Grey Correlation Analysis of Haze Impact Factor PM2.5

Xu, Jiayi; Liu, Zhixin; Yin, Lirong; Liu, Yan; Tian, Jiawei; Gu, Yang; Zheng, Wenfeng; Yang, Bo; Liu, Shan

doi:10.3390/atmos12111513

Cited by 25 publications

(14 citation statements)

References 39 publications

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“…The algorithm based on data learning can not only learn the feature detector like Quarknetworks, but also learn the feature descriptor. With the improvement of machine learning [20][21][22][23][24], Simoserra et al proposed Deepdesc [25] for key point descriptor learning. This method uses a convolutional neural network to learn the discriminant representation of image blocks (patches), trains a Siamese network with paired inputs, and processes a large number of paired image blocks by combining the random extraction of training sets and the mining strategy for patch pairs that are difficult to classify.…”

Section: Introductionmentioning

confidence: 99%

Soft Tissue Feature Tracking Based on Deep Matching Network

Lu¹,

Liu²,

Hou³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Research in the field of medical image is an important part of the medical robot to operate human organs. A medical robot is the intersection of multi-disciplinary research fields, in which medical image is an important direction and has achieved fruitful results. In this paper, a method of soft tissue surface feature tracking based on a depth matching network is proposed. This method is described based on the triangular matching algorithm. First, we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm. The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set. After the training, the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame. From this matrix, the inter-frame feature matching results can be obtained. In this way, the inter-frame speckle tracking is completed. On this basis, the results of this method are compared with the matching results based on the convolutional neural network. The experimental results show that the proposed method has higher matching accuracy. In particular, the accuracy of the MNIST handwritten data set has reached more than 90%.

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Section: Introductionmentioning

confidence: 99%

Soft Tissue Feature Tracking Based on Deep Matching Network

Lu¹,

Liu²,

Hou³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…As stated earlier, the best model was determined LS-SVM with the input parameters, including pressure, temperature, acentric factor, and critical pressure and temperature. In order to study the influence of input parameters on the dissolved mole fraction of H 2 S in ionic liquids, the relevancy factor was utilized 89 . This relevancy factor ( ) is defined for all independent variables (i) as follows 90 : where , , , , and represent input parameters, an average of inputs, number of the data points, output parameter, and average of output, respectively.…”

Section: Resultsmentioning

confidence: 99%

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Abdi

Hadipoor

Esmaeili-Faraj

et al. 2022

Sci Rep

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Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

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“…Instead, short-term predictions were used as variables to predict long-term concentrations of PM2.5/PM10 [38,39]. In the next step, we will choose the long-time scale and PM2.5 /PM10 concentration content range to increase to the degree of heavy pollution, and conduct the time series analysis of the severe haze areas to study its development mechanism [40,41].…”

Section: Discussionmentioning

confidence: 99%

Haze Prediction Model Using Deep Recurrent Neural Network

et al. 2021

Self Cite

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In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes.

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Grey Correlation Analysis of Haze Impact Factor PM2.5

Cited by 25 publications

References 39 publications

Soft Tissue Feature Tracking Based on Deep Matching Network

Soft Tissue Feature Tracking Based on Deep Matching Network

A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids

Haze Prediction Model Using Deep Recurrent Neural Network

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