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5th international conference on Signal Processing and computer science, SPCS 2024

作者： Li, Yuan Liu, Xiangqian Beijing Jiaotong University Beijing China School Institute of Information Science Beijing Jiaotong University Beijing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

An efficient foggy weather vehicle recognition framework is proposed to address the challenges of vehicle identification under adverse weather conditions in this study. Enhanced high-resolution data is used as input, and an image restoration model is employed to effectively capture multi-scale feature information. Additionally, an image dehazing model is incorporated to restore clear vehicle features from foggy and blurred images, thereby improving the accuracy of vehicle detection in foggy conditions. In the experimental analysis, visualized results are first presented, and the model's performance is analyzed by comparing it with the detection results of other algorithms. The data is further analyzed, with several dehazing algorithms integrated into the vehicle detection model for ablation experiments to validate the model's effectiveness. © 2025 SPIE.

关键词： Restoration

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multi-Scale HyperGraph Node Embedding via Random Walking for Music Recommendation 5

Multi-Scale HyperGraph Node Embedding via Random Walking for...

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5th international conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Yuan, Zhengwu Wang, Qiuliang Yang, Hao Yang, Hanqing School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798331528911

Recommender systems are designed to deliver tailored recommendations to users, demonstrating efficacy in addressing the issue of data overload prevalent in numerous online platforms. Current music recommendation systems have produced considerable results, but due to the complexity and diversity of the interaction between users and music and the background of listening time, as well as the dynamics of user preferences, making full use of music listening records remains a challenging and important problem. Therefore, a novel multi-Scale HyperGraph Node (MSHN) Embedding approach for sequential music recommendation is proposed in this paper. Firstly, a devised Weighted Random Walk Algorithm is used to learn the First-order Markov node sequence. Secondly, Users' preferences are subsequently inferred from their listening habits, leading to recommendations of music pieces that align with their immediate needs. Finally, extensive experiments are performed on two real-world datasets, demonstrating that MSHN outperforms baseline models. © 2024 IEEE.

关键词： Embeddings

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 international conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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CNN-Based Detection of DDoS Attacks in multi-Cloud Environments

CNN-Based Detection of DDoS Attacks in Multi-Cloud Environme...

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2024 international conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Verma, Rashmi Jailia, Manisha Kumar, Munish Kaliraman, Bhawna Banasthali Vidyapith Department Of Computer Science And Engineering Rajasthan Tonk India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering Vaddeswaram India Dpg Institute Of Technology And Management Department Of Computer Science And Engineering Gurugram India

ISBN: (纸本)9798350366846

With the increasing reliance on cloud computing services, ensuring the security of cloud-based infrastructures has become paramount. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) to detect Distributed Denial of Service (DDoS) attacks in a multi-cloud environment. The utilization of CNNs, known for their proficiency in feature extraction from structured and unstructured data, offers an innovative solution to the dynamic and evolving nature of DDoS attacks. The datasets utilized in this study include NSL-KDD. As the global frequency of cyberattacks rises, the digital landscape faces significant threats impacting both individual online presence and corporate entities. This paper employs deep learning techniques to enhance security against DDoS attacks, utilizing the inherent ability of deep learning to extract intricate patterns from vast datasets. This makes it a potent tool for constructing effective detection and mitigation systems for the DDoS threat. The research presents a comprehensive approach for detecting DDoS attacks by leveraging CNNs (Convolutional Neural Networks) and advanced data preprocessing methods, focusing on the widely recognized NSL-KDD dataset. The research findings reveal that the proposed CNN-based approach consistently outperforms, achieving an impressive accuracy score of 97.46%. These results underscore the promising potential of the proposed methodology in significantly improving the accuracy and effectiveness of intrusion detection systems. In a parallel exploration, another research paper introduces an alternative approach by offering a CNN model on the same dataset, achieving an accuracy outcome of 96.61%. While this alternative approach demonstrates commendable performance, the findings highlight the superior accuracy achieved by the proposed CNN methodology in the context of DDoS attack detection. © 2024 IEEE.

