The Experts below are selected from a list of 18411 Experts worldwide ranked by ideXlab platform
V. Parthasarathy - One of the best experts on this subject based on the ideXlab platform.
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An advanced Artificial Intelligence Technique for resource allocation by investigating and scheduling parallel-distributed request/response handling
Journal of Ambient Intelligence and Humanized Computing, 2020Co-Authors: R. Geetha, V. ParthasarathyAbstract:Cloud computing is an emerging technology undergoing various challenges that integrate parallel and distributed computing together. In the multi-tenant environment cloud applications can be utilized as a service. User request are enormous and therefore the attributes to be concerned about are scalability, reliability, and resource availability and server response. Utilization of software, platform and infrastructure increases in this environment paving way for resource consumption. This scenario arises various types of issues through collision, traffic jam, data loss, request dropout and delay in response. The past research provides solutions for aspects like scalability, resource allocation, scheduling, load balancing and optimized request and response handling, resource management through virtualization. The process of virtualization and migration of environment is difficult. The cost for allocating VM for a single user is less. The paper proposed a novel scheduling approach for handling unlimited incoming request with quality of service through energy and throughput. The allocated resource focus on maintaining incoming job request, request for dispatch to the server and an acknowledgement for the receipt of response. The paper provides resource allocation methodology through scheduling approaches called integrating of AI Techniques namely Genetic Algorithms (GA) and Artificial Neural Networks (ANN). The property of the request is analyzed and priorityis applied for scheduling the request using resource allocation.
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an advanced Artificial Intelligence Technique for resource allocation by investigating and scheduling parallel distributed request response handling
Journal of Ambient Intelligence and Humanized Computing, 2020Co-Authors: R. Geetha, V. ParthasarathyAbstract:Cloud computing is an emerging technology undergoing various challenges that integrate parallel and distributed computing together. In the multi-tenant environment cloud applications can be utilized as a service. User request are enormous and therefore the attributes to be concerned about are scalability, reliability, and resource availability and server response. Utilization of software, platform and infrastructure increases in this environment paving way for resource consumption. This scenario arises various types of issues through collision, traffic jam, data loss, request dropout and delay in response. The past research provides solutions for aspects like scalability, resource allocation, scheduling, load balancing and optimized request and response handling, resource management through virtualization. The process of virtualization and migration of environment is difficult. The cost for allocating VM for a single user is less. The paper proposed a novel scheduling approach for handling unlimited incoming request with quality of service through energy and throughput. The allocated resource focus on maintaining incoming job request, request for dispatch to the server and an acknowledgement for the receipt of response. The paper provides resource allocation methodology through scheduling approaches called integrating of AI Techniques namely Genetic Algorithms (GA) and Artificial Neural Networks (ANN). The property of the request is analyzed and priorityis applied for scheduling the request using resource allocation.
Moawad Abdeen - One of the best experts on this subject based on the ideXlab platform.
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simulation and prediction for energy dissipaters and stilling basins design using Artificial Intelligence Technique
Cogent engineering, 2015Co-Authors: Moawad Abdeen, Alaa E. Abdin, W AbbasAbstract:AbstractWater with large velocities can cause considerable damage to channels whose beds are composed of natural earth materials. Several stilling basins and energy dissipating devices have been designed in conjunction with spillways and outlet works to avoid damages in canals’ structures. In addition, lots of experimental and traditional mathematical numerical works have been performed to profoundly investigate the accurate design of these stilling basins and energy dissipaters. The current study is aimed toward introducing the Artificial Intelligence Technique as new modeling tool in the prediction of the accurate design of stilling basins. Specifically, Artificial neural networks (ANNs) are utilized in the current study in conjunction with experimental data to predict the length of the hydraulic jumps occurred in spillways and consequently the stilling basin dimensions can be designed for adequate energy dissipation. The current study showed, in a detailed fashion, the development process of different ...
