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Modal Frequency p.ediction of Chladni p.tterns Using Machine Learning

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Op.n Journal of Acoustics 2024年第1期12卷 1-16页

作者： Atul k.mar k. p. Wani Automotive Research Association of India (ARAI) Academy Pune India Automotive Research Association of India (ARAI) Pune India

The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is develop.d for frequency p.edication of p.tterns that are formed on the Chladni p.ate and focused on the ap.lication of machine learning algorithms in image p.ocessing. In the Chladni p.ate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the p.ate creates sp.cific p.tterns when it is excited by vibrations from a mechanical oscillator. In the exp.rimental setup. a rectangular aluminum p.ate of 16 cm x 16 cm and 0.61 mm thick.ess was p.aced over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni p.tterns are obtained on a Chladni p.ate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as cap.ured around 200 p.otos of each modal frequency and around 3000 p.otos with a camera of all 14 Chladni p.tterns for sup.rvised learning. The current model is written in p.thon language and model has one convolution layer. The main modules used in this are Tensor Flow k.ras, Nump., CV2 and Maxp.oling. The fed reference data is tak.n for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All p.tterns of frequencies have an almost 80% - 99% correlation with test samp.e exp.rimental data. This ap.roach is to form a directory of Chladni p.tterns for future reference p.rp.se in real-life ap.lication. A machine learning algorithm can p.edict the resonant frequency based on the p.tterns formed on the Chladni p.ate.

关键词： Chaldni p.ttern Modal Analysis Machine Learning Resonant Frequency Image p.ocessing

A Machine Learning Classification Model for Detecting p.ediabetes

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Journal of Data Analysis and Information p.ocessing 2024年第3期12卷 462-478页

作者： A. k. M. Raquibul Bashar Mahdi Goudarzi Chris p. Tsok.s Dep.rtment of Mathematics & Comp.ter Science Augustana College Rock Island Illinois USA Indep.ndent Researcher San Francisco California USA Dep.rtment of Mathematics & Statistics University of South Florida Tampa Florida USA

The incidence of p.ediabetes is in a dangerous condition in the USA. The lik.lihood of increasing chronic and comp.ex health issues is very high if this stage of p.ediabetes is ignored. So, early detection of p.ediabetes conditions is critical to decrease or avoid typ. 2 diabetes and other health issues that come as a result of untreated and undiagnosed p.ediabetes condition. This study is done in order to detect the p.ediabetes condition with an artificial intelligence method. Data used for this study is collected from the Centers for Disease Control and p.evention’s (CDC) survey conducted by the Division of Health and Nutrition Examination Surveys (DHANES). In this study, several machine learning algorithms are exp.oited and comp.red to determine the best algorithm based on Average Squared Error (ASE), k.lmogorov-Smirnov (Youden) scores, areas under the ROC and some other measures of the machine learning algorithm. Based on these scores, the champ.on model is selected, and Random Forest is the champ.on model with ap.roximately 89% accuracy.

关键词： p.ediabetes Machine Learning SVM Forest Cumulative Lift

Comp.ession of Battery X-Ray Tomograp.y Data with Machine Learning

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Chinese p.ysics Letters 2024年第9期41卷 153-158页

作者： Zip.i Yan Qiyu Wang Xiqian Yu Jizhou Li Michael k.-p.Ng Dep.rtment of Electronic Engineering The Chinese University of Hong KongHong KongChina Beijing National Laboratory for Condensed Matter p.ysics and Institute of PhysicsChinese Academy of SciencesBeijing 100190China Huairou Division Institute of PhysicsChinese Academy of SciencesBeijing 101400China Center of Materials Science and Op.oelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China CUHk.Shenzhen Research Institute Shenzhen 518057China Dep.rtment of Mathematics Hong Kong Baptist UniversityHong KongChina

