scholarly journals A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index

2019 ◽  
Vol 8 (6) ◽  
pp. 289 ◽  
Author(s):  
Shuangxi Miao ◽  
Chengqi Cheng ◽  
Weixin Zhai ◽  
Fuhu Ren ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Trajectory Planning ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Computational Time ◽  
Safety Control ◽  
Time Dynamic ◽  
Computation Efficiency ◽  
Grid Database ◽  
Order Of Magnitude ◽  
Low Altitude

Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization.

scholarly journals Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

2021 ◽  
Vol 11 (2) ◽  
pp. 813
Author(s):  
Shuai Teng ◽  
Zongchao Liu ◽  
Gongfa Chen ◽  
Li Cheng
Keyword(s):  
Feature Extraction ◽  
Crack Detection ◽  
Computational Cost ◽  
Concrete Structures ◽  
Detection Algorithm ◽  
Computational Time ◽  
Image Size ◽  
Important Indicator ◽  
Feature Extractor ◽  
Model Training

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

An Improved Assembling Algorithm in Boundary Elements With Galerkin Weighting Applied to Three-Dimensional Stokes Flows

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Sofia Sarraf ◽  
Ezequiel López ◽  
Laura Battaglia ◽  
Gustavo Ríos Rodríguez ◽  
Jorge D'Elía
Keyword(s):  
Boundary Element Method ◽  
Linear System ◽  
Boundary Element ◽  
Numerical Solutions ◽  
Three Dimensional ◽  
Boundary Integral ◽  
Computational Time ◽  
Creeping Flows ◽  
Order Of Magnitude ◽  
Element Method

In the boundary element method (BEM), the Galerkin weighting technique allows to obtain numerical solutions of a boundary integral equation (BIE), giving the Galerkin boundary element method (GBEM). In three-dimensional (3D) spatial domains, the nested double surface integration of GBEM leads to a significantly larger computational time for assembling the linear system than with the standard collocation method. In practice, the computational time is roughly an order of magnitude larger, thus limiting the use of GBEM in 3D engineering problems. The standard approach for reducing the computational time of the linear system assembling is to skip integrations whenever possible. In this work, a modified assembling algorithm for the element matrices in GBEM is proposed for solving integral kernels that depend on the exterior unit normal. This algorithm is based on kernels symmetries at the element level and not on the flow nor in the mesh. It is applied to a BIE that models external creeping flows around 3D closed bodies using second-order kernels, and it is implemented using OpenMP. For these BIEs, the modified algorithm is on average 32% faster than the original one.

scholarly journals Parallel Algorithms for Spatial Rainfall Distribution

Jurnal INKOM ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Arnida Lailatul Latifah ◽  
Adi Nurhadiyatna
Keyword(s):  
Parallel Algorithms ◽  
Computation Time ◽  
Computational Time ◽  
Rainfall Distribution ◽  
Flood Modelling ◽  
Computation Efficiency ◽  
Distance Weighting ◽  
Speed Up ◽  
Important Input ◽  
Serial Algorithms

This paper proposes parallel algorithms for precipitation of flood modelling, especially applied in spatial rainfall distribution. As an important input in flood modelling, spatial distribution of rainfall is always needed as a pre-conditioned model. In this paper two interpolation methods, Inverse distance weighting (IDW) and Ordinary kriging (OK) are discussed. Both are developed in parallel algorithms in order to reduce the computational time. To measure the computation efficiency, the performance of the parallel algorithms are compared to the serial algorithms for both methods. Findings indicate that: (1) the computation time of OK algorithm is up to 23% longer than IDW; (2) the computation time of OK and IDW algorithms is linearly increasing with the number of cells/ points; (3) the computation time of the parallel algorithms for both methods is exponentially decaying with the number of processors. The parallel algorithm of IDW gives a decay factor of 0.52, while OK gives 0.53; (4) The parallel algorithms perform near ideal speed-up.

A Panoramic Video Monitoring System Using Low-Altitude UAV Based on Fish-Eye Lens

2013 ◽  
Vol 341-342 ◽  
pp. 1128-1132
Author(s):  
Zi Ming Xiong ◽  
Ke Li ◽  
Yang Ding ◽  
Gang Wan ◽  
Jin Min Liao
Keyword(s):  
Monitoring System ◽  
Distortion Correction ◽  
Video Data ◽  
Video Monitoring ◽  
Monitoring Method ◽  
Time Dynamic ◽  
Panoramic Video ◽  
Data Source ◽  
Low Altitude ◽  
Fish Eye

This paper uses the panoramic video data obtained by the UAV as data source, constructs low-altitude Panoramic Video Monitoring System based on eye-fish lens, and provides a new monitoring method which is real-time, dynamic, continuous, extensive and measurable by making a breakthrough in some key technologies, such as, distortion correction of eye-fish, geo-location based on panoramic video and projection transformation.

scholarly journals Identification of conflicting questions in the PARES system

2012 ◽  
Vol 13 (3) ◽  
pp. 297 ◽  
Author(s):  
Avgoustos Tsinakos ◽  
Ioannis Kazanidis
Keyword(s):  
Continuing Education ◽  
Statistical Evaluation ◽  
Academic Staff ◽  
Vector Space Model ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Knowledge Assessment ◽  
Space Model ◽  
Practice Tests ◽  
Test Suites

