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arXiv 2025年

作者： Xin, Jinghao Liang, Zhichao Zhang, Zihuan Wang, Peng Li, Ning Department of Automation Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Deep Reinforcement Learning (DRL) has demonstrated potential in addressing robotic local planning problems, yet its efficacy remains constrained in highly unstructured and dynamic environments. To address these challenges, this study proposes the ColorDynamic framework. First, an end-to-end DRL formulation is established, which maps raw sensor data directly to control commands, thereby ensuring compatibility with unstructured environments. Under this formulation, a novel network, Transqer, is introduced. The Transqer enables online DRL learning from temporal transitions, substantially enhancing decision-making in dynamic scenarios. To facilitate scalable training of Transqer with diverse data, an efficient simulation platform E-Sparrow, along with a data augmentation technique leveraging symmetric invariance, are developed. Comparative evaluations against state-of-the-art methods, alongside assessments of generalizability, scalability, and real-time performance, were conducted to validate the effectiveness of ColorDynamic. Results indicate that our approach achieves a success rate exceeding 90% while exhibiting real-time capacity (1.2–1.3 ms per planning). Additionally, ablation studies were performed to corroborate the contributions of individual components. Building on this, the OkayPlan-ColorDynamic (OPCD) navigation system is presented, with simulated and real-world experiments demonstrating its superiority and applicability in complex scenarios. The codebase and experimental demonstrations have been open-sourced on our website1 to facilitate reproducibility and further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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Multi-scale feature fused stacked autoencoder and its application for soft sensor modeling

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Chinese Journal of Chemical engineering 2025年 81卷 241-254页

作者： Li, Zhi Xia, Yuchong Long, Jian Liu, Chensheng Zhang, Longfei State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai 200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai200237 China

Deep Learning has been widely used to model soft sensors in modern industrial processes with nonlinear variables and uncertainty. Due to the outstanding ability for high-level feature extraction, stacked autoencoder (SAE) has been widely used to improve the model accuracy of soft sensors. However, with the increase of network layers, SAE may encounter serious information loss issues, which affect the modeling performance of soft sensors. Besides, there are typically very few labeled samples in the data set, which brings challenges to traditional neural networks to solve. In this paper, a multi-scale feature fused stacked autoencoder (MFF-SAE) is suggested for feature representation related to hierarchical output, where stacked autoencoder, mutual information (MI) and multi-scale feature fusion (MFF) strategies are integrated. Based on correlation analysis between output and input variables, critical hidden variables are extracted from the original variables in each autoencoder's input layer, which are correspondingly given varying weights. Besides, an integration strategy based on multi-scale feature fusion is adopted to mitigate the impact of information loss with the deepening of the network layers. Then, the MFF-SAE method is designed and stacked to form deep networks. Two practical industrial processes are utilized to evaluate the performance of MFF-SAE. Results from simulations indicate that in comparison to other cutting-edge techniques, the proposed method may considerably enhance the accuracy of soft sensor modeling, where the suggested method reduces the root mean square error (RMSE) by 71.8%, 17.1% and 64.7%, 15.1%, respectively. © 2025 Chemical Industry and engineering Society of China, and Chemical Industry Press Co., Ltd.

关键词： Frequency estimation

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Low-Earth Orbital Lifetime Prediction Based on Parameter Sensitivity Analysis and Deep Learning

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Chen, Shun-Yi Yang, Zhen Wang, Hua Lu, Lin State Key Laboratory of Space System Operation and Control Changsha410073 China National University of Defense Technology College of Aerospace Science and Engineering Changsha410073 China China Astronaut Research and Training Center Beijing100094 China

This article proposes an intelligent prediction method for the orbital lifetime of resident space objects (RSOs) in low Earth orbit. This method is intended to satisfy the computational efficiency and accuracy requirements when predicting the large-scale RSOs. It helps to analyze the distribution and uncertainty of RSOs' lifetime. First, based on the semi-analytical orbit prediction model, a parameter sensitivity analysis method for the orbital lifetime is proposed, and the parameters that affect the orbital lifetime are investigated. Second, using a fully connected neural network, an orbital lifetime prediction model is established based on deep learning. The hyperparameters of the deep learning model are optimized using the black-winged kite algorithm. Finally, by combining the sensitivity analysis with the deep learning model, an intelligent prediction method is proposed for the rapid prediction of orbital lifetime. Simulation results show that the proposed intelligent prediction method accurately determines the orbital lifetime of RSOs and has higher prediction efficiency than the traditional prediction method. The research results provide an effective calculation and analysis tool for predicting the orbital lifetime of large-scale RSOs in the future. © 2025 IEEE.

