Safety Early Warning for Air Traffic Management

In recent years, with the rapid development of China's transportation industry, air traffic management has attracted more and more attention, and its safety problems have become increasingly prominent. In civil aviation transport, it mainly involves various types of transportation tools, such as high-altitude and long-span channels.Therefore, it is one of the most important contents to strengthen the safety management in the civil aviation transportation. Air traffic safety early warning management is not only related to the safety of the aircraft flight, but also can very good reflect the aviation technology level in our country, said from the book, the main content of air traffic safety management is to keep the normal order of air traffic, at the same time avoid mutual collision between all kinds of aircraft and obstacles, in the current situation, to strengthen the management of air safety early warning research is very important

Parameter estimation for a ship's roll response model in shallow water using an intelligent machine learning method

In order to accurately identify the ship's roll model parameters in shallow water, and solve the problems of difficult estimating nonlinear damping coefficients by traditional methods, a novel Nonlinear Least Squares Support Vector Machine (NLS-SVM) is introduced. To illustrate the validity and applicability of the proposed method, simulation and decay tests data are combined and utilized to estimate unknown parameters and predict the roll motions. Firstly, simulation data is applied in the NLS-SVM model to obtain estimated damping parameters, compared with pre-defined parameters to verify the validity of the proposed method. Subsequently, decay tests data are used in identifying unknown parameters by utilizing traditional models and the new NLS-SVM model, the results illustrate that the intelligent method can improve the accuracy of parametric estimation, and overcome the conventional algorithms' weakness of difficult identification of the nonlinear damping parameter in the roll model. Finally, to show the wide applicability of the proposed model in shallow water, experimental data from various speeds and Under Keel Clearances (UKCs) are applied to identify the damping coefficients. Results reveal the potential of using the NLS-SVM for the problem of the roll motion in shallow water, and the effectiveness and accuracy are verified as well

Parameter estimation for a ship's roll response model in shallow water using an intelligent machine learning method

In order to accurately identify the ship's roll model parameters in shallow water, and solve the problems of difficult estimating nonlinear damping coefficients by traditional methods, a novel Nonlinear Least Squares Support Vector Machine (NLS-SVM) is introduced. To illustrate the validity and applicability of the proposed method, simulation and decay tests data are combined and utilized to estimate unknown parameters and predict the roll motions. Firstly, simulation data is applied in the NLS-SVM model to obtain estimated damping parameters, compared with pre-defined parameters to verify the validity of the proposed method. Subsequently, decay tests data are used in identifying unknown parameters by utilizing traditional models and the new NLS-SVM model, the results illustrate that the intelligent method can improve the accuracy of parametric estimation, and overcome the conventional algorithms' weakness of difficult identification of the nonlinear damping parameter in the roll model. Finally, to show the wide applicability of the proposed model in shallow water, experimental data from various speeds and Under Keel Clearances (UKCs) are applied to identify the damping coefficients. Results reveal the potential of using the NLS-SVM for the problem of the roll motion in shallow water, and the effectiveness and accuracy are verified as well

Ship manoeuvring model parameter identification using intelligent machine learning method and the beetle antennae search algorithm

In order to identify more accurately and efficiently the unknown parameters of a ship motions model, a novel Nonlinear Least Squares Support Vector Machine (NLSSVM) algorithm, whose penalty factor and Radial Basis Function (RBF) kernel parameters are optimised by the Beetle Antennae Search algorithm (BAS), is proposed and investigated Aiming at validating the accuracy and applicability of the proposed method, the method is employed to identify the linear and nonlinear parameters of the first-order nonlinear Nomoto model with training samples from numerical simulation and experimental data. Subsequently, the identified parameters are applied in predicting the ship motion. The predicted results illustrate that the new NLSSVM-BAS algorithm can be applied in identifying ship motion's model, and the effectiveness is verified. Compared among traditional identification approaches with the proposed method, the results display that the accuracy is improved. Moreover, the robust and stability of the NLSSVM-BAS are verified by adding noise in the training sample data

Hybrid method for predicting ship manoeuvrability in regular waves

The ship's manoeuvring behaviour in waves is significantly different from that in calm water. In this context, the present work uses a hybrid method combining potential flow theory and Computational Fluid Dynamics (CFD) techniques for the prediction of ship manoeuvrability in regular waves. The mean wave-induced drift forces are calculated by adopting a time domain 3D higher-order Rankine panel method, which includes the effect of the lateral speed and forward speed. The hull-related hydrodynamic derivatives are determined based on a RANS solver using the double body flow model. The two-time scale method is applied to integrate the improved seakeeping model in a 3-DOF modular type Manoeuvring Modelling Group (MMG model) to investigate the ship's manoeuvrability in regular waves. Numerical simulations are carried out to predict the turning circle in regular waves for the 5175 container carrier. The turning circle's main characteristics as well as the wave-induced motions are evaluated. A good agreement is obtained by comparing the numerical results with experimental data obtained from existing literature. This demonstrates that combining potential flow theory with CFD techniques can be used efficiently for predicting the manoeuvring behaviour in waves. This is even more true when the manoeuvring derivatives cannot be obtained from model tests when there is lack of such experimental data

A novel method for purifying bluetongue virus with high purity by co-immunoprecipitation with agarose protein A

<p>Abstract</p> <p>Background</p> <p>Bluetongue virus (BTV) is an icosahedral non-enveloped virus within the genus <it>Orbivirus </it>of <it>Reