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2nd International Conference on Nonlinear Dynamics and Applications, ICNDA 2024

作者： Chettri, Yogesh Micheal, David Raj Abdul Rahim, M.F. Department of Mathematics Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Majitar737136 India Vellore Institute of Technology Tamil Nadu Vellore India University Malaysia of Computer Science and Engineering Aras 1 and 2 Block 2 VSQ@PJ City Centre Jalan Utara Section 14 Selangor Petaling Jaya46200 Malaysia

ISBN: (纸本)9783031668739

The research delves into investigating the propagation of two-soliton in a compressible hyperelastic rods in the framework of the KdV equation. Using suitable transformation, the KdV equation is transformed to the standard KdV equation. By utilizing the Hirota bilinear method, the two-soliton solution of the KdV equation is obtained. Time evaluation of the propagation of two-soliton are plotted. Effect of various parameters on the two soliton solution are also investigated. Phase shift and their variation for different parameter values are also presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Solitons

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AI-based Gas Turbine Multi-Component Health Prognosis via Recurrent Expansion of Gas Path Parameters

AI-based Gas Turbine Multi-Component Health Prognosis via Re...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Berghout, Tarek Amirat, Yassine Bounceur, Ahcene Benbouzid, Mohamed University of Batna 2 Laboratory of Automation and Manufacturing Engineering Batna Algeria Isen Yncréa Ouest L@bISEN Brest France King Fahd University of Petroleum & Minerals Department of Information and Computer Science Dhahran Saudi Arabia University of Brest Umr Cnrs 6027 Irdl Brest France

ISBN: (纸本)9798350361025

Investigating Gas Turbine (GT) degradation based on Gas Path Parameters (GPPs) is essential for its maintenance and operation. Monitoring GPPs is crucial for early detection of degradation, enabling timely maintenance interventions and preventing potential failures. Integrating physics-based thermodynamic models with representation learning significantly improves predictive maintenance studies and provides a solution to challenges posed by expensive and impractical accelerated aging experiments. However, this integration also presents challenges in managing both data complexity and data drift. In this context, this article aims to address these gaps by extending and improving previous research through (i) exclusive use of GPPs;(ii) the implementation of advanced data preprocessing techniques;and (iii) the use of innovative representation learning strategies. Specifically, it introduces ProgMachina, a tool for data quality analysis in prognosis studies that addresses issues related to data complexity in GPPs. Furthermore, to diversify the feature space and improve the adaptability of representation learning to degradation patterns, this paper proposes using Multiverse Recurrent Expansion with Multiple Repeats (MV-REMR) approach, which is based on a series of Recurrent Neural Networks (RNNs). For evaluation, this study incorporates cross-validation and multiple metrics while comparing against multiple RNNs and state-of-the-art works, demonstrating stable and promising performance, making it a suitable choice for GT prognosis tasks. © 2024 IEEE.

关键词： Gas turbines

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Segmentation and Classification of Stomach Abnormalities Using Deep Learning

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computers, Materials & Continua 2021年第10期69卷 607-625页

作者： Javeria Naz Muhammad Attique Khan Majed Alhaisoni Oh-Young Song Usman Tariq Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistanl 2College of Computer Science and EngineeringUniversity of Ha’ilHa’ilSaudi Arabial 3Department of SoftwareSejong UniversitySeoulKoreaGwangjin-guKoreal 4College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabial 5Faculty of Applied Computing and TechnologyNoroff University CollegeKristiansandNorway

An automated system is proposed for the detection and classification of GI *** proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep *** first bleeding region is segmented using a hybrid *** threshold is applied to each channel extracted from the original RGB ***,all channels are merged through mutual information and pixel-based *** a result,the image is *** and deep learning features are extracted in the proposed classification *** transfer learning(TL)approach is used for the extraction of deep *** Local Binary Pattern(LBP)method is used for texture ***,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture *** selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning *** experimental process is evaluated on the basis of two datasets:Private and *** accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data *** can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.