关键词： Prediction models

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Efficient Trajectory Generation Based on Traversable Planes in 3D Complex Architectural Spaces

arXiv

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arXiv 2025年

作者： Zhang, Mengke Tian, Zhihao Xia, Yaoguang Xu, Chao Gao, Fei Cao, Yanjun The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou Key Laboratory of Autonomous System Huzhou313000 China China Tobacco Zhejiang Industrial Co. Ltd. Hangzhou310024 China

With the increasing integration of robots into human life, their role in architectural spaces where people spend most of their time has become more prominent. While motion capabilities and accurate localization for automated robots have rapidly developed, the challenge remains to generate efficient, smooth, comprehensive, and high-quality trajectories in these areas. In this paper, we propose a novel efficient planner for ground robots to autonomously navigate in large complex multi-layered architectural spaces. Considering that traversable regions typically include ground, slopes, and stairs, which are planar or nearly planar structures, we simplify the problem to navigation within and between complex intersecting planes. We first extract traversable planes from 3D point clouds through segmenting, merging, classifying, and connecting to build a plane-graph, which is lightweight but fully represents the traversable regions. We then build a trajectory optimization based on motion state trajectory and fully consider special constraints when crossing multi-layer planes to maximize the robot’s maneuverability. We conduct experiments in simulated environments and test on a CubeTrack robot in real-world scenarios, validating the method’s effectiveness and practicality. Copyright © 2025, The Authors. All rights reserved.

关键词： Flight paths

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Selection for Schottky Barrier Diodes with Various On-Resistance in Full-SiC Voltage Source Converters

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IEEE Transactions on Industry Applications 2025年

作者： Chen, Yuzhi Li, Chi Liu, Jianwei Wu, Yifan Liu, Zicheng Zheng, Zedong Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan430074 China

In Silicon Carbide based voltage source converters (VSCs), pairing Schottky barrier diodes (SBDs) presents trade-offs. While SBDs reduce the freewheeling loss and mitigate the adverse effects of bipolar conduction, their junction capacitance also contributes to increased switching loss. This paper proposes a systematic selection strategy for SBDs aimed at enhancing converter efficiency, using on-resistance as the design variable. A three-phase VSC with 1.2 kV MOSFETs and four sets of 1.2 kV SBDs is demonstrated as the case study. The static and switching characteristics for MOSFET/SBD pair are measured and analyzed over a wide temperature range. VSC efficiency is evaluated in both inverter and rectifier modes under different operating conditions. It appears that VSC using SBDs with 126% of the MOSFETs' resistance exhibits an overall superior performance. A 4 kW full-SiC VSC prototype is developed to validate the proposed selection strategy. Compared to other SBDs, using the recommended selection enhances VSC efficiency by up to 2% (± 0.5%) under light loads while maintaining high performance under medium and heavy loads. Compared to freewheeling by the body diode, using the recommended selection enhances VSC efficiency by an average of 0.4% (± 0.1%) across nearly the entire load range. © 1972-2012 IEEE.

关键词： Schottky barrier diodes

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3D bioprinting of a dermal scaffold for full-thickness skin tissue regeneration

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Bio-Design and Manufacturing 2025年第1期8卷 68-84,I0039-I0041页

作者： Lu Han Zixian Liu Meng Li Zhizhong Shen Jianming Wang Shengbo Sang Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Electronic Information and Optical EngineeringTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China General Hospital of TISCO Taiyuan 030809China

