Magnetorheological elastomers (MRE) have gained popularity due to their ability to control viscoelastic properties by varying the strength of the magnetic field. Due to the obvious nonlinear and complex behavior of MR...
Magnetorheological elastomers (MRE) have gained popularity due to their ability to control viscoelastic properties by varying the strength of the magnetic field. Due to the obvious nonlinear and complex behavior of MRE, machine learning approaches were used to predict the MRE viscoelastic properties, which are storage and loss modulus. In comparison to the traditional viscoelastic model, which is complex in mathematical derivation, machine learning method easily identifies trends and patterns by mapping the input-output relationship. It can also handle nonlinear problems by training on data. Support vector regression (SVR), Gaussian process regression (GPR), Backpropagation neural network (BP-ANN), and Extreme learning machine (ELM) were introduced and compared to simulate the field-dependent viscoelastic behavior of MRE with frequency and magnetic field strength as model input. As a result, the ELM model produced the highest accuracy, with more than 98 percent accuracy on model generalization capability. Therefore, this demonstrates that machine learning can replace traditional modelling approaches and serve as a basis for material and device development.
Manual irrigation is still widely used in agricultural field using traditional drip and can watering. However, traditional irrigation systems are inefficient and inexact, leading to either insufficient or excessive wa...
Manual irrigation is still widely used in agricultural field using traditional drip and can watering. However, traditional irrigation systems are inefficient and inexact, leading to either insufficient or excessive watering. Moreover, it is difficult for farmers to predict suitable quantities at the appropriate time. Manual monitoring of the crop field may also lead to human error and is potentially risky for rural areas. Farmers may also not be aware of intrusions if they are not on location. Therefore, this project is designed to develop a smart monitoring and automated irrigation system to provide not only efficient water consumption based on specific conditions, but also enables real-time monitoring of the environment. Furthermore, this system prevents damage to plants and reduces the likelihood of plant theft. This system uses NodeMCU ESP32 as a microcontroller that collects environmental data such as humidity, temperature, soil moisture levels from sensors. The NodeMCU is integrated with a relay and RTC module to irrigate plants at specific times and is also equipped with a passive infrared sensor to detect intruders near the crop-field. Upon detection, an ESP32 camera is used to automatically capture the current conditions and farmers will be subsequently notified. Warnings are also sent to farmers upon detection of unwanted circumstances such as extreme temperature, which could prevent instances of open burning. The utility of the developed prototype is evident in the way it automatically irrigates the crop field without human intervention. Farmers may monitor and manually control the irrigation process using an attached Android application. Additionally, they may manually activate a buzzer warn off any potential malicious actors.
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