Analysis,simulation and implementation of a novel dual bridge asymmetric cascaded multi level inverter using MGWO-PI-PWM controller

This paper proposes a novel dual bridge asymmetric cascaded multilevel inverter to produce 21 level output which requires twelve switches and three sources only when compared with the conventional topologies such as multilevel DC link (MLDCL) and dual H-bridge (DHB). The successful working of the projected topology is validated by carrying out the simulation in MATLAB/Simulink. Modified GWO based PI controller with PWM technique (MGWO-PI-PWM) is applied for controlling the switches, which results in reduced Total Harmonic Distortion (THD).A detailed analysis and comparison study of the number of switches and DC sources required to produce the twenty one level output for the proposed topology is performed and compared with the conventional MLI topologies. A prototype of the inverter is developed and its spectral quality is analyzed. The simulation results are verified experimentally.     

Reset: A Reconfigurable state encoding technique for FSM to achieve security and hardware optimality

Digital controllers are prone to side-channel and fault-insertion attacks that lead hardware security as the primary issue in its creation. On the other hand, optimal hardware design is also the prime concern while crafting a digital controller. A finite state machine (FSM) presents a novel framework for any complex digital controller, and a state assignment technique is used for its optimization. In this article, a reconfigurable state encoding technique (ReSET) is proposed for FSM to obtain security and hardware optimality. ReSET is a deterministic method, which employs algorithms such as, (a) robust quadratic sum code based state assignment, and (b) gradient-based interior point approach based state assignment. A user-defined reconfiguration factor is introduced in ReSET by which the degree of security and hardware optimality is configured for the FSM. An extensive set of experiments are executed to validate the ReSET’s feasibility, which also proves ReSET’s superiority in terms of area, computation time, power, and error masking probabilities over the state-of-art literature. To the best of author’s knowledge, ReSET has made the first successful attempt to achieve security as well as hardware optimality for an FSM by setting the reconfiguration factor.     

3DFP-FCGAN: Face completion generative adversarial network with 3D facial prior

Face completion is a domain-specific image inpainting problem. Most existing face completion methods fail to synthesize fine-grained facial structures due to the undifferentiated treatment of face images and other scene images. To handle this problem, we propose an end-to-end deep generative model based approach which makes full use of the facial prior knowledge, including 2D facial geometry priors from facial parsing maps and landmarks, as well as the 3D depth prior. We adopt a coarse-to-fine inpainting framework where the 2D facial geometry priors based on coarse faces are extracted to guide the refinement network for better planar facial textures and structures. Moreover, a novel 3D regularized reconstruction loss is proposed for the enhancement of the stereo perception of generated faces. Experimental results on two large-scale benchmarks CelebA and CelebA-HQ show that our method significantly outperforms the state-of-the-arts in generating more visually realistic and pleasing faces. Code is available at .     

A Comparison of the Performance Indices of Embedded EZ-Source Inverter Using Different PWM Techniques

Wireless Personal Communications - Many converters were proposed for boost/buck operation for power supply. But these converters have more stages due to separate boost or buck converters, as a...     

Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque

In this paper, a novel adaptive neuro-fuzzy inference system (ANFIS)-based control technique optimized by Bacterial Foraging Optimization Algorithm for speed control of matrix converter (MC)-fed brushless direct current (BLDC) motor is presented. ANFIS is considered to be one of the most promising technologies for control of electrical drives fed by MC. Optimizing the training parameters of ANFIS, to improve its performance, is still being considered by several researchers recently. Parameters of the online ANFIS controller such as learning rate (η), forgetting factor (λ) and steepest descent momentum constant (α) are optimized by using the proposed algorithm. For the purpose of comparison, proportional integral derivative controller, fuzzy logic controller, PSO-ANFIS and BAT-ANFIS are considered. Set point tracking performances of the proposed system are carried out at various operating points for an industrial BLDC motor operating at a maximum rated speed of 380 rpm and torque of 6.4 N m. Time domain specifications such as rise time, settling time, peak time, steady-state error and peak overshoot in the presence and absence of load torque disturbances are presented. Time integral performance measures such as integral square error, integral absolute error, and integral time multiplied absolute error are analyzed for various operating conditions. Speed fluctuation in the output of BLDC motor is dependent on the source current harmonics of the inverter/converter. To illustrate this, total harmonic distortion (THD) analysis is carried out for the existing PWM inverter and the proposed MC, and it is proved that MC results in reduced THD, as compared to PWM inverter. Simulation results confirm that the proposed controller outperforms the other existing control techniques under various set speed and torque conditions. Statistical analysis is effectively carried out to prove the effectiveness of the proposed controller. Experimental analysis is performed to validate the performance of the proposed control scheme.

