Convergence of Neutrosophic Random Variables GRANADOS, Carlos
Advances in the theory of nonlinear analysis and its applications,
03/2023, Letnik:
7, Številka:
1
Journal Article
Recenzirano
Odprti dostop
In this paper, we propose and study convergence of neutrosophic random variables. Besides, some relations among these convergences are proved. Besides, we define the notion of neutrosophic weak law ...of large number and neutrosophic central limit theorem, also some applications examples are shown.
The online monitoring data in distribution networks contain rich information on the running states of the networks. By leveraging the data, this paper proposes a spatio-temporal correlation analysis ...approach for anomaly detection and location in distribution networks. First, spatio-temporal matrix for each feeder line in a distribution network is formulated and the spectrum of its covariance matrix is analyzed. The spectrum is complex and exhibits two aspects: 1) bulk, which arises from random noise or fluctuations and 2) spikes, which represents factors caused by anomaly signals or fault disturbances. Then, by connecting the estimation of the number of factors to the limiting empirical spectral density of covariance matrices of residuals, the spatio-temporal parameters are accurately estimated, during which free random variable techniques are used. Based on the estimators, anomaly indicators are designed to detect and locate the anomalies by exploring the variations of spatio-temporal correlations in the data. The proposed approach is sensitive to the anomalies and robust to random fluctuations, which makes it possible for detecting early anomalies and reducing false alarming rate. Case studies on both synthetic data and real-world online monitoring data verify the effectiveness and advantages of the proposed approach.
This paper develops a new probabilistic optimization framework based on chance constrained programming for bi-objective optimal energy management in microgrids considering intermittent ...characteristics of wind and photovoltaic power, and customers' load profile. The proposed approach uses a method based on jointly distributed random variables to calculate the chance of meeting the load requirements while maintaining the operation cost below a present value. The framework benefits from the new improved hybrid artificial bee colony and differential evolution algorithm as the optimization technique to solve the optimal energy management of a grid-connected microgrid. A sample average approximation approach is used to verify the results of the proposed method in comparison with the scenario-based and the Monte Carlo-based stochastic programming.
Reconstructing a scene's 3D structure and reflectivity accurately with an active imaging system operating in low-light-level conditions has wide-ranging applications, spanning biological imaging to ...remote sensing. Here we propose and experimentally demonstrate a depth and reflectivity imaging system with a single-photon camera that generates high-quality images from ∼1 detected signal photon per pixel. Previous achievements of similar photon efficiency have been with conventional raster-scanning data collection using single-pixel photon counters capable of ∼10-ps time tagging. In contrast, our camera's detector array requires highly parallelized time-to-digital conversions with photon time-tagging accuracy limited to ∼ns. Thus, we develop an array-specific algorithm that converts coarsely time-binned photon detections to highly accurate scene depth and reflectivity by exploiting both the transverse smoothness and longitudinal sparsity of natural scenes. By overcoming the coarse time resolution of the array, our framework uniquely achieves high photon efficiency in a relatively short acquisition time.
We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the ...selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the selected coefficients and test whether all relevant variables have been included in the model.
Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific ...knowledge contained in physical–analytical models. The focus is on solving ill-posed inverse problems that are at the core of many challenging applications in the natural sciences, medicine and life sciences, as well as in engineering and industrial applications. This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.
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