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We introduce a novel semi-supervised video segmentation approach based on an efficient video representation, called as “super-trajectory”. Each super-trajectory corresponds to a group of compact trajectories that exhibit consistent motion patterns, similar appearance and close spatiotemporal relationships. We generate trajectories using a probabilistic model, which handles occlusions and drifts in...
Nowadays, life unfolds in a digitised world, in which, each person can have access to a huge amount of information through the use of Internet. In this situation, most of daily activities are being influenced by a new kind of society that allows ubiquitous and instantaneous interaction among its members. The creation of social platforms (SPs) has strengthened human relationships at such point that...
Partial discharge (PD) in power transformer degrades the dielectric insulation and results in insulation failure and breakdown after a long period. In fact, a partial discharge detector can gather signals from two or more PD sources which increase the difficulty on pattern recognition and insulation state assessment. How to separate multiple PD signals is meaningful to subsequent data processing....
Monitoring energy consumption and diagnosing abnormal behavior will enable utilities to introduce strategies to improve system resiliency, stability, and to meet energy efficiency targets. The deployment of advanced metering infrastructure (AMI) enables utilities to collect various raw data from its customers and networks. This paper presents contextual anomaly detection algorithm to detect irregular...
IoT systems deployed in industrial and smart factory settings generate large volumes of data at high velocity. Context awareness is mandatory for knowledge discovery and actionable insights from such high-velocity, high-volume IoT data streams. Changes to the context of a data stream are represented in the underlying data distribution. Research in concept drift aims to detect and adapt to such changes...
The world is witnessing a remarkable increase in the usage of video surveillance systems. Besides fulfilling an imperative security and safety purpose, it also contributes towards operations monitoring, hazard detection and facility management in industry/smart factory settings. Most existing surveillance techniques use hand-crafted features analyzed using standard machine learning pipelines for action...
Coverage issue in directional sensor networks (DSNs) is different from traditional omni-directional wireless sensor networks (WSNs) due to the limited angle of view and adjustable working direction. This paper sets up a model of monitoring area and a model of sensing area according to the unique characteristics of directional sensor and then derives an optimization model for area coverage ratio maximization...
The role of machine vision detection technology as the core component in the surface mount technology devices (SMDs) cannot be denied. Single in-line package chip is an important type in IC chips and its leads forms varies a lot. This paper proposes an efficient method to soft measure single in-line package chips position and orientation. Chip image is taken by the industrial camera in the form of...
Currently, the demand on portable medical electronic systems are increasing, for they provide services and information to both patients and doctors that the traditional medical methods cannot achieve. However, the development of portable medical electronic systems has many limitations, i.e., one has to find a sweet spot among performance, power consumption, size, and cost. For instance, if one only...
As an effective method in dealing with the massive data, the serial processing, aiming to obtain the useful information quickly, cannot satisfy our calculation requirements with high-performance. However, both distributed computing and parallel computing are good choices in calculating high-volume data with high-performance. As a parallel computing framework based on memory computing large data, Spark...
This paper presents a comparison between custom fixed-point (FxP) and floating-point (FlP) arithmetic, applied to bidimensional K-means clustering algorithm. After a discussion on the K-means clustering algorithm and arithmetic characteristics, hardware implementations of FxP and FlP arithmetic operators are compared in terms of area, delay and energy, for different bitwidth, using the ApxPerf2.0...
In the training of the radial basis function network (RBFN), feature selection and classifier design are two tasks commonly addressed in separated processes. The former is related to the number of input nodes, whereas the latter is associated with the design of the hidden layer. Hence, this paper presents an algorithm to train a RBFN based on differential evolution (DE), which simultaneously adjusts...
Data is becoming more and more valuable as technology advances. Through crowdsourcing organizations are able to collect large amounts of data at an effective rate with little cost. This paper proposes a crowdsourcing-based music playing system, where the next song to play is determined by the listening preferences of realtime online users. Unlike conventional radio play system, where songs are randomly...
We study the classic k-median and k-means clustering objectives in the beyond-worst-case scenario. We consider three well-studied notions of structured data that aim at characterizing real-world inputs:• Distribution Stability (introduced by Awasthi, Blum, and Sheffet, FOCS 2010)• Spectral Separability (introduced by Kumar and Kannan, FOCS 2010)• Perturbation Resilience...
Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best known algorithm for k-means with a provable guarantee is a simple local search heuristic yielding an approximation guarantee of 9+≥ilon, a ratio that is known to be tight with respect to such methods.We overcome this barrier...
Clustering is an important tool for analyzing gene expression data. Many clustering algorithms have been proposed for the analysis of gene expression data. In this article we have clustered real life gene expression data via K-Means which is one of clustering algorithms. Also, we have proposed a new method determining the initial cluster centers for K-means. We have compared results of our method...
Clustering is an important unsupervised data analysis technique, which divides data objects into clusters based on similarity. Clustering has been studied and applied in many different fields, including pattern recognition, data mining, decision science and statistics. Clustering algorithms can be mainly classified as hierarchical and partitional clustering approaches. Partitioning around medoids...
We consider the problem of efficiently learning mixtures of a large number of spherical Gaussians, when the components of the mixture are well separated. In the most basic form of this problem, we are given samples from a uniform mixture of k standard spherical Gaussians with means mu_1,...,mu_k in R^d, and the goal is to estimate the means up to accuracy δ using poly(k,d, 1/δ)...
A tight criterion under which the abstract version Lovász Local Lemma (abstract-LLL) holds was given by Shearer [41] decades ago. However, little is known about that of the variable version LLL (variable-LLL) where events are generated by independent random variables, though variable- LLL naturally models and is enough for almost all applications of LLL. We introduce a necessary and sufficient...
A new training scheme for neural-network-based controller for power electronics systems is proposed. It utilizes the circuit model of the power conversion stage (PCS) in the training process. The training algorithm is a distributed form of evolutionary computation, being able to run on a computer cluster equipped with multiple graphics processing units (GPUs). As a design example, a boost converter...
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