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Decentralized damage diagnostic technique for tall buildings using VARMAX model

Earthquake Engineering and Engineering Vibration 2022年第2期21卷 417-439页

作者： K.lakshmi J.Prawin CSIR-Structural Engineering Research Centre CSIR RoadChennai 600113Tamil NaduIndia

Civil infrastructure,especially buildings,are becoming more slender,tall,and multipurpose,creating a need to continuously monitor their health to ensure the safety and security of human lives and assets.While the majority of structural health monitoring techniques use measurements from the entire structure,in this study,an output-only damage diagnostic technique using a decentralized concept(subdomain-based)for tall buildings and employing a vector form of the autoregressive moving average with exogenous input(VARMAX)model is proposed,which offers reduced instrumentation and data handling requirements.Unlike other decentralized approaches,this technique predicts more than one DOF at a time so the number of subdomains required for the diagnosis of the complete structure is minimized.The proposed subdomain-based damage diagnostic algorithm works with ambient loads and does not require any correlated numerical models since it is solely based on measured data.The proposed algorithm can identify the time instant of damage,spatial location(s)and characterize the damage intensity.Efforts have been made to account for confounding factors such as environmental and operational variabilities separate from measurement noise to avoid false positive alarms.The effectiveness of the proposed technique is illustrated using synthetic time history responses from a twenty-story framed structure under ambient loading and an experimental study on a ten-story framed structure.Both numerical and experimental investigations confirm the effectiveness of the method and its robustness to environmental/operational variabilities and measurement noise.

关键词： structural health monitoring subdomain high-rise buildings shear-type buildings VARMAX model environmental and operational variability measurement noise

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An Optimal Deep Learning for Cooperative Intelligent Transportation System

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Computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

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Profile and Rating Similarity Analysis for Recommendation Systems Using Deep Learning

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Computer Systems Science & Engineering 2022年第6期41卷 903-917页

作者： lakshmi Palaniappan k.Selvaraj Department of Computer Science and Engineering Veerammal Engineering CollegeK.SingarakottaiDindigul624708India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India

Recommendation systems are going to be an integral part of any E-Business in near future.As in any other E-business,recommendation systems also play a key role in the travel business where the user has to be recommended with a restaurant that best suits him.In general,the recommendations to a user are made based on similarity that exists between the intended user and the other users.This similarity can be calculated either based on the similarity between the user profiles or the similarity between the ratings made by the users.First phase of this work concentrates on experimentally analyzing both these models and get a deep insight of these models.With the lessons learned from the insights,second phase of the work concentrates on developing a deep learning model.The model does not depend on the other user's profile or rating made by them.The model is tested with a small restaurant dataset and the model can predict whether a user likes the restaurant or not.The model is trained with different users and their rating.The system learns from it and in order to predict whether a new user likes or not a restaurant that he/she has not visited earlier,all the data the trained model needed is the rating made by the same user for different restaurants.The model is deployed in a cloud environment in order to extend it to be more realistic product in future.Result evaluated with dataset,it achieves 74.6%is accurate prediction of results,where as existing techniques achieves only 64%.

关键词： Deep learning restricted boltzman machine profile based similarity rating based similarity item based similarity

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Improving the performance of query processing using proposed resilient distributed processing technique

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International Journal of Intelligent Computing and Cybernetics 2021年第2期14卷 158-169页

作者： C.lakshmi k.Usha Rani Department of Computer Science Sri Padmavati Mahila VisvavidyalayamTirupatiIndia

Purpose-Resilient distributed processing technique(RDPT),in which mapper and reducer are simplified with the Spark contexts and support distributed parallel query processing.Design/methodology/approach-The proposed work is implemented with Pig Latin with Spark contexts to develop query processing in a distributed environment.Findings-Query processing in Hadoop influences the distributed processing with the MapReduce model.MapReduce caters to the works on different nodes with the implementation of complex mappers and reducers.Its results are valid for some extent size of the data.Originality/value-Pig supports the required parallel processing framework with the following constructs during the processing of queries:FOREACH;FLATTEN;COGROUP.

