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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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Research on Motion Control of Gantry Carbon Fiber Prepreg Tape Laying Machine Based on Visual Feedback

Research on Motion Control of Gantry Carbon Fiber Prepreg Ta...

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international conference on computers, Communications, and systems (ICCCS)

作者： Qin He Wenhui Li Mechanical and Electrical Engineering Department Wenhua College Wuhan Hubei China

Advanced Composites had many advantages, such as light weight, high strength, high modulus, integrated structure and function, integrated design and manufacture, easy forming, etc. At present, after many years of continuous efforts, the domestic automatic placement technology had achieved certain results, but compared with foreign countries, there was still a big gap in the research and development of composite raw materials, automatic production, equipment technology, software optimization and so on. therefore, this project was based on the demand of aerospace manufacturing industry to ensure the quality of large composite components and improve processing efficiency, the research on motion control of gantry type carbon fiber prepreg tape laying machine was carried out. In this study, the motion control theory based on visual feedback was adopted, and the machine vision technology was applied to the automatic placement technology, and the visual measurement system was added to the open control system based on PC and motion controller, by combining position-based visual servo with iterative learning control, an iterative learning control system based on visual feedback was proposed to determine the movement control system of paver. the aim was to realize the automatic placement of placement machine, and improve placement efficiency, to ensure the placement quality of large-scale conforming material components. the effective implementation of this project would had a certain reference value for the research of fiber placement technology.

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Vibration Characteristics of Perforated Plate using Experimental and Numerical Approaches

Vibration Characteristics of Perforated Plate using Experime...

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ieee international conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Hazim Jamali M. N Mohammed H.S. S. Aljibori Adnan Al-Tamimi Intisar Swedain Ali Oday I. Abdullah M. A. Abdelgnei Zuhair Nafea Alani Marwan Salah Hameed College of Engineering University of Kerbala Karbala Iraq Mechanical Engineering Depaertment College of Engineering Gulf University Sanad Bahrain College of Engineering University of Warith Al-Anbiyaa Karbala Iraq College of Technical Engineering Al-Farahidi University Baghdad Iraq Department of Automated Manufacturing Engineering AI-Khwarizmi college of Engineering University of Baghdad Baghdad Iraq Dept of Energy Engineering University of Baghdad Baghhdad Iraq Mechanical Engineering Dept College of Engineerin Gulf University Sanad Bahrain Hamburg University of Technology Institute of Laser and Systems Technologies Hamburg Germany Department of Mechanical Engineering Faculty of Mechanical Engineering Omar Al-Mukhtar University Bayda Libya Architectural and Interior Design Engineering Department College of Engineering Gulf University Sanad Kingdom of Bahrain Department of Mechanical Engineering American University of Bahrain Riffa Bahrain

Vibration analysis plays a vital role in understanding and analyzing the behavior of the structure. Where, it can be utilized from this analysis in the design process of the structures in different engineering applications, check the quality and safety of the structure under different working conditions. this work presents experimental measurements and numerical solutions to an out of plane vibration of a rectangular plate with a circular hole. Free edges rectangular plates with different circular holes diameters were studied. the effects of hole location on the plate natural frequencies were also investigated. A finite element modeling (using ANSYS software) has been used to analyze the vibration characteristics of the plates. A good agreement between finite elements predictions and experimental measurements were found for the plates that have been tested.

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Alzheimer's Disease Imaging Recognition Based on Improved 3D-ShufflenetV2 Network 1

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Tao, Xinjie Wei, Lisheng Wang, Qin Anhui Key Laboratory of Electric Drive and Control Anhui Polytechnic University Wuhu241000 China School of Electrical Engineering Anhui Polytechnic University Wuhu241000 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

To solve the problems of low accuracy, data leakage, and low efficiency in current deep learning based two dimensional Alzheimer's disease (AD) imaging recognition, an improved 3D-ShufflenetV2 network has been proposed. Firstly, the network utilizes 3D convolution to extract spatial continuity information from three dimensional images, enabling the extraction of more brain lesion information. Secondly, a Squeeze-and-Excitation attention mechanism is introduced into the backbone network, recalibrating channel-level feature responses based on global information to enhance the model's focus on important features, thereby enhancing the model's classification performance. Finally, to increase the model's ability to extract global features, a global feature fusion branch has been added to the network structure. Experiments have shown that the improved network is capable of recognizing AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN). the classification performance of the modified network model shows significant improvement over the original network, demonstrating its practical value. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neurodegenerative diseases

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Analysis and Design of Intelligent Music Player System Based on Gesture Recognition

Analysis and Design of Intelligent Music Player System Based...

