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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wei, Bingcai Liu, Hui Qian, Chuang Jia, Yi Wu, Wangyu Li, Zhishan School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool United Kingdom School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns. © 2025 IEEE.

关键词： Cross-view Inter-view Sand removal Stereo image Uncertainty estimation

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Research on the Assessment of Dairy Cow Dry Matter Intake Using Itso-Optimizedstacking Ensemble Learning

SSRN

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SSRN 2025年

作者： Wang, Shuairan Long, Ting Wei, Xiaoli Guo, Qinzu Guo, Hongrui Shen, Weizheng Gu, Zhixin College of Computer and Control Engineering Northeast Forestry University Harbin150040 China College of Electric and Information Northeast Agricultural University Harbin150030 China

Dry matter intake (DMI) in dairy cows is a critical indicator of nutrient intake from feed, serving as the cornerstone of precision feeding practices, playing a critical role in improving production efficiency and enhancing the quality of dairy products. To address the high costs of traditional measurement methods and the structural complexity and large parameter counts of neural network models, this study proposes a Stacking ensemble learning model to assess DMI, with model parameters optimized using the Tuna Swarm Optimization (TSO) algorithm to enhance assessment accuracy, taking cow body weight, lying duration, lying times, rumination duration, foraging duration, walking steps, and the concentrate-to-roughage feed ratio as input variables. To further improve TSO's search efficiency and spatial exploration, this study introduces Sine-Logistic chaotic mapping, Levy flight, and Gaussian random walk strategy to optimize the TSO algorithm, developing the improved Tuna Swarm Optimization (ITSO). ITSO-optimized Stacking model achieved superior performance in DMI assessment, with an accuracy of 95.84%, significantly outperforming SVR, RF, DT, GBR, ETR, and AdaBoost models. This study provides a robust tool for precision feeding, contributing to optimizing cow feeding strategies, improving farm efficiency, and supporting sustainable dairy farming practices. © 2025, The Authors. All rights reserved.

关键词： Dairy products

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The DSOGI-PLL with the repetitive control internal model suppresses the SSO in the PV system

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Solar Energy 2025年 296卷

作者： Liu, Yiqi Tao, Peng Wu, Yucheng Yin, Laicheng College of Computer and Control Engineering Northeast Forestry University Harbin China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China

The interaction between PV system and inductive grid impedance under weak grid is easy to cause sub-/super-synchronous oscillation (SSO). The coupling phenomenon between grid impedance and PLL cannot be ignored. In this paper, the influence of output phase of PLL on coordinate transformation is analyzed, and a mathematical model of PV inverter considering oscillation phase is established. By establishing and analyzing the mathematical model, the mechanism of SSO disturbance of photovoltaic inverter is expounded, and the disturbance can be divided into error disturbance and grid disturbance. According to the mechanism of the disturbance, a scheme combining DSOGI-PLL (Dual Second Order Generalized Integrator) and repetitive control internal model is proposed to suppress the SSO. For grid disturbance, the designed repetitive control internal model can eliminate the harmonics in the system. For error disturbance, DSOGI-PLL can eliminate the error of PLL output phase caused by grid voltage oscillation, and accurately lock the phase. The performance and robustness of the presented scheme are proved in time-domain simulations. © 2025 International Solar Energy Society

关键词： Time domain analysis

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Invariant Set Analysis and control Synthesis of Multi-Equilibrium Switched Systems under control Constraints

Invariant Set Analysis and Control Synthesis of Multi-Equili...

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International Conference on Intelligent control and Information Processing (ICICIP)

作者： Yuejiang Han Lixian Zhang Yihang Ding Yifei Dong Ye Liang School of Astronautics Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

This paper investigates the problems of invariant set analysis and control synthesis for multi-equilibrium switched systems under control constraints. A control strategy based on the invariant set method is proposed, accompanied by criteria formulated as linear matrix inequalities, ensuring that the state remains within the mode-dependent invariant sets. Furthermore, considering the control constraints in practical scenarios, a multi-equilibrium switched controller with control constraints (MESCC controller) is designed. Compared to existing works, the proposed method incorporates mode-dependent dwell time conditions and mode-dependent invariant sets, thereby offering enhanced design flexibility. An example of an aero-engine control system is presented to demonstrate the effectiveness and potential of the theoretical results.

