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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Adaptive neural finite-time self-triggered control for nonstrict-feedback nonlinear systems with sensor faults

Robotic Intelligence and Automation

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Robotic Intelligence and Automation 2025年第3期45卷 373-385页

作者： Zhang, Wenxin Xu, Ning Zhao, Ning Al-Barakati, Abdullah A. College of Control Science and Engineering Bohai University Jinzhou China Department of Information Systems Faculty of Computing and Information Technology Communication Systems and Networks Research Group King Abdulaziz University Jeddah Saudi Arabia

Purpose: This paper aims to investigate the problem of adaptive neural finite-time self-triggered tracking control for interconnected large scale nonlinear systems in nonstrict-feedback forms with sensor faults. Design/methodology/approach: To begin with, by combining backstepping techniques and neural networks (NNs), an adaptive NN controller is designed to compensate for sensor faults. Then, command filters are introduced to deal with the complexity explosion problem in backstepping design processes. Moreover, to reduce unnecessary data transmissions, a self-triggered control strategy is presented. Findings: Based on self-triggered strategy, an adaptive neural finite-time control scheme for interconnected large-scale systems with sensor faults is proposed. Originality/value: This article considers sensor faults in interconnected large-scale nonlinear systems with nonstrict-feedback forms. Moreover, the introduction of command filters not only effectively avoids the complexity explosion problem arising from the repetitive differentiation of virtual control inputs, but also simplifies the controller design process. Besides, this paper proposes a self-triggered mechanism that calculates the next trigger point based on current system data, overcoming the need for continuous monitoring of measurement errors in event-triggered mechanisms. Furthermore, the controller guarantees the finite-time stability of interconnected large-scale systems, with the tracking error converging to a small neighborhood of the origin within a finite time frame. © 2025, Emerald Publishing Limited.

关键词： Large scale systems

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Neural Network Ensemble Method for Deepfake Classification Using Golden Frame Selection

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Big Data and Cognitive computing 2025年第4期9卷 109-109页

作者： Lipianina-Honcharenko, Khrystyna Melnyk, Nazar Ivasechko, Andriy Telka, Mykola Illiashenko, Oleg Department of Information and Computing Systems and Control Faculty of Computer Information Technologies West Ukrainian National University Ternopil46000 Ukraine Department of Computer Systems Networks and Cybersecurity Faculty of Radio Electronics Computer Systems and Infocommunications National Aerospace University "KhAI" Kharkiv61000 Ukraine Via Giuseppe Moruzzi 1 Pisa56124 Italy

Deepfake technology poses significant threats in various domains, including politics, cybersecurity, and social media. This study uses the golden frame selection technique to present a neural network ensemble method for deepfake classification. The proposed approach optimizes computational resources by extracting the most informative video frames, improving detection accuracy. We integrate multiple deep learning models, including ResNet50, EfficientNetB0, Xception, InceptionV3, and Facenet, with an XGBoost meta-model for enhanced classification performance. Experimental results demonstrate a 91% accuracy rate, outperforming traditional deepfake detection models. Additionally, feature importance analysis using Grad-CAM highlights how different architectures focus on distinct facial regions, enhancing overall model interpretability. The findings contribute to of robust and efficient deepfake detection techniques, with potential applications in digital forensics, media verification, and cybersecurity. © 2025 by the authors.

关键词： Digital forensics

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Reconfigurable Intelligent Surface Enhanced Wireless Localization: Phase Optimization for Malicious Interference Mitigation

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IEEE Transactions on Communications 2025年

作者： Zhang, Yi Xie, Yajing Wang, Lijia Liwang, Minghui Wang, Xianbin Xiamen University Ministry of Education of China Department of Information and Communication Engineering Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai China Western University Department of Electrical and Computer Engineering ON Canada

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks. © 1972-2012 IEEE.

