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Modeling and prediction of nonlinear cable slab dynamics using Koopman operators

Mechatronics 2025年 110卷

作者： Michael Pumphrey Almuatazbellah M. boker Mohammad Al Saaideh Natheer Alatawneh Yazan M. Al-Rawashdeh Khaled Aljanaideh Mohammad Al Janaideh School of Engineering University of Guelph Guelph ON N1G 2W1 Canada Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA Department of Electrical and Computer Engineering Memorial University St. John’s NL A1B 3X5 Canada School of Computer Science and Engineering Sacred Heart University 3135 Easton Turnpike Fairfield CT USA Department of Electrical Engineering Al-Zaytoonah University of Jordan Amman Jordan Department of Aeronautical Engineering Jordan University of Science and Technology Irbid 22110 Jordan University of Jordan Amman Jordan Fraunhofer Institute for Applied Optics and Precision Engineering Jena Germany

A novel approach for modeling the nonlinear dynamics of cable slabs using Koopman operator theory is presented. Cable slab dynamics are a critical challenge in precision motion systems, as the cables can induce undesired vibrations and disturbances on motion stages. To address this, a higher-dimensional state-space model with nonlinear observable functions is developed to approximate the cable slab dynamics. The proposed model achieves a prediction error within ∼ 1 % over the specified motion range and demonstrates robustness in predicting untrained, randomized, acyclic cable slab motions. A systematic evaluation of various observable functions was conducted to minimize the modeling errors, leading to an optimized model with fractional-order exponents. When compared with a neural network-based state-space model (NN-SS), the Koopman approach demonstrated faster training and better performance. For force prediction, the Koopman approach achieved a reduction of three-quarters in maximum error when compared with the NN-SS method. This work offers a concise and experimentally validated analytical framework specifically for developing accurate predictive models of nonlinear cable slab dynamics.

关键词： Koopman operator Cable slab Motion control Nonlinear dynamics Data-driven modeling System identification

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Mining social network data for predictive personality modelling by employing machine learning techniques

Mining social network data for predictive personality modell...

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International Conference on Computational Advancement in Communication Circuit and System, ICCACCS 2018

作者： Sengupta, Arjun Ghosh, Anupam Tata Consultancy Services Ltd Candor Kolkata One Hi-Tech Structures Pvt. Ltd IT/ITES SEZ Block G III 5th Floor Tower B-II New Town Kolkata700160 India Department of Computer Science & Engineering Netaji Subhash Engineering College Techno City Garia Kolkata700152 India

ISBN: (纸本)9789811386862

Facebook, Twitter, LinkedIn and Tumblr are online social networking platforms where the users send and receive messages on the topic of their choice and express their sentiments. The usage of these sites has exponentially increased over the last few years, thereby increasing the information posted on online social media sites. The quantity of information/tweets keeps increasing on a daily basis. Twitter has become a stable platform to identify personality-related indicators and encrypted in user profiles and pages related to a subject. In this proposed work, we present a scalable real-time system for sentiment analysis of Twitter data. This work will collect tweets of the users in real time and thus provide a basis to identify each tweet into either positive or negative based on the mind-set of the user, thereby providing a real-time analysis of the users regarding a certain topic. The system relies on feature extraction from the tweets generated in real time. A supervised learning approach based on ensemble learning is used to train various classifiers based on the features extracted. A design and implementation in Flask and Celery has been carried out which contains the feature extraction and classification tasks. The system is scalable with respect to the size of the input data and the rate of data arrival. The merits of the proposed system in terms of scalability, performance and classification accuracy was evaluated experimentally. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Real time systems

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Android Malware Detection Amid COVID-19

Android Malware Detection Amid COVID-19

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Rohit Srivastava R.P. Mishra Vivek Kumar Himanshu Kumar Shukla Neha Goyal Chandrabhan Singh Computer Science and Engineering F.O.E.T. University Lucknow India Electronics and Communication Dr R.M.L. Awadh University Ayodhya India Computer Science and Engineering Teerthanker Mahaveer University Moradabad India Computer Science and Engineering K.M. C. Language University Lucknow India Computer Science and Engineering B.N. College of Engg. and Tech. Lucknow India

ISBN: (数字)9781728189086

ISBN: (纸本)9781728189093

Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious.

关键词： COVID-19 XML Static analysis Machine learning Feature extraction Malware Smart phones

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Grain refinement process of AZ61 alloy by plastic deformation

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Materials Today: Proceedings 2024年

作者： Rajalaxmi Hegde S. Mohanasundaram S. Krishnaraj S. Arunkumar M. Udayakumar D. Suresh Department of Computer Science and Engineering NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karnataka India Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Mechanical Engineering Sri Sai Ram Engineering College West Tambaram Chennai India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sankunthala Engineering College Avadi-Veltech Road Chennai India University of Technology and Applied Sciences Salalah Oman Department of Mechanical Engineering V.S.B Engineering College Karur Tamil Nadu India