关键词： Gastrointestinal tract contrast stretching segmentation deep learning features selection

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Applications of Machine Learning for Face Mask Detection During COVID-19 Pandemic 2nd

Applications of Machine Learning for Face Mask Detection Dur...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Khan, Sarfraz Fayaz Khan, Mohammad Ahmar Al-Quadah, Rabiah SAT Algonguin College 1385 Woodroffe Ave OttawaONC211K2G1V8 Canada Department of MIS-CCBA Dhofar University Salalah Salalah Oman Department of Computer Science and Software Engineering Concordia University Mackay Street MontrealQC2070H3G 2J1 Canada

ISBN: (纸本)9789819934805

Covid-19 pandemic has forced us to adapt to the new lifestyle. World Health Organization (WHO) recommends that people should adhere to the public health expert’s guidelines to fight against the spread of Covid-19. The most essential Covid-19 guideline has been the use of facemask which has been enforced throughout the globe and has proven to contain the spread of corona virus. The proposed study aims at examining the detection of mask usage by people through Machine learning approach. The research employs binary classification problem to detect and classify people wearing masks from the people not-wearing masks. Three machine learning models namely InceptionV3, VGGNet and Resnet have been adapted in this research for pre-processing the input images. Similarly, XGBoost, Random Forest and fully connected DNN models have been used for decoding and classification. Performance evaluation has also been done for the different models and a comparison of the performances has been carried out as a part of the research. The results obtained through performance evaluation technique showed that ResNet+Fully Connected DNN is the best among the developed models where the precision was 99.73%, accuracy was 99.7%, F1 Score was 99.69% and the recall score was 99.66%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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A Truly Decentralized Blockchain Consensus Protocol that Avoids Concentration of Power

A Truly Decentralized Blockchain Consensus Protocol that Avo...

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International Conference on Industrial and Information Systems, ICIIS

作者： Kesara Wimal Geethapriya Liyanage School of Computer Science and Engineering University of Westminster London W1B 2HW United Kingdom Department of Computer Science and Engineering University of Moratuwa Moratuwa 10400 Sri Lanka

A consensus protocol plays a vital role in any blockchain platform by ensuring all components agree on common data values and follow the same course of action, even in the presence of defective components. This research project has discovered a problem associated with contemporary blockchain consensus protocols, which has an impact on both users of blockchain technology and investors in cryptocurrencies. The question of whether current blockchain platforms are truly decentralized has been raised by the blockchain community over a period of time. The most popular blockchains use Proof of Work (PoW), Proof of Stake (PoS), or a combination of both, but both systems have limitations in terms of decentralization. Several alternative consensus protocols have emerged recently, but none have achieved total decentralization or welcomed any level user to be the next miner or forger. Therefore, true decentralization of a blockchain system is more challenging as most of the popular consensus protocols were invented to accommodate decentralization but eventually, driven by the centralization of power. This paper proposes a truly decentralized consensus protocol that is capable of avoiding the centralization of power even in the future regardless of the number of peers. The protocol also makes sure it avoids the use of specialized hardware resources, decreases the level of energy consumption, and encourages network activities. Additionally, the novel consensus protocol offers a more democratic and fair approach to blockchain consensus.

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Multi-Source Fusion User Authentication Based on Channel and Image Features 2

Multi-Source Fusion User Authentication Based on Channel and...

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2nd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies, SMC-IoT 2023

作者： Wang, Aiwen Tang, Jie Luo, Hongyu Wen, Hong Han-Ho, Pin Chang, Shih Yu University of Electronic Science and Technology of China Usetc Institute of Aeronautics and Astronautics Chengdu611731 China The University of Waterloo 200 University Avenue West Waterloo Department of Electrical and Computer Engineering Uw Engineering UW ONN2L 3G1 Canada

ISBN: (纸本)9798350373257

This paper aims to address the real-time transmission of image information verification in industrial operations through the fusion of multi-source information. It combines channel and image feature extraction, employing a neural network model based on self-supervised learning for deep feature learning. The model utilizes channel data collection, image data processing, and multiple convolutions to extract profound physical features, achieving a more accurate reflection of user environmental characteristics. Experimental results indicate that compared to traditional unsupervised learning and single-feature verification approaches, the proposed solution in this paper achieves a higher success rate in verification within industrial settings. © 2023 IEEE.

关键词： Neural network models

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5G Core PFCP Intrusion Detection Dataset 12

5G Core PFCP Intrusion Detection Dataset

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Amponis, George Radoglou-Grammatikis, Panagiotis Nakas, George Goudos, Sotirios Argyriou, Vasileios Lagkas, Thomas Sarigiannidis, Panagiotis K3Y Ltd. Sofia1612 Bulgaria International Hellenic University Kavala Campus Department of Computer Science 65404 Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece Aristotle University of Thessaloniki Department of Physics Thessaloniki54124 Greece Kingston University London Department of Networks and Digital Media SurreyKT1 2EE United Kingdom

