The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach—The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings—The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.
Pure steam generated by conventional boiler is not devoid of chemical additives, rust or other undesirable materials and fails to meet essential quality of steam needed for pharmaceutical and health care process industries for WFI(Water for Injection), sterilization of medical equipment and clean room humidification on account of the limitations in achieving desired standards needed by the qualities of ideal pure steam such as pH value, conductivity, bacteria count etc. as prescribed by the International Society of Pharmaceutical Engineers (ISPE). Dry and pure steam is produced by ‘pure steam generators’ using purification process of the plant steam and doesn’t contain any liquid droplets, any anti scaling additives and corrosion inhibitors. - Thermo siphon type Pure Steam Generator has been chosen in the present project for its extremely good heat transfer performance at low temperature differences, less sensitivity to large changes in process condition and more stable operation. In this paper design of the heat exchanger for generation of pure steam has been discussed. Here shell and tube type heat exchanger has been selected due to its flexibility of design, which allows wide range of pressures and temperatures. Standards laid out in ASME BPE for selection of material, CGMP and TEMA for design and manufacturing methods are followed for efficient and safe steam generation. Parameters given by the industry to meet customers’ requirements have been considered while designing the HE, its accessories, piping, valves and instruments. Thermal design of the two phase shell and tube heat exchanger has been done manually and validated using HTRI suite. The mean % error between HTRI results and manual results is 12.05%.
Lean and flexible manufacturing is a matter of necessity for the automotive industries today. Rising consumer expectations, higher raw material and processing costs, and dynamic market conditions are driving the auto sector to become smarter and agile. This paper presents a machine learning-based soft sensor approach for identification and prediction of lean manufacturing (LM) levels of auto industries based on their performances over multifarious flexibilities such as volume flexibility, routing flexibility, product flexibility, labour flexibility, machine flexibility, and material handling. This study was based on a database of lean manufacturing and associated flexibilities collected from 46 auto component enterprises located in the Pune region of Maharashtra State, India. As many as 29 different machine learning models belonging to seven architectures were explored to develop lean manufacturing soft sensors. These soft sensors were trained to classify the auto firms into high, medium or low levels of lean manufacturing based on their manufacturing flexibilities. The seven machine learning architectures included Decision Trees, Discriminants, Naive Bayes, Support Vector Machine (SVM), K-nearest neighbour (KNN), Ensembles, and Neural Networks (NN). The performances of all models were compared on the basis of their respective training, validation, testing accuracies, and computation timespans. Primary results indicate that the neural network architectures provided the best lean manufacturing predictions, followed by Trees, SVM, Ensembles, KNN, Naive Bayes, and Discriminants. The trilayered neural network architecture attained the highest testing prediction accuracy of 80%. The fine, medium, and coarse trees attained the testing accuracy of 60%, as did the quadratic and cubic SVMs, the wide and narrow neural networks, and the ensemble RUSBoosted trees. Remaining models obtained inferior testing accuracies. The best performing model was further analysed by scatter plots of predicted LM classes versus flexibilities, validation and testing confusion matrices, receiver operating characteristics (ROC) curves, and the parallel coordinate plot for identifying manufacturing flexibility trends for the predicted LM levels. Thus, machine learning models can be used to create effective soft sensors that can predict the level of lean manufacturing of an enterprise based on the levels of its manufacturing flexibilities.
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