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Research on the crowd evacuation in the classrooms based on the wave equation in seismic environments

Physica A: Statistical Mechanics and its Applications 2025年 670卷

作者： LongCheng, Yang Jun, Hu Juan, Wei Lei, You Ying, Liang Sichuan Key Laboratory of Indoor Space Layout Optimization and Security Guarantee Chengdu Normal University Chengdu611130 China Key Laboratory of Earth Exploration and Information Technology of Ministry of Education Chengdu University of Technology Chengdu610059 China

In seismic environments, the state of crowd movement in a conventional classroom differs significantly from that of steady-state evacuation. An improved social force model based on the wave equation theory is proposed to improve the authenticity of crowd movement in classrooms under the simulated seismic environment. The optimized model first extracts the evacuation velocity values in diverse scenarios by combining the video data of crowd evacuation in real-life seismic environments, and then corrects the expected speed values of the social force model, so that the optimized model is suitable for crowd evacuation in seismic environments. Secondly, the diffusion process of the wave equation is analogized to the spread process of individual panic during an earthquake. Different individuals are affected by the superposition effect of seismic waves from different directions, which is directly manifested in the interactions that affect the acting forces in the social force model. This enables the optimized model to accurately depict the nonlinear interactions between pedestrians and between pedestrians and obstacles in seismic environments. Experiments are performed by integrating a self-built simulation platform with actual seismic data. The experimental results show that the ideal expected speed range in seismic environments is 1.6−3.2m/s, and the wave equation is effective in characterizing the panic of pedestrians, and the overall stress trend of the optimized model is consistent with the actual seismic environment. Therefore, correcting the expected speed and using the wave equation to characterize the panic of pedestrians provide an effective theoretical approach for crowd evacuation in classrooms when an earthquake occurs. © 2025

关键词： Seismic response

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Performance Analysis of Intelligent Reflecting Surface Assisted Wireless Communication System

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 775-787页

作者： Weiqiang Tan Quanquan Zhou Weijie Tan Longcheng Yang Chunguo Li School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China Key Laboratory of Advanced Manufacturing Technology Ministry of EducationGuizhou UniversityGuiyang550025China Sichuan Key Laboratory of Indoor Space Layout Optimization and Security Guarantee Chengdu Normal UniversityChengdu611130China School of Information Science and Engineering Southeast UniversityNanjing210096China

In this paper,we investigate the end-to-end performance of intelligent reflecting surface(IRS)-assisted wireless communication *** consider a system in which an IRS is deployed on a uniform planar array(UPA)configuration,including a large number of reflecting elements,where the transmitters and receivers are only equipped with a single *** objective is to analytically obtain the achievable ergodic rate,outage probability,and bit error rate(BER)of the ***,to maximize the system’s signal-to-noise ratio(SNR),we design the phase shift of each reflecting element and derive the optimal reflection phase of the IRS based on the channel state information(CSI).We also derive the exact expression of the SNR probability density function(p.d.f.)and show that it follows a non-central Chi-square *** the p.d.f.,we then derive the theoretical results of the achievable rate,outage probability,and *** accuracy of the obtained theoretical results is also verified through numerical *** shown that the achievable rate,outage probability,and BER could be improved by increasing the number of reflecting elements and choosing an appropriate SNR ***,we also find that the IRS-assisted communication system achieves better performance than the existing end-to-end wireless communication.

关键词： Massive MIMO intelligent reflecting surface uniform planar array achievable ergodic rate

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Application of a neural network model with multimodal fusion for fluorescence spectroscopy

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Nuclear Science and Techniques 2024年第10期35卷 135-148页

作者： Lin Tang Shuang Zhou Kai-Bo Shi Hong-Tao Shen Lei You College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Vanadium and Titanium Resource Comprehensive Utilization Key Laboratory of Sichuan Province Panzhihua UniversityPanzhihua 617000China Key Laboratory of Interior Layout optimization and Security Institutions of Higher Education of Sichuan ProvinceChengdu Normal UniversityChengdu 611130China

