检索结果-南通市图书馆

CSEE Journal of Power and Energy Systems 2023年第3期9卷 1203-1213页

作者： Dulichand Jaraniya Urvashi gupta Shailendra Kumar Arvind Mittal department of Electrical Engineering Maulana Azad National Institute of TechnologyBhopal(M.P.)India Energy Centre Maulana Azad National Institute of TechnologyBhopal(M.P.)India

Electric vehicles(EVs)are one of the more emerging and significant areas,representing the future of ***,the increased demand for EVs brings greater attention toward the development of the charging ***,this paper addresses the multifunctional charging system integrated with the grid,EV battery and household *** presents an improved idea about the charging system with grid connection and its connection to the varying local nonlinear *** consists of a voltage source converter(VSC)tied to the grid and bidirectional converter,which charges/discharges the battery and also regulates the constant voltage across the DC bus,where all the vehicles are connected for *** also shows the operations of charging/discharging the battery of EVs and the voltage and current profile across the battery as per the state of charge(SOC)***,the sparse constrained proportionate normalized least mean fourth(SCP-NLMF)based method is used to control the charging system by estimating the active component of load *** derived mean square error(MSE)of load current is improved due to low convergence rates in later *** adaptation process becomes more accurate by mitigating the MSE error of load current to converge faster and the dynamic response is further *** simulated and tested results validate the accuracy and efficacy of the SCPNLMF technique and charging system.

关键词： Multifunctional charging power quality SCPNLMF voltage source converter

Sustainable remediation of failed slope using helical soil nails

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Mountain Science 2023年第6期20卷 1742-1758页

作者： Saurabh RAWAT Ashok Kumar gupta Pankaj SHARMA Department of Civil Engineering Jaypee University of Information Technology Department of Civil Engineering Chitkara University Institute of Engineering and TechnologyChitkara University

The performance of a helical soil nailed structure is dependent on the installation torque required and the consequent pullout resistance *** present research work aims at proposing theoretical models to estimate the required torque during installation of helical soil ***,theoretical models are also developed to predict the pullout capacity of single and group of the helical nail for uniform and staggered *** proposed model predicts the pure-elastic and elastic-plastic pullout behavior of different helical *** equation for estimating the capacity-totorque Ratio(Kt)has also been developed for different nail shaft *** results from the proposed models are validated with experimental results obtained from model testing of both single and group of helical *** predicted results are also compared for validation with the published *** results for installation torque and pullout load depict that the developed models predict values which are in accordance with the experimental results and are also found in good agreement with the published ***,the proposed models can effectively be used by the filed engineers for estimating the required installation torque and corresponding pullout capacities for single or double plate helical soil nails in cohesionless soil under surcharge pressure range of 0–50k Pa.

关键词： Theoretical torque Pullout force Pure elastic Elastic plastic Capacity to torque ratio

Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第4期71卷 1849-1866页

作者： Swati Singh Isha gupta Sheifali gupta Deepika Koundal Sultan Aljahdali Shubham Mahajan Amit Kant Pandit Department of Electronics and Communication Engineering University Institute of TechnologyHimachal Pradesh UniversityShimla171005India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Systemics School of Computer ScienceUniversity of Petroleum and Energy StudiesDehradunIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia School of Electronics&Communication Shri Mata Vaishno Devi UniversityKatra185320India

In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticrecognition of the diseases at earlier stages is important as well as economicalfor better quality and quantity of fruits. Computer aided detection (CAD)has proven as a supportive tool for disease detection and classification, thusallowing the identification of diseases and reducing the rate of degradationof fruit quality. In this research work, a model based on convolutional neuralnetwork with 19 convolutional layers has been proposed for effective andaccurate classification of Marsonina Coronaria and Apple Scab diseases fromapple leaves. For this, a database of 50,000 images has been acquired bycollecting images of leaves from apple farms of Himachal Pradesh (H.P)and Uttarakhand (India). An augmentation technique has been performedon the dataset to increase the number of images for increasing the *** performance analysis of the proposed model has been compared with thenew two Convolutional Neural Network (CNN) models having 8 and 9 layersrespectively. The proposed model has also been compared with the standardmachine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimentalresults, it has been observed that the proposed model has outperformed theother CNN based models and machine learning models with an accuracy of99.2%.

