scholarly journals Voltage variations identification using gabor transform and rule-based classification method

2020 ◽  
Vol 10 (1) ◽  
pp. 681
Author(s):  
Weihown Tee ◽  
M. R. Yusoff ◽  
M. Faizal Yaakub ◽  
A. R. Abdullah
Keyword(s):  
Mathematical Model ◽  
Classification Method ◽  
Gabor Transform ◽  
Voltage Sag ◽  
Rule Based ◽  
Time Frequency ◽  
Voltage Variation ◽  
Frequency Representation ◽  
The Mathematical Model

This paper presents a comparatively contemporary easy to use technique for the identification and classification of voltage variations. The technique was established based on the Gabor Transform and the rule-based classification method. The technique was tested by using mathematical model of Power Quality (PQ) disturbances based on the IEEE Std 519-2009. The PQ disturbances focused were the voltage variations, which included voltage sag, swell and interruption. A total of 80 signals were simulated from the mathematical model in MATLAB and used in this study. The signals were analyzed by using Gabor Transform and the signal pattern, time-frequency representation (TFR) and root-mean-square voltage graph were presented in this paper. The features of the analysis were extracted, and rules were implemented in rule-based classification to identify and classify the voltage variation accordingly. The results showed that this method is easy to be used and has good accuracy in classifying the voltage variation.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

scholarly journals Enzyme-Based Biosensors: Tackling Electron Transfer Issues

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3517 ◽  
Author(s):  
Paolo Bollella ◽  
Evgeny Katz
Keyword(s):  
Mathematical Model ◽  
Electron Transfer ◽  
Fuel Cells ◽  
Glucose Oxidase ◽  
Rate Constant ◽  
Direct Electron Transfer ◽  
Redox Enzymes ◽  
Practical Applications ◽  
The Mathematical Model

This review summarizes the fundamentals of the phenomenon of electron transfer (ET) reactions occurring in redox enzymes that were widely employed for the development of electroanalytical devices, like biosensors, and enzymatic fuel cells (EFCs). A brief introduction on the ET observed in proteins/enzymes and its paradigms (e.g., classification of ET mechanisms, maximal distance at which is observed direct electron transfer, etc.) are given. Moreover, the theoretical aspects related to direct electron transfer (DET) are resumed as a guideline for newcomers to the field. Snapshots on the ET theory formulated by Rudolph A. Marcus and on the mathematical model used to calculate the ET rate constant formulated by Laviron are provided. Particular attention is devoted to the case of glucose oxidase (GOx) that has been erroneously classified as an enzyme able to transfer electrons directly. Thereafter, all tools available to investigate ET issues are reported addressing the discussions toward the development of new methodology to tackle ET issues. In conclusion, the trends toward upcoming practical applications are suggested as well as some directions in fundamental studies of bioelectrochemistry.

scholarly journals Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO BasedK-Means Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Suraj ◽  
Purnendu Tiwari ◽  
Subhojit Ghosh ◽  
Rakesh Kumar Sinha
Keyword(s):  
Motor Imagery ◽  
Execution Time ◽  
Feature Vector ◽  
Time Frequency ◽  
Real World Application ◽  
Frequency Representation ◽  
High Level ◽  
Electroencephalogram Eeg ◽  
The Brain

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO basedK-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO basedK-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) basedK-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

scholarly journals A Utilisation of Improved Gabor Transform for Harmonic Signals Detection and Classification Analysis

2017 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
M.H. Jopri ◽  
A.R. Abdullah ◽  
T. Sutikno ◽  
M. Manap ◽  
M.R. Yusoff
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Gabor Transform ◽  
Classification Analysis ◽  
Power Distribution System ◽  
Percent Correct ◽  
Time Frequency ◽  
Harmonic Signals ◽  
Threshold Setting

This paper presents a utilization of improved Gabor transform for harmonic signals detection and classification analysis in power distribution system.  The Gabor transform is one of time frequency distribution technique with a capability of representing signals in jointly time-frequency domain and known as time frequency representation (TFR). The estimation of spectral information can be obtained from TFR in order to identify the characteristics of the signals. The detection and classification of harmonic signals for 100 unique signals with numerous characteristic of harmonics with support of rule-based classifier and threshold setting that been referred to IEEE standard 1159 2009. The accuracy of proposed method is determined by using MAPE and the outcome demonstrate that the method gives high accuracy of harmonic signals classification. Additionally, Gabor transform also gives 100 percent correct classification of harmonic signals. It is verified that the proposed method is accurate and cost efficient in detecting and classifying harmonic signals in distribution system.

Probabilistic model of structural phase transition in perovskites

2021 ◽  
pp. 2150211
Author(s):  
S. H. Jabarov ◽  
R. T. Aliyev ◽  
N. A. Ismayilova
Keyword(s):  
Mathematical Model ◽  
Phase Transitions ◽  
Crystal Structures ◽  
Structural Phase ◽  
Random Variable ◽  
Structural Phase Transitions ◽  
The Moment ◽  
The Mathematical Model ◽  
First Time

In this work, the crystal structures and phase transitions of compounds with perovskite structure were investigated. The classification of structural phase transitions in perovskites was carried out, the most common crystal structures and structural phase transitions were shown. A mathematical model was constructed, a theorem was given and proved for the probability of a possible transition. The formulas [Formula: see text] and [Formula: see text] are given for the mathematical expectation and variance of random variable [Formula: see text], which is the moment when the stochastic process [Formula: see text] deviation from the boundary [0, [Formula: see text]] interval for the first time. According to the mathematical model, one of the trajectories of random processes corresponding to the phase transitions that occur in perovskites is constructed.

Sparse time-frequency representation based on multiwindow discrete Gabor transform

2018 ◽  
Vol 16 (05) ◽  
pp. 1850041
Author(s):  
Rui Li ◽  
Jian Zhou
Keyword(s):  
Frequency Spectrum ◽  
Frequency Resolution ◽  
Good Time ◽  
Gabor Transform ◽  
Analysis Tool ◽  
Sparse Regularization ◽  
Heisenberg Uncertainty Principle ◽  
Arithmetic Average ◽  
Time Frequency ◽  
Frequency Representation

The multiwindow discrete Gabor transform (M-DGT) is an important time-frequency analysis tool in many applications. In this paper, sparse time-frequency representation (TFR) based on M-DGT with sparse regularization theory is presented. The M-DGT is first formulated as a convex constrained optimization model by minimizing the objective function with a mixed [Formula: see text]–[Formula: see text] norm of the M-DGT coefficients. Then, an iterative algorithm based on the split Bergman method is utilized to compute the sparse Gabor time-frequency spectrum of the analyzed signal. According to the Heisenberg uncertainty principle, using an analysis window with good time resolution in M-DGT will lead to the Gabor TFR with high frequency resolution and vice versa. To obtain the sparse TFR with good time-frequency resolution (or concentration), the sparse spectra of M-DGT can be combined by the arithmetic average or the geometric average. Numerical experiments clearly show that the proposed method is an effective and powerful tool for analyzing nonstationary signals, by which the high time-frequency concentration (or resolution) of the Gabor time-frequency spectrum can be obtained as compared to traditional M-DGT.

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