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Symmetry features for license plate classification

CAAI Transactions on Intelligence Technology 2018年第3期3卷 176-183页

作者： Karpuravalli Srinivas Raghunandan Palaiahnakote Shivakumara Lolika Padmanabhan govindaraju hemantha kumar Tong Lu Umapada Pal Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia PES Institute of Technology Bangalore Karnataka India National Key Lab for Novel Software Technology Nanjing University Nanjing People's Republic of China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Achieving high recognition rate for license plate images is challenging due to multi-type images. We present new symmetry features based on stroke width for classifying each input license image as private, taxi, cursive text, when they expand the symbols by writing and non-text such that an appropriate optical character recognition （OCR） can be chosen for enhancing recognition performance. The proposed method explores gradient vector flow （GVF） for defining symmetry features, namely, GVF opposite direction, stroke width distance, and stroke pixel direction. Stroke pixels in Canny and Sobel which satisfy the above symmetry features are called local candidate stroke pixels. Common stroke pixels of the local candidate stroke pixels are considered as the global candidate stroke pixels. Spatial distribution of stroke pixels in local and global symmetry are explored by generating a weighted proximity matrix to extract statistical features, namely, mean, standard deviation, median and standard deviation with respect the median. The feature matrix is finally fed to an support vector machine （SVM） classifier for classification. Experimental results on large datasets for classification show that the proposed method outperforms the existing methods. The usefulness and effectiveness of the proposed classification is demonstrated by conducting recognition experiments before and after classification.

关键词：车牌图像图像识别识别技术计算机技术

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Finite element analysis of dynamic stability of skeletal structures under periodic loading

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第2期8卷 245-256页

作者： THANA hemantha kumar AMEEN Mohammed Department of Civil Engineering National Institute of Technology Calicut Kerala 673601 India

This paper addresses the dynamic stability problem of columns and frames subjected to axially applied periodic loads. Such a structure can become unstable under certain combinations of amplitudes and frequencies of the imposed load acting on its columns/beams. These are usually shown in the form of plots which describe regions of instability. The finite element method (FEM) is used in this work to analyse dynamic stability problems of columns. Two-noded beam elements are used for this purpose. The periodic loading is decomposed into various harmonics using Fourier series expansion. Computer codes in C++ using object oriented concepts are developed to determine the stability regions of columns subjected to periodic loading. A number of nu-merical examples are presented to illustrate the working of the program. The direct integration of the equations of motions of the discretised system is carried out using Newmark’s method to verify the results.

关键词： Finite element analysis Dynamic stability Mathieu-Hill equation

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Modelling and predicting the dynamic response of an axially graded viscoelastic core sandwich beam

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Defence Technology（防务技术） 2023年第12期30卷 32-46页

作者： Suryarao Nagiredla Sharnappa Joladarashi hemantha kumar Department of Mechanical Engineering National Institute of Technology KarnatakaSurathkalMangaluruKarnataka 575025India

The present study explored the influence of axial gradation of viscoelastic materials on the dynamic response of the sandwich beam for structural *** finite element(FE)formulations are used to model and investigate dynamic response of the sandwich *** classical beam theory is used to develop the FE formulations and Lagrange's approach is considered to obtain the equations of motion(EOM).FE code is developed and validated with the existing literature and also conducted the convergence study for the developed FE ***,the influence of different viscoelastic materials and boundary conditions on the dynamic response of the sandwich beam is *** different axial gradation configurations of viscoelastic materials are considered for the present work to explore the influence on natural frequency,loss factor and frequency response of the sandwich *** modeled axial gradation of viscoelastic material has displayed a considerable impact on the peak vibrational amplitude response of the sandwich beam for all the boundary conditions and these configurations improved the damping capabilities at different configurations for the structural applications.

