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Design of full band differentiators of integer and non-integer/fractional order using Black Hole Optimization (BHO) algorithm is presented in this paper. The discrete models of the differentiators are obtained without using any s-domain to z-domain generating function. The average performance, pole-zero characteristics, and the error convergence are thoroughly analysed to determine the efficacy of...
This paper presents a graph-based direct approach for distribution systems with PV nodes. The contribution of this paper mainly includes two points: First, without building the bus-injection to branch-current (BIBC) matrix and the branch-current to bus-voltage (BCBV) matrix, we based on graph theory develop an uniformed formulation between bus current injections and bus voltages for both radial and...
In the paper, an improved nonsingular fast terminal sliding mode (INFTSM) guidance law with impact angle constraints is proposed. The guidance law, which employs a double power reaching law and an attractor with negative exponential factor, has a fast speed no matter far from the sliding surface or approach. For the maneuvering targets, extended state observer is designed through which the unknown...
Recurrent neural network has been widely used as auto-regressive model for time series. The most commonly used training method for recurrent neural network is back propagation. However, recurrent neural networks trained with back propagation can get trapped at local minima and saddle points. In these cases, auto-regressive models cannot effectively model time series patterns. In order to address these...
In robotics, non-linear least squares estimation is a common technique for simultaneous localization and mapping. One of the remaining challenges are measurement outliers leading to inconsistency or even divergence within the optimization process. Recently, several approaches for robust state estimation dealing with outliers inside the optimization back-end were presented, but all of them include...
In this paper, an implicit iterative algorithm is developed to obtain the unique positive definite solution of the generalized algebraic Riccati matrix equation. For this proposed algorithm, there exisits a tuning parameter which can be chosen such that this algorithm achieves better convergence performance. Some convergence results are given for the proposed algorithm. Moreover, an approach is also...
This paper studies the average consensus problem for a discrete-time multi-agent system with first-order dynamics, we provide the sufficient and necessary stable condition that the step-size needs to satisfy for the multi-agent systems with an iterative method, and the cause of oscillation is investigated with use of matrix diagonal and eigenvalue analysis method. Some conclusions about the largest...
Background: Python is one of the most popular modern programming languages. In 2008 its authors introduced a new version of the language, Python 3.0, that was not backward compatible with Python 2, initiating a transitional phase for Python software developers. Aims: The study described in this paper investigates the degree to which Python software developers are making the transition from Python...
We consider the problem of modeling data matrices with locally low rank (LLR) structure, a generalization of the popular low rank structure widely used in a variety of real world application domains ranging from medical imaging to recommendation systems. While LLR modeling has been found to be promising in real world application domains, limited progress has been made on the design of scalable algorithms...
Local community detection (or local clustering) is of fundamental importance in large network analysis. Random walk based methods have been routinely used in this task. Most existing random walk methods are based on the single-walker model. However, without any guidance, a single-walker may not be adequate to effectively capture the local cluster. In this paper, we study a multi-walker chain (MWC)...
Visible spectrum video based fire detection using non-stationary cameras has been an overlooked research problem. While many authors have successfully developed algorithms to identify and measure the proportions of uncontrolled fire using thermal or stationary surveillance cameras, the development of non-stationary systems provides a much larger application scope. We present a deep learning based...
Laser rangefinders are very popular sensors in robot localization due to their accuracy. Typically, localization algorithms based on these sensors compare range measurements with previously obtained maps of the environment. As many indoor environments are highly symmetrical (e.g., most rooms have the same layout and most corridors are very similar) these systems may fail to recognize one location...
RGB-D view registration has been widely studied by the robotics and computer vision community. The well known Iterative Closest Points (ICP) method and its variants prevail for estimating the relative pose between sensors. However, the optimization is performed locally and by consequence it can get trapped in local minima. Global registration methods have been introduced as an approach to solve the...
In this paper, we present a method for stereo super-resolution which employs a deep network. The network is trained using the residual image so as to obtain a high resolution image from two, low resolution views. Our network is comprised by two deep sub-nets which share, at their output, a single convolutional layer. This last layer in the network delivers an estimate of the residual image which is...
Maintaining the balance between convergence and diversity plays a vital role in multi-objective evolutionary algorithms (MOEAs). However, most MOEAs cannot reach a satisfying balance, especially when solving problems having complicated pareto optimal sets. In this paper, we present a modified cooperative co-evolution approach for achieving better convergence and diversity simultaneously (namely DPP2)...
In this paper, we propose an approach to distributed localization and motion control of unicycle mobile agents for the target circumnavigation problem. The bearing angle measurement-based localization performs when not all the agents get access to the target, but its performance is decided by the motion behaviors, and vice versa. Therefore we propose a coupled framework where we estimate the relative...
This article aims to the problems that the particle swarm optimization (PSO) algorithm has slow convergence and easy to fall into local optimum, provides an improved adaptive particle swarm optimization algorithm based on Levy flight mechanism (LFAPSO). The long jumps of Levy flight will step out of the local optimum in the local search. The convergence speed and accuracy of the LFAPSO algorithm are...
Orthogonal frequency division multiple (OFDM) has been introduced into long term evolution (LTE) because of its high spectral efficiency and robust anti-multipath fading ability. However, a major drawback of OFDM signals is high fluctuations of signal envelope. Peak-to-average power ratio (PAPR) is a well-known measure for the envelope fluctuations. Recently, another metric named cubic metric (CM)...
We consider a constrained multi-agent optimization problem where the bit rate of communication in the network is limited. This problem arises in a network with time-varying connectivity where all the agents try to minimize a sum of nonsmooth but Lipschitz continuous functions, and the estimates of each agent are restricted to lie in the same convex set. We design a uniform quantizer and present a...
Joint sparse representation (JSR) models have been widely applied into the field of hyperspectral image (HSI) classification. However, most of JSR-based models adopt the Frobenius norm to measure the reconstruction error, which ignores the structural information of the small patch. In this paper, we propose a nuclear-norm joint sparse representation (NuJSR) model for hyperspectral image classification...
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