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Using the backstepping design, we achieve exponential stabilization of the coupled Saint-Venant-Exner (SVE) PDE model of water dynamics in a sediment-filled canal with arbitrary values of canal bottom slope, friction, porosity, and water-sediment interaction under subcritical or supercritical flow regime. This model consists of two rightward and one leftward convecting transport Partial Differential...
Accurate forecasts of future climate with numerical models of atmosphere and ocean are of vital importance. However, forecast quality is often limited by the available computational power. This paper investigates the acceleration of a C-grid shallow water model through the use of reduced precision targeting FPGA technology. Using a double-gyre scenario, we show that the mantissa length of variables...
ALMA is a revolutionary instrument in its scientific concept, its engineering design and its organisation as a global effort. ALMA and new incoming radio-telescopes delivery big amounts of data that are useful to the sky image reconstruction. In this context, MEM is one of the most recognized reconstruction algorithms in radio-interferometry and is based on a Bayesian approach. Our results show that...
This paper introduces a novel method to conserve the shape of smoke simulation based on fast Fourier transform. Through the advection step of simulating Navier-Stokes equation, semi-Lagrange method loses the high frequency part of fluid, since the interpolation method is equal to low-pass filter, which causes the shape of fluids variable in different resolution. The method consists of the dissipation...
Human tracking in crowded scenes is a challenging problem because occlusion is frequently occurred. In this paper, we propose an online human tracking method which can handle occlusion effectively. Our method automatically changes a learning rate for updating tracking model according to the situation. If the tracking target is under occlusion, the learning rate decreases to reduce the influence of...
Finding the bottlenecks in the execution of a kernel in a GPU is essential to improve the performance of the implementation. Although there are several expertise techniques such as Assess, Parallelize, Optimize, Deploy (APOD), proposed by NVIDIA, the use of those techniques in computationally expensive algorithms such as Reverse Time Migration (RTM) is not an option. To solve this problem several...
The research is aimed at developing algorithms for the construction of automated systems to control active components of the electrical network. The construction of automated systems intended for the control of electric power systems requires high-speed mathematical tools. The method applied in the research to describe the object of control is based on the universal approach to the mathematical modelling...
We present the source-to-source TRACO compiler allowing for increasing program locality and parallelizing arbitrarily nested loop sequences in numerical applications. Algorithms for generation of tiled code and extracting synchronization-free slices composed of tiles are presented. Parallelism of arbitrary nested loops is obtained by creating a kernel of computations represented in the OpenMP standard...
Multi-output regression estimation aims at mining a vector-valued function from multi-dimensional input vector to multi-dimensional output vector. However, the output variables may be correlative. It is desirable to develop a multi-dimensional regression model taking advantage of the possible correlations. Therefore, this paper proposes a novel multi-output support vector regression model via double...
To deal with inference and reasoning problems, Gaussian process has been considered as a promising tool due to the robustness and flexibility features. Especially, solving the regression and classification, Gaussian process coupling with Bayesian learning is one of the most appropriate supervised learning approaches in terms of accuracy and tractability. Unfortunately, this combination tolerates high...
Traditionally, programmers and software tools have focused on mapping a single data-parallel kernel onto a heterogeneous computing system consisting of multiple general-purpose processors (CPUS) and graphics processing units (GPUs). These methodologies break down as application complexity grows to contain multiple communicating data-parallel kernels. This paper introduces MKMD, an automatic system...
Performance modeling can be utilized in a number of scenarios, starting from finding performance bugs to the scalability study of applications. Existing dynamic and static approaches for automating the generation of performance models have limitations for precision and overhead. In this work, we explore combination of a number of static and dynamic analyses for life-long performance modeling and investigate...
Designed with the goal of mimicking key features of real HPC workloads, mini-apps have become an important tool for co-design. An investigation of mini-app behavior can provide system designers with insight into the impact of architectures, programming models, and tools on application performance. Mini-apps can also serve as a platform for fast algorithm design space exploration, allowing the application...
A shift is underway in high performance computing (HPC) towards heterogeneous parallel architectures that emphasize medium and fine grain thread parallelism. Many scientific computing algorithms, including simple finite-differencing methods, have already been mapped to heterogeneous architectures with order-of-magnitude gains in performance as a result. Recent case studies examining high-resolution...
Deformable structures are abundant in various domains such as biology, medicine, life sciences, and ocean engineering. Our previous work created a numerical method, named LBM-IB method [1], to solve the fluid-structure interaction (FSI) problems. Our LBM-IB method is particularly suitable for simulating flexible (or elastic) structures immersed in a moving viscous fluid. Fluid-structure interaction...
The paper presents an informational technology for improving the reliability of nonlinear dynamic objects fault diagnosing using model-based diagnostics method of nonparametric identification. Diagnostic models build on the base of Volterra kernels moments. The efficiency of the proposed diagnostic models analyzes on an nonlinear dynamic objects simulation model.
The paper presents an informational technology for improving the reliability of nonlinear dynamic objects fault diagnosing using model-based diagnostics method of nonparametric identification. Diagnostic models build on the base of Volterra kernels moments. The efficiency of the proposed diagnostic models analyzes on an nonlinear dynamic objects simulation model.
Designers of the upcoming digital-centric More-than-Moore systems are lacking a common design and simulation environment able to efficiently manage all the multi-disciplinary aspects of its components of various nature that closely interact with each other. A key to successful design and verification lies in a SystemC-based virtual prototyping environment that is able to simulate a complex heterogeneous...
Nowadays, one of the main challenges in marketing is connecting consumers with brands, and of course with products. This connection could be possible when there are involved feelings, emotions and sensations of the consumer, to find them, there are techniques such as focus groups, projective methods or personal interviews, among other. Despite the progress of these techniques in recent years by psychologists...
This paper proposes a novel kernel-based mixture of experts model for linear regression. The method is novel in that it formulates the mixture of experts model for linear regression so that kernel functions can be used. This allows the method to work directly in terms of kernels and avoids the explicit introduction of the feature vector, allowing one to use feature spaces of high, even infinite dimensionality...
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