This paper presents a comparison study between the measured solar radiations on site and the calculated solar radiation based on the most theoretical models presented in the literature up to date. Indeed, for such purposes, this paper focusses on the analysis of the data of the measured solar radiation collected on two sites in Algeria such as Tlemcen (34°52′58″ N 01°19′00″ W, elevation 842 m) and Senia (35°39′ N 0°38′ W, elevation: 77 m). In order to check the accuracy of the proposed model, the experimental collected data of the solar radiation obtained from the existing radiometric stations installed at the two locations under investigation, are compared with the estimated or predicted solar radiations obtained from the Capderou and R.Sun models, where four days under clear skies are selected from different seasons to achieve this comparison. Second, the daily averages of the experimental global solar irradiation are compared to those predicted by Mefti model for both the sites. Finally, a validation is carried out based on the obtained experimental monthly global irradiations and with those estimated by Coppolino and Sivkov models. A relative difference is used in this case to judge the reliability and the accuracy of each model for both sites

This paper focuses on the application of the direct torque control based on space vector modulation (DTC-SVM), combined with the input–output feedback linearization (IOFL) technique on a three-phase dual open-end windings induction motor (DOEWIM) fed by two dual indirect matrix converters. The main aim of integrating the non-linear technique is to overcome the main drawbacks met within the application of the conventional DTC-SVM on dual-stator induction motor (DSIM), such as the torque and flux ripples reduction, the stator harmonics current minimization, and the elimination of the common-mode voltage (CMV). Furthermore, it is proved in this paper that the proposed control on DOEWIN can ensure more flexibility versus speed reverse and variation, load torque changes, and motor parameters variation. The obtained results prove the validity of the proposed control on the studied induction motor topology in ensuring the main aforementioned advantages compared to the conventional DTC-SVM control on DSIM, which presents a promising solution, especially in industrial applications in which high-power motors are required

In this paper, a deadbeat predictive control (DBPC) technique for doubly-fed induction generators (DFIGs) in wind turbine applications is proposed. The major features of DBPC scheme are its quick dynamic performance and its fixed switching frequency. However, the basic concept of DBPC is computing the reference voltage for the next sample from the mathematical model of the generator. Therefore, the DBPC is highly sensitive to variations of the parameters of the DFIG. To reduce this sensitivity, a disturbance observer is designed in this paper to improve the robustness of the proposed DBPC scheme. The proposed observer is very simple and easy to be implemented in real-time applications. The proposed DBPC strategy is implemented in the laboratory. Several experiments are performed with and without mismatches in the DFIG parameters. The experimental results proved the superiority of the proposed DBPC strategy over the traditional DBPC technique.

Active magnetic bearing (AMB) is a suspension system to levitate a rotating shaft freely without any physical contact which allows extremely fast rotation speeds. One big control challenge of the AMB systems, which appears during high rotation speeds, is the non-uniform distribution of the rotor weight about its rotating axis. This is usually referred to as the rotor imbalance problem which produces sinusoidal disturbance forces. These disturbances lead to undesirable vibrations and large deviations of the rotor shaft from its desired trajectories. We adopt in this work model predictive control (MPC) to reduce the effect of these sinusoidal disturbances and to achieve a stable levitation of the rotor shaft while tracking a reference trajectory. Owing to the MPC capability of handling constraints in an optimal manner, physical input constraints can be committed. Moreover, state 

Recently, Wireless Body Area Network (WBAN) has revolutionized e-health-care. WBAN boosts monitoring vital signs utilizing tiny wireless sensors implanted in or around the human body. In February 2012, the IEEE 802.15.6 WBAN standard was released for low-power and short-range communication around the human body. The standard defines one medium access control layer and three different physical layers: narrow band , ultrawideband, and Human Body Communication (HBC) layers. We are motivated by exploiting the human body as a communication medium. We propose a novel optimized architecture for the HBC baseband transceiver based on deep learning. The receiver utilizes two deep neural networks: one for frame synchronization to recover data and timing precisely and the other for the channel decoder to improve transceiver performance and reduce power consumption. In addition, low-complexity Preamble/SFD generator, Walsh modulation, and FSC spreader modules are proposed to reduce the power consumption while preserving the transceiver performance. Compared with the traditional hard-decision channel decoder, the proposed neural network decoder improves the block error rate by 2 dB. The proposed HBC transceiver supports 1.312 Mbps data rate at 42 MHz clock rate. The transceiver is implemented in RTL and synthesized on 90 nm CMOS technology. It consumes 493 pJ/bit on the receiver side and 105 pJ/bit on the transmitter side.

