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A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

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Digital Communications and Networks 2023年第5期9卷 1113-1122页

作者： Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Department of Computer Science and Information Systems BITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Systems and Computer Engineering Carleton UniversityOttawaONK1S 5B6Canada

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of

关键词： Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT)

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Vibration-Based Failure Prediction in Machinery: Novel Application of Time Series Decomposition and Neural Networks 5

Vibration-Based Failure Prediction in Machinery: Novel Appli...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Nirmal, W.C. Lakmal, H.K.I.S. Fernando, K.J.P. Nsbm Green University Faculty of Engineering Department of Mechatronic & Industrial Engineering Homagama Sri Lanka Nsbm Green University Faculty of Engineering Department of Electrical Electronic & Systems Engineering Homagama Sri Lanka

ISBN: (纸本)9798331530983

Predictive maintenance (PdM) is critical in reducing downtime and minimizing operational costs in industrial settings. This study introduces a novel framework for vibration-based failure prediction by combining Seasonal and Trend Decomposition using LOESS (STL) and deep learning models, specifically Long Short-Term Memory (LSTM) networks. The framework processes multivariate vibration data collected from two-roller mills in a tire manufacturing facility. STL is employed to decompose the data into trend, seasonal, and residual components, enabling noise reduction and enhanced feature extraction. These components serve as inputs to LSTM models for predicting machine condition scores. A comprehensive analysis compared five deep learning architectures: LSTM, Bidirectional LSTM, Gated Recurrent Unit (GRU), Deeper LSTM, and Conv1D LSTM. The GRU and Conv1D LSTM demonstrated superior performance across multiple vibration axes, achieving low errors and high R2 values. Results highlight the effectiveness of STL in preparing vibration data for sequential learning and the robust predictive capabilities of deep learning models. This study provides a scalable approach to PdM, enhancing reliability and efficiency in industrial machinery maintenance. Future work includes extending the framework to other machinery types and real-time monitoring systems. © 2025 IEEE.

关键词： Rollers (machine components)

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Machine Learning Techniques in Additive Manufacturing: A Review

Machine Learning Techniques in Additive Manufacturing: A Rev...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Alsalem, Ahmed Zohdy, Mohamed Oakland University Department of Industrial and Systems Engineering RochesterMI United States Oakland University Department of Electrical and Computer Engineering RochesterMI United States

ISBN: (纸本)9798350322347

Additive manufacturing (AM) is a 3D printing process widely used in industries such as automotive, healthcare, aerospace, and consumer goods. It creates different 3D objects by adding different layers over one another. AM manufacturing processes are capable enough to produce complex parts, consisting of intricate designs and geometries, characterized by well-established grain structure and properties without expensive tooling in reduced lead time and at low cost. It is a multi-physical process, including various parameters that impact the final product's quality. Moreover, Machine Learning (ML) techniques are suitable approaches aimed at understanding and predicting the complex processes of AM. In this study, an overview of AM and the challenges in the AM is presented, as well as the ML approaches to address the challenges in AM processes are also discussed. The recently introduced ML techniques in AM processes are evaluated explicitly. To be more precise, limited training data, material choice, and standardization regarding data pose major challenges that need to be addressed when applying ML techniques to AM methods. © 2023 IEEE.

关键词： 3D printing

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Automated Warehouse 5G Infrastructure Modeling Using Variational Autoencoders

Automated Warehouse 5G Infrastructure Modeling Using Variati...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Gulia, Rahul Ganguly, Amlan Kwasinski, Andres Kuhl, Michael E. Rashedi, Ehsan Hochgraf, Clark Rochester Institute of Technology Department of Computer Engineering RochesterNY United States Rochester Institute of Technology Department of Industrial and Systems Engineering RochesterNY United States Rochester Institute of Technology Department of Electrical and Computer Engineering Technology RochesterNY United States

ISBN: (纸本)9798350364910

The next decade is poised for a transformative shift in wireless communication technologies, driven by the increasing demand for data-intensive applications. Innovations in signal processing, network architecture estimation and design, and spectrum utilization will be critical in realizing the potential of 5G and beyond. These advancements will enable the seamless integration of emerging technologies and empower the digital transformation of industries and society. In this paper, we introduce a Variational Autoencoder (VAE) based model for indoor radio propagation modeling within an automated Industry4.0 warehouse or factory floor in the 5G wireless bands. We detail the creation of training data tensors, the architecture of our proposed VAE model, and its training using tensors that capture the impact of various interacting objects within a 5G-enabled smart warehouse infrastructure. The model is trained on multiple warehouse configurations to predict the signal-to-interference-plus-noise ratio (SINR) for both the validation dataset and unknown warehouse configurations. We present reconstruction error heatmaps to demonstrate the accuracy of our model and analyze its performance in different complex warehouse environments. © 2024 IEEE.

