On Steering Dominated Points in Hypervolume Indicator Gradient Ascent for Bi-Objective Optimization

2016 ◽  
pp. 175-203 ◽  
Author(s):  
Hao Wang ◽  
Yiyi Ren ◽  
André Deutz ◽  
Michael Emmerich
Keyword(s):  
Gradient Ascent ◽  
Hypervolume Indicator

Gradient Ascent Optimization for Fault Detection in Electrical Power System Based on Wavelet Transformation

2019 ◽  
Vol 14 ◽  
Author(s):  
Iyappan Murugesan ◽  
Karpagam Sathish
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Power System ◽  
Electrical Power ◽  
Daubechies Wavelet ◽  
Electrical Power System ◽  
Gradient Ascent ◽  
Neural Learning ◽  
Weight Value ◽  
Transmission And Distribution

: This paper presents electrical power system comprises many complex and interrelating elements that are susceptible to the disturbance or electrical fault. The faults in electrical power system transmission line (TL) are detected and classified. But, the existing techniques like artificial neural network (ANN) failed to improve the Fault Detection (FD) performance during transmission and distribution. In order to reduce the power loss rate (PLR), Daubechies Wavelet Transform based Gradient Ascent Deep Neural Learning (DWT-GADNL) Technique is introduced for FDin electrical power sub-station. DWT-GADNL Technique comprises three step, normalization, feature extraction and FD through optimization. Initially sample power TL signal is taken. After that in first step, min-max normalization process is carried out to estimate the various rated values of transmission lines. Then in second step, Daubechies Wavelet Transform (DWT) is employed for decomposition of normalized TLsignal to different components for feature extraction with higher accuracy. Finally in third step, Gradient Ascent Deep Neural Learning is an optimization process for detecting the local maximum (i.e., fault) from the extracted values with help of error function and weight value. When maximum error with low weight value is identified, the fault is detected with lesser time consumption. DWT-GADNL Technique is measured with PLR, feature extraction accuracy (FEA), and fault detection time (FDT). The simulation result shows that DWT-GADNL Technique is able to improve the performance of FEA and reduces FDT and PLR during the transmission and distribution when compared to state-of-the-art works.

scholarly journals A Robust Noise Resistant Algorithm for POI Identification from Flickr Data

2017 ◽  
Author(s):  
Yiyang Yang ◽  
Zhiguo Gong ◽  
Qing Li ◽  
Leong Hou U ◽  
Ruichu Cai ◽  
...  
Keyword(s):  
Social Media ◽  
Joint Space ◽  
Zero Crossing ◽  
Social Media Data ◽  
Local Maxima ◽  
Gradient Ascent ◽  
Data Density ◽  
Density Values ◽  
Data Points ◽  
Media Data

Point of Interests (POI) identification using social media data (e.g. Flickr, Microblog) is one of the most popular research topics in recent years. However, there exist large amounts of noises (POI irrelevant data) in such crowd-contributed collections. Traditional solutions to this problem is to set a global density threshold and remove the data point as noise if its density is lower than the threshold. However, the density values vary significantly among POIs. As the result, some POIs with relatively lower density could not be identified. To solve the problem, we propose a technique based on the local drastic changes of the data density. First we define the local maxima of the density function as the Urban POIs, and the gradient ascent algorithm is exploited to assign data points into different clusters. To remove noises, we incorporate the Laplacian Zero-Crossing points along the gradient ascent process as the boundaries of the POI. Points located outside the POI region are regarded as noises. Then the technique is extended into the geographical and textual joint space so that it can make use of the heterogeneous features of social media. The experimental results show the significance of the proposed approach in removing noises.

scholarly journals Practical pulse engineering: Gradient ascent without matrix exponentiation

2018 ◽  
Vol 13 (3) ◽  
Author(s):  
Gaurav Bhole ◽  
Jonathan A. Jones
Keyword(s):  
Gradient Ascent

A Test Case Prioritization Genetic Algorithm Guided by the Hypervolume Indicator

2020 ◽  
Vol 46 (6) ◽  
pp. 674-696 ◽  
Author(s):  
Dario Di Nucci ◽  
Annibale Panichella ◽  
Andy Zaidman ◽  
Andrea De Lucia
Keyword(s):  
Genetic Algorithm ◽  
Test Case ◽  
Test Case Prioritization ◽  
Hypervolume Indicator

A Reliable Resilient Backpropagation Method with Gradient Ascent

2006 ◽  
pp. 236-244 ◽  
Author(s):  
Xugang Wang ◽  
Hongan Wang ◽  
Guozhong Dai ◽  
Zheng Tang
Keyword(s):  
Gradient Ascent ◽  
Backpropagation Method

A gradient ascent algorithm based on possibilistic fuzzy C-Means for clustering noisy data

2021 ◽  
pp. 116153
Author(s):  
Hossein Saberi ◽  
Reza Sharbati ◽  
Behzad Farzanegan
Keyword(s):  
Noisy Data ◽  
Fuzzy C Means ◽  
Gradient Ascent
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