Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses

Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin’geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

doi:10.1016/j.chemosphere.2017.04.015

Cited by 73 publications

(19 citation statements)

References 43 publications

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“…Most statistical calculations are performed using linear regression models, which have been frequently applied in different fields [18][19][20]. Almost every discipline utilizes regression analyses as a basis for comparing improved models [21][22][23].…”

Section: Multiple Linear Regression Modelmentioning

confidence: 99%

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

Yin

Jia

et al. 2018

Water

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Water-energy nexus has been a popular topic of rese arch in recent years. The relationships between the demand for water resources and energy are intense and closely connected in urban areas. The primary, secondary, and tertiary industry gross domestic product (GDP), the total population, the urban population, annual precipitation, agricultural and industrial water consumption, tap water supply, the total discharge of industrial wastewater, the daily sewage treatment capacity, total and domestic electricity consumption, and the consumption of coal in industrial enterprises above the designed size were chosen as input indicators. A feedforward artificial neural network model (ANN) based on a back-propagation algorithm with two hidden layers was constructed to combine urban water resources with energy demand. This model used historical data from 1991 to 2016 from Wuxi City, eastern China. Furthermore, a multiple linear regression model (MLR) was introduced for comparison with the ANN. The results show the following: (a) The mean relative error values of the forecast and historical urban water-energy demands are 1.58 % and 2.71%, respectively; (b) The predicted water-energy demand value for 2020 is 4.843 billion cubic meters and 47.561 million tons of standard coal equivalent; (c) The predicted water-energy demand value in the year 2030 is 5.887 billion cubic meters and 60.355 million tons of standard coal equivalent; (d) Compared with the MLR, the ANN performed better in fitting training data, which achieved a more satisfactory accuracy and may provide a reference for urban water-energy supply planning decisions.

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Section: Multiple Linear Regression Modelmentioning

confidence: 99%

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

Yin

Jia

et al. 2018

Water

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“…This research focuses on constructing an SF 6 component feature set for DC-GIE fault diagnosis and uses minimum-redundancy-maximum-relevance (mRMR) [18]- [21] criterion to filter the constructed component feature set. Back propagation neural network (BPNN) [22]- [25] is used for fault identification to verify the validity of the selected component feature quantity set. To solve the problem of using SF 6 decomposition characteristic quantities to estimate the DC-GIE insulation condition, this research adopts C4.5 algorithm [26], [27] to construct a decision tree model for evaluating the PD condition of typical insulation defect of DC-GIE.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis and Condition Division Criterion of DC Gas Insulating Equipment Based on SF₆ Partial Discharge Decomposition Characteristics

Zeng

Yang

et al. 2019

IEEE Access

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SF 6 decomposition characteristics directly reflect the generation, development, and evolution of internal faults in gas-insulated equipment (GIE). However, establishing the DC-GIE internal insulation condition comprehensive division criterion and fault diagnosis method based on SF 6 decomposition characteristics remains unsolved. Therefore, partial discharge (PD) and SF 6 decomposition characteristics of typical insulation defects from initial discharge to the breakdown process are discussed in this paper. The average discharge magnitude in a second Q sec is selected as an electrical feature to characterize the PD condition, and the PD state is divided into three levels, namely, mild PD, medium PD, and severe PD. SF 6 decomposition experiments are conducted under different PD conditions of each defect, and the SF 6 decomposition characteristics under different failure conditions are obtained. On this basis, we construct five feature sets of 36 characteristic quantities for GIE PD fault diagnosis and analyze the validity. A minimum-redundancymaximum-relevance-based feature selection algorithm is used to optimize the characteristic quantities, and an optimal feature set is selected, i.e., {C(SO 2)/C(T), C(SOF 2)/C(CT), C(SO 2 F 2)/C(ST)}. The characteristic quantities of SF 6 decomposition component, which characterizes PD condition, are reconstructed, and the component characteristic criterion of PD condition division is proposed. Thus, a decision tree evaluation model for the PD condition of the typical insulation defect inside DC-GIE is constructed. This paper lays a foundation for the insulation condition evaluation and fault diagnosis of DC-GIE.

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“…The performance of BPNN is affected by many parameters such as the amount of training data (TD), number of hidden layers (HL), number of neuron nodes (NN), leaning rate (LR), goal accuracy (GA), and active function (AF) [22,23]. To obtain the optimal parameters, extensive simulations were conducted, where BP algorithm will be used to obtain the optimized neural network's weights.…”

Section: Structure Of the Bpnnmentioning

confidence: 99%

Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network

et al. 2017

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Articulated arm coordinate measuring machine (AACMM) is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the accuracy of AACMM. In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and the coordinates of the probe are used as the output of neural network. To avoid tedious training and improve the training efficiency and prediction accuracy, a data acquisition strategy is developed according to the actual measurement behavior in the joint space. A neural network model is proposed and analyzed by using the data generated via Monte-Carlo method in simulations. The structure and parameter settings of neural network are optimized to improve the prediction accuracy and training speed. Experimental studies have been conducted to verify the proposed algorithm with neural network compensation, which shows that 97% error of the AACMM can be eliminated after compensation. These experimental results have revealed the effectiveness of the proposed modeling and compensation method for AACMM.

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Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses

Cited by 73 publications

References 43 publications

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

Fault Diagnosis and Condition Division Criterion of DC Gas Insulating Equipment Based on SF₆ Partial Discharge Decomposition Characteristics

Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network

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Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses

Cited by 73 publications

References 43 publications

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

Comprehensive Forecast of Urban Water-Energy Demand Based on a Neural Network Model

Fault Diagnosis and Condition Division Criterion of DC Gas Insulating Equipment Based on SF6 Partial Discharge Decomposition Characteristics

Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network

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Fault Diagnosis and Condition Division Criterion of DC Gas Insulating Equipment Based on SF₆ Partial Discharge Decomposition Characteristics