关键词： Signal to noise ratio

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Flexible, Super-stable, and Multifunctional Capacitive Pressure Sensors with Tilted Micropillar Array by Inclined Laser Ablation and Demolding

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IEEE Sensors Journal 2025年第6期25卷 9367-9374页

作者： Xu, Tianlong Li, Kaixin Zhou, Yihao Zhang, Dawei Sheng, Bin College of Optical Electrical and Computer Engineering Unversity of Shanghai for Science and Technology Shanghai 200093 China. Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instruments and System Shanghai 200093 China

Flexible capacitive pressure sensors have garnered considerable interest across diverse applications, including medical monitoring, electronic skin, and robotic tactile systems, owing to their straightforward fabrication process, low energy consumption, and robust stability. In this paper, we introduce a new inclined laser ablation (ILA) combined with demolding technique for the fabrication of flexible capacitive sensors featuring a microstructured dielectric layer composed of a tilted micropillar array (TMA). The design parameters of TMA were systematically optimized, including the tilt angle of the micropillars, array gap, aspect ratio, and bottom film thickness. The resulting capacitive pressure sensor, equipped with 75° tilted micropillar arrays (aspect ratio of 5:1), demonstrates exceptional sensitivity (1.12 kPa-1) within the low-pressure range (0-2 kPa), rapid response and recovery times, a low pressure detection limit of 0.25 Pa, and transient response stability factor as high as 243.9. The proposed capacitive pressure sensor was validated for the detection and recognition of human motions when attached to various body parts. Additionally, the sensors also demonstrated significant potential for diverse applications, including tactile sensing, and Morse code communication. © 2025 IEEE.

关键词： Aspect ratio

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Reliability evaluation for time-based stochastic manufacturing networks with time-dependent reliability

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Quality Technology and Quantitative Management 2024年第6期21卷 1025-1041页

作者： Chang, Ping-Chen Huang, Cheng-Fu Fiondella, Lance Department of Industrial Engineering and Management National Taipei University of Technology Taipei Taiwan Department of Business Administration Feng Chia University Taichung Taiwan Department of Electrical & Computer Engineering University of Massachusetts Dartmouth MA United States

This study aims to evaluate the system reliability for a time-based stochastic manufacturing network (TSMN). The architecture of a TSMN includes arcs and nodes where each node is an inspection station and each arc is a workstation of identical machines. Due to the possibility of partial or complete maintenance and machine failures, the reliability should be time-dependent and the arcs could be considered as stochastic capacities. To consider time factor, the Weibull reliability function is utilized to characterize the time-dependent reliability of each machine. Then, based on the reliability of each machine, the workstation reliability can be derived, where the workstation reliability is the probability that a workstation can provide sufficient number of machines to complete a demand until a given time. Focusing on a given TSMN with the specified demand and constrained operating time, the performance for a TSMN, which is referred to as TSMN reliability, can be calculated in terms of workstation reliability. In addition, the situation for machines having different times under operation is analyzed for TSMN reliability. A step-by-step procedure for evaluating the TSMN reliability is developed, and a printed circuit board manufacturing system is discussed for the proposed TSMN reliability procedure. © 2023 International Chinese Association of Quantitative Management.

关键词： Reliability

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Feature Importance Analysis and Machine Learning for Alzheimer’s Disease Early Detection: Feature Fusion of the Hippocampus, Entorhinal Cortex, and Standardized Uptake Value Ratio

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Digital Biomarkers 2024年第1期8卷 59-74页

作者： Hassouneh, Aya Bazuin, Bradley Danna-Dos-Santos, Alessander Acar, Ilgin Abdel-Qader, Ikhlas Electrical and Computer Engineering Western Michigan University KalamazooMI United States Department of Physical Therapy Western Michigan University KalamazooMI United States Department of Industrial and Entrepreneurial Engineering and Engineering Management Western Michigan University KalamazooMI United States

