Intelligent quantification of natural gas pipeline defects using improved sparrow search algorithm and deep extreme learning machine

Xiong, Jingyi; Liang, Wei; Liang, Xiaobin; Yao, Junqiang

doi:10.1016/j.cherd.2022.06.001

Cited by 25 publications

(6 citation statements)

References 12 publications

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“…Xiong et al [20] used an adaptive T-distribution mutation operator in conjunction with an opposition-based learning technique to optimize the SSA. The Deep Extreme Learning Machine's (DELM) input layer weights and bias settings were optimized using this method.…”

Section: Application Of Sparrow Algorithm In Deep Learningmentioning

confidence: 99%

Overview and Application of Sparrow Search Algorithm in Deep Learning

Wang,

Fei,

Liu

et al. 2024

Frontiers in Artificial Intelligence and Applications

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A brand-new swarm intelligence optimization algorithm called the Sparrow Search Algorithm (SSA) was put forth in 2020. By simulating the foraging process of sparrows, SSA efficiently solves optimization problems, exhibiting advantages such as faster convergence and excellent optimization abilities. This paper introduces the basic principles of the Sparrow Search Algorithm, analyzes its existing issues, summarizes improvements made to existing Sparrow algorithms, and then discusses the application of SSA in deep learning.

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Section: Application Of Sparrow Algorithm In Deep Learningmentioning

confidence: 99%

Overview and Application of Sparrow Search Algorithm in Deep Learning

Wang,

Fei,

Liu

et al. 2024

Frontiers in Artificial Intelligence and Applications

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“…Furthermore, ELM-AE, which integrates ELM and autoencoder, inherits the stronger adaptability and learning ability of ELM [21,22]. When solving deep engineering problems, the weights or thresholds of the DELM can be improved using optimization algorithms [23]. The SSA was proposed by [24] in 2020 by simulating sparrow predation and defensive behavior [25].…”

Section: Introductionmentioning

confidence: 99%

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Zhou,

Xiao,

et al. 2024

Meas. Sci. Technol.

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Accurate tool wear monitoring is crucial for increasing tool life and machining productivity. Although many prediction models can achieve high prediction accuracy, there are problems such as poor stability in the face of different working conditions or tool signals. A tool wear prediction method based on improved deep extreme learning machines (DELM) was proposed as a solution to this issue; it uses the sparrow search algorithm (SSA) to upgrade the input weight of DELM to improve the model, and then extracts the time-domain, frequency-domain, and time-frequency domain characteristics from multi-sensor signals to construct and test the improved model SSA-DELM. The verification results show that the proposed model accurately reflects the tool wear. Meanwhile, the RMSE of the proposed model decreased by 53.39%, 19.95%, 43.86%, 23.80%, 24.80%, and 3.72%, respectively, and the MAE decreased by 67.81%, 17.87%, 32.70%, 29.90%, 30.30%, and 6.78%, respectively, compared to the with unimproved DELM, PSO-LSSVM, LSTM, SSAE, RNN, and DBO-DELM.

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“…Deep Extreme Learning Machine (DELM) is a deep learning model based on Extreme Learning Machine (ELM). Compared to traditional deep learning models, DELM does not require backpropagation algorithms and therefore has faster training speed and better generalisation performance [19]. Grey Wolf Optimizer (GWO), a swarm intelligence optimisation algorithm proposed in 2014 by Mirjalili et al, scholars from Griffith University, Australia.…”

Section: Introductionmentioning

confidence: 99%

Research on Employee Performance Management Method Based on Big Data Improvement GWO-DELM Algorithms

Wang,

Liu

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: Accurate and objective human resources performance management evaluation methods are conducive to a comprehensive understanding of the real and objective situation of teachers, and are conducive to identifying the management, teaching and academic level of teachers, which enables teacher managers to have a clear understanding of the gaps and problems among teachers. OBJECTIVES: Aiming at the current human resources performance management evaluation method, there are evaluation indexes exist objectivity is not strong, poor precision, single method and other problems. METHODS: This research puts forward an intelligent optimisation algorithm based on the improvement of the depth of the limit of the learning machine network of human resources performance management evaluation method. (1) Through the analysis of the problems existing in the current human resources performance management, select the human resources performance management evaluation indexes, and construct the human resources performance management evaluation system; (2) Through the multi-strategy grey wolf optimization algorithm method to improve the deep learning network, and construct the evaluation model of the human resources performance management in colleges; (3) The analysis of simulation experiments verifies the high precision and real-time nature of the proposed method. RESULTS: The results show that the proposed method improves the precision of the evaluation model, improves the prediction time. CONCLUSION: This research solves the problems of low precision and non-objective system indicators of human resource performance management evaluation.

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Intelligent quantification of natural gas pipeline defects using improved sparrow search algorithm and deep extreme learning machine

Cited by 25 publications

References 12 publications

Overview and Application of Sparrow Search Algorithm in Deep Learning

Overview and Application of Sparrow Search Algorithm in Deep Learning

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

Research on Employee Performance Management Method Based on Big Data Improvement GWO-DELM Algorithms

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