Research on Green Management Effect Evaluation of Power Generation Enterprises in China Based on Dynamic Hesitation and Improved Extreme Learning Machine

Qin, Yunfu; Li, Menglu; De, Gejirifu; Huang, Liling; Yang, Shenbo; Tan, Qinkun; Tan, Zhongfu; Zhou, Feng’ao

doi:10.3390/pr7070474

Cited by 8 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…S. Mojrian [61] used the radial-base-function (RBF) kernel of the Extreme Learning Machine (ELM) classification model with the Online Support Vector Machine and other baseline models. The Extreme Learning Machine with RBF kernel performs well under the evaluation metrics of accuracy, precision, mindfulness, and specificity; meanwhile, the algorithm of RBF-ELM proposed by Y. Qin [62] is validated on a lowcarbon engineering problem, which further illustrates the computational classification advantages brought by RBF in mapping input vectors to a high-dimensional special space. The RBF formulation is shown in Equation ( 5), where σ is the kernel parameter:…”

Section: Establishment Of the Sunflower Origin Identification Model 2...mentioning

confidence: 92%

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

Suo,

Liu,

et al. 2024

Agronomy

View full text Add to dashboard Cite

This study constructs a model for the rapid identification of the origins of edible sunflower (Helianthus) using Kernel Extreme Learning Machine (KELM) with multi-source information fusion technology. Near-infrared spectroscopy (NIRS) and nuclear magnetic resonance spectroscopy (NMRS) were utilized to analyze 180 sunflower samples from the Xinjiang, Heilongjiang, and Inner Mongolia regions. Initially, the identification models for the origin of sunflowers using NIR and NMR data were compared between two algorithms: the Extreme Learning Machine (ELM) and KELM, combined with various spectral preprocessing methods. The experiment found that the NIR spectral model preprocessed with standard normal variate (SNV) using the KELM algorithm was the most accurate, achieving accuracies of 98.7% in the training set and 97.2% in the test set. The spin-echo NMR spectral model preprocessed with non-local means (NLMs) using the KELM algorithm was the second best, with accuracies of 98.4% in the training set and 96.4% in the test set. To further improve the accuracy of the identification models, innovative sunflower origin identification models were developed based on data layer fusion and feature layer fusion using NIRS and NMRS. In the data layer fusion model, the KELM algorithm model was optimal, achieving a test set accuracy and F1 score of 98.2% and 98.18%, respectively, an improvement of 1.0% over the best single data source model. In the feature layer fusion model, four types of feature-layer information-fusion identification models were established using two feature extraction algorithms, Competitive Adaptive Reweighted Sampling (CARS) and Variable Importance Projection (VIP), combined with joint feature and simple merging feature strategies. The CARS-KELM algorithm combined with the joint feature method was found to be the best, achieving 100% accuracy in both the training and test sets, an improvement of 2.8% over the best single data source model. Identifying the origin of edible sunflower using NIRS and NMRS is demonstrated as feasible by the results. The best single-spectrum sunflower origin identification model was achieved using the KELM algorithm with SNV preprocessing. The feature layer fusion method combining NIRS and NMRS data is suitable for handling the task of sunflower origin identification. This method significantly improves the recognition accuracy of the model compared to a single model, achieving fast and accurate origin identification of edible sunflowers. The research results provide a new method for rapid identification of sunflower origin.

show abstract

Section: Establishment Of the Sunflower Origin Identification Model 2...mentioning

confidence: 92%

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

Suo,

Liu,

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

“…A comprehensive set of manufacturer evaluation criteria in utilizing AHP was constructed by Hossein [14], aiming to allocate different weight proportions to eight evaluation indicators. Qin [15], who conducted research on the green operation benefits of power generation enterprises, proposed an intuitionistic fuzzy comprehensive analytic hierarchy process based on improved dynamic hesitation degree (D-IFAHP) and an improved extreme learning machine algorithm optimized by the RBF (radial basis function) kernel function (RELM), which simplifies the manufacturing evaluation process. Another expert scoring method is the Delphi method, which has been mentioned in the context of smart shipping [16] and smart health [17], but has not been applied to smart manufacturer evaluation.…”

Section: State Of the Art Of Smart Manufacturer Evaluationmentioning

confidence: 99%

The Evaluation Technology of Manufacturer Intelligence Regarding the Selection of the Decision Support System of Smart Manufacturing Technologies: Analysis of China–South Africa Relations

et al. 2023

View full text Add to dashboard Cite

With the development of international cooperation, South Africa (SA) has been China’s largest trading partner in Africa for several consecutive years. China and SA can build the digital “Belt and Road” to modernize the manufacturing system locally and optimize process control by benchmarking with the best-in-class manufacturers in each country. In this research, an evaluation technology of manufacturer intelligence regarding the selection of decision support system (DSS) of smart manufacturing technologies, analyzing China–South Africa relations, is described. Firstly, the three keys aspects that enable the technologies of DSS are discussed in detail. Then, one key technology, the manufacturers’ intelligent evaluation system with 15 indexes, was built. The indexes and their measurements are also proposed. Finally, a fusion method based on boosting with multi-kernel function (online sequential extreme learning machine based on boosting, Boosting-OSELM) is introduced. The purpose of Boosting-OSKELM is to combine several weak learners into a strong learner (lower mean square error, MSE) through an acceptable time delay. Finally, the case study is presented to demonstrate the improvement on the MSE and process time, showing a relative MSE improvement of 96.19% and a relative time delay ratio of 31.46%. Totally, the largest contribution of the proposed evaluation method in this study is the conversion of the history data saved by the manual scoring method into knowledge in accessible MES and resealable time delay, which will free up the expert workforce in the entire process. We expect this paper will help future research in this field.

show abstract

“…Experiment results from UCI data revealed that their method was better in generalization compared to original ELM. Qin and Li [139] applied ELM to evaluate the green management in power generation enterprises in China. Firstly, an evaluation indicator system for low carbon sustainability was created by an improved dynamic hesitation degree.…”

Section: Chemistry Applicationmentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

271

View full text Add to dashboard Cite

Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

show abstract

Research on Green Management Effect Evaluation of Power Generation Enterprises in China Based on Dynamic Hesitation and Improved Extreme Learning Machine

Cited by 8 publications

References 18 publications

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

The Evaluation Technology of Manufacturer Intelligence Regarding the Selection of the Decision Support System of Smart Manufacturing Technologies: Analysis of China–South Africa Relations

A review on extreme learning machine

Contact Info

Product

Resources

About