关键词： Switched systems Robust control Switches Information processing control systems Linear matrix inequalities Closed loop systems Aircraft propulsion Intelligent control

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection control Algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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Counteracting New Attacks in CPS: A Few-shot Class-incremental Adaptation Strategy for Intrusion Detection System

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IEEE Transactions on Network and Service Management 2025年

作者： Dong, Xinrui Lai, Yingxu Zhang, Xiao Xu, Xinyu Beijing University of Technology College of Computer Science Beijing100124 China Ministry of Education Engineering Research Center of Intelligent Perception and Autonomous Control Beijing100124 China

The deep integration of physical devices and communication networks has increased the security risks of cyber-physical systems (CPSs) compared to traditional control systems. Deep learning-based intrusion detection systems (IDSs) play a crucial role in ensuring CPSs security. However, the existing IDSs often rely on known attack features, rendering them unable to withstand emerging new attacks arising from the dynamic evolution of intrusion behaviors. This paper aims to develop an IDSs with high adaptability and strong generalization capabilities, which is capable of rapidly adapting to new attack classes with only a few new samples. To achieve this objective, we propose CAT-IDS, a few-shot class-incremental adaptation strategy for an IDS to counteract new attacks on CPSs. We design a highly symmetric classifier structure for CAT-IDS that can flexibly adjust the classification space to adapt to new attacks. Furthermore, we calibrate the biased distribution formed by a few training samples through statistical feature transfer. In order to prevent the model from forgetting old attack information during the adaptation process, we devise hybrid features for attack detection. These features contain essential information for both old and new class classifications. We demonstrate the effectiveness of CAT-IDS through multiple experiments on three CPSs datasets. The results show that CAT-IDS achieves an average accuracy improvement of approximately 4. 5% compared to the state-of-the-art methods, demonstrating its superior ability to adapt to new attacks while maintaining high performance in classifying existing attacks. © 2004-2012 IEEE.

关键词： Network intrusion

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CPL-SLAM: Centralized Collaborative Multi-Robot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center The Ministry of Education for Intelligent Control System and Intelligent Equipment Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering China Carleton University Canada

Traditional visual-inertial SLAM (Simultaneous Localization and Mapping) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intra-robot and inter-robot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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CASI: Context-aware Automatic Semantic Inference by fusing video and network traffic information in industrial control systems

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Information Fusion 2025年 122卷

作者： Hetu, Zheqiu Zhang, Zhenyong Wang, Mufeng Xv, Zidong Meng, Jie State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China State Key Laboratory of Industrial Control Technology and College of Control Science and Engineering Zhejiang University Hangzhou310027 China

The increasing adoption of Internet of Things devices in Industrial control Systems (ICS) has provided both attackers and security professionals with new perspectives on these traditionally safety-critical systems. Our observations reveal that video captured by field-deployed cameras contains process variables (PVs) essential for both ICS attack and defense. Based on this insight, we propose a Context-Aware Semantic Inference (CASI) method. CASI integrates video information from cameras with network traffic to infer the physical semantics of PVs in network packets, serving as a tactical tool for attackers or security professionals. A key advantage of CASI is its non-intrusive nature, deriving PVs location in the packets and semantics solely from video and network traffic without interacting with the runtime ICS, which is both covert and ensures the uninterrupted operation of the system. We constructed an ICS testbed using real devices to replicate a simplified industrial process, collecting traffic from 2 industrial protocols and creating 4 datasets. We evaluated CASI's precision and recall in detecting PVs within this context, comparing it with similar tools. Experimental results demonstrate that CASI achieves a recall rate of 1.00 in PV semantic extraction, significantly outperforming other tools. Furthermore, leveraging CASI-derived semantic information for data tampering attacks successfully disrupted monitoring and data acquisition programs, posing a severe security threat to the ICS. © 2025 Elsevier B.V.

关键词： Reverse engineering

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Geometric Reachability for Attitude control Systems via Contraction Theory

arXiv

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arXiv 2025年

作者： Xu, Chencheng Jafarpour, Saber Zhao, Chengcheng Shi, Zhiguo Chen, Jiming College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States College of Control Science and Engineering Zhejiang University Hangzhou China

In this paper, we present a geometric framework for the reachability analysis of attitude control systems. We model the attitude dynamics on the product manifold SO(3) × 3 and introduce a novel parametrized family of Riemannian metrics on this space. Using contraction theory on manifolds, we establish reliable upper bounds on the Riemannian distance between nearby trajectories of the attitude control systems. By combining these trajectory bounds with numerical simulations, we provide a simulation-based algorithm to over-approximate the reachable sets of attitude systems. We show that the search for optimal metrics for distance bounds can be efficiently performed using semidefinite programming. Additionally, we introduce a practical and effective representation of these over-approximations on manifolds, enabling their integration with existing Euclidean tools and software. Numerical experiments validate the effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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