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Chinese Automation Congress (CAC)

作者： Ling Wang Rongxian Wang Gaofeng Jia Key Laboratory of Knowledge Automation of Industrial Process of Ministry of education School of Automation and Electrical Engineering Shunde Innovation School University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation of Industrial Process of Ministry of education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

The precise forecasting of traffic data holds significant importance for numerous practical applications in the real world. The challenge lies in the nonlinear spatio-temporal dependencies of the data, as well as rapid error accumulations for multi-step predictions. Therefore, we propose a Generative Adversarial Nets model based on a Spatio-Temporal Graph Attention Network (STGAT-GAN). Utilizing spatio-temporal graph attention proves to be an effective method for capturing the spatio-temporal dependencies, while GANs can learn a rich distribution over spatio-temporal data implicitly to produce realistic data adversarially. Experiments are carried out on two public traffic datasets. Comparative experimental analysis reveals that the proposed STGAT-GAN has better performance.

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Genomic evolution reshapes cell-type diversification in the amniote brain

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Developmental cell 2025年

作者： Duoyuan Chen Zhenkun Zhuang Maolin Huang Yunqi Huang Yuting Yan Yanru Zhang Youning Lin Xiaoying Jin Yuanmei Wang Jinfeng Huang Wenbo Xu Jingfang Pan Hong Wang Fubaoqian Huang Kuo Liao Mengnan Cheng Zhiyong Zhu Yinqi Bai Zhiwei Niu Ze Zhang Ya Xiang Xiaofeng Wei Tao Yang Tao Zeng Yuliang Dong Ying Lei Yangang Sun Jian Wang Huanming Yang Yidi Sun Gang Cao Muming Poo Longqi Liu Robert K Naumann Chun Xu Zhenlong Wang Xun Xu Shiping Liu BGI Research Hangzhou 310030 China BGI Research Shenzhen 518083 China Key Laboratory of Spatial Omics of Zhejiang Province BGI Research Hangzhou 310030 China State Key Laboratory of Genome and Multi-omics Technologies BGI Research Hangzhou 310030 China. School of Biology and Biological Engineering South China University of Technology Guangzhou 510006 China School of Life Sciences Zhengzhou University Zhengzhou 450001 China. BGI Research Hangzhou 310030 China. School of Biology and Biological Engineering South China University of Technology Guangzhou 510006 China. Key Laboratory of Spatial Omics of Zhejiang Province BGI Research Hangzhou 310030 China. College of Life Sciences University of Chinese Academy of Sciences Beijing 100049 China HIM-BGI Omics Center Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Hangzhou 310018 China. The Institute of Biomedical and Health Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions Shenzhen 1068 Xueyuan Avenue Shenzhen University Town Nanshan District Shenzhen 518055 China. Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou 310024 China College of Life Sciences Northwest University Xi'an 710069 China. China National GeneBank BGI Research Shenzhen 518120 China Guangdong Genomics Data Center BGl research Shenzhen 518120 China. BGI Research Shenzhen 518083 China. Shanxi Medical University BGI Collaborative Center for Future Medicine Shanxi Medical University Taiyuan 030001 China. Institute of Neuroscience Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology CAS Center for Excellence in Brain Science and Intelligence Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai 200031 China. HIM-BGI Omics Center

Over 320 million years of evolution, amniotes have developed complex brains and cognition through largely unexplored genetic and gene expression mechanisms. We created a comprehensive single-cell atlas of over 1.3 million cells from the telencephalon and cerebellum of turtles, zebra finches, pigeons, mice, and macaques, employing single-cell resolution spatial transcriptomics to validate gene expression patterns across species. Our study identifies significant species-specific variations in cell types, highlighting their conservation and diversification in evolution. We found pronounced differences in telencephalon excitatory neurons (EXs) and cerebellar cell types between birds and mammals. Birds predominantly express SLC17A6 in EX, whereas mammals express SLC17A7 in the neocortex and SLC17A6 elsewhere, possibly due to loss of function of SLC17A7 in birds. Additionally, we identified a bird-specific Purkinje cell subtype (SVIL+), implicating the lysine-specific demethylase 11 (LSD1)/KDM1A pathway in learning and circadian rhythms and containing numerous positively selected genes, which suggests an evolutionary optimization of cerebellar functions for ecological and behavioral adaptation. Our findings elucidate the complex interplay between genetic evolution and environmental adaptation, underscoring the role of genetic diversification in the development of specialized cell types across amniotes.

