Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

Unveiling the structural, charge density distribution and supercapacitor performance of NiCo2O4 nano flowers for asymmetric device fabrication

Abstract: The flower-like nano sized nickel cobalt oxide was successfully synthesized by facial hydrothermal method. Supercapacitor [NiCo2O4)||Activated carbon] asymmetric supercapacitor device (ASC) has been fabricated. The prepared self-assembled flower-like nanostructured NiCo2O4 nanomaterials were subjected to various analytical tools such as Powder X-ray diffraction (PXRD), Scanning electron microscopy (SEM) equipped with energy dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR). Furthermore, the bond length and electron cloud density between the atoms were analyzed by maximum entropy method (MEM). The electrochemical studies were probed using Cyclic Voltammetry (CV), chronopotentiometry charge-discharge (CD) and electrochemical impedance spectroscopy (EIS) using electrochemical workstation (CHI6008e) instrument. The activated carbon was used as an anode electrode material for device fabrication. The maximum specific capacitance of NiCo2O4 NFs was achieved to be 1030 Fg−1 at 1 Ag−1 with good cyclic stability. The maximum specific capacitance of ASC device is 41 Fg−1 at 1 Ag−1. More interestingly, the maximum specific energy (10 W h kg−1) and specific power (2000 W kg−1) values are achieved for asymmetric supercapacitor energy storage device.

Minimal complexity attack classification intrusion detection system

Abstract: In general, the kind of users and the injection of network packets into the internet sectors are not under specific control. There is no clear description as to what packets can be considered normal or abnormal. If the invasions are not detected at the appropriate level, the loss to system may be some times unimaginable. Although many intrusion detection system (IDS) methods are used to detect the existing types of attacks within the network infrastructures, reducing false negative and false positives is still a major issue. In our paper an intrusion detection system is designed to classify by the incorporation of enhanced rules as learnt from the network behavior with less computational complexity of O(n). The method demonstrates the achievements of promising classification rate. The bench mark data KDD Cup99 data is used in our method.

Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification

Abstract: Cognitive impairment must be diagnosed in Alzheimer’s disease as early as possible. Early diagnosis allows the person to receive effective treatment benefits apart from helping him or her to remain independent longer. In this paper, different feature selection techniques are utilized with different classifiers in the classification of this chronic disease as normal control (NC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) based on the MRI images of ADNI dataset. Dimensionality reduction plays a major role in improving classification performance when there are fewer records with high dimensions. After different trials to select the ample features, support vector machine (SVM) with radial basis function kernel is found to produce better results with 96.82%, 89.39% and 90.40% accuracy for binary classification of NC/AD, NC/MCI and MCI/AD, respectively, with repeated tenfold stratified cross-validation. Combining mini-mental state examination (MMSE) score to the MRI data, there has been an improvement of 2.7% in the MCI/AD classification, but it does not have much influence in the NC/AD and NC/MCI classification.

Modeling Evaporation-Seepage Losses for Reservoir Water Balance in Semi-arid Regions

Abstract: In the water balance of reservoir system, evaporation plays a crucial role particularly so for the reservoir systems of smaller size located in the semi-arid or arid regions Such regions are most often characterized by significant seepage losses from reservoirs, besides evaporation losses Usually, in the optimization of a reservoir system, it is a common practice to assume evaporation loss either as some constant value or as negligible Such assumptions, however, may affect the results of reservoir optimization This is demonstrated in this study by a case study in the optimal scheduling of Pilavakkal reservoir system in Vaipar basin of Tamilnadu, India For modeling reservoir losses, many models are available, of which, Penman combination model is most commonly used In this study, an alternative approach based on Genetic Programming (GP) is proposed The results of GP and Penman model for both evaporation loss estimation and reservoir scheduling are compared It is found that while GP and Penman combination model performs equally well for estimating evaporation losses, GP is also able to model seepage losses (or other losses from reservoir) to a much better degree It is also shown the reservoir scheduling does get influenced based on how the reservoir losses are modeled in the reservoir water balance equation

Minimum ignition energy for micro and nano flash powders

Abstract: The minimum ignition energy (MIE) of dust clouds is required to assess the electrostatic ignition risk. Recently, the growing number of accidents shows that fire and explosions occurring in fireworks industry are due to electrostatic discharge (ESD). The objective of the this paper is to discuss the various practical concerns during the handling of flash powders of various compositions containing potassium nitrate, sulfur, and aluminum in fireworks. These powders form dust clouds and cause fires and explosions because the MIEs of these dust clouds are very low. This study is carried out with powders of various sizes ranging from micron and nanometer. The measurements are done by using a 1.2-L Hartmann apparatus. The experimental results show that the MIEs for flash powders are in the range from 89.2 to 19.8 mJ for micron- and nano-sized particles. The experimental results are given for various changes, such as: electrode gap, electrode material, and dust concentration, dust composition, etc. If the nano flash powder is mixed with micron powders, the MIE is greatly reduced, and it becomes extremely combustible. Therefore, it is imperative for fireworks manufacturers, or anyone handling these dusts, to take precautionary measures to prevent fires and explosions involving ESDs. © 2011 American Institute of Chemical Engineers Process Saf Prog, 2012