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2024 China Automation Congress, CAC 2024

作者： Huang, Yuan Xu, Chi 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)9798350368604

Recent advancements in Novel View Synthesis using neural radiance fields and 3D Gaussian splatting have demonstrated promising results. However, these methods conventionally rely on accurate initial poses and point clouds from the model of structure from motion, which poses challenges in sparse input views. To addressing these drawbacks, we introduces an novel method for view synthesis based on 3D Gaussian splatting. Our approach operates on sparse input views without the prerequisite of pose priors, achieving reconstruction and real-time rendering of novel perspectives. For the deficiency of camera poses and point cloud, we employ an end-to-end deep learning model to recover the 3D structure from a few of 2D images to initialize the 3D Gaussian point cloud. In addressing the issue with few-shot, we utilize the differentiable rasterization module to render depth map and apply regulation on it. Additionally, we incorporate supervision between input views by interpolating to generate novel perspectives during training. Experimental results demonstrate that our proposed method significantly enhances the capabilities of 3D Gaussian splatting for novel view synthesis in sparse views and without the necessity of camera pose priors. © 2024 IEEE.

关键词： 3D reconstruction

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Establishment of a humanized ST6GAL1 mouse model for influenza research

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Animal Models and Experimental Medicine 2024年第3期7卷 337-346页

作者： Lyu Chao Han Feng Gao Qian Lv Limin Lu Ziwei Lu Shuangshuang Li Xiaoyan Hu Yuechao Yang Mengjie Zhao Yingze Liu Jun Lu Xuancheng Duo Shuguang National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Chinese Center for Disease Control and PreventionBeijingChina Institute of Zoology Chinese Academy of SciencesBeijingChina NHC Key Laboratory of Biosafety National Key Laboratory of Intelligent Tracking and Forecasting for Infectious DiseasesNational Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina Zhejiang Provincial Key Laboratory of Medical Genetics Key Laboratory of Laboratory MedicineMinistry of EducationSchool of Laboratory Medicine and Life SciencesWenzhou Medical UniversityWenzhouChina

Background:This study aimed to construct and characterize a humanized influenza mouse model expressing ***:Humanized fragments,consisting of the endothelial cell-specific K18 promoter,human ST6GAL1-encoding gene,and luciferase gene,were microinjected into the fertilized eggs of *** manipulated embryos were transferred into the oviducts of pseudopregnant female *** offspring were identified using *** exhibiting elevated expression of the hST6GAL1 gene were selectively bred for propagation,and in vivo analysis was performed for *** of the humanized gene was tested by performing immunohistochemical(IHC)*** and biochemical analyses using the whole blood and serum of humanized hST6GAL1 mice were ***:Successful integration of the human ST6GAL1 gene into the mouse genome led to the overexpression of human SiaT *** mice were identified as carrying copies of the humanized gene,and the in vivo analysis indicated that hST6GAL1gene expression in positive mice mirrored influenza virus infection *** IHC results revealed that hST6GAL1 was expressed in the lungs of humanized ***,the hematologic and biochemical parameters of the positive mice were within the normal ***:A humanized influenza mouse model expressing the hST6GAL1 gene was successfully established and characterized.

关键词： hST6GAL1 humanized mice influenza animal model

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Temperature Effect on the Optical Characteristics of a Microoptoelectromechanical Accelerometer 25

Temperature Effect on the Optical Characteristics of a Micro...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Barbin, Evgenij S. Nesterenko, Tamara G. Koleda, Aleksej N. Talovskaia, Alena A. Kulinich, Ivan V. Mokhovikov, Denis M. Laboratory of Microsystems Engineering Tomsk State University of Control Systems and Radioelectronics Laboratory of Radiophotonics V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia Laboratory of Microsystems Engineering Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Radiophotonics Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Radiophotonics Tomsk State University of Control Systems and Radioelectronics Laboratory of Intelligent Computer Systems V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia

ISBN: (纸本)9798350389234

The treatise studies the temperature sensitivity of the optical transmission coefficient of an microoptoelectromechanical accelerometer (MOEMA) based on the optical tunneling effect in a temperature range from minus 40 °C to plus 100 °C. Two types of optical measuring transducers are studied: based on a directional coupler or a resonator. It is shown that the major contribution to the temperature error of the MOEMA’s transmission coefficient is made by the alteration of the optical transmission coefficient (OTC). For a measuring transducer based on a directional coupler, the temperature sensitivity of the OTC amounted to 0.00058 °C-1. That for a transducer based on a resonator amounted to 0.33 °C-1, which definitely speaks of its inability to operate without a thermostatic system. Following the results, the contribution of the refraction index alteration depending on temperature more than 5 times exceeds that of the MOEMA's mechanical parameters for optical measuring transducers based on the directional coupler and more than 3000 times for the resonator. © 2024 IEEE.

