Measurement最新文献

Equivalent stiffness is one of the most important mechanical parameters of any concrete structure which allows engineers to predict the behaviour of the structure. This paper identifies the equivalent stiffness parameter through an embedded piezo sensor (EPS) in the strength development of cement paste using the electro-mechanical impedance (EMI) technique through experimental and numerical investigation. The experiments were conducted on the cement paste specimens; and the compressive strength (destructively) and EMI response were obtained during curing process. Further, a numerical model of cement paste specimen with EPS is developed to validate the EMI response. In addition, an equivalent stiffness parameter is identified from the experimental and simulation EMI data. It is found that the variation in conductance signature during curing process and strength development is effectively captured by the EPS. The identified equivalent stiffness either calculated from experimental or simulation data followed a similar behaviour of the compressive strength.

A methodology based on an optimized surrogate model and Non-dominated Sorting Genetic Algorithm III(NSGA-III) algorithm is proposed to address the long design cycle issues that conventional antennas encounter, with fast multiple parameters processing capability. A Temporal Convolutional Network (TCN) network model with high fitting efficiency and a simple structure is first developed as a surrogate model for antenna performance prediction, taking antenna dimensional parameters as input and outputting the reflection coefficient (S11) and gain of the antenna. Then, an adaptive NSGA-III algorithm with enhanced search efficiency is presented. By combing it with the prior TCN surrogate model, the parameter optimization for a 5G millimeter-wave antenna design is realized. Results show that the proposed method achieves a remarkable reduction in Mean Squared Error (MSE) by 12.489 % and 3.277 % respectively, while also presents a decreased running time by 2.670 % and 70.419 % respectively, as compared to the Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network models. The proposed method can rapidly and accurately perform the optimization task of multiple antenna parameters, demonstrating its feasibility and effectiveness for antenna applications requiring wide bandwidth, low loss and high gain.

In this work, the significant characterization of the neutron beam used in the PGNAA facility of the Isfahan MNSR research reactor has been determined using both calculation and measurement. The important parameters of the beam include flux and energy distribution of neutrons, cadmium ratio, thermal neutron content (TNC), and neutron-to-gamma ratio (n/γ). In this regard, Monte Carlo simulation and experiments were implemented to find these characteristics. The experiments have been carried out through the relative and absolute activation methods using irradiation of indium foil and mica in combination with uranium foil. Moreover, the condition of the neutron beam has been investigated in terms of contamination with fast neutrons using the germanium triangle method. The results showed that the MNSR has acceptable characteristics for performing PGNAA analyses despite its low power.

The uniform field coils with large uniform region are important for improving the resolution in bio-magnetic signal measurements. The traditional coil design methods suffer from small space utilization ratio which limits the mobility and accuracy of bio-magnetic signal measurements. In this paper, a novel cubic grid coil (CGC) design method is presented, with significantly improved space utilization ratio and simple structure. The iterative fastest current drop method (IFCD) is proposed to simplify coil structure, thus ensuring the controllability of the cubic grid coil system. The superiority of CGCs has been demonstrated by finite element simulations and a series of experiments. The space utilization ratio of CGCs reaches 22.4% while maintaining 2% inhomogeneity, improved by about 6 times compared with traditional coils. Furthermore, the CGC-based system for directly eliminating the geomagnetic field achieves a low residual magnetic field noise of 29.7 pT/Hz/${}^{1/2}$ at 1 Hz. The CGCs can provide new ideas for high-resolution, lightweight mobile measurements of weak bio-magnetic signals in the human body.

The accurate prediction of Remaining Useful Life (RUL) is crucial for various applications, and the relative position of time steps between features plays a significant role in this process. However, traditional deep learning models often struggle with extracting and positional corresponding information in temporal features, especially in the presence of noise and limited labeled data. To overcome these challenges, we propose a novel semi-supervised Residual-denoising Temporal Convolutional Expansive Attention Network (Res-TCEANet). This approach introduces a unique expansive attention mechanism (EAM) that enhances the modeling of long-term dependencies by addressing the positional correspondence of features across layers. The proposed EAM distinguishes itself from existing attention mechanisms by enabling TCEANet to model long sequences with a focus on positional coherence, resulting in more robust feature extraction. The Root Mean Square Error and Score of the proposed method on C-MAPSS dataset are 10.75, 12.27, 10.82, 12.33 and 114.82, 427.05, 161.96 746.93, respectively, which have demonstrated that our method achieves the start-of-the-art performance and outperforms other models.

