No abstract
The COVID-19 pandemic has had catastrophic consequences all over the world since the detection of the first case in December 2019. Currently, exponential growth is expected. In order to stop the spread of this pandemic, it is necessary to respect sanitary protocols such as the mandatory wearing of masks. In this research paper, we present an affordable artificial intelligence-based solution to increase the protection against COVID-19, covering several relevant aspects to facilitate the detection and prevention of this pandemic: noncontact temperature measurement, mask detection, automatic gel-dispensing, and automatic sterilization. Our main contribution is to provide high-quality, real-time learning and analysis. To achieve this goal, we used a deep convolutional neural network (CNN) based on MobileNetV2 architecture as the learning algorithm and Advanced Encryption Standard (AES) as an encryption protocol for sending secure data to notify hospital staff. The experimental results show the effectiveness of our model by providing 99.7% accuracy in detecting masks with a runtime of 1.54 s.
The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require novel, distributed, and intelligent security models, controls, and decisions. In addition, the ever-evolving hacking techniques and methods and the expanded risks surfaces have demonstrated the importance of attacks detection systems. This proves that even advanced solutions face difficulties in discovering and recognizing these small variations of attacks. In fact, to address the above problems, Artificial Intelligence (AI) methods could be applied on the millions of terabytes of collected information to enhance and optimize the processes of IoT and fog systems. In this respect, this research is designed to adopt a new security scheme supported by an advanced machine learning algorithm to ensure an intelligent distributed attacks detection and a monitoring process that detects malicious attacks and updates threats signature databases in IoT-Fog environments. We evaluated the performance of our distributed approach with the application of certain machine learning mechanisms. The experiments show that the proposed scheme, applied with the Random Forest (RF) is more efficient and provides better accuracy (99.50%), better scalability, and lower false alert rates. In this regard, the distribution character of our method brings about faster detection and better learning.
Fuzzy gain-scheduled PID (FuGSPID) controllers have attracted significant interest in contemporary research. This paper provides mathematical description help to reduce the code program for a Fuzzy gain-scheduled controller FuGSPID that is subsequently used to stabilize the altitude of a home-constructed Quadcopter. The subject particle swarm optimization approach was employed to optimize the fuzzy output sets of the controller. MATLAB was used to generate the dynamic model, the FuGSPID, and the optimization. A simulation exercise revealed that the new parameters employed in the FuGSPID that were generated as a result of the particle swarm optimization produced fewer trajectory tracking errors. Fuzzy systems are required to process large volumes of tasks and processes. This subsequently impedes the performance of the microcontroller. In light of this, this paper outlines a mathematical description that can potentially reduce the algorithm code employed in the FuGSPID controller and, thereby, making it more intuitive and reducing the processing speed of the microcontroller and reducing the sampling time of the controller and the whole flight controller. The findings of this study revealed that the mathematical description it be useful to reduce instruction of fuzzy controller program to implement it in low cost microcontroller and tested effectively for a quadcopter altitude stabilization.
In this paper, improved electrothermal models of the power diode and IGBT have been developed. The main local physical effects have been considered. The proposed models are able to deal with electrical and thermal effects. The models were confirmed by comparison with other models having similar characteristics for different circuits and different temperatures. The developed models are implemented in a traction unit to study the electrothermal performance in an electric vehicle system. The models were implemented in the Pspice circuit simulation platform using standard Pspice components and analog behavior modeling (ABM) blocks. The switching performance of the diode and the IGBT have been studied under the influence of different circuit elements in order to study and estimate the on-state and switching losses pre-required for the design of various topologies of converters and inverters. The comparison shows that these models are simple, configurable with the electrical circuit simulator software. They are better able to predict the main circuit parameters needed for power electronics design. Transient thermal responses have been demonstrated for single pulse and repetition modes. The obtained results show that our model is suitable for a fully electrothermal use of power electronic circuit simulations.
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