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A Multi-Vehicle Self-Organized Cooperative Control Strategy for Platoon Formation in Connected Environment

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 4002-4018页

作者： Zhang, Mengqi Wang, Chunyan Zhao, Wanzhong Liu, Jinqiang Zhang, Ziyu Nanjing Univ Aeronaut & Astronaut Dept Vehicle Engn Nanjing 210016 Peoples R China Jiangsu Engn Res Ctr Vehicle Distributed Drive & I Nanjing 210016 Peoples R China

Aiming at the problem of low merging efficiency, poor platoon stability, and high collision risk when multiple connected and automated vehicles merge into the same target platoon, we propose a multi-vehicle self-organized cooperative control strategy for platoon formation, which includes vehicle self-organizing formation control and platoon cooperative merging control. The vehicle self-organizing formation control module organizes the merging vehicles within the V2V communication range into multiple local platoons. The dynamic self-adjusting critical interval and a fixed-topology second-order platoon consistency control protocol are proposed to divided the vehicles reasonably and make the states of local platoon vehicles consistent. The merging vehicles merge into the target platoon as a whole in the form of a local platoon, which transforms the complex multi-vehicle merge problem into a platoon cooperative control problem and improves the merging efficiency. The platoon cooperative merging control module adopts a distributed model predictive control (DMPC) theory to design two longitudinal cooperative merging controllers, which control the target platoon to split to create a merging gap and the local platoon to align with this gap longitudinally. The lateral merging controller controls the local platoon change the lane to merge into the target platoon safely and smoothly. Simulation experiments are conducted in typical scenarios, and it is verified that the proposed control strategy can enable multi-vehicles to merge into a platoon efficiently, safely, and stably.

关键词： Merging Stability analysis Asymptotic stability Optimization Formation control Vehicle dynamics Predictive control prediction algorithms Safety Protocols Connected and automated vehicles multi-vehicle cooperative control platoon formation and control local platoon distributed model predictive control

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Deinterleaving of Pulse Streams With Conditional Autoregressive Kernel Mixture Network

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 1901-1913页

作者： Feng, Han Cong Li Jiang, Kai Zhou, Zhixin Zhao, Yuxin Tian, Kailun Tang, Bin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610056 Peoples R China

Deinterleaving emitters with complex patterns presents a significant challenge for electronic support measure systems. In this article, we address this issue by formulating deinterleaving as the optimization of an autoregressive likelihood function. We then propose the conditional autoregressive kernel mixture network, a conditional generative model that estimates the conditional density of pulse parameters based on previous noisy observations and source labels to estimate the solution of this optimization problem. The model, trained with a modified loss function for denoising, can extract pulses belonging to the class of a given source label. Therefore, the denoising-based deinterleaving is achieved with a single model. We evaluate our model with the proposed algorithms on a challenging synthetic dataset under various nonideal conditions and compare it against existing approaches for both conventional and open-set deinterleaving. The results indicate that our method significantly outperforms the comparative techniques, especially in open-set deinterleaving.

关键词： Noise reduction Dictionaries Streams Predictive models Noise Kernel prediction algorithms

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SDFC-YOLO: A YOLO-Based Model With Selective Dynamic Feature Compensation for Pavement Distress Detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 6556-6569页

作者： Geng, Huantong Liu, Zhenyu Wang, Yingrui Fang, Long Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing 210044 Peoples R China Jiangsu Open Univ Sch Informat Technol Nanjing 210036 Peoples R China

Timely detection and treatment of road cracks are crucial to prevent further deterioration of pavement. An accurate road crack detection algorithm can significantly reduce the human resources required for pavement maintenance. However, existing CNN-based object detectors, such as the You Only Look Once (YOLO) series of algorithms, face challenges such as receptive field fixation and information loss during feature extraction, resulting in lower accuracy in road crack detection. Therefore, we propose the Selective Dynamic Feature Compensation-YOLO (SDFC-YOLO) algorithm for pavement distress detection. Firstly, we introduce the Dynamic Downsampling Module (DDM), which adaptively adjusts the sampling positions of the convolutional kernel during the feature extraction process, addressing the issue of a fixed receptive field. Secondly, we propose a novel feature fusion method compensating for lost feature information in the path aggregation network. Lastly, we design a multi-scale weight selection module based on the above feature fusion method. It aims to utilize the channel weights of high-level features to guide bottom-level features and select more important features for compensation, thereby further enhancing detection accuracy. Experimental results demonstrate that compared to the benchmark model YOLOv8s, our method improves Precision (P), Recall (R), mean Average Precision@0.5 (mAP@0.5), and F1 score on the UAPD dataset by 6.8%, 2.4%, 5.3%, and 4.3%, respectively. Similarly, on the UAV-PDD2023 dataset, the aforementioned metrics are enhanced by 3.1%, 4.4%, 2.7%, and 3.8%, respectively. Furthermore, our method takes only 10.53ms to process a 1280 x 1280 resolution image, which fully meets the requirement of real-time detection.