Introduction: Alzheimer’s disease (AD) is a progressive neurological disorder characterized by mild memory loss and ranks as a leading cause of mortality in the USA, accounting for approximately 120,000 deaths per year. It is also the primary form of dementia. Early detection is critical for timely intervention as the neurodegenerative process often starts 15–20 years before cognitive symptoms manifest. This study focuses on determining feature importance in AD classification using fused texture features from 3D magnetic resonance imaging hippocampal and entorhinal cortex and standardized uptake value ratio (SUVR) derived from positron emission tomography (PET) images. Methods: To achieve this objective, we employed four distinct classifiers (Linear Support Vector Classification, Linear Discriminant Analysis, Logistic Regression, and Logistic Regression Classifier with Stochastic Gradient Descent Learning). These classifiers were used to derive both average and top-ranked importance scores for each feature based on their outputs. Our framework is designed to distinguish between two classes, AD-negative (or mild cognitive impairment stable [MCIs]) and AD-positive (or MCI conversion [MCIc]), using a probabilistic neural network classifier and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results: The findings from the feature importance highlight the crucial role of the GLCM texture features extracted from the hippocampus and entorhinal cortex, demonstrating their superior performance compared to the volume and SUVR. GLCM texture AD classification achieved approximately 90% sensitivity in identifying MCIc cases while maintaining low false positives (below 30%) when fused with other features. Moreover, the receiver operating characteristic curves validate the GLCMs’ superior performance in distinguishing between MCIs and MCIc. Additionally, fusing different types of features improved classification performance compared to relying solely on any single

关键词： Discriminant analysis

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I2C Bridge on FPGA to Facilitate Vast Connection of Heterogeneous IoT Devices in Agriculture Applications 24

I2C Bridge on FPGA to Facilitate Vast Connection of Heteroge...

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8th International Conference on Algorithms, Computing and systems, ICACS 2024

作者： Patsianotakis, Charalampos Xenakis, Apostolos Chaikalis, Costas Kosmanos, Dimitrios Stamoulis, George Kakarontzas, George Electrical and Computer Engineering University of Thessaly Volos Greece Digital Systems University of Thessaly Larissa Greece

ISBN: (纸本)9798400718304

The integration of sensor devices into an IoT network is experiencing significant growth. Along with the rise of several application demands, it is necessary to continuously develop new designs to accommodate these changes. To facilitate the integration of new features into the IoT system, the electronics of end - node devices are comprised of the parts related to the Main board (MCU unit), various Radio - Frequency (RF) modules and other essential components. These include the peripheral units, designed specifically to meet the user's application requirements. The goal is to maintain a single Main Board for all devices, thereby reducing logistics management effort and costs. To this end, this paper presents a solution, tailored at consolidating the production parts diversity, within the Agriculture IoT applications domain, thereby streamlining manufacturing processes and enhancing logistics personnel productivity. The proposed approach revolves around the initialization of a communication protocol translator, between the Integrated Circuits (ICs) of Peripheral - Boards and the Main - Board, effectively simplifying the interface, across all productions. Finally, we design and test a software drive, to facilitate the integration at firmware level. Our energy efficient experiments prove the applicability of our proposed solution. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Firmware

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Real time enhancement of operator's ergonomics in physical human - robot collaboration scenarios using a multi-stereo camera system

Real time enhancement of operator's ergonomics in physical h...

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2023 IEEE International Conference on industrial Technology, ICIT 2023

作者： Arvanitis, Gerasimos Piperigkos, Nikos Anagnostopoulos, Christos Lalos, Aris S. Moustakas, Konstantinos University of Patras Department of Electrical and Computer Engineering Patra Greece Industrial Systems Institute Athena Research Center Patra Greece

ISBN: (纸本)9798350336504

In collaborative tasks where humans work alongside machines, the robot's movements and behaviour can have a significant impact on the operator's safety, health, and comfort. To address this issue, we present a multi-stereo camera system that continuously monitors the operator's posture while they work with the robot. This system uses a novel distributed fusion approach to assess the operator's posture in real-time and to help avoid uncomfortable or unsafe positions. The system adjusts the robot's movements and informs the operator of any incorrect or potentially harmful postures, reducing the risk of accidents, strain, and musculoskeletal disorders. The analysis is personalized, taking into account the unique anthropometric characteristics of each operator, to ensure optimal ergonomics. The results of our experiments show that the proposed approach leads to improved human body postures and offers a promising solution for enhancing the ergonomics of operators in collaborative tasks. © 2023 IEEE.

关键词： Ergonomics

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Synergia: Device-Edge Server Association for ISAC-assisted Mobile Edge Computing systems

Synergia: Device-Edge Server Association for ISAC-assisted M...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Charatsaris, Panagiotis Penafiel, Arianna Santamaria Diamanti, Maria Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350351255

The efficient operation of the unified Integrated Sensing and Communication (ISAC) - Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices' externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices' externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia's operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability. © 2024 IEEE.

关键词： Mobile edge computing

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