The proposed paper presents the DC/AC microgrid modeling using the Energy storage units and photovoltaic (PV) panels. The modal consists of a two stage power conversion. The power is supplied to the both DC and AC loads by this PV solar panels. The suitable way to explore the PV generation model is by using manufacturer datasheet. A bidirectional converter is connected to the battery storage system and dc bus. To keep the bus voltage stable, the storage system absorbs the excess power whenever generation is more and delivers power to the load when generation is less. This system eliminates hazards of islanding by supply the local loads continuously incase of grid discontinuity. This paper emphasizes on control and stability of dc bus voltage and energy management scheme. Matlab/Simulink is used for integration of system modeling and efficiency of the system is verified by simulation.
Face Recognition is important Biometric credentials for identification or verification of a person. In this paper, we propose a novel technique of generating compressed unique features of face images which helps in improving matching speed of recognition. The training face database samples are applied to 2D-DWT to obtain LL band features. The LL band features are subjected to normalization to scale the magnitude values in the range 0 to 1. The output of normalization is further convolved with the original face sample to obtain unique features. The convolved output is subjected to Gaussian filter to obtain smoothened image features. Further, The feature vector of several image samples of single person are compressed to convert into single vector to database feature vectors are created by compressing feature vectors of single person face samples in to single column unique vectors which helps in scaling down of feature vectors and improve matching speed. The test samples are subjected to same process to generate unique compressed test feature vectors and are compared with database vectors using Euclidean distance. The results are tabulated for different set of face databases and also compared with existing techniques to validate the performance of proposed method.
The article reports on a development of RP-HPLC method for the quantitative determination of Levetiracetam in tablet dosage forms. The chromatographic separations were performed using Phenomenex_ C18 (250 mm x 4.6 mm i.d, 5 μm particle size) column at 40 ºC temperatures. The optimum mobile phase consisted of methanol, water and acetonitrile in the ratio of 30:10:60. Auto sampler 20 μl was used and kept at 15 ºC temperature. Analysis was done with flow rate of 1.0 ml/min at 212 nm (_ max of Levetiracetam) wavelength by using photodiode array (PDA) detector. The drug was analyzed for acid, alkaline, oxidative, hydrolytic, photolytic and thermal degradation studies. The standard calibration curve was plotted for the drug and results showed that the drug was linear (r2 = 0.999) in the concentration range between 0.01 – 1.5 μg/ml. The results of stress testing undertaken according to the International Conference on Harmonization (ICH) guidelines reveal that the selected method is selective and stability-indicating for determination of levitiracetam in pharmaceutical formualtion.
Number of malware detection models has been proposed recently, which still poses major limitations in terms of detection rate. Hence, to overcome this, this paper introduces a new malware detection model with three stages: Feature Extraction, Feature selection and Classification. In feature extraction phase, the Term Frequency-Inverse Document Frequency (TF-IDF) and Information gain (IG) features are extracted. More importantly, the IG feature is subjected with the Holoentropy evaluation. Following the feature extraction phase feature selection is performed using Principle Component Analysis (PCA). Finally, to do the classification process, Deep Belief Network (DBN) is used with optimized activation function. To work out this optimization scenario, this paper intends to propose a new hybrid algorithm that combines the concept of Lion Algorithm (LA) and Glowworm Swarm Algorithm (GSO). The performance of proposed Lion Updated GSO (LU-GSO) is compared over other conventional models with respect to various evaluation measures and proves the betterments over others. Through the performance analysis, it was observed that the proposed model attains high accuracy, which is 10.21%, 10.04%, 9.18% and 6.42% better than LA, GSO, GWO and PSO, respectively.
Objective: The aim of the present study was to purify and determine the molecular weight of keratinase isolated from Streptomyces malaysiensis.
Methods:For that purpose purification was done using ammonium sulphate and Sephadex-LH 100 column chromatography. Further, the fractions were pooled and subjected to molecular weight determination using sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE).
Results:The obtained results showed keratinase with 47.57% recovery, 3.5-fold purification and an estimated molecular mass of 27,000 Da. Keratinase showed an optimal activity at 60 ο
Conclusion:The production of keratinase on simple media with feathers as sole source allowing its production from the cheap substrate and a commercial production with low production cost. Stability in the presence of detergents, surfactants and solvents make this keratinase extremely useful for a biotechnological process involving keratin.C and pH 8. Keratinase activity of the purified product was assayed with feather powder as a substrate. The isolated strain was identified as Streptomyces malaysiensis based on phylogenetic tree analysis. The strain isolated from termite mound soil showed the highest keratinase activity, which could be considered a microorganism of environmental origin.
The important issue for Designing architecture isthe evolution of Artificial Neural Network (ANN). There is no systematic method to design a near-optimal architecture for a given application or task. The pattern classification methods are used to design the neural network architectures and efforts towards the automatic design of network topologies, constructive and destructive algorithms can be used. In the proposed work the optimization of architectures and connection weights uses the evolutionary process. A singlepoint crossover is applied with selective schemas on the network space and evolution is introduced in the mutation stage so that an optimized ANNs are achieved.
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