Intelligent System of Limited Resource Allocation for Large-Scale Agent Systems

2016 ◽  
pp. 201-215
Author(s):  
Jakub Weclawski ◽  
Stanislaw Jankowski
Keyword(s):  
Resource Allocation ◽  
Large Scale ◽  
Intelligent System ◽  
Limited Resource ◽  
Agent Systems

Design of an Intelligent System to Support Tutors in Learning Communities Using Multi-Agent Systems and Fuzzy Logic

2015 ◽  
Vol 10 (8) ◽  
pp. 845 ◽  
Author(s):  
Youness Chaabi ◽  
R. Messoussi ◽  
V. Hilaire ◽  
Y. Ruichek ◽  
K. Lekdioui ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Learning Communities ◽  
Intelligent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Resource Allocation in Large-Scale Wireless Control Systems with Graph Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 2634-2641
Author(s):  
Vinicius Lima ◽  
Mark Eisen ◽  
Konstatinos Gatsis ◽  
Alejandro Ribeiro
Keyword(s):  
Neural Networks ◽  
Resource Allocation ◽  
Control Systems ◽  
Large Scale ◽  
Wireless Control ◽  
Graph Neural Networks

scholarly journals Resource allocation in the Covid-19 health crisis: are Covid-19 preventive measures consistent with the Rule of Rescue?

2021 ◽  
Author(s):  
Julian W. März ◽  
Søren Holm ◽  
Michael Schlander
Keyword(s):  
Resource Allocation ◽  
Large Scale ◽  
Preventive Measures ◽  
Risk Groups ◽  
Healthcare Resources ◽  
Health Crisis ◽  
Rule Of Rescue ◽  
Post War ◽  
Vaccination Campaigns ◽  
N95 Respirators

AbstractThe Covid-19 pandemic has led to a health crisis of a scale unprecedented in post-war Europe. In response, a large amount of healthcare resources have been redirected to Covid-19 preventive measures, for instance population-wide vaccination campaigns, large-scale SARS-CoV-2 testing, and the large-scale distribution of protective equipment (e.g., N95 respirators) to high-risk groups and hospitals and nursing homes. Despite the importance of these measures in epidemiological and economic terms, health economists and medical ethicists have been relatively silent about the ethical rationales underlying the large-scale allocation of healthcare resources to these measures. The present paper seeks to encourage this debate by demonstrating how the resource allocation to Covid-19 preventive measures can be understood through the paradigm of the Rule of Rescue, without claiming that the Rule of Rescue is the sole rationale of resource allocation in the Covid-19 pandemic.

Research on Modulation Multiplexing System and Technique Employing Tilted Grating

2013 ◽  
Vol 397-400 ◽  
pp. 1643-1647
Author(s):  
Hui Bo Wang ◽  
Zhi Quan Li
Keyword(s):  
Delay Time ◽  
Large Scale ◽  
Intelligent System ◽  
Simulation Analysis ◽  
Response Speed ◽  
Signal Frequency ◽  
System Response ◽  
Photodiode Array ◽  
Speed Simulation ◽  
Demodulation Method

A dual demodulation technique based on tilted grating and InGaAs photodiode array is proposed; using the coupling modes of the cladding, a wavelength demodulation method with the tilted grating as the spectroscopic device is realized. This method can achieve that the demodulation of the channel in which the sensing information changed and the optimization of collection rules of the system. Two tunable F-P filters scan and demodulate the sensing path simultaneously to further improve the system response speed. Simulation analysis and experiments results indicate that the average demodulation time is 40ms and the average signal frequency can reach 15Hz. In addition, the demodulation bandwidth is 40nm, and its wavelength demodulation precision can reach 20pm. The system has advantages of the shorter delay time, and the demodulation time is immune to the number of channels, etc.. Therefore, this system is able to meet the smart requirement of some complex systems and large scale distributed intelligent system.

Hierarchical nearly cyclic pursuit for consensus in large-scale multi-agent systems

2017 ◽  
Vol 11 (5) ◽  
pp. 740-746 ◽  
Author(s):  
Muhammad Iqbal ◽  
John Leth ◽  
Trung Dung Ngo
Keyword(s):  
Large Scale ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Cyclic Pursuit ◽  
Multi Agent

scholarly journals Yield Prediction for Tomato Greenhouse Using EFuNN

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Kefaya Qaddoum ◽  
E. L. Hines ◽  
D. D. Iliescu
Keyword(s):  
Large Scale ◽  
Management Practices ◽  
Intelligent System ◽  
Vapour Pressure Deficit ◽  
Knowledge Bases ◽  
Yield Prediction ◽  
Tomato Yield ◽  
Fuzzy Neural ◽  
Rule Sets ◽  
Human Operators

In the area of greenhouse operation, yield prediction still relies heavily on human expertise. This paper proposes an automatic tomato yield predictor to assist the human operators in anticipating more effectively weekly fluctuations and avoid problems of both overdemand and overproduction if the yield cannot be predicted accurately. The parameters used by the predictor consist of environmental variables inside the greenhouse, namely, temperature, CO2, vapour pressure deficit (VPD), and radiation, as well as past yield. Greenhouse environment data and crop records from a large scale commercial operation, Wight Salads Group (WSG) in the Isle of Wight, United Kingdom, collected during the period 2004 to 2008, were used to model tomato yield using an Intelligent System called “Evolving Fuzzy Neural Network” (EFuNN). Our results show that the EFuNN model predicted weekly fluctuations of the yield with an average accuracy of 90%. The contribution suggests that the multiple EFUNNs can be mapped to respective task-oriented rule-sets giving rise to adaptive knowledge bases that could assist growers in the control of tomato supplies and more generally could inform the decision making concerning overall crop management practices.

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