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Effects of weld penetration on tensile properties of 2219 aluminum alloy TIG-welded joints

Transactions of Nonferrous Metals Society of China 2019年第6期29卷 1161-1168页

作者： Deng-kui ZHANG Yue ZHAO Ming-ye DONG Guo-qing WANG Ai-ping WU Ji-guo SHAN Dan-yang MENG Xian-li LIU Jian-ling SONG Zhong-ping ZHANG Department of Mechanical Engineering Tsinghua University Beijing 100084 China Key Laboratory for Advanced Materials Processing Technology Ministry of EducationTsinghua University Beijing 100084 China China Academy of Launch Vehicle Technology Beijing 100076 China State Key Laboratory of Tribology Tsinghua University Beijing 100084 China Tianjin Long March Launch Vehicle Manufacturing Co. Ltd. Tianjin 300462 China Capital Aerospace Machinery Corporation Limited Beijing 100076 China

Numerical simulation and experimental methods were used to investigate the effects of weld penetration on tensile properties of 2219 aluminum alloy tungsten inert gas(TIG)welded *** results show that when other geometric parameters are consistent,within a certain range,the deeper the weld penetration of the capping weld is,the lower the tensile strength of the j oint *** deeper weld penetration of the capping weld can cause the more concentrated stress at the weld toe and the joint is more likely to crack *** on necessary assumptions,a model for analyzing the mathematical relation between the weld penetration of the capping weld and the tensile strength of the joint was proposed to validate the experimental results. The decrease of weld penetration of capping weld can be controlled by decreasing welding current,helium content or increasing welding voltage.

关键词： 2219-C10S aluminum alloy weld penetration tensile strength numerical simulation correlation

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A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot

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Frontiers of Information technology & Electronic Engineering 2022年第8期23卷 1217-1228页

作者： Manjun TIAN Xiali LI Shihan KONG Licheng WU Junzhi YU First Research Institute of the Ministry of Public Security of PRC Beijing 100048China School of Information Engineering Minzu University of ChinaBeijing 100081China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

To tackle the problem of aquatic environment pollution,a vision-based autonomous underwater garbage cleaning robot has been developed in our *** propose a garbage detection method based on a modified YOLOv4,allowing high-speed and high-precision object ***,the YOLOv4 algorithm is chosen as a basic neural network framework to perform object *** the purpose of further improvement on the detection accuracy,YOLOv4 is transformed into a four-scale detection *** improve the detection speed,model pruning is applied to the new *** virtue of the improved detection methods,the robot can collect garbage *** detection speed is up to 66.67 frames/s with a mean average precision(mAP)of 95.099%,and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.

关键词： Object detection Aquatic environment Garbage cleaning robot Modified YOLOv4

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Optimization of Anti-Whiplash Seat

Optimization of Anti-Whiplash Seat

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International Conference on Digital manufacturing & automation

作者： Hongying YANG Jikuang YANG State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Department of Applied Mechanics Chalmers University of Technology Gothenburg Sweden

The study aimed at optimizing the anti-whiplash performance of the seat in rear impact by using orthogonal experimental design (OED) and comprehensive equilibrium methods. Firstly, according to the Euro NCAP evaluation procedure for whiplash injury, a rear impact model was developed with BioRID II dummy, sled system, seat and seat belt by using MADYMO code. Then, the anti-whiplash performance of the seat was evaluated by using five criterions (NIC, Nkm, etc). In order to improve the anti-whiplash performance of the seat, the effects of 7 design parameters (the position of the headrest, the stiffness of the headrest, etc) on the five criterions was analyzed. The results from the study show that NIC is reduced by 36.8%, and other four criterions are also decreased in the best configuration.

关键词： Rear impact Whiplash injury OED Comprehensive equilibrium method

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Automatic Target Recognition in GPR Images Based on Time-Frequency Texture Features and Support Vector Machine

Automatic Target Recognition in GPR Images Based on Time-Fre...