关键词： Denial-of-service attack

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Video conference System in Mixed Reality Using a Hololens

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computer Modeling in engineering & sciences 2023年第1期134卷 383-403页

作者： Baolin Sun Xuesong Gao Weiqiang Chen Qihao Sun Xiaoxiao Cui HaoGuo Cishahayo Remesha Kevin Shuaishuai Liu Zhi Liu School of Information Science and Engineering Shandong UniversityQingdao266000China State Key Laboratory of Digital Multi-Media Technology Hisense Co.Ltd.Qingdao266000China

The mixed reality conference system proposed in this paper is a robust,real-time video conference application software that makes up for the simple interaction and lack of immersion and realism of traditional video conference,which realizes the entire process of holographic video conference from client to cloud to the *** paper mainly focuses on designing and implementing a video conference system based on AI segmentation technology and mixed *** mixed reality conference system components are discussed,including data collection,data transmission,processing,and mixed reality *** data layer is mainly used for data collection,integration,and video and audio *** network layer uses Web-RTC to realize peer-to-peer data *** data processing layer is the core part of the system,mainly for human video matting and human-computer interaction,which is the key to realizing mixed reality conferences and improving the interactive *** presentation layer explicitly includes the login interface of the mixed reality conference system,the presentation of real-time matting of human subjects,and the presentation *** the mixed reality conference system,conference participants in different places can see each other in real-time in their mixed reality scene and share presentation content and 3D models based on mixed reality technology to have a more interactive and immersive experience.

关键词： Mixed reality AI segmentation hologram video conference Web-RTC

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Study on multi-stage adjust mechanism of downhole stratification control tool for natural gas hydrate multi-gas combined production gas lift

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international JOURNAL OF HYDROGEN ENERGY 2024年 72卷 800-814页

作者： Tang, Yang Zhang, Yulin He, Yufa Zhou, Yunjian Zhao, Peng Wang, Guorong Southwest Petr Univ Sch Mechatron Engn Chengdu 610500 Peoples R China State Key Lab Nat Gas Hydrates Beijing 102200 Peoples R China Southwest Petr Univ Energy Equipment Inst Chengdu 610500 Peoples R China Southwest Petr Univ Key Lab Oil & Gas Equipment Minist Educ Chengdu 610500 Peoples R China CNOOC Res Inst Co Ltd Beijing 100028 Peoples R China

Accurate control of natural gas flow and pressure is essential to achieving multi-gas combined production of the natural gas hydrates. In this study, a downhole stratification control tool for controlling natural gas flow rate and pressure with a multi-stage throttling structure was innovatively proposed. The multi-stage throttling structure combined an adjustable cage sleeve and a fixed nozzle. The numerical model of the internal flow field of the downhole stratification control tool was established. Through the computer fluid dynamics numerical simulation, the flow field change law inside the spool module of the downhole stratification control tool under different spool structures, different outlet pressures, and different spool openings was analyzed. Moreover, the spool module prototype was processed, and the indoor experiment of the spool's performance of reducing pressure and regulating production was carried out. The results show that the multi-stage throttling structure has the advantages of adjustable spool opening and moderate cooling range and the mass flow of gas inside the spool changes linearly with the opening of the spool. The spool module can accurately control the mass flow of natural gas under high-pressure conditions, and meet the needs of the downhole stratification control tool for accurate gas control.

