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Real-Time Proportional-Integral-Derivative(PID)Tuning Based on Back Propagation(BP)Neural Network for Intelligent Vehicle Motion Control

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Computers, Materials & Continua 2025年第5期83卷 2375-2401页

作者： Liang Zhou Qiyao Hu Xianlin Peng Qianlong Liu School of Information Science and Technology Northwest UniversityXi’an710127China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest UniversityXi’an710127China Generative Artificial Intelligence and Mixed Reality Key Laboratory of Higher Education Institutions in Shaanxi Province Xi’an710127China Shaanxi Silk Road Cultural Heritage Digital Protection and Inheritance Collaborative Innovation Center Xi’an710127China School of Art Northwest UniversityXi’an710127China Network and Data Center Northwest UniversityXi’an710127China

Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control *** modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and ***,deficiencies in these systems can lead to significant operational *** paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter *** paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and *** BP neural network is first trained to capture the nonlinear dynamic characteristics of the *** network is then combined with the PID controller to forma hybrid control *** output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight *** experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 ***,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.

关键词： PID control backpropagation neural network hybrid control nonlinear dynamic processes edge intelligence

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Performance of fiber-based QAM/FSO systems in turbulence with anisotropic tilt angle and random angular jitter

arXiv

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arXiv 2025年

作者： Zhai, Chao Xue, Zhenyuan School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University at Qinhuangdao Qinhuangdao066004 China

Nowadays, the subsistent anisotropic non-Kolmogorov (ANK) turbulence models are all established on the supposition that the long axis of turbulence cell ought to be level with the ground. Nevertheless, Beason et al. and Wang et al. have illustrated through their recent experimental results that there is an anisotropic tilt angle in the turbulence cell, i.e., the long axis of turbulence cell is probably not level with the ground but has a particular angle with the ground. For the practical free-space optical (FSO) communication link, bias error and random angular jitter are critical elements which influence the fiber-based FSO communication system performance. In this paper, employing the new ANK turbulence spectrum models in the horizontal link with anisotropic tilt angle, we derive the normalized probability density function (PDF) for Gamma-Gamma distribution with anisotropic tilt angle, and the PDF of fiber-coupling efficiency in the presence of bias error, anisotropic tilt angle, and random angular jitter. And then the average bit error rate (BER) expression of fiber-based J × Q rectangular quadrature amplitude modulation (QAM) FSO systems for a plane wave transmission through the weak ANK horizontal link in the presence of bias error, anisotropic tilt angle, and random angular jitter is developed. © 2025, CC BY-NC-ND.

关键词： Quadrature amplitude modulation

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A Plug-and-Play Bregman ADMM Module for Inferring Event Branches in Temporal Point Processes

arXiv

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arXiv 2025年

作者： Wang, Qingmei Wu, Yuxin Long, Yujie Huang, Jing Ran, Fengyuan Su, Bing Xu, Hongteng Gaoling School of Artificial Intelligence Renmin University of China China School of Cyber Science and Engineering Wuhan University China Beijing Key Laboratory of Big Data Management and Analysis Methods China

An event sequence generated by a temporal point process is often associated with a hidden and structured event branching process that captures the triggering relations between its historical and current events. In this study, we design a new plug-and-play module based on the Bregman ADMM (BADMM) algorithm, which infers event branches associated with event sequences in the maximum likelihood estimation framework of temporal point processes (TPPs). Specifically, we formulate the inference of event branches as an optimization problem for the event transition matrix under sparse and low-rank constraints, which is embedded in existing TPP models or their learning paradigms. We can implement this optimization problem based on subspace clustering and sparse group-lasso, respectively, and solve it using the Bregman ADMM algorithm, whose unrolling leads to the proposed BADMM module. When learning a classic TPP (e.g., Hawkes process) by the expectation-maximization algorithm, the BADMM module helps derive structured responsibility matrices in the E-step. Similarly, the BADMM module helps derive low-rank and sparse attention maps for the neural TPPs with self-attention layers. The structured responsibility matrices and attention maps, which work as learned event transition matrices, indicate event branches, e.g., inferring isolated events and those key events triggering many subsequent events. Experiments on both synthetic and real-world data show that plugging our BADMM module into existing TPP models and learning paradigms can improve model performance and provide us with interpretable structured event branches. The code is available at https://***/qingmeiwangdaily/BADMM *** Codes 60G55, 62M10 Copyright © 2025, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Characterizing the set of quantum correlations in prepare-and-measure quantum multichain-shaped networks

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Physical Review A 2025年第2期111卷 022439-022439页

作者： Yanning Jia Fenzhuo Guo Yukun Wang Haifeng Dong Fei Gao School of Science Beijing University of Posts and Telecommunications Beijing 100876 China Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum-Beijing Beijing 102249 China School of Instrumentation Science and Opto-Electronics Engineering Beihang University Beijing 100191 China