In energy-dispersive X-ray fluorescence spectroscopy,the estimation of the pulse amplitude determines the accuracy of the spectrum *** error generated by the amplitude estimation of the pulse output distorted by the measurement system leads to false peaks in the measured *** eliminate these false peaks and achieve an accurate estimation of the distorted pulse amplitude,a composite neural network model is proposed,which embeds long and short-term memory(LSTM)into the UNet *** UNet network realizes the fusion of pulse sequence features and the LSTM model realizes pulse amplitude *** model is trained using simulated pulse datasets with different amplitudes and distortion *** the pulse height estimation,the average relative error of the trained model on the test set was approximately 0.64%,which is 27.37% lower than that of the traditional trapezoidal shaping *** processing of a standard iron source further validated the pulse height estimation performance of the UNet-LSTM *** estimating the amplitude of the distorted pulses using the model,the false peak area was reduced by approximately 91% over the full spectrum and was corrected to the characteristic peak region of interest(ROI).The corrected peak area accounted for approximately 1.32%of the characteristic peak ROI *** results indicate that the model can accurately estimate the height of distorted pulses and has substantial corrective effects on false peaks.

关键词： UNet Long-and short-term memory Pulse distortion Pulse height estimation Fluorescent spectroscopy

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Influence of Excitation Signal Type and Period Selection on Detection Capability of TEM System 1

Influence of Excitation Signal Type and Period Selection on ...

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2022 1st International Conference on Advances in Modern Physics Sciences and Engineering Technology, ICPSET 2022

作者： Yang, Longcheng Wang, Huajun Liu, Jicheng Kuang, Yin Wei, Juan Wang, Xiaofei School of Computer Science Chengdu Normal University Chengdu China Key Laboratory of Earth Exploration and Information Techniques Ministry of Education Chengdu University of Technology Chengdu China School of Electronic Engineering Chengdu University of Information Technology Chengdu China Key Laboratory of Interior Layout Optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Chengdu China

The type and period of excitation signal are the important technical indexes of TEM system detection, which have an important influence on the detection accuracy and detection depth of the system. In this paper, by simulating the magnetic field distribution characteristics and magnetic focusing effects of transmitting coils with different excitation signals at the same depth, the differences of excitation signal types are analyzed and studied. Then, the echo effect diagram of receiving coils and the change curve of magnetic flux at the equivalent depth are simulated under different periods to determine the minimum period of excitation signal by comparative analysis. The simulation results show that the focusing and propagation depth of the magnetic field energy generated by the excitation signal are better when the rectangular pulse is selected and its period is determined to be 500 ms. Experimental tests under different thickness of steel plate show that the induced voltage on the receiving coil is different with different thickness. The thicker the ferromagnetic material is, the smaller the peak value of echo signal is. Moreover, in the case of no ferromagnetic material model, the induced voltage of the receiving coil is the maximum. This provides a theoretical basis for evaluating the corrosion of buried metal pipes by transient electromagnetic detection system. © Published under licence by IOP Publishing Ltd.

关键词： Ferromagnetic materials

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Remote Sensing Images Semantic Segmentation Method Based on Improved Nested UNet

Remote Sensing Images Semantic Segmentation Method Based on ...

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2022 International Conference on Geographic Information and Remote Sensing Technology, GIRST 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Geophysics Chengdu University of Technology Chengdu610059 China College of Computer Science Chengdu Normal University Chengdu611130 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Sichuan Chengdu611130 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China

ISBN: (纸本)9781510662186

With the development of remote sensing technology, remote sensing images of buildings are of great significance in urban planning, disaster response, and other directions. When we use a neural network containing batch normalization layers for semantic segmentation, the neural network is sensitive to batch size and has low segmentation accuracy for occluded and dense buildings. This paper proposes a method for building segmentation in remote sensing images based on Nested UNet (UNet++) deep neural network. First, the UNet++ network is used to extract features, and the Group Normalization (GN) method is used instead of Batch Normalization (BN) to alleviate the model's sensitivity to batch size. Then the weighted combination of Cross-Entropy Loss (CELoss) and DiceLoss is used as the loss function to improve the feature extraction ability of the neural network for unbalanced buildings. Finally, experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet++-GN) improves Mean Intersection over Union (MIoU) and Mean Pixel Accuracy (Macc) by 12.16% and 2.92%, respectively, compared with the original model (UNet++-BN). © 2023 SPIE.