关键词： Accelerometers

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An improved road target detection approach based on YOLO-v8

An improved road target detection approach based on YOLO-v8

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2024 China Automation Congress, CAC 2024

作者： Hou, Hongyi Ci, Wenyan Tian, Lu Ge, Yangxun School of Engineering Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou University Huzhou313000 China

ISBN: (纸本)9798350368604

To address the challenges of high equipment demands, low detection accuracy, and high miss rates for overlapping and small targets in traditional road target detection models, we propose an improved detection method named YOLOv8-GPT. The C2f module in the backbone network is restructured with the inclusion of a lightweight attention mechanism, Triplet Attention, improving the model’s feature extraction capabilities. Additionally, a new lightweight convolution accumulation technique, GSConv, is integrated into the Neck, reducing model burden while preserving accuracy and enhancing computational cost efficiency. Then, to better detect small vehicles in crowded traffic, a dedicated small target detection head has been added, aiming to capture detailed features and contextual cues effectively. Ultimately, the Wise-IoU, capable of adaptively adjusting weight coefficients, is employed as the loss function for the refined model, boosting the regression performance of bounding boxes and overall detection robustness. Experiments on the KITTI dataset show that the YOLOv8-GPT model significantly excels beyond the original, with a 3.5 percentage point increase in mAP, a reduction of 1.3 million parameters, and superior performance in both volume and accuracy compared to other classic detection models, highlighting its strong practical applicability. © 2024 IEEE.

关键词： Feature extraction

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Impact of COVID-19 Interventions on Respiratory and Intestinal Infectious Disease Notifications-Jiangsu Province,China,2020–2023

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China CDC weekly 2024年第41期6卷 1059-1064,I0003页

作者： Ziying Chen Xin Liu Jinxing Guan Yingying Shi Wendong Liu Zhihang Peng Jianli Hu School of Public Health Nanjing Medical UniversityNanjing CityJiangsu ProvinceChina Department of Acute Infectious Diseases Control and Prevention Jiangsu Provincial Center for Disease Control and PreventionNanjing CityJiangsu ProvinceChina National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Chinese Center for Disease Control and PreventionBeijingChina Jiangsu Province Engineering Research Center of Health Emergency Nanjing CityJiangsu ProvinceChina.

Introduction:Many measures implemented to control the coronavirus disease 2019(COVID-19)pandemic have reshaped the epidemic patterns of other infectious *** study estimated the impact of the COVID-19 pandemic on respiratory and intestinal infectious diseases and potential changes following ***:The optimal intervention and counterfactual models were selected from the seasonal autoregressive integrated moving average(SARIMA),neural network autoregression(NNAR),and hybrid models based on the minimum mean absolute percentage error(MAPE)in the test *** relative change rate between the actual notification rate and that predicted by the optimal model was calculated for the entire COVID-19 epidemic prevention period and the“reopening”***:Compared with the predicted notification rate based on the counterfactual model,the total relative change rates for the 9 infectious diseases were−44.24%,respiratory infections(−55.41%),and intestinal infections(−26.59%)during 2020–*** with the predicted notification rate based on the intervention model,the total relative change rates were+247.98%,respiratory infections(+389.59%),and intestinal infections(+50.46%)in *** them,the relative increases in influenza(+499.98%)and hand-foot-mouth disease(HFMD)(+70.97%)were ***:Measures taken in Jiangsu Province in response to COVID-19 effectively constrained the spread of respiratory and intestinal infectious *** and HFMD rebounded significantly after the lifting of COVID-19 intervention restrictions.

关键词： Impact lifting prevention

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Temperature Effect on the Mechanical Transmission Coefficient of a Microoptoelectromechanical Accelerometer 25

Temperature Effect on the Mechanical Transmission Coefficien...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Barbin, Evgenij S. Nesterenko, Tamara G. Koleda, Aleksej N. Talovskaia, Alena A. Kulinich, Ivan V. Mokhovikov, Denis M. Laboratory of microsystems engineering Tomsk State University of Control Systems and Radioelectronics Laboratory of Radiophotonics V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia Laboratory of microsystems engineering Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Radiophotonics Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Laboratory of Radiophotonics Tomsk State University of Control Systems and Radioelectronics Laboratory of Intelligent Computer Systems V.E. Zuev Institute of Atmospheric Optics of SB RAS Tomsk Russia