关键词： Feature extraction Convolution Roads Accuracy Kernel Heuristic algorithms Image segmentation YOLO Data mining prediction algorithms Pavement distress detection YOLOv8 dynamic downsampling feature compensation multi-scale weight selection

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Online End-to-End Learning-Based Predictive Control for Microgrid Energy Management

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2025年第2期33卷 463-478页

作者： Casagrande, Vittorio Ferianc, Martin Rodrigues, Miguel R. D. Boem, Francesca UCL Dept Elect & Elect Engn London WC1E 7JE England Helsing London W1T 3BL England

This article proposes an innovative Online Learning (OL) algorithm designed for efficient microgrid energy management, integrating Recurrent Neural Networks (RNNs), and Model Predictive Control (MPC) in an End-to-End (E2E) learning-based control architecture. The algorithm leverages the RNN capabilities to predict uncertain and possibly evolving profiles of electricity price, load demand, and renewable generation. These are then exploited in an integrated MPC optimization problem to minimize the overall microgrid electricity consumption cost while guaranteeing operation constraints. The proposed methodology incorporates a specifically designed online version of the Stochastic Weight Averaging (O-SWA) and Experience Replay (ER) methods to enhance OL capabilities, ensuring more robust and adaptive learning in real-time scenarios. In addition, to address the challenge of model uncertainty, a task-based loss approach is proposed by integrating the MPC optimization as a differentiable optimization layer within the Neural Network (NN), allowing the OL architecture to jointly optimize prediction and control performance. The performance of the proposed methodology is evaluated through extensive simulation results, showcasing its Transfer Learning (TL) capabilities across different microgrid sites, which are crucial for deployment in real microgrids. We finally show that our OL algorithm can be used to estimate the prediction uncertainty of the unknown profiles.

关键词： Microgrids Energy management Uncertainty Renewable energy sources prediction algorithms Real-time systems Predictive models Predictive control Electricity Adaptation models End-to-End (E2E) learning Energy Management System (EMS) learning-based control microgrid Model Predictive Control (MPC) online learning (OL)

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QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Meghanath, Ashtakala Das, Subham Behera, Bikash K. Khan, Muhammad Attique Al-Kuwari, Saif Farouk, Ahmed Indian Inst Sci Educ & Res Thiruvananthapuram Dept Phys Thiruvananthapuram 695551 India Bikashs Quantum OPC Pvt Ltd Mohanpur 741246 West Bengal India Prince Mohammad Bin Fahd Univ Coll Comp Engn & Sci Dept AI Al Khobar 34217 Saudi Arabia Hamad Bin Khalifa Univ Coll Sci & Engn Qatar Ctr Quantum Comp Doha Qatar Hurghada Univ Fac Comp & Artificial Intelligence Dept Comp Sci Hurghada 83523 Egypt

In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU dagger method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate QDCNN's -key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework.

关键词： Transportation Decision making Safety prediction algorithms Roads Reliability Real-time systems Quantum state Logic gates Clustering algorithms Quantum deep convolutional neural network autonomous transportation shadow detection safety and reliability real-time decision-making

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An Inexact Sequential Quadratic Programming Method for Learning and Control of Recurrent Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第2期36卷 2762-2776页

作者： Adeoye, Adeyemi D. Bemporad, Alberto IMT Sch Adv Studies Lucca I-55100 Lucca Italy

This article considers the two-stage approach to solving a partially observable Markov decision process (POMDP): the identification stage and the (optimal) control stage. We present an inexact sequential quadratic programming framework for recurrent neural network learning (iSQPRL) for solving the identification stage of the POMDP, in which the true system is approximated by a recurrent neural network (RNN) with dynamically consistent overshooting (DCRNN). We formulate the learning problem as a constrained optimization problem and study the quadratic programming (QP) subproblem with a convergence analysis under a restarted Krylov-subspace iterative scheme that implicitly exploits the structure of the associated Karush-Kuhn-Tucker (KKT) subsystem. In the control stage, where a feedforward neural network (FNN) controller is designed on top of the RNN model, we adapt a generalized Gauss-Newton (GGN) algorithm that exploits useful approximations to the curvature terms of the training data and selects its mini-batch step size using a known property of some regularization function. Simulation results are provided to demonstrate the effectiveness of our approach.

