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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Zhu, Huijie Shen, Zhuohao Yuan, Shenggui Chen, Fuliang Li, Jinhui Science and Technology on Near-Surface Detection Laboratory Wuxi214035 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Software Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789811623356

Smartphone recycling is a heated topic recently because of environmental and economical concerns. However, the limited recognition ability of naked eyes to find defects on phones impedes further growth of the second-hand markets. Related work for this problem focuses on detection and feature extraction, which can be tough because of the thin shapes of the defects and similar characteristics obstructing discrimination between scratches and cracks. In this paper, we propose an algorithm using semantic segmentation based on HRNet to build an effective model to evaluate defects of screens quickly and precisely. We improve the performance of the model by introducing the Lovász-Softmax loss function and data augmentation. Peripheral methods including subdivision and postprocessing are proposed to reduce misprediction on small scratches and enforce reliability in practice. We compare our model with other networks on mIoU and Kappa scores, concluding on an empirical basis. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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Multi-center solitary solid pulmonary nodule classification algorithm based on mutual information of graph federation

Multi-center solitary solid pulmonary nodule classification ...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Jiangfeng Shi Bao Feng Yu Liu Xiangmeng Chen School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin China Laboratory of Intelligent Detection and Information Processing Guilin University of Aerospace Technology Guilin City Guangxi Province China Department of Radiology Jiangmen Central Hospital Jiangmen China

With the continuous growth of medical image data and the imbalance in distribution across multiple medical centers, accurate and reliable classification of Solitary solid pulmonary nodules has become increasingly important. Firstly, to address the issue of data silos, a federated learning framework is employed, which ensures data security while improving the diagnostic performance of the model. Secondly, to tackle the non-independent and identically distributed (non-IID) nature of multi-center data, a graph federated personalized strategy based on mutual information is proposed. Graph convolutional algorithms and mutual information are utilized to explore the topological structure and correlation information among the data for personalized modeling. Additionally, considering the variations in shape and size of pulmonary nodules in different CT images, this paper combines a Diverse Branch Block (DBB) convolutional structure with ResNet18, serving as the framework for local and global models, to enhance the model’s capability of extracting multi-scale features. The results demonstrate promising classification performance of the proposed algorithm across five pulmonary nodule data centers, with AUC values of 0.7572, 0.8378, 0.7616, 0.7813, and 0.9130 on the respective test sets.

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Fusing Vehicle Trajectories and GNSS Measurements to improve GNSS Positioning Correction based on Actor-Critic Learning

Fusing Vehicle Trajectories and GNSS Measurements to improve...

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2023 International Technical Meeting of The Institute of Navigation, ITM 2023

作者： Zhao, Haoli Li, Zhenni Chen, Ci Wang, Lujia Xie, Kan Xie, Shengli School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Detection The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China

ISBN: (纸本)9781713871378

Obtaining high-accuracy vehicle localization is essential for safe navigation in automated vehicles, however, complex urban environments result in inaccurate positioning because of multipath and non-line-of-sight errors, making precise positioning in complex environments an important and unsolved problem. Different from conventional model-based approaches which require rigid assumptions on sensors and noise models, data-driven artificial intelligent approaches can provide new solutions for Global Navigation Satellite System (GNSS) positioning problems in multipath environments. Recent learning-based works employ the generalization ability of the deep neural network to model these complex errors in urban environments, and provide effective positioning corrections for rough GNSS localizations by learning from a vehicle trajectory-based Reinforcement Learning (RL) environment. However, existing RL-based approaches employ insufficient features as input, and cannot take advantage of the vehicle trajectory *** tackle these issues, we propose a novel Deep Reinforcement Learning-based (DRL) positioning correction framework with fused features considering the vehicle trajectory information and GNSS features in the multi-input *** help the agent learn the correction policy efficiently with comprehensive inputs, we develop a new positioning correction RL environment with multi-input observations, which augments GNSS device readings of different satellites as complementary to the vehicle trajectory, and moreover employ a correction advantage reward to help the agent learn an efficient policy in positioning correction. To fully utilize comprehensive multi-input observations in the proposed environment, we employ the LSTM module to separately extract features to fuse brief states from input time series data. Finally, to address the long-term positioning accuracy, we construct the learning model based on actor-critic DRL structure with the cumulative reward

关键词： Statistical tests

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Adaptive Spectral Broadening Filter-Based Phased Array Focused Ultrasound Imaging for High-Echo Target detection

SSRN

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SSRN 2024年

作者： Lou, Cuijuan Zhou, Quan Li, Ranyang Liu, Wufeng Li, Yan Yuchi, Ming Ding, Mingyue Key Laboratory of Grain Information Processing and Control Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China Department of Automation School of Automation Wuhan University of Technology Wuhan430070 China Department of Biomedical Engineering School of Life Science and Technology Key Laboratory of Molecular Biophysics of the Ministry of Education Medical Ultrasound Laboratory Huazhong University of Science and Technology Wuhan430074 China

