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Primary frequency standards serve the function of calibrating the rate of International Atomic Time, TAI, and therefore play a critical role in the accuracy of the world's time. The Working Group on Primary and Secondary Frequency Standards, WGPSFS, is an advisory body to the Time Department of the Bureau International des Poids et Mesures and to the Consultative Committee for Time and Frequency on...
Uncertain data has been emerged as an important problem in database systems due to the imprecise nature of many applications. To handle the uncertainty, probabilistic databases can be used to store uncertain data, and querying facilities are provided to yield answers with confidence. However, the uncertainty may propagate, hence the returned results from a query or mining process may not be useful...
The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
In this paper the robust stabilization problem for a class nonlinear systems with incomplete state availability is addressed and solved by using a second order sliding mode approach. In previous works a discontinuous control input that guarantees the asymptotic stabilization of a class of systems expressed in the Brunowsky normal form, in which the last state variable was assumed to be not measurable,...
Ranging signals in the urban environment suffer from significant attenuation and additive errors caused by reflections. Special design considerations for ranging-based positioning systems that operate in the Urban Canyon are presented. The tradeoff between acquisition sensitivity and acquisition time is presented. The integrity of the measurements is improved by verifying that the signals exhibit...
The feasibility of guaranteeing the accuracy at filtering with substantial prior uncertainty in a class of input signals is analyzed. The class y2 containing nonstationary stochastic signals with the only known prior information — upper bounds of some derivatives is considered. The procedure of estimating the extreme attainable error dispersion at filtering the input signals of the class y2 at the...
In this paper, an Interval Type-2 neuro-fuzzy inference system based emotion recognition system is proposed. The employed fuzzy inference system is a four layer network realizing Takagi-Sugeno-Kang fuzzy inference mechanism, with an input layer, a rule layer, a normalization layer and an output layer. The rule layer employs an Interval Type-2 fuzzy membership function to handle the uncertainty in...
Accurate State Of Charge estimations are one of the critical functionalities of Battery Management Systems, whether it is for single-cell batteries or multiple-cells batteries. However, battery-SOCs are usually generalized without any consideration about cell disparities. In this presentation, we propose to define a more relevant battery-SOC by analyzing the already existing studies about SOC estimation...
This paper reports that uncertainty of detected edges on a fish-eye image depends on the direction to observe the edges. In fish-eye cameras, dimensions of observation space corresponding to a pixel greatly change with the pixel location. This dimension is defined as “spatial uncertainty” in this paper and formulated with typical projection models of fish-eye cameras. Experimental results showed that...
Any measurement process has always its inherent uncertainty due to different error sources. Identifying measurement uncertainties is important to make any measurement results reliable and credible. In this paper, 3 different induction motors were tested for efficiency using the direct method (dynamometer procedure) and a proposed algorithm of estimating full-load efficiency from only one no-load test.
Energy management has always been a crucial issue in efficient operation of energy systems. Recently, application of stochastic distributed energy resources and energy storage units in microgrids has made the energy management problem more complex. This paper focuses on the energy management of a cluster of demands, solar power stations and storage units, which are interconnected through a microgrid...
Data mining technique in the history of medical data found with enormous investigations found that the prediction of heart disease is very important in medical science. In medical history it is observed that the unstructured data as heterogeneous data and it is observed that the data formed with different attributes should be analyzed to predict and provide information for making diagnosis of a heart...
Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most "valuable"...
Low-rank matrix completion methods have been successful in a variety of settings such as recommendation systems. However, most of the existing matrix completion methods only provide a point estimate of missing entries, and do not characterize uncertainties of the predictions. In this paper, we propose a Bayesian hierarchical probabilistic matrix factorization (BHPMF) model to 1) incorporate hierarchical...
In this paper, bootstrap percolation is introduced to control the information propagation for efficient cooperative positioning in wireless networks. Particularly, we obtain a novel linear least square (LLS) estimator for the localization of agent nodes. Exploiting the idea of bootstrap percolation, agent nodes sequentially get activated and estimate their positions with an adaptive location updating...
Based on new results on ideal adaptive sliding mode control (ASMC) design for nonlinear systems with uncertainties discussed in Part I of the present contribution, we extend the new designs to the real case in this paper (Part II) by using boundary layer method and filtered rate of the sliding variable. These modifications are proposed to enhance accuracy without overestimation of the uncertainty...
Gene selection plays a crucial role in the analysis of microarray data with high dimensionality and small sample size. Incremental wrapper based feature subset selection (FSS) methods, among various feature selection approaches, tend to obtain high quality feature subset and better classification accuracy than filter methods, while it is much more time consuming since the interdependence and redundancy...
The difficulties of having expertise in expert systems, the increasing of the data volume, self adaptation and prediction, all those problems are solved in the presence of learning. The classical definition of learning in cognitive science is the ability to improve the performance as the exercise of an activity. With learning, knowledge is automatically extracted from a data set. In this paper, we...
This paper introduces a new approach for estimating the uncertainty in the forecast through the construction of Triangular Fuzzy Numbers (TFNs). The interval of the proposed TFN presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE). Here, instead of the representing the forecast with a crisp value with a Prediction Interval (PI), the level of uncertainty associated...
Cross-situational learning, the ability to learn word meanings across multiple scenes consisting of multiple words and referents, is thought to be an important tool for language acquisition. The ability has been studied in infants, children, and adults, and yet there is much debate about the basic storage and retrieval mechanisms that operate during cross-situational word learning. It has been difficult...
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