关键词： Frequency division multiplexing

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Full life-cycle cost model for practical application of solar energy system

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Energy Informatics 2025年第1期8卷

作者： Sha, Ang Xiong, Zhen Zang, Xiaolin Zhao, Wei Ge, Ruibang Yao, Wanxiang Aiello, Marco Innovation Institute for Sustainable Maritime Architecture Research and Technology Qingdao University of Technology Qingdao266033 China School of Architecture and Urban Planning Qingdao University of Technology Qingdao266033 China School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China School of Continuing Education Qingdao University of Technology Qingdao266033 China Service Computing Department Institute of Architecture of Application Systems University of Stuttgart Universitätsstraße 38 Stuttgart70569 Germany

In pursuit of carbon neutrality, a swift transformation is underway in the global energy structure, marked by a consistent rise in the installed capacity of solar energy systems. Meanwhile, the substantial reduction of government subsidies in the solar industry intensifies focus on the economic viability of solar energy installations. In this study, we propose a full life-cycle cost model, named the F-LCC model, for calculating the cost of the solar energy system on the long term, e.g., 20–30 years. This model integrates replacement costs, residual value calculation, interest rate, and inflation impacts while supporting market price estimation for individual components, thereby aiding feasibility analysis in the early project phase. We design an investment cost recovery algorithm based on the F-LCC model to calculate the break-even electricity price for solar energy system. Moreover, we analyze component cost distributions, Net Present Value (NPV), and Discounted Payback Period (DPP) for grid-connected and off-grid solar energy systems with capacities of 10 kWp and 100 kWp in the Chinese market. The results show that the proposed model, compared to other models, captures the fact that payback times are longer. In a solar energy system without storage, solar panels have the highest component cost share at 28.8%. With battery storage, batteries dominate the total cost, reaching up to 74.6%. And the the grid-connected systems DPP ranging from a minimum of 5.5 to a maximum of 7.0 years by grid-connected electricity price, while off-grid systems require at least 19.9 years. The 10 kWp off-grid fixed mounting system’s break-even price being 137.1% higher than its grid-connected counterpart. In addition, tracking-mount systems offer greater cost-reduction potential than fixed installations, with the payback period reduced by 20% for 100 kWp grid-connected systems and 15% for off-grid systems. Finally, we develop a plugin based on the F-LCC model. These findings deepen unde

关键词： Commerce

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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An Intelligent Method for Recognizing Real and Generated Ukrainian-Language Voices 5

An Intelligent Method for Recognizing Real and Generated Ukr...

引用

5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Lipianina-Honcharenko, Khrystyna Lendiuk, Dmytro Ivaniush, Adam Boguta, Gena Soia, Mariana Yurkiv, Khrystyna Department for Information-Computing Systems and Control West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9798350384499

The modern development of speech synthesis technologies and convolutional neural networks (CNN) creates new opportunities and challenges in the field of voice recognition. One of the key tasks is to ensure the security and authenticity of audio information, especially in the context of information warfare and disinformation campaigns. In this context, the ability to recognize and identify generated voices becomes particularly important. This study aims to develop an intelligent method for recognizing real and generated Ukrainian-language voices using convolutional neural networks. The proposed method is based on the careful collection and preparation of data, the development and training of a CNN model, and its validation on test samples. Special attention is paid to ensuring the balance of the dataset, which is crucial for preventing model bias and improving its ability to generalize to unknown data. The model shows high performance in detecting real and generated voices, making this approach useful for local applications, particularly in Ukraine. ©2024 IEEE.

关键词： Convolutional neural networks

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Prices and Decentralization of control 15

Prices and Decentralization of Control

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Gorelov, Mikhail Ereshko, Felix Federal Research Center 'Informatics and Control' of Ras Department of Information Computing Systems Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The paper considers some models of a two-level hierarchical system for different-degree awareness of the Center and its subsystems. The authors investigate control procedures through the distribution of resources, the output of products, and pricing. They also formulate a problem to design the mechanisms coordinating the decisions of subsystems on voluntary fulfillment of the general restrictions that is fundamentally new for hierarchical games. © 2022 IEEE.

关键词： Hierarchical systems

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Computational Experiments on Back-Testing Complex Using the Forecast of Artificial Neural Network 15

Computational Experiments on Back-Testing Complex Using the ...

引用

15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Gasanov, Igor Ereshko, Anton Federal Research Center 'Informatics & Control' of Ras Department of Information Computing Systems Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Neural networks

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A Model of Economic Dynamics with Labor Resources 15

A Model of Economic Dynamics with Labor Resources

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Sytov, Anatoly Bayramov, Orudzh Federal Research Center Computing Systems 'Informatics & Control' of Ras Department of Information Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The presented model of an economic system is the generalization of the Arrow-Debreu model for the dynamics case. The authors use the description techniques to develop an optimal enterprise by taking into account the dynamics of material products and financial indicators. They present a record of a multi-criterion problem and a procedure for coordinating the interests of industries. © 2022 IEEE.

关键词： Economics Industries Conference management Dynamic scheduling Large-scale systems Labor resources

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