关键词： amniote brain science cerebellum evolution of cell types single-nucleus RNA-seq telencephalon

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Median query research in wireless sensor networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2010年第2A期38卷 133-137页

作者： Wu, Zhong-Bo Zhang, Hui Chen, Hong Information School Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China School of Mathematics and Computer Science Xiangfan University Xiangfan Hubei 441053 China

Poor quality and harsh condition can result in faulty and outlier data in sampling data of sensor nodes. So we need median query to reflect average level of monitoring region. First, we put forward HMA algorithm. Second, we extend HMA algorithm and put forward HFMA algorithm. In HFMA, We only need collect data inside filter and aggregate influence coefficient during sampling period. Base station can compute median result according to the sample data inside filter and influence coefficient aggregation value. Experimental results have shown that HFMA outperforms Naive algorithm and HMA algorithm and can prolong the lifetime of sensor network.

关键词： Sensor nodes

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L-Band Dissipative Soliton Generation in An All-Fiber Er-Doped Laser with A 45° Tilted Fiber Grating

L-Band Dissipative Soliton Generation in An All-Fiber Er-Dop...

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2018 Asia Communications and Photonics Conference, ACP 2018

作者： Huang, Qianqian Zou, Chuanhang Yan, Zhijun Mou, Chengbo Zhou, Kaiming Zhang, Lin Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China School of Optical and Electronic Informaiton National Engineering Laboratory for Next Generation Internet Access System Huazhong University of Science and Technology Wuhan China Aston University Birmingham United Kingdom

We demonstrate an all-fiber dissipative soliton Er-doped laser in L-band region using a 45° titled fiber grating for the first time. 697fs dissipative soliton with 30.19 nm spectral bandwidth at 1596.12 nm is rea... 详细信息

ISBN: (纸本)9781538661581

关键词： Fiber Bragg gratings

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A Multi-Step Speed Prediction Method for Traffic Networks Based on MAF-GCN-Seq2Seq Model 2

A Multi-Step Speed Prediction Method for Traffic Networks Ba...

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2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Long, Yi Xiao, Hongbo Xiao, Jianhua Peng, Xiaoning School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Hunan Huaihua418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9781665464017

Accurately predicting the future trend of traffic flow is of great significance to the planning of urban traffic. The great challenge of predicting traffic flow stems from the complexity and uncertainty of traffic flow. To address the complexity of traffic flow, this paper proposes a MAF-GCN 2 Seq model. The model uses a moving average filter to filter the time series data, extracts the spatial features between each node through a graph convolutional neural network, and forms a time series matrix for input into the encoder (GRU), and introduces a self-attention mechanism to mine the spatial features. time-domain features for multi-step traffic speed prediction. In order to verify the advanced nature of the model, this paper uses the traffic flow data set in Shenzhen to predict the traffic flow. Through the comparison experiments, it can be seen that the prediction accuracy of the MAF-GCN 2 Seq model reaches 90%, which has obvious advantages compared with other models. © 2022 IEEE.

关键词： Convolutional neural networks

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Research on Pedestrian Gender Detection Based on A Lightweight Model

Research on Pedestrian Gender Detection Based on A Lightweig...

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Chinese Control and Decision Conference, CCDC

作者： Hu Yuling Wang Xinyi Zou Weiguang Institute of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In recent years, a variety of emergency events have been occurring in buildings in countries. For there are some differences between male and female in movement ability and evacuation behavior, gender has a non-negligible impact on evacuation and decision-making of emergency events. Thus, it is of great practical significance to detect the gender of pedestrians at entrances or exits of buildings in real time to grasp the proportion of different gender people in buildings and to formulate emergency warning or evacuation strategies scientifically. In response to the requirement of rapid emergency response, this paper proposes a pedestrian gender detection method based on a lightweight object detection model YOLOX-Tiny. In addition, considering the inaccurate pedestrian gender detection caused by the change of pedestrian posture and camera angle, the attention mechanism CBAM is introduced to enhance the ability of filtering a large amount of redundant information, obtaining key information and acquiring pedestrian gender characteristic information. Through the experiments of self-made pedestrian gender datasets, the results show that the detection accuracy of the improved model is increased by 1.8%, and the detection speed reaches 251FPS, which can meet both speed and accuracy of pedestrian gender detection in emergencies.

