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Structural Optimization of Photoacoustic Transducer with Pdms/Csnps Nanocomposite for Laser-Induced Nonlinear Surface Wave and its Application in Fatigue Crack Detection

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SSRN 2023年

作者： Yao, Zhijun Li, Yehai Lv, Gaolong Chen, Dan Yang, Jian Guo, Shifeng Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic Shenzhen518055 China Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Ningbo China Ningbo315100 China

Laser ultrasonics is a promising non-contact inspection technique but faces the challenges of low signal-to-noise ratio and low amplitude under the non-destructive thermoelastic regime. In this paper, an ultrasonic surface wave modulation method based on laser ultrasonics and photoacoustic transducer (PAT) is proposed and combined with nonlinear wave mixing technique to achieve non-contact detection of fatigue crack. The PAT comprised of candle soot nanoparticles and polydimethylsiloxane (PDMS) as well as a line-arrayed laser source modulated by a slit mask are firstly introduced to generate narrowband surface wave with high intensity and SNR under the thermoelastic regime. The thicknesses of the PDMS layers of PAT are thereafter optimized by analytical, numerical and experimental investigations to acquire narrowband surface wave with pure fundamental frequency component. Two modulated ultrasonic surface waves with frequencies of 2.1 MHz and 2.9 MHz are excited on a fatigued aluminum plate to generate nonlinear mixed components, and the ultrasonic responses over the fatigue crack regions are acquired by using a scanning laser Doppler vibrometer. The localization of fatigue crack with microscale is eventually acquired by mapping the nonlinear parameter of the mixed components, which proves it a reliable and nondestructive technique to inspect the fatigue crack. © 2023, The Authors. All rights reserved.

关键词： Transducers

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Design and Experiment of a Soft Gripper Based on Cable-Driven Continuum Structures

Design and Experiment of a Soft Gripper Based on Cable-Drive...

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IEEE International Conference on robotics and Biomimetics

作者： Qiong Wu Zhenglong Yi Hongqiang Wang Han Yuan School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen University Town of Shenzhen Shenzhen P.R. China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen P.R. China

ISBN: (纸本)9781665405362

Soft grippers can be used as the end effectors of robots due to their good adaptability to the object being grasped. This is because of the passive compliance of the material. However, for this reason, the grippers have difficulties applying enough forces to the objects. Therefore, how to design the grippers to realize universal adaptive and compliant grasping of the object is worthy of study. In this paper, we design a novel cable-driven soft gripper, the shape and function of which are similar to a human hand. It is able to realize a stable self-adaptive grasp. Kinematic and kinetic models of the finger are established, simulation analysis and finite element analysis are carried out to optimize the finger. Experimental validation proves that the gripper designed can realize stable self-adaptive grasping.

关键词： Analytical models Shape Prototypes Grasping Bending Numerical simulation Mathematical models

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Research on PMSM Vector Control System Based on Fuzzy PI Parameter Self-Tuning 10

Research on PMSM Vector Control System Based on Fuzzy PI Par...

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10th International Conference on Power and Energy systems, ICPES 2020

作者： Ding, Jianhua Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

Based on the analysis of the permanent magnet synchronous motor (PMSM) mathematical model, a PMSM rotor field-oriented double closed-loop vector control system is constructed. Aiming at the defects of slow speed response, large overshoot, severe torque ripple, and poor anti-disturbance ability in the traditional PI controller used in the system speed loop, a fuzzy PI parameter self-tuning controller is proposed. The controller can set the two parameters of proportional and integral online according to fuzzy control rules. The system simulation model is built based on Matlab/Simulink. The simulation results show that the fuzzy PI parameter self-tuning controller has the advantages of fast dynamic response, high steady-state precision and strong anti-disturbance capability. © 2020 IEEE.

关键词： Permanent magnets

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An Anomaly Detection Method of Industrial Data Based on Stacking Integration

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Journal on Artificial Intelligence 2021年第1期3卷 9-19页

作者： Kunkun Wang Xianda Liu College of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang110159China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China Shenyang Institute of Automation Chinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China

With the development of Internet technology,the computing power of data has increased,and the development of machine learning has become faster and *** the industrial production of industrial control systems,quality inspection and safety production of process products have always been our *** at the low accuracy of anomaly detection in process data in industrial control system,this paper proposes an anomaly detection method based on stacking integration using the machine learning *** are collected from the industrial site and processed by feature *** component analysis(PCA)and integrated rule tree method are adopted to reduce the dimension of the process data,which can restore the original feature information of the data to the maximum *** forest(RF),Adaboost,XGboost,SVM were selected as the first layer of basic *** regression(LR)was used as the secondary learner to build the exception detection model based on stacking integrated *** data was used to train the base learner model and the integrated *** comparing and analyzing the experimental results of between integrated model and each basic learning *** comparing and analyzing the experimental results of the constructed anomaly detection model and the basic learning model,the accuracy of process data anomaly detection is effectively improved,and the false alarm rate of process data anomaly detection is effectively reduced.

