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Modeling and Risk Analysis of Cooperative Adaptive Cruise Control Systems Based on Petri Nets and Distributed Edge Intelligence

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IEEE Internet of Things Journal 2025年第12期12卷 19995-20006页

作者： Yu, Wangyang Cheng, Yumeng Fang, Xianwen Zhai, Xiaojun Wang, Yinglong Jing, Hongyuan Shaanxi Normal University Chang'an District Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi'an710119 China Shaanxi Normal University School of Computer Science Chang'an District Xi'an710119 China Anhui University of Science and Technology Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui232001 China University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Beijing Union University College of Robotics Beijing100101 China

Fueled by advancements in intelligent transportation systems, the Internet of Vehicles (IoV) seeks to connect smart vehicles, road infrastructure, and users into a unified network, enhancing traffic efficiency and reducing accident risks. Centralized cloud data collection raises concerns about privacy and communication overhead. To address these, Distributed Edge Intelligence (DEI) reduces transmission costs and improves privacy by implementing machine learning at the network edge. In this context, Cooperative Adaptive Cruise Control (CACC) systems, combined with DEI in the IoV framework, enhance transportation system intelligence through real-time data processing and decentralized decision-making. This paper proposes a modeling and analysis method for CACC systems based on Petri nets. The datasets are automatically generated using tools developed by our team, and machine learning methods are utilized to perform risk prediction analysis on the CACC model. From the perspective of Petri nets synchronization, we propose risk mitigation strategies from a design standpoint. The research results show that the proposed method significantly reduces signal accumulation and enhances synchronization in CACC systems. This improvement provides new theoretical support and technical guidance for the design and implementation of CACC systems, ultimately enhancing their safety and reliability. © 2025 IEEE.

关键词： Adaptive cruise control

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Forensicability Assessment of Questioned Images in Recapturing Detection

arXiv

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arXiv 2022年

作者： Chen, Changsheng Zhao, Lin Cai, Rizhao Yu, Zitong Huang, Jiwu Kot, Alex C. The Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Department of Electrical and Electrics Engineering Nanyang Technological University Singapore639798 Singapore

Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing (FAS) and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

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Stabilization of Continuous-Time Markov/Semi-Markov Jump Linear Systems Via Finite data-Rate Feedback

SSRN

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SSRN 2022年

作者： Wang, Jingyi Feng, Jianwen Xu, Chen Wu, Xiaoqun Lü, Jinhu College of Mathematics and Statistics Shenzhen University Shenzhen518060 China School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan China State Key Laboratory of Software Development Environment School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

This letter investigates almost sure exponential stabilization of continuous-time Markov jump linear systems (MJLSs) under communication data-rate constraints by introducing sampling and quantization into the feedback control. Different from previous works, the sampling times and the jump times are independent of each other in this letter. The quantization is recursively adjusted on the sampling time, and its updating strategy does not depend on the switching in a sampling interval. In other words, the explicit value of the switching signal in a sampling interval is not necessary. The numerically testable condition is developed to ensure almost sure exponential stabilization of MJLSs under the proposed communication and control protocols. We also drop the assumption of stabilizability of all individual modes required in previous works about the switched systems. Moreover, we extend the result to the case of continuous-time semi-Markov jump linear systems (semi-MJLSs) via the semi-Markov kernel approach. Finally, some numerical examples are presented to illustrate the effectiveness on stabilization of the proposed communication and control protocols. © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Z16b,a natural compound from Ganoderma cochlear is a novel RyR2 stabilizer preventing catecholaminergic polymorphic ventricular tachycardia

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中国药理学报（英文版） 2022年第9期43卷 2340-2350页

作者： Jiang-fan Wan Gang Wang Fu-ying Qin Dan-ling Huang Yan Wang Ai-ling Su Hai-ping Zhang Yang Liu Shao-yin Zeng Chao-liang Wei Yong-xian Cheng Jie Liu Guangdong Key Laboratory of Genome Stability and Human Disease Prevention Department of PathophysiologySchool of MedicineShenzhen UniversityShenzhen 518000China College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Genome Stability and Human Disease Prevention Department of PathophysiologySchool of MedicineShenzhen UniversityShenzhen 518000China School of Pharmaceutical Sciences Shenzhen University Health Science CenterShenzhen 518000China Center for Translation Medicine Research and Development Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China Guangdong Cardiovascular Institute Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510000China Guangdong Provincial key laboratory of South China Structure Heart Disease Department of Pediatric CardiologyGuangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510000China

