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The use of elevation changes in an undulating terrain surface can be an effective alternative for vehicle navigation in GPS-denied underwater environments, since subsea terrain elevation data can be obtained using sonar systems. The performance of terrain-referenced navigation varies significantly depending on how informative a given terrain is, however it is not straightforward to quantify the amount...
Passive optical networks are the most promising solution for access networks. Nowadays, Gigabit passive optical networks are very popular for Internet services providers. These networks have currently been limited by transmission speeds (up to 2.5 Gbit/s in downstream) which is not sufficient for all services (such as 4k video transfer etc.) because this speed is shared with all users in the network...
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...
We show that the KLS constant for n-dimensional isotropic logconcavemeasures is O(n^{1/4}), improving on the current best bound ofO(n^{1/3}√{\log n}). As corollaries we obtain the same improvedbound on the thin-shell estimate, Poincar\e constant and Lipschitzconcentration constant and an alternative proof of this bound forthe isotropic constant; it also follows that the ball walk for samplingfrom...
This study rewrote a fractional-order particle swarm optimizer algorithmic equation and used an improved uniform design method (IUDM) to find the best combination for parameters of FPSO. Compared to PSO, FPSO makes a high convergence rate. In the improved FPSO, there are 4 parameters to influence effectiveness. Uniform design is an experimental method and suitable for multiple parameters and multiple...
Quantum-behaved particle swarm optimization (QPSO) is a novel variant of particle swarm optimization (PSO), inspired by quantum mechanics. Compared with traditional PSO, the QPSO algorithm guarantees global convergence and has less number of controlling parameters. However, QPSO is likely to get trapped into a local optimum because of using a single search strategy. This paper proposes a cooperative...
The bare bones particle swarm optimization (BBPSO) is a population-based algorithm. The BBPSO is famous for easy coding and fast applying. A Gaussian distribution is used to control the behavior of the particles. However, every particle learning from a same particle may cause the premature convergence. To solve this problem, a new hierarchical bare bones particle swarm optimization algorithm is proposed...
These article shows efficiency of Problem-Based Learning (PBL) in academic performance of course “Physics I”, specifically in how learning arises across experience. For this, existing methodologies were adapted in PBL in order to generate six methodological proposals, developed during an academic semester. Impact of PBL was evident at end of semester. It was validated through non-parametric test from...
Iterative learning control is applicable to systems that make sweeps or passes through dynamics defined over a finite duration. Once each pass is complete all information generated as its dynamics evolve are available for use in designing the control action to be applied on the next sweep. The design problem is to construct a sequence of control inputs to enforce convergence to a specified reference...
This paper presents the application of runs test for indirect consideration of observation's autocorrelation in estimation of a standard uncertainty of arithmetic mean value. At first stage researches were performed by Monte Carlo (MC) simulation for two kind's random signals: first order autoregression (AR) and moving averaging (MA). Comparison of theoretical values of effective number of observations...
In this paper, a particle swarm optimization method with a new strategy for inertia weight has been considered. The author abandoned the commonly used linear inertia weight and proposed a new dynamic inertia weight based on fitness of the particles. The new weight is a function of the best and the worst fitness of the particles. The considered NIWPSO algorithm was tested on a set of benchmark functions...
With NVIDA Tegra Jetson X1 and Pascal P100 GPUs, NVIDIA introduced hardware-based computation on FP16 numbers also called half-precision arithmetic. In this talk, we will introduce the steps required to build a viable benchmark for this new arithmetic format. This will include the connections to established IEEE floating point standards and existing HPC benchmarks. The discussion will focus on performance...
Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness...
This paper carries out a large dimensional analysis of the standard regularized quadratic discriminant analysis (QDA) classifier designed on the assumption that data arise from a Gaussian mixture model. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace. Under...
The α-stable distribution is highly intractable for inference because of the lack of a closed form density function in the general case. However, it is well-established that the α-stable distribution admits a Poisson series representation (PSR) in which the terms of the series are a function of the arrival times of a unit rate Poisson process. In our previous work, we have shown how to carry out inference...
In this paper, a subgradient method is developed to solve the system of (nonsmooth) equations. First, the system of (nonsmooth) equations is transformed into a nonsmooth optimization problem with zero minimal objective function value. Then, a subgradient method is applied to solve the nonsmooth optimization problem. During the processes, the pre-known optimal objective function value is adopted to...
In this work we explored the high-order Flux Reconstruction/correction procedure via reconstruction (FR/CPR) in inviscid low Mach number flow calculation. The method presented here is designed to improve the accuracy of the solution at low Mach numbers of the compressible Euler equations. The algorithm is based on modified low dissipation numerical Euler flux functions within FR framework, and the...
In this paper, the iterative learning control problem using uniform quantizer with encoding and decoding scheme is considered, in which the system output is transformed and encoded firstly, and then transmitted back to the controller and decoded for input updating. Zero-error convergence of the output to the desired reference is realized by utilizing this updating scheme. The results are extended...
Using a semigroup framework, this paper is concerned with existence, uniqueness and regularity of weak solutions for the stochastic wave equation driven by additive noise.
In this paper, an implicit iterative algorithm is proposed to obtain the unique positive definite solution of the continuous algebraic Riccati matrix equation. In this proposed algorithm, there exists a tuning parameter which can be appropriately chosen such that the algorithm achieves better convergence performance. A sufficient condition is given for the convergence of the proposed algorithm. Moreover,...
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