     

Cross-modality interactive attention network for multispectral pedestrian detection

Multispectral pedestrian detection is an emerging solution with great promise in many around-the-clock applications, such as automotive driving and security surveillance. To exploit the complementary nature and remedy contradictory appearance between modalities, in this paper, we propose a novel cross-modality interactive attention network that takes full advantage of the interactive properties of multispectral input sources. Specifically, we first utilize the color (RGB) and thermal streams to build up two detached feature hierarchy for each modality, then by taking the global features, correlations between two modalities are encoded in the attention module. Next, the channel responses of halfway feature maps are recalibrated adaptively for subsequent fusion operation. Our architecture is constructed in the multi-scale format to better deal with different scales of pedestrians, and the whole network is trained in an end-to-end way. The proposed method is extensively evaluated on the challenging KAIST multispectral pedestrian dataset and achieves state-of-the-art performance with high efficiency.     

Application of Modified Bacterial Foraging Optimization algorithm for optimal placement and sizing of Distributed Generation

In this paper, a new approach in Bacterial Foraging Optimization algorithm is proposed to reduce the total power loss and to improve the voltage profile of the radial distribution systems, in the presence of Distributed Generation unit. The proposed method aims to modify the performance of the Bacterial Foraging Optimization algorithm. The test results obtained from Modified Bacterial Foraging Optimization algorithm are also compared and found to be better and in close agreement with the Bacterial Foraging Optimization algorithm. The achievability and convenience of the optimization methods proposed have been demonstrated on 12-bus, 34-bus, and 69-bus radial distribution system consisting of 11, 33, and 68 sections, respectively. MATLAB, Version 7.10 software is used for simulation.     

Oppositional krill herd algorithm for optimal location of capacitor with reconfiguration in radial distribution system

The delivery of power from sources to the consumer points is always accompanied of power losses. Basically, active losses in distribution systems can be reduced by optimal reconfigurations of the network. Optimal capacitor allocation problem in reconfigured distribution network is a challenge of researchers for several decades. This paper presents a computationally efficient methodology namely, krill herd (KH) algorithm to find optimal location of capacitor and optimal reconfiguration in order to minimize real power loss of radial distribution systems. Moreover, the opposition based learning (OBL) concept is integrated with KH algorithm for improving the convergence speed and simulation results. In order to show the usefulness and supremacy, the conventional KH and proposed oppositional KH (OKH) algorithms are tested on 33-bus and 69-bus radial distribution networks. The simulation results of the proposed methods are compared with fuzzy multi-objective approach and non dominated sorting genetic algorithm (NSGA). The solution results show that OKH technique could generate better quality solutions and better convergence characteristics than those obtained by conventional KH algorithm and other existing optimization techniques available in the literature. Results also show the robustness of the proposed methodology to solve reconfigured distribution network (RDN) problems.     

On minimal realizations of first-degree 3D systems with separable denominators

In this paper, we focus on first-degree three-dimensional (3D) causal systems which have separable denominators. Gröbner basis is applied to prove that not all first-degree 3D systems with separable denominators have minimal realizations (of order 3). This is in contrast to 2D systems with separable denominators which always admit absolutely minimal realizations. Two illustrative examples are presented.     

Joint cost and secured node disjoint energy efficient multipath routing in mobile ad hoc network

Wireless Networks - The severe resource constraints and deployment environments of mobile ad hoc networks (MANETs) pose challenges for the security and energy consumption during data transmission...