<p>Student testing and knowledge assessment is a significant aspect of the learning process. In a number of cases, it is expedient not to present the exact same test to all learners all the time (Pritchett, 1999). This may be desired so that cheating in the exam is made harder to carry out or so that the learners can take several practice tests on the same subject as part of the course.</p><p><br />This study presents an e-testing platform, namely PARES, which aims to provide assessment services to academic staff by facilitating the creation and management of question banks and powering the delivery of nondeterministically generated test suites. PARES uses a conflict detection algorithm based on the vector space model to compute the similarity between questions and exclude questions which are deemed to have an unacceptably large similarity from appearing in the same test suite. The conflict detection algorithm and a statistical evaluation of its accuracy are presented. Evaluation results show that PARES succeeds in detecting question types at about 90% and its efficiency can be further increased through continuing education and enrichment of the system’s correlation vocabulary.<br /><br /></p><p> </p>

Mathematical Models for Model-Based Control in Offshore Pipelay Operations

2009 ◽  
Author(s):  
Gullik A. Jensen ◽  
Thor I. Fossen
Keyword(s):  
Mathematical Models ◽  
Dynamic Models ◽  
Controller Design ◽  
Computational Time ◽  
Beam Model ◽  
Time Dynamic ◽  
Model Based ◽  
On Line ◽  
Stability And Accuracy ◽  
Control Application

This paper considers mathematical models for model-based controller design in offshore pipelay operations. Three classes of models for control design are discussed, real-world models suitable for controller design verification, controller and observer models which are used on-line in the control system implementation. The control application place requirements on the model with respect to the computational time, dynamic behavior, stability and accuracy. Models such as the beam model, two catenary models, as well as general finite element (FE) models obtained from computer programs were not able to meet all of the requirements, and two recent dynamic models designed for control are presented, which bridge the gap between the simple analytical and more complex FE models. For completeness, modeling of the pipelay vessel, stinger and roller interaction, soil and seabed interaction and environmental loads are discussed.

scholarly journals CANNY ALGORITHM, COSMIC STRINGS AND THE COSMIC MICROWAVE BACKGROUND

2010 ◽  
Vol 19 (02) ◽  
pp. 183-217 ◽  
Author(s):  
REBECCA J. DANOS ◽  
ROBERT H. BRANDENBERGER
Keyword(s):  
Cosmic Strings ◽  
Simulated Data ◽  
String Tension ◽  
Detection Algorithm ◽  
Small Scale ◽  
Scale Invariant ◽  
South Pole Telescope ◽  
Scaling Solution ◽  
Order Of Magnitude ◽  
Canny Algorithm

We describe a new code to search for signatures of cosmic strings in cosmic microwave anisotropy maps. The code implements the Canny algorithm, an edge detection algorithm designed to search for the lines of large gradients in maps. Such a gradient signature which is coherent in position-space is produced by cosmic strings via the Kaiser–Stebbins effect. We test the power of our new code to set limits on the tension of the cosmic strings by analyzing simulated data, with and without cosmic strings. We compare maps with a pure Gaussian scale-invariant power spectrum with maps which have a contribution of a distribution of cosmic strings obeying a scaling solution. The maps have angular scale and angular resolution comparable to what current and future ground-based small-scale cosmic microwave anisotropy experiments will achieve. We present tests of the codes, indicate the limits on the string tension which could be set with the current code, and describe various ways to refine the analysis. Our results indicate that when applied to the data of ongoing cosmic microwave experiments such as the South Pole Telescope project, the sensitivity of our method to the presence of cosmic strings will be more than an order of magnitude better than the limits from existing analyses.

scholarly journals A Ship Trajectory Prediction Framework Based on a Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5133
Author(s):  
Yongfeng Suo ◽  
Wenke Chen ◽  
Christophe Claramunt ◽  
Shenhua Yang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Computational Time ◽  
Identification System ◽  
Trajectory Prediction ◽  
Ground Truth Data ◽  
Redundant Data ◽  
Computation Efficiency ◽  
Path Distance

Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship’s trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.

Aircraft Conflict Risk Assessment Based on Particle Filter

2014 ◽  
Vol 602-605 ◽  
pp. 3416-3420
Author(s):  
Wen Peng Zhai ◽  
Hao Wu ◽  
Lan Ma
Keyword(s):  
Particle Filter ◽  
Early Stage ◽  
Random Perturbation ◽  
Kinematic Model ◽  
Detection Algorithm ◽  
Conflict Detection ◽  
Collision Risk ◽  
Free Flight ◽  
Risk Probability ◽  
Particle Filter Algorithm

Free flight is a method to resolve airspace congestion problem, but raise safety problem. In this paper, with the influence of wind and the presence of positioning error, the model of conflict detection based on particle filter algorithm is presented. According to the flight kinematic model with the influence of random factors, the target trajectory is generated. The particle filter algorithm is used for estimating the real flight trajectory. The flight collision risk probability is calculated. By simulation calculation, the conflict detection with particle filter algorism improves the accuracy of collision risk probability estimation. The results show that the particle filter conflict detection algorithm reduces the estimation and conflict detection error caused by random perturbation. The method can be applied to identify conflict in the early stage in the study of flight free flight.

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