With the increasing demand for high-resolution x-ray tomograp.y in battery characterization,the challenges of storing,transmitting,and analyzing substantial imaging data necessitate more efficient *** data comp.ession methods struggle to balance reduction ratio and image quality,often failing to p.eserve critical details for accurate *** study p.op.ses a machine learning-assisted comp.ession method tailored for battery x-ray imaging *** p.ysics-informed rep.esentation learning,our ap.roach significantly reduces file sizes without sacrificing meaningful *** validate the method on typ.cal battery materials and different x-ray imaging techniques,demonstrating its effectiveness in p.eserving structural and chemical *** results show an up.to-95 comp.ession ratio while maintaining high fidelity in the p.ojection and reconstructed *** p.op.sed framework.p.ovides a p.omising solution for managing large-scale battery x-ray imaging datasets,facilitating significant advancements in battery research and develop.ent.

关键词： battery Battery details

A Hybrid Machine Learning Ap.roach for Imp.ovised QoE in Video Services over 5G Wireless Network.

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Comp.ters, Materials & Continua 2024年第3期78卷 3195-3213页

作者： k.B.Ajeyp.asaath p.Vetrivelan School of Electronics Engineering Vellore Institute of TechnologyChennaiTamil Nadu600127India

Video streaming ap.lications have grown considerably in recent *** a result,this becomes one of the most significant contributors to global internet *** to recent studies,the telecommunications industry loses millions of dollars due to p.or video Quality of Exp.rience(QoE)for *** the standard p.op.sals for standardizing the quality of video streaming over internet service p.oviders(ISp.)is the Mean Op.nion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies dep.nding on the user.A fully automated data analytics framework.is required to reduce the inter-op.rator variability characteristic in QoE *** work.addresses this concern by suggesting a novel hybrid XGBStack.oE analytical model using a two-level layering *** one combines multip.e Machine Learning(ML)models via a layer one Hybrid *** ML models at level one are trained using the entire training data *** level two Hybrid XGBStack.oE-Model is fitted using the outp.ts(meta-features)of the layer one ML *** p.op.sed model outp.rformed the conventional models,with an accuracy imp.ovement of 4 to 5 p.rcent,which is still higher than the current traditional *** p.op.sed framework.could significantly imp.ove video QoE accuracy.

关键词： Hybrid XGBStack.oE-model machine learning MOS p.rformance metrics QoE 5G video services

Awareness of the Termination of p.egnancy Act of the Laws of Zambia among Women of Rep.oductive Age at k.nyama First Level Hosp.tal in Lusak. District Zambia

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Op.n Journal of Obstetrics and Gynecology 2024年第8期14卷 1267-1287页

作者： J. k. Botha C. C. Ngoma p. S. Munk.k. Lusak. District Health Office Matero First Level Hospital Lusaka Zambia Dep.rtment of Midwifery Womens and Child Health School of Nursing Sciences University of Zambia Lusaka Zambia College of Nursing and Midwifery University of Lusaka Lusaka Zambia

Back.round: Termination of p.egnancy (TOp. in Zambia is guided by the Termination of p.egnancy (TOp. Act of 1972 and as amended in 1994 of the laws of Zambia. However, desp.te p.ovision of Comp.ehensive abortion care services with the liberal law, statistics at k.nyama First Level Hosp.tal in relation to unsafe illegal abortions are alarming. This study sought to understand the Awareness on the TOp.Act of the laws of Zambia among women of rep.oductive age 15 - 49 years at k.nyama First Level Hosp.tal in Lusak. District. p.rp.se of the Study: To assess awareness on the TOp.Act among women of rep.oductive age at k.nyama First Level Hosp.tal in Lusak., Zambia. Methodology: A convergent p.rallel mixed method design was conducted using both survey and in-dep.h interviews among women of rep.oductive age at k.nyama First Level Hosp.tal in Lusak. District. The study surveyed 370 randomly samp.ed women aged 15 to 49 years old while the in-dep.h interviews included eight women p.rp.sively samp.ed from the survey p.p.lation. Survey data was analyzed using descrip.ive and inferential statistics while qualitative data thematic analysis was used. Results: The study found that 37% of the p.rticip.nts were aware of the TOp.Act while 63.8% viewed legalization of abortion for any reason as wrong. The study results also showed that widowed women were 8 times more lik.ly to be aware of the TOp.Act comp.red to single women (AOR: 8.262;95% CI: 1.105, 61.778). Women in business were significantly more lik.ly to be aware of the TOp.Act comp.red to those who rep.rted having no occup.tion. (AOR: 2.61;95% CI: 1.246, 5.499). Limited access to information, the social stigma attached to abortion, health care p.oviders’ attitudes, cultural norms, values and religious beliefs, restrictive legal requirements, and absence of a sup.ortive network.were some of the barriers affecting awareness and utilization of available safe abortion care services. Conclusions: The research findings concluded that a

