Farrukh Hassan - Academia.edu (original) (raw)
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CSIC (Consejo Superior de Investigaciones Científicas-Spanish National Research Council)
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Papers by Farrukh Hassan
2021 International Conference on Computer & Information Sciences (ICCOINS)
Structural degradation takes place in pipelines with the passage of time. Hence. The restoration ... more Structural degradation takes place in pipelines with the passage of time. Hence. The restoration of proper functioning of these pipelines requires these defects to be identified and localized. Acoustic emission (AE) is a powerful non-destructive evaluation (NDE) technique for the detection of defects. Acoustic emission signals contain a significant amount of noise. In this paper, machine learning technique has been used to accurately classify and localize the corrosion defect. Experiments were performed on a 10’’ steel pipeline to show the relationship between the location of the corrosion defect and the acoustic emission signal. The results show that by using SVR, corrosion defect can identified and localized. This method is capable of providing a reference value for the real-time pipeline monitoring being operational in status, with broad application prospects.
2021 International Conference on Computer & Information Sciences (ICCOINS)
Cloud computing is a paradigm that has already evolved. Cloud computing moved widely to microserv... more Cloud computing is a paradigm that has already evolved. Cloud computing moved widely to microservices from monoliths. The modular cloud application has gained attention for Microservices. Intensive network communication is required to call the interdependent microservices operating inside the cloud nodes. This research focuses on container-based microservices pre-distribution techniques and proposes two distribution strategies i.e. design pattern distribution and random distribution. The microservices are arbitrarily distributed to the available data centers in the random allocation method. While the microservices are clustered in the pattern distribution based on behavioral design patterns, which identify common contact patterns between entities. A custom-built modeling environment has been used to evaluate the proposed method. The findings revealed that the pre-distribution of microservices in accordance with the application architecture trend led to substantial less response time for the calls made to services hosted at geographically dispersed data centers.
2021 International Conference on Computer & Information Sciences (ICCOINS)
Structural degradation takes place in pipelines with the passage of time. Hence. The restoration ... more Structural degradation takes place in pipelines with the passage of time. Hence. The restoration of proper functioning of these pipelines requires these defects to be identified and localized. Acoustic emission (AE) is a powerful non-destructive evaluation (NDE) technique for the detection of defects. Acoustic emission signals contain a significant amount of noise. In this paper, machine learning technique has been used to accurately classify and localize the corrosion defect. Experiments were performed on a 10’’ steel pipeline to show the relationship between the location of the corrosion defect and the acoustic emission signal. The results show that by using SVR, corrosion defect can identified and localized. This method is capable of providing a reference value for the real-time pipeline monitoring being operational in status, with broad application prospects.
2021 International Conference on Computer & Information Sciences (ICCOINS)
Cloud computing is a paradigm that has already evolved. Cloud computing moved widely to microserv... more Cloud computing is a paradigm that has already evolved. Cloud computing moved widely to microservices from monoliths. The modular cloud application has gained attention for Microservices. Intensive network communication is required to call the interdependent microservices operating inside the cloud nodes. This research focuses on container-based microservices pre-distribution techniques and proposes two distribution strategies i.e. design pattern distribution and random distribution. The microservices are arbitrarily distributed to the available data centers in the random allocation method. While the microservices are clustered in the pattern distribution based on behavioral design patterns, which identify common contact patterns between entities. A custom-built modeling environment has been used to evaluate the proposed method. The findings revealed that the pre-distribution of microservices in accordance with the application architecture trend led to substantial less response time for the calls made to services hosted at geographically dispersed data centers.