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Chinese Chemical Letters 2025年第2期36卷 108-117页

作者： Weidan Meng Yanbo Zhou Yi Zhou State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process East China University of Science and TechnologyShanghai 200237China National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery East China University of Science and TechnologyShanghai 200237China Shanghai Engineering Research Center for Multi-media Environmental Catalysis and Resource Utilization East China University of Science and TechnologyShanghai 200237China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Carbon dioxide photocatalytic reduction (CO_(2)-PR) is an efficient method for controlling CO_(2)emissions and generating cleaner energy while mitigating global *** oxides (WxOy) have attracted considerable attention for CO_(2)-PR due to their excellent spectral ***,comprehensive reviews are lacking on the use of WxOyfor CO_(2)-***,this review provides a detailed summary of t research progress made with WxOy-based catalysts in CO_(2)-*** also explains the fundamental principles of CO_(2)-PR and evaluates key performance indicators that affect the activity of WxOy-based photocatalysts,including yield,selectivity,stability,and apparent quantum ***,this review explores opportunities for synthesizing high-performance WxOy-based photocatalysts and highlights their potential for the green preparation of C1/C2 products through CO_(2)-*** innovative strategies aim to address the challenges and pressures associated with energy and environmental issues,particularly by enhancing artificial photosynthesis efficiency.

关键词： Carbon dioxide photoreduction Tungsten-based nanocatalyst Mechanism Performance evaluation Selectivity

Behavior Recognition Based on Multi-view Through Walls Radar 11th

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Behavior Recognition Based on Multi-view Through Walls Radar

11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024

作者： Huang, Ling Mei, Tan College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou730050 China Key Lab of Advanced Control for Industrial Process in Gansu Province Lanzhou University of Technology Lanzhou730050 China National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou University of Technology Lanzhou730050 China

ISBN: (纸本)9789819788279

Aiming at the single-view angle through-wall radar in the process of human behavior recognition there is a viewpoint blind area, resulting in the inability to accurately identify the movement state of the human target behind the wall. In this paper, based on the multi-view through-wall radar system, a multi-view through-wall human behavior recognition method based on attention fusion is proposed. Firstly, the features of the radar distance image are enhanced by the method of clutter suppression and compensated gain to, and the dataset is constructed in this way. Secondly, we use the improved DenseNet to extract features from the distance image data of the two viewpoints, and finally, we use the attention mechanism to calculate the weight information of the radar echo signals of the two viewpoints, and then we fuse the two action feature information according to this weight data, and input the classifier to get the behavior recognition results. The experimental results show that the behavior recognition method proposed in this paper achieves better results compared with the existing behavior recognition methods. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Radar imaging

Three-Dimensional Sparse Random Mode Decomposition for Mode Disentangling with Crossover Instantaneous Frequencies

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arXiv 2025年

作者： Luo, Chen Chen, Tao Xie, Lei Su, Hongye State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

Sparse random mode decomposition (SRMD) is a novel algorithm that constructs a random time-frequency feature space to sparsely approximate spectrograms, effectively separating modes. However, it fails to distinguish adjacent or overlapped frequency components, especially, those with crossover instantaneous frequencies. To address this limitation, an enhanced version, termed three-dimensional SRMD (3D-SRMD), is proposed in this letter. In 3D-SRMD, the random features are lifted from a two-dimensional space to a three-dimensional (3D) space by introducing one extra chirp rate axis. This enhancement effectively disentangles the frequency components overlapped in the low dimension. Additionally, a novel random feature generation strategy is designed to improve the separation accuracy of 3D-SRMD by combining the 3D ridge detection method. Finally, numerical experiments on both simulated and real-world signals demonstrate the effectiveness of our method. Copyright © 2025, The Authors. All rights reserved.

