Fujian Metrology Institute

With the rapid development of industry, landfill and other environmental problems have arisen due to the coal mining and industrial solid waste generated during coal extraction and industrial production. In this study, coal gangue was utilized as the filling aggregate, along with industrial solid waste as the principal constituent, supplemented by cement, to develop a novel type of cementitious material and address environmental problems arising from the storage of solid waste. The impacts of sodium silicate, lime, and cement on the excitation characteristics and micro-evolution of steel slag–slag-based composite cementitious materials were investigated through experimental proportioning. The mineral composition, chemical composition, particle size distribution, microstructure, and hydration products of the filling materials were analyzed through XRD, XRF, a laser particle size analyzer, and SEM. The results show the following: (1) When the mass ratio of steel slag, slag, cement, sodium silicate, and lime is 30:38:15:2:15, the compressive strength of the Cemented Gangue Filling Body (CGFB) reaches the optimum level. At this juncture, the compressive strength of CGFB at 3 days is 2.16 MPa, and that at 28 days is 4.18 MPa. (2) Na2SiO3 and lime can activate the latent active substances within slag and steel slag, generating C-S-H gel and AFt through hydration reaction. (3) As the curing time escalates, the microstructure of the filling body becomes increasingly compact, and the porosity decreases from 10.5% to 3.8%. This study not only presents a new technical means for the resource treatment of solid waste such as coal gangue but also provides powerful support for the development and application of mine filling materials.

Dynamic characteristics play a crucial role in evaluating the performance of weight sensors and are essential for achieving fast and accurate weight measurements. This study focuses on a weight sensor based on optical coherence displacement. Using finite element analysis, the sensor was numerically simulated. Frequency domain and time domain dynamic response characteristics were explored through harmonic response analysis and transient dynamic analysis. The superior dynamic performance and reduced conditioning time of the non-contact optical coherence-based displacement weight sensor were confirmed via a negative step response experiment that compared the proposed sensing method to strain sensing. Moreover, dynamic performance metrics for the optical coherence displacement-type weight sensor were determined. Ultimately, the sensor’s dynamic performance was enhanced using the pole-zero placement method, decreasing the overshoot to 4.72% and reducing the response time to 0.0132 s. These enhancements broaden the sensor’s operational bandwidth and amplify its dynamic response capabilities.

Abstract In order to address the issue of metering inaccuracies in charging stations that directly affect the development of electric vehicles, a prediction method for the relative error of charging stations based on the ConvFormer model is proposed. First, charging station data is preprocessed using forward interpolation and normalization methods, and the dataset is transformed into a dataset of input relative errors. Then, a neural network with an improved unidirectional convolutional and attention combination for time-series forecasting is constructed, and common regression performance evaluation metrics, MAE and MSE, are selected for evaluation. Finally, based on seven days of charging station data, the relative error of charging stations for the next 24 hours is predicted, and compared to traditional Transformer and LSTM (Long Short-Term Memory) time-series models. The results show that the improved model yields the lowest values for both MAE and MSE, with a 57.05% reduction in MAE compared to the Transformer model and a 51.48% reduction compared to LSTM, and a 66.95% reduction in MSE compared to the Transformer model and approximately 62.23% reduction compared to LSTM.