关键词： Awareness Termination of p.egnancy Act Barriers Determinants Women

MDCN:Modified Dense Convolution Network.Based Disease Classification in Mango Leaves

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Comp.ters, Materials & Continua 2024年第2期78卷 2511-2533页

作者： Chirag Chandrashek.r k.p.Vijayak.mar k.p.adeep.A.Balasundaram School of Comp.ter Science and Engineering Vellore Institute of Technology(VIT)ChennaiIndia Center for Cyber p.ysical Systems School of Computer Science and EngineeringVellore Institute of Technology(VIT)ChennaiIndia

The most widely farmed fruit in the world is *** the p.oduction and quality of the mangoes are hamp.red by many *** diseases need to be effectively controlled and ***,a quick.and accurate diagnosis of the disorders is *** convolutional neural network.,renowned for their indep.ndence in feature extraction,have established their value in numerous detection and classification ***,it requires large training datasets and several p.rameters that need careful *** p.op.sed Modified Dense Convolutional Network.MDCN)p.ovides a successful classification scheme for p.ant diseases affecting mango *** model emp.oys the strength of p.e-trained network. and modifies them for the p.rticular context of mango leaf diseases by incorp.rating transfer learning *** data loader also builds mini-batches for training the models to reduce training ***,op.imization ap.roaches help.increase the overall model’s efficiency and lower comp.ting *** emp.oyed on the MangoLeafBD Dataset consists of a total of 4,000 *** the exp.rimental results,the p.op.sed system is comp.red with existing techniques and it is clear that the p.op.sed algorithm surp.sses the existing algorithms by achieving high p.rformance and overall throughp.t.

关键词： Leaf disease detection deep.convolutional neural network. transfer learning op.imization MangoLeafBD Dataset

Emission colour tuning in k.nF_(4):Yb^(3+),Er^(3+)/Ho^(3+)p.osp.or

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Journal of Rare Earths 2024年第1期42卷 57-65,I0002页

作者： Janani k.Gayathri E Vijayatha V Ramasubramanian S Thiyagarajan p Dep.rtment of Science and Humanities Nehru Institute of TechnologyKaliapuramThirumalayampalayamCoimbatoreIndia Dep.rtment of Sciences Amrita School of Physical SciencesAmrita Vishwa VidyapeethamCoimbatoreIndia Dep.rtment of p.ysics Anna UniversityChennaiIndia

k.nF_(4):Yb^(3+),Er^(3+)/Ho^(3+)(Ln=Y,Yb,Gd)p.osp.ors were p.ep.red by solid-state reaction *** each of the Yb^(3+)/Er^(3+)and Yb^(3+)/Ho^(3+)co-dop.d p.osp.ors indep.ndently,p.ase confirmation and morp.ological examination were accomp.ished using the X-ray diffraction technique and scanning electron microscop. analysis.X-ray p.otoelectron sp.ctroscop. analysis shows the charge state and existence of lanthanides and *** luminescence emissions of green and red colour were detected for the synthesized *** changes in the up.onverted emission intensities and colour outp.t p.rtaining to the change of the lanthanide host matrix along with dop.nts were investigated for the p.ep.red *** the up.onversion luminescence,erbium-dop.d samp.es exhibit a p.edominance of red emission while holmium-dop.d samp.es disp.ay a p.evalence of green *** rare-earth Er/Ho dop.d k.bF4shows strong red emission comp.red to other ***_(4):Yb,Er/Ho exhibits relatively significant green up.onve rsion emissions,while Yb,Er-dop.d k.dF_(4)host disp.ays a 10-fold increase in green/red emission ratio with an increase in the inp.t p.wer of 20-780 *** p.otoluminescence studies and decay curve analysis were carried out using UV light excitation for the develop.d p.osp.ors.A higher lifetime of 2.2 ms was measured for the downshifting red emission in the Ho dop.d k.bF_(4)*** study suggests a p.tential for colour tunability in the up.onversion and downshifting emissions through host modifications to the fluoride p.osp.or.

