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A Multi-Task Autonomous Driving Environment Perception Network Based on CA-YOLOPv8 20

A Multi-Task Autonomous Driving Environment Perception Netwo...

引用

20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wang, Shuyan Liu, Ya Zhang, Feng School of Computer Science Xi'an University of Posts and Telecommunications University Xi'an China School of Mechanical and Electrical Engineering Shihezi University Shihezi China

ISBN: (纸本)9798350356328

To enhance environmental perception in autonomous driving and address the shortcomings of single-task methods, this study introduces a multi-task perception network called You Only Look Once for Panoptic Driving Perception version 8 (YOLOPv8). Leveraging Cross Stage Partial Hierarchical Enhancement (CSPHet) and Attention and Convolution Mix (ACmix), the CA-YOLOPv8 model integrates object detection, lane detection, and drivable area segmentation tasks. The underlying architecture of the network is built upon the You Only Look Once version 8 (YOLOv8) framework, further augmented by incorporating the Spatial Pyramid Pooling-Fast (SPPF) component to bolster its feature extraction efficiency. The YOLOv8 framework has integrated the CSPHet module, which has been designed to supersede the Cross Convolution fusion (C2f) component. This enhancement aims to bolster the efficiency of feature extraction and simultaneously curtail computational demands. Furthermore, the ACmix module amalgamates the self-attention mechanism with convolutional processes, adeptly harnessing both global and local feature insights to augment the model's overall performance. Experiments on the BDD100K dataset demonstrate competitive results in the tasks of target detection (mAP50: 81.1%), dividing of drivable area (mIoU: 91.0%) and lane line detection (IoU: 28.8%). © 2024 IEEE.

关键词： Feature extraction

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Extremum Seeking and Adaptive Dynamic Programming for Distributed Feedback Optimization 63

Extremum Seeking and Adaptive Dynamic Programming for Distri...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Liu, Tong Krstic, Miroslav Jiang, Zhong-Ping New York University Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering BrooklynNY11201 United States University of California Department of Mechanical and Aerospace Engineering San DiegoCA92093-0411 United States

ISBN: (纸本)9798350316339

This paper studies the distributed feedback optimization problem for linear multi-agent systems without precise knowledge of local costs and agent dynamics. The proposed solution is based on a hierarchical approach that uses upper-level coordinators to adjust reference signals toward the global optimum and lower-level controllers to regulate agents' outputs toward the reference signals. In the absence of precise information on local gradients and agent dynamics, an extremum-seeking mechanism is used to enforce a gradient descent optimization strategy, and an adaptive dynamic programming approach is taken to synthesize an internal-model-based optimal tracking controller. The whole procedure relies only on measurements of local costs and input-state data along agents' trajectories. Moreover, under appropriate conditions, the closed-loop signals are bounded and the output of the agents exponentially converges to a small neighborhood of the desired extremum. A numerical example is conducted to validate the efficacy of the proposed method. © 2024 IEEE.

关键词： Dynamic programming

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IoT-Integrated Convolutional Neural Network for Accurate Vehicle Acoustic Signal Analysis 3

IoT-Integrated Convolutional Neural Network for Accurate Veh...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sasikala, P. Sharmini, K Subha Ponnappan, Venkatesan Sorakka Kavitha, K.R. Gunasekaran, K. Srinivasan, C. Department of Mathematics Salem India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Sathyabama Institute of Science and Technology Department of Mechanical Engineering Chennai India Sona College of Technology Department of Electronics and Communication Engineering Salem India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508456