Grain refinement is a pivotal technique for enhancing the mechanical attributes of magnesium alloys, offering improvements in strength, ductility, and fatigue resistance. Among the methods for achieving grain refinement, Severe Plastic Deformation (SPD) stands out due to its effectiveness in imposing extreme strains on materials, either at room temperature or under moderately elevated thermal conditions. This process significantly alters the microstructure by transforming larger grains into much finer ones, thereby augmenting the alloy’s mechanical performance. The preparatory treatment of an alloy prior to SPD plays a critical role in determining the resultant microstructure and its associated mechanical properties. Although various preliminary treatments have been investigated for their impact on magnesium alloys, the specific influence of these treatments on the grain refinement of the AZ61 alloy necessitates further exploration. This gap in knowledge highlights the need for rigorous research to comprehend how different initial treatments, such as pre-annealing, influence the effectiveness of subsequent SPD processes. This study aims to meticulously examine the impact of various initial treatments on the grain refinement of AZ61 alloy through SPD. by systematically investigating these effects, the research seeks to deepen our understanding of microstructural evolution in AZ61 alloy and to derive strategies for optimizing processing routes that enhance its mechanical properties. The findings from this investigation are expected to significantly advance the application of AZ61 alloy in critical engineering domains where the demand for lightweight yet high-performance materials is crucial. This research could therefore have substantial implications for industries such as aerospace and automotive, where optimizing the balance between weight and strength is paramount.

关键词： AZ61 alloy Magnesium alloys Grain refinement Severe plastic deformation Initial treatment

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Variant component principal linear reduction for prediction of hypothyroid disease using machine learning

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Journal of Physics: Conference Series 2021年第1期1767卷

作者： M. Shyamala Devi P. Swathi Ayesha Jahangir A. Dhanunjai Sai Kumar A. Naga bhushan Naidu M. Raghuram Manas Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Third Year B.Tech Students Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

With the tremendous technological growth, the world is shifted to adapt the different food and life style by the people that results in the improper working of the body organs. The change in the food habits leads to a major problems that we face in the current scenario is the presence of hypothyroid in the body. The likelihood of hypothyroid still ruins as a challenging issue due to the uncertainty of proper symptoms. With this background, the machine learning can be used towards health care scenarios for the prediction of disease based on the patients past history. This paper focus on predicting the existence of hypothyroid with respect to the patients' medical parameters. The hypothyroid patient dataset is taken from the UCI Metadata repository with 24 columns and 3163 unique patient's records is used for the experimentation of hypothyroid with the following contributions. Firstly, the hypothyroid dataset from UCI machine repository is subjected with the data processing and exploratory analysis of the dataset. Secondly, the unrefined data set is fixed with different classifier algorithm to find the presence of hypothyroid and to examine the efficiency metrics before and after feature scaling. Thirdly, the data is processed to PCA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Fourth, the data is processed to LDA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Experimental results show that the Kernel Support Vector Machine classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced PCA dataset. Similarly, the Logistic Regression, Kernel Support Vector Machine and Gaussian Naïve bayes classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced LDA dataset.

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5G Tiny-ML AI-based IoT eNose system for Hazardous Odour Detection and Classification

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IEEE Sensors Journal 2025年

作者： Fayos-Jordan, Rafael Alselek, Mohammad Khadmaoui-bichouna, Mohamed Segura-Garcia, Jaume Alcaraz-Calero, Jose M. University of the West of Scotland School of Computing Engineering and Physics Scotland PA1 2BE United Kingdom Tech Lead London United Kingdom ETSE Universitat de Valencia Valencia46100 Spain Aston University School of Computer Science and Digital Technologies BirminghamB4 7ET United Kingdom

More than 2 million people have died in the world in 2019 exposed to hazard substances. In this context, it is of paramount importance to deliver effective systems to help minimizing such number of dead. The usage of advanced technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), plays a critical role in the detection of hazardous substances within Industry 4.0. The combination of these technologies enhance the efficiency and accuracy of monitoring harmful materials, improving safety standards and operational processes in industrial environments. AI can be used to analyse vast amounts of data for identifying patterns and predicting potential hazards, while IoT connects various devices and sensors to ensure real-time tracking and prompt responses to risks. This technological synergy is essential for modern industries aiming to create safer and more automated systems. In this work, we propose a 5G AI-IoT e-Nose system for the real-time detection and classification of 5 hazardous odours. The proposed AI model is very lightweight and it is affordable for our IoT micro controller unit. The system has been validated in laboratory conditions, but has the advantage and potential impact to be effective in any scenario. © 2001-2012 IEEE.

