Vehicle exhaust prediction is of great importance for exhaust emissions control and public environment protection, and very challenging because it is influenced by various complex factors, including multi-component temporal dependencies (closeness, period, trend) and external factors, e.g., meteorological data and workday information. To address these issues, we propose a novel deep-learning-based framework, called multi-component fusion temporal networks (MCFT-Net), to collectively forecast emission concentration of carbon monoxide (CO). The proposed MCFT-Net consists of three parts, 1) a one-dimensional Convolution Neural Networks (1D-CNNs) module to capture temporal dependencies of vehicle emissions by merging closeness, period, and trend time sequences; 2) an artificial neural network (ANN) module to capture features of external factors, such as wind direction, weekday and weekend; and 3) a cascade fusion component to merge the temporal patterns, which are derived from temporal dependencies processed by multiple 1D-CNNs, and then assigned different weights according to an attention mechanism after fusion. The output of the attention operation is further combined with external features through sum and average operations to predict the final emission concentration. We evaluate the proposed MCFT-Net using two real-world vehicle exhaust datasets, ExhaustBMS and ExhaustHMS, which were collected from Beijing monitoring station (BMS) and Hefei monitoring station (HMS). The results demonstrate that our MCFT-Net outperforms 8 baseline methods, such as ARIMA, RNN, GRU, BiLSTM.

This paper aims to estimate the carbon monoxide (CO) and hydrocarbon (HC) concentrations of vehicle exhaust. For this purpose, an improved Stacking model is designed. Compared with individual estimation models, the improved Stacking model can achieve better concentration estimation performance. That model has a three-layer structure. The first layer is made up of multiple estimation models, which produce intermediate estimation results from the original exhaust data based on the K -fold cross-validation. The second layer takes these intermediate estimation results as input and trains a statistical learning model which generates preliminary estimation results of the concerned exhaust concentrations. The first two layers actually constitute the Stacking model, which is extended by the additional third layer of our improved one. The third layer implements a weighted summation method. More specifically, the preliminary estimation results generated by the second layer are linearly combined with the concentration estimation results of some strong estimation models, such as XGBoost and LightGBM, to produce the final estimation results in the third layer. Our improved Stacking model is verified through experimental data, which were collected by a Urban Road Network Vehicle Emissions Monitoring System and small weather stations in two Chinese cities, including Beijing and Jiaozuo. Experimental results show that compared with some regression and neural network estimation models, especially the Stacking model and Boosting models, our improved Stacking model achieves higher exhaust concentration estimation accuracy.

Rotating mechanical systems (RMSs) are widely applied in various industrial fields. Intelligent fault diagnosis technology plays a significant role in improving the reliability and safety of industrial equipment. A new algorithm based on improved multiscale fuzzy entropy and support vector machine (IMFE-SVM) is proposed for the automatic diagnosis of various fault types in elevator rotating mechanical systems. First, the empirical mode decomposition (EMD) method is utilized to construct a decomposition model of the vibration data for the extraction of relevant parameters related to the fault feature. Secondly, the improved multiscale fuzzy entropy (IMFE) model is employed, where the scale factor of the multiscale fuzzy entropy (MFE) is extended to multiple subsequences to resolve the problem of insufficient coarse granularity in the traditional MFE. Subsequently, linear discriminant analysis (LDA) is applied to reduce the dimensionality of the extracted features in order to overcome the problem of feature redundancy. Finally, a support vector machine (SVM) model is utilized to construct the optimal hyperplane for the diagnosis of fault types. Experimental results indicate that the proposed method outperforms other state-of-the-art methods in the fault diagnosis of elevator systems.

With the rapid development of global industrial production, the demand for reliability in power equipment has been continuously increasing. Ensuring the stability of power system operations requires accurate methods to detect potential faults in power equipment, thereby guaranteeing the normal supply of electrical energy. In this article, the performance of YOLOv5, YOLOv8, YOLOv9, YOLOv10, and the state-of-the-art YOLOv11 methods was comprehensively evaluated for power equipment object detection. Experimental results demonstrate that the mean average precision (mAP) on a public dataset for power equipment was 54.4%, 55.5%, 43.8%, 48.0%, and 57.2%, respectively, with the YOLOv11 achieving the highest detection performance. Moreover, the YOLOv11 outperformed other methods in terms of recall rate and exhibited superior performance in reducing false detections. In conclusion, the findings indicate that the YOLOv11 model provides a reliable and effective solution for power equipment object detection, representing a promising approach to enhancing the operational reliability of power systems.