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Genes that share transcription factors are biologically driven to show a more likely measurable correlation in their gene expression. No modern method of visualization displays these intricate co-expression and correlation patterns better than a graph. Structural observations about a co-expression graph can reveal the secrets of the biological system that it models, but experimentally validated co-expression...
Emergency decision itself is a scientific and complicated process, clarifying scarcity factors' influence on individual decision-making risk behaviors can help decision-making makers make a scientific decision. From scarcity factor in emergency, this study analyzes the decision-making risk behaviors, then puts forward the research hypotheses, Based on the questionnaire data, the research hypotheses...
This paper investigates the uncertainty of the day-ahead distribution system scheduling considering the random variations of both Photovoltaic-based distributed generator (PV-DG) output power and load. Instead of Monte-Carlo simulation (MCS), a two-point estimation method (2PEM) is applied to obtain accurate and computation-efficient analysis results. Based on the two-year real-world hourly weather...
This paper investigates the various aspects of the capacity value of photovoltaic systems (PV-systems) and the effect of their penetration levels on power system reliability. Unlike wind power, the available output power of PV-systems can be classified into two specific time periods: certainly unavailable during the night and uncertainly available during the day. In other words, during the night,...
In the course of the scheduling of a project we always have to face uncertainties in the planned durations of the individual activities. The most frequently used method to handle this situation, is PERT. But PERT offers some detriments like the systematic underestimation of the real risk. Therefore a further development led to the “critical field” approach, which implies the use of Monte Carlo simulation...
This paper presents the application of runs test for indirect consideration of observation's autocorrelation in estimation of a standard uncertainty of arithmetic mean value. At first stage researches were performed by Monte Carlo (MC) simulation for two kind's random signals: first order autoregression (AR) and moving averaging (MA). Comparison of theoretical values of effective number of observations...
The Dempster-Shafer evidence combination method will appear inconsistent conclusions for the conflict evidence. One new universal evidence combination method was proposed. According to the concept of the Pearson correlation coefficient. Evidence distances which represent the conflict degree were calculated, and then the weight coefficient were further converted. The evidences probability were redistributed...
Distributed energy resources (DER) systems introduce uncertainties in the electrical grid that cannot be addressed by classical deterministic methods. Power system analytic tools, such as Load Flow (LF), should be revisited to address such uncertainties. Probabilistic Load Flow (PLF) provides a solution to this problem by handling these uncertainties as random variables. Among the existing sampling...
The prediction of the movement of a floating object in the ocean, such as an iceberg, is a challenging problem. Large uncertainties in the driving forces and possibly in the geometry of the object itself prevent accurate forecasts. However, if observations of the past trajectory of the object are available the forecast can be improved considerably. This article proposes an adaptive data-driven forecast...
The purpose of the present study is to empirically investigate whether national culture has an impact on cybersecurity development. We used methods of correlation and hierarchical regression to analyse two sets of indices; the global cybersecurity index of 2015 and Hofstede cultural dimension index. The research discovered that there exist a significant correlation between cybersecurity development...
Decision theoretic rough set is a typical generalization model of rough set, which has fault tolerance based on Bayes minimum decision risk. How to mine knowledge from the information collected from different sources is one of the focuses of current artificial intelligence. From a cognitive point of view, especially from the point of granulation, this paper studies decision theory of multi-source...
Adaptive Educational Systems (AES) make use of Artificial Intelligence techniques aiming at adapting themselves to the real needs of the student, and through such provide a personalized and individualized teaching. In order for this adaptation to be successful, it is important that the system knows the level of knowledge concerning the real cognitive state of the students. In this manner, this article...
This paper provides a summary of general weighted sensor estimate Mean Square Error (MSE) fusion and recently established Minimizing Euclidean Error Estimation (MEEE) fusion. Based on the MEEE setting, we propose a general heterogeneous sensor estimation fusion method. Unlike the previous estimation fusion method, the statistical correlations between sensor estimation errors are not needed, as well...
There are two ways to improve the D-S evidence theory, the methods based on modification for Dempster rule, and the methods based on modification for original evidence sources. For modification of evidence sources, there are mainly two methods: discounting factor method and weighted average method. Although the weighted average method has better focusing degree, it ignores conflicting degree of combination...
In multi-label image classification, each image is always associated with multiple labels and labels are usually correlated with each other. The intrinsic relation among labels can definitely contribute to classifier training. However, most previous studies on active learning for multi-label image classification purely mine label correlation based on observed label distribution. They ignore the mapping...
This paper investigates the capability of universal Kriging (UK), or Kriging with a trend, approximator enhanced with the efficient global optimization (EGO) method to solve expensive multi-objective design optimization problem. Engineering optimization problems typically can be well described with smooth and polynomial-like behavior, which is the main rationale to apply UK over the ordinary Kriging...
We propose a scheme to reduce monitoring uncertainties in optical networks. The proposed scheme uses monitoring data of optical connections (lightpaths) which can be obtained from coherent optical receivers that can also function as optical performance monitors (OPM). We exploit both space and time correlation of the monitoring data in order to reduce the monitoring uncertainties. The improved accuracy...
Soft sensors are used to infer the quality variable from easy-to-measure process variables. The conventional static soft sensor is incapable of handling the dynamic of processes. For data-based soft sensor development, with abundance of the raw sensor data, the problem of variable correlations and large number of sample are encountered. This work presents a latent variable model (LVM) based active...
The uncertainty quantification problem in Transcranial Magnetic Stimulation is solved by means of a realistic head model and considering the statistical correlation between grey matter and white matter electrical conductivities. The fast model order reduction approach, introduced by the authors, is extended here to the general case of correlated random parameters. Numerical results prove that correlations...
Analog circuits lack perfect accuracy; noise, PVT-Variations, non-linearities, crosstalk and many other effects cause unforeseen deviations that we also call “uncertainties”. In the paper we classify various causes of uncertainties and describe a simple, generic, mathematical model of uncertain signals and systems that is applicable from circuit level up to system level. We show in particular how...
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