Effect of Freeze-Thaw Cycles on the Internal Structure and Performance of Semirigid Base Materials

In this study, we investigate the spatial distributions of the internal structures in semirigid base materials (SRBMs) and explore their effect on the service performance of the SRBMs. X-ray computed tomography (X-ray CT) was used to conduct a spatial voids structure analysis. Three variates were selected to study the factors influencing the spatial distributions of the internal structures, including freeze-thaw cycles, curing time, and cement content. The results show that, with the increase in the number of freezing and thawing cycles, the average porosity, void area, and void number of the SRBM samples increased, and the average void diameters of all samples initially increased and then decreased. These trends led to an increase in the mass loss ratio and strength loss ratio. Increasing the cement content and extending the curing time decreased the average number of voids, average void area, and average void diameter and decreased the mass loss ratio and strength loss ratio of the SRBMs. The top and bottom of the SRBM samples were more porous than the middle of the samples, whereas the maximum value of the average void diameter was observed in the middle of the samples

GA-SVR and pseudo-position-aided GPS/INS integration during GPS outage

The performance of Global Positioning System and Inertial Navigation System (GPS/INS) integrated navigation is reduced when GPS is blocked. This paper proposes an algorithm to overcome the condition where GPS is unavailable. Together with a parameter-optimised Genetic Algorithm (GA), a Support Vector Regression (SVR) algorithm is used to construct the mapping function between the specific force, angular rate increments of INS measurements and the increments of the GPS position. During GPS outages, the real-time pseudo-GPS position is predicted with the mapping function, and the corresponding covariance matrix is estimated by an improved adaptive filtering algorithm. A GPS/INS integration scheme is demonstrated where the vehicle travels along a straight line and around a curve, with respect to both low-speed-stable and high-speed-unstable navigation platforms. The results show that the proposed algorithm provides a better performance when GPS is unavailable

One Case of Autopsy Pathological Analysis of Acute Pancreatitis Combined with Hemorrhage in Pericardial Cavity

Senile male, physically ft at usual, he died suddenly without any clinical symptoms. By autopsy dissection, it was found that large amount of bleeding was presented in pericardial cavity, the abdominal cavity and thoracic cavity had a small amount of hemorrhage, partial pancrea tissue had coagulation necrosis accompanied with infltration of neutrophile granulocyte and degeneration and necrosis of liver cell accompanied with acute or chronic inflammation cell infltration. Laboratory examination of the patient when he was alive suggested that liver function and coagulation function had obstacles, there was not any timely clinical process, and he died suddenly. Autopsy examination results suggested that acute pancreatitis caused a large quantity of bleeding in pericardial cavity, which led to cardiac tamponade and it cause acute circulation failure, which initiated cardiac arrest and then death. Coronary heart disease may exert certain facilitation effect in the death process. Patients with pancreatitis, especially the senile and pancreatitis patients with coronary artery disease, should be evaluated and prevented ahead of schedule, for those patients who had coma suddenly, it should be thought that it had possibility of combining with hemorrhage in the interior of pericardial cavity, the patient's doctor should try his or her best to reduce death rate

Neural Network Aided Adaptive UKF Algorithm for GPS/INS Integration Navigation

The predicted residual vectors should be zero-mean Gaussian white noise, which is the precondition for multiple fading factors adaptive filtering algorithm based on statistical information in GPS/INS integration system. However the abnormalities in observations will affect the distribution of the residual vectors. In this paper, a neural network aided adaptive unscented Kalman filter (UKF) algorithm with multiple fading factors based on singular value decomposition(SVD) is proposed. The algorithm uses the neural network algorithm to weaken the influence of the observed abnormalities on the residual vectors. Singular value decomposition instead of unscented transformation is adopted to suppress negative definite variation in priori covariance matrix of UKF. Since single fading factor in poor tracking of multiple variables has the limitation, multiple fading factors to adjust the predicted-state covariance matrix are constructed with better robustness so that each filter channel has different adjustability. Finally, vehicle measurement data are collected to validate the proposed algorithm. It shows that the neural network algorithm can prevent the observed abnormalities from affecting the distribution of the residual vectors, expanding the applied range of the adaptive algorithm. The neural network algorithm aided SVD-UKF algorithm with multiple fading factors is able to remove influences of state anomalies on condition of the observed abnormalities. The accuracy and reliability of the navigation solution can be improved by this algorithm