关键词： Finite element method Sandwich beam Viscoelastic material Natural frequency Loss factor Boundary condition Axial gradation

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Multimodal Biometric Score Fusion Using Gaussian Mixture Model and Monte Carlo Method

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Journal of Computer Science & Technology 2010年第4期25卷 771-782页

作者： R Raghavendra Rao Ashok G hemantha kumar Department of Studies in Computer Science University of Mysore Department of E&C Channabasaveshwara Institute of Technology

Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on Gaussian Mixture Mode] （GMM） and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle： 1） small size of match scores as is more commonly encountered in biometric fusion, and 2） arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio （LR） method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.

关键词： multimodal biometric system match score level fusion Gaussian mixture model Monte Carlo method.

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Multi-length scale modeling of carburization,martensitic microstructure evolution and fatigue properties of steel gears

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Journal of Materials Science & Technology 2020年第14期47卷 157-165页

作者： Edward Charles Henry Crawford O’Brien hemantha kumar Yeddu School of Engineering Newcastle UniversityNewcastle upon TyneNE17RUUnited Kingdom

Multi-length scale modeling is performed to(i)predict the carburized case depth of SAE8620 steel gears by solving the Fick’s second law of diffusion,(ii)model the martensitic microstructure evolution in a grain inside the carburized case as well as to study the effect of stress cycling on retained austenite(RA)and martensite using a 3D phase-field model,(iii)simulate the effect of carburization and different RA contents on macroscale fatigue behavior of SAE8620 steel spur gear using the finite element *** diffusion model predicts that the case depth increases with increasing heat treatment time and *** phase-field simulations show that RA can transform to martensite during fatigue loading,where the extent of the transformation will depend on the type of stresses applied,*** in a high stress regime or low stress regime of fatigue *** transformation of martensite to austenite is also observed in low RA sample under high stress *** macroscale simulations show that the carburized case with high RA gives rise to better fatigue life compared to that with low RA.

关键词： Phase-field model Martensitic transformation Microstructure Gear steel Carburization

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Dynamic behavior of sandwich beams with different compositions of magnetorheological fluid core

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International Journal of Smart and Nano Materials 2021年第1期12卷 88-106页

作者： Subash Acharya Vipin J Allien Puneet N P hemantha kumar Department of Mechanical Engineering NITK SurathkalMangaloreKarnatakaIndia

Magnetorheological fluid(MRF)sandwich beams belong to a class of adaptive beams that consists of MRF sandwiched between two or more face layers and have a great prospective for use in semi-active control of beam vibrations due to their superior vibration suppression *** composition of MRF has a strong influence on the MRF properties and hence affects the vibration characteristics of the *** this work,six MRF samples(MRFs)composed of combination of two particle sizes and three weight fractions of carbonyl iron pow-der(CIP)were prepared and their viscoelastic properties were *** MRFs were used to fabricate different MRF core sandwich ***,a sandwich beam with commer-cially available MRF 132DG fluid as core was *** modal parameters of the cantilever MRF sandwich beams were determined at different magnetic ***,sinusoidal sweep excitation tests were performed on these beams at different magnetic fields to investigate their vibration suppres-sion *** having larger particle size and higher weight fraction of CIP resulted in higher damping ratio and vibration ***,optimal particle size and weight fraction of CIP were determined based on the maximization of damping ratio and minimization of weight of MRF.

关键词： Smart materials adaptive sandwich beam damping ratio vibration suppression complex shear modulus

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Condition Monitoring of Roller Bearing by K-star Classifier andK-nearest Neighborhood Classifier Using Sound Signal

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Structural Durability & Health Monitoring 2017年第1期11卷 1-17页

作者： Rahul kumar Sharma V.Sugumaran hemantha kumar M.Amarnath V.I.T.university ChennaiIndia N.I.T.Karnataka SrinivasanagarSurathkalMangaloreKarnatakaIndia I.I.I.T.D.&M.Jabalpur KhamariaJabalpurIndia Department of Mechanical Engineering School of Mechanical and Building SciencesV.I.T University

Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault ***, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.

