Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

Remote monitoring and control of pressure and flow in oil pipelines transport system using PLC based controller

Abstract: One of the major sources for the petroleum products is obtained from the seas (offshore and onshore). Here the major problem faced by the worker is maintaining the required pressure and flow till the extreme ends. In this paper, the parameters such as pressure and flow are maintained constantly by implementing control valves depends on the different pressure and flow rate of the transmitting pipe and these parameters are monitored and controlled using HMI screen. Hence the automation in pipeline transportation system is an important research area. In order to fulfill the above requirement, there is a continuing need for research on improved forms of control. Hence PLC (Programmable Logical Controller) is used to automatically regulate the flow and pressure of the petroleum product by controlling the percentage of opening of the control valves. The required set point for pressure and flow are obtained by implementing a suitable controller that regulates the pressure range and flow rate in the long transmitting concrete pipes. For this, a PLC based controller is developed and their closed loop response is identified and the simulation is carried out to ensure the performance of the controller.

Influence of ultrathin gahnite anti-reflection coating on the power conversion efficiency of polycrystalline silicon solar cell

Abstract: Current research has concentrated on the development of ZnAl2O4 (gahnite) spinel nanostructure through anti-reflection coating (ARC) material for improved power conversion efficiency (PCE) of polycrystalline silicon solar cells. Radio frequency magnetron sputtering technique was adopted to deposit transparent polycrystalline gahnite nano-microfilms at room temperature. Material deposition was performed in a pure argon atmosphere on polycrystalline silicon solar cell substrates with a coating duration of 5–45 min. The influence of gahnite spinel nanostructure-integrated coating on the efficiency of silicon solar cell was explored by investigating physical, electrical, optical characteristics and temperature distribution profiles. The synthesized ARC material has gahnite spinel crystal structure composed of two-dimensional (2D) nanosheets. Atomic force microscopy study revealed that the thickness of synthesized gahnite 2D nanosheets was about 50 nm. The resistivity of gahnite coated with the time duration of 35 (T-IV) minutes on silicon solar cell was measured to be 1.93 × 10−3 Ω cm. The nano-microfilms showed a great optical transmittance (97%) in the wavelength range of 300–800 nm. The maximum PCE of 21.27% at open atmospheric condition and 23.83% at controlled atmospheric condition had been achieved for 35 (T-IV) minutes of gahnite nano-microfilm coating and it has been proved that gahnite nano-microfilms assists the absorption of more photons on a polycrystalline silicon solar cell substrate. The results acquired indicate that the gahnite nano-microfilm is an appropriate ARC material for polycrystalline silicon solar cells to enhance the PCE.

A novel approach for classification of abnormalities in digitized mammograms

Abstract: Feature extraction is an important process for the overall system performance in classification. The objective of this article is to reveal the effectiveness of texture feature analysis for detecting the abnormalities in digitized mammograms using Self Adaptive Resource Allocation Network (SRAN) classifier. Thus, we proposed a feature set based on Gabor filters, fractal analysis, multiscale surrounding region dependence method (MSRDM) to identify the most common appearance of breast cancer namely microcalcification, masses and architectural distortion. The results of the experiments indicate that the proposed features with SRAN classifier can improve the classification performance. The SRAN classifier produces the classification accuracy of 98.44% for the proposed features with 192 images from MIAS dataset.

Intrusion detection system- An efficient way to thwart against Dos/DDos attack in the cloud environment

Abstract: One of the emerging and glooming technologies in the IT is Cloud computing where the information is permanently stored in the third party cloud servers and cached temporarily on clients that include different devices like desktops, entertainment centers, table computers, notebooks, wall computers, sensors, etc. Internet connection is the basic requirement for accessing the cloud. Virtualization technology is another technology which goes along with the cloud environment which is used most widely to reduce the cost purchasing the hardware infrastructures in organizations. Henceforth, the Cloud Computing Systems can be easily intruded by various types of cyber attacks including Denial of Serv-ice (DoS) attack and Distributed Denial of Service (DDoS) attack which utilize the entire resources like CPU, Memory, etc and makes the server to starve. This creates a major impact reducing the efficiency of the virtual machines. In the proposed method, attack can be overcome by Transmission Control Protocol (TCP) Mitigation Strategy which uses the SYN Cookie to prevent the attack in the cloud to prevent the attack in which the server ignores the connection packets when it does not receive the correct Acknowledgement (ACK) from the client which requested the connection. The server here has rules to be check whether it is a legitimate client or the spoofed one using the first layer of security for hop count filtering mechanism and second layer of security is encoding the sequence number of the SYN packet so that only a legitimate client can decode it. Additionally security is also provided for the data packets using the Message Authentication Code (MAC) and thus client is authenticated.

Recast Layer Formation during Wire Electrical Discharge Machining of Titanium (Ti-Al6-V4) Alloy

Abstract: Titanium alloys, in particularly Ti-6Al-4V alloy is used enormously in many high-tech sectors specially in aerospace industries due to its superior properties. Machining process (for example wire electrical discharge machining) to reshape this alloy affects the integrity of the newly generated surfaces. This experimental study has identified three affected layers using scanning electron microscopy on the cross section of the machined titanium (Ti-6Al-4V) alloy surface generated from wire electrical discharge machining (WEDM). This study also explained the formation mechanism of those three layers as no detail investigation is available in this area so far. It was found that the top flaky layers are formed due to the highest cooling rate at the outermost surface, which is induced due to the low thermal conductivity of the titanium alloy as well as the quenching effect because of the existence of dielectric. The recast layer is formed at a cooling rate lower than that at the outer surface, where the melted material is resolidified very quickly without having any grain boundaries. The heat-affected zone appears at a slightly different color, which does not melt but experience heat treatment during the machining process.