PSG College of Technology

About: PSG College of Technology is a based out in . It is known for research contribution in the topics: Machining & Thin film. The organization has 3174 authors who have published 3575 publications receiving 40690 citations.

Long term shoreline oscillation and changes of Cauvery delta coastline inferred from satellite imageries

Abstract: Coastal zone is highly volatile ecosystem which is always in adjustments. Loss of shore line will cause severe impact on human life and as well as their properties. Remote sensing is a reliable technique to study the historical shoreline changes. Therefore in this paper long term shoreline oscillations of Cauvery delta shorelines at Poompuhar, Tharangambadi and Nagapattinam were studied using satellite imageries and the same was physically observed at the above three locations with the help of reference pillars and compared mutually. It was observed that the shoreline at Poompuhar is under accretion at the rate of 1.79m/ year and other shoreline stretches at Tharangambadi and Nagapattinam were under erosion at 0.4888m/ year and 0.4985m/ year respectively. It was also observed that the remote sensing study qualitatively matches with the physical observation for all the three coastal stretches of the study area.

Improved hybrid segmentation of brain mri tissue and tumor using statistical features

Abstract: Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Relevant application in neuroradiology is the segmentation of MRI data sets of the human brain into the structure classes gray matter, white matter and cerebrospinal fluid (CSF) and tumor. In this paper, brain image segmentation algorithms such as Fuzzy C means (FCM) segmentation and Kohonen means(K means) segmentation were implemented. In addition to this, new hybrid segmentation technique, namely, Fuzzy Kohonen means of image segmentation based on statistical feature clustering is proposed and implemented along with standard pixel value clustering method. The clustered segmented tissue images are compared with the Ground truth and its performance metric is also found. It is found that the feature based hybrid segmentation gives improved performance metric and improved classification accuracy rather than pixel based segmentation.

An Efficient Predictive technique to Autoscale the Resources for Web applications in Private cloud

Abstract: Cloud computing refers to the delivery of computing resources over the network based on user demand. Some web applications may experience different workload at different times, automatic provisioning needs to work efficiently and viably at any point of time. Autoscaling is a feature of cloud computing that has the capability to scale the resources according to demand. Autoscaling provides better fault tolerance, availability and cost management. Although Autoscaling is beneficial, it is not easy to implement. Effective Autoscaling requires techniques to foresee future workload as well as the resources needed to handle the workload. Reactive Autoscaling strategy adds or reduces resources based on threshold set. The predictive strategy is used to address the issues like rapid spike in demand, outages and variable traffic patterns from web applications by providing necessary scaling actions beforehand. In the proposed work, Auto Regressive Integrated Moving Average (ARIMA) and Recurrent Neural Network–Long Short Term Memory (RNN-LSTM) techniques are used for predicting the future workload of based on CPU and RAM usage rate collected from three tier architecture of web application integrated in private cloud. On comparing the performance metrics of both the techniques, the RNN-LSTM deep learning technique gives the minimum error rate and can be applied on large datasets for predicting the future workload of web applications in a private cloud.

Friction stir welding of hybrid AA 6061-ZrO2-C composites FSW process optimization using desirability approach

Abstract: In the present work, friction stir welding (FSW) was employed to fabricate components made of metal matrix composites (MMC) by incorporating aluminium alloy, Zirconium Di-oxide (ZrO2) and Graphite (C) reinforcement particles into the aluminium AA6061 matrix. The weight percentage of MMC composition is 92% AA6061, 6% ZrO2, and 2% C. The stir cast MMC specimens were welded at the different FSW condition by changing the process parameters tool rotational speed, welding speed, and axial load. The desirability approach was used to optimize the FSW process parameters for simultaneous improvement of tensile strength and hardness of welded composites. From the investigation, it was found that the 190 MPa tensile strength and 50 HRB hardness with respective welding process parameters 852.82 rpm tool rotational speed,48.3 mm min−1 welding speed and 5.11 kN axial load.