No abstract

In this paper, a multi-LAVs’ aerial delivery areas-choosing method is brought forward, which aims at known targets which are located in a fixed area. This method takes the performance of LAV, especially minimum turning radius, into consideration, and incorporates AHP and Dubins’ path. Based on the discussion of general GA method, we design the IGA method to increase the convergence rate. By using this method, the LAVs can reach the target located at the longest distance within the minimum time. Simulation results show that the IGA method has a higher convergence rate than the general GA method, while the delivery areas can be chosen in an optimized way. The proposed method can be extended to the problem of multi-Dubins vehicles’ task assignment and path planning.

Ultrasonic detection has been widely used in underwater detectoscopes as an important method for underwater detection. Feature extraction of echo signal time-delay and amplitude is the main task of processing underwater ultrasonic signal. Underwater target ultrasonic echo signal is influenced by reverberation and noise from the sea and system itself, reverberation interference of signal background is the main difficulty for target echo detection. So we use denoising algorithm to denoise echo signal. At first this paper denoises the measured weighted background clutter data using wavelet threshold denoising method, then the paper extracts breaking points of echo signal through wavelet transform, at last the paper makes an envelope extraction using Hilbert transform combined with wavelet transform methods, and acquires the feature information of echo signal amplitude.

In this paper, ECO-friendly titanium magnesium catalyst with high activity was synthesized, and titanium based bottle polyester was synthesized on a 30,000 ton annual industrial plant. At the same time, the characterization and processing properties of titanium based and antimony based polyester were studied. Compared with commercial antimony based polyester, TMPC chips had faster SSP rate and lower AA content, easier crystallization and high catalyst activity, easy to form macromolecular chains, and the rheology of polyester melt also showed a wider processing range. However, the chain end thermal degradation reaction of titanium based bottle chips was slightly larger than that of antimony based bottle chips, and the α transition temperature in DMA test also led to the molecular chain motion of titanium bottle chips is more easily affected by temperature, which was not conducive to maintaining a better viscosity drop and AA value of bottle preform for a long time in the high-temperature injection molding process. To sum up, according to the performance test results, by using TMPC catalyst and adjusting the processing conditions to control the injection molding process of TMPC polyester, titanium bottle preform with better viscosity drop and AA content can be obtained. Finally, the bottle preform with a weight of 28.5g was prepared by using titanium TMPC bottle chips. The injection molding processing temperature was reduced by 10 °C, the viscosity drop was equivalent to that of commercial antimony bottle preform, the end carboxyl group was reduced by 54%, the color index b value was reduced by 17.3%, and the AA content was reduced by 19.8%.

This paper brings out a new sparse line-optical flow field computing method. Firstly, we establish a lines matching algorithm based on Kalman Filter (KF). In this algorithm, we map lines in an image into Hough space, after that we employ KF to predict the position in the following frame in order to match lines in image sequence. Secondly, we present the concept of sparse line-optical flow field of images and propose the calculation method of it. By using the camera perspective projection model and the optical flow Identity, we can get the sparse line-optical flow field. Simulations is made in the following step, and results show that the lines matching algorithm works well and the accuracy of the calculation method proposed in this paper is as good as that of the classic Horn algorithm, while the calculating time-cost of it is only 1/30 Horn algorithms.