关键词： Industrial poisons

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Activation Layers Implication of CNN Sequential Models for Facial Expression Recognition

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： M. Shyamala Devi Ankita Sagar Karan Thapa Mreenmoy Hazarika P. Swathi Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial Expression Recognition is the critical part in the human emotional detection in the field of image processing. The application tends to soft or hard real time application based on the power of expression detection of idle image or videos. The Social communication with any object can be done by verbal or non-verbal format. Expression and Emotion detection completely rely on the non-verbal communication and facial expression. Machine Learning play an important role in the recognition of facial expression. An attempt is made in this paper to analyse the performance of Convolutional neural network models with diverse activation layer for the performance ***, the facial expression dataset is extracted from the website http://***/ckagree/, http://***/database/faces/ is subjected with the data processing. Secondly, the data analysis is done for the distribution of expression image in the training and testing dataset. Thirdly, the facial expression images are detected with HAAR cascade and then the images are cropped with (350, 350). Fourth, the facial expression images is applied with normalized and the bottleneck features are created for training and testing data. Fifth, the training dataset is fitted with convolutional sequential neural network models with various activation layers like Sigmoid, Elu, Relu, Selu, Tanh, Softsign and Softplus. Sixth, the performance analysis is done with loss and accuracy for all the epoch of all CNN models for all the activation layers. Experimental results show that CNN sequential model with Relu activation layer is found to have the accuracy of 100%.

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Epoch interrogation for skin cancer detection using convolutional neural network models

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： A Poornima M Shyamala Devi M Sumithra Mullaguri Venkata bharath Swathi S Sathishkumar K Yogesh Shubham Santosh Upadhyay Nitesh Kumar Sah Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Skin cancer is very important notable disease and it is probable to everyone nowadays, it flourishes on the area of body where it exposed to ultraviolet rays. It leads anomalous gain in skin cells. It initiate on various parts of body like face, hand and bottoms of the feet as cautious hole or spot. The initial investigation of anomalous gain is essence to cure the disease at early stage, and it still remains a feasible challenge in the scientific improvements. From the analysis, this paper endeavour to inspect the category of disease with the following improvements. Initially, the skin dataset from ISIC machine archive is utilized for image processing. Secondly, the values of dataset images are normalized by dividing all the RGb values by 255. Thirdly, keras sequential API is used to add one layer at a time, initiating from the input. The CNN can extract the features that are useful for classifying the image, by using the kernel filter matrix. MaxPool reduce the computational cost by down-sampling the image, and the relu activation function is implemented to provide non linearity to the network. The flatten layer is utilized to remodel the final feature maps into 1D vector. CNN model provides accuracy of 94.83% with 3297 images and ResNet 50 model has attained accuracy of 90.78% due to less number of images used for classification. AlexNet model has attained accuracy of 81.8% with 1300 images and GoogleNet V3 inception has attained accuracy of 96% with 3374 images. Finally Vgg16 model has attained accuracy of 97.3% with 5636 samples.

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Extracting Facial Features in Filter Modified Images using Neural Networks

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： M Sumithra M Shyamala Devi A Poornima Shatadru Sarkar Harish Patel Preethi Mahankali boggula Navya Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial identification plays a vital role in this digital era. Recent advancements in technology provide various filtering effects while capturing the images. Extracting the facial features from the filter modified image becomes challenging. To identify the important facial attribute values from those images, special features extraction techniques needs to be applied. Facial points are the key factors to recognize the face. When these factors are altered, facial detection becomes crucial. Initially apply the image pre-processing techniques to reduce the noise level in the images then perform feature selection. To overcome the curse of dimensionality, Occam's razor approach is followed to limit the selection of attributes. In different stages the features are going to be filtered by replacing the weaker attributes. Later Pearson Correlation, a filter based correlation technique is applied to evaluate the deviated numerical value from the target. The deviated features can be treated with Lasso effect to suppress the poor weighted features. Finally through recursive feature elimination, highest influencing attribute factor can be identified. In this approach some of the facial feature which is used in training the neural network can be discarded based on the deviation rates. In terms of accuracy the proposed system can recognize the dataset with 70% of accuracy with filter effected images.

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Annotation and Retrieval of Retinal Images using Support Vector Machine with Active Learning

Annotation and Retrieval of Retinal Images using Support Vec...

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2019 IEEE International Conference on Intelligent techniques in Control, Optimization and Signal Processing, INCOS 2019

作者： Hephzi Punithavathi, I.S. Ganeshkumar, P. Duraipand, P. Shadan College of Engg. Tech Department of Computer Science Engineering Hyderabad India Anna University Regional Campus Department of Information Technology Coimbatore India J.B.Institute of Engg. Technology Department of Electrical Electronics Engineering Hyderabad India

ISBN: (纸本)9781538695425

in the medical field, many images are acquired using different modalities like X-ray, Computed Tomography and Magnetic Resonance Imaging. Organizing these images and retrieving them is a critical task. in this paper, a novel retinal image retrieval and automatic annotation of ten different retinal diseases is presented and is comprised of three steps namely, feature extraction, automatic annotation and retrieval of similar images. in feature extraction, visual features like texture, color and shape were derived from the images and stored in a database. The retinal images are affected by various diseases and have to be identified easily and quickly for better treatment. For automatic annotation, we have considered ten different retinal diseases and annotated the retinal image with its corresponding disease using Support Vector Machine (SVM) with Active Learning (AL) and discriminative based automatic image annotation. in retrieval of images, bray Curtis distance measure was used to retrieve the similar images by comparing the feature values of images stored in the database. Our proposed system has achieved a precision of 82% and specificity of 96.3%. © 2019 IEEE.

关键词： Support vector machines

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