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Route Towards Road Freight Electrification in India:Examining Battery Electric Truck Powertrain and Energy Consumption

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Chinese Journal of Electrical Engineering 2022年第3期8卷 57-75页

作者： Sreedhar Madichetty Avram John Neroth sukumar Mishra B.Chitti Babu Department of Electrical and Electronics Engineering Mahindra University Ecole CentraleHyderabad 500043India Department of Electrical Engineering Indian Institute of TechnologyDelhi 110016India Department of Electronics and Communication Engineering Indian Institute of Information TechnologyDesign and ManufacturingKanchipuram 600127India

Medium-duty/heavy-duty trucks(MD/HDTs)are yet to be included in India’s electric mobility plans.With the improvement of electric vehicle(EV)technologies,there is a growing interest in battery-electric trucks(BETs)from original equipment manufacturers(OEMs).The time is opportune to consider electrification as a future direction for road freight in India.Accordingly,this article presents the results of an energy consumption simulation study of a BET under Indian conditions.This study specifically considered an MDBET over a domestic drive cycle.These energy consumption figures can facilitate future studies that analyze the technical and practical feasibility of BETs in the country.In addition,the article provides the requisite groundwork for BET modeling for a simulation study by reviewing available EV powertrain systems and components.Appropriate powertrain considerations are thereby obtained for a typical medium-duty/heavy-duty battery-electric truck(MD/HDBET)in the Indian context.

关键词： Electric truck heavy-duty battery electric truck medium-duty battery electric truck energy consumption vehicle powertrain

Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI

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Computers, Materials & Continua 2023年第8期76卷 2551-2570页

作者： Umair Muneer Butt Rimsha Arif sukumar Letchmunan Babur Hayat Malik Muhammad Adil Butt School of Computer Sciences Universiti Sains MalaysiaPenang1180Malaysia Department of Computer Science and IT The University of ChenabGujrat50700Pakistan

The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.

关键词： Brain diseases deep learning feature enhanced stacked auto encoder stack auto encoder

Imbalanced Classification in Diabetics Using Ensembled Machine Learning

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Computers, Materials & Continua 2022年第9期72卷 4397-4409页

作者： M.Sandeep Kumar Mohammad Zubair Khan sukumar Rajendran Ayman Noor A.Stephen Dass J.Prabhu School of Information Technology and Engineering Vellore Institute of TechnologyVelloreTamil Nadu632014India Department of Computer Science and Information Taibah UniversityMedinaSaudi Arabia College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia

Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar.The risk of diabetics can be lowered if the diabetic is found at the early stage.In recent days,several machine learning models were developed to predict the diabetic presence at an early stage.In this paper,we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics.The proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of classification.Different measures such as Accuracy,Precision,Recall,and F1-Score are used to evaluate the model.The results we obtained using K-Nearest Neighbor(kNN),Naïve Bayes(NB),Support Vector Machines(SVM),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)were 89.32%,91.44%,95.78%,89.3%,81.76%,and 80.38%respectively.The SVM model is more efficient than other models which are 21.38%more than exiting machine learning-based works.

关键词： Diabetics classification imbalanced data split-vote instance duplication

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

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Computers, Materials & Continua 2023年第3期74卷 5017-5033页

作者： Umair Muneer Butt Hadiqa Aman Ullah sukumar Letchmunan Iqra Tariq Fadratul Hafinaz Hassan Tieng Wei Koh School of Computer Sciences Universiti Sains MalaysiaPenang1180Malaysia Department of Computer Science The University of ChenabGujrat50700Pakistan Department of Software Engineering and Information System Universiti Putra MalaysiaSelangor43400Malaysia

Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.

关键词： Human activity recognition deep learning transfer learning neural network ensemble learning spatio-temporal

Spike-and nucleocapsid-based gold colloid assay toward the development of an adhesive bandage for rapid SARS-CoV-2 immune response detection and screening

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微系统与纳米工程(英文) 2023年第3期9卷 371-386页

作者： Imen Boumar Muhammedin Deliorman Pavithra sukumar Mohammad A.Qasaimeh Division of Engineering New York University Abu Dhabi(NYUAD)Abu DhabiUAE NYU Tandon School of Engineering New York UniversityNew YorkUSA

