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Culture-Oriented Science and technology (CoST), International Conference on

作者： Penghao Ren Mengnan Li Zhibin Su Beijing Radio & Television Station Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Communication University of China Beijing Key Laboratory of Modern Entertainment Technology Communication University of China Beijing China

In order to solve the problem of fast switching of special equipment in the cultural complex, the improved Moth-Flame Optimization algorithm (MFO) is combined with the improved Dynamic Window Approach (DWA) to realize the hybrid path planning of handling robots in different scenarios of cultural complexes in this paper. Specifically, the traditional Moth-Flame Optimization algorithm is improved and applied to the global path planning problem. On the basis of the planned global path, the intersection of dynamic obstacles with the global path is detected, and the improved Dynamic Window Approach is used for local obstacle avoidance in the section with collision risk, finally, the hybrid path planning in the complex environment of the cultural complex is realized. The simulation experimental results prove the effectiveness of the proposed algorithm.

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Cooperative Prediction Approach for the Small-Signal Stability Margin of Renewable Power system Based on Vertical Federated Learning

Cooperative Prediction Approach for the Small-Signal Stabili...

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Power systems and Electrical technology (PSET), IEEE International Conference on

作者： Xiang Lai Zhihao Chen Yun Liu Tianchen Dai Haiqing Cai Haohan Gu Wei Chen School of Electric Power Engineering South China University of Technology Guangzhou China State Key Laboratory of HVDC CSG Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Since sub-synchronous oscillations have been frequently witnessed in large-scale wind farms under weak grid conditions, it is significant to predict the critical stability situations in short term so that timely preventative actions can be taken to avoid potential small-signal stability (SS) risks. To this end, this paper proposes a SS prediction approach based on the grid strength metric of generalized short-circuit ratio (gSCR) under the mechanism of vertical federated learning. Specifically, the improved ridge regression prediction model is employed and its corresponding distributed training and the distributed predicting are derived by utilizing the separable characteristic of gradient vector calculation in the context of federated learning. On this basis, the original largescale predicting problem is decomposed into a large number of small-scale subproblems, and each subproblem is calculated locally by the corresponding wind farm. The feature data information of adjacent wind farms can be effectively used to improve the accuracy of the prediction of the stability margin while ensuring the data privacy and security of all participants. Finally, simulation results illustrate the effectiveness and efficiency of the proposed approach under various scenarios.

关键词： Training Accuracy Federated learning Simulation Power system stability Wind farms Predictive models Stability analysis Vectors Security

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Music emotion prediction based on multidimensional feature fusion

Music emotion prediction based on multidimensional feature f...

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Information technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xuanye Zhou Zhibin Su Hui Ren School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

Audience sentiment analysis is a popular research task in Music Information Retrieval (MIR). In this work, we aim to lean the prediction model of fine-grained music emotion for intelligent editing and retrieval. To satisfy the properties of time-series of music, the RNN-based model of BILSTM was selected as the bone. In particular, the traditional features and deep features extracted from the CNN architecture of spectrogram was fused with attention mechanism to enhance the recognition ability for different emotions. The ablation experiment has proved the necessary of the multi-dimensional features. Finally, the contrasting experiments on recent algorithms demonstrate that our proposed method for emotion prediction is effective, which is also suitable for small data sets.

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Application of automated laser seam tracking straight seam welding technology in production

Application of automated laser seam tracking straight seam w...

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2024 International Conference on Precision Instruments and Optical Engineering, PIOE 2024

作者： Qin, Jingchuan Liu, Yongkai Hu, Haoran Liu, Xian Beijing AriTime Intelligent Control Co. Ltd. 6 Fufeng Road Fengtai District Beijing Science and Technology Park Beijing100070 China Automation Research And Design Institute Of Metallurgical Industry Co. LTD. 72 South Fourth Ring Road Fengtai District Beijing100070 China National Key Laboratory of Metallurgical Intelligent Manufacturing System Beijing100081 China Shanxi Limin Industrial Co. Ltd. TaiGu030812 China

ISBN: (纸本)9781510686120

In the manufacturing process of a specific type of conical cylinder, high-frequency argon arc welding is employed for butt welding. The uncertainty of the butt weld position relative to the welding gun results in excessive manual labor during production, leading to suboptimal product qualification rates;Consequently, the implementation of a fusion laser welding seam tracking automated control system was initiated. This system utilizes a PLC to gather various sensor data signals, control servomotors, valves, and motors, thereby establishing a comprehensive automated butt welding tracking control system. Ultimately, this achieves the goals of reducing labor costs, diminishing labor intensity, and enhancing both production efficiency and product quality. © 2024 SPIE.