Noise suppression and resolution improvement are important issues in high-echo target ultrasound imaging detection. To address this problem, this paper proposes a phased array focused ultrasound imaging method based on an adaptive spectral broadening filter. The proposed method first standardizes the received signals after fixed-point transmission by subtracting the mean of the received aperture signals and dividing by the standard deviation of the received aperture signals. Then, spectralbroadening was achieved through covariance and temporal autocorrelation coefficient calculations. Finally, the variance factor proposed for the first time is calculated as a weight from standardized received data after fixed-point transmission focusing to obtain the final scanline data. To evaluate the performance of the proposed method in high-echo target ultrasound imaging detection, experimental phantom images and two views of the interventricular septum including the parasternal long-axisviews and the apical four-chamber views, are reconstructed. In three scenarios, some finer structures of the targets in the images by the proposed method can be more easily identified compared to delay and sum (DAS), square-neighborhood robust signal mean-to-standard-deviation factor (SN-RSMSF), and coherence factor beamforming (CF). For the experimental phantom data, the image resolution measured by -20dB mainlobe width of the proposed method can reach 14.2 percents of DAS, 18.3 percents of SN-RSMSF and 18.3 percents of CF. The proposed method also outperforms the other three methods in terms of noise suppression. The proposed method can be used to detect diseases that involve high-echo targets such as stones, heart valves, and calcifications. © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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Multi-objective optimization of heat transfer performance of power battery cold plate based on bionic spider web flow channel

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Case Studies in Thermal engineering 2025年 72卷

作者： Junfu Qiao Jinqin Guo Yongwei Li Department of Automation Taiyuan Institute of Technology Taiyuan 030008 China Intelligent Detection and Control Engineering(Technology) Research Cente Taiyuan 030008 China

With the rapid advancement of new energy vehicles, effective thermal management of power batteries has become increasingly critical. This study presents a comprehensive numerical investigation using Computational Fluid Dynamics (CFD) to develop an innovative bionic spider web-inspired liquid cooling system enhanced with nanofluids. We establish a sophisticated physical model to analyze the thermal performance by systematically optimizing three key parameters: nanofluid inlet temperature, copper nanoparticle volume fraction, and flow velocity. Our comprehensive multi-parameter optimization using Response Surface Method (RSM) revealed that a 5 % copper nanoparticle concentration achieves optimal cooling, reducing average and maximum battery temperatures by 0.68 % and 0.67 % respectively, while maintaining a manageable 16.85 % increase in pressure drop. The study further identifies the most efficient operating conditions as 1.896 % nanoparticle volume fraction, 283K inlet temperature, and 0.05 m/s flow velocity, establishing an optimal balance between cooling efficiency and system energy consumption. These findings provide valuable insights for the design of high-performance thermal management systems in electric vehicles, offering a scientifically validated solution that enhances both cooling effectiveness and operational efficiency.

关键词： Bionic cobweb Nanofluid Heat transfer Response surface method optimization

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Improving GNSS positioning correction using Deep Reinforcement Learning with Adaptive Reward Augmentation Method 36

Improving GNSS positioning correction using Deep Reinforceme...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Tang, Jianhao Li, Zhenni Guo, Rui Zhao, Haoli Wang, Qianming Liu, Ming Xie, Shengli Polycarpou, Marios School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510006 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (纸本)9780936406350

High-precision global navigation satellite system (GNSS) positioning for automatic driving in urban environments is an unsolved problem because of the influence of multipath effects. Recently, methods based on data-driven deep reinforcement learning (DRL) have been used to learn continual positioning-correction strategies without strict assumptions about model parameters, and are adaptable to nonstationary urban environments. However, these methods face two remaining challenges: 1) real-time data for training collected in nonstationary urban environments is inadequate because of issues such as response delay and signal interruption, which causes the performance degradation in DRL, 2) the existing methods use vehicle positions as the environment observations, ignoring the complex errors caused by multipath effects in urban environments. In this paper, we propose a novel DRL-based positioning-correction method with an adaptive reward augmentation method (ARAM), and use GNSS measurements instead of vehicle positions as the environment observations, for improving the GNSS positioning accuracy in nonstationary urban environments. To specify the accurate current state of the vehicle agent, we use the GNSS measurement observations, including the line-of-sight (LOS) vector and the pseudorange residual, to model complex environmental errors, and employ a long and short term memory (LSTM) module to learn the temporal aspects of the observations, which include the interference by multipath effects on the GNSS positioning in urban environments. To address the performance degradation caused by inadequate real-time training data, we employ ARAM to adaptively modify the matching of data between the source domain and target domain of the nonstationary urban environments, to leverage sufficient data from the source domain for training, and thus to improve the performance of DRL. Hence, based on ARAM and using the GNSS measurement observations, we construct an LSTM-based proximal pol

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Efficient Graph Neural Network Driven Recurrent Reinforcement Learning for GNSS Position Correction 36

Efficient Graph Neural Network Driven Recurrent Reinforcemen...