The control of flexible robot manipulators is a challenging task, especially when one considers parallel and interconnected manipulators under flexibility considerations. This paper proposes a method to estimate the position of the end-effector of a flexible interconnected manipulator based on a virtual sensor principle and function approximation schemes. By using SolidWorks/MSC ADAMS software, we developed a virtual prototype of a flexible interconnected manipulator, and rigorously evaluated the feasibility of using function approximation schemes such as Neural Networks (NN), Support Vector Machines (SVM), and Gaussian Process (GP) in estimating the deflection error arising due to the flexibility of the robot structure. Our rigorous computational experiments have shown that: (1) the NN, SVM, and GP models were are able to attain the promising and reasonable prediction accuracy, (2) a feedforward NN with 535 neurons and an Ascending distribution of its nodes achieves the best prediction and generalization to unseen environments (the upper bound of the error was 0.15 × 10−3 m); implying the robust estimation of the position of the end-effector under flexibility considerations, and (3) the control based on the inverse Jacobian and a NN-based estimator was able to follow a sinusoidal trajectory with reasonable tracking and error performance in MSC ADAMS & MATLAB/Simulink co-simulation. Our results show the feasibility and effectiveness of the nonlinear relationships learned by NN, SVM, and GP in aiding estimation and control of the position of the end-effector of the flexible manipulator with a promising/desirable capability.

Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite their important and intriguing nature. Previous studies on jacquard fabrics are of simple patterns. This paper introduces a new and novel field in fabrics defect detection. Complex-patterned jacquard fabrics are much more challenging. In this paper, novel defect detection models for jacquard-patterned fabrics are presented. Owing to the lack of available databases for jacquard fabrics, we compiled and experimented on our own novel dataset. Our dataset was collected from plain, undyed jacquard fabrics with different complex patterns. In this study, we used and tested several deep learning models with image pre-processing and convolutional neural networks (CNNs) for unsupervised detection of defects. We also used multispectral imaging, combining normal (RGB) and near-infrared (NIR) imaging to improve our system and increase its accuracy. We propose two systems: a semi-manual system using a simple CNN network for operation on separate patterns and an integrated automated system that uses stateof-the-art CNN architectures to run on the entire dataset without prior pattern specification. The images are preprocessed using contrast-limited adaptive Histogram Equalization (CLAHE) to enhance their features. We concluded that deep learning is efficient and can be used for defect detection in complex patterns. Proposed method of EfficientNet CNN gave high accuracy reaching 99% approximately. We also found that multispectral imaging is more advantageous and yields higher accuracy

In this paper, we propose a frequency-reconfigurable antenna structure consisting of a dielectric resonator (DR) topped by a superstrate material. Two metasurfaces (MSs) are placed upon the DR and the superstrate, where these two MSs are utilized to synthesize a localized reduction of the dielectric constant of the DR. By placing switches into one of the MSs, the distribution of dielectric constant of the DR can be switched to one of two predefined distributions, which is equivalent to switching the DR length to two different lengths. Consequently, the frequency response of the proposed structure can be tuned to one of two operating bands. The excited modes inside the proposed antenna were obtained analytically and through simulations. Also, the dielectric constant value of substrates topped by MSs was analyzed. 

A breast cancer hyperthermia system has been developed for the targeted of increasing hyperthermia therapy efficiency after lumpectomy surgery with the assistance of fat grafting technique. Enhancing the efficiency of hyperthermia treatment will decrease the required radiation doses after surgery. A fat grafting technique is applied around the tumor region that is located into glandular tissue for the purpose of enhancing wave penetration through glandular tissue. The proposed hyperthermia system employs a single horn antenna operating at 2.45 GHz. Here, a modified hyperthermia system was tested on a hemisphere phantom of two different densities with various tumor sizes at depth 2.8 cm from skin layer. Transmission and reflection coefficients of applied electromagnetic waves at the air‐phantom interface were calculated analytically.