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In order to improve the posture estimation accuracy of small ship, a regularized particle filter based on multidimensional autoregressive model (MARM) is proposed in this paper. The small ship has the characteristics of nonlinear stochastic dynamical systems. Particle filter algorithm has been proposed to deal with nonlinear problems for many years. Although it is effective, two problems often arise:...
This paper proposes a novel approach to global localization using high-level features. The new probabilistic framework enables to incorporate uncertain localization cues into a probability distribution that describes the likelihood of the current robot pose. We use multiple triplets of planes segmented from RGB-D data to generate this probability distribution and to find the robot pose with respect...
This paper proposes a new TDOA estimation based on phase-voting cross correlation and circular standard deviation. Based on phase delay and kernel function, the proposed method generates a probability density function (PDF) of TDOA for each frequency bin. TDOA estimate is determined by voting the PDFs generated for all frequency bins. Peak positions of the bin-wise PDFs for the target signal are concentrated...
In this paper, we describe a set of robust algorithms for group-wise registration using both rigid and non-rigid transformations of multiple unlabelled point-sets with no bias toward a given set. These methods mitigate the need to establish a correspondence among the point-sets by representing them as probability density functions where the registration is treated as a multiple distribution alignment...
We propose a block-based sampling method (I-sampling) which randomly selects the base data blocks from the block pool of a large-scale dataset rather than directly chooses records from the original dataset. I-sampling firstly partitions the given large-scale dataset into the non-overlapping primary data blocks. Secondly, the records in each primary data block are randomly shuffled and the corresponding...
Large-scale convolutional neural network (CNN), conceptually mimicking the operational principle of visual perception in human brain, has been widely applied to tackle many challenging computer vision and artificial intelligence applications. Unfortunately, despite of its simple architecture, a typically-sized CNN is well known to be computationally intensive. This work presents a novel stochastic-based...
Conventional yield optimization approaches rely on accurate yield estimation for given design parameters, which would be computational intensive. In this paper, a novel Bayesian yield optimization approach is proposed for analog and SRAM circuits. An equivalent problem is formulated via applying Bayes' theorem on the augmented yield problem. The yield optimization problem is converted to identifying...
Intelligent Transportation System (ITS) uses traffic data gathered by crowdsensing technology, which can easily get vast amounts of data from ordinary people's mobile devices, to ease congestion. However, crowdsensing also highlights the problem that the abnormal data, which we often call as outliers, may be collected for analyzing and then decrease the performances of ITS. To deal with this problem,...
Data can encapsulate different object groupings in subspaces of arbitrary dimension and orientation. Finding such subspaces and the groupings within them is the goal of generalized subspace clustering. In this work we present a generalized subspace clustering technique capable of finding multiple non-redundant clusterings in arbitrarily-oriented subspaces. We use Independent Subspace Analysis (ISA)...
This paper is concerned with the input design for Kernel-Based system identification methods. It proposes a method for input design which maximizes the information obtained through experiment based on a prior information on the target systems. The mutual information is adopted as such an information measure, and its closed form expression is obtained in terms of the kernel matrix, which expresses...
In this paper, we provide an anisotropic statistical-based algorithm for the removal of noise from triangular meshes. We show that the probability density function (pdf) of the real noise data, that takes into account the heavy-tailed outliers characterizing noise, can be estimated by the Generalized Gaussian distribution(GGD). We use the structure tensor to identify the covariance matrix of the GG...
Most Wi-Fi based localization algorithms are cooperative as user device is required to associate with an AP. However, user may not associate with AP in scenarios such as supermarkets which calls for non-cooperative localization. In this paper, the probe request (PR) frame sent by device is analyzed and the weighted kernel density estimation assisted Bayes (w-KAB) algorithm is utilized for localization...
Images with weak contrast, overlapped noise and texture of the object and background make many PDE based methods disabled. To address these problems, this paper presents a novel combined multi-scale variational framework level set segmentation model. Its level set formulation consists edge-based term, region-based term and shape constraint term. The edge-based term is constructed using a newly defined...
Survival information potential (SIP) is defined by the survival distribution function instead of the probability density function (PDF) of a random variable. SIP can be used as a risk function equipped with learning error compensation ability while this SIP based risk function does not involve the estimation of PDF. This is desirable for a robust learning application in view of the error compensation...
This paper studies how to sample load more realistically and efficiently for security constraint unit commitment (SCUC) problems in order to achieve a high degree of robustness of the unit commitment (UC) solution. For example, given the UC solution, 95% of load profiles can be supplied. Principal component analysis (PCA) is introduced to find a clear feature of the historical load in two-dimensional...
We look at the neural network as a non-linear probability density function (pdf) transformer by stochastic learning cumulative (SLC) technique. We formulate a potential function that drives a neural network to non-linearly transform the input pdf to the desired pdf. We show the working of the algorithm using synthetic data drawn from three different pdfs and estimate the parameters of the distributions...
While the transmission voltage levels are increasing higher and higher to meet the needs of industrial automation in China, flashover of insulators are seriously threaten the reliability of power system. Leakage Current (LC) is one of the most important characteristics of flashover on high voltage insulators, thus the prediction of LC near to flashover offers significant information to prevent flashover...
Service performance degradation and downtimes are a common on the Internet today. Many on-line services (e.g. Amazon.com, Spotify, and Netflix, etc.) report huge loss in revenue and traffic per episode. This is perhaps due to the correlation between performance and end-users's satisfaction.
The paper presents an assessment of the reliability of medium voltage networks within a power company. Reliability of power supply in medium voltage networks is one of the commonly recognized targets of Smart Grid. Novel approaches are needed for evaluating the reliability of electricity distribution and the reliability of supply in distribution network planning. This paper presents a stochastic supply...
Intrusion behavior and detection analysis particularly rely upon the type of data. Most of the datasets used in intrusion analysis are heterogeneous and imbalanced data sets. In these data sets, the features vary with a huge difference in between and within the feature values. This is very effective while taking decision, especially in the supervised learning. To analyze the intrusion problem, support...
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