Performance Analysis on Carrier Phase-Based Tightly-Coupled GPS/BDS/INS Integration in GNSS Degraded and Denied Environments

The integration of Global Navigation Satellite Systems (GNSS) carrier phases with Inertial Navigation System (INS) measurements is essential to provide accurate and continuous position, velocity and attitude information, however it is necessary to fix ambiguities rapidly and reliably to obtain high accuracy navigation solutions. In this paper, we present the notion of combining the Global Positioning System (GPS), the BeiDou Navigation Satellite System (BDS) and low-cost micro-electro-mechanical sensors (MEMS) inertial systems for reliable navigation. An adaptive multipath factor-based tightly-coupled (TC) GPS/BDS/INS integration algorithm is presented and the overall performance of the integrated system is illustrated. A twenty seven states TC GPS/BDS/INS model is adopted with an extended Kalman filter (EKF), which is carried out by directly fusing ambiguity fixed double-difference (DD) carrier phase measurements with the INS predicted pseudoranges to estimate the error states. The INS-aided integer ambiguity resolution (AR) strategy is developed by using a dynamic model, a two-step estimation procedure is applied with adaptively estimated covariance matrix to further improve the AR performance. A field vehicular test was carried out to demonstrate the positioning performance of the combined system. The results show the TC GPS/BDS/INS system significantly improves the single-epoch AR reliability as compared to that of GPS/BDS-only or single satellite navigation system integrated strategy, especially for high cut-off elevations. The AR performance is also significantly improved for the combined system with adaptive covariance matrix in the presence of low elevation multipath related to the GNSS-only case. A total of fifteen simulated outage tests also show that the time to relock of the GPS/BDS signals is shortened, which improves the system availability. The results also indicate that TC integration system achieves a few centimeters accuracy in positioning based on the comparison analysis and covariance analysis, even in harsh environments (e.g., in urban canyons), thus we can see the advantage of positioning at high cut-off elevations that the combined GPS/BDS brings

Influence of Emulsified Asphalt on the Mechanical Property and Microstructure of Cement-Stabilized Gravel under Freezing and Thawing Cycle Conditions

Properties of cement-stabilized gravel modified by emulsified asphalt under freezing and thawing cycle conditions were investigated by adjusting the dosage of cement. Mercury intrusion porosimetry (MIP) and Scanning electron microscopy (SEM) were introduced to analyze the influential mechanism. The results indicate that cement emulsified asphalt stabilized gravel with 5 wt % of cement performed well in both mechanics and frost-resistance. Although the addition of emulsified asphalt would lead to a partial decrease of strength, it can extend the process of strength loss and improve the freezing resistance. The main reason for this is that the permeability can be improved by the filling effects of emulsified asphalt. The frost-heave stress caused by the phase transition of water can also be remitted by emulsified asphalt, the elasticity modulus of which is much lower than the matrix. The generating speed of the micro crack can also be slowed down by emulsified asphalt

An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas

Inertial Navigation System (INS) is often combined with Global Navigation Satellite System (GNSS) to increase the positioning accuracy and continuity. In complex urban environments, GNSS/INS integrated systems suffer not only from dynamical model errors but also GNSS observation gross errors. However, it is hard to distinguish dynamical model errors from observation gross errors because the observation residuals are affected by both of them in a loosely-coupled integrated navigation system. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors. In the proposed method, firstly a test statistic based on Mahalanobis distance is treated as judging index to achieve fault detection. Then, an optimal RBF neural network strategy is trained on-line by the optimality principle. The network’s output will bring benefits in recognizing the above two kinds of filtering fault and the system is able to choose a robust or adaptive Kalman filtering method autonomously. A field vehicle test in urban areas with a low-cost GNSS/INS integrated system indicates that two types of errors simulated in complex urban areas have been detected, distinguished and eliminated with the proposed scheme, success rate reached up to 92%. In particular, we also find that the novel neural network strategy can improve the overall position accuracy during GNSS signal short-term outages