关键词： Butt welding

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Microstructure Evolution and Dynamic Recrystallization Mechanisms During Hot Deformation of As-Cast Udimet720Li Superalloy

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Journal of Materials Engineering and Performance 2025年 1-17页

作者： Sha, Lei Wang, Bingbing Chen, Xiaokang Zhong, Kangdi Zhou, Zongjie Xue, Lian Wang, Xiaoning College of Aeronautical Changji University Changji China Key Laboratory of Intelligent Perception and Air Traffic Control Operation Safety Technology of Unmanned System in Changji University Changji China Xinjiang Advanced Manufacturing Engineering and Technology Research Centre Xinjiang University Urumqi China Department of Mechanical Engineering Xinjiang University Urumqi China

This study investigated the microstructure evolution and DRX mechanism during the hot deformation of an as-cast Ni–Co–Cr-based precipitation–hardening superalloy, specifically Udimet720Li. Thermal simulation compression tests were conducted at two temperatures of 1050 °C and 1150 °C, with varying strains to analyze the microstructure evolution. A CA model was established to simulate the influence of hot deformation parameters on the microstructure of the alloy during the DRX process. EBSD and TEM were employed to investigate the microstructure evolution and the nucleation mechanisms of DRX at 1050 °C and 1150 °C. The results indicated that at the lower temperature (1050 °C), fine DRX grains tended to cluster at the initial grain boundaries under low strain, with DRX gradually increasing with strain. Conversely, at the higher temperature (1150 °C), DRX grains formed a necklace-like distribution, thereby achieving a more homogeneous microstructure at high strain. The CA model simulations of the microstructure evolution correlated with the experimental results. Finally, it was observed that both DDRX and CDRX occurred at the lower temperature, with DDRX being the dominant mechanism. At the higher temperature, DDRX, characterized by grain boundary bulging, predominated the DRX process, while the overall contribution of CDRX decreased. The findings of this study are helpful to understand the microstructure evolution and DRX mechanism of as-cast Udimet720Li alloy during hot forming process, and provide a model for the simulation and prediction of microstructure, which provides a basis for the precise control of microstructure during hot-working process.

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Research on intelligent Fault Diagnosis Method of Planetary Gearbox Based on ITD and CNN

Research on Intelligent Fault Diagnosis Method of Planetary ...

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Prognostics and system Health Management Conference (PHM-Qingdao)

作者： Songsong Zhu Jiwei Chen Ruitong Xie Yuhao Song Mian Zhang Mengxiong Zhao Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education (Tianjin University of Technology) Tianjin China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

Accurate fault diagnosis of planetary gearboxes (PGs) is crucial for preventing equipment failures and extending their lifespan. To enhance the efficiency and accuracy of PG fault diagnosis, this paper proposes a novel method integrating intrinsic time scale decomposition (ITD) with convolutional neural network (CNN). The proposed method leverages the demodulation capability of ITD to decompose and reconstruct the original signal, effectively removing irrelevant noise and isolating core fault features, and the learning ability of CNN to accurately identify the fault features. Compared to methods relying on CNN only, the proposed method not only improves the computational efficiency but also achieves higher fault diagnosis accuracy.

关键词： Fault diagnosis Accuracy Computational modeling Noise Feature extraction Computational efficiency Convolutional neural networks Prognostics and health management Demodulation

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Significant Wave Height Prediction Based on VMD-SA-MLP-BP

Significant Wave Height Prediction Based on VMD-SA-MLP-BP

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2023 China Automation Congress, CAC 2023

作者： Wu, Yining Zhang, Runfeng Liu, Fang Liu, Zhenzhong Wu, Jianguo Dong, Haonan School of Mechanical Engineering Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin300384 China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education China Yichang Research Institute of Testing Technology Yichang443003 China Tianjin Hanhailanfan Marine Technology Co. Ltd. Tianjin300457 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9798350303759

