scholarly journals A sharp Bombieri inequality, logarithmic energy and well conditioned polynomials

2021 ◽  
pp. 1
Author(s):  
Ujué Etayo
Keyword(s):  
Logarithmic Energy

scholarly journals An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2701 ◽  
Author(s):  
Masoud Ahmadipour ◽  
Hashim Hizam ◽  
Mohammad Lutfi Othman ◽  
Mohd Amran Mohd Radzi
Keyword(s):  
Neural Network ◽  
Shannon Entropy ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Detection Technique ◽  
Islanding Detection ◽  
Energy Entropy ◽  
Cost Parameters ◽  
Logarithmic Energy

This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

Free-energy calculation via mean-force dynamics using a logarithmic energy landscape

2012 ◽  
Vol 85 (6) ◽  
Author(s):  
Tetsuya Morishita ◽  
Satoru G. Itoh ◽  
Hisashi Okumura ◽  
Masuhiro Mikami
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
Free Energy Calculation ◽  
Energy Calculation ◽  
Force Dynamics ◽  
Logarithmic Energy

Automatic Speaker Localization and Tracking

2010 ◽  
Vol 2 (3) ◽  
pp. 15-33
Author(s):  
Siham Ouamour ◽  
Halim Sayoud ◽  
Salah Khennouf
Keyword(s):  
Correlation Method ◽  
Differential Method ◽  
Localization Task ◽  
Fusion Technique ◽  
Decision Level ◽  
Speaker Tracking ◽  
Speaker Localization ◽  
Localization And Tracking ◽  
Logarithmic Energy

This paper presents a system of speaker localization for a purpose of speaker tracking by camera. The authors use the information given by the two microphones, placed in opposition, to determine the position of the active speaker in trying to supervise the audio-visual recording. To achieve the speaker localization task, the authors have proposed and employed two methods, which are called respectively: the filtered correlation method and the energy differential method. The principle of the first method is based on the calculation of the correlation between the two signals collected by the two microphones and a special filtering. The second is based on the computation of the logarithmic energy differential between these two signals. However, when different methods are used simultaneously to make a decision, it is often interesting to use a fusion technique combining those estimations or decisions in order to enhance the system performances. For that purpose, this paper proposes two fusion techniques operating at the decision level which are used to fuse the two estimations into one that should be more precise.

scholarly journals Detection of Epileptic Seizure Using Wavelet Analysis based Shannon Entropy, Logarithmic Energy Entropy and Support Vector Machine

2018 ◽  
Vol 7 (4.10) ◽  
pp. 935
Author(s):  
Vasudha Harlalka ◽  
Viraj Pradip Puntambekar ◽  
Kalugotla Raviteja ◽  
P. Mahalakshmi
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Shannon Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Energy Entropy ◽  
Electroencephalogram Eeg ◽  
Logarithmic Energy

Epilepsy is a prevalent condition, mainly affecting the nervous system of the human body. Electroencephalogram (EEG) is used to evaluate and examine the seizures caused due to epilepsy. The issue of low precision and poor comprehensiveness is worked upon using dual tree- complex wavelet transform (DT-CWT), rather than discrete wavelet transform (DWT). Here, Logarithmic energy entropy (LogEn) and Shannon entropy (ShanEn) are taken as input features. These features are fed to Linear Support Vector Machine     (L-SVM) Classifier. For LogEn, accuracy of 100% for A-E, 99.34% for AB-E, and 98.67% for AC-E is achieved. While ShanEn combinations give accuracy of 96.67% for AB-E and 95.5% for ABC-E. These results showcase that our methodology is suitable for overcoming the problem and can become an alternate option for clinical diagnosis.  

scholarly journals Energy Stable Discontinuous Galerkin Finite Element Method for the Allen–Cahn Equation

2018 ◽  
Vol 15 (03) ◽  
pp. 1850013 ◽  
Author(s):  
Bülent Karasözen ◽  
Murat Uzunca ◽  
Ayşe Sariaydin-Fi̇li̇beli̇oğlu ◽  
Hamdullah Yücel
Keyword(s):  
Galerkin Finite Element Method ◽  
Discrete Energy ◽  
Galerkin Finite Element ◽  
Energy Functionals ◽  
Fully Discrete ◽  
Cahn Equation ◽  
Degenerate Mobility ◽  
Discontinuous Galerkin Finite Element ◽  
Energy Stable ◽  
Logarithmic Energy

In this paper, we investigate numerical solution of Allen–Cahn equation with constant and degenerate mobility, and with polynomial and logarithmic energy functionals. We discretize the model equation by symmetric interior penalty Galerkin (SIPG) method in space, and by average vector field (AVF) method in time. We show that the energy stable AVF method as the time integrator for gradient systems like the Allen–Cahn equation satisfies the energy decreasing property for fully discrete scheme. Numerical results reveal that the discrete energy decreases monotonically, the phase separation and metastability phenomena can be observed, and the ripening time is detected correctly.

Sign in / Sign up

Export Citation Format

Share Document