scholarly journals Artificial neural network based antenna sensitivity assignments for chaotic internet service provider network architecture

2018 ◽  
Vol 7 (2.3) ◽  
pp. 14 ◽  
Author(s):  
Lolit Villanueva ◽  
Reggie C. Gustilo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Service Provider ◽  
Network Architecture ◽  
The Philippines ◽  
Internet Service Provider ◽  
The Internet ◽  
Internet Service ◽  
Access Points ◽  
Artificial Neural

The connectivity and grade of service of an Internet Service Provider (ISP) in the Philippines is observed and analysed in this research. Traditionally, the sensitivity of the antennas for wireless access points are done manually by monitoring the signal levels onsite during the installation process. Ten subscriber locations are randomly selected as test points. The connectivity of these subscribers is observed given that their sensitivities are set manually. Finally, a proposed artificial neural network algorithm is presented to improve the availability of the internet link. The proposed algorithm incorporates the random variations of the received signal levels of the internet access points and possible degradation of signals from attenuation due to rain. Experiment results show that at least 75% increase in availability is observed using the proposed algorithm during rainy events. 

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

CO-EVOLVING BUSINESS MODELS: A CASE STUDY WITH THE INTERNET SERVICE PROVIDER (ISP) INDUSTRY

2005 ◽  
Vol 01 (03) ◽  
pp. 359-369
Author(s):  
IAN FENTY ◽  
ERIC BONABEAU ◽  
JUERGEN BRANKE
Keyword(s):  
Service Provider ◽  
Business Models ◽  
Long Term Evolution ◽  
Internet Service Provider ◽  
Entire Population ◽  
The Internet ◽  
Internet Service ◽  
Term Evolution

In this paper, co-evolution is used to examine the long-term evolution of business models in an industry. Two types of co-evolution are used: synchronous, whereby the entire population of business models is replaced with a new population at each generation, and asynchronous, whereby only one individual is replaced.

A Contract Service Provider Model for Virtual Assets

2022 ◽  
Author(s):  
Thomas Hardjono ◽  
Alexander Lipton ◽  
Alex Pentland
Keyword(s):  
Business Model ◽  
Service Provider ◽  
Design Principles ◽  
End Users ◽  
Internet Service Provider ◽  
The Internet ◽  
Internet Service ◽  
Smart Contract ◽  
Service Models ◽  
The Cost

With the recent rise in the cost of transactions on blockchain platforms, there is a need to explore other service models that may provide a more predictable cost to customers and end-users. We discuss the Contract Service Provider (CSP) model as a counterpart of the successful Internet Service Provider (ISP) model. Similar to the ISP business model based on peered routing-networks, the CSP business model is based on multiple CSP entities forming a CSP Community or group offering a contract service for specific types of virtual assets. We discuss the contract domain construct which encapsulates well-defined smart contract primitives, policies and contract-ledger. We offer a number of design principles borrowed from the design principles of the Internet architecture.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

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