Waves seriously impact port construction, worldwide route planning, military activities, and wave power generation. To improve the accuracy of significant wave height prediction, we proposed a novel prediction method, a multi-layer perceptron combined with a backpropagation adjustment (MLP-BP) prediction model that combines mutation mode decomposition (VMD) and a simulated annealing optimization algorithm (SA). Firstly, we use the variable modulus method to decompose the significant wave height sequence data and transform the wave height sequence into multiple different sub-modes (IMF) to reduce the complexity and non-stationarity of the data. Secondly, the simulated annealing algorithm (SA) is used to optimize the weight and bias of the MLP-BP neural network to find the optimal parameter configuration and improve the performance and generalization ability of the prediction model. Finally, the decomposed sub-modal components of each significant wave height sequence are inserted into the MLP-BP neural network and the predicted values of each element are summed to obtain the final significant wave height prediction. The prediction results of the SSA-MLP-BP, PSO-MLP-BP, SA-MLP-BP, and VMD-SA-MLP-BP were compared and demonstrated that the VMD-SA-MLP-BP model performed best. The MAE, MAPE, MSE, RMSE, and R2 of the prediction evaluation indexes were 0.036 m, 11.7%, 0.004 m2, 0.067 m, and 0.983, respectively, which performed well in predicting significant wave height. © 2023 IEEE.

关键词： Variational mode decomposition

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Kinematic analysis and trajectory planning of two-limb 4-DOF high-speed parallel robot

Kinematic analysis and trajectory planning of two-limb 4-DOF...

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IEEE International Conference on Mechatronics and Automation

作者： Weihua Sun Yue Ma Changrui Li Bin Li Yansen Zhao Qi Liu Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Focusing on the pick-and-place operation in the electronic, packaging, and other light industries, this paper proposed a new type 3T1R (T-Translation, R-Rotation) high-speed parallel robot with two limbs. Compared with other 4-DOF high-speed parallel robots with four limbs, its structure is simpler while the working space is larger, such that the potential for cheaper usage in the industry is achieved. The robot's kinematic analysis and trajectory planning, including key aspects, are also outlined here. Firstly, the robot's closed-loop constraint equation is established using the vector chain method. The displacement, velocity, and acceleration models are derived, and the Jacobian matrix is obtained. On this basis, the gate shape trajectory is planned using the 3-4-5 polynomial motion rule, such that the displacement, velocity, and acceleration of the driving joint are obtained using the kinematic models above. Ultimately, the accuracy of the kinematic models is confirmed by comparing the theoretical outcomes with the simulations carried out in SolidWorks. The study presented in this paper establishes a firm basis for the dimensional integration and the creation of a physical model of the robot.

关键词： Industries Solid modeling Parallel robots Trajectory planning Kinematics Logic gates Vectors

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Mechanical Design of Out-pipe Detection Mechanisms for Pipeline Defects

Mechanical Design of Out-pipe Detection Mechanisms for Pipel...

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IEEE International Conference on Mechatronics and Automation

作者： Guangyin Nie Xinhua Zhao Yihan Ren Liang Liu Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Pipes in various are dominated in in the process of industrial production. To avoid the pipeline leak due to inner corrosion, it is essential to develop pipe inspection mechanisms to detect the wall thickness of the pipes used in production. The paper presents three kinds of out-pipe detection mechanisms (ODM) for different petrochemical pipeline detection demands based on eddy current testing (ECT) method. A linear inspection mechanism with fixed length is firstly proposed to detect defects of straight pipe. Secondly, a rotary inspection mechanism is designed for pipeline weld detection. Besides, an out-pipe scanning wheeled robotic mechanism is developed to detect wall thickness of pipes and avoid small obstacles such as valves on the pipelines. Lastly, detection experiments and simulations are carried out to verify the effectiveness of the designed out-pipe detection mechanisms.

关键词： Mechatronics Welding Pipelines Production Inspection Valves Petrochemicals

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An Algorithm for Cooperative Navigation using Neural Network to Identify Colored Noise Online

An Algorithm for Cooperative Navigation using Neural Network...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Danting Sheng Jin Cheng Fengyi Li Mingrui Hao Beijing Electro-mechanical Engineering Institute CASIC Beijing China HIWING Technology Academy of CASIC CASIC Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation CASIC Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

In practical applications of cooperative navigation algorithms based on Kalman filtering, noise is often colored noise, which does not meet the requirement of Gaussian white noise for Kalman filtering. Therefore, an algorithm is proposed to identify and compensate for colored noise online using a BP neural network. Firstly, a cooperative navigation model based on Kalman filtering is established. Then, by studying the update process of Kalman filtering, the neural network is incorporated into the prediction calculation of the observations, following the online learning and updating of the Kalman filter. Mathematical simulations show that under conditions of colored noise, the results of the Kalman filter diverge. However, after correction by the BP neural network, the filtering converges, providing high reference value for improving the accuracy of cooperative navigation.

关键词： Accuracy Filtering Navigation Neural networks Collaboration White noise Mathematical models Kalman filters Colored noise Convergence

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