关键词： Chirp modulation

Text2UA: Automatic OPC UA information modeling from textual data with large language model

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Wang, Rongkai Gu, Chaojie He, Shibo Chen, Jiming State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310000 China

FLOAT Drone: A Fully-actuated Coaxial Aerial Robot for Close-Proximity Operations

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arXiv 2025年

作者： Lin, Junxiao Ji, Shuhang Wu, Yuze Wu, Tianyue Han, Zhichao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

How to endow aerial robots with the ability to operate in close proximity remains an open problem. The core challenges lie in the propulsion system’s dual-task requirement: generating manipulation forces while simultaneously counteracting gravity. These competing demands create dynamic coupling effects during physical interactions. Furthermore, rotor-induced airflow disturbances critically undermine operational reliability. Although fully-actuated unmanned aerial vehicles (UAVs) alleviate dynamic coupling effects via six-degree-of-freedom (6-DoF) force-torque decoupling, existing implementations fail to address the aerodynamic interference between drones and environments. They also suffer from oversized designs, which compromise maneuverability and limit their applications in various operational scenarios. To address these limitations, we present FLOAT Drone (FuLly-actuated cOaxial Aerial roboT), a novel fully-actuated UAV featuring two key structural innovations. By integrating control surfaces into fully-actuated systems for the first time, we significantly suppress lateral airflow disturbances during operations. Furthermore, a coaxial dual-rotor configuration enables a compact size while maintaining high hovering efficiency. Through dynamic modeling, we have developed hierarchical position and attitude controllers that support both fully-actuated and underactuated modes. Experimental validation through comprehensive real-world experiments confirms the system’s functional capabilities in close-proximity operations. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones

DeepFilter: An Instrumental Baseline for Accurate and Efficient Process Monitoring

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arXiv 2025年

作者： Wang, Hao Chen, Zhichao Pan, Licheng Jiang, Xiaoyu Song, Yichen He, Qunshan Liu, Xinggao The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Effective process monitoring is increasingly vital in industrial automation for ensuring operational safety, necessitating both high accuracy and efficiency. Although Transformers have demonstrated success in various fields, their canonical form based on the self-attention mechanism is inadequate for process monitoring due to two primary limitations: (1) the step-wise correlations captured by self-attention mechanism are difficult to capture discriminative patterns in monitoring logs due to the lacking semantics of each step, thus compromising accuracy;(2) the quadratic computational complexity of self-attention hampers efficiency. To address these issues, we propose DeepFilter, a Transformer-style framework for process monitoring. The core innovation is an efficient filtering layer that excel capturing long-term and periodic patterns with reduced complexity. Equipping with the global filtering layer, DeepFilter enhances both accuracy and efficiency, meeting the stringent demands of process monitoring. Experimental results on real-world process monitoring datasets validate DeepFilter’s superiority in terms of accuracy and efficiency compared to existing state-of-the-art models. © 2025, CC BY.

关键词： Wiener filtering

Automatic Point Cloud Clustering for Surface Defect Diagnosis

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 12538-12547页

作者： Ye, Jidong Liu, Xingjian Madhusudanan, Harikrishnan Wang, Yue Zhu, Junhui Wang, Yong Ru, Changhai Liu, Xinyu Sun, Yu The School of Control Science and Engineering Dalian University of Technology Dalian116024 China The State Key Laboratory of High-Performance Precision Manufacturing Dalian University of Technology Dalian116024 China University of Toronto Department of Mechanical and Industrial Engineering TorontoONM5S 3G8 Canada Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China

Point cloud clustering is a promising method for 3D surface defect diagnosis in manufacturing but requires manual clustering parameter selection, reducing usability. This paper proposes an automatic point cloud clustering method to address this issue. It employs a strategy that progresses from coarse to fine. In the coarse searching stage, a K-Nearest Neighbor (KNN) graph analysis technique is developed to recognize potential defective regions in parallel. Moving on to the fine stage of extracting detailed defects, a modified DBSCAN algorithm is proposed, in which the clustering parameters are calculated automatically from the KNN graph analysis results. Experimental results showed that the proposed method achieved cloud clustering with automatically calculated clustering parameters for surface defect diagnosis. The proposed method outperformed the traditional region growing algorithm in accuracy (0.942 vs. 0.680) and processing speed (21500 points/sec vs. 8740 points/sec) without requiring manual *** to Practitioners - This paper presents a method for diagnosing defects on automobile and flat steel surfaces. Current 3D point cloud techniques for surface defect diagnosis require manual parameter adjustments, reducing usability. This paper proposes an automatic method without manual intervention. The proposed method uses a coarse-to-fine strategy. The 3D point cloud is divided into sub-blocks to locate potential defects, and a clustering algorithm then extracts detailed defects with automatically determined parameters. We mathematically characterize changes in point density caused by surface defects and show how these features can be used for clustering parameter calculation. Experimental results demonstrate the method's efficiency on flat as well as some curved surfaces, but it has yet to be evaluated on complex structures. Future work will aim to broaden its application to include a more extensive variety of surfaces and integrate it with robotic visi

