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Latest articles for Measurement Science and Technology

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  • Prediction of SO2 concentration at desulfurization outlet of thermal power units based on reliefF-SC and ISAO-ARELM
    The flue gas desulfurization (FGD) system of thermal power units operates under complex conditions and exhibits significant nonlinearity. Establishing an accurate prediction model for outlet SO2 concentration is crucial for optimizing the control of the FGD system. The study constructs an autoregressive limit learning machine (ARELM) prediction model for SO2 concentration at the desulfurization outlet of thermal power units, leveraging the improved feature selection algorithm ReliefF-SC and the improved snow ablation optimizer (ISAO). Initially, the time delays of the input variables are compensated and the ReliefF-SC algorithm, which incorporates the Spearman correlation coefficient and cosine similarity, is designed to obtain the optimal feature set for predicting SO2 concentration at the outlet. To enhance the extreme learning machine (ELM)’s capacity to process time-series data, the AR concept is integrated into the ELM framework. Furthermore, to mitigate the impact of random initialization of ARELM network parameters on model stability, the ISAO algorithm is proposed by introducing the sine–cosine position update strategy and adaptive adjustment of subpopulation size. Finally, experimental validation is conducted using actual plant operation data. The results indicate that the established SO2 concentration prediction model for desulfurization outlets of thermal power units is highly accurate and offers valuable theoretical guidance and technical support for optimizing the control of the FGD systems.

  • Augmented deep transfer learning for SRP condition monitoring via physically-informed WGAN-GP approach
    Traditional dynamometer card sensors are costly and complex, making them unsuitable for real-time sucker rod pumping (SRP) well diagnostics. Recently, SRP diagnosis models using motor power curves offer an alternative, but irregular power curves and limited labeled data present challenges. To address this, we propose a deep transfer model enhanced by Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) data augmentation. First, a physics-driven model reconstructs polished rod torque from motor power, emphasizing fault features. Second, WGAN-GP generates faulty SRP torque-displacement samples to expand training data. Finally, a fully parameter-tuned deep transfer SRP diagnosis framework is established, which improves the automatic learning of advanced fault features and enhances diagnostic accuracy using the augmented dataset. Experiments confirm the model’s superior performance and generalization.

  • S-curve bias optimization of navigation signals based on a pre-distortion method
    Nonideal radio-frequency (RF) emission channels directly affect navigation signal quality. In particular, the deviation in the pseudo-range caused by differences among the RF channels of multiple satellites can decrease user positioning accuracy. Previous studies have mainly focused on designing pre-distortion filters to improve the performance of non-ideal RF channels in navigation signal generators. However, our findings indicate that using a pre-distortion filter alone results in limited improvement of the S-curve bias because the constant-envelope character is compromised, and the nonlinearity of the high-power amplifier (HPA) becomes more pronounced. More applications that require better accuracy require a smaller S-curve bias. This study proposes a method to compensate for the nonideal RF channel by using pre-distortion that considers both the nonlinear HPA and nonideal filter characteristics. The proposed method was validated through numerical analysis and simulations. The results show that the proposed method can reduce the S-curve bias across different receiver configurations. This study provides a reference for improving the quality of navigation signals, particularly in terms of correcting the S-curve bias.

  • Multi-parameter correlation analysis and multi-step prediction of seawater quality based on graph spatio-temporal analysis network
    In recent years, with the increasing pollution of near-shore waters, the water quality pollution incidents have been aggravated, which seriously threatens many aspects of coastal economic development, ecological environment and living health. Therefore, there is an urgent need for an effective method to predict the water quality of near-shore waters. However, due to seasonal changes, ocean currents, biological activities and other factors, the marine environment has strong complexity and uncertainty, which leads to the monitoring data of seawater quality parameters are unstable, non-linear and other characteristics. At the same time, there are interactions between different parameters, so it is not easy to dig deeper into the information in the data, and the accuracy of the existing prediction methods for multi-parameter multi-step prediction of seawater quality is generally low. To solve the above problems, a new graph neural network model is proposed in this paper. The model can effectively extract the local time correlation, global time correlation and spatial correlation in non-Euclidean space of seawater quality parameter data from multiple dimensions. Finally, this paper evaluates the model performance using the seawater parameter data from the near-shore waters of Beibu Gulf, and compared with the five baseline models, the model proposed in this paper shows the best performance in all the defined evaluation indexes.

  • Impact of different range bias corrections on orbit and Earth rotation parameters determination using BDS-3 satellite laser ranging observations
    Satellite laser ranging (SLR) is an important technique that determines geodetic parameters, and its observation processing often calibrates range bias corrections to offset systematic errors. However, the impact of different range bias calibration methods on estimating the BDS-3 satellite orbit and Earth Rotation Parameters (ERP) has not been fully studied. The aim of this study is to explore the impact of employing different SLR range bias corrections on the accuracy of SLR-based BDS-3 satellite orbit and ERP. Eight months of experimental analysis revealed that the station–satellite-pair-dependent range bias correction resulted in the optimal orbit accuracy. Regarding orbit differences relative to precise ephemerides and overlap differences, the 3D root-mean-square (RMS) of satellites manufactured by the China Academy of Space Technology (CAST) are 1.00 and 0.94 m, respectively. The corresponding values of satellites manufactured by the Shanghai Engineering Center for Microsatellites (SECM) are 0.98 and 0.90 m, respectively. The station–satellite-pair-dependent range bias correction performed the best in terms of pole coordinate accuracy. The RMS of the XP and YP differences relative to the International Earth Rotation and Reference Systems Service (IERS) 20 C04 product are 1.32 and 1.41 mas, respectively. The solution using satellite-dependent range bias corrections has the optimal length of day (LOD) accuracy, with a 44.92 μs rms of the LOD difference. However, due to the apparent satellite-related error characteristic reflected in the SLR residual, the station-dependent range bias correction is unsuitable for simultaneously processing the SLR observations of all BDS-3 satellites.