关键词： Query processing MapReduce Scalability Resilient distributed processing Spark

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Elimination of lymphatic filariasis-Challenging but not impossible

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Asian Pacific Journal of Tropical Medicine 2023年第11期16卷 522-523,I0001页

作者： lakshmi krishnasamy Professor of Microbiology Sree Balaji Medical College and HospitalBharath Institute of Higher Education and ResearchChennaiTamilnaduIndia

We recently went through your article titled“Elimination of lymphatic filariasis:Where do we stand so far?”by Aashna Sinha et al[1].The authors have brought about relevant information regarding lymphatic filariasis focussing on history,diagnostic aspects,treatment strategies and various control measures.Lymphatic filariasis is a neglected parasitic infection affecting millions of people worldwide.

关键词： filariasis lymphatic neglected

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Adaptive Particle Swarm Optimization Data Hiding for High Security Secret Image Sharing

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Computer Systems Science & Engineering 2022年第12期43卷 931-946页

作者： S.lakshmi Narayanan Electronics and Communication Engineering Sri Ramakrishna Engineering CollegeCoimbatore641022Tamil NaduIndia

The main aim of this work is to improve the security of data hiding forsecret image sharing. The privacy and security of digital information have becomea primary concern nowadays due to the enormous usage of digital technology.The security and the privacy of users’ images are ensured through reversible datahiding techniques. The efficiency of the existing data hiding techniques did notprovide optimum performance with multiple end nodes. These issues are solvedby using Separable Data Hiding and Adaptive Particle Swarm Optimization(SDHAPSO) algorithm to attain optimal performance. Image encryption, dataembedding, data extraction/image recovery are the main phases of the proposedapproach. DFT is generally used to extract the transform coefficient matrix fromthe original image. DFT coefficients are in float format, which assists in transforming the image to integral format using the round function. After obtainingthe encrypted image by data-hider, additional data embedding is formulated intohigh-frequency coefficients. The proposed SDHAPSO is mainly utilized for performance improvement through optimal pixel location selection within the imagefor secret bits concealment. In addition, the secret data embedding capacityenhancement is focused on image visual quality maintenance. Hence, it isobserved from the simulation results that the proposed SDHAPSO techniqueoffers high-level security outcomes with respect to higher PSNR, security level,lesser MSE and higher correlation than existing techniques. Hence, enhancedsensitive information protection is attained, which improves the overall systemperformance.

关键词： Image sharing separable data hiding using adaptive particle swarm optimization(SDHAPSO) security access control

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Influence of volume percentage of NanoTiB2 particles on tribological&mechanical behaviour of 6061-T6 Al alloy nano-surface composite layer prepared via friction stir process

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Defence Technology（防务技术） 2017年第1期13卷 16-21页

作者： V.kishan Aruri Devaraju k.Prasanna lakshmi Department of Mechanical Engineering MRCETHyderabadT.S.India Department of Mechanical Engineering SR Engineering CollegeWarangalT.S.India Department of Mechanical Engineering College of EngineeringJNTU ManthiniT.S.India

The aim of present study is to analyze the influence of volume percentage(vol.%) of nano-sized particles(TiB_2: average size is 35 nm) on microstructure, mechanical and tribological behavior of 6061-T6 Al alloy surface nano composite prepared via Friction stir process(FSP). The microstructure of the fabricated surface nanocomposites is examined using optical microscopy(OM) and scanning electron microscope(SEM) for distribution of TiB_2 nano reinforcement particles, thickness of nano composite layer formed on the Aluminum alloy substrate and fracture features. The depth of surface nano composite layer is measured as 3683.82 m m along the cross section of stir zone of nano composite perpendicular to FSP. It was observed that increase in volume percentage of TiB_2 particles, the microhardness is increased up to132 Hv and it is greater than as-received Al alloy's microhardness(104 Hv). It is also observed that at 4volume percentage higher tensile properties exhibited as compared with the 2 and 8 vol. %. It is found that high wear resistance exhibited at 4 volume percentage as-compared with the 2 and 8 vol. %. The observed wear and mechanical properties are interrelated with microstructure, fractography and worn morphology.

