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arXiv 2025年

作者： Lian, Shiwei Zhang, Feitian Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China

The object goal visual navigation is the task of navigating to a specific target object using egocentric visual observations. Recent end-to-end navigation models based on deep reinforcement learning have achieved remarkable performance in finding and reaching target objects. However, the collision problem of these models during navigation remains unresolved, since the collision is typically neglected when evaluating the success. Although incorporating a negative reward for collision during training appears straightforward, it results in a more conservative policy, thereby limiting the agent’s ability to reach targets. In addition, many of these models utilize only RGB observations, further increasing the difficulty of collision avoidance without depth information. To address these limitations, a new concept—collision-free success is introduced to evaluate the ability of navigation models to find a collision-free path towards the target object. A two-stage training method with collision prediction is proposed to improve the collision-free success rate of the existing navigation models using RGB observations. In the first training stage, the collision prediction module supervises the agent’s collision states during exploration to learn to predict the possible collision. In the second stage, leveraging the trained collision prediction, the agent learns to navigate to the target without collision. The experimental results in the AI2-THOR environment demonstrate that the proposed method greatly improves the collision-free success rate of different navigation models and outperforms other comparable collision-avoidance methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Moving-Window Integrated Physics-Data-Based Modeling of Four-Wheel Vehicle Lateral Dynamics

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Automotive Innovation 2025年 1-11页

作者： Wei, Wenpeng Wang, Jinxiang Pi, Dawei Zhuang, Weichao He, Tianyi Yin, Guodong School of Mechanical Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Safety and Intelligence of Vehicle Equipment Research Center Nanjing University of Science and Technology Nanjing China Department of Mechanical and Aerospace Engineering Utah State University Logan USA

This paper presents an Integrated physics-data-based (IPDB) modeling of lateral vehicle dynamics with moving-window data snapshots. The IPDB model encodes the fundamental physical principle of four-wheel vehicle motions and simultaneously carries out the adaptiveness of the data-driven approach. Specifically, the traditional physics-based lateral dynamics considering four-wheel interaction are first derived into an affine linear-parameter-varying model, in which the vehicle-related parameters and motion variables are separated. Then, by using the Kronecker product, the IPDB model, directly formulated by the data snapshots in the moving-window fashion, is obtained for system representation. As a result, the IPDB technique rendered model is physically interpretable. The impacts of moving window length on modeling performance are numerically studied. The IPDB model accuracy is validated with data from CarSim simulations and experiments with passenger vehicles under various scenarios. It is further demonstrated that the proposed IPDB model is more data-efficient than other data-driven methods since it only uses the data snapshot in the moving window to update the model recursively. This characteristic enables the IPDB method with online modeling capability to adapt to varying driving scenarios.

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Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method

arXiv

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arXiv 2025年

作者： Zhang, Zhong-Yi Xu, Chen-Yang Zhao, Li-Xuan Hou, Hui-Rang Meng, Qing-Hao Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China

Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring, widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on GCN model. This model tackles two key challenges: (1) subjectivity in depression-level labeling due to patient self-report biases, and (2) class imbalance across severity categories. Inspired by the model learning patterns, we introduced two novel modules: the sample confidence module and the minority sample penalty module. The former leverages the L2-norm of prediction errors to progressively filter EEG samples with weak label alignment during training, thereby reducing the impact of subjectivity;the latter automatically upweights misclassified minority-class samples to address imbalance issues. After testing on two public EEG datasets, DepL-GCN achieved accuracies of 81.13% and 81.36% for multi-class severity recognition, outperforming baseline models. Ablation studies confirmed both modules’ contributions. We further discussed the strengths and limitations of regression-based models for depression-level recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Electroencephalography

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Unraveling tensor structures in correct-by-design controller synthesis

arXiv

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arXiv 2025年

作者： Wang, Ruohan Sun, Zhiyong Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

Formal safety guarantees on the synthesis of controllers for stochastic systems can be obtained using correct-by-design approaches. These approaches often use abstractions as finite-state Markov Decision Processes (MDPs). As the state space of these MDPs grows, the curse of dimensionality makes the computational and memory cost of the probabilistic guarantees, quantified with dynamic programming, scale exponentially. In this work, we leverage decoupled dynamics and unravel, via dynamic programming operations, a tree structure in the Canonical Polyadic Decomposition (CPD) of the value functions. For discrete-time stochastic systems with syntactically co-safe linear temporal logic (scLTL) specifications, we provide provable probabilistic safety guarantees and significantly alleviate the computational burden. We provide numerical studies and validations of the theoretical results on several typical case studies and showcase that the uncovered tree structure enables efficient reductions in the computational burden. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Short-term Load Forecasting with Transformer Based on Fusion CNN-BiGRU

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第5期52卷 1344-1364页

作者： Xie, Tao Xiao, Zhongchuang He, Xiaojun Fan, Yinchun Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Jiaotong University's School of Electromechanical and Vehicle Engineering Chongqing400065 China SINOPEC Sales Co Ltd's Chongqing EJI branch Chongqing400065 China

