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.

An Enhanced Handwritten Digit Recognition Using Convolutional Neural Network

Abstract: Handwritten digit recognition have great impact in the applications of deep learning. Convolutional Neural Network in the deep learning has become one of the major methods and one of the important factors in the various success in recent times and deep learning is used majorly in the area of object recognition. In the paper work, the speech output feature is integrated along with the text output. Convolutional Neural Network model is applied in the image classification. The dataset used to train and test is the MNIST dataset. There are various applications of handwritten digit recognition in the real time. It is applied in detection of vehicle number, reading of bank cheques, the arrangement of letters in the post office.

Optimization of Big Data Using Rough Set Theory and Data Mining for Textile Applications

Abstract: Prediction of any property of the material has attracted the attention of many scientists all over the world. In order to produce better products, building construction, remote sensing, prediction of water availability, etc., information technology (IT) has played a dominant role. Textile industries are one of the sources to decide our economy of our country. Among various fabrics, single jersey finished cotton knitted fabrics are liked by many people because of its comfort. It has many comfort properties such as fabric width, fabric weight, fabric shrinkage, and fabric handle. Although attempts have been made to predict fabric width and fabric weight, the type of network that used was old and no new approaches have been made. In order to remedy this situation, new techniques such as rough set theory and data mining techniques have been applied with a view to predicting fabric width and this research paper addresses this issue in depth. We propose a new algorithm scalable rough priority prediction model (SrPPM) to predict fabric width of single jersey finished cotton knitted fabric with real-time textile big dataset over Hadoop MapReduce framework. Our results show that our proposed framework works well, in terms of time efficiency and scalability.

Super Capacitor based Solar and Wind Grid Connected Storage System

Abstract: Due to the ever-increasing concern for the environment and the progression of technology, renewable energy such as solar photovoltaic (PV), wind, and super capacitor is being widely used. Many creative approaches have been used to convert the power from renewable sources. One such creative solution is using power electronic converters to match the load and grid requirements so that the renewable generation's dynamic but instead steady-state characteristics are enhanced, with the goal of achieving maximum power point tracking (MPPT) regulate and energy storage to resolve this issue. This new design seeks to increase circuit efficacy and power density by using a multiple DC-DC converter [3] which has a DC input port for renewable sources, an unidirectional Input voltage port for energy storage, as well as an Output signal port for operating the load. A few new DC-DC four converters have developed in recent years and are now being researched in the literature. This study reviews several three-port DC/DC converter topologies that have been developed by different research organizations. The study concludes that topologies based on three-port Power converter with power terminals and a single inductor are likely for further research. The suggested system's simulation is done using MATLAB/SIMULINK.

New luminescent tetracoordinate boron complexes: an in-depth experimental and theoretical characterisation and their application in OLEDs

Abstract: A group of new tetracoordinate mononuclear 2-(N-phenylformimino)pyrrolyl boron chelates [BX2{κ2N,N′-NC4H3-2-C(H)N-C6H5}] (X = F 3; mesityl (2,4,6-trimethylphenyl, Mes) 4; C6F55; X2 = 1,1′-biphenyl-2,2′-diyl 6) and the related binuclear complex [(C6F5)2B{κ2N,N′-NC4H3-2-C(H)N-C6H4-NC(H)-C4H3N-κ2N,N′}B(C6F5)2] 7 were synthesised via metathetic exchange reactions of sodium 2-(N-phenylformimino)pyrrolyl with BF3·Et2O (3), BMes2F (4) and 9-chloro-9-borafluorene (6), whereas 5 and 7 were obtained from the acid–base reactions between the corresponding neutral ligand precursors, respectively 2-(N-phenylformimino)pyrrole (1) and 1,4-(HNC4H3-C(H)N)2-C6H4 (2), with B(C6F5)2OEt. These complexes were designed to evaluate the influence of the boron co-ligands on the molecular properties of the corresponding 2-iminopyrrolyl tetrahedral boron derivatives, particularly on luminescence. Compounds 3–7 were photophysically characterised in solution and in solid state, exhibiting blue to yellowish-green emissions and fluorescence quantum yields (ϕf) up to 0.40. The exception was complex 4, which revealed full fluorescence quenching owing to a dynamic equilibrium involving the bidentate (tetracoordinate) 2-iminopyrrolyl boron complex and the corresponding monodentate (tricoordinate) species. DFT and TDDFT studies were carried out, considering the effect of solvent and also of dispersion forces, in order to elucidate the change in geometries of compounds 3–7 from the ground to the singlet excited state, to understand the dynamic equilibrium of 4, to ascribe electronic transitions, and to rationalise the observed luminescence and also the main trends of thermal stabilities. These complexes were applied in organic light-emitting diodes (OLEDs), the ones based on complex 6 showing the best performances (maximum luminance of 170 cd m−2 and electroluminescence efficiency of 0.037 cd A−1).