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international conference on computer and Communications (ICCC)

作者： Yiliang Xing Huajun Lei Software Engineering Department Hainan College of Software Technology Qionghai City Hainan Province China

Gesture recognition based on computer vision is an important part of artificial intelligence. Gesture recognition is an important way of human-computer interaction and is of great significance to improve user experience. Aiming at the problem of intelligent control of traditional music player, this paper designed and developed an intelligent music player system based on gesture recognition, this paper discusses the method of gesture contour extraction, presents the gesture recognition method about fourier descriptor and neural network, and gives the design of the intelligent music player based on software engineering. Experiments show that the system has the advantages of high recognition rate, excellent performance and good user experience.

关键词： Human computer interaction computer vision Neural networks Gesture recognition Reliability engineering User experience Real-time systems

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Sustainable IoT-Based Data Logger System for Marine Applications Using Renewable Energy

Sustainable IoT-Based Data Logger System for Marine Applicat...

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ieee international conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Ahmed Rimaz Faizabadi Khandakar Ameem Adnan Muhammad Aiman Norazaruddin Hasan Firdaus Mohd Zaki Abang Mohd Haziq Bin Abang Akral Osamah Ebrahim Mohammed Shweesh Department of Mechatronics Engineering Kulliyyah of Engineering International Islamic University Malaysia Kuala Lumpur Malaysia Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Department of Mechatronics Engineering Kulliyyah of Engineering International Islamic University Malaysia Autonomous Marine Systems Hidrokinetik Technologies Sdn Bhd Kuala Lumpur Malaysia

Navigational data is vital for various fields of applications. Satellite communications are costly while Automatic Identification System (AIS) data has considerable interval which can make crucial incidents during the voyage unrecoverable. the storage attached to the existing loggers are limited and the GSM900 communication link is slow. this paper presents the design and implementation of an Internet of things (IoT) based data logging system tailored for marine applications that improves cost, power consumption and durability while providing efficient and reliable data logging capabilities in the harsh marine environment. A programmable multithreaded low-power UR32 industrial router from Milesight replaces the simple Microcontroller Unit (MCU), providing a dual-SIM 4G connectivity failsafe. the proposed design minimizes the required components and enables seamless data acquisition and remote monitoring. Using photovoltaic (PV) solar panels, our logger can run entirely on renewable energy giving an endurance of almost 24 hours on a single charge. It can log data from various sensors over serial communication ports with uninterrupted log intervals. the communication link is improved from 2kbps to more than 7 mbps while storage is improved by 32 times. the complete system is packaged into an IP68 rating enclosure. the proposed solution contributes to cost-effective and energy-efficient IoT-based data logging systems for marine applications, enabling better understanding and managing marine resources and ecosystems using renewable energy.

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Abnormal Image Classification Using Vision Transformer for Smart Agriculture

Abnormal Image Classification Using Vision Transformer for S...

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ieee international conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Talha Hafeez Muhammad Farrukh Shahid Tariq J.S. Khanzada Fast School of Computing FAST-NUCES Karachi Pakistan Department of Information System Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Computer Systems Engineering Departments Mehran UET Jamshoro Pakistan

Smart agriculture is an emerging innovative sector that harnesses technological advancements like sensor monitoring, image processing, soil quality evaluation, and automated water systems, all of which contribute towards enhanced crop yields and superior quality of produce. With the progression towards automated systems in smart agriculture, the need for a robust data infrastructure that assures data integrity is critical. this demands an integrated approach with anomaly detection algorithms and a robust classification technique to ensure an error-free data pipeline. this study employs two distinct datasets to achieve abnormal image classification using a vision transformer (ViT) and a convolutional neural network (CNN). the primary dataset, obtained through direct collection from cotton fields in Karachi and Balochistan, Pakistan, comprises high-resolution agricultural images captured using specialised cameras and Unmanned Aerial Vehicles (UAVs). the secondary dataset was sourced from a publicly available database. the two classification models, ViT and CNN, were applied to both datasets. these supervised models are primarily used to classify images as normal or abnormal. the results of the two classification models were also compared, with the ViT model surpassing the CNN model in performance and achieving the highest accuracy on both datasets. the Vision Transformer model proved to be robust for supervised data, showing remarkable accuracy of 96.00% on the public dataset, 99.18% on both camera based images and 97.56% on UAV images of collected dataset as compared to the CNN model's accuracy that are 82.00% on the public dataset and 98.36% on camera and 95.12% on UAV images of collected dataset.

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GAN-IVDS: An Intrusion Detection System for Intelligent Connected Vehicles Based on Generative Adversarial Networks

GAN-IVDS: An Intrusion Detection System for Intelligent Conn...

