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To improve the uniformity of asphalt mixtures, optimizing the particle distribution is essential, and the screw conveyor of paver plays a critical role in this process. In this regard, a parameter optimization strategy based on the discrete element method and response surface methodology (RSM) is proposed. First, a discrete element model is developed for spiral material separation and asphalt mixture particles. Next, uniformity evaluation indicators are used to analyze the impact of individual parameters on particle uniformity. Then, simulation experiments are conducted under varying material level coefficients and rotational speeds, and a regression model is developed to analyze the impact of coupling factors using RSM. Afterward, different algorithms are employed to optimize the parameters of the model, and the results show that the particle swarm optimization algorithm is better than the traditional hill climbing algorithm. Last, field tests are conducted to verify the feasibility of the proposed optimization strategy. The optimal theoretical parameter of the material level coefficient is 0.95, and the rotational speed is 53 rpm. Overall, the proposed strategy offers an effective and accurate model for simulating the process of particle separation, which can lead to improved uniformity of asphalt mixtures. [ABSTRACT FROM AUTHOR]

Rotational inertial navigation system (RINS) could improve the navigation performance by rotating the IMU around gimbals, and the structure and function of the dual-axis RINS also help realize the process of self-calibration. However, the gyro–accelerometer asynchronous time is not considered in current studies. Coupled into the navigation error, the gyro–accelerometer asynchronous time can lead to velocity accuracy decline during the process of rotation. Therefore, the modeling and calibration of the gyro–accelerometer asynchronous time would enhance the systemic reliability. In this article, in order to solve the abovementioned problem: 1) we model the gyro–accelerometer asynchronous time and perform an error analysis of gyro–accelerometer asynchronous time; 2) we propose a compensation method of the outer lever arm to improve the accuracy of the observed velocity and position; and 3) we derive a continuous linear 40-D RINS error model considering the abovementioned two error parameters compared with the traditional method, the PWCS method, and the SVD method is utilized to provide the observability degrees of the proposed state during the self-calibration process. Experimental results illustrated that the proposed method can improve the accuracy in the long-endurance navigation. [ABSTRACT FROM AUTHOR]

A significant issue of land vehicle navigation is in-motion attitude alignment of the odometer (OD)-aided strapdown inertial navigation system (SINS). The consecutive OD outliers can occur due to sudden wheel slipping and skidding while vehicle maneuvering. They seriously reduce the robustness and precision of attitude alignment. In this paper, we investigate a robust in-motion attitude alignment method for the OD-aided SINS. The method consists of in-motion coarse alignment and in-motion fine alignment. In the in-motion coarse alignment process, we developed Huber's M-estimation and integral formula based robust Kalman filter (HRKF/IF-CA), which can restrain the interference of consecutive OD outliers on reconstructed observation vectors. Thus, HRKF/IF-CA can contribute to better coarse attitude results. The next process is in-motion fine alignment. Under the popular repeated backtracking scheme, we investigate HRKF based fine alignment (HRKF-FA) with the SINS/OD summed measurement model. HRKF-FA can refine attitude alignment and restrain the interference of consecutive OD outliers simultaneously. Finally, the proposed method is evaluated by simulation and vehicle test. The attitude alignment results show that this method can achieve reasonable attitude results, and the interference of consecutive OD outliers caused by sudden wheel slipping and skidding can be greatly restrained. [ABSTRACT FROM AUTHOR]

Most image encryption algorithms encrypt the whole image, but only part of the data is important in the image. In this paper, we propose a multidimensional chaotic image encryption algorithm based on the region of interest (ROI). The histogram of oriented gradients (HOG) feature extraction and support vector machine (SVM) are used to separate the region of interest from the whole image. Then, the region of interest pixels is messed up by using the improved Henon sequence, Joseph sequence and the region of interest pixels are diffused by using the unified chaotic sequence to hide the sensitive information in the image, so as to achieve the purpose of private protection. Furthermore, the improved logistic sequence is used to hide the edge information of the target image to achieve the tradeoff between the secrecy of information and the complexity of encryption. A series of analyses are carried out including key space analysis, key sensitivity analysis, statistical analysis, information entropy analysis, analysis of the fixed-point obscuration analysis, quality analysis and image decryption for our encryption algorithm. Through experiments and comparisons, the proposed algorithm has good performance in encrypting image and coping with various invasions. The image encryption algorithm based on ROI has a good performance of security, moreover through the main encryption of ROI can effectively shorten the encryption time, so as to achieve the compromise of security and computational complexity. [ABSTRACT FROM AUTHOR]