Automation of monitoring of drying parameters in hybrid solar-electric dryer for agricultural products

ABSTRACT Increasing the efficiency of solar dryers with ensuring that the system remains accessible to all users can be achieved with their automation through low-cost and easy-to-use technique sensors. The objective was to develop, implement and evaluate an automatic system for monitoring drying parameters in a hybrid solar-electric dryer (HSED). Initially, an automated data acquisition system for collecting the parameters of sample mass, air temperature, and relative air humidity was developed and installed. The automatic mass data acquisition system was calibrated in the hybrid solar-electric dryer. The automated system was validated by comparing it with conventional devices for measuring the parameters under study. The data obtained were subjected to analysis of variance, Tukey test and linear regression at p ≤ 0.05. The system to turn on/off the exhaust worked efficiently, helping to reduce the errors related to the mass measurement. The GERAR Mobile App showed easy to be used since it has intuitive icons and compatibility with the most used operating systems for mobile devices. The responses in communication via Bluetooth were fast. The use of Arduino, a low-cost microcontroller, to automate the monitoring activity allowed estimating the mass of the product and collecting the drying air temperature and relative air humidity data through the DHT22. This sensor showed a good correlation of mass and air temperature readings between the automatic and conventional system, but low correlation for relative air humidity. In general, the automatic data acquisition system monitored in real time the parameters for drying agricultural products in the HSED.

A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling

Fused deposition modelling (FDM)-based 3D printing is a trending technology in the era of Industry 4.0 that manufactures products in layer-by-layer form. It shows remarkable benefits such as rapid prototyping, cost-effectiveness, flexibility, and a sustainable manufacturing approach. Along with such advantages, a few defects occur in FDM products during the printing stage. Diagnosing defects occurring during 3D printing is a challenging task. Proper data acquisition and monitoring systems need to be developed for effective fault diagnosis. In this paper, the authors proposed a low-cost multi-sensor data acquisition system (DAQ) for detecting various faults in 3D printed products. The data acquisition system was developed using an Arduino micro-controller that collects real-time multi-sensor signals using vibration, current, and sound sensors. The different types of fault conditions are referred to introduce various defects in 3D products to analyze the effect of the fault conditions on the captured sensor data. Time and frequency domain analyses were performed on captured data to create feature vectors by selecting the chi-square method, and the most significant features were selected to train the CNN model. The K-means cluster algorithm was used for data clustering purposes, and the bell curve or normal distribution curve was used to define individual sensor threshold values under normal conditions. The CNN model was used to classify the normal and fault condition data, which gave an accuracy of around 94%, by evaluating the model performance based on recall, precision, and F1 score.

Design of Intelligent Voice Acquisition System Based on Cloud Resource Scheduling Model

The existing acquisition system has the problem of imperfect communication link, which leads to the weak signal receiving strength of the system. This paper designs an intelligent voice acquisition system based on cloud resource scheduling model. Hardware: select S3C6410 as hardware platform, optimize audio access port, connect IIS serial bus and other components; Software part: extract the frequency agility characteristics of intelligent voice signal, predict the future sample value, establish the communication link with cloud resource scheduling model, obtain the communication rate information, code and generate digital voice data, set the transmission function of intelligent acquisition system with overlay algorithm. Experimental results: the average signal receiving strength of the designed system and the other two intelligent voice intelligent acquisition systems is 106.40 dBm, 91.33 dBm and 90.23 dBm, which proves that the intelligent acquisition system integrated with cloud resource scheduling model has higher use value.

Computerised calibration system for radiosonde pressure sensors

A computer bast1d data acquisition system has been designed for the calibration of pressure sensors used in MK III Indian radiosondes. The system can calibrate 64 baroswitchesin one cycle and gives a tabular printout of pressure values at various contact numbers. This replaces the analog type of recording system, which was in use for the last few decades. The present system gives better accuracy and resolution and avoids human judgement and errors.