This paper examines the use of IoT-integrated Convolutional Neural Networks (CNNs) for precise analysis of vehicle audio signals. The context highlights the growing intricacy of vehicle systems and the need for advanced monitoring methods. Acoustic signals generated by different vehicle components may provide significant insights about vehicle health;nevertheless, traditional analysis techniques are often ineffective. The Internet of Things (IoT) technology allows real-time data collection from sensors installed on vehicles, which is then analyzed using CNNs to identify certain patterns and abnormalities in auditory signals. The approaches used include using IoT sensors to record various vehicle noises and utilizing CNNs for feature extraction and classification, providing great defect detection precision. The results indicate the system's capacity to detect engine faults, braking deficiencies, and other mechanical concerns early, enabling quick treatments. It presents a novel methodology for vehicle monitoring, enhancing predictive maintenance, safety, and performance, with prospects for future developments in autonomous diagnostics. The proposed system improved vehicle acoustic signal processing using IoT and CNN models, achieving high accuracy of 99% in anomaly detection, preventive maintenance, and real-time traffic monitoring. © 2025 IEEE.

关键词： Convolutional neural networks

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Effect of vacuum isothermal high-pressure gas quenching on heat treatment deformation control and hardness of Cr12MoV dies 10

Effect of vacuum isothermal high-pressure gas quenching on h...

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2024 10th International Conference on Applied Materials and Manufacturing Technology, ICAMMT 2024

作者： Fan, Zhenyu Wang, Huizhen Zhai, Yuewen Jiang, Chao Zhou, Leyu Guo, Shuo Zhang, Zibo Zhan, Yuheng Department of Materials Science China Academy of Machinery Beijing Research Institute of Mechanical & Electrical Technology Co. Ltd. Beijing100083 China Department of Aerospace Engineering Faculty of Aerospace Engineering Beijing Institute of Technology Beijing100081 China

Heat treatment produces cumulative deformation of the dies, and it is particularly important to control deformation through appropriate heat treatment processes. The influence of vacuum gas quenching on the heat treatment deformation control of Cr12MoV cold working dies is explored. The heat treatment deformation and hardness of Cr12MoV dies by different heat treatment processes were tested. The dimensional deformation of dies was measured by the three-dimensional detection method. A characterization method for the deformation rate was designed based on the shape characteristics of Cr12MoV dies. The results indicate that the vacuum isothermal gas quenching process contributes to controlling deformation while maintaining the high hardness of the die. The dimensional deformation rate is controlled below 0.05% and the hardness of the sample reached 59.0 HRC after quenching and tempering. © Published under licence by IOP Publishing Ltd.

关键词： Quenching

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Neural Balance Control of Human Quiet Stance in Construction Tasks 19

Neural Balance Control of Human Quiet Stance in Construction...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Sreenivasan, Gayatri Zhu, Chunchu Yi, Jingang Rutgers University The Department of Electrical and Computer Engineering PiscatawayNJ08854 United States Rutgers University The Department of Mechanical and Aerospace Engineering PiscatawayNJ08854 United States

ISBN: (纸本)9798350320695

Quiet stance is one of the most common gaits that construction workers perform during their daily activities. We present modeling and experiments to quantify the neuromechanical balance control at quiet stance. Most existing biomechanical models focus on ankle and hip strategies of human neural balance for quiet stance. Instead of using a single or double-link modeling approach, a triple-link inverted pendulum model is proposed to facilitate the study of knee balance control strategies in quiet stance. The frequency behavior of the intersection point of ground reaction forces is used to quantify the balance strategy chosen by the human. We also attempt to understand how high elevation and wearable assistive robots can affect the balance of construction workers. Extensive experiments and simulations are conducted to highlight changes in the favored human balance strategy under different testing conditions. © 2023 IEEE.

关键词： Inverted pendulum

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Prediction for Dynamical Systems via Transfer Learning 63

Prediction for Dynamical Systems via Transfer Learning

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Du, Zhe Oymak, Samet Pasqualetti, Fabio University of California Department of Mechanical Engineering RiversideCA92521 United States University of Michigan Electrical Engineering and Computer Science Department Ann ArborMI48109 United States

ISBN: (纸本)9798350316339

In robotics and reinforcement learning, transfer learning helps to pass the knowledge obtained in the design phase to the deployment phase, allowing for faster adaptation. However, this practice is yet to be fundamentally and theoretically understood for dynamical systems. In this work, we approach the prediction problems for dynamical systems under the transfer learning setup. Specifically, we propose methods that are able to utilize data from different training systems to learn a predictive model for an unknown target system. Feasibility condition and finite sample guarantees are developed. Particularly, through experimental studies, our methods demonstrate the ability to handle hidden model structures such as parameterization redundancy and nonlinearity, which outperforms the system identification method. © 2024 IEEE.