关键词： Pedestrians Accuracy Attention mechanisms Filtering Buildings Urban areas Object detection

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Integrating Machine Learning into a Fully Coupled Current-Wave-Sediment Model: Characterizing Particle Size in the Settling Process in Estuaries of the Great Barrier Reef, Australia

SSRN

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SSRN 2025年

作者： Xiao, Ziyu Livsey, Daniel N. Schroeder, Thomas Blondeau-Patissier, David Santa Cruz, Rodrigo Su, Jiasheng Song, Dehai Wang, Xiao Hua Carlin, Geoffrey Steven, Andrew D.L. Crosswell, Joseph R. CSIRO Environment Brisbane4102 Australia Southern Cross University National Marine Science Centre Coffs Harbour2450 Australia Agroclimate and Hydraulic Engineering Research Unit U.S. Department of Agriculture EI Reno43036 United States Imaging and Computer Vision Group CSIRO Data61 Brisbane4069 Australia CSIRO Health & Biosecurity Brisbane4102 Australia Key Laboratory of Physical Oceanography Ministry of Education Ocean University of China Qingdao266100 China Laoshan Laboratory Qingdao266237 China The Sino-Australian Research Consortium for Coastal Management School of Science University of New South Wales Canberra2600 Australia CSIRO Health & Biosecurity Australia CSIRO Oceans and Atmosphere Australia

Accurate prediction of sediment settling is critical for management of coastal ecosystems, but complex estuarine processes that influence sediment deposition and erosion present a major modelling challenge. This study introduces a proof-of-concept framework that integrates machine learning (ML) into environmental simulations to improve accuracy and efficiency by modelling dynamic sediment flocculation processes and their influence on particle size, enabling a more precise determination of settling velocity. Environmental factors influencing in-situ sediment particle size were used to train a regression model based on coeval measurements of three key parameters: salinity, shear rate and suspended sediment concentration (SSC). This regression model was developed using ML and integrated into a fully coupled current-wave-sediment model to simulate the flocculation response to these three parameters. The integrated model framework demonstrates its reliability and accuracy when evaluated against the in-situ measurements, satellite-derived SSC for the Fitzroy Estuary (Great Barrier Reef), and a parametric flocculation model that only relates settling velocity to SSC. We present an example of the ML-based approach outperforming a parametric model by capturing nonlinear particle-hydrodynamic interactions while maintaining computational efficiency, enabling high-resolution SSC simulations. This work demonstrates an advancement for hybrid modelling using rapidly evolving ML applications, offering a scalable tool for sediment transport and water quality management. © 2025, The Authors. All rights reserved.

关键词： Logistic regression

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Effects of Various Proportions Tib2p-Ticp Reinforced Al-Cu-Mg Composites in High-Temperature Mechanical Properties and Sliding Wear Behaviors

SSRN

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SSRN 2024年

作者： Zhong, Xinmiao Li, Qing-Yuan Meng, Meng Yang, Hong-Yu Qiu, Feng Jiang, Qichuan Duan, Xiang-Zheng Key Laboratory of Automobile Materials Ministry of Education Department of Materials Science and Engineering Jilin University Renmin Street NO. 5988 Jilin Province Changchun130025 China School of Mechanical and Aerospace Engineering Jilin University Renmin Street NO. 5988 Jilin Province Changchun130025 China State Key Laboratory of Automotive Simulation and Control China

Synergistic reinforcement of dual-phase particles with different sizes was always considered superior to single-phase reinforcement in composites, but there were few studies to investigate the role of each part in enhancing properties. In this work, 40 vol.% (TiB2p-TiCp)/Al-Cu-Mg composites with different proportions of TiB2p and TiCp (3:1, 2:1, 1:1, 1:2 and 1:3), were fabricated by the combustion synthesis to contrast the strengthening effect of dual-phase particles from the interface stability and the characteristics of dual-phase particles. Calculated by first principle, TiB2 (3.12 J/m2) presented better interfacial stability with Al matrix than TiC (1.61 J/m2). Under the circumstance of the same volume fraction, the composites with a higher proportion of TiB2p usually show higher yield and maximum compressive strength and effectively prevent crack growth of composites. However, excess submicron sized TiB2p tends to produce cracks at the hexagon edge when the loads were applied and formed crack loops delaminating materials. When the TiB2p/TiCp proportion was 1:1, plasticity and strength were beneficially balanced at the elevated temperature. Otherwise, the composite with TiB2: TiC=3:1 possessed the best wear resistance (coefficient of friction: 0.22) at a sliding velocity of 0.32 mm/s, 30N load) among other composites ([[EQUATION]],[[EQUATION]],[[EQUATION]], and [[EQUATION]]) at 473 K, which reduced 45%, 64%, 73% and 86%, respectively. The main wear mechanism was abrasive wear when the TiB2 proportion was dominant. Submicron sized TiB2p with better interface bonding can keep the rigidity of the composites to resist the plastic flow, while TiC nanoparticles are easier to be extracted during the wear process. © 2024, The Authors. All rights reserved.