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civil environmental engineering research article simulation and prediction for energy dissipaters and stilling basins design using Artificial Intelligence Technique
2015Co-Authors: Mostafa Ahmed, Moawad Abdeen, Alaa E. Abdin, W AbbasAbstract:3 Abstract: Water with large velocities can cause considerable damage to channels whose beds are composed of natural earth materials. Several stilling basins and energy dissipating devices have been designed in conjunction with spillways and outlet works to avoid damages in canals' structures. In addition, lots of experimental and traditional mathematical numerical works have been performed to profoundly investigate the accurate design of these stilling basins and energy dissipaters. The current study is aimed toward introducing the Artificial Intelligence Technique as new modeling tool in the prediction of the accurate design of stilling basins. Specifically, Artificial neural networks (ANNs) are utilized in the current study in conjunction with experimental data to predict the length of the hydraulic jumps occurred in spill- ways and consequently the stilling basin dimensions can be designed for adequate energy dissipation. The current study showed, in a detailed fashion, the development process of different ANN models to accurately predict the hydraulic jump lengths acquired from different experimental studies. The results obtained from implement- ing these models showed that ANN Technique was very successful in simulating the hydraulic jump characteristics occurred in stilling basins. Therefore, it can be safely
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MHD Stability of Streaming Jet Using Artificial Intelligence Technique
Journal of Mechanics, 2012Co-Authors: Moawad Abdeen, Alfaisal A. HasanAbstract:Mathematical formulation for Magnetohydrodynamic (MHD) stability of a streaming cylindrical model penetrated by varying transverse magnetic field is presented. Eigen value relation is derived and discussed analytically. In the current paper, Artificial Neural Network (ANN) model, one of the Artificial Intelligence Techniques, is developed to simulate the stability of streaming jet penetrated by magnetic field. The ANN results presented in the current study showed that ANN Technique, with less effort and time, is very efficiently capable of simulating and predicting the effect of magnetic field variation and axial exterior field on the stability of the streaming jet. The influence of magnetic field has a stabilizing effect for all short and long wavelengths. However the streaming is strongly destabilizing.
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structural investigation and simulation of acoustic properties of some tellurite glasses using Artificial Intelligence Technique
Journal of Alloys and Compounds, 2011Co-Authors: M S Gaafar, Moawad Abdeen, Samir Y MarzoukAbstract:Abstract The developments in the field of industry raise the need for simulating the acoustic properties of glass materials before melting raw material oxides. In this paper, we are trying to simulate the acoustic properties of some tellurite glasses using one of the Artificial Intelligence Techniques (Artificial neural network). The Artificial neural network (ANN) Technique is introduced in the current study to simulate and predict important parameters such as density, longitudinal and shear ultrasonic velocities and elastic moduli (longitudinal and shear moduli). The ANN results were found to be in successful good agreement with those experimentally measured parameters. Then the presented ANN model is used to predict the acoustic properties of some new tellurite glasses. For this purpose, four glass systems x Nb 2 O 5 –(1 − x )TeO 2 , 0.1PbO– x Nb 2 O 5 –(0.9 − x )TeO 2 , 0.2PbO– x Nb 2 O 5 –(0.8 − x )TeO 2 and 0.05Bi 2 O 3 – x Nb 2 O 5 –(0.95 − x )TeO 2 were prepared using melt quenching Technique. The results of ultrasonic velocities and elastic moduli showed that the addition of Nb 2 O 5 as a network modifier provides oxygen ions to change [TeO 4 ] tbps into [TeO 3 ] tps.