关键词： Contrastive Learning

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A Deep Learning Approach to Recognizing New Thai Currency Using Transfer Learning for Visually Impaired Individuals 17

A Deep Learning Approach to Recognizing New Thai Currency Us...

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17th International Conference on Signal Processing and Communication Systems, ICSPCS 2024

作者： Ahmad, Irfan Sharif, Abubakar Azmat, Aneeka Jamil, Muhammad Usman Maynooth University Maynooth International Engineering College Faculty of Science and Engineering Ireland Maynooth University Maynooth International Engineering College Faculty of Science and Engineering Fuzhou China University of Glasgow James Watt School of Engineering United Kingdom National Tsinghua University College of Electrical Engineering and Computer Science Institute of Information Systems and Application Hsinchu Taiwan Chuzhou University School of Mechanical and Electrical Engineering Chuzhou China

ISBN: (纸本)9798350389630

Assistive mobile applications play a pivotal role for visually impaired individuals worldwide. These applications often face challenges in currency recognition due to varying perspectives, inconsistent illumination, and background clutter. This issue is especially pressing in developing countries like Thailand, where there is a notable gap in robust currency recognition systems, particularly for the new Thai currency notes. This study employs a deep learning approach using a convolutional neural network (CNN) to automate the recognition of the new Thai currency notes. Using transfer learning, we fine-tuned the CNN using the Xception model, renowned for its depth-wise separable convolution. The network trained on a meticulously curated dataset comprising 3600 images (without data augmentation) of five different denominations of the new Thai currency (20, 50, 100, 500, and 1000 baht) notes, captured under various conditions. The resulting model achieved an average training accuracy of 99.5% and a validation accuracy of 99.8%. Given its robustness and high accuracy, the model can be integrated into an Android application. Such an application would offer a user-friendly and reliable tool for visually impaired individuals to effortlessly identify the new Thai currency notes in their everyday transactions. © 2024 IEEE.

关键词： Convolutional neural networks

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RECONSTRUCTION OF INTERNAL MORPHOLOGY IN MOLDED PLATELET COMPOSITE USING RESIDUAL STRESSES AND DEEP CONVOLUTIONAL NEURAL NETWORK 10

RECONSTRUCTION OF INTERNAL MORPHOLOGY IN MOLDED PLATELET COM...

引用

10th Composites and Advanced Materials Expo, CAMX 2024

作者： Saquib, Mohammad N. Larson, Richard Li, Jiang Kravchenko, Sergii G. Kravchenko, Oleksandr G. Mechanical and Aerospace Engineering Department Old Dominion University NorfolkVA United States Electrical and Computer Engineering Department Old Dominion University NorfolkVA United States Department of Materials Engineering University of British Columbia VancouverBC Canada