关键词： K-star k-nearest neighborhood k-NN machine learning approach conditionmonitoring fault diagnosis roller bearing decision tree algorithm J-48 random treealgorithm decision making two-layer feature selection sound signal statistical features

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Use of Discrete Wavelet Features and Support Vector Machine for Fault Diagnosis of Face Milling Tool

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Structural Durability & Health Monitoring 2018年第2期12卷 111-127页

作者： C.K.Madhusudana N.Gangadhar hemantha kumar S.Narendranath National Institute of Technology Karnataka SurathkalMangalorePin-575025India

This paper presents the fault diagnosis of face milling tool based on machine learning *** machining,spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired.A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform(DWT)*** decision tree technique is used to select significant features out of all extracted wavelet features.C-support vector classification(C-SVC)andν-support vector classification(ν-SVC)models with different kernel functions of support vector machine(SVM)are used to study and classify the tool condition based on selected *** the results obtained,C-SVC is the best model thanν-SVC and it can be able to give 94.5%classification accuracy for face milling of special steel alloy 42CrMo4.

关键词： Fault diagnosis face milling decision tree discrete wavelet transform support vector machine

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Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique

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Structural Durability & Health Monitoring 2020年第2期14卷 127-145页

作者： Suhas S.Aralikatti K.N.Ravikumar hemantha kumar H.Shivananda Nayaka V.Sugumaran NationalInstitute of Technology Karnataka Mangaluru575025India Vellore Institute of Technology University Chennai600127India

The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic ***,it is necessary to monitor tool condition for *** process to have superior quality and economic *** the pre-sent study,fault classification of single point cutting tool for hard turning has been carried out by employing machine learning *** force and vibration signals were acquired to monitor tool condition during machining.A set of four tooling conditions namely healthy,worn flank,broken insert and extended tool overhang have been considered for the *** machine learning technique was applied to both vibration and cutting force *** wavelet features of the signals have been extracted using discrete wavelet trans formation(DWT).This transformation represents a large dataset into approximation coeffcients which contain the most useful information of the *** features,among features extracted,were selected using J48 decision tree ***-sification of tool conditions was carried out us ing Naive Bayes algorithm.A 10 fold cross validation was incorporated to test the validity of classifier.A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.

关键词： Fault diagnosis of cutting tool Naive Bayes classifer,decision tree technique

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In vitro anti-biofilm and anti-bacterial activity of Junceella juncea for its biomedical application

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Asian Pacific Journal of Tropical Biomedicine 2012年第12期2卷 930-935页

作者： P kumar S Senthamil Selvi M govindaraju Department of Environmental Biotechnology Bharathidasan University Department of Plant Science Bharathidasan University

Objective:To investigate the anti-biofilm and anti-bacterial activity of Junceella juncea(***)against biofilm forming pathogenic ***:Gorgonians were extraeted with methanol and analysed with fourier transform infrared *** forming pathogens were identified by Congo red agar supplemented with sucrose.A quantitative spectrophotometric method was used to monitor in vitro biofilm reduction by microtitre plate ***-bacterial activity of methanolic gorgonian extract(MGE)was carried out by disc diffusion method followed by calculating the percentage of increase with crude methanol(CM).Results:The presence of active functional group was exemplified by FT-IR ***,black,crystalline colonies confirm the production of extracellular polymeric substances responsible for biofilm formation in Congo red *** exhibited potential anti-biofilm activity against all tested bacterial *** anti-bacterial activity of methanolic extract was comparably higher in Salmonella typhii followed by Escherichia colt,Vibrio cholerae and Shigella *** overall percentage of increase was higher by 50.2%to ***:To conclude,anti-biofilm and anti-bacterial efficacy of *** is impressive over biofilm producing pathogens and are good source for novel anti-bacterial compounds.

关键词： Junceella juncea Anti-biofilm Anti-bacterial FT-IR Congo red agar Microtitre plate assay

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