ISBN: (纸本)9798350321074

The rapid evolution of the 5G environments introduces several benefits, such as faster data transfer speeds, lower latency and energy efficiency. However, this situation brings also critical cybersecurity issues, such as the complex and increased attack surface, privacy concerns and the security of the 5G core network functions. Therefore, it is evident that the role of intrusion detection mechanisms empowered with Artificial Intelligence (AI) models is crucial. Therefore, in this paper, we introduce a labelled security dataset called 5GC PFCP Intrusion Detection Dataset. This dataset includes a set of network flow statistics that can be used by AI detection models to recognise cyberattacks against the Packet Forwarding Control Protocol (PFCP). PFCP is used for the N4 interface between the Session Management Function (SMF) and the User Plane Function (UPF) in the 5G core. In particular, four PFCP attacks are investigated in this paper, including the relevant network traffic data in terms of pcap files and the Transmission Control Protocol (TCP)/Internet Protocol (IP) and application-layer statistics. This dataset is already publicly available in IEEE Dataport and Zenodo. © 2023 IEEE.

关键词： Artificial intelligence

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Assessing the Impact Of Time Intervals On PM2.5 Prediction Using LSTM Neural Networks

Assessing the Impact Of Time Intervals On PM2.5 Prediction U...

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International Conference on Advanced Electrical engineering (ICAEE)

作者： Selma Boulkamh Nadia Zeghib Yacine Yaddaden LIRE Laboratory University of Constantine 2-Abdelhamid Mehri Constantine Algeria Department of Mathematics Computer Science and Engineering University of Quebec at Rimouski Canada

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349368

Air pollution is a global issue with profound implications to human health and environmental sustainability. Especially, PM2.5, which refers to particulate matter with a size of 2.5 microns or smaller, poses significant health risks. Therefore, accurately predicting PM2.5 levels is crucial. In this aim, various machine-learning methods are employed for PM2.5 prediction. The problem arises from the use of collected datasets at varied time intervals (5 minutes, 15 minutes, 1 hour, etc.), resulting in inconsistency across predictive approaches. While shorter time intervals seems to offer heightened sensitivity to changes, implying simpler prediction, this presumption may not be universally applicable. This paper investigates the impact of time intervals on PM2.5 prediction through a comparative analysis of datasets collected at different intervals (15 minutes, 1 hour, and 8 hours) using Long Short Term Memory Neural Networks (LSTM). By leveraging statistical analysis and predictive modeling techniques, we assess the dataset's distributions and evaluate prediction model performance. This study underscores the importance of interval selection in PM2.5 prediction and informs future efforts to enhance air quality surveillance and public health protection measures.

关键词： Analytical models Sensitivity Statistical analysis Atmospheric modeling Surveillance Neural networks Predictive models Public healthcare Protection Long short term memory

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Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving

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engineering Applications of Artificial Intelligence 2024年 135卷

作者： Azam, Shoaib Munir, Farzeen Kyrki, Ville Kucner, Tomasz Piotr Jeon, Moongu Pedrycz, Witold Department of Electrical Engineering and Automation Aalto University Espoo Finland Finnish Center for Artificial Intelligence Finland School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

Learning contextual and spatial environmental representations enhances autonomous vehicle's hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack global environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset. In closed-loop evaluations on CARLA's Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions. © 2024 The Authors

关键词： Autonomous vehicles

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Research on Biomimetic Design Methods for Humanoid Robot Thigh

Research on Biomimetic Design Methods for Humanoid Robot Thi...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Nie, Daming Xie, Anhuan Kong, Lingyu Zhang, Yu Zheng, Gang Fu, Yili Gu, Jason Zhejiang Laboratory Intelligent Robot Research Center Hangzhou311100 China Zhejiang University of Science and Technology School of Mechanical and Energy Engineering Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxB3M 1A2 Canada

ISBN: (纸本)9798350325706

Human bones have formed the preferred configuration for high-strength and lightweight after long-time evolution. Taking human's longest and strongest bone - the femur - as an example, it is consist of two characteristic layers, i.e. the substantia compacta and the substantia spongiosd. This article innovatively imitates the structural characteristics of human femur, the thigh of humanoid robot is designed in form of "variable thickness shell + variable density lattice". The thickness of shell and the density of lattice are adjusted by the initial stress distribution individually. Results show that the weight of shell and lattice of the thigh structure can be reduced by 20% under reasonable mapping relationship of "stress - shell thickness"and "stress - lattice rod diameter", while the structural stiffness meets the application requirements. Finally, the limiting factors of the "variable thickness shell + variable density lattice"structure designing approach are analyzed, and potential measures for optimizing the design method of the humanoid robot thigh in the future are described. © 2023 IEEE.

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