ISBN: (纸本)9781934551462

Compression molding of discontinuous long fiber composite materials is crucial for meeting industry demands for lightweight, durable products manufactured at a high rate. However, the variability in orientation state of prepreg platelet molded composites, resulting from stochastic molding conditions, poses challenges to reliability. This study introduces a novel computational tool aimed at rapid non-destructive reconstruction of fiber orientation distribution (FOD) in each layer in prepreg platelet molded composite (PPMC) plates. While traditional micro CT would require significant time to generate layer based FOD, a deep convolutional neural network (DCNN) can achieve an order of magnitude increase in part inspection speed. The DCNN utilizes thermal residual strain components on the surface as input to predict FOD throughout the volume. The neural network was trained using thousands of synthetic PPMC morphologies from finite element simulation data. The trained DCNN was used to reconstruct the microstructure of the entire PPMC synthetic coupon. Progressive failure analysis (PFA) was performed on the reconstructed tensile coupons and compared to the PFA simulation of the initial synthetic morphology, which was considered as a ground truth. The failure analysis of the reconstructed PPMC coupon showed 14.53% error in predicting a tensile strength. Furthermore, the observed damage modes and failure locations closely matched between the true and reconstructed morphology, demonstrating that the proposed DCNN model is capable of full morphology reconstruction in PPMC materials. Copyright 2024. Used by CAMX – The Composites and Advanced Materials Expo.

关键词： Compression molding

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Numerical Analysis of Cooling of PV Panel Embedded with PCM, Fins and Water

Numerical Analysis of Cooling of PV Panel Embedded with PCM,...

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2023 International Conference on Sustainable Technology and engineering, i-COSTE 2023

作者： Elavarasan, Rajvikram Madurai Nadarajah, Mithulananthan Pugazhendhi, Rishi Gangatharan, Sivasankar Rengasamy, Mageswaran School of Electrical Engineering and Computer Science The University of Queensland St LuciaQLD4072 Australia Department of Mechanical and Aerospace Engineering Los AngelesCA90095 United States Thiagarajar College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Madurai625015 India S.A Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai600077 India

ISBN: (纸本)9798350329711

Thermal management of photovoltaics (PV) is significant to improve the performance of PV panel. A novel passive cooling techniques with PCM, fins, and water was developed in the previous research work conducted by the authors. The objective of this paper is to numerically simulate the performance of the cooling technique. An attempt to predict the temperature is made with the considered cooling technique. With the numerical analysis aimed for 5 W panel, the model is studied for its validation and applicability to large-scale PV panels. The governing equations are sorted out from the literature review. This research is performed using ANSYS software to simulate the preliminary model. The research outcomes show that the numerical results have a deviation of about 1.6% for uncooled PV and 9% for cooled PV when compared with the experimental results. This deviation is primarily attributed to the unaccounted losses due to wind velocity, water temperature and varying irradiation that occurs while performing the real time experimentation. Which was not considered in the proposed Finite Element Analysis (FEA) modelling. © 2023 IEEE.

关键词： Finite element method

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An Effective Detection of Ransomware Using Improved Long Short-Term Memory 3

An Effective Detection of Ransomware Using Improved Long Sho...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Vijayalakshmi, V. Fallahhusein, Muhamed Balamurugan, D. Suresh, T. Muthu Krishnan, K. Lakshmi Bangaru Arts and Science College Department of Computer Science with Artificial Intelligence Melmaruvathur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Computer Science and Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Electrical and Electronics Engineering Mamallapuram India

ISBN: (纸本)9798331508456

Ransomware detection focuses on recognizing the unusual system activities which indicate an unauthorized data access and restriction. It continuously monitors the system behavior continuously to detect potential threats before significant damage occurs. By identifying anomalies and patterns, it effectively distinguishes ransomware attacks from normal operation. However, Ransomware attacks cause a serious threat because of far-reaching effects and its zero-day variants are even more hazardous. This research proposes Improved Long Short-Term Memory (LSTM) to detect the ransomware effectively and accurate detection of Ransomware. The proposed improved LSTM learns the sequential patterns in system behavior and detects subtle abnormalities over time. The enhanced model improves detection accuracy by learning past data and adapts to new attack variants including zero-day threats. By identifying anomalies, the proposed method enables timely detection and reduces the impact of ransomware attacks. To select the appropriate features, the Dung Beetle Optimization (DBO) is used to optimize the feature subset for better detection performance. The proposed improved LSTM obtains a better accuracy of 95% when compared to existing method like cost sensitive Pareto Ensemble classifier (CSPE-R). © 2025 IEEE.

关键词： Zero-day attack

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