R. Geetha - One of the best experts on this subject based on the ideXlab platform.
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An advanced Artificial Intelligence Technique for resource allocation by investigating and scheduling parallel-distributed request/response handling
Journal of Ambient Intelligence and Humanized Computing, 2020Co-Authors: R. Geetha, V. ParthasarathyAbstract:Cloud computing is an emerging technology undergoing various challenges that integrate parallel and distributed computing together. In the multi-tenant environment cloud applications can be utilized as a service. User request are enormous and therefore the attributes to be concerned about are scalability, reliability, and resource availability and server response. Utilization of software, platform and infrastructure increases in this environment paving way for resource consumption. This scenario arises various types of issues through collision, traffic jam, data loss, request dropout and delay in response. The past research provides solutions for aspects like scalability, resource allocation, scheduling, load balancing and optimized request and response handling, resource management through virtualization. The process of virtualization and migration of environment is difficult. The cost for allocating VM for a single user is less. The paper proposed a novel scheduling approach for handling unlimited incoming request with quality of service through energy and throughput. The allocated resource focus on maintaining incoming job request, request for dispatch to the server and an acknowledgement for the receipt of response. The paper provides resource allocation methodology through scheduling approaches called integrating of AI Techniques namely Genetic Algorithms (GA) and Artificial Neural Networks (ANN). The property of the request is analyzed and priorityis applied for scheduling the request using resource allocation.
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an advanced Artificial Intelligence Technique for resource allocation by investigating and scheduling parallel distributed request response handling
Journal of Ambient Intelligence and Humanized Computing, 2020Co-Authors: R. Geetha, V. ParthasarathyAbstract:Cloud computing is an emerging technology undergoing various challenges that integrate parallel and distributed computing together. In the multi-tenant environment cloud applications can be utilized as a service. User request are enormous and therefore the attributes to be concerned about are scalability, reliability, and resource availability and server response. Utilization of software, platform and infrastructure increases in this environment paving way for resource consumption. This scenario arises various types of issues through collision, traffic jam, data loss, request dropout and delay in response. The past research provides solutions for aspects like scalability, resource allocation, scheduling, load balancing and optimized request and response handling, resource management through virtualization. The process of virtualization and migration of environment is difficult. The cost for allocating VM for a single user is less. The paper proposed a novel scheduling approach for handling unlimited incoming request with quality of service through energy and throughput. The allocated resource focus on maintaining incoming job request, request for dispatch to the server and an acknowledgement for the receipt of response. The paper provides resource allocation methodology through scheduling approaches called integrating of AI Techniques namely Genetic Algorithms (GA) and Artificial Neural Networks (ANN). The property of the request is analyzed and priorityis applied for scheduling the request using resource allocation.
Shaorong Wang - One of the best experts on this subject based on the ideXlab platform.
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Optimal planning of RDS considering PV uncertainty with different load models using Artificial Intelligence Techniques
International Journal of Web and Grid Services, 2020Co-Authors: Zia Ullah, M R Elkadeem, Shaorong Wang, Syed Muhammad Abrar AkberAbstract:This article presents the optimised planning of RDS and proposes the Artificial Intelligence Technique using hybrid optimisation combined with phasor particle swarm optimisation and a gravitational algorithm, called PPSO/GSA for optimal planning of RDS considering photovoltaic distributed generators in RDSs. The main objective is to maximise the RDS performance by optimally allocating the PV generators. The proposed PPSO/GSA is implemented and validated on 94-bus practical RDS located in Portuguese considering single and multiple scenarios of PV generators installation along with various loading conditions. The results reveal that the optimised planning of RDS enhance the system reliability in term of a substantial reduction in active power loss and yearly economic loss as well as improving system voltage profile. Moreover, the convergence characteristics, computational efficiency, and applicability of the proposed Artificial Intelligence Technique are evaluated by comparative analysis and comparison with other optimisation Techniques.
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Artificial Intelligence Technique for optimal allocation of renewable energy based dgs in distribution networks
Broadband and Wireless Computing Communication and Applications, 2019Co-Authors: Zia Ullah, M R Elkadeem, Shaorong WangAbstract:This paper proposes the Artificial Intelligence Technique based on hybrid optimization phasor particle swarm optimization and a gravitational search algorithm, called PPSO-GSA for optimal allocation of renewable energy-based distributed generators (OA-RE-DGs), particularly wind and solar power generators, in distribution networks. The main objective is to maximize the techno-economic benefits in the distribution system by optimal allocation and integration of RE-DGs into distribution system. The proposed PPSO-GSA is implemented and validated on 94-bus practical distribution system located in Portuguese considering single and multiple scenarios of RE-DGs installation. The results reveal that optimizing the location and size of RE-DGs results in a substantial reduction in active power loss and yearly economic loss as well as improving system voltage profile and stability. Moreover, the convergence characteristics, computational efficiency and applicability of the proposed Artificial Intelligence Technique is evaluated by comparative analysis and comparison with other optimization Techniques.
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BWCCA - Artificial Intelligence Technique for Optimal Allocation of Renewable Energy Based DGs in Distribution Networks
Lecture Notes in Networks and Systems, 2019Co-Authors: Zia Ullah, M R Elkadeem, Shaorong WangAbstract:This paper proposes the Artificial Intelligence Technique based on hybrid optimization phasor particle swarm optimization and a gravitational search algorithm, called PPSO-GSA for optimal allocation of renewable energy-based distributed generators (OA-RE-DGs), particularly wind and solar power generators, in distribution networks. The main objective is to maximize the techno-economic benefits in the distribution system by optimal allocation and integration of RE-DGs into distribution system. The proposed PPSO-GSA is implemented and validated on 94-bus practical distribution system located in Portuguese considering single and multiple scenarios of RE-DGs installation. The results reveal that optimizing the location and size of RE-DGs results in a substantial reduction in active power loss and yearly economic loss as well as improving system voltage profile and stability. Moreover, the convergence characteristics, computational efficiency and applicability of the proposed Artificial Intelligence Technique is evaluated by comparative analysis and comparison with other optimization Techniques.
Kallippatti Ramsamy Vadivelu - One of the best experts on this subject based on the ideXlab platform.
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Artificial Intelligence Technique based Reactive Power Planning incorporating FACTS Controllers in Real Time Power Transmission System
2014 IEEE 2nd International Conference on Electrical Energy Systems (ICEES), 2014Co-Authors: Kallippatti Ramsamy Vadivelu, Venkata G. MarutheswarAbstract:Reactive Power Planning is a major concern in the operation and control of power systems This paper compares the effectiveness of Evolutionary Programming (EP) and Differential Evolution to solve Reactive Power Planning (RPP) problem incorporating FACTS Controllers like Static VAR Compensator (SVC), Thyristor Controlled Series Capacitor (TCSC) and Unified power flow controller (UPFC) considering voltage stability. With help of Fast Voltage Stability Index (FVSI), the critical lines and buses are identified to install the FACTS controllers. The optimal settings of the control variables of the generator voltages, transformer tap settings and allocation and parameter settings of the SVC, TCSC, UPFC are considered for reactive power planning. The test and Validation of the proposed algorithm are conducted on IEEE 30-bus system and 72-bus Indian system. Simulation results shows that the UPFC gives better results than SVC and TCSC and the FACTS controllers reduce the system losses.
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Artificial Intelligence Technique based reactive power planning using FVSI
2013 International Conference on Advanced Computing and Communication Systems, 2013Co-Authors: Kallippatti Ramsamy Vadivelu, G. V. MarutheswarAbstract:This paper proposes an application of Fast Voltage Stability Index (FVSI) to Reactive Power Planning (RPP) using Artificial Intelligence Technique based Differential Evolution (DE). FVSI is used to identify the weak buses for the Reactive Power Planning problem which involves process of experimental by voltage stability analysis based on the load variation. The point at which Fast Voltage StabilityIndex close to unity indicates the maximum possible connected load and the bus with minimum connected load is identified as the weakest bus at the point of bifurcation. The proposed approach has been used in the IEEE 30-bus system. Results show considerable reduction in system losses and improvement of voltage stability with the use of Fast Voltage Stability Index for the Reactive Power Planning problem.
