A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm

Ge, Hengqing; Xu, Guang; Huang, Jinxin; Ma, Xiaoping

doi:10.1177/1687814019829281

Cited by 3 publications

(2 citation statements)

References 17 publications

(21 reference statements)

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“…Peng and Liu (2011) studied the pth moment stability of stochastic Grossberg-Hopfield neural networks. Besides the mentioned articles, there are other applications in which ml is used to forecast direction volatility, predict requirement change volatility, analyze the profitability of a trading strategy and other applications (Tino et al 2001;Bekiros & Georgoutsos, 2008;Medeiros et al, 2008;Xia et al, 2011;Zhu et al, 2013;Ge et al, 2019;Wang & Liu, 2020;Patnaik, 2020;Vrontos et al, 2021;Hein et al, 2021;Arvin et al, 2021;Rahman et al, 2018;Xu, 2021).…”

Section: Other Applicationsmentioning

confidence: 99%

Use of Machine Learning in Volatility: A Review Using K-Means

Muñoz

Castañeda²

2023

rev.univ.empresa

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Recently, the use of machine learning (ML) in scientific disciplines has experienced an unprecedented increase. Finance has not been an exception. Several works have been published in recent years using mltechniques. However, one of the topics with the least number of developed papers is volatility in this context. Nevertheless, the data analyzed here suggest changes regarding this issue. Data obtained from the Web of Science database show that between 2001 and 2010 there were 33 published papers associated with this topic. Surprisingly, between 2019 and 2023, 189 manuscripts have been published related to this topic. The purpose of this work is to review the works related to the applications of ml in volatility. For this, a classification of the main proposals on this topic is proposed following a narrative methodology, accompanied by a statistical and bibliometric analysis in which novel techniques such as K-means were used. The results are suggestive. Although most papers focus on volatility prediction through neural networks and support vector machines, there is a lack of studies related to volatility transmission, calibration of volatility surfaces,and corporate finance. Moreover, the obtained results indicate that there is a gap in the production of worksrelated to these topics in finance and economics specialized journals.

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Section: Other Applicationsmentioning

confidence: 99%

Use of Machine Learning in Volatility: A Review Using K-Means

Muñoz

Castañeda²

2023

rev.univ.empresa

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“…is fresh air dilutes and discharges harmful gases and dust such as gas and carbon monoxide and ensures the safe operation of the coal mine. e performance and operating conditions of the main fan directly determine the safety of mine workers and the production efficiency in the coal mine [1][2][3]. e performance of the main fan, in turn, depends largely on the performance of its spindle bearings.…”

Section: Introductionmentioning

confidence: 99%

Vibration Reliability Evaluation of Main Fan Spindle Bearings

Pang

Qiu

Liang

et al. 2019

Shock and Vibration

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Based on an analysis of failure causes, the axial force is the main reason why spindle bearings vibrate abnormally and cause frequent failures of the main fans in mines. In this paper, the probability density function of the spindle bearing vibration was established through the bootstrap maximum entropy method, the Lagrangian multiplier and the empirical coefficient were introduced, and models of the upper- and lower-bound functions of the probability density and the true value function of the failure probability were established. The true value function and interval estimation values of vibration reliability were obtained on the basis of maximum entropy. The analysis results showed that the estimation true values of vibration decreased with an increase in the empirical coefficient. When the empirical coefficient was less than 0.1, the estimated true value decreased sharply. When the empirical coefficient was greater than 0.1, the vibration value decreased linearly with the empirical coefficient. When the empirical coefficient was 0.1, the estimated true value was 0.85 mm/s2, which is consistent with the change in the actual vibration value. Therefore, when the main fan is fault predicted, the vibration value should be 0.85 mm/s2 as the basis for judgment and then the theoretical basis for the fault diagnosis of the spindle bearing are provided.

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