Multirotor unmanned aerial vehicle video observation can obtain accurate information about traffic flow of large areas over extended times. This paper aims to construct an open data test platform for updated traffic data accumulation and traffic simulation model verification by analyzing real time aerial video. Common calibration boards were used to calibrate internal camera parameters and image distortion correction was performed using a high-precision distortion model. To solve external parameters calibration problems, an existing algorithm was improved by adding two sets of orthogonal equations, achieving higher accuracy with only four calibrated points. A simplified algorithm is proposed to calibrate cameras by calculating the relationship between pixel and true length under the camera optical axis perpendicular to road conditions. Aerial video (160 min) from the Shanghai inner ring expressway was collected and real time traffic parameter values were obtained from analyzing and processing the aerial visual data containing spatial, time, velocity, and acceleration data. The results verify that the proposed platform provides a reasonable and objective approach to traffic simulation model verification and improvement. The proposed data platform also offers significant advantages over conventional methods that use historical and outdated data to run poorly calibrated traffic simulation models.
This paper realizes the simultaneous optimization of a vessel’s course and speed for a whole voyage within the estimated time of arrival (ETA), which can ensure the voyage is safe and energy-saving through proper planning of the route and speed. Firstly, a dynamic sea area model with meteorological and oceanographic data sets is established to delineate the navigable and prohibited areas; secondly, some data are extracted from the records of previous voyages, to train two artificial neural network models to predict fuel consumption rate and revolutions per minute (RPM), which are the keys to route optimization. After that, speed configuration is introduced to the optimization process, and a simultaneous optimization model for the ship’s course and speed is proposed. Then, based on a customized version of the A* algorithm, the optimization is solved in simulation. Two simulations of a ship crossing the North Pacific show that the proposed methods can make navigation decisions in advance that ensure the voyage’s safety, and compared with a naive route, the optimized navigation program can reduce fuel consumption while retaining an approximately constant time to destination and adapting to variations in oceanic conditions.
Due to its unique technological characteristics, coal mining and production often encounter an acid corrosion environment caused by acid gases. Acid erosion and a series of chemical reactions caused by it often led to the deterioration of coal, rock, support structure, etc. and induced serious safety accidents. To further explore the macro-mesoscopic damage evolution law and failure mechanisms of rock masses under corrosion conditions through numerical simulation, a zonal refined numerical model that can reflect the acid corrosion characteristics of sandstone is established based on CT and digital image processing (DIP). The uniaxial compression test of corroded sandstone is simulated by ABAQUS software. Comparing the numerical simulation results with the physical experiment results, we found that the three-dimensional finite element model based on CT scanning technology can genuinely reflect sandstone’s corrosion characteristic. The numerical simulation results of the stress-strain curve and macroscopic failure mode of the acid-corroded sandstone are in good agreement with the experimental results, which provides a useful method for further studying the damage evolution mechanism of the acid-corroded rock mass. Furthermore, the deformation and damage evolution law of the corroded sandstone under uniaxial compression is qualitatively analyzed based on the numerical simulation. The results show that the rock sample’s axial displacement decreases gradually from top to bottom under the axial load, and the vertical variation is relatively uniform. In contrast, the rock sample’s removal gradually increases with the increase of axial pressure, and the growth presents a certain degree of nonuniformity in the vertical. The acid-etched rock sample’s damage starts from both the end and the middle; it first appears in the corroded area. Moreover, with the displacement load increase, it gradually develops and is merged in the middle of the rock sample and forms macroscopic damage.
Hyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–1000 nm) of the desert background, jungle background, desert camouflage netting, jungle camouflage netting, and jungle camouflage clothing through the hyperspectral imaging system, and the samples were preprocessed by denoising and black-and-white correction. Then, we analysed the region of interest (ROI) of the training samples by principal component analysis (PCA). After the pixels in the region of interest and their surrounding areas were averaged, 60% of the data was used as the training samples, and the remaining 40% was used as the test samples. According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. The results show that among the four models, SVM model has the highest accuracy of classification and the recognition rate of jungle camouflage clothing is the highest. This study verifies the scientific and feasibility of hyperspectral imaging technology for camouflage identification and classification in a simulated operational environment, which has some practical significance.
According to the principle of phase-shifting interferometry and spiral phase characteristics of the vortex beam, this article proposes a method for detecting the surface profile of a transparent object, in which the +1 order vortex beam is generated by a spatial light modulator and is taken as the reference light. The influence of the nonlinear phase modulation characteristics of the spatial light modulator on the measurement precision is studied. The results show that nonlinear phase modulation has a great impact on the measurement. Then, the vortex lights with initial phases of 0, π/2, π, and 3π/2 are used to measure the H-type thin film sample based on the Twyman-Green interference system after correcting the nonlinear phase modulation characteristics. The experimental results show that the measurement error of the surface profile to an object with the theoretical value of 20 nm is 1.146 nm, and the feasibility of the optical vortex phase-shifting technique used to measure the surface profile of an object is verified.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.