Browsing by Author "Heidari,A."

Now showing 1 - 3 of 3

Citation Count: 4
Cloud-based non-destructive characterization
(Elsevier, 2023) Heidari,A.; Navimipour,N.J.; Otsuki,A.
Cloud services have grown in popularity; businesses, organizations, industries, and academic institutions use cloud services such as Cloud Non-destructive Characterization Testing (CNDCT), also known as Cloud Testing (CT). Vendors compete to deliver highly reliable services, diverse requirements, and product qualities. The CT platforms can test cloud-based systems or use the cloud for testing purposes: both approaches have sparked interest in the research. Cloud testing draws many companies and sectors worldwide by offering potential solutions for managing software applications and providing convenient testing environments. Because of cloud computing, Testing as a Service (TaaS) was born. Given the capabilities of TaaS, it has created several issues and obstacles, particularly in cloud-based, non-destructive testing environments. So, this chapter reviews and addresses the obstacles and benefits of CNDCT, including a theoretical comparison between the cloud-based testing environment and traditional standard system testing. © 2024 Elsevier Inc. All rights reserved.
Citation Count: 5
A new service composition method in the cloud-based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework
(John Wiley and Sons Ltd, 2024) Vakili,A.; Jafari Navimipour, Nima; Al-Khafaji,H.M.R.; Darbandi,M.; Heidari,A.; Jafari Navimipour,N.; Unal,M.
Cloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP-hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy-saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost. © 2024 John Wiley & Sons Ltd.
Citation Count: 0
Probabilistic approach to assess and minimize the voltage violation risk in active distribution networks
(Institute of Electrical and Electronics Engineers Inc., 2024) Kenari,M.T.; Ozdemir,A.; Heidari,A.
The increasing trend in using renewable energy resources in distribution systems has encouraged system operators to find the best methods to decrease the growing uncertainty's impact on system operation. A probabilistic approach based on the combination of Monte Carlo simulation and Particle Swarm Algorithm is proposed in this paper to reduce the risk of voltage magnitude violations. Also, a novel criterion is used to assess the risk of voltage magnitude violations in distribution system operation. This index is based on providing voltage samples using a probabilistic approach. Therefore, enhancing the confidence level of voltage risk is considered an objective function in finding the optimum location of energy storage systems. The proposed approach is applied to the IEEE 33-bus test system, and the results show that two ESS units installed at appropriate locations can solve all the voltage magnitude violation problems. © 2024 IEEE.