Catecholaminergic polymorphic ventricular tachycardia(CPVT)is an inherited,lethal ventricular arrhythmia triggered by *** in genes that encode cardiac ryanodine receptor(RyR2)and proteins that regulate RyR2 activity cause enhanced diastolic Ca2+release(leak)through the RyR2 channels,resulting in *** therapies for CPVT are *** found that Z16b,a meroterpenoid isolated from Ganoderma cochlear,inhibited Ca2+spark frequency(CaSF)in R2474S/+cardiomyocytes in a dose-dependent manner,with an IC50 of 3.2 μM.Z16b also dose-dependently suppressed abnormal post-pacing Ca2+release *** injection(i.p.)of epinephrine and caffeine stimulated sustained ventricular tachycardia in all R2474S/+mice,while pretreatment with Z16b(0.5 mg/kg,i.p.)prevented ventricular arrhythmia in 9 of 10 mice,and Z16b administration immediately after the onset of VT abolished sVT in 9 of 12 *** translational significance,Z16b significantly inhibited CaSF and abnormal Ca2+release events in human CPVT ***,Z16b interacts with RyR2,enhancing the"zipping"state of the N-terminal and central domains of RyR2.A molecular docking simulation and point mutation and pulldown assays identified Z16b forms hydrogen bonds with Arg626,His1670,and Gln2126 in RyR2 as a triangle shape that anchors the NTD and CD interaction and thus stabilizes RyR2 in a tight"zipping"*** findings support that Z16b is a novel RyR2 stabilizer that can prevent *** may also serve as a lead compound with a new scaffold for the design of safer and more efficient drugs for treating CPVT.

关键词： arrhythmia catecholaminergic polymorphic ventricular tachycardia natural product ryanodine receptor induced pluripotent stem cell-derived cardiomyocytes

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PROM: A persistent routing optimization method based on supervised learning

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Journal of Network and Computer Applications 2025年 242卷

作者： Yingya Guo Zebo Huang Mingjie Ding Bin Lin Huan Luo College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China

Traditional Traffic Engineering (TE) typically improves network performance by formulating and solving routing optimization problems based on measured or predicted Traffic Matrices (TMs). However, since network traffic is dynamic, routing strategies optimized for the current measured TMs may not be suitable for future traffic patterns. To adapt to dynamic traffic, one possible solution is to perform more frequent routing updates, but this may lead to service disruptions. Another approach is the two-step method of prediction followed by optimization, which could lead to degraded performance of routing strategies due to inaccuracies in traffic prediction. Additionally, existing approaches focus solely on either distributed or centralized networks, lacking flexibility to adapt to different network architectures. To address these challenges, we propose PROM, a Machine Learning (ML)-based routing optimization model that directly predicts an effective routing strategy in an end-to-end manner based on historical traffic. This routing strategy is applicable across multiple future TMs, thereby reducing the need for frequent routing updates. Furthermore, PROM can be easily extended from a centralized implementation to a distributed one, adapting to different network architectures. To enhance model performance, we have also designed a custom loss function for routing optimization scenarios to avoid overfitting. Extensive simulation results demonstrate that PROM can predict high-quality routing strategies in future unknown traffic scenarios and is adaptable to both centralized and distributed network architectures.

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization

Adaptive Latent Factor Analysis via Generalized Momentum-Inc...

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IEEE International Conference on Networking, Sensing and Control

作者： Jiufang Chen Ye Yuan School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China College of Computer and Information Science Southwest University Chongqing China

ISBN: (纸本)9781665472449

Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e., learning rate and regularization coefficient, self-adaptation. However, a standard PSO algorithm may suffer from accuracy loss caused by premature convergence. To address this issue, this paper incorporates more historical information into each particle's evolutionary process for avoiding premature convergence following the principle of a generalized-momentum (GM) method, thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a GM-PSO-based LFA (GMPL) model is further achieved to implement efficient self-adaptation of hyper-parameters. The experimental results on three HDI matrices demonstrate that the GMPL model achieves a higher prediction accuracy for missing data estimation in industrial applications.

关键词： Adaptation models Analytical models Tensors Computational modeling Predictive models Prediction algorithms Time complexity

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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