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Chinese automation Congress (CAC)

作者： Kehui Chen Wei Xue Zexi Wang Shaopeng Ma School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

Ground penetrating radar (GPR) images are easily disturbed by noise, which causes a lot of difficulties for target recognition. To improve the target recognition performance in GPR images, a novel recognition method based on time-frequency texture features and support vector machine (SVM) is proposed. The proposed method first extracts texture features from the gray co-occurrence matrix of S transform time-frequency images for the A-scan data, then establishes the SVM model and realizes the target recognition. The proposed method is assessed using synthetic GPR images and compared with other two time-frequency analysis methods, combined with SVM. The expermental results show that the proposed method can achieve higher recognition accuracies than the other two methods in heavily noisy environment.

关键词： Support vector machines Time-frequency analysis Image recognition Target recognition Support vector machine classification Transforms Radar imaging

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Radar Vital Signs Detection Method Based on Variational Mode Decomposition and Wavelet Transform

Radar Vital Signs Detection Method Based on Variational Mode...

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Chinese automation Congress (CAC)

作者： Shaopeng Ma Wei Xue Kehui Chen Zexi Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9781665426480

To deal with noise interference in frequency modulated continuous wave (FMCW) radar vital signs and the interference of breathing harmonics on the heartbeat signal, a vital signs detection method based on variational mode decomposition (VMD) and wavelet transform is proposed. First, VMD is applied to decompose the vital signs into a series of intrinsic mode function (IMF) components. Then, the IMF components with spectral peaks in the frequency range of breathing and heartbeat are selected to reconstruct the breathing and heartbeat signals respectively. Finally, the wavelet transform threshold method is used to remove the noise in the breathing and heartbeat signals. The experimental results show the proposed method can overcome the influence of breathing harmonics, accurately extract the breathing and heartbeat signals, and effectively improve the signal-to-noise ratio and detection accuracy of heart rate.

关键词： Frequency modulation Continuous wavelet transforms automation Heart beat Radar detection Radar Interference

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Norm-Integrated Softmax Loss For Deep Face Recognition

Norm-Integrated Softmax Loss For Deep Face Recognition

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IEEE International Conference on Image Processing

作者： Jun Chen Yiwei Wang Haiyan Zhang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The key to face recognition lies in how to improve the model’s ability to extract facial features. To this end, numerous loss functions based on different metrics have been proposed to increase the margin of feature distinction between different classes. Methods based on Cosine distance significantly enhance face recognition performance by focusing on angular constraints between samples and classes, demonstrating their superiority over those based on Euclidean distance. However, a significant oversight in these methods is the neglect of feature magnitude’s importance in representing facial features. To address this gap, our study introduces the NormIntegrated Softmax loss (NIface loss), a novel loss function that amalgamates feature norms with angular information. This integration offers a comprehensive perspective for feature classification, augmenting the compactness of intra-class features. Extensive evaluations on large-scale public datasets have demonstrated the efficacy of NIface loss in enhancing recognition accuracy and stability.

关键词： Measurement Technological innovation Image recognition Accuracy Face recognition Focusing Euclidean distance

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Current Controller Based on Recurrent Neural Network for Grid-Connected Converter

Current Controller Based on Recurrent Neural Network for Gri...

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Chinese Control Conference (CCC)

作者： Xuefen Wang Hongbin Wang Qingyi Wang Min Ding Quan Yin Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China State Grid Corporation of China Wuhan China School of Automation Huazhong University of science and technology Wuhan China

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Due to system nonlinearity and time-variant characteristic, there are limitations in standard decoupled d-q vector control mechanism. To mitigate these limitations, a RNN vector controller trained with Levenberg-Marquardt and FATT(Forward accumulation through time) algorithm is designed. The simulation is researched by using MATLAB software, and the results show that training neural-network algorithm is effective and the system using RNN vector control method outperforms the system using conventional PI control method under low sampling rate conditions.