关键词： Training Recurrent neural networks Optimization Neural networks Quadratic programming Process control prediction algorithms Gauss-Newton methods markov decision processes numerical optimization recurrent neural networks (RNNs) reinforcement learning (RL) sequential quadratic programming (SQP)

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Scribble-Supervised Video Object Segmentation via Scribble Enhancement

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第4期35卷 2999-3012页

作者： Gao, Xingyu Li, Zuolei Shi, Hailong Chen, Zhenyu Zhao, Peilin Chinese Acad Sci Inst Microelect Beijing 100029 Peoples R China State Grid Corp China Big Data Ctr Beijing 100031 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Tencent AI Lab Shenzhen 518000 Peoples R China

Current video object segmentation methods heavily rely on pixel-level mask annotations when training, which are expensive and time-consuming to acquire. To address this problem, some approaches try to train with sparse scribble annotations and take sparse target scribble as initial information for inference. However, due to the sparsity of scribble annotations, the performance is often limited, and the corresponding loss function needs to be designed. Inspired by the powerful ability of Segment Anything Model (SAM) to leverage prompt for segmentation, we argue that this problem can be alleviated by improving the quality of scribble. Therefore, we propose SEVOS, a framework for scribble-supervised video object segmentation, which contains a scribble enhancement algorithm and an semi-supervised video object segmentation network. Specifically, the scribble enhancement algorithm first samples corresponding positive sample points and negative sample points from target scribbles, and then feeds them into the SAM in turn, achieving high-quality scribble enhancement without human intervention. This algorithm augments the scribble-annotated video dataset, which is used for additional training of the model. Furthermore, we design a post-processing enhancement algorithm to further improve the prediction results. The obtained model outperforms state-of-the-art methods with a considerable performance gap, indicating the generalization and effectiveness of the proposed model.

关键词： Annotations Training Object segmentation prediction algorithms Circuits and systems Reliability Accuracy Costs Transformers Target tracking Video object segmentation weakly supervised segment anything scribble enhancement

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A Hybrid Machine Learning Model for Efficient XML Parsing

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IEEE ACCESS 2025年 13卷 382-393页

作者： Ali, Muhammad Khan, Minhaj Ahmad Ur Rasool, Raihan Bahauddin Zakariya Univ Dept Comp Sci Multan 60800 Pakistan IBM Technol Melbourne Vic 3006 Australia

The Extensible Markup Language (XML) files are extensively used for representing structured data on the web for file configuration, exchanging data between distinct applications, web development, and many other applications. Consequently, effective parsing techniques are necessary for XML files to enhance the performance of applications. The existing parsing techniques have their strengths and weaknesses affecting the performance of applications. Researchers point out that the selection of an efficient and appropriate parser is the most challenging issue regarding a particular condition. This paper proposes a framework XML Parsing Optimization using Hybrid Machine Learning (XPOHML) that makes use of Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning techniques for efficient XML parsing. The newly developed framework performs analysis and prediction of different XML parsers using profiling, classification, performance evaluation, and finally generates code for efficient parsing. The XML profiling phase of the XPOHML framework generates a dataset by evaluating the performance of PXTG, SAX, StAX, DOM, and JDOM parsing models on separate cores by applying numerous file sizes. The Classification phase produces the classification model by applying ANN and SVM techniques to identify the appropriate parsing model. The performance evaluation phase of XPOHML assesses the performance of both parsing models through classification metrics (accuracy). Additionally, based on evaluation outcomes, the code generation phase produces an efficient parsing model of XML files. The newly designed and developed XPOHML framework has shown a meaningful improvement in the performance of parsing XML files.