Dermal substitutes have provided a template for the regeneration and reconstruction of the ***,the healed skin tissue often exhibits abnormal morphology and functionality,including scarring and *** this study,a composite bioink composed of methacrylated gelatin(GelMA)and chitosan oligosaccharide(COS)was proposed for printing a dermal scaffold using digital light processing(DLP)*** GelMA/COS bioink exhibited suitable porosity,swelling,degradation rate,and mechanical *** inclusion of COS demonstrated antibacterial effects against both Gram positive and Gram-negative bacteria,while simultaneously fostering the proliferation of human dermal fibroblasts(HDFs).Additionally,the application of COS could effectively reduce the expression levels of fibrosis-related genes,such as collagen I,collagen III,and fibronectin *** three-dimensionally printed cell-laden dermal scaffold exhibited excellent shape fidelity and high cellular viability,facilitating the extension of HDFs along the scaffold and the simultaneous secretion of extracellular matrix ***,the HDF-laden dermal scaffold transplanted into full-thickness skin defect sites in nude mice was shown to accelerate wound closure,reduce inflammation,and improve wound ***,the DLP-printed dermal scaffold provides an appealing approach for effectively treating full-thickness skin defects in clinical settings.

关键词： 3D printing Dermal scaffold Photo-cross-linking Skin tissue regeneration

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Approximately Invertible Neural Network for Learned Image Compression

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Yanbo Gao Shuai Li Meng Fu Chong Lv Zhiyuan Yang Xun Cai Hui Yuan Mao Ye School of Software Shandong University Jinan China Shandong University-WeiHai Research Institute of Industrial Technology Shandong University Weihai China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China University of Electronic Science and Technology of China Chengdu Sichuan China

Learned image compression has attracted considerable interests in recent years. An analysis transform and a synthesis transform, which can be regarded as coupled transforms, are used to encode an image to latent feature and decode the feature after quantization to reconstruct the image. Inspired by the success of invertible neural networks in generative modeling, invertible modules can be used to construct the coupled analysis and synthesis transforms. Considering the noise introduced in the feature quantization invalidates the invertible process, this paper proposes an Approximately Invertible Neural Network (A-INN) framework for learned image compression. It formulates the rate-distortion optimization in lossy image compression when using INN with quantization, which differentiates from using INN for generative modelling. Generally speaking, A-INN can be used as the theoretical foundation for any INN based lossy compression method. Based on this formulation, A-INN with a progressive denoising module (PDM) is developed to effectively reduce the quantization noise in the decoding. Moreover, a Cascaded Feature Recovery Module (CFRM) is designed to learn high-dimensional feature recovery from low-dimensional ones to further reduce the noise in feature channel compression. In addition, a Frequency-enhanced Decomposition and Synthesis Module (FDSM) is developed by explicitly enhancing the high-frequency components in an image to address the loss of highfrequency information inherent in neural network based image compression, thereby enhancing the reconstructed image quality. Extensive experiments demonstrate that the proposed A-INN framework achieves better or comparable compression efficiency than the conventional image compression approach and state-of-the-art learned image compression methods.

关键词： Image coding Transforms Decoding Neural networks Quantization (signal) Image reconstruction Noise Entropy Optimization Transform coding

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Quantitative detection of alkali metal Na based on laser-induced breakdown spectroscopy technology

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Meitan Xuebao/Journal of the China Coal Society 2025年第4期50卷 2262-2270页

作者： Shen, Sijie Li, Chi Zhou, Wangzheng Wang, Zhenzhen Yan, Junjie Hou, Zongyu Wang, Zhe Deguchi, Yoshihiro School of Energy and Power Engineering Xi'an Jiaotong University Xi'an710049 China State Key Laboratory of Multiphase Flow in Power Engineering Xi'an Jiaotong University Xi'an710049 China State Key Laboratory of Power System Operation and Control Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China Department of Energy and Power Engineering Tsinghua University Beijing100084 China Shanxi Research Institute for Clean Energy Tsinghua University Taiyuan030032 China Graduate School of Technology Industrial and Social Sciences Tokushima University Tokushima770-8506 Japan

