dependent yield stress which is defined as an actuating force of the MRF actuator without causing significant errors

Estimating dairy young stock rearing cost of different management systems in Keningau, Sabah, Malaysia

Background: Rearing young stock is costly, yet study on the economics of dairy young stock rearing in Malaysia is scarce. This study aims to determine the cost of rearing dairy young stock in different management system at the largest milk producer area located in Keningau, Sabah, Malaysia. Methods: A survey was conducted at 7 smallholders, 6 semi-commercial sand 1 commercial dairy farms between July and August 2019 to estimate costs of rearing young stock from birth until first calving age (FCA) with the average number of milking cows as 16±2.495, 40±3.256 and 1,303 heads, respectively and the average number of young stocks was 2±0.769, 14±4.578 and 2,221 heads, respectively. Only feed costs were estimated. Data were analysed using IBM SPSS software. Result: The average cost of rearing was RM4, 052 (USD1,000)/heifer across three different management systems, which were RM3,478 (USD858)/heifer in small scale, RM4,380 (USD1,081)/heifer) in semi-commercial and RM4,300 (USD1,061)/heifer) in commercial farms

A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument

Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, deformities and fissure. The nomogram’s area under the curve (AUC) was 0.9421 and 0.946 for the internal and external datasets, respectively. The probability of DSPN was predicted from the nomogram and a DSPN severity grading system for MNSI was created using the probability score. An independent dataset was used to validate the model’s performance. The patients were divided into four different severity levels, i.e., absent, mild, moderate, and severe, with cut-off values of 10.50, 12.70 and 15.00 for a DSPN probability of less than 50, 75 and 100%, respectively. We provide an easy-to-use, straightforward and reproducible approach to determine prognosis in patients with DSPN