关键词： Industrial control system anomaly detection random forest SVM stacking

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Short-Term Photovoltaic Power Prediction Modeling Based on AdaBoost Algorithm and Elman 10

Short-Term Photovoltaic Power Prediction Modeling Based on A...

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10th International Conference on Power and Energy systems, ICPES 2020

作者： Sun, Wenxuan Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important[1]. The application of renewable energy production forecasting has also been rapidly developed, especially in the field of solar photovoltaic (PV)[2][3]. In the example of solar PV output prediction, machine learning and hybrid technologies have been implemented for many applications. In this paper, a high-precision PV system output power prediction model based on improved AdaBoost and Elman is proposed. Multiple model using integrated AdaBoost algorithm fusion with the bat algorithm for the parameters of optimized combination of weak Elman neural network predictor to become a higher prediction precision, the method of strong predictor of the model can according to the weather information, such as temperature, solar radiation and the history of the output of the PV system data, the probability of photovoltaic power generation for 12 hours and deterministic prediction. The prediction accuracy of the model is determined by Root Mean Squared Error (RMSE). Experimental results show that the prediction accuracy of this algorithm is better than that of other benchmark models, and the algorithm can effectively predict the volatility and irregularity of complex time series. © 2020 IEEE.

关键词： Adaptive boosting

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Monocular Visual SLAM based on VGG Feature Point Extraction

Monocular Visual SLAM based on VGG Feature Point Extraction

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Chinese Control Conference (CCC)

作者： Xu-Yang Dai Qing-Hao Meng Wen-Jian Zheng Shao-Kai Zhu Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Significant progress has been made in the field of visual SLAM (Simultaneous Localization and Mapping). However, the localization accuracy of visual SLAM can be significantly reduced in low-texture and illumination-changing environments. To solve these problems, an enhanced visual SLAM algorithm based on VGG (Visual Geometry Group) network was proposed in this paper. Firstly, the VGG network for feature point extraction was incorporated into the visual odometry (VO) to achieve robust camera pose estimation. Secondly, an automatic corner annotation method was adopted to set up the training database, which could reduce the workload of data annotation. Thirdly, to make the backend optimization more suitable for the VGG based VO, the BA (bundle adjustment) optimization process was improved. Experimental results showed that, the proposed visual SLAM method outperformed the mainstream ORB-SLAM (Oriented FAST and Rotated BRIEF SLAM) method, in terms of the number of effective feature points, the robustness of feature matching process to light changes, and the accuracy of robot pose estimation.

关键词： Feature extraction Simultaneous localization and mapping Cameras Visualization Optimization Training Robot vision systems

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Anisotropic tensile properties and high-cycle fatigue behavior of Ti6Al4V fabricated by single and dual laser-powder bed fusion

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Materials Science and engineering: A 2025年 941卷

作者： Li, Guotai Hu, Peichen Fan, Yuzhan Wu, Ming Chen, Mingjun Yu, Tianyu State Key Laboratory of Robotics and System Harbin Institute of Technology Heilongjiang Harbin150001 China Key Laboratory of Micro-systems and Micro-structures Manufacturing Ministry of Education Heilongjiang Harbin150001 China Department of Computer Science KU Leuven Leuven3001 Belgium Department of Mechanical Engineering KU Leuven Leuven3001 Belgium

The development of multi laser-powder bed fusion (PBF) technology has significantly reduced the production time for large engine components. However, the impact of thermal effects and microstructural characteristics in the laser beam overlap region on the anisotropic mechanical behavior of metallic materials remains unclear. This study investigated the anisotropic tensile properties and high-cycle fatigue behavior of Ti6Al4V produced by both single laser-PBF (SL-PBF) and dual laser-PBF (DL-PBF). As-built DL-PBF samples exhibited high strength (1404 MPa) but low ductility (3.6 %). After heat treatment, the samples achieved a strength-ductility balance, with an ultimate tensile strength of 1089 MPa, and an elongation of 17.1 %. The mechanical properties of Ti6Al4V produced by DL-PBF were influenced by the α lath thickness, dislocation density and texture. The precipitation of small-angle grain boundaries during DL-PBF process was found to contribute to the strong anisotropy. Due to the influence of internal pore forming, the fatigue strength of the DL-PBF samples was lower than that of the SL-PBF samples. Pores in the dual-laser beam overlap region accelerated the fatigue crack propagation, resulting in a venation-like pattern. In contrast, cracks in the SL-PBF samples propagated in a circular pattern around internal pore. A high-speed camera was employed to capture the dual-laser beams dynamics during pore formation. This study provides a comprehensive analysis of the mechanical properties and pore formation mechanisms in the DL-PBF process. © 2025 Elsevier B.V.

