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CANDECOMP/PARAFAC Decomposition (CPD) is one of the most popular tensor decomposition methods that has been extensively studied and widely applied. In recent years, sparse tensors that contain a huge portion of zeros but a limited number of non-zeros have attracted increasing interest. Existing techniques are not directly applicable to sparse tensors, since they mainly target dense ones and usually...
In machine learning, data augmentation is the process of creating synthetic examples in order to augment a dataset used to learn a model. One motivation for data augmentation is to reduce the variance of a classifier, thereby reducing error. In this paper, we propose new data augmentation techniques specifically designed for time series classification, where the space in which they are embedded is...
In competitive energy markets, the growing adoption of renewable energy sources such as wind energy and photovoltaics causes power grid fluctuations due to the intermittency and variability in their power output. To balance the demand and generation by renewable sources conventional thermal power plants must operate with greater flexibility in the way they increase or decrease output. Furthermore,...
Biological molecules (e.g. DNAs, RNs, proteins, etc.) do not function in isolation themselves but in a sophisticated manner of various interactions between them to carry out every biological processes in living cells. Data about these interactions are exponentially increased thanks to advanced technologies. Analyzing biological interaction networks is essential to get insights into how biological...
Most state-of-the-art graph kernels only take local graph properties into account, i.e., the kernel is computed with regard to properties of the neighborhood of vertices or other small substructures. On the other hand, kernels that do take global graph properties into account may not scale well to large graph databases. Here we propose to start exploring the spacebetween local and global graph kernels,...
Consider a microgrid with a hybrid collection of distributed energy resources (DERs) that include fossil, renewables and energy storage battery systems. Given the dynamic variability of the DERs in terms of power production, this paper presents an optimization approach for the optimal power dispatch in such a hybrid microgrid by taking into account amplitude and rate constraints on each DER. The constrained...
The paper presents an approach for tracking a variable number of objects by using a multi-layer particle filter combined with an extended Expectation Maximization (EM) clustering. The approach works on basis of binary foreground images coming from previous background subtraction. The multi-layer particle filter is an improvement of a conventional particle filter approach. It uses an introduced adaptive...
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...
Algorithm research of task scheduling is one of the key techniques in grid computing. This paper firstly describes an DAG task scheduling model used in grid computing environment, then discusses generational scheduling (GS) and communication inclusion generational scheduling (CIGS) algorithms. Finally, an improved CIGS algorithm is proposed to use in grid computing environment, and it has been proved...
This paper formulates and studies the problem of distributed filtering based on randomized gossip strategy in order to estimate the state of a dynamic system via all sensors in a network. First we introduce the randomized gossip algorithm by which the fastest averaging strategy can be obtained for a network with an arbitrary topology. Then we combine the randomized gossip algorithm with the information...
Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on...
We revisit the K Nearest Neighbors (KNN) problem in large binary datasets which is of major importance in several applied areas. The goal is to find the K nearest items in a dataset to a query point where both the query and the items lie in the Hamming cube. The problem is addressed in its online setting, that is, data items are inserted sequentially into the dataset. To accommodate efficient similarity...
The rapid growth of data transmission over digital networks, especially of delay sensitive traffic, has meant that research into improved network control and management has increased. Network domain boundaries are key points in the network where service provisioning, flow control, and management occur between organizations. This paper presents a flexible automated approach that utilizes Software Defined...
An Improved A∗ algorithm based on time window is proposed to solve conflict-free path planning problem for the Automated Guided Vehicles (AGV) in flexible manufacturing systems. Based on the planned AGV paths, conflict-free routing was received by utilizing the improved A∗ algorithm in the next AGV routing. The algorithm of improving evaluation function to introduce the turning factors not only avoids...
In this paper, we first propose a Quality of Experience (QoE) evaluation model for dynamic adaptive streaming over HTTP (DASH) services. The proposed model predicts the perceived quality of user based on segment media quality, playback continuity and perceptual quality fluctuations caused by bitrate switching. Large quantities of subjective mean-opinion-score (MOS) tests demonstrate that our QoE evaluation...
A modern web service or the Internet of Things (IoT) based service is composed of various loosely-coupled service components, called microservices, running on the cloud resource. It enables that the number of active servers be adjusted following the load fluctuation, so an efficient cloud resource allocation is required. This situation is modeled as variable-sized dynamic bin packing problem where...
In recent years, there has been growing interest in learning to rank. We considered the current state of learning to rank in information retrieval systems. We proposed an approach for learning to rank problem based on multi-criteria optimization using the method of Pareto optimization and Genetic Algorithms. The performance of the method has been investigated on test data collections, also a comparison...
Binarization is an important task in image processing. Numerous approaches to this problem are known: the thresholding algorithm, the Otsu method, etc. A promising direction in image processing is the use of fuzzy logic methods and the theory of fuzzy sets. Their use makes it possible to improve the quality of processing by providing information in a fuzzy form. Most of the existing fuzzy image processing...
This paper deals with the results on the reconstruction accuracy of the irregularly sampled discrete-time signal (DTS) with unknown sampling locations. Reconstruction is performed by means of special reconstruction algorithms based on the DTS values correction. The first algorithm is drawn on applying of the non-recursive smoothing filter (NRSF). The second algorithm is based on the local least squares...
We introduce the fast digital algorithms for the coherent demodulation of binary and four position differential phase-shift keyed signals. These algorithms can be practically implemented with the minimum number of simple arithmetic operations over the input signal period and they are is fully compatible with the modern hardware components available. We obtain the timing diagrams of the demodulators...
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