New Time-Frequency Transient Features for Nonintrusive Load Monitoring

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.

Wavelet Entropy Energy Measure (WEEM): A multiscale measure to grade a geophysical system's predictability

<p>Model-free gradation of predictability of a geophysical system is essential to quantify how much inherent information is contained within the system and evaluate different forecasting methods' performance to get the best possible prediction. We conjecture that Multiscale Information enclosed in a given geophysical time series is the only input source for any forecast model. In the literature, established entropic measures dealing with grading the predictability of a time series at multiple time scales are limited. Therefore, we need an additional measure to quantify the information at multiple time scales, thereby grading the predictability level. This study introduces a novel measure, Wavelet Entropy Energy Measure (WEEM), based on Wavelet entropy to investigate a time series's energy distribution. From the WEEM analysis, predictability can be graded low to high. The difference between the entropy of a wavelet energy distribution of a time series and entropy of wavelet energy of white noise is the basis for gradation. The metric quantifies the proportion of the deterministic component of a time series in terms of energy concentration, and its range varies from zero to one. One corresponds to high predictable due to its high energy concentration and zero representing a process similar to the white noise process having scattered energy distribution. The proposed metric is normalized, handles non-stationarity, independent of the length of the data. Therefore, it can explain the evolution of predictability for any geophysical time series (ex: precipitation, streamflow, paleoclimate series) from past to the present. WEEM metric's performance can guide the forecasting models in getting the best possible prediction of a geophysical system by comparing different methods. </p>

The problem of active power measurement in the case of distorted waveforms

The article discusses the influence of deformed waveforms on the accuracy of power measurement in the energy meters installed by the distributors of the power system. The basic sub-logical and reliability aspects of the real energy measure are presented. The discussed issue is presented based on the analysis of two cases: deformed waveforms with a fundamental component and higher harmonic content and distorted waveforms with subharmonic and interharmonic contents.

On Approximation of Any Ordered Fuzzy Number by A Trapezoidal Ordered Fuzzy Number

In this paper, the model of imprecise quantity information is an ordered fuzzy number. The purpose of our study is to propose some methods of approximating any ordered fuzzy number using a trapezoidal ordered fuzzy number. The information ambiguity is evaluated by means of an energy measure. The information indistinctness is evaluated by Kosko’s entropy measure. We discuss the problem of approximation of an arbitrary ordered fuzzy number by the nearest trapezoidal ordered fuzzy number. This way, we can simplify arithmetical operations on the linear space of ordered fuzzy numbers. The set of feasible trapezoidal ordered numbers is limited by the combination of the following conditions: invariance of energy measure, invariance of entropy measure, and invariance of information support. Evaluating the influence of individual limits combinations on the utility of given approximations, two combinations of those restraints, recommended for use, were chosen. It was also indicated that one of the recommended approximation problems can be used only for ordered fuzzy numbers characterized by a low level of entropy. The obtained results are currently used in such multi-criterial decision making models as financial portfolio management, evaluation of negotiations offers, the fuzzy TOPSIS model, and the fuzzy SAW model.

Using Texture Analysis Image Processing Technique to Study the Effect of Microwave Plasma on the Living Tissue

The present study is a hybrid method of studying the effect of plasma on the living tissue by using the image processing technique. This research explains the effect of microwave plasma on the DNA cell using the comet score application, texture analysis image processing and the effect of microwave plasma on the liver using texture analysis image processing. The study was applied on the mice cells. The exposure to the plasma is done by dividing the mice for four groups, each group includes four mice (control group, 20, 50, 90 second exposure to microwave plasma). The exposure to microwave plasma was done with voltage 175v and gas flow on 2 with room temperature; the statistical features are obtained from the comet score images and the textural features are calculated from the texture matrix energy measure. The result shows that the plasma has a clear effect on the DNA by reaper the damage cell and affecting the liver enzyme. The microwave plasma affected the ALP (Alkaline phosphatase) enzyme Alanine amino Transferase (ALT), Aspartate amino Transferase (AST) by decreasing their value with time exposure. This has been analyzed and studier by the textural analysis.