关键词： Friction stir process Al alloy Surface nano composites Tensile Tribology

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Impact of temperature and precipitation lapse rate on hydrological modelling over Himalayan Gandak River Basin

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Journal of Mountain Science 2022年第12期19卷 3487-3502页

作者： Brijesh kUMAR Dipankar ROY Venkataraman lakshmi Department of Hydraulic and Water Resource Engineering Bule Hora UniversityBule Hora 144Ethiopia Department of Civil Engineering Madanapalle Institute of Technology&ScienceMadanapalle 517325India Engineering Systemsand Environment Universityof VirginiaCharlottesvilleVA22904USA

The hydrology of Himalayan region is influenced by temperature lapse rate(TLAPS)and precipitation lapse rate(PLAPS).Therefore,hydrological modeling considering TLAPS and PLAPS is crucial to manage the water resources in these terrains.In this research,Himalayan Gandak River basin is considered as the study area where TLAPS and PLAPS vary significantly due to high altitude of Himalayas.To assess the impact of TLAPS and PLAPS on water balance components,Soil Water Assessment Tool(SWAT)model was calibrated(2000-2007)and validated(2008-2014)on daily time step for three projects i.e.,Reference Project(RP),Snowmelt Project(SP)and distributed elevation band snowmelt project(SWAT-ETISM).The analysis discloses that SWAT-ETISM model(which has TLAPS and PLAPS parameters)outperforms the RP and the SP models in predicting streamflow with improved statistical indicators R2=0.88,NSE=0.84 and PBIAS=11.9.Furthermore,it was observed that SWAT-ETISM model comprehensively improved the streamflow statistics by improving the snow water equivalent and water balance components through the consideration of TLAPS and PLAPS values for the region.Hence,the proposed SWAT-ETISM model can be used for estimation of the water budget at the high-altitude and data scarce alpine Himalayan regions and worldwide,where PLAPS and TLAPS are substantial due to altitudinal variation.

关键词： Streamflow SWAT-ETISM Himalayan Regions Water balance Temperature lapse rate Precipitation lapse rate

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Dust Deposition’s Effect on Solar Photovoltaic Module Performance:An Experimental Study in India’s Tropical Region

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Journal of Renewable Materials 2022年第8期10卷 2133-2153页

作者： k.R.Chairma lakshmi Geetha Ramadas Department of Electrical and Electronics Engineering R.M.K.Engineering CollegeChennaiIndia

A solar PV panel works with maximum efficiency only when it is operated around its optimum operating point or maximum power point.Unfortunately,the performance of the solar cell is affected by several factors like sun direction,solar irradiance,dust accumulation,module temperature,as well as the load on the system.Dust deposition is one of the most prominent factors that influence the performance of solar panels.Because the solar panel is exposed to the atmosphere,dust will accumulate on its surface,reducing the quantity of sunlight reaching the solar cell and diminishing output.In the proposed work,a detailed investigation of the performance of solar PV modules is carried out under the tropical climatic condition of Chennai,India,where the presence of dust particles is very high.The data corresponding to four different dust samples of various densities at four solar irradiation levels of 220,525,702,and 905 W/m^(2)are collected,and performance analysis is carried out.Based on the analysis carried out,the maximum power loss is found to be 73.51%,66.29%,65.46%,and 61.42%,for coal,sand,brick powder,and chalk dust respectively.Hence,it can be said that coal dust contributes to the maximum power loss among all four dust samples.Due to heat dissipation produced by dust deposition,the performance of solar PV modules is degraded as the temperature rose.

关键词： Solar photovoltaic power generation system dust effect electrical efficiency loss dust samples surface contamination

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Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

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Intelligent Automation & Soft Computing 2023年第3期35卷 3403-3413页

作者： S.Ayshwarya lakshmi k.Anandavelu Department of Computer Science and Engineering University College of Engineering Panruti607106TamilnaduIndia Department of Mechanical Engineering MRK Institute of TechnologyCuddalore608301TamilnaduIndia

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer detection.The suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation process.Because time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difﬁculties.One of the most signiﬁcant requirements is to design optimal solutions to optimize their use.There is no particular technique that can answer all optimization issues.The proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized diagnosis.The accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization algorithm.The total sensitivity and speciﬁcity attained in the proposed system are 99.56%and 99.73%respectively.The proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional methods.The proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefﬁcient.The model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.

关键词： Cukoo search optimization technique ﬁtness function cancer

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