To improve the accuracy of short-term power load forecasting, a short-term power load forecasting method based on Transformer with fused CNN-BiGRU is proposed. First, the best input data sequence is selected using the Partial Autocorrelation Function (PACF). Next, the importance scores and rankings of the feature data are obtained through the gradient boosting tree algorithm of CatBoost, and the optimal input features are selected. Then, the feature data and load data are combined. Finally, the combined data is used as input for the Transformer fused with CNN-BiGRU. In model training and forecasting, a hybrid forecasting strategy is employed, incorporating elements of multi-step forecasting into single-step forecasting. For the data of each moment, personalized and independent model training is performed, along with forecasting that include hybrid elements. The model replaces the original word embedding and position encoding components of Transformer. It uses CNN-BiGRU to extract high-dimensional feature representations of latent feature information and relative positional information from the input data. The proposed model demonstrates higher forecasting accuracy through validation on two different datasets and comparison with other forecasting models. Additionally, two ablation experiments are conducted. Through systematic ablation experiments, we demonstrate that modifications to the Transformer input layer significantly improve model performance in time series tasks. These results validate the rationality and effectiveness of the proposed approach. The ablation experiments on the method of PACF selecting the optimal input data sequence and CatBoost filtering the optimal input feature data, as well as the hybrid forecasting strategy, further verify the effectiveness and rationality of the data selection methods and forecasting strategies used in this study for short-term power load forecasting. Moreover, to eliminate the zigzagging jitter phenomenon in the foreca

关键词： CatBoost hybrid forecasting strategy PACF Transformer fuses CNN-BiGRU

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Unit commitment with bidirectional ramping constraints of flexibility retrofit thermal power unit

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International Journal of Electrical Power and Energy systems 2025年 168卷

作者： Jiang, Jingxiao Zhu, Yanran Wang, Ying Guo, Xiaorui Lv, Jianhu Qin, Jianxiang Yan, Xingyun Zhang, Kaifeng Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China China Electric Power Research Institute Nanjing210003 China State Grid Ningxia Electric Power Co. Ltd. Yinchuan 750001 China

Flexibility retrofits of thermal power units have been widely implemented to improve power system balancing. These retrofits enable operation below the regular minimum output, known as deep peak regulation, to accommodate increasing renewable energy integration. Due to the inherent characteristics of thermal power units, ramp-up rates and ramp-down rates generally differ. Existing studies often overlook this difference by assuming equal ramping rates. To bridge this gap, this paper systematically models and analyses bidirectional ramping constraints in unit commitment (UC) models, providing a more accurate and realistic representation of the ramping process. Specifically, this paper proposes three innovative bidirectional ramping models. The first is the all-period fixed bidirectional ramping (AFBR) model, which assumes a constant ramp rate across all periods. The second model is the interperiod dynamic bidirectional ramping (InterDBR) model, which captures variations in ramping rates across different peak regulation states, including deep peak regulation with oil (DPRO), deep peak regulation (DPR), and regular peak regulation (RPR). These ramping rate adjustments are restricted to interperiod scheduling periods. The third is the intraperiod dynamic bidirectional ramping (IntraDBR) model, which considers transitions between DPRO, DPR, and RPR within an intraperiod scheduling period, thereby reflecting actual operational conditions of the unit. We validate the effectiveness of the proposed bidirectional ramping models on the IEEE 118-bus system and a real-world Ningxia Power Grid system. Results demonstrate that bidirectional ramping models more accurately and effectively support dispatch scheduling decisions, improving system security and economic efficiency. © 2025 The Authors

关键词： Retrofitting

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Adapting Human Mesh Recovery with Vision-Language Feedback

arXiv

引用

arXiv 2025年

作者： Xu, Chongyang Huang, Buzhen Zhang, Chengfang Feng, Ziliang Wang, Yangang College of Computer Science Sichuan University Chengdu610065 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The School of Automation Southeast University Nanjing210096 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China

Human mesh recovery can be approached using either regression-based or optimization-based methods. Regression models achieve high pose accuracy but struggle with model-to-image alignment due to the lack of explicit 2D-3D correspondences. In contrast, optimization-based methods align 3D models to 2D observations but are prone to local minima and depth ambiguity. In this work, we leverage large vision-language models (VLMs) to generate interactive body part descriptions, which serve as implicit constraints to enhance 3D perception and limit the optimization space. Specifically, we formulate monocular human mesh recovery as a distribution adaptation task by integrating both 2D observations and language descriptions. To bridge the gap between text and 3D pose signals, we first train a text encoder and a pose VQ-VAE, aligning texts to body poses in a shared latent space using contrastive learning. Subsequently, we employ a diffusion-based framework to refine the initial parameters guided by gradients derived from both 2D observations and text descriptions. Finally, the model can produce poses with accurate 3D perception and image consistency. Experimental results on multiple benchmarks validate its effectiveness. The code will be made publicly available. © 2025, CC BY.