关键词： XML Support vector machines Classification algorithms prediction algorithms Machine learning Codes Machine learning algorithms Predictive models Performance evaluation Training Artificial intelligence artificial neural network eXtensible markup language framework machine learning model parsing support vector machine XML parsing optimization

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Research on Clean-Up Technique to Improve Short-Term Stability of Rubidium Clocks

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2025年第1期28卷 37-44页

作者： Han, Songyue Liu, Ya Xiao, Bo Wang, Jiachen Fan, Duosheng Li, Xiaohui Zhu, Feng Chinese Acad Sci Natl Time Serv Ctr Measurement & Testing Technol & Instrumentat Xian Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Key Lab Time Reference & Applicat Beijing Peoples R China Chinese Acad Sci Natl Time Serv Ctr Xian Peoples R China

In order to improve the poor short-term stability of rubidium (Rb) clock in time transfer, this paper propose a self-adaptive dual-state control algorithm to clean up the frequency standard. Compared with control algorithms that remain in the laboratory, this algorithm has a strong engineering application background. It has the advantages of long-term stable operation, combining efficiency and simplicity. By measuring the phase difference between the Rb clock and the crystal oscillator, and calculating the predicted value to change the control voltage, a digital phase-locked loop (PLL) is formed. Based on this principle, this paper designs a digital PLL control circuit for the VCXO tracking the Rb clock. After continuous testing, the second-level stability of the Rb clock is 7.68E-13, which is improved by an order of magnitude. The enhancement greatly reduces the usage limitations of Rb clocks, offering the cost-effective of any frequency source. The "crystal oscillator+" control strategy in the paper can also be applied to short-term stability improvement of other frequency sources. When the reference signal is traced to UTC (k), the VCXO can track and replicate the standard time signal.

关键词： Voltage measurement Crystals prediction algorithms Circuit stability Rubidium Phase locked loops Standards Frequency control Clocks Testing

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Explainable Depression Classification Based on EEG Feature Selection From Audio Stimuli

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 1411-1426页

作者： Zhu, Lixian Wang, Rui Jin, Xiaokun Li, Yuwen Tian, Fuze Cai, Ran Qian, Kun Hu, Xiping Hu, Bin Yamamoto, Yoshiharu Schuller, Bjorn W. Beijing Inst Technol Sch Med Technol Key Lab Brain Hlth Intelligent Evaluat & Intervent Minist Educ Beijing 100081 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Beijing Inst Technol Minist Educ Key Lab Brain Hlth Intelligent Evaluat & Intervent Beijing 100081 Peoples R China Beijing Inst Technol Sch Med Technol Beijing 100081 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci CAS Ctr Excellence Brain Sci & Intelligence Techno Shanghai 200234 Peoples R China Chinese Acad Sci Joint Res Ctr Cognit Neurosensor Technol Lanzhou U Inst Semicond Lanzhou 730000 Peoples R China Univ Tokyo Grad Sch Educ Educ Physiol Lab Tokyo 1130033 Japan Imperial Coll London GLA Grp Language Audio & Mus London SW7 England Tech Univ Munich Chair Hlth Informat CHI MRI D-81675 Munich Germany Munich Data Sci Inst MDSI D-85748 Munich Germany Munich Ctr Machine Learning MCML D-80538 Munich Germany

With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have been widely proposed. However, existing studies have mostly focused on the accuracy of depression recognition, ignoring the association between features and models. Additionally, there is a lack of research on the contribution of different features to depression recognition. To this end, this study introduces an innovative approach to depression detection using EEG data, integrating Ant-Lion Optimization (ALO) and Multi-Agent Reinforcement Learning (MARL) for feature fusion analysis. The inclusion of Explainable Artificial Intelligence (XAI) methods enhances the explainability of the model's features. The Time-Delay Embedded Hidden Markov Model (TDE-HMM) is employed to infer internal brain states during depression, triggered by audio stimulation. The ALO-MARL algorithm, combined with hyper-parameter optimization of the XGBoost classifier, achieves high accuracy (93.69%), sensitivity (88.60%), specificity (97.08%), and F1-score (91.82%) on a auditory stimulus-evoked three-channel EEG dataset. The results suggest that this approach outperforms state-of-the-art feature selection methods for depression recognition on this dataset, and XAI elucidates the critical impact of the minimum value of Power Spectral Density (PSD), Sample Entropy (SampEn), and R & eacute;nyi Entropy (Ren) on depression recognition. The study also explores dynamic brain state transitions revealed by audio stimuli, providing insights for the clinical application of AI algorithms in depression recognition.

关键词： Electroencephalography Feature extraction Depression Brain modeling Electrodes Predictive models Optimization Accuracy prediction algorithms Classification algorithms EEG depression detection ant lion optimization - multi-agent reinforcement learning (ALO-MARL) explainable artificial intelligence (XAI)

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