关键词： Natural gas hydrates Downhole stratification control tool multi-gas combined production multi-stage throttling computer fluid dynamics

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Cooperative guidance control design for multi-agent vehicles 3

Cooperative guidance control design for multi-agent vehicles

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3rd international conference on Intelligent Mechanical and Human-computer Interaction Technology, IHCIT 2024

作者： Yang, Kaiyuan Ming, Chao Yan, Suyu Wei, Zhihua Mechanical Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China

ISBN: (纸本)9781510683105

Aiming at the collaborative encirclement and interception problem of multi-agent vehicles with terminal line-of-sight (LOS) angle constraints, this paper designs a cooperative guidance control by using the multi-agent leader-follower structure coordination theory. Regarding the collaborative interception problem between the leader vehicle and the follower vehicle, a multi-agent leader-follower structure consensus protocol is proposed. Based on this consensus protocol, a time-collaborative guidance control is designed to achieve a synchronous attack on the target by the follower vehicles and the leader vehicle. For the terminal LOS angle constraint of each vehicle, the terminal sliding mode control method is combined with a variable exponential power reaching law to design a guidance control that satisfies the constraint. Finally, in this article, the designed time collaborative guidance control with terminal LOS angle constraints is used to conduct simulations to verify its feasibility for high-speed target interception problems. © 2024 SPIE.

关键词： Electronic guidance systems

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Extending Defense-in-Depth to multi-Domain Networks 24

Extending Defense-in-Depth to Multi-Domain Networks

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8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Xu, Yaobing Yang, Junhai Zhou, Ziyan Shi, Xinyi Wang, Jing Zhang, Junhao Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400710094

multi-domain networks, consisting of interconnected management domains with distinct control mechanisms, introduce unique cybersecurity challenges that are often overlooked in current research. This paper addresses these gaps by providing a formal definition of multi-domain networks and their management domains, emphasizing the security implications of inter-domain relationships. We extend the traditional Defense-in-Depth (DiD) strategy to account for both intra-domain security and cross-domain interactions, presenting a novel framework for DiD in multi-domain environments. A tri-objective optimization problem is formulated to balance defense effectiveness, resource costs, and operational impacts, which is solved using an improved genetic algorithm (IGADS). Simulations demonstrate the efficacy of this approach, offering a robust defense schema for securing multi-domain networks. © 2024 Copyright held by the owner/author(s).

关键词： Genetic algorithms

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A multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction 5

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

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5th international conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China South China University of Technology School of Electronic and Information Engineering Guangzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350373820

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. © 2024 IEEE.

关键词： Trajectories

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AI-Based multi-Fault Diagnostic Correlation Web Model for the Analysis of Electronic Vehicle Charging System

AI-Based Multi-Fault Diagnostic Correlation Web Model for th...

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2024 international conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Ettyem, Sajjad Ali Fouad, Laith Hasan, Hiba Abdulameer Saber Mohammed, Ali Ali Majed, Safa Mohammed, Mohammed Abdulamer Al-Farouni, Mohammed National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kitab University Department of Computer Engineering Technologies Kirkuk Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Hilla University College Babil Iraq College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350378177

A sort of massive, high-power, package-loading electronics, the electric car charging battery exhibits flexibility and power-sharing traits. However, comparable battery failure signatures, sensor defects, and interaction issues frequently lead to misdiagnosis. Based on fault diagnostic systems and AI, this work implements the online multi-fault diagnostic correlation model (OM-FDCM) to quantify electronic vehicle charging. Indicators of errors and assessments of human error have been subjected to the technology of correlation coefficients. Battery problems can be discovered using techniques that analyse the relationship between subsequent voltages and failure signals. Furthermore, connection and sensor voltage faults separate the surrounding voltage differential and power correlation coefficients. The online multi-diagnostic can tolerate battery variations in temperature range, loading state, and health. Creating an integrated early warning system for electric car charging and discharging can significantly increase assessing safety and promote the long-term growth of electric vehicles. Power supply equipment, battery charging stacks and fault correlation are evaluated to build an integrated online diagnostic that may considerably enhance the safety of charging electronic cars. When compared to other methods, the simulation's outcomes perform better in terms of accuracy (94.7%), interaction (92.6%), safety (93.5%), probability (92.9%), reduced temperature (35.7%), voltage (26.9%), and energy consumption (22.8%). © 2024 IEEE.

关键词： State of charge

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