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ieee international conference on Data Science in Cyberspace (DSC)

作者： Xiansen Chen Kun Xiao Lei Luo Yun Li Lirong Chen School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Guangdong Weichen Information Technology Co. Ltd Dongguan China

this paper proposes an anomaly detection algorithm based on Generative Adversarial Networks (GANs) for intrusion detection in Intelligent Connected Vehicles (ICVs). To address the imbalance between normal network traffic and attack traffic in vehicular networks, as well as the difficulty in collecting attack data, we train the neural network solely with normal traffic. We employ the Gini index to perform data dimensionality reduction, selecting 34 features with the highest purity of information and enhancing model training speed via normalization functions. Our algorithm combines the generative capabilities of GANs and the reconstructive power of autoencoders, detecting abnormal network data by learning the distribution of normal network data, thus eliminating the need to analyze packet payloads and safeguarding user privacy. We also develop a vehicular network traffic data collection scheme to collect network traffic data from ICVs. Experimental results indicate that our algorithm demonstrates high accuracy in detecting anomalous network traffic in ICVs, with an accuracy rate of 94.9%.

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A Probabilistic Approach for Scheduling of Wind-Energy-Integrated Power systems Incorporating Dynamic Lines and Transformers Rating

A Probabilistic Approach for Scheduling of Wind-Energy-Integ...

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Technology and Energy Management (ICTEM), international conference on

作者： Mohammad Akhlaghi Zahra Moravej Amir Bagheri Faculty of Electrical & Computer Engineering Semnan University Semnan Iran Department of Electrical Engineering Faculty of Engineeing University of Zanjan Zanjan Iran

In today's electric power systems, transmission congestion and integration of renewable-based power generations are challenging issues. To tackle with these problems, power system operators can make use of smart-grid technologies. Dynamic transformer rating (DTR) and dynamic line rating (DLR) are employed as two smart-grid technologies for stochastic day-ahead optimal power flow (OPF) under uncertainty condition. the uncertainty scenarios are generated using Monte-Carlo simulation where the K-means clustering algorithm is employed for the scenario reduction purpose. A convexified version of AC power flow and dynamic rating relations are employed to formulate the optimization problem as a mixed-integer quadratically-constrained programming (MIQCP). the conducted approach is applied to the ieee-24-bus network in GAMS, and the simulation results are investigated to validate efficiency of the proposed method. the simulations show that the wind power curtailment and load shedding becomes zero in the case of utilizing dynamic ratings. Also, a total cost reduction of 41% is observed by the aid of DTR and DLR.

关键词： Uncertainty Power system dynamics Voltage Wind power generation Dynamic scheduling Transformers Probabilistic logic

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CAKG: A Framework for Cybersecurity threat Detection of Automotive via Knowledge Graph

CAKG: A Framework for Cybersecurity Threat Detection of Auto...

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ieee international conference on Data Science in Cyberspace (DSC)

作者： Peng Yang Wang Lijie Li Yun Song Xuedong Wang Yaxin Guo Biheng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Cybersecurity Research Institute Guangdong Weichen Information Technology Co. Ltd Dongguan China

With the increasing complexity and connectivity of vehicles, ensuring their security has become a critical concern. In this study, we propose CAKG: A Framework For Cybersecurity threat Detection Of Automotive Via Knowledge Graph, achieved with a knowledge graph for vehicle vulnerability and threat intelligence. We integrate existing cyber security knowledge bases to analyze potential attack surfaces and scenarios specific to automobiles. By using keyword extraction and text similarity analysis, we identify threats relevant to the event produced by Intrusion Detection systems(IDS) in automotive. Moreover, we leverage another knowledge graph to analyze attack logs gained from actual vehicles, enabling us to further correlate security events and product situational analysis. Our framework provides a holistic perspective on vehicle security, facilitating threat modeling and enhancing our understanding of potential attack scenarios.

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A Real-Time Approach to Detecting API Abuses Based on Behavioral Patterns

A Real-Time Approach to Detecting API Abuses Based on Behavi...

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Cryptography, Security and Privacy (CSP), ieee international conference on

作者： Sameeraa Prinakaa Bavanika V Sanjana S Sneha Srinivasan Sarasvathi V Dept. of Computer Science Engineering PES University Bengaluru India

ISBN: (数字)9798350386325

ISBN: (纸本)9798350386332

Over the past two decades, the internet has undergone a remarkable expansion, giving rise to the development of web-based software solutions. this evolution has led to widespread web Application Programming Interface (API) usage, facilitating remote software communication. However, the increased frequency of API calls has unfortunately resulted in a corresponding surge in API misuse instances. Monitoring API traffic becomes imperative to ensure data security, service availability, and system integrity. this paper introduces an API abuse detection system within a Log Storage Application utilizing Spring Boot with OAuth2.0 and JSON Web Token (JWT) based authentication. the proposed system effectively safeguards against critical security threats, such as Economical Denial of Service (EDoS) attacks (OWASP top 4), Unauthorized Access attempts (OWASP top 3), Brute Force Broken User Authentication, and Behavioral API abuses, with alerts promptly sent to the server. the prevalence of API usage has created numerous attack surfaces, offering a rich landscape for further research opportunities.

关键词： Privacy Force Authentication software Real-time systems Servers Surges

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