关键词： Deep learning convolutional neural network apple leaves apple scab support vector machine

Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Defence Technology（防务技术） 2023年第6期24卷 58-82页

作者： S.gupta T.Mukhopadhyay V.Kushvaha Department of Civil Engineering Indian Institute of Technology JammuJ&KIndia Department of Aerospace Engineering Indian Institute of Technology KanpurUPIndia

The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive ***-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical ***,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational *** order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element *** composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)*** study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high *** CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction *** this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite ***

关键词： Micromechanics of fiber-reinforced composites Machine learning assisted stress prediction Microstructural image-based machine learning CNN based stress analysis

Drop weight impact analysis of GFRP tubes with hollow glass particle-filled matrix

在线全文

学校读者我要写书评

暂无评论

Defence Technology（防务技术） 2023年第11期29卷 1-9页

作者： Daniel Paul R.Velmurugan N.K.gupta Department of Aerospace Engineering Indian Institute of Technology MadrasChennaiIndia Department of Applied Mechanics Indian Institute of Technology DelhiNew DelhiIndia

Protecting occupants or payloads in crashes and blasts is of utmost importance in both moving and immobile *** way of achieving this is by using a sacrificial energy *** tubes have been studied as potential energy absorbers due to their ability to fail progressively under axial *** this study,the energy absorption capability of these tubes is enhanced by adding hollow glass particles to the ***-weight tests are performed on composite tubes,and a digital image correlation(DIC)-based technique is used to capture their load-displacement *** eliminates the use of electronic data acquisition systems,load cells,and *** load-displacement curves of the tubes are obtained from the DIC-based technique and examined to understand their crushing *** the mean crush load shows a drop,an increase in crush length is *** specific energy absorbed by the tubes improves with an increase in GMB volume *** addition of 0.1,0.2,0.3 and 0.4 vol fractions of GMB results in the specific energy absorption increasing by6.6%,14.7%,24%and 36.6%,respectively,compared to neat glass fibre-epoxy *** examination of the tubes and comparison with tubes subject to quasi-static compression is also performed.

关键词： Composite tubes Syntactic foam Impact Axial compression Digital image correlation

Fault Detection for Microgrid Feeders Using Features Based on Superimposed Positive-sequence Power

在线全文

学校读者我要写书评

暂无评论

Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1948-1958页

作者： Salauddin Ansari Om Hari gupta Om P.Malik Department of Electrical Engineering National Institute of TechnologyJamshedpur 831014India IEEE Department of Electrical and Software Engineering University of Calgary2500 University Drive NWCalgary Alberta T2N 1N4Canada

With the integration of distributed generation(DG) into a microgrid, fault detection has become a major task to accomplish. A scheme for microgrid feeder protection based on a newly proposed feature, ΔRP, defined as the ratio of the sum of positive-sequence real power(PSRP) at the two ends of the feeder to the larger of the PSRPs among the two ends, is proposed. If ΔRP is larger than a threshold, an internal fault is detected;otherwise, the fault is external or there is no fault. The proposed scheme is tested in various scenarios, including fault type, fault resistance, fault location, fault inception angle, varying DG penetration levels, and simultaneous, evolving, and composite faults. In addition to this, the proposed scheme offers a robust performance when subjected to noise, synchronization error, changes in sampling frequency, and changes in the topology of a microgrid. The dominancy of the proposed scheme is proven by a comprehensive comparative study with various available recent schemes. Test results on the IEEE 13-bus network indicate the viability of the proposed protection scheme for a microgrid. Finally, the proposed scheme has been validated on a real-time simulator.

关键词： Fault detection distributed generation microgrid protection superimposed power

Visual Object Tracking Based on Modified LeNet-5 and RCCF

在线全文

学校读者我要写书评

暂无评论

Computer Systems Science & Engineering 2023年第7期46卷 1127-1139页

作者： Aparna Gullapelly Barnali gupta Banik Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationDeemed to be UniversityHyderabad500075TelanganaIndia

The field of object tracking has recently made significant ***,the performance results in both deep learning and correlation filters,based trackers achieved effective tracking ***,there are still some difficulties with object tracking for example illumination and deformation(DEF).The precision and accuracy of tracking algorithms suffer from the effects of such *** this situation,finding a solution is *** research proposes a new tracking algorithm to handle this *** features are extracted by using Modified LeNet-5,and the precision and accuracy are improved by developing the Real-Time Cross-modality Correlation Filtering method(RCCF).In Modified LeNet-5,the visual tracking performance is improved by adjusting the number and size of the convolution kernels in the pooling and convolution *** high-level,middle-level,and handcraft features are extracted from the modified LeNet-5 *** handcraft features are used to determine the specific location of the target because the handcraft features contain more spatial information regarding the visual *** LeNet features are more suitable for a target appearance change in object *** experiments were conducted by the Object Tracking Benchmarking(OTB)databases like OTB50 and *** experimental results reveal that the proposed tracker outperforms other state-of-the-art trackers under different *** experimental simulation is carried out in *** overall success rate and precision of the proposed algorithm are 93.8%and 92.5%.The average running frame rate reaches 42 frames per second,which can meet the real-time requirements.

