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Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

Energy Engineering 2023年第8期120卷 1747-1761页

作者： Nisha Vasudevan Vasudevan Venkatraman A.ramkumar T.Muthukumar A.Sheela M.Vetrivel r.j.vijaya saraswathi F.T.josh Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Computer Science Engineering Kalasalingam Academy of Research and EducationTamil NaduIndia Department of Electrical and Electronics Engineering Kongunadu College of Engineering and TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Kongu Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Narasu’s Sarathy Institute of TechnologyTamil NaduIndia Department of Electrical and Electronics Engineering Panimalar Engineering CollegeTamil NaduIndia Department of Electrical and Electronics Engineering Karunya Institute of Technology&SciencesTamil NaduIndia

MigroGrid(MG)has emerged to resolve the growing demand for *** because of its inconsistent output,it can result in various power quality(PQ)*** is a problem that is becoming more and more important for the reliability of power systems that use renewable energy ***,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply ***,this research focuses on power quality enhancement in the MG using hybrid shunt ***,the performance of the filter mainly depends upon the design,and stability of the *** efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)*** performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this ***,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in *** the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

关键词： Artificial intelligence resistive inductive load shunt hybrid filter smart grid adaptive fuzzy back-stepping power factor

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Deep Learning Enabled Social Media recommendation Based on User Comments

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Computer Systems Science & Engineering 2023年第2期44卷 1691-1702页

作者： K.saraswathi V.Mohanraj Y.Suresh j.Senthilkumar Department of Computer Science and Engineering Government College of EngineeringSalem636011India Department of Information Technology Sona College of TechnologySalem636005India

Nowadays,review systems have been developed with social media recommendation systems(rS).Although research on rS social media is increas-ing year by year,the comprehensive literature review and classiﬁcation of this rS research is limited and needs to be *** previous method did notﬁnd any user reviews within a time,so it gets poor accuracy and doesn’tﬁlter the irre-levant comments efﬁ*** recursive Neural Network-based Trust recom-mender System(rNN-TrS)is proposed to overcome this method’s *** it is efﬁcient to analyse the trust comment and remove the irrelevant sentence ***ﬁrst step is to collect the data based on the transactional reviews of social *** second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the *** the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the ***,In the classiﬁcation method,the recursive Neural Network-based Trust recommender System(rNN-TrS)for User reviews based on the Positive and negative scores is analysed by the *** simulation results improve the predicting accuracy and reduce time complexity better than previous methods.

关键词： recommendation systems(rS) social media recursive neural network-based trust recommender system(rNN-TrS) user reviews

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Improved Secure Identification-Based Multilevel Structure of Data Sharing in Cloud Environments

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Computer Systems Science & Engineering 2022年第11期43卷 785-801页

作者： saraswathi Shunmuganathan Sridharan Kannan T.V.Madhusudhana rao K.Ambika T.jayasankar Department of Computer Science and Engineering SSN College of EngineeringChennaiTamil Nadu603110India Department of Computer Science and Engineering J.K.K.Munirajah College of TechnologyErodeTamil Nadu638506India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalliTamil Nadu620024India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India

The Cloud Computing Environment(CCE)developed for using the dynamic cloud is the ability of software and services likely to grow with any *** has transformed the methodology for storing the enterprise data,accessing the data,and Data Sharing(DS).Big data frame a constant way of uploading and sharing the cloud data in a hierarchical architecture with different kinds of separate privileges to access the *** the requirement of vast volumes of storage area in the CCEs,capturing a secured data access framework is an important *** paper proposes an Improved Secure Identification-based Multilevel Structure of Data Sharing(ISIMSDS)to hold the DS of big data in *** complex file partitioning technique is proposed to verify the access privilege context for sharing data in complex *** access control Encryption Method(EM)is used to improve the *** Complexity is measured to increase the authentication *** active attack is protected using this ISIMSDS *** proposed ISIMSDS method assists in diminishing the Complexity whenever the user’s population is increasing *** security analysis proves that the proposed ISIMSDS methodology is more secure against the chosen-PlainText(PT)attack and provides more efficient computation and storage space than the related *** performance of the proposed ISIMSDS methodology provides more efficiency in communication costs such as encryption,decryption,and retrieval of the data.