We introduce a hierarchy of tests satisfied by any probability distribution P that represents the quantum correlations generated in prepare-and-measure (P&M) quantum multichain-shaped networks, assuming only the inner-product information of prepared states. The P&M quantum multichain-shaped networks involve multiple measurement parties, with each measurement party potentially having multiple sequential receivers. We adapt the original Navascués, Pironio, and Acín (NPA) hierarchy by incorporating a finite number of linear and positive semi-definite constraints to characterize the quantum correlations in P&M quantum multichain-shaped networks. These constraints in each hierarchy are derived from sequential measurements and the inner-product matrix of prepared states. The adapted NPA hierarchy is further applied to tackle some quantum information tasks, including sequential quantum random access codes (QRACs) and semi-device-independent randomness certification. First, we derive the optimal trade-off between the two sequential receivers in the 2→1 sequential QRACs, and investigate randomness certification in its double violation region. Second, considering the presence of an eavesdropper in actual communication, we show how much local and global randomness can be certified using the optimal trade-off of 2→1 sequential QRACs. We also quantify the amount of local and global randomness that can be certified from the complete set of probabilities generated by the two sequential receivers. Our conclusion is that utilizing the full set of probabilities certifies more randomness than relying solely on the optimal trade-off relationship.

关键词： Probability distributions

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How do we improve the reproducibility and accuracy of EIS and Tafel testing in corrosion science?

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Surface Science and Technology 2025年第1期3卷 1-22页

作者： Shao, Zexi Ma, Mingyuan Liu, Xuhui Yuan, Sicheng Lin, Dan Bai, Ziheng Hu, Haodong Yu, Jingtai Du, Yafei Zhu, Yanji Wang, Huaiyuan School of Chemical Engineering and Technology and State Key Laboratory for Chemical Engineering Tianjin University Tianjin P.R. China Tianjin Key Laboratory of Chemical Process Safety and Equipment Technology Tianjin University Tianjin P.R. China School of Materials Science and Engineering Northeastern University Shenyang China School of Materials and New Energy Ningxia University Yinchuan P.R. China Institute for Advanced Materials and Technology National Materials Corrosion and Protection Scientific Data Center University of Science and Technology Beijing Beijing China

Electrochemical is crucial in corrosion, energy storage, and electrocatalysts and are essential for mitigating carbon emissions. However, deviations in electrochemical testing can result from experiment errors and electrode preparation differences. Therefore, electrochemical experiment results are required to assess the reproducibility. Herein we found that the surface resistance is a key factor of electrochemical testing, and the difference of surface resistance could inform future anode oxidation devices, and enhance the reproducibility and accuracy of electrochemical testing.

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Azimuth-based antenna group delay variation modeling for dual-frequency multi-constellation GBAS

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Chinese Journal of Aeronautics 2025年第2期 374-384页

作者： Yuan LIU Yanbo ZHU Kun FANG Zhipeng WANG National Key Laboratory of CNS/ATM School of Electronic and Information Engineering Beihang University Aviation Data Communication Corporation CAAC

Antenna Group Delay Variation(AGDV) is a hardware error source that affects the performance of Dual-Frequency Multi-Constellation(DFMC) Ground-based Augmentation System(GBAS), and these errors are difficult to distinguish from multipath errors. Currently, AGDV is usually modeled as a part of the multipath error, which is called the multipath-AGDV model. However, because of the inconsistency of AGDV and multipath when switching among different positioning modes of GBAS, and because the traditional model does not consider the impact of the azimuth on AGDV, using the traditional multipath-AGDV model will cause the protection levels to be inaccurately calculated. In this paper, azimuth-based modeling of AGDV is conducted by using anechoic chamber measurements. The biases and standard deviations of AGDV based on azimuths are analyzed and modeled, and the calculation method for the DFMC GBAS protection level is optimized. The results show that the azimuth-based AGDV model and protection level optimization algorithm can better avoid the error exceeding the protection level than the multipath-AGDV model. Compared with AGDV elevation model, the VPLs of the B1C signal are increased by 0.24 m and 0.06 m, and the VPLs of the B2a signal are reduced by 0.01 m and 0.16 m using the 100 s and 600 s DFree filtering positioning modes, respectively. The changes in the B1C and B2a protection levels reflect the changes in AGDV corresponding to the azimuth for the respective frequencies, further ensuring the integrity of airborne users, especially when they turn near the airport.

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Collision-Aware MPCC Integrated with Neural Augmentation for Quadrotor Trajectory Tracking

Collision-Aware MPCC Integrated with Neural Augmentation for...

