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Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 103-141页

作者： narayanan ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Effect of tool plunge depth on joint formation and mechanical performance of friction stir forming joints made between AA 5052-H32 and AA 6061-T6 sheet metals

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Transactions of Nonferrous Metals Society of China 2018年第4期28卷 613-628页

作者： Tinu P.SAJU R.ganesh narayanan Department of Mechanical Engineering Indian Institute of Technology Guwahati

The potential of friction stir forming for joining dissimilar grades of aluminum alloys namely,AA 5052-H32 and AA 6061-T6,was *** on the effect of tool plunge depth revealed that,lap shear load of 7.16 kN and cross-tensile load of 3.51 kN,recorded at medium tool plunge depth range from 0.5 to 0.7 mm,measured using a universal testing machine,were much larger than those of friction stir welded and friction stir spot welded joints fabricated on the same *** macrostructure observed with optical microscope revealed that joints were strengthened either by mechanical pin interlocking or by metallurgical *** effect of tool plunge depth on the stir zone formation and the influence of frictional heat flux on the lower sheet were revealed through the microhardness measurement using Vickers hardness *** studies revealed that tool plunge depth has a significant influence on the pin formation and the geometric features,generated in these *** of various failure modes such as pin pull-out,pin shear,partial bond delamination,and tear-off,were governed by the formation of critical weak zones at various tool plunge depths.

关键词： friction stir forming tool plunge depth aluminum alloy lap shear test cross-tension test failure modes

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Enhancing the Surface Properties of a Bioengineered Anterior Cruciate Ligament Matrix for Use with Point-of-Care Stem Cell Therapy

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Engineering 2021年第2期7卷 153-161页

作者： Xiaohua Yu Paulos Y.Mengsteab ganesh narayanan Lakshmi S.Nair Cato T.Laurencin Connecticut Convergence Institute for Translation in Regenerative Engineering University of Connecticut HealthFarmingtonCT 06030USA Raymond and Beverly Sackler Center for Biological Physical and Engineering SciencesUniversity of Connecticut HealthFarmingtonCT 06030USA Department of Orthopedic Surgery University of Connecticut HealthFarmingtonCT 06030USA Department of Biomedical Engineering University of ConnecticutStorrsCT 06269USA Department of Materials Science and Engineering University of ConnecticutStorrsCT 06269USA Department of Chemical and Biomolecular Engineering University of ConnecticutStorrsCT 06269USA Department of Reconstructive Sciences University of Connecticut HealthFarmingtonCT 06030USA

We have previously developed a poly(L-lactic)acid(PLLA)bioengineered anterior cruciate ligament(ACL)matrix that has demonstrated enhanced healing when seeded with primary ACL cells prior to implantation in a rabbit model,as compared with the matrix *** suggests that improving cell adhesion on the matrix may beneficially affect the healing response and long-term performance of the bioengineered ACL *** regenerative engineering approach involves enhancing the surface properties of the matrix to support cell adhesion and growth in combination with point-of-care stem cell ***,we studied the cell adhesion properties of PLLA braided microfiber matrices enhanced through the physical adsorption of fibronectin and air plasma *** evaluated the kinetics and binding efficiency of fibronectin onto matrices at three time points and three fibronectin *** the matrix for 120 min in a solution of 25 lgmL1 fibronectin achieved the greatest binding efficiency to the matrix and cellular *** the matrices to air plasma treatment for 5 min before fibronectin adsorption significantly enhanced the cell adhesion of rabbit bone marrow-derived mesenchymal stem cells(R-BMMSCs)24 h post cell ***,cellular proliferation was monitored for up to 21 d,the matrices were exposed to air plasma treatment,and fibronectin adsorption was found to result in enhanced cell *** findings suggest that exposure to air plasma treatment and fibronectin adsorption enhances the cellular adhesion of PLLA braided microfiber matrices and may improve the clinical efficacy of the matrix in combination with point-of-care stem cell therapies.