关键词： Ductile fracture

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Radiomics based on dual-energy CT for noninvasive prediction of cervical lymph node metastases in patients with nasopharyngeal carcinoma

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Radiography 2025年第4期31卷 102989-102989页

作者： L. Li D. Yang Y. Wu R. Sun Y. Qin M. Kang X. Deng M. Bu Z. Li Z. Zeng X. Zeng M. Jiang B.T. Chen Department of Radiology First Affiliated Hospital of Guangxi Medical University Nanning 530021 Guangxi PR China Department of Radiology Guizhou Provincial People Hospital No.83 East Zhongshan Road Nanming District Guizhou Province 550000 Guiyang PR China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University No. 2870 Huaxi Avenue South Guiyang 550025 Guizhou PR China Department of Nuclear Medicine First Affiliated Hospital of Guangxi Medical University Nanning 530021 Guangxi PR China Department of Radiation Oncology First Affiliated Hospital of Guangxi Medical University Nanning 530021 Guangxi PR China Department of Diagnostic Radiology City of Hope National Medical Center Duarte CA USA

Introduction To develop and validate a machine learning model based on dual-energy computed tomography (DECT) for predicting cervical lymph node metastases (CLNM) in patients diagnosed with nasopharyngeal carcinoma (NPC). Methods This prospective single-center study enrolled patients with NPC and the study assessment included both DECT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Radiomics features were extracted from each region of interest (ROI) for cervical lymph nodes using arterial and venous phase images at 100 keV and 150 keV, either individually as non-fusion models or combined as fusion models on the DECT images. The performance of the random forest (RF) models, combined with radiomics features, was evaluated by area under the receiver operating characteristic curve (AUC) analysis. DeLong's test was employed to compare model performances, while decision curve analysis (DCA) assessed the clinical utility of the predictive models. Results Sixty-six patients with NPC were included for analysis, which was divided into a training set (n = 42) and a validation set (n = 22). A total of 13 radiomic models were constructed (4 non-fusion models and 9 fusion models). In the non-fusion models, when the threshold value exceeded 0.4, the venous phase at 100 keV (V100) (AUC, 0.9667; 95 % confidence interval [95 % CI], 0.9363–0.9901) model exhibited a higher net benefit than other non-fusion models. The V100 + V150 fusion model achieved the best performance, with an AUC of 0.9697 (95 % CI, 0.9393–0.9907). Conclusion DECT-based radiomics effectively diagnosed CLNM in patients with NPC and may potentially be a valuable tool for clinical decision-making. Implications for practice This study improved pre-operative evaluation, treatment strategy selection, and prognostic evaluation for patients with nasopharyngeal carcinoma by combining DECT and radiomics to predict cervical lymph node status prior to treatment.

关键词： Dual-energy computed tomography (DECT) Nasopharyngeal carcinoma (NPC) Cervical lymph node metastases (CLNM) Radiomics Machine learning model

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Image Segmentation via Improving Clustering Algorithms with Density and Distance

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Procedia Computer Science 2015年 55卷 1015-1022页

作者： Zhensong Chen Zhiquan Qi Fan Meng Limeng Cui Yong Shi School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing 100190 China Sino-Danish Center for Education and Research Beijing 100190 China Research Center on Fictitious Economy and Data Science CAS Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Image segmentation problem is a fundamental task and process in computer vision and image processing applications. It is well known that the performance of image segmentation is mainly influenced by two factors: the segmentation approaches and the feature presentation. As for image segmentation methods, clustering algorithm is one of the most popular approaches. However, most current clustering-based segmentation methods exist some problems, such as the number of regions of image have to be given prior, the different initial cluster centers will produce different segmentation results and so on. In this paper, we present a novel image segmentation approach based on DP clustering algorithm. Compared with the current methods, our method has several improved advantages as follows: 1) This algorithm could directly give the cluster number of the image based on the decision graph; 2) The cluster centers could be identified correctly; 3) We could simply achieve the hierarchical segmentation according to the applications requirement. A lot of experiments demonstrate the validity of this novel segmentation algorithm.

关键词： Image Segmentation Clustering Algorithm Feature Representation

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