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International Conference on Mechatronics (ICM)

作者： Chuanqi Hu Jin Wang Yikai Chen Yonghang Chen Zhi Zheng Jun Meng Guodong Lu Polytechnic Institute Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China School of Mechanical Engineering Zhejiang University Hangzhou China National Elite Institute of Engineering Chongqing University Chongqing China College of Electrical Engineering Zhejiang University Hangzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This paper presents a trajectory tracking algorithm designed for autonomous quadrotors to navigate complex dy-namic environments that is resilient to collision disturbances. Since it combines Model Predictive Contouring Control (MPCC) with the capabilities of neural networks, our method is named MPCC Integrated with Neural Augmentation (MINA). By equip-ping the quadrotor with PVDF piezoelectric sensors, we achieve real-time perception of collision forces and utilize S4D-Conv Adaptive Net Architecture (SANA) for state computation, a network that integrates the advantages of S4D and convolutional kernels to enhance the accuracy of state predictions. We incorporate the predicted results into the MPCC framework to optimize the thrust output of the quadrotor for the next time step, ensuring precise trajectory tracking. Experimental validation demonstrates the reliability and effectiveness of our algorithm, providing an innovative solution for autonomous flight and trajectory tracking of quadrotors in complex dynamic environments.

关键词： Trajectory tracking Navigation Heuristic algorithms Neural networks Reliability theory Predictive models Prediction algorithms Real-time systems Sensors Quadrotors

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Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting

arXiv

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arXiv 2025年

作者： Yang, Chen Wang, Jingyuan Jiang, Xiaohan Wu, Junjie School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Engineering School of Economics and Management Beihang University Beijing China MIIT Key Laboratory of Data Intelligence and Management School of Economics and Management Beihang University Beijing China

Recent years have witnessed the perfect encounter of deep learning and quantitative trading has achieved great success in stock investment. Numerous deep learning-based models have been developed for forecasting stock returns, leveraging the powerful representation capabilities of neural networks to identify patterns and factors influencing stock prices. These models can effectively capture general patterns in the market, such as stock price trends, volume-price relationships, and time variations. However, the impact of special irrationality factors – such as market sentiment, speculative behavior, market manipulation, and psychological biases – has not been fully considered in existing deep stock forecasting models due to their relative abstraction as well as lack of explicit labels and data description. To fill this gap, we propose UMI, a Universal multi-level Market Irrationality factor model to enhance stock return forecasting. The UMI model learns factors that can reflect irrational behaviors in market from both individual stock and overall market levels. For the stock-level, UMI construct an estimated rational price for each stock, which is cointegrated with the stock’s actual price. The discrepancy between the actual and the rational prices serves as a factor to indicate stock-level irrational events. Additionally, we define market-level irrational behaviors as anomalous synchronous fluctuations of stocks within a market. Using two self-supervised representation learning tasks, i.e., sub-market comparative learning and market synchronism prediction, the UMI model incorporates market-level irrationalities into a market representation vector, which is then used as the market-level irrationality factor. We also developed a forecasting model that captures both temporal and relational dependencies among stocks, accommodating the UMI factors. Extensive experiments on U.S. and Chinese stock markets with competitive baselines demonstrate our model’s effectiveness and

关键词： Self-supervised learning

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SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking 25

SoAy: A Solution-based LLM API-using Methodology for Academi...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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Task Prioritization in Multiagent Environments: A Novel Approach Using Nash Q-Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Kexin Hu, Yu Wang, Jielei Li, Lin Zhang, Shuyuan Luo, Guangchun Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

—With the rapid proliferation of smartphones and various terminal devices, edge computing has gained increasing importance in overcoming computational limitations and enhancing service quality through task offloading. However, most existing approaches handle tasks on a first-come, first-served basis, neglecting task prioritization, which is critical in real-world scenarios where tasks have different levels of urgency and resource requirements. This paper addresses the challenge of effective task prioritization in multi-agent task offloading by proposing a Nash Q-learning-based strategy. Our methodology involves optimizing task allocation decisions using Nash Q-learning to integrate Nash equilibrium principles, thereby enhancing adaptability and dynamic decision-making capabilities within a multi-agent environment. The proposed algorithm is evaluated through extensive simulations, comparing total delay, resource utilization, and task success rates across various task offloading strategies. The results demonstrate that our Nash Q-learning approach outperforms conventional techniques by dramatically lowering overall latency, optimizing resource allocation, and maintaining high task success rates. This work demonstrates the efficacy of Nash Q-learning for dynamically setting priorities for tasks in multi-agent edge computing environments, providing a robust framework for effective resource management and collaborative task completion. And also highlight the potential of Nash Q-learning in advancing intelligent task management in edge computing, suggesting its applicability in real-world, resource-constrained environments. © 2025 IEEE. All rights reserved,

关键词： Computation offloading

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