Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model

Ma, Jialin; Zeng, Xin; Xue, Xiaoping; Deng, Ranran

doi:10.3390/app12031644

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…Network. LSTM network is evolved from recurrent neural network, and its components are input layer, hidden layer, and output layer [15][16][17]. Te data used in this study are the AE signal released by the rock mass rupture, and the efective information on the time series of the AE signal data can be fully extracted by LSTM.…”

Section: Principle Structure Of Blstm Neuralmentioning

confidence: 99%

Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM

Wang

2024

Journal of Electrical and Computer Engineering

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Acoustic emission (AE) signals produced by different types of rocks have different characteristics of information. Determining the brittle mineral content of rock according to the acoustic emission characteristics of rock is helpful to understand the mechanical behavior of rock in field monitoring. This article constructs a deep learning algorithm model to identify acoustic emission signals released from rock fractures with different brittle mineral contents. In response to the interference characteristics of acoustic emission signal data, a multiscale one-dimensional convolutional neural network embedded with efficient channel attention (ECA) module was incorporated into the model, and multiscale convolutional kernels were used to extract features of different levels of precision. In the latter half of the model, the BLSTM network was incorporated to extract time series-related features, local spatial uncorrelated features, and weak periodic pattern features from the acoustic emission signal data. To solve the problem that the recognition accuracy of minority samples decreases, this study replaces ReLU activation function with SELU. The results show that the multiscale 1DCNN-BLSTM model embedded in ECA module has a good antinoise performance, and the recognition accuracy can reach over 90%. The discovery of this work provides a new idea for exploring the mechanism of rock mass instability.

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Section: Principle Structure Of Blstm Neuralmentioning

confidence: 99%

Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM

Wang

2024

Journal of Electrical and Computer Engineering

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“…The essence of the double integral: the volume of the curved top cylinder [21], the double integral is a binary function z = f(x, y), where f(x, y) ∈ 𝐷. The integral over the plane region D, like the definite integral, is the limit of a sum of a certain form.…”

Section: Double Integral To Find Volumementioning

confidence: 99%

Quantitative Analysis of the Influence of Coal Mining on the Underflow Zone of Rivers by Using Double Integro-differential Equations

Yuan

Zhang

Wang

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The global ecological environment is fragile, and large-scale coal mining has accelerated the loss of water resources, but it is difficult to quantify its impact. Studies have found that large-scale coal mining has a particularly large impact on the underflow zone of rivers, but blind mining affects environmental damage and loses the water volume of the river. The lack of a compensation mechanism for water damage caused by coal mining often exacerbates the province’s fragile water environment. This paper analyzes the prediction of the impact of coal mining on the underflow zone of the river. First of all, determine the mining area of the mine and collect and collect data. Then, the lost water volume was calculated as a predicted score using the double integro-differential equation. Finally, the GA-BiLSTM model is proposed to predict the lost water capacity. The experimental results show that the mean absolute error (MAE), root mean square (RMSE), and prediction pass rate are 11.78/%, 24.87/%, and 92/%, respectively, and the average relative error is only 9.98%. Compared with BP, SVM, LSTM, Bi-LSTM neural network model has better prediction accuracy. The experimental results also show that the model has good reliability and practicability, which can provide a new idea and method for the new analysis of the impact of coal mining on the underflow zone of the river.

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“…In formula (18), δ represents the standard deviation of the source image. Higher and lower pixel values in the infrared frame indicate hotter and cooler regions, respectively.…”

Section: W2dpca Fusion Base Layermentioning

confidence: 99%

“…In response to the above problems, this study proposes a model based on dimension fusion optimization and long short-term memory (LSTM) [18]. In view of the defect that the input layer weights and hidden layer biases of the network model need manual experience tuning, an improved sparrow is introduced.…”

Section: Introductionmentioning

confidence: 99%

Visual Monitoring Technology for Substation Vulnerable High-Voltage Electrical Equipment Based on ISSA-LSTM Deep Learning Model Coupling Video Overlay Algorithm

Wang

Ding

Tan

2022

Computational Intelligence and Neuroscience

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To enhance the visualization effect of substation high-voltage electrical equipment vulnerability, this study proposes an ISSA-LSTM coupled video overlay algorithm-based substation high-voltage electrical equipment vulnerability visualization and monitoring model. Using the improved α blending algorithm combined with the inverse sampling of video background color, overlaying visible video as well as infrared video, using the improved adaptive weighted two-dimensional principal component analysis (W2DPCA) to fuse the base layer, selecting the detail layer as the final detail layer, obtaining the final fusion frame, and realizing the visualization and monitoring of substation high-voltage electrical equipment vulnerability, and introducing the improved sparrow search algorithm (ISSA) to establish long and short-term memory network prediction model to reduce the prediction error and improve the monitoring accuracy rate. The experimental results show that the monitoring frames obtained by this method can reflect rich details of substation high-voltage electrical equipment, and the texture color and equipment edge contrast are enhanced to facilitate accurate determination of substation high-voltage electrical equipment vulnerability, and the prediction accuracy of ISSA-LSTM model is as high as 99.85%.

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Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model

Cited by 9 publications

References 17 publications

Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM

Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM

Quantitative Analysis of the Influence of Coal Mining on the Underflow Zone of Rivers by Using Double Integro-differential Equations

Visual Monitoring Technology for Substation Vulnerable High-Voltage Electrical Equipment Based on ISSA-LSTM Deep Learning Model Coupling Video Overlay Algorithm

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