关键词： Data sharing cloud environments big data chosen-plaintext attack security

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Analysis of Surface Integrity in Drilling Metal Matrix and Hybrid Metal Matrix Composites

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journal of Materials Science & Technology 2012年第8期28卷 761-768页

作者： T.rajmohan K.Palanikumar j.Paulo Davim Department of Mechanical Engineering Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya University Department of Mechanical Engineering Sri Sai Ram Institute of Technology Department of Mechanical Engineering University of AveiroCampus Santiago3810-193 AveiroPortugal

Hybrid metal matrix composites consist of at least three constituents-a metal or an alloy matrix and two reinforcements in various forms, bonded together at the atomic level in the composite. Despite their higher specific properties of strength and stiffness, the non homogeneous and anisotropic nature combined with the abrasive reinforcements render their machining difficult. In this paper, the surface integrity of machining in drilling hybrid composites has been discussed. Drilling tests are carried out at different spindle speed, feed rates, and different drill tool materials to investigate the effect of the various cutting parameters on the surface quality and the extent of the deformation of drilled surface due to drilling. Materials used for the present investigation are A1356/IOSIC （wt%） metal matrix and A1356/10SiC-3mica （wt%） hybrid composites. The composites are fabricated using stir casting route. The drilling tests are conducted on vertical computer numeric control （CNC） machining center using carbide, coated carbide and polycrystalline diamond （PCD） drills. The surface roughness decreases with increasing spindle speed and increases with increasing feed rate. The machined surface is analyzed by scanning electron microscopy （SEM）. SEM images of the machined surfaces indicate the presence of grooves and pits. Microhardness depth profiles indicate that the subsurface damage is limited to the top of 100-250 μm.

关键词： Hybrid composites Scanning electron microscopy (SEM) Subsurface Drilling

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An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

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Computer Systems Science & Engineering 2023年第2期44卷 1647-1657页

作者： M.Usharani B.Sakthivel S.Gayathri Priya T.Nagalakshmi j.Shirisha Department of Electronics and Communication Engineering Er.Perunal Manimekalai College of EngineeringKonneripalliHosur635117India Department of Electronics and Communication Engineering Pandian Saraswathi Yadav Engineering CollegeSivagangaiTamilnadu630561India Department of Electronics and Communication Engineering R.M.D Engineering CollegeGummidipundiTamilnadu601206India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur522502India Department of Electronics and Communication Engineering Malla Reddy Engineering CollegeHyderabadTelangana500015India

Approximate computing is a popularﬁeld for low power consumption that is used in several applications like image processing,video processing,multi-media and data *** Approximate computing is majorly performed with an arithmetic circuit particular with a *** multiplier is the most essen-tial element used for approximate computing where the power consumption is majorly based on its *** are several researchers are worked on the approximate multiplier for power reduction for a few decades,but the design of low power approximate multiplier is not so *** seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher *** overcome these issues,the digital circuits are applied to the Deep Learning(DL)approaches for higher *** recent times,DL is the method that is used for higher learning and prediction accuracy in severalﬁ***,the Long Short-Term Memory(LSTM)is a popular time series DL method is used in this work for approximate *** provide an optimal solution,the LSTM is combined with a meta-heuristics jel-lyﬁsh search optimisation technique to design an input aware deep learning-based approximate multiplier(DLAM).In this work,the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate *** optimal hyperparameters of the LSTM model are identiﬁed by jelly search ***ﬁne-tuning is used to obtain an optimal solution to perform an LSTM with higher *** proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a func-tion of area,delay,power and error *** experimental results on an 8-bit multiplier with an image processing application shows that the proposed approx-imate computing multiplier achieved a superior area and power reduction with very good results on error rates.

关键词： Deep learning approximate multiplier LSTM jellyﬁsh

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