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Simulations are becoming ever more common as a tool for designing complex products. Sensitivity analysis techniques can be applied to these simulations to gain insight, or to reduce the complexity of the problem at hand. However, these simulators are often expensive to evaluate and sensitivity analysis typically requires a large amount of evaluations. Metamodeling has been successfully applied in...
This paper studies on the Day-of-the-week effect by means of several binary classification algorithms in order to achieve the most effective and efficient decision trading support system. This approach utilizes the intelligent data-driven model to predict the influence of calendar anomalies and develop profitable investment strategy. Advanced technology, such as time-series feature extraction, machine...
We present two algorithms for fast time-domain Volterra filtering. The first algorithm computes the required products of input samples using only one multiplication per term. Since the products are explicitly computed, this algorithm can be used for adaptation as well as for filtering. The second algorithm generalizes Horner's method for polynomial evaluation and directly computes output samples without...
Many modern highly scalable scientific simulations packages rely on small matrix multiplications as their main computational engine. Math libraries or compilers are unlikely to provide the best possible kernel performance. To address this issue, we present a library which provides high performance small matrix multiplications targeting all recent x86 vector instruction set extensions up to Intel AVX-512...
In this paper, we present a data decomposition method for multi-dimensional data, aiming at realizing multi graphics processing unit (GPU) acceleration of a compute unified device architecture (CUDA) code written for a single GPU. Our multi-dimensional method extends a previous method that deals with one-dimensional (1-D) data. The method performs a sample run of selected GPU threads to decompose...
Feature extraction plays an important role in machinery fault diagnosis and prognosis. The features extracted from time, frequency and time-frequency domains are widely investigated to describe the properties of overall signal from different perspectives, seldom considering the sequential characteristic of time-series signal in which the fault information may be embedded. This paper investigates a...
PM2.5 concentration can have significant impacts on solar irradiation and thus on photovoltaic (PV) power output. This paper presents a method to model impacts of PM2.5 concentration on PV power. A non-parametric kernel density estimation is used to fit the probability distribution of PM2.5 concentration. An incremental relation between the increase of PM2.5 concentration and the decrease of solar...
Water is classified into four status of water quality, which good condition, lightly polluted, medium polluted and heavyly polluted. The classification status of water quality is very important to know the proper use and handling. Accuracy in classification of the quality status is very important, so that both of the classification algorithm K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)...
We developed a methodology to improve the cache behavior and overall performance of sparse linear algebra kernels used in graph analytics. Large scale graph processing typically has low performance because it cannot effectively use processor caches, resulting in high-latency for memory accesses. This is particularly true in a sparse linear algebra formulations of graph algorithms, which use well known...
In contrast to the network coding problem wherein the sinks in a network demand subsets of the source messages, in a network computation problem the sinks demand functions of the source messages. Similarly, in the functional index coding problem, the side information and demands of the clients include disjoint sets of functions of the information messages held by the transmitter instead of disjoint...
The computational complexity of kernel methods grows at least quadratically with respect to the training size and hence low rank kernel approximation techniques are commonly used. One of the most popular approximations is constructed by sub-sampling the training data. In this paper, we present a sampling algorithm called Enhanced Distance Subset Approximation (EDSA) based on a novel kernel function...
In this paper we propose a new regularized technique for identification of piecewise affine systems which combines the ℓ1 loss and the recently introduced stable spline kernel. This latter is used to define a quadratic penalty which embeds information on the stability of each isolated subsystem. Our procedure determines sequentially the complexity of each affine subsystem, and then its impulse response,...
In this paper we introduce a novel representation for the classification of 3D images. Unlike most current approaches, our representation is not based on a fixed pyramid but adapts to image content and uses image regions instead of rectangular pyramid scales. Image characteristics, such as depth and color, are used for defining regions within images. Multiple region scales are formed in order to construct...
This paper presents an efficient constant-time bilateral filter where constant-time means that computational complexity is independent of filter window size. Many state-of-the-art constant-time methods approximate the original bilateral filter by an appropriate combination of a series of convolutions. It is important for this framework to optimize the performance tradeoff between approximate accuracy...
In this study we address the problem of face segmentation in thumbnail images. While there have been several approaches for face detection, none performs detection in such low resolution and segmentation with pixel accuracy. In this paper, we propose convolutional segmentation networks (CSNs) that can be trained to learn segmentation of human faces. Unlike the deep classifiers such as Convolutional...
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Support vector machine (SVM) plays an important part in fault diagnosis of chemical plant, and intelligent optimization algorithms are used to optimize the SVM parameters, including the penalty parameter C and parameter g of different kernel function, to improve performance of its faults classification. To assess SVM faults classification capability based on diverse optimization algorithms and various...
In this paper, we study discrete windowed linear canonical transform. we first provide several necessary conditions for discrete windowed linear canonical transform being a frame. Then we give a sufficient condition for discrete windowed linear canonical transform being a frame. Finally, we derive a necessary and sufficient condition for discrete windowed linear canonical transform being a Riesz basis.
Fault diagnosis is an important procedure to ensure the equipment efficiency and stability. The diagnosis process is actually a pattern recognition process, and usually, the fault samples are lack of tags of fault types. In this case, the non-supervised learning method is more available, and kernel clustering is one of the most effective methods. In this paper, a novel electromagnetic particle swarm...
Short, transient radio-frequency interference (RFI) events could threaten the quality of astronomical observations made by new and planned radio telescopes such as MeerKAT, the SKA and HERA in the radio quiet reserve in South Africa. Because they are so short, often of the order of microseconds long, these events are difficult to detect and identify in the time-frequency plots typically produced by...
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