Deep Learning Methods for Industry and Healthcare

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Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Face Detection using Min-Max Features Enhanced with Locally Linear Embedding

Face detection is critical function in many embedded applications such as computer vision and security as it is widely used as preprocessor for face recognition systems. As a preprocessor, the face detection system needs to extract features from a region of interest and classify them quickly as either face or non-face. In our previous works, we have devised a feature representation method called Min-Max (MMX) feature that allows representation of a region of interest using a few data points based on the unique characteristics of vertical and horizontal summation of face regions. In this paper, we attempt to improve the classification accuracy of MMX by integrating a technique called Locally Linear Embedding (LLE), a powerful dimensionality and feature enhancement algorithm that has been used successfully in many pattern recognition tasks. To test the performance of the proposed enhancement, the LLE-treated features were compared with non-treated features using a Multi-Layer Perceptron (MLP) neural network classifier. The results indicate an increase (+1.2%) in classification accuracy of the MLPs, demonstrating the ability of LLE to enhance the representation of MMX features

Development of an intelligent system for the detection of corona virus using artificial neural network

This paper presents the development of an intelligent system for the detection of coronavirus using artificial neural network. This was done after series of literature review which indicated that high fever accounts for 87.9% of the COVID-19 symptoms. 683 temperature data of COVID-19 patients at >= 38C^o were collected from Colliery hospital Enugu, Nigeria and used to train an artificial neural network detective model for the detection of COVID-19. The reference model generated was used converted into Verilog codes using Hardware Description Language (HDL) and then burn into a Field Programming Gate Array (FPGA) controller using FPGA tool in Matlab. The performance of the model when evaluated using confusion matrix, regression and means square error (MSE) showed that the regression value is 0.967; the accuracy is 97% and then MSE is 0.00100Mu. These results all implied that the new detection system for is reliable and very effective for the detection of COVID-19.Comment: 13 pages, 8 Figure

Soft sensors in automotive applications

2017 - 2018In this work, design and validation techniques of two soft sensors for the estimation of the motorcycle vertical dynamic have been proposed. The aim of this work is to develop soft sensors able to predict the rear and front stroke of a motorcycle suspension. This kind of information are typically used in the control loop of semi‐active or active suspension systems. Replacing the hard sensor with a soft sensor, enable to reduce cost and improve reliability of the system. An analysis of the motorcycle physical model has been carried out to analyze the correlation existing among motorcycle vertical dynamic quantities in order to determine which of them are necessary for the development of a suspension stroke soft sensor. More in details, a first soft sensor for the rear stroke has been developed using a Nonlinear Auto‐Regressive with eXogenous inputs (NARX) neural network. A second soft sensor for the front suspension stroke velocity has been designed using two different techniques based respectively on Digital filtering and NARX neural network. As an example of application, an Instrument Fault Detection (IFD) scheme, based on the rear stroke soft sensor, has been shown. Experimental results have demonstrated the good reliability and promptness of the scheme in detecting different typologies of faults as losing calibration faults, hold‐faults, and open/short circuit faults thanks to the soft sensor developed. Finally, the scheme has been successfully implemented and tested on an ARM microcontroller, to confirm the feasibility of a real‐time implementation on actual processing units used in such context. [edited by Author]XXX cicl

Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids

Energy management systems (EMSs) are nowadays considered one of the most relevant technical solutions for enhancing the efficiency, reliability, and economy of smart micro/nanogrids, both in terrestrial and vehicular applications. For this reason, the recent technical literature includes numerous technical contributions on EMSs for residential/commercial/vehicular micro/nanogrids that encompass renewable generators and battery storage systems (BSS) The volume “Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids”, was released as a Special Issue of the journal Energies, published by MDPI, with the aim of expanding the knowledge on EMSs for the optimal operation of electrical micro/nanogrids by presenting topical and high-quality research papers that address open issues in the identified technical field. The volume is a collection of seven research papers authored by research teams from several countries, where different hot topics are accurately explored. The reader will have the possibility to benefit from original scientific results concerning, in particular, the following key topics: distribution systems; smart home/building; battery energy storage; demand uncertainty; energy forecasting; model predictive control; real-time control, microgrid planning; and electrical vehicles

Advanced Control of a Multi-Port Autonomous Reconfigurable Solar Power Plant

The multi-port autonomous reconfigurable solar power plant (MARS), which is an integration of photovoltaic (PV) and energy storage system (ESS) to the transmission ac grid and a high-voltage direct current (HVdc) link, is designed to provide frequency response and reject disturbances in the grid with continued operation and reduced transient instability. The complex architecture of the MARS and the intermittent nature of PV underlies the need for developing simple, efficient, and easily generalizable control methods for MARS and MARS-type systems that integrate multiple power sources to the submodules (SMs) in each arm. The presence of different sources such as PV and ESS in each arm of the MARS causes uneven distribution of active power among different SMs present in MARS, thereby leading to unbalanced modules’ capacitor voltages that may impact system stability under various operating conditions. Moreover, in the case of partial shadings, shaded PV SMs will suffer from decreased injected PV power, causing power mismatch between different SMs in the MARS system. An energy balancing control (EBC) method is introduced to balance the capacitor voltages of different types of SMs. Moreover, the system operation region is explored through data-driven method and a machine learning-based EBC criteria are proposed to improve the system efficiency and reduce the switching frequency. The proposed EBC criteria can disable/enable the EBC depending on the MARS input power dispatch commands with high accuracy according to the operation region. To simplify the design process and improved the system performance, the thesis further proposed a neural network-based power mismatch elimination (NNPME) strategy. The NNPME strategy employs ESS to its maximum capacity and the dc and ac circulating currents to transfer power between the SMs, arms, and legs of the MARS and stabilize the system under partial shedding conditions. The aforementioned controls are data-driven methods that require a large amount of simulation data. A model predictive control (MPC) is proposed for more accurate and efficient control of MARS. It can optimally allocate uneven power of ESS and PV in one arm and counteract capacitor voltage deviations. The system dynamic response is largely improved with the implementation of MPC. The proposed advanced controls facilitate the efficient control and energy management of a system with multiple input power sources like MARS to fully utilize its potential with an extended operating region while maintaining high efficiency.Ph.D