关键词： Tensile strength

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Joint Task Offloading and Uav Trajectory Optimization in Dynamic Edge Computing systems: A Deep Reinforcement Learning Approach

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SSRN 2025年

作者： Wang, Yitian Wang, Hui Ding, Jingfang Yu, Haibin School of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

This paper addresses latency challenges in cloud-based task offloading caused by geographical server-user disparities by integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC). We propose a dynamic UAV-assisted framework that optimizes real-time parameter adjustments to minimize system latency. Key challenges include joint task offloading, UAV trajectory optimization, and User Equipment (UE) occlusion detection under mobility constraints. To resolve these, we transform the problem into a Markov Decision Process (MDP) and develop an enhanced Adaptive Delayed Deep Deterministic Policy Gradient (AD3PG) algorithm, which improves upon DDPG by incorporating delayed updates and neural network tuning. The algorithm dynamically optimizes three critical aspects: UAV-UE connectivity establishment, occlusion-aware dual noise power configuration, and adaptive task offloading ratios. Extensive simulations demonstrate AD3PG’s superiority over baseline methods such as DDPG and Twin-Delayed DDPG (TD3) in reducing total system latency by 13.2–35.3% under dynamic scenarios. These results validate the framework’s efficacy for latency-sensitive applications in UAV-MEC systems. © 2025, The Authors. All rights reserved.

关键词： Drones

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A Novel E-Nose Chamber Design for VOCs Detection in Automobiles

A Novel E-Nose Chamber Design for VOCs Detection in Automobi...

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Chinese Control Conference (CCC)

作者： Lu Cheng Yin-Bo Liu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

To detect parts per billion (ppb)-level volatile organic compounds (VOCs) in automobiles, a pre-concentrator is generally needed for electronic noses (e-noses), which can enrich ppb-level concentration to parts per million (ppm) level. However, the current e-nose chamber structure could dilute the pre-concentrated gases and then the detection performance is reduced. To solve this problem, a novel chamber design was proposed in this paper. The core design idea was to reduce the space for the gas flow in the chamber, taking into account the shape and size of the gas sensors. Specially, for the pin-type gas sensors, a series of cylindrical structure design was adopted, and for the micro-electro-mechanical system (MEMS) patch-type sensors, the headspace was compressed. The results of computational fluid dynamics (CFD) based simulation showed that the concentration of pre-concentrated gases in the novel chamber was higher than that of the traditional chamber. The designed chambers were manufactured using polylactic acid (PLA) material by 3D printing. Experimental gases were prepared from polyurethane (PU) leather and polyvinyl chloride (PVC) leather to verify the performance of the chambers, and the results demonstrated that the proposed new e-nose chamber was superior to the traditional one in terms of the pre-concentration effect, repeatability and classification performance.

关键词： Gas detectors Sensor arrays Micromechanical devices Fluid flow Gases Automobiles

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Indoor Layout Estimation by Fusing Monocular RGB Image Features Extracted with HRNet

Indoor Layout Estimation by Fusing Monocular RGB Image Featu...

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Chinese Control Conference (CCC)

作者： Rong-Ze Huang Yin-Bo Liu Meh Jabeen Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Indoor layout estimation (ILE) is a challenging research that segments the natural indoor scene image into floor, walls and ceiling, and it has a wide range of application prospects in the fields such as scene understanding, reconstruction, robot positioning, and virtual reality. An ILE method by merging multiple features from a monocular red-green-blue (RGB) image was proposed. Firstly, the High-resolution networks (HRNet) was used to extract the features of indoor corner keypoints (CKs) and informative edges (IEs) in the form of heatmaps. Secondly, the extracted features were then selected through a room type (RT) classification network. Finally, a feature fusion algorithm was proposed, which obtained the initial layout hypothesis through CK features and then continuously optimized the layout hypothesis with IE features. The proposed ILE method was validated on the large scene understanding challenge (LSUN) dataset.

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