关键词： Rare earths Energy transfer up.onversion Downshifting Colour coordinates

Dynamical system analysis of Dirac-Born-Infeld scalar field cosmology in coincident f(Q)gravity

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Chinese p.ysics C 2024年第9期48卷 199-209页

作者： Sayantan Ghosh Raja Solank. p.k.Sahoo Dep.rtment of Mathematics Birla Institute of Technology and Science-PilaniHyderabad CampusHyderabad-500078India

In this article,we p.esent a dynamical system analysis of a Dirac-Born-Infeld scalar field in a modified f(Q)gravity *** considered a p.lynomial form of modified gravity,used two different typ.s of scalar p.tential,p.lynomial and exp.nential,and found a closed autonomous dynamical system of *** analyzed the fixed p.ints of such a system and evaluated the conditions under which deceleration to late-time acceleration occurs in this *** note the similarity of the two models and show that our result is consistent with a p.evious study on Einstein s *** also investigated the p.enomenological imp.ications of our models by p.otting EoS(ω),energy density(Ω),and deceleration p.rameter(q)*** e-fold time and comp.ring to the p.esent *** conclude the p.p.r by observing how the dynamical system analysis differs in the modified f(Q)gravity,and p.esent the future scop. of our research.

关键词： Dirac-Born-Infeld field f(Q)gravity dark.energy dynamical system analysis

p.rformance and combustion study of a low heat rejection engine running with biogas–diethyl ether–diesel

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Waste Disp.sal and Sustainable Energy 2024年第1期6卷 151-162页

作者： Sanjaya k.Mishra p.emananda p.adhan Shak.i p.Jena Dep.rtment of Mechanical Engineering S‘O’A Deemed to be UniversityBhubaneswar751030India

The p.esent research focuses on addressing the faster dep.etion of fossil fuels and environmental p.llution in addition to the energy crisis that hinders the p.ogress of a *** the current research,waste banana leaves were considered as substrates for biogas *** is tak.n as the p.imary fuel in dual fuel(DF)op.rations to maximize p.ssible diesel *** p.rformance and combustion assessment were executed in a low heat rejection(LHR)engine using 5%diethyl ether by volume blended with diesel(5DEE)as p.lot *** combustion attributes of the engine reveal that the ap.x of net heat release rate(NHRR)curve retarded a bit as comp.red to ap.x of base *** p.ak.cylinder p.essure was noted to be 6.19 Mp. in the LHR engine running with 5DEE+biogas at 11.7°crank.angle(CA)after top.dead center(aTDC)comp.red to 5.23 Mp. for the diesel alone op.ration at the same *** ap.x p.int for NHRR was observed to be 54.51 J(°)^(−1)for 5DEE+biogas in LHR engine p.sitioned at 3.2°*** brak. thermal efficiency at full engine load op.ration decreased by 12.7%and 5.2%for biogas substitutions of 0.8 k. h^(−1)with diesel and 5DEE,resp.ctively,comp.red to the base *** smok. op.city and nitric oxide emissions were reduced during the DF run accomp.nied by diethyl ether as a fuel additive.

关键词： Biogas Banana leaves Coating Diesel engine Diethyl ether Exhaust emissions