关键词： Anterior cruciate ligament Ligament Poly(L-lactic)acid plasma treatment Fibronectin Stem cells Adhesion

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Machine-learned interatomic potentials by active learning:amorphous and liquid hafnium dioxide

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npj Computational Materials 2020年第1期6卷 803-810页

作者： ganesh Sivaraman Anand narayanan Krishnamoorthy Matthias Baur Christian Holm Marius Stan Gábor Csányi Chris Benmore Álvaro Vázquez-Mayagoitia Leadership Computing Facility Argonne National LaboratoryArgonneIL 60439USA Institute for Computational Physics Universität StuttgartAllmandring 3Stuttgart 70569Germany Helmholtz-Institute Münster:Ionics in Energy Storage(IEK-12) Forschungszentrum Jülich GmbHCorrensstrasse 46Münster 48149Germany Applied Materials Division Argonne National LaboratoryArgonneIL 60439USA Department of Engineering University of CambridgeTrumpington StreetCambridge CB21PZUK X-ray Science Division Argonne National LaboratoryArgonneIL 60439USA Computational Science Division Argonne National LaboratoryArgonneIL 60439USA

We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential(GAP).Our active learning scheme consists of an unsupervised machine learning(ML)scheme coupled with a Bayesian optimization technique that evaluates the GAP *** apply this scheme to a Hafnium dioxide(HfO2)dataset generated from a“melt-quench”ab initio molecular dynamics(AIMD)*** results show that the active learning scheme,with no prior knowledge of the dataset,is able to extract a configuration that reaches the required energy fit ***,molecular dynamics(MD)simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates(i.e.,1.0 K/ps)not accessible via *** melt and amorphous X-ray structural factors generated from our simulation are in good agreement with *** addition,the calculated diffusion constants are in good agreement with previous ab initio studies.

关键词： amorphous dioxide scheme

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Ivabradine in the treatment of systolic heart failure- A systematic review and meta-analysis

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World Journal of Cardiology 2017年第2期9卷 182-190页

作者： Mahesh Anantha narayanan Yogesh NV Reddy Janani Baskaran Abhishek Deshmukh David G Benditt ganesh Raveendran Division of Cardiovascular Diseases University of Minnesota Medical Center Division of Cardio-vascular Diseases Mayo ClinicRochester

AIM To perform a systematic-review and meta-analysis to compare outcomes of ivabradine combined with betablocker to beta-blocker alone in heart failure with reduced ejection fraction(HFr EF).METHODS We searched PubM ed, Cochrane, EMBASE, CINAHL and Web of Science for trials comparing ivabradine + betablocker to beta-blocker alone in HFr EF. We performed a systematic-review and meta-analysis of published literature. Primary end-point was combined end point of cardiac death and hospitalization for heart *** Six studies with 17671 patients were included. Mean follow-up was 8.7 ± 7.9 mo. Combined end-point of heart failure readmission and cardiovascular death was better in ivabradine + beta-blocker group compared to beta-blocker alone(RR: 0.93, 95%CI: 0.79-1.09, P = 0.354). Mean difference(MD) in heart rate was higher in the ivabradine + beta-blocker group(MD: 6.14, 95%CI: 3.80-8.48, P < 0.001). There was no difference in all cause mortality(RR: 0.98, 95%CI: 0.89-1.07, P = 0.609), cardiovascular mortality(RR: 0.99, 95%CI: 0.86-1.15, P = 0.908) or heart failure hospitalization(RR: 0.87, 95%CI: 0.68-1.11, P = 0.271). CONCLUSION From the available clinical trials, ivabradine + betablocker resulted in a significantly greater reduction in HRcoupled with improvement in combined end-point of heart failure readmission and cardiovascular death but with no improvement in all cause or cardiovascular mortality. Given the limited evidence, further randomized controlled trials are essential before widespread clinical application of ivabradine + beta-blocker is advocated for HFrEF.

关键词： Ivabradine Heart failure

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