Yuxuan Zhao^1,2,*, Bo Wang¹, Shu Wang¹, Wenjun Xu², Gang Ma²

Energy Engineering, Vol.121, No.12, pp. 3711-3733, 2024, DOI:10.32604/ee.2024.054032 - 22 November 2024

Abstract The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather… More >

Yuefan Liu, Ducheng Zhang, Chen Guo^*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3281-3299, 2024, DOI:10.32604/cmc.2024.057294 - 18 November 2024

Abstract Craniocerebral injuries represent the primary cause of fatalities among riders involved in two-wheeler accidents; nevertheless, the prevalence of helmet usage among these riders remains alarmingly low. Consequently, the accurate identification of riders who are wearing safety helmets is of paramount importance. Current detection algorithms exhibit several limitations, including inadequate accuracy, substantial model size, and suboptimal performance in complex environments with small targets. To address these challenges, we propose a novel lightweight detection algorithm, termed GL-YOLOv5, which is an enhancement of the You Only Look Once version 5 (YOLOv5) framework. This model incorporates a Global DualPooling… More >

Liang Yang^1,2, Jie Zhang¹, Jinfeng Xia¹, Siwen Zhang¹, Yang Yang³, Zhigang Chu^2,*

Sound & Vibration, Vol.58, pp. 171-183, 2024, DOI:10.32604/sv.2024.056968 - 21 October 2024

Abstract Helmholtz resonators are widely used to control low frequency noise propagating in pipes. In this paper, the elastic bottom plate of Helmholtz resonator is simplified as a single degree of freedom (SDOF) vibration system with acoustic excitation, and a one-dimensional lumped-parameter analytical model was developed to accurately characterize the structure-acoustic coupling and sound transmission loss (STL) of a Helmholtz resonator with an elastic bottom plate. The effect of dynamical parameters of elastic bottom plate on STL is analyzed by utilizing the model. A design criterion to circumvent the effect of wall elasticity of Helmholtz resonators More >

Licheng Sun¹, Heping Li^2,3, Liang Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4543-4560, 2024, DOI:10.32604/cmc.2024.055115 - 12 September 2024

Abstract It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents, such as construction sites and mine tunnels. Although existing methods can achieve helmet detection in images, their accuracy and speed still need improvements since complex, cluttered, and large-scale scenes of real workplaces cause server occlusion, illumination change, scale variation, and perspective distortion. So, a new safety helmet-wearing detection method based on deep learning is proposed. Firstly, a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details… More >

Yahia Said^1,*, Yahya Alassaf², Refka Ghodhbani³, Yazan Ahmad Alsariera⁴, Taoufik Saidani³, Olfa Ben Rhaiem⁴, Mohamad Khaled Makhdoum¹, Manel Hleili⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 733-749, 2024, DOI:10.32604/cmes.2024.052369 - 20 August 2024

Abstract Enhancing road safety globally is imperative, especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations. Acknowledging the critical role of helmets in rider protection, this paper presents an innovative approach to helmet violation detection using deep learning methodologies. The primary innovation involves the adaptation of the PerspectiveNet architecture, transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone, aimed at bolstering detection capabilities. Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset (IDD) for training and validation, the system demonstrates exceptional More >

Qunyue Mu^1,2, Qiancheng Yu^1,2,*, Chengchen Zhou^1,2, Lei Liu^1,2, Xulong Yu^1,2

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 449-466, 2024, DOI:10.32604/cmc.2024.051728 - 18 July 2024

Abstract Wearing helmets while riding electric bicycles can significantly reduce head injuries resulting from traffic accidents. To effectively monitor compliance, the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles. However, manual enforcement by traffic police is time-consuming and labor-intensive. Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques. This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles, addressing these challenges. The More >

Zerui Xiao, Wei Liu, Zhiwei Ye^*, Jiatang Yuan, Shishi Liu

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1009-1029, 2024, DOI:10.32604/csse.2023.040475 - 17 July 2024

Abstract Nowadays, construction site safety accidents are frequent, and wearing safety helmets is essential to prevent head injuries caused by object collisions and falls. However, existing helmet detection algorithms have several drawbacks, including a complex structure with many parameters, high calculation volume, and poor detection of small helmets, making deployment on embedded or mobile devices difficult. To address these challenges, this paper proposes a YOLOv5-based multi-head detection safety helmet detection algorithm that is faster and more robust for detecting helmets on construction sites. By replacing the traditional DarkNet backbone network of YOLOv5s with a new backbone… More >

Chen Xing¹, Leihua Yao^1,*, Yingdong Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2825-2848, 2024, DOI:10.32604/cmes.2024.049330 - 08 July 2024

Abstract In pumped storage projects, the permeability of rock masses is a crucial parameter in engineering design and construction. The rock mass permeability coefficient (K) is influenced by various geological parameters, and previous studies aimed to establish an accurate relationship between K and geological parameters. This study uses the improved sparrow search algorithm (ISSA) to optimize the parameter settings of the deep extreme learning machine (DELM), constructing a prediction model with flexible parameter selection and high accuracy. First, the Spearman method is applied to analyze the correlation between geological parameters. A sample database is built by comprehensively… More >

Jing Gao^*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158 - 20 June 2024

Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

Zhuo Chen^1,*, Ningning Wang², Wenbo Jin³, Dui Li¹

Energy Engineering, Vol.121, No.4, pp. 1007-1026, 2024, DOI:10.32604/ee.2023.045270 - 26 March 2024

Abstract A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines. To ensure the safe operation of crude oil pipelines, an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines. Aiming at the shortcomings of the ENN prediction model, which easily falls into the local minimum value and weak generalization ability in the implementation process, an optimized ENN prediction model based on the IRSA is proposed. The validity of the new model was confirmed by the accurate prediction of two sets of… More > Graphic Abstract