Sustainable development early warning and financing risk management of resource-based industrial clusters using optimization algorithms

Wang, Yawen; Xue, Weixian

doi:10.1108/jeim-03-2021-0152

Cited by 9 publications

(6 citation statements)

References 42 publications

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“…It gradually eliminates solutions with low fitness values and increases solutions with high fitness values. After evolving for N generations, it is highly likely to obtain individuals with high fitness values, which represent the optimal results of the objective function [30,31]. The steps for selecting the optimal kernel function parameters and penalty factor using the genetic algorithm are as follows.…”

Section: Svr After Pca Of Pid Signal Featuresmentioning

confidence: 99%

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

Feng,

Liu,

Ren

et al. 2024

Chemosensors

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In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring volatile organic compounds (VOCs) using a photoionization detector (PID). This study is based on the PID water-soil-gas VOC online monitor developed by this group, online monitoring of the concentration of different constituents of VOCs in different production enterprises of the petroleum and chemical industries in Shandong Province, with the concentration of the laboratory test, to build a relevant model. The correlation coefficient about the PID test concentration and the actual concentration correlation coefficient was obtained through the collection of a large number of data trainings. Based on the application of PID in VOC monitoring, the establishment of a PID high-precision calibration model is important for the precise monitoring of VOCs. In this paper, multiple quantitative analyses were conducted, based on SVM regression of PID response to VOC signals, to study the high-precision VOC monitoring method. To select the response signals of PID under different concentrations of environmental VOCs measured by the research group, first, the PID response to VOC signals was modeled using the support vector machine principle to verify the effect of traditional SVM regression. For the problem of raw data redundancy, calculate the time-domain and frequency-domain characteristics of the PID signal, and conduct the principal component analysis of the time-domain of the PID signal. In order to make the SVM regression more generalized and robust, the selection of kernel function parameters and penalty factor of SVM is optimized by genetic algorithm. By comparing the accuracy of PID calibration models such as PID signal feature extraction, SVM regression, and principal component analysis SVM regression, the superiority of photoionization detector using the signal feature extraction PCA-GA-SVM method to monitor VOCs is verified.

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Section: Svr After Pca Of Pid Signal Featuresmentioning

confidence: 99%

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

Feng,

Liu,

Ren

et al. 2024

Chemosensors

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show abstract

“…It gradually eliminates solutions with low fitness values and increases solutions with high fitness values. After evolving for N generations, it is highly likely to obtain individuals with high fitness values, which represent the optimal results of the objective function [26]. The steps for selecting the optimal kernel function parameters and penalty factor using genetic algorithm are as follows.…”

Section: Svr After Pca Of Pid Signal Featuresmentioning

confidence: 99%

Monitoring Method of VOCs Based on PID in Soil-water-gas Environment

Dong,

Ren,

Zhang

et al. 2024

Preprint

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In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring Volatile Organic Compounds (VOCs) using a Photoionization Detector (PID). This paper analyzes the reasons for the low accuracy of the traditional Support Vector Machine (SVM) regression method. To address the issue, the PID signal is subjected to feature extraction and Principal Component Analysis (PCA) to reduce the data dimensionality. Moreover, the optimal SVM parameters are selected using a Genetic Algorithm (GA), and a combined approach of SVM regression with PCA and GA is utilized for PID signal regression analysis. And the effectiveness of the method is validated through extensive experiments and simulations. Furthermore, the influence of the sample quantity on the regression accuracy is analyzed, enabling accurate monitoring of VOCs concentration in a moist environment.

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“…The data interface adopts the mainstream 16-channel structure design and imports the CIM model directly from the grid system and the monitoring module to get all the original static data and real-time grid operation information for fault analysis and processing [6] . The Hadoop data analysis system is based on the open source framework developed by Apahce, using a distributed HDFS system and MapReduce module.…”

Section: Hardware Design Of Intelligent Early Warning System For Elec...mentioning

confidence: 99%

Intelligent early warning system for power theft in distribution network based on IOT technology

Heng

2023

3rd International Conference on Internet of Things and Smart City (IoTSC 2023）

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Because the existing methods ignore the influence of the boundary characteristics of the disturbance stability region, the early warning effect is not good. For this reason, an intelligent early warning system for power theft in distribution network based on Internet of Things technology is proposed. The hardware part of the system is composed of monitoring module, human-machine monitoring module and early warning display module, which eliminates interference data and noise data and improves the accuracy of finding abnormal grids. In the software part of the system, the boundary characteristics of the stability region of the electric theft risk are extracted, and the big data parameter fusion model of the risk assessment is established to achieve the quantitative assessment and decision-making of the theft risk. Finally, the improved equivalent resistance method is used to calculate the theoretical line loss of the distribution line characteristics, analyze the fluctuation of the parallel line loss, and then compare the time coincidence of the line loss and the change point of the current difference curve with Pearson correlation coefficient to realize the identification of the power theft risk, further improving the accuracy of the early warning. The experimental results show that when the method is applied to the early warning of electricity theft, the MAPE value of the system is less than 0.1%, which has a better early warning accuracy.

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Sustainable development early warning and financing risk management of resource-based industrial clusters using optimization algorithms

Cited by 9 publications

References 42 publications

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

Monitoring Method of VOCs Based on PID in Soil-water-gas Environment

Intelligent early warning system for power theft in distribution network based on IOT technology

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