The alkali metal compounds released from high alkali fuels such as low-quality coal during thermal utilization can affect the normal operation of thermal equipment. Therefore, rapid online detection of alkali metal content in low-quality coal is of great significance for controlling alkali metal release during combustion. The alkali metal Na element is used as the detection object, and the mixed samples of graphite and sodium chloride powder with different ratios are used as experimental samples. The influencing factors of measuring Na element in samples using laser induced breakdown spectroscopy (LIBS) technology are studied. The impact of two signal strength calculation methods on signal stability is compared. The influence of experimental parameters on signal strength and signal-to-noise ratio is analyzed. The quantitative calculation models for Na element have been established. Research has shown that the characteristic spectral lines of Na element, Na I 588.995 nm and Na I 589.592 nm, are suitable as the main analytical spectral lines. Using the area intensity of the dual line characteristic spectral lines of Na element as the signal intensity can effectively improve signal stability. When the laser energy is 60 mJ and the delay time is 1 000 ns, the relative standard deviation of spectral signal intensity is low and the signal-to-noise ratio is high. The quantitative calculation models are established using traditional calibration method, partial least squares (PLS) method, and support vector machine (SVR) with spectral signal intensity as the input and Na element addition in the sample as the output. The accuracy of each model is compared and analyzed. The results indicate that the PLS model may exhibit overfitting when the sample size is small and the input quantity is large. The fitting accuracy of the SVR model is 0.978 3, the root mean square percentage error of the training set is 13.42%, and the root mean square percentage error of the test set is 13.51

关键词： Sodium chloride

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Analysis of multi-field cavitation structure and dynamic mode decomposition based on a separated venturi cavitation generator

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European Journal of Mechanics, B/Fluids 2025年 113卷

作者： Wang, Xiaoyu Wang, Tian Ma, Junqi Sun, Qiang Lv, WanYu School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Shenyang110819 China AECC Shenyang Engine Research Institute Shenyang110015 China

Cavitation is generated due to the reduction of fluid pressure, which will cause instability and erosion inside the pipeline, valve or pump etc. In this paper, based on the improved venturi cavitation generator, the cavitation under several different flow conditions is studied, and a high-speed camera is used to capture the dynamic process of cavitation at the venturi throat. The flow field simulation data have been analysed using the dynamic mode decomposition method, and the coherent structures of sheet cavitation, transition zone cavitation, and cloud cavitation and their displacements with cycle time have been investigated. The periodic variation of cavitation is analysed using a combination of experimental and numerical simulations, and the state of cavitation in the flow field is investigated in detail using data-driven tools. The results show that periodic changes in the cavitation structure can be observed intuitively through the improved venturi generator and agrees well with the results of simulation. Cavitation intensity is continuously enhanced with the increase of inlet velocity. At flow velocity of 4.68 m/s, the cavitation is manifested as a piecewise cavitation that can only be observed at the throat position, since then the length of the cavitation cloud continues to increase, finally the detachment of a bubble cloud and localized vortex cavitation are observed at velocity of 6.08 m/s. The frequency and growth characteristics of individual flow modes can be accurately captured using the dynamic modal decomposition of the flow field. © 2025

关键词： High speed cameras

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Hierarchical Path Planning and Motion control Framework Using Adaptive Scale Based Bidirectional Search and Heuristic Learning Based Predictive control

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IEEE Transactions on Vehicular Technology 2025年

作者： Du, Guodong Zou, Yuan Zhang, Xudong Li, Zirui Liu, Qi ETH Zurich Institute of Dynamic System and Control Sonneggstrasse 3 Zurich Switzerland Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Beijing China Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Collaborative Innovation Center of Electric Vehicles in Beijing Beijing China Delft University of Technology Department of Transport and Planning CD Delft2628 Netherlands

Autonomous vehicles have been used for a variety of driving tasks, in which path planning and motion control are important research parts to realize the autonomous driving. A hierarchical framework consisting of path planning and motion control of the vehicle for non-specific scenarios is proposed in this paper. Firstly, the description and the formulations of the problem are given, and the corresponding models are constructed. Then, the logical construction of proposed framework is expounded with several logical associations and algorithmic improvements. The bidirectional heuristic planning with adaptive scale search is designed and incorporated with robust weighted regression algorithm to plan the optimal global path, while the multi-step predictive control method based on heuristic reinforcement learning algorithm is proposed to improve the effect of the motion control. The results show that the proposed framework for autonomous driving achieves better performance in both path planning and motion control than several existing algorithms and methods. The adaptability of hierarchical framework is demonstrated. Furthermore, the effectiveness of the hierarchical framework in real world scenario application is also validated. © 1967-2012 IEEE.

关键词： Predictive control systems

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