关键词： Mesh generation

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MT_GAN: A SAR-to-optical image translation method for cloud removal

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 225卷 180-195页

作者： Wang, Peng Chen, Yongkang Huang, Bo Zhu, Daiyin Lu, Tongwei Mura, Mauro Dalla Chanussot, Jocelyn Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing210016 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Wuhan430074 China Department of Geography The University of Hong Kong 999077 Hong Kong Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430073 China Grenoble Images Parole Signals Automatics Laboratory Grenoble Institute of Technology Saint Martin d'Heres38402 France Paris75231 France Universite Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France

Synthetic Aperture Radar (SAR) is an active microwave imaging and earth observation device capable of penetrating through clouds, rain, and fog, enabling it to operate effectively regardless of the weather conditions and throughout the day. However, speckle noise in SAR images can make them difficult to interpret, and optical images are often difficult to observe when they are covered by clouds. Therefore, after preprocessing, SAR images can be directly converted to optical images through end-to-end translation learning without optical images as auxiliary information, improving the interpretability of SAR images and realizing cloud removal. Due to the relatively simple structure design of the existing generator based on residual network, it is not perfect to capture and extract the feature information of the image, and the relationship between the features is not well connected, resulting in the existing SAR-optical translation results are not accurate enough. To mitigate this issue, we propose an image translation method utilizing a multilayer translation generative adversarial network (MT_GAN) for cloud removal. First, we design a despeckling module (DSM) to preprocess the speckle noise in SAR. Furthermore, a multilayer translation generator (MTG) is designed for SAR-to-optical (S-O) image translation. It can perform multi-scale image translation on different layers and combine them to enrich the semantic information of features and optimize the translation results. In addition, MTG combined with PatchGAN discriminator is used to compose the optical image generation sub-network (OGS) and SAR image regression sub-network (SRS). Finally, the SRS and OGS are used to establish the connection of cycle consistency loss and optimize the generated optical image. We prepare four datasets for experiments, two of which are used for image translation experiments and the other two for cloud removal experiments. The findings demonstrate that our proposed approach outperforms ex

关键词： Image coding

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Nonlinear Dynamic Modeling and Analysis of Rigid-Flexible Coupling Spacecrafts with Time-Varying Temperature Distribution

SSRN

引用

SSRN 2025年

作者： Zhao, Bingxin Gu, Xudong Hu, Rongchun Wu, Sihan Deng, Zichen Department of Engineering Mechanics Northwestern Polytechnical University Xi’an China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an China

When establishing the dynamic model of a rigid-coupled spacecraft, the traditional linear method requires the flexible structure to satisfy the small deformation assumption. However, the wide range of motion of the large spacecraft may lead to nonlinear deformation of the flexible structure. In this paper, the nonlinear dynamic model of the rigid-flexible coupling spacecraft is established during in-orbit operation. Firstly, the kinetic energy of the rigid-flexible spacecraft is derived by incorporating the nonlinear deformations with second-order coupling terms. Secondly, the time-varying temperature distribution along the thickness direction of the flexible structure is derived by using a one-dimensional thermal conduction equation. Then, the nonlinear time-varying coupled dynamics equations of the rigid-flexible coupling spacecraft are obtained by the Hamilton’s variational principle. The discrete equations are derived by using the assumed modal method (AMM). Finally, the results obtained through numerical calculations indicate that the nonlinear model accurately predicts the dynamical stiffening phenomenon as the attitude rotational velocity of the rigid hub increases. At the same time, the effects of the temperature, the orbital radius and structure size on the response amplitude of the spacecraft are obtained. © 2025, The Authors. All rights reserved.

关键词： Kinetics

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Expand the synchronization range of MEMS linear oscillators based on mode coupling and phase control

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Sensors and Actuators A: Physical 2025年 389卷

作者： Dai, Hongsheng Wang, Xuefeng Shi, Zhan Yang, Qiqi Xiao, Zunhao Liu, Zhiqiang Wei, Xueyong Huan, Ronghua Department of Mechanics Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou China Department of Engineering Mechanics MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi'an China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an China School of Instrument Science and Technology Xi'an Jiaotong University Xi'an China Huanjiang Laboratory Shaoxing China

Synchronization is a widespread phenomenon observed in both natural and societal systems. The advancement of MEMS technology has facilitated the study and application of synchronization, significantly enhancing the performance of MEMS sensors. However, the limited synchronization range of linear systems has restricted its broader application in sensor technologies. In this article, we propose a method to expand the synchronization range based on mode coupling and phase control. Theoretical derivations and experimental validations were conducted to demonstrate the effectiveness of the proposed approach. Experimental results reveal that the method achieves a synchronization range expansion of more than 50 times for both downward and upward frequency sweeps. Furthermore, the maintenance time of the synchronized state is significantly extended. This achievement is expected to provide a new solution for the application of synchronization-enhanced sensing in various fields, paving the way for improved sensor performance and broader applicability. © 2025 Elsevier B.V.

关键词： Synchronization

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