关键词： Object tracking correlation filters feature extraction experimental results semantic information

Modeling and targeting an essential metabolic pathway of Plasmodium falciparum in apicoplast using Petri nets

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Applied Mathematics(A Journal of Chinese Universities) 2022年第1期37卷 91-110页

作者： Sakshi gupta Gajendra Pratap Singh Sunita Kumawat Department of Mathematics Amity School of Applied SciencesAmity University HaryanaGurugramIndia School of Computational and Integrative Sciences and Concurrent Faculty at Special Centre for Systems Medicine Jawaharlal Nehru UniversityNew DelhiIndia

Petri net(PN) is one of the promising computational and mathematical formalisms used to represent and study the behavior of complex metabolic networks. The various available analysis techniques of PN could be used to validate and analyze the network in different scenarios. Plasmodium falciparum is one of the threatening parasites which causes malaria, a deadly disease affecting a large number of today’s world population. The development of antimalarial drug resistance is an emerging global threat, highlighting the need to discover novel antimalarial targets. The fatty acid biosynthesis of malarial parasite is one of the essential metabolic pathways required for its growth and is present in apicoplast, a non-photosynthetic plastid. The malarial parasite obtains fatty acids by using type two fatty acid synthase(FAS II) enzyme,which is different from type one enzyme used by human host, making it an ideal drug *** article proposes and studies the PN model of the parasite’s FAS II pathway to analyze the mechanism of potential drug targets in this pathway. The proposed PN model can serve as a base for further findings in the field of antimalarial drug targets to decrease the malaria mortality rate.

关键词： Petri net malaria Plasmodium falciparum type II fatty acid biosynthesis drug target property analysis

Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第3期70卷 4523-4543页

作者： Ashutosh Kumar Dubey Umesh gupta Sonal Jain Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Institute of Engineering and Technology JK Lakshmipat UniversityJaipurIndia

This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of clustering and *** the first phase,the clustering(k-means and FCM)algorithms were employed independently and the clustering accuracy was evaluated using different *** the second phase,the non-clustered data obtained from the first phase were preprocessed with *** was performed using k-means(TLBO-KM)and FCM(TLBO-FCM)(TLBO-KM/FCM)*** objective function was determined by considering both minimization and maximization ***-clustered data obtained from the first phase were further utilized and fed as input for threshold *** benchmark datasets were considered from theUniversity of California,Irvine(UCI)Machine Learning Repository for comparative study and *** are breast cancer Wisconsin(BCW),Pima Indians Diabetes,Heart-Statlog,Hepatitis,and Cleveland Heart Disease *** combined average accuracy obtained collectively is approximately 99.4%in case of TLBO-KM and 98.6%in case of *** approach is also capable of finding the dominating *** findings indicate that TLBO-KM/FCM,considering different computational measures,perform well on the non-clustered data where k-means and FCM,if employed independently,fail to provide significant *** different feature sets,the TLBO-KM/FCM and SVM(GS)clearly outperformed all other classifiers in terms of sensitivity,specificity and ***/FCM attained the highest average sensitivity(98.7%),highest average specificity(98.4%)and highest average accuracy(99.4%)for 10-fold cross validation with different test data.

关键词： K-means FCM TLBO TLBO-KM TLBO-FCM TLBO-KM/FCM machine learning algorithms

Memory response in a nonlocal micropolar double porous thermoelastic medium with variable conductivity under Moore-Gibson-Thompson thermoelasticity theory

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Ocean Engineering and Science 2023年第3期8卷 263-277页

作者： Shishir gupta Rachaita Dutta Soumik Das Department of Mathematics and Computing Indian Institute of Technology(ISM)DhanbadJharkhand 826004India

The present study enlightens the two-dimensional analysis of the thermo-mechanical response for a mi-cropolar double porous thermoelastic material with voids(MDPTMWV)by virtue of Eringen’s theory of nonlocal ***-Gibson-Thompson(MGT)heat equation is introduced to the considered model in the context of memory-dependent derivative and variable *** employing the normal mode technique,the non-dimensional coupled governing equations of motion are solved to determine the an-alytical expressions of the displacements,temperature,void volume fractions,microrotation vector,force stress tensors,and equilibrated stress *** two-dimensional graphs are presented to demon-strate the influence of various parameters,such as kernel functions,thermal conductivity,and ***,different generalized thermoelasticity theories with variable conductivity are compared to visualize the variations in the distributions associated with the prior mentioned *** particu-lar cases are also discussed in the presence and absence of different parameters.

关键词： Memory-dependent derivative Eringen’s nonlocal elasticity theory Micropolar double porous thermoelastic material with voids Moore-Gibson-Thompson thermoelasicity Variable conductivity