The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Failure boundaries are always far away from the center of the design space of variables for low failure probability problems. In order to make full use of samples, a partitioning method of experimental levels is proposed for improving general design of experiments in this paper. The method is implemented by non-uniformly partitioning experimental levels according to the probability density function...
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,...
With the continuously increasing demand for higher bandwidth, bit rates of high speed serial links (USB, SATA, PCI-express, etc.) have reached the multi-gigabits per second. The transmitted jitter at a given bit error rate is one of the major electrical parameters used to characterize SerDes Integrated Circuit performance. The standard organizations specify RJ and DJ budgets. Decomposing the jitter...
Recently, nonsymmetric measures of dependence have started to attract attention, and several continuous entropy-like nonsymmetric dependence measures have been proposed. Based on Onicescu's information energy, we have introduced in previous work a nonsymmetric dependence measure between two discrete random variables. In the present paper, we analyze the continuous version of this measure. We deduct...
Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data have proven to be very effective. METAPHOR (Machine-learning...
Several approaches have been developed to estimate probability density functions (pdfs). The pdf has two important properties: the integration of pdf over whole sampling space is equal to 1 and the value of pdf in the sampling space is greater than or equal to zero. The first constraint can be easily achieved by the normalisation. On the other hand, it is hard to impose the non-negativeness in the...
The node localization is one of the most important research issues in wireless sensor networks (WSNs). Traditionally, hop-count-based localization methods only take on integer value and have a same distance estimation to all of a node's one-hop neighbors. In this paper, a novel approach termed hop-count-based expectation of distance (HCED) algorithm is proposed. By partitioning an anchor's neighbor...
A new method for signal estimation in the magnitude resonance imaging (MRI) which follows Rician distribution data is proposed. Sigma estimation in the MRI is importance for the various post-processing tasks. Although different methods for sigma estimation of MRI are available, most of these methods require multiple images. In this paper, Nonlinear spatial domain first order moment (NSDFOM) estimator...
In this paper, we deal with covariance matrix estimation in complex elliptically symmetric (CES) distributions. We focus on Tyler's estimator (TyE) and the well-known sample covariance matrix (SCM). TyE is widely used in practice, but its statistical behavior is still poorly understood. On the other hand, under Gaussian assumption, the SCM is Wishart-distributed, but its properties degrade in non-Gaussian...
A data-driven diagnostic approach is developed and proved successful to identify the weld breakage of the connector in battery pack according to the data-upload by the remote monitoring platform. Firstly, two modes of the weld breakage: complete breakage and loose contact, as well as their statistical patterns are illustrated. To further assure the weld breakage fault, the capacity of battery pack...
Spectrum Sensing (SS) techniques play an important role in the Cognitive Radio (CR) systems. In recent years, many spectrum sensing techniques have been proposed in the literature to identify the state of the Primary Users (PUs) in the temporal domain. However, these techniques are usually interested in the current state of channel without consideration to their status in the past. In this paper,...
Accurately estimating the probability distribution of renewable power production is a fundamental and challenging task in the probabilistic analysis of power systems with a high penetration of renewable energy. In this study, a novel hybrid method of minimum frequency and maximum entropy (MFME) is proposed for accurately and rapidly estimating the probability density function (PDF) of renewable power...
It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem. In this paper, we focus on the comparison of oneclass classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection...
In passive mobile positioning, the cell-level measurements must be translated into a geographical location, which can be expressed as spatial probability density function (SPDF). In this paper, we present the results of a study where we compared different methods to estimate SPDF of passive mobile positioning. The mobile operators provide spatial data about network cells. It is called cellplan. Cellplan...
In this paper, a novel empirical data analysis approach (abbreviated as EDA) is introduced which is entirely data-driven and free from restricting assumptions and pre-defined problem- or user-specific parameters and thresholds. It is well known that the traditional probability theory is restricted by strong prior assumptions which are often impractical and do not hold in real problems. Machine learning...
Remaining useful life (RUL) prediction is an important part of the prognostics and health management (PHM). This article presents a methodology to predict the RUL of a class of multi-component systems with hidden degradation processes. In the real industrial process, components of a system are usually in the same environment, so their degradations may be affected by a common factor which is assumed...
Dereverberation methods based on coherent-to-diffuse power ratio (CDR) estimates exploit the spatial properties of signals to suppress late reverberation components. Naturally, the quality of the CDR estimate has a major effect on the quality of the dereverberated signals. This paper presents a statistical study of the performance of state-of-the-art CDR estimators under a Gaussian signal model. The...
In this paper we propose a new machine learning concept called randomized machine learning, in which model parameters are assumed random and data are assumed to contain random errors. Distinction of this approach from “classical” machine learning is that optimal estimation deals with the probability density functions of random parameters and the “worst” probability density of random data errors. As...
Initialization is an extremely important part of the mixture estimation process. There exists a series of initialization approaches in the literature concerning the mixture initialization. However, the majority of them is directed at initialization of the expectation-maximization algorithm widely used in this area. This paper focuses on the initialization of the mixture estimation with normal components...
This paper deals with clustering non-gaussian data with fixed bounds. It considers the problem using recursive mixture estimation algorithms under the Bayesian methodology. Such a solution is often desired in areas, where the assumption of normality of modeled data is rather questionable and brings a series of limitations (e.g., non-negative, bounded data, etc.). Here for modeling the data a mixture...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.