Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components. Shunt active filters (SAF) with current controlled voltage source inverters (CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents. However, CCVSI with traditional controllers have a limited transient and steady state performance. In this paper, we propose an adaptive dynamic programming (ADP) controller with online learning capability to improve transient response and harmonics. The proposed controller works alongside existing proportional integral (PI) controllers to efficiently track the reference currents in the d -q domain. It can generate adaptive control actions to compensate the PI controller. The proposed system was simulated under different nonlinear (three-phase full wave rectifier) load conditions. The performance of the proposed approach was compared with the traditional approach. We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison. The online learning based ADP controller not only reduced average total harmonic distortion by 18.41 %, but also outperformed traditional PI controllers during transients.      

Evaluation of compressive behaviour of porous structures under large deformation using micro-CT,DVC and micro-FE

Compressive behaviour of open and closed cell polyurethane foam samples under large deformation is studied using micro-Computed Tomography (micro-CT), Digital Volume Correlation (DVC) technique and micro-Finite Element (micro-FE) modelling. The micro-CT images of the foam samples at different compression strains are used to determine anisotropy in the foams, to obtain qualitative information on deformation mechanisms, to quantify the deformation and strains using a local DVC approach and to generate images for micro-FE modelling of the foam samples. Micro-FE modelling predicts the deformation using an elastoplastic material model coupled with continuum damage mechanics. Two different types of boundary conditions, experimentally derived (ExBC) and interpolated from DVC (IPBC), were implemented to evaluate the displacements in the micro-FE models. A reduced integration scheme in micro-FE analysis resulted in high artificial energy and was discarded in favour of full integration. The displacement predicted by IPBC matched with DVC displacement contours for closed cell foam. The ExBC-predicted axial displacement (W) showed a better agreement with DVC than transverse displacements (U, V) contours. However, a significant statistical comparison (R² > 0.70) of all displacements was obtained for both IPBC and ExBC. For open cell foam, both boundary conditions predicted a significant difference in the displacement contours with respect to DVC measurements. Still, the axial displacements of ExBC and IPBC showed a better statistical significance (R² > 0.70).     

1-Bit FinFET Carry Cells for Low Voltage High-Speed Digital Signal Processing Applications

Silicon - In this paper, new high speed and low voltage 1-bit FinFET full adder carry cells are proposed for multi-bit arithmetic applications used in Digital Signal Processing (DSP) architectures....     

Influence of Titanium Content on Thermal,Mechanical and Corrosion Behaviour Anomalies of Nickel-Molybdenum-Silicate Bulk Metallic Glasses

Silicon - This work discussed the correlation between structural, mechanical, and corrosion studies variation on the milling time of (Ni75Mo15Si10)100-xTix (x = 0, 3, 6, and...     

Parachute shape ultra-wideband wearable antenna for remote health care monitoring

Wireless body area networks (WBAN) is used to measure patients' health conditions continuously. Different kinds of sensors are required to measure health conditions. When such types of antennas are used on the human body, they are flexible with the movements. The usage of wearable devices is currently increasing in the biomedical field. The presented wearable antenna is suitable for biomedical applications. The presented ultra-wideband (UWB) flexible parachute shape wearable antenna is fabricated on a jeans textile substrate. The prototype antenna has a −10 dB measured impedance bandwidth of 5800 MHz (7 to 12.8 GHz) with average radiation efficiency of 75.28%. The prototype antenna's size is 40 × 40 mm² (1.32 × 1.32 ${\lambda }_0^2$ at centre frequency 9.9 GHz) and a peak gain of 4.5 dB at 12.33 GHz. The fabricated antenna is suitable for biomedical applications in X-band frequencies and can be implemented with a low-cost manufacturing process. The radiating element is made by conductive copper tape. Muscle-equivalent phantoms are used to analyze the body effect on antenna performance. The radiation effect emitted by the presented antenna on the human body is calculated by the specific absorption rate (SAR) value. The maximum SAR value of the proposed antenna is 1.84 W/kg at 12.33 GHz. This leads to promising results for wearable applications related to remote health care monitoring, such as biotelemetry and mobile health with a sensor-driven approach.     

Early Skin Disease Identification Using eep Neural Network

Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists. Skin disease is the most common disorder triggered by fungus, viruses, bacteria, allergies, etc. Skin diseases are most dangerous and may be the cause of serious damage. Therefore, it requires to diagnose it at an earlier stage, but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy. This advance therapy involves financial burden and some other ill effects. Therefore, it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage. Several techniques have been proposed to detect skin disease at an earlier stage but fail to get accuracy. Therefore, the primary goal of this paper is to classify, detect and provide accurate information about skin diseases. This paper deals with the same issue by proposing a high-performance Convolution neural network (CNN) to classify and detect skin disease at an earlier stage. The complete methodology is explained in different folds: firstly, the skin diseases images are pre-processed with processing techniques, and secondly, the important feature of the skin images are extracted. Thirdly, the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network (DCNN). The approach proposed in this paper is simple, fast, and shows accurate results up to 98% and used to detect six different disease types.     

Experimental,Numerical and Microscopic Analysis of Contaminated Clay with Precipitated Silica

Silicon - Petrol-contaminated clay (PCC) is a severe environmental risk and has a detrimental effect on the engineering properties of soil. Since precipitated silica has a powerful adsorption...