关键词： Mathematical model Training Artificial neural networks Recurrent neural networks Voltage control Convergence

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Improving Urban Canyons Based on a Graph Transformer

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Navigation, Journal of the Institute of Navigation 2024年第4期71卷

作者： Zheng, Shaolong Zeng, Kungan Li, Zhenni Wang, Qianming Xie, Kan Liu, Ming Xie, Shengli School of Automation Guangdong University of Technology Guangzhou China Guangdong–HongKong–Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China 111 Center for Intelligent Batch Manufacturing Based on Internet of Things Technology Guangzhou China Techtotop Microelectronics Technology Co. Ltd Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou China

Signals from global navigation satellite systems (GNSSs) in urban areas suffer from serious multipath errors caused by building blockages and reflections. The use of deep neural networks offers great potential for predicting and eliminating com-plex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first challenge is an inability to effectively exploit irregular GNSS measurement data caused by an inconsistent number of visible satellites in different epochs. The second challenge is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network (GTNN) for predicting satellite visibility that effectively learns environment representations from irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from irregular GNSS measurements, a sky satellite graph is constructed as input to a graph neural network by using satellites captured in the same epoch, which can represent the spatial relationships between satellites and enable the model to learn satellite-related features sufficiently well. To improve the generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites to extract the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, our novel GTNN for predicting satellite visibility can not only handle irregular GNSS measurements but can also learn an environment representation via graph attention. Comparative experiments were conducted on real-world GNSS measurement data in urban areas, demonstrating that

关键词： Graph neural networks

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Variational Autoencoder-Based Topological Optimization of an Anechoic Coating: An Efficient- and Neural Network-Based Design

SSRN

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SSRN 2022年

作者： Sun, Yiping Li, Zhaoyu Chen, Jiadui Zhao, Xuefeng Tao, Meng School of Mechanical Engineering Guizhou University Guiyang550025 China Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang550025 China

An anechoic coating is an artificial heterogeneous composite material composed of periodic cells with cavities. Using the local resonance of the cavity to reduce the sound absorption frequency and widen the frequency band has been a research hotspot in recent years. One of the main challenges is to optimize the cavity structure of an anechoic coating to obtain low-frequency, broadband, and strong sound-absorbing properties. In this paper, a variational autoencoder (VAE) model-based topology optimization method was investigated. First, the finite element method (FEM) was used to calculate the sound absorption coefficient, and the dataset was constructed with samples whose average sound absorption coefficients ranged from 200 to 6000 Hz and were greater than 0.75. Then, the VAE model was trained to learn the key features of an anechoic coating. Finally, the data were reconstructed with Gaussian distributions. The decoder network of the trained VAE model was used to design a new anechoic coating. It took only approximately 3 seconds to generate 100 new topologies, and the average absorption coefficients were all greater than 0.754. This efficient- and neural network-based method can be further generalized to optimize the design of various mechanical structural materials with specific functions. © 2022, The Authors. All rights reserved.

关键词： Coatings

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Road Underground Target Detection in Ground Penetrating Radar Images Based on YOLO Models

Road Underground Target Detection in Ground Penetrating Rada...

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Chinese automation Congress (CAC)

作者： Ting Li Wei Xue Xinya Yang Dongchang Wu Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Ground penetrating radar (GPR) is extensively employed for subsurface road target detection, offering benefits such as convenience, nondestructive testing, rapid data acquisition, and superior resolution. Despite these advantages, interpreting GPR data often depends on the expertise of professionals, resulting in low detection efficiency and low accuracy. To address these challenges, this study introduces an intelligent detection technique for GPR images, utilizing an enhanced YOLOv5 framework. First, considering the problems of the small amount of GPR image datasets and the unclear characteristics caused by the complex underground media, a Dense-C3 module is built by utilizing the structure of DenseNet to enhance the network's capability for extracting features. Subsequently, a channel and spatial hybrid attention module is introduced into the backbone for feature refinement and improving the efficiency. Finally, the multi-class focal loss function is devised to enhance the precision in cases of imbalanced sample classes. Experimental results show that the proposed model surpasses the original YOLOv5 model and various contemporary advanced models.

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