关键词： Feature extraction

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Research on the identification of obstacle image based on convolutional neural network

Research on the identification of obstacle image based on co...

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2021 International Conference on Signal Image Processing and Communication, ICSIPC 2021

作者： Zhongyu, Li Huajun, Wang Yuhang, Cao Yuting, Dai College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China College of Computer Science Chengdu Normal University Chengdu611130 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of Interior Layout Optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Chengdu Sichuan611130 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

To effectively realize the reasonable obstacle avoidance of the detection robot, VGG based obstacle discrimination method is proposed. Above all, the image captured by the robot is input into the multi-layer convolutional neural network to obtain the high-level image features, which are used to construct the more accurate neural network model parameters and to train the softmax classifier with these parameters. Then the distance between the imported images and the data images is calculated by using the softmax classifier, and the similarity between the obstacles and non-obstacles is estimated. The experimental results show that the discrimination accuracy increase to above 94%. And the proposed method is more effectively compared with traditional ultrasonic and radar methods. © 2021 SPIE.

关键词： Convolution

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A Semantic Segmentation Method of Buildings in Remote Sensing Image Based on Improved UNet 2

A Semantic Segmentation Method of Buildings in Remote Sensin...

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2nd International Conference on Signal Image Processing and Communication, ICSIPC 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Computer Science Chengdu Normal University Chengdu611130 China College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Sichuan Chengdu611130 China College of Movie and Media Sichuan Normal University Chengdu610066 China

ISBN: (纸本)9781510657694

Aiming at the problem of model instability and overfitting of deep neural networks with the deepening of the number of network layers, the current mainstream method is to use batch normalization (BN) to alleviate them. However, since the BN method is more sensitive to batch size when the batch size is small, the model performance will be poor. For a relatively large model, due to the limitation of video memory, the batch size cannot take a large value, limiting the model's performance. Because of the dependence of BN on batch size, this paper adopts group normalization (GN) instead of batch normalization (BN) in the UNet network to alleviate the impact of the model on batch size. Then experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet-GN) improves the mean intersection over union (MIoU) and mean pixel accuracy (MPA) by 10.66% and 1.65% respectively compared with the original model (UNet-BN). © 2022 SPIE.

关键词： Deep neural networks

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Study on Pedestrian Evacuation Model Base on Reinforcement Learning

SSRN

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SSRN 2024年

作者： Rui, Zhu Hu, Jun Ling, Fan Qi, Zhang Juan, Wei School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Key Laboratory of Interior Layout Optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Chengdu611130 China School of Intelligent Manufacturing Chengdu Technological University Chengdu611730 China Key Laboratory of Multidimensional Data Sensing and Intelligent Information Processing Dazhou Key Laboratory Dazhou635000 China

This paper proposed a pedestrian evacuation model combined with reinforcement learning in order to study how to better guide pedestrians to complete evacuation in specific indoor scenes. This model introduced the way of establishing a scene in cellular automata and formulated reward rules according to the characteristics of the scene. It fitted the psychological activities of pedestrians in the actual evacuation process and trained the strategy of pedestrians at the overall level through the Q-learning algorithm from the reinforcement learning area. A speed control mechanism combined with real statistical data was introduced to simulate the speed attenuation. A simulation platform was built to compare the evacuation conditions under different scenarios and the different total numbers of pedestrians. The research showed that the model could automatically realize the exit selection function of pedestrians and part of conformity behavior. In the same evacuation scenario, this model could show adaptability for the different total numbers of pedestrians. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Influence of Excitation Signal Type and Period Selection on Detection Capability of TEM System

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Journal of Physics: Conference Series 2022年第1期2242卷

作者： Longcheng Yang Huajun Wang Jicheng Liu Yin Kuang Juan Wei Xiaofei Wang School of Computer Science Chengdu Normal University Chengdu China Key Laboratory of Earth Exploration and Information Techniques Ministry of Education Chengdu University of Technology Chengdu China Key Laboratory of Interior Layout Optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Chengdu China School of Electronic Engineering Chengdu University of Information Technology Chengdu China

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