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36th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2023

作者： Zhao, Haoli Tang, Jianhao Li, Zhenni Wu, Zhuoyu Xie, Shengli Wu, Zhaofeng Liu, Ming Kumara, Banage T.G.S. School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd. Guangzhou510000 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (纸本)9780936406350

With the wide applications of the Global Navigation Satellite System (GNSS) in autonomous driving scenarios, the demand for high-precision positioning of navigation systems has increased dramatically in complex multipath environments. Conventional model-based methods are constrained by strict assumptions about noise models and can hardly model complex environment errors. In contrast, approaches based on artificial intelligent learning have become an important direction to solving the problem of high-precision positioning because learning-based approaches only require simple assumptions. However, current learning-based approaches are facing the following issues. The existing Graph Neural Network-based (GNN) method could hardly adapt to dynamically changing driving environment scenarios since it considers positioning discretely. On the other hand, existing Reinforcement Learning-based (RL) approaches ignore the relationship between multi-constellation satellites, resulting in an inadequate description of the driving correction environment observations. In this paper, we construct a GNN-driven recurrent reinforcement learning method to consider the GNSS measurement of multi-constellation satellites and to learn real-time correction strategy in the dynamic driving environment. To establish a comprehensive positioning correction environment, we construct a multi-constellation graph observation, based on the feature vector concerning GNSS measurement of multi-constellation satellites and edges for satellites in and between constellations. To make more effective use of GNSS measurements, we employ the graph embedding module to deal with the multi-constellation graph inputs, to extract hidden topological features to form the brief states about relationships between multi-constellation satellites for the RL environment. Finally, we construct a recurrent actor-critic structured RL model with cumulative reward and continuous action space to exploit historical information and a

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Frequency-modulated continuous-wave quantum interferometric lidar with enhanced accuracy and resolution

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Physical Review Applied 2025年第3期23卷 034055-034055页

作者： Ming-Da Huang Zhan-Feng Jiang Hong-Yi Chen Ying Zuo Xiao-Peng Hu Hai-Dong Yuan Li-Jian Zhang Qi Qin College of Physics and Optical Engineering Shenzhen University Shenzhen 518060 China State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Shenzhen 518060 China Institute of Intelligent Optical Measurement and Detection Shenzhen University Shenzhen 518060 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China International Quantum Academy Shenzhen 518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China National Laboratory of Solid State Microstructures and College of Engineering and Applied Sciences Nanjing University Nanjing 210093 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong SAR China Quantum Science Center of Guangdong-HongKong-Macao Greater Bay Area (Guangdong) Shenzhen 518045 China

The range and velocity of a moving object can be ascertained using the sensing technique known as light detection and ranging (lidar). It has recently been suggested that quantum lidar, which uses entangled states of light, can enhance the capabilities of lidar. Entangled pulsed light is used in prior quantum lidar approaches to assess both range and velocity at the same time using the time of flight and Doppler shift. However, the detection of entangled pulsed light is often inefficient and constrained by the temporal resolution of the detectors. Here, we investigate a quantum lidar utilizing a linear interferometer operating on a frequency-modulated continuous wave (FMCW) instead of pulses. Our findings demonstrate that the quantum FMCW lidar enables simultaneous measurements of range and velocity with quantum-enhanced accuracy and resolution, as well as superresolution phase imaging. Through self-heterodyne detection with a linear optical design, the signals of the quantum FMCW lidar with entangled photon pair can be efficiently carried out electronically by down-converting the optical signal to the microwave region. As a result, it is better suited for practical implementations.

关键词： Frequency modulation

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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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Searching for neutrinoless double-beta decay of 136Xe with PandaX-4T

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Science Bulletin 2025年第11期70卷 1779-1785页

作者： Zhang, Shu Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Ning, Xuyang Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Guofang Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Jiayi Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China School of Physics Sun Yat-sen University Guangzhou510275 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province Xichang615000 China Yalong River Hydropower Development Company Chengdu610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai519082 China Department of Physics Yantai University Yantai264005 China Institute of Modern Physics Fudan University Shanghai200433 China School of Physics Beihang University Beijing102206 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing100191 China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing102206 China Institute of Modern Physics Chinese Academy of Sciences Huizhou516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Physics University of Maryland College Park Maryland20742 United States School of Physics Nankai University Tianjin300071 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Qingdao266237 China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Qingdao266237 China School of Physics Peking University Beijing100871 China College of Nuclear Technol

We report the search for neutrinoless double-beta decay of 136Xe from the PandaX-4T experiment with a 3.7-tonne natural xenon target. The data reconstruction and the background modeling are optimized in the MeV energy region. A blind analysis is performed with data from the commissioning run and the first science run. No significant excess of signal over the background is observed. A lower limit on the half-life of 136Xe neutrinoless double-beta decay is established to be 2.1×1024 yr at the 90% confidence level, with a 136Xe exposure of 44.6 kg⋅yr. Our result represents the most stringent constraint from a natural xenon detector to date. © 2025 Science China Press

关键词： Majorana neutrino Natural xenon detector Neutrinoless double-beta decay

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