关键词： k-nearest neighbors

Compliance while resisting: a shear-thickening fluid controller for physical human-robot interaction

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arXiv 2025年

作者： Chen, Lu Chen, Lipeng Chen, Xiangchi Lu, Haojian Zheng, Yu Wu, Jun Wang, Yue Zhang, Zhengyou Xiong, Rong State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou China Tencent Robotics X Lab Shenzhen China

Physical human-robot interaction (pHRI) is widely needed in many fields, such as industrial manipulation, home services, and medical rehabilitation, and puts higher demands on the safety of robots. Due to the uncertainty of the working environment, the pHRI may receive unexpected impact interference, which affects the safety and smoothness of the task execution. The commonly used linear admittance control (L-AC) can cope well with high-frequency small-amplitude noise, but for medium-frequency high-intensity impact, the effect is not as good. Inspired by the solid-liquid phase change nature of shear-thickening fluid, we propose a Shear-thickening Fluid control (SFC) that can achieve both an easy human-robot collaboration and resistance to impact interference. The SFC’s stability, passivity, and phase trajectory are analyzed in detail, the frequency and time domain properties are quantified, and parameter constraints in discrete control and coupled stability conditions are provided. We conducted simulations to compare the frequency and time domain characteristics of L-AC, nonlinear admittance controller (N-AC), and SFC, and validated their dynamic properties. In real-world experiments, we compared the performance of L-AC, N-AC, and SFC in both fixed and mobile manipulators. L-AC exhibits weak resistance to impact. N-AC can resist moderate impacts but not high-intensity ones, and may exhibit self-excited oscillations. In contrast, SFC demonstrated superior impact resistance and maintained stable collaboration, enhancing comfort in cooperative water delivery tasks. Additionally, a case study was conducted in a factory setting, further affirming the SFC’s capability in facilitating human-robot collaborative manipulation and underscoring its potential in industrial applications. Copyright © 2025, The Authors. All rights reserved.

关键词： industrial robots

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

CT-NeRF: Incremental Optimization of Neural Radiance Field and Camera Poses with Complex Trajectory

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Ran, Yunlong Li, Yanxu Ye, Qi Huo, Yuchi Cui, Zhaopeng Bai, Zechun Sun, Jiahao Chen, Jiming Zhejiang University College of Control Science and Engineering Hangzhou China Zhejiang University College of Control Science and Engineering State Key Laboratory of Industrial Control Technology China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China Zhejiang University State Key Lab of CAD and CG China

Neural radiance field (NeRF) has achieved impressive results in high-quality 3D scene reconstruction. However, NeRF heavily relies on precise camera poses. While recent works like BARF have introduced camera pose optimization within NeRF, their applicability is limited to simple trajectory scenes. Existing methods struggle while tackling complex trajectories involving large rotations. To address this limitation, we propose CT-NeRF, an incremental reconstruction and optimization pipeline using only RGB images without pose and depth input. In this pipeline, we first propose a local-global bundle adjustment under a pose graph connecting neighboring frames to enforce the consistency between poses to escape the local minima caused by only pose consistency with the scene structure. Further, we instantiate the consistency between poses as a reprojection error constraint resulting from pixel-level correspondences between input image pairs. Through the incremental reconstruction, CT-NeRF enables the recovery of both camera poses and scene structure and is capable of handling scenes with complex trajectories. We evaluate the performance of CT-NeRF on two real-world datasets, NeRF-Buster and Free-Dataset, which feature complex trajectories. Results show CT-NeRF outperforms existing methods in novel view synthesis and pose estimation accuracy. © 1991-2012 IEEE.

关键词： 3D Reconstruction bundle adjustment NeRF novel view synthesis structure from motion