ISBN: (纸本)9798350389234

The treatise studies a temperature effect on the microoptoelectromechanical (MOEMS) accelerometer with an optical measuring transducer based on evanescent wave coupling. The work investigates the temperature sensitivity of the mechanical part of the accelerometer in a temperature range from minus 65 °C to plus 125 °C. The temperature alteration induces stresses in the mechanical structure. Maximum mechanical stresses emerging in the structure at a temperature of minus 65 °C reach 556.61 MPa for the layer of aluminum that can be used for in-package wire bonding. However, these stresses are smaller than the admissible value for aluminum (690 MPa). The maximum stresses in residual layers of the mechanical structure are several times smaller than those in the aluminum layer. At different temperatures, the eigenfrequency of the accelerometer also changes, which alters its mechanical transmission coefficient. The temperature sensitivity of normalized mechanical transmission coefficient per 1 °C amounts to 0.00012 °C-1. To compensate the temperature effect on the mechanical transmission coefficient, various design schemes can be used and the output signal can be adjusted according to the temperature sensor readings. © 2024 IEEE.

关键词： Accelerometers

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Semi-Supervised Soft Sensor Modeling for Industrial Dynamic Information Mining Based on Two-Path Weight Comparison 13

Semi-Supervised Soft Sensor Modeling for Industrial Dynamic ...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Jia, Ruining Jin, Xinyi Ni, Xinru Ruan, Mengjiao Yang, Zeyu Wang, Peiliang School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou313000 China

ISBN: (纸本)9798350361674

Deep learning methods can extract reliable feature representations from massive process data to build accurate soft sensor models. However, the data in actual industrial production is often nonlinear, dynamic, and even limited in label samples due to untimely sampling and analysis. Aiming at the above problems, this paper proposes a semi-supervised soft sensor modeling method (SSACF-LSTM) for industrial dynamic information mining based on two-path weight comparison. An unsupervised long short-term memory network (LSTM) encoder-decoder is utilized to extract the hidden dynamic nonlinear information in unlabeled samples. This information is then spliced and masked with the initial input, which is subsequently fed into a deep neural network encoder-decoder for pretraining to learn a self-supervised task that mines correlation information between sequences as well as between samples that is useful for the model. In addition, the LSTM with attentional convolutional fusion serves as an auxiliary path that mines correlation information between variables directly from the initial input to complement the information extracted by self-supervision. Finally, the results of both are linearly weighted to obtain the final prediction. The method is validated in a debutanizer process. The results show that SSACF-LSTM can utilize both labeled and unlabeled data to extract quality-related features for soft sensor modeling, which is better than the semi-supervised LSTM network with historical feature fusion (HFF-ssLSTM), semi-supervised stacked auto-encoder (SSAE), and variational auto-encoder regression (SVAER). © 2024 IEEE.

关键词： Self-supervised learning

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Linear Quadratic Pursuit and Evasion Differential Game Guidance Strategy with Obstacle Avoidance 34th

Linear Quadratic Pursuit and Evasion Differential Game Guida...

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34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024

作者： Wang, Xintao Yang, Ming Ma, Ping Chao, Tao Control and Simulation Center Harbin Institute of Technology Harbin150080 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin150080 China

In this paper, a class of linear quadratic differential game guidance scheme is presented to study the pursuit and evasion conflict scenario that an evader establishes a static obstacle and performs the evasion maneuver to avoid a pursuer. First of all, the engagement kinematic model among the obstacle, the pursuer, and the evader is linearized according to the assumption. The issue of the pursuit and evasion game is transformed into a linear quadratic differential game through the dead zone and the cost functions. Furthermore, the linear quadratic differential game approach is utilized to obtain the guidance strategy with obstacle avoidance. Numerical simulation results are employed to validate the performance of the guidance policy. © 2024, International Council of the Aeronautical Sciences. All rights reserved.

关键词： Cost functions

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Adaptive optimal output regulation: A parametric Lyapunov equation approach 5

Adaptive optimal output regulation: A parametric Lyapunov eq...

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5th International Conference on control, Robotics, and intelligent System, CCRIS 2024

作者： ul Aabidin Lodhi, Zain Jiang, Huaiyuan Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

This article delves into a data-driven approach for solving the adaptive optimal output regulation problem in continuous-time linear systems with uncertain dynamics. Employing a class of parametric Lyapunov equations in conjunction with a nominal system model, the initial stabilizing feedback gain is determined to initiate the iterative method. Then, by utilizing a policy-iteration-based technique, we design an optimal control policy to achieve precise output tracking and disturbance rejection. The prescribed convergence characteristics of the closed-loop system directly result from the properties embedded within the solution of the parametric Lyapunov equation. The applicability and performance of the proposed method are demonstrated through a numerical example involving an input-constrained double integrator system. © 2024 SPIE.

关键词： Continuous time systems

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation 26

Efficient Facial Expression Recognition Network Based on Spo...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 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 China

ISBN: (纸本)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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