Monitoring the immediate evolution of concrete’s strength is essential to ensure structural integrity and construction efficiency, requiring Forecasting to avoid unforeseen and severe failures during construction. The present investigation introduces an Equivalent structural parametric (ESP) study using a surface-bonded piezo sensor and electromechanical impedance (EMI) methodology to monitor and forecast the strength of concrete by implementing machine learning and deep learning methods. The concrete hydration processes are simulated using COMSOL™ 5.5. The concrete cube’s hydration is represented by altering Young’s modulus and damping ratio to show the rate of curing. Concrete strength development is examined in terms of conductance resonant frequency (CRF) and Conductance resonant peak (CRP). Continuous conductance signature monitoring and data analysis show that CRF and CRP increase with compressive strength. The system’s mechanical impedance is measured, and EMI signatures vs. frequency plots within a specific frequency range are compared to healthy impedance graphs. A mass-spring-damper system with identical properties is identified, and relevant structural parameters are computed. Modern ML algorithms include linear regression (LR), interaction LR, fine, medium, and coarse Gaussian SVM, etc. reliably predict strength with an error rate of less than 2%. Convolutional neural networks (CNN) have advanced image-based recognition, but their usage in EMI-based structural strength assessment is still being studied. A unique strategy using 2D CNN, 2D CNN– Long short-term memory (LSTM), and 2D CNN Bidirectional-LSTM to forecast concrete structure compressive strength shows the potential of deep learning. The proposed 2D CNN-Bi-LSTM model excels in compressive strength prediction, obtaining an R² value of 0.99 and stabilizing loss error over a few epochs.

Accurate prediction of multi-parameter gas extraction in mines is vital for intelligent gas extraction regulation. This study establishes a linear and nonlinear multi-parameter prediction model to address excessive prediction residuals resulting from parameter interactions. Initially, a combined linear-nonlinear model is constructed using ARIMA and RBFNN. Real-time multi-parameter data under varying negative pressures is collected via a custom experimental platform. After preprocessing and inspection, the linear models is obtained: ARIMA (0,1,1) for gas concentration, ARIMA (0,1,1) for flow rate, ARIMA (2,1,2) for air leakage volume. Finally, the prediction residuals were optimised using RBFNN to obtain the combined model predictions. Results demonstrate closer proximity of combined model predictions to actual values, with gas extraction multi-parameter R² surpassing 0.7. Additionally, negligible values of MAE、MSE and MAPE attest to the model’s robust performance, laying theoretical groundwork for dynamic gas extraction regulation and control.

A 3D profile imaging method is proposed by using digital holography swept at dual frequency intervals. The dual frequency interval sweeping is achieved by altering the frequency of the tunable laser with two selected frequency intervals. An infrared camera was used to capture the interferometric patterns during two sweeping periods, and depth profile was reconstructed from multiple digital holograms. Phase image across the target region is extracted at each frequency and the phase at each pixel is varied as the frequencies are swept at a prefixed interval in each period. From measured phases, the congruence equations are constructed to achieve the 3D profile of the target object. The proposed sweeping method extends the measurement range by about ten times compared to the classical single frequency interval sweeping method, with a high depth accuracy of within 20 μm over a range of 4 cm. The proposed method is verified by both numerical simulation and experiment. Automatic extraction of reconstructed distances is achieved to facilitate automated 3D profile imaging.

To realize the compressive-pressure and non-contacting-distance measurements in the curved-interlayer-structure of modern-industrial-equipment under temperature-interference, the influences of temperature on the impedance-variations of the spiral conductive polymer composite (which possesses the tunneling-effect-based-conduction, the intrinsically-flexibility and the ability to induce eddy-current-effect) caused by the pressure and distance are studied. The pressure/distance-impedance-property repeatability increases with the increment of experimental-times, and the pressure/distance-impedance-characteristic is monotonically decreasing/increasing. When the temperature increases from 10 °C to 40 °C, the normalized-impedance-amplitude attenuates/increases and the normalized-impedance-angle raises/decreases under the same pressure/distance, and the sensitivities for the pressure/distance-impedance-property increases. The measurement system based on the two-levels-interpolation is developed and tested under temperature-interference. The testing-results show that the pressure-measurement-error is 8 % F.S. (0–0.65 MPa) and the distance-measurement-error is 5 % F.S. (0–2 mm). The results lay the foundation on revealing the distance/pressure/temperature-sensing-mechanisms of spiral composite, and verify the feasibility of using the spiral composite to measure the pressure/distance under different temperatures.

In the domain of geodetic adjustment, current methodologies addressing additive and multiplicative error models operate under the assumption of independence between additive and multiplicative random errors. However, limited research has been conducted on elucidating the correlation between the components of additive and multiplicative random errors. To extend a methodology of mixed error models with additive and multiplicative correlation observations, this paper derives three parameter adjustment methods: correlation observation least squares, correlation observation weighted least squares, and correlation observation bias-corrected weighted least squares, all based on the principles of least squares. Additionally, corresponding unit weight variance estimations are constructed. Finally, it is verified by two numerical simulation experiments and a real-world application example of a geodetic network that the correlated observation bcWLS studied in this paper proves to be the most efficient among the six methods for solving the correlated observation multiplication mixture error model.