Immunoglobulin M(IgM)and immunoglobulin G(IgG)antibodies are important biomarkers used for the diagnosis and screening of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infections in both symptomatic and asymptomatic individuals.These antibodies are highly specific to the spike(S)and nucleocapsid(N)proteins of the SARS-CoV-2 virus.This paper outlines the development steps of a novel hybrid(vertical-lateral-vertical)flow assay in the form of a finger-stick point-of-care device,similar to an adhesive bandage,designed for the timely detection and screening of IgM and IgG immune responses to SARS-CoV-2 infections.The assay,comprising a vertically stacked plasma/serum separation membrane,conjugate pad,and detection(readout)zone,utilizes gold nanoparticles(AuNPs)conjugated with SARS-CoV-2 S and N proteins to effectively capture IgM and IgG antibodies from a pinprick(~15µL)of blood in just one step and provides results of no immune IgM−/IgG−,early immune IgM+/IgG−,active immune IgM+/IgG+or immune IgM−/IgG+in a short amount of time(minutes).The adhesive bandage-like construction is an example of the design of rapid,low-cost,disposable,and easy-to-use tests for large-scale detection and screening in households.Furthermore,the bandage can be easily adjusted and optimized to detect different viral infections as they arise by simply selecting appropriate antigens related to pandemics and outbreaks.

关键词： diagnosis utilize selecting

Discrete Event System Framework for Fault Diagnosis with Measurement Inconsistency:Case Study of Rogue DHCP Attack

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 789-806页

作者： Mayank Agarwal Santosh Biswas sukumar Nandi IEEE the Department of Computer Science and Engineering Indian Institute of Technology Guwahati

Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters.Due to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.

关键词： Fault diagnosis instrasion detection system (IDS) measurement inconsistent discrete event system (DES) rogue dynamic host configuration protocol (DHCP) server

Memristor based object detection using neural network

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High-Confidence Computing

High-Confidence Computing 2022年第4期2卷 52-56页

作者： Ravikumar KI sukumar R Department of E&CE Jain UniversityBangaloreKarnatakaIndia Department of E&CE Jain Institute of TechnologyDavangereKarnatakaIndia

With the increasing growth of AI,big data analytics,cloud computing,and Internet of Things applications,devel-oping memristor devices and related hardware systems to compute the deep learning application needs extensive data calculations with low power consumption and lesser chip area.Deep learning model is one of the AI methods which is gaining importance in object detection,natural language processing,and pattern recognition.A large amount of data handling is essential for driving the deep learning model with less power consumption.To address these issues,the paper proposed the Memristor-based object detection on the CIFAR-10 dataset and achieved an accuracy of 85 percent.The memtorch package in python is employed to predict the objects for implementation.

关键词： Memristor Deep learning Object detection CIFAR-10

A comprehensive review of the effect of different kinetic promoters on methane hydrate formation

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Chinese Journal of Chemical Engineering 2021年第4期34卷 1-16页

作者： Ekta Chaturvedi sukumar Laik Ajay Mandal Gas Hydrates Laboratory Petroleum Engineering DepartmentIndian Institute of Technology(ISM)DhanbadIndia

Gas hydrates have recently emerged as a better alternative for the production,storage,and transportation of natural gases.However,factors like slow formation rate and limited storage capacity obstruct the possible industrial application of this technique.Different types of promoters and synergists have been developed that can improve the kinetics and storage capacity of gas hydrates.This review focuses on different kinetic promoters and synergists that can be utilized to enhance the storage capacity of hydrates.The main characteristics,structure and the possible limitations of the use of these promoters are likewise portrayed in detail.The relationship between structure and storage capacity of hydrates have also been discussed in the review.Current status of production of gas from hydrates,their restrictions,and future difficulties have additionally been addressed in the ensuing areas of the review.

关键词： Gas hydrates Structure Storage capacity Kinetic promoters Nano-materials Surfactants

CircRNP complexes:from nature to design

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分子细胞生物学报:英文版 2023年第1期15卷 7-12页

作者： Stephen sukumar Nuthalapati Corinna Jessica Ulshofer Albrecht Bindereif Institute of Biochemistry Justus Liebig University of Giessen35392 GiessenGermany

Circular RNAs(circRNAs)have recently been established as a new and large class of noncoding RNAs,conserved in all eukaryotes investigated.However,functional and mechanistic investi-gations lag behind the rapid progress in circRNA expression studies,and no uniform function has emerged so far.Here,we focus on circRNA–protein(circRNP)complexes(circRNPs),since proteins associated with natural circRNAs likely guide us in the search for physiological functions of individual circRNAs.First,approaches for how to systematically search for circRNA-interacting proteins in natural circRNPs will be summarized,including some experimental considerations important for good scientific practice.Second,building on natural protein-sponging circRNAs,we introduce the concept of designer circRNAs functioning as specific protein sponges,using the heterogeneous nuclear ribonucleoprotein L(hnRNP L)and IMP3 proteins as two examples of classical RNA-binding proteins.Third,we discuss the general implications of this concept,in particular how it can be applied as a new way to intervene with RNA-based gene-regulatory networks.In sum,designer circRNAs sponging specific proteins open up exciting new perspectives in molecular medicine and on therapeutic options to treat human disease.

关键词： complexes sponge likely