A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network

Zhang, Ziyan; Tian, Jiawei; Huang, Weizheng; Yin, Lirong; Zheng, Wenjun; Liu, Shan

doi:10.3390/atmos12101327

Cited by 95 publications

(38 citation statements)

References 43 publications

(44 reference statements)

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“…Li et al [16] developed a hybrid called the CNN-LSTM model, which is used to predict the concentration of PM2.5 in Beijing in the next 24 h. Bai et al [30] proposed an E-LSTM neural network, which constructs multiple LSTM models in different modes for integrated learning with an hourly PM 2.5 concentration forecast. Our time series model did show a better result [26,[31][32][33].…”

Section: Introductionmentioning

confidence: 70%

A Haze Prediction Model in Chengdu Based on LSTM

Liu

Yin

et al. 2021

Atmosphere

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Air pollution with fluidity can influence a large area for a long time and can be harmful to the ecological environment and human health. Haze, one form of air pollution, has been a critical problem since the industrial revolution. Though the actual cause of haze could be various and complicated, in this paper, we have found out that many gases’ distributions and wind power or temperature are related to PM2.5/10’s concentration. Thus, based on the correlation between PM2.5/PM10 and other gaseous pollutants and the timing continuity of PM2.5/PM10, we propose a multilayer long short-term memory haze prediction model. This model utilizes the concentration of O3, CO, NO2, SO2, and PM2.5/PM10 in the last 24 h as inputs to predict PM2.5/PM10 concentrations in the future. Besides pre-processing the data, the primary approach to boost the prediction performance is adding layers above a single-layer long short-term memory model. Moreover, it is proved that by doing so, we could let the network make predictions more accurately and efficiently. Furthermore, by comparison, in general, we have obtained a more accurate prediction.

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Section: Introductionmentioning

confidence: 70%

A Haze Prediction Model in Chengdu Based on LSTM

Liu

Yin

et al. 2021

Atmosphere

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“…In the past few decades, a variety of mathematical computational approaches have been implemented in different research fields such as fluid mechanic engineering [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], chemical engineering , electrical engineering , robotics and automation , urban planning engineering [73][74][75][76], petroleum engineering [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94], energy engineering [95,96], mathematics [97][98][99][100][101][102][103][104], environmental engineering [105][106]…”

Section: Introductionmentioning

confidence: 99%

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

et al. 2022

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The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high degree of correlation with the original model. The proposed procedure is considered in three cases: 100 points LUT modeling, 1000 points LUT modeling, and 10,000 points LUT modeling. Indeed, by removing the high-cost functions in the original model, the presented model can be implemented in a low-error, high-speed, and low-area resources state in comparison with the original system. To test and validate the proposed final hardware, a digital FPGA board (Xilinx Virtex-II FPGA board) is used. Digital hardware synthesis illustrates that our presented approach can follow the Izhikevich neuron in a high-speed state (more than the original model), increase efficiency, and also reduce overhead costs. Implementation results show the overall saving of 84.30% in FPGA and also the higher frequency of the proposed model of about 264 MHz, which is significantly higher than the original model, 28 MHz.

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“…Because this study realizes feature matching by constructing a convolutional neural network [16,32], it does not need to calculate the descriptors of each frame when tracking the feature points, which avoids the defects caused by the insufficient performance of the descriptors. The feature points of the tracked category are found in each frame of the endoscope video [33], and the 3D coordinates are recovered by the binocular geometric relationship to complete the tracking. In this paper, the tracking process of feature points can be realized by iterative calculation.…”

Section: Feature Point Trackingmentioning

confidence: 99%

Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

Zhang¹,

Liu

Tian

et al. 2021

Sensors

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At present, feature-based 3D reconstruction and tracking technology is widely applied in the medical field. In minimally invasive surgery, the surgeon can achieve three-dimensional reconstruction through the images obtained by the endoscope in the human body, restore the three-dimensional scene of the area to be operated on, and track the motion of the soft tissue surface. This enables doctors to have a clearer understanding of the location depth of the surgical area, greatly reducing the negative impact of 2D image defects and ensuring smooth operation. In this study, firstly, the 3D coordinates of each feature point are calculated by using the parameters of the parallel binocular endoscope and the spatial geometric constraints. At the same time, the discrete feature points are divided into multiple triangles using the Delaunay triangulation method. Then, the 3D coordinates of feature points and the division results of each triangle are combined to complete the 3D surface reconstruction. Combined with the feature matching method based on convolutional neural network, feature tracking is realized by calculating the three-dimensional coordinate changes of the same feature point in different frames. Finally, experiments are carried out on the endoscope image to complete the 3D surface reconstruction and feature tracking.

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A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network

Cited by 95 publications

References 43 publications

A Haze Prediction Model in Chengdu Based on LSTM

A Haze Prediction Model in Chengdu Based on LSTM

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

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