Development and Temperature Correction of Piezoelectric Ceramic Sensor for Traffic Weighing-In-Motion.

Weighing-In-Motion (WIM) technology is one of the main tools for pavement management. It can accurately describe the traffic situation on the road and minimize overload problems. WIM sensors are the core elements of the WIM system. The excellent basic performance of WIMs sensor and its ability to maintain a stable output under different temperature environments are critical to the entire process of WIM. In this study, a WIM sensor was developed, which adopted a PZT-5H piezoelectric ceramic and integrated a temperature probe into the sensor. The designed WIM sensor has the advantages of having a small size, simple structure, high sensitivity, and low cost. A sine loading test was designed to test the basic performance of the piezoelectric sensor by using amplitude scanning and frequency scanning. The test results indicated that the piezoelectric sensor exhibits a clear linear relationship between input load and output voltage under constant environmental temperature. The linear correlation coefficient R2 of the fitting line is up to 0.999, and the sensitivity is 4.04858 mV/N at a loading frequency of 2 Hz at room temperature. The sensor has good frequency-independent characteristics. However, the temperature has a significant impact on it. Therefore, the output performance of the piezoelectric ceramic sensor is stabilized under different temperature conditions by using a multivariate nonlinear fitting algorithm for temperature compensation. The fitting result R2 is 0.9686, the root mean square error (RMSE) is 0.2497, and temperature correction was achieved. This study has significant implications for the application of piezoelectric ceramic sensors in road WIM systems.

An Aeromagnetic Compensation Method Based on a Multimodel for Mitigating Multicollinearity.

Aeromagnetic surveys play an important role in geophysical exploration and many other fields. In many applications, magnetometers are installed aboard an aircraft to survey large areas. Due to its composition, an aircraft has its own magnetic field, which degrades the reliability of the measurements, and thus a technique (named aeromagnetic compensation) that reduces the magnetic interference field effect is required. Commonly, based on the Tolles-Lawson model, this issue is solved as a linear regression problem. However, multicollinearity, which refers to the case when more than two model variables are highly linearly related, creates accuracy problems when estimating the model coefficients. The analysis in this study indicates that the variables that cause multicollinearity are related to the flight heading. To take this point into account, a multimodel compensation method is proposed. By selecting the variables that contribute less to the multicollinearity, different sub-models are built to describe the magnetic interference of the aircraft when flying in different orientations. This method restricts the impact of multicollinearity and improves the reliability of the measurements. Compared with the existing methods, the proposed method reduces the interference field more effectively, which is verified by a set of airborne tests.

An adaptive joint CCA-ICA method for ocular artifact removal and its application to emotion classification.

BACKGROUND: The quality of Electroencephalogram (EEG) signals is critical for revealing the neural mechanism of emotions. However, ocular artifacts decreased the signal to noise ratio (SNR) and covered the inherent cognitive component of EEGs, which pose a great challenge in neuroscience research. NEW METHOD: We proposed a novel unsupervised learning algorithm to adaptively remove the ocular artifacts by combining canonical correlation analysis (CCA), independent component analysis (ICA), higher-order statistics, empirical mode decomposition (EMD), and wavelet denoising techniques. Specifically, the combination of CCA and ICA aimed to improve the quality of source separation, while the higher-order statistics further located the source of ocular artifacts. Subsequently, these noised sources were further corrected by EMD and wavelet denoising to improve SNR of EEG signals. RESULTS: We evaluated the performance of our proposed method with simulation studies and real EEG applications. The results of simulation study showed our proposed method could significantly improve the quality of signals under almost all noise conditions compared to four state-of-art methods. Consistently, the experiments of real EEG applications showed that the proposed methods could efficiently restrict the components of ocular artifacts and preserve the inherent information of cognition processing to improve the reliability of related analysis such as power spectral density (PSD) and emotion recognition. COMPARISON WITH EXISTING METHODS: Our proposed model outperforms the comparative methods in EEG recovery, which further improve the application performance such as PSD analysis and emotion recognition. CONCLUSIONS: The superior performance of our proposed method suggests that it is promising for removing ocular artifacts from EEG signals, which offers an efficient EEG preprocessing technology for the development of brain computer interface such as emotion recognition.

Rheological Behaviors and Damage Mechanism of Asphalt Binder under the Erosion of Dynamic Pore Water Pressure Environment.

Asphalt binder plays an important role in the overall resistance of asphalt mixture to the moisture damage induced by a dynamic pore water pressure environment. This study evaluates the moisture sensitivity of asphalt binder from the perspective of rheological behaviors using the dynamic shear rheometer (DSR) and the bending beam rheometer (BBR) methods at high, medium, and low temperatures. The damage mechanism is further discussed quantitatively based on the Fourier transform infrared spectroscopy (FTIR) method. The results indicate that a longer conditioning duration is beneficial for asphalt binder to recover its adhesion at 60 °C in multiple stress creep recover (MSCR) tests, but the increasing pore water pressure magnitude of 60 psi held an opposite effect in this study. The asphalt binder's fatigue life at 20 °C in linear amplitude sweep (LAS) tests decreased obviously with conditioning duration and environmental severity, but the reducing rate gradually slowed down, while the groups of 50 psi-4000 cycles and 60 psi-4000 cycles held a comparable erosion effect. Both the stiffness and relaxation moduli at -12 °C in the BBR tests exhibited an obvious decreasing trend with conditioning duration and environmental severity. The erosion effect on the asphalt binder was gradually enhanced, but it also exhibited a slightly more viscous performance. Water conditioning induced several obvious characteristic peaks in the FTIR absorbance spectra of the asphalt binder. The functional group indexes presented a trend of non-monotonic change with conditioning duration and environmental severity, which made the asphalt binder show complicated rheological behaviors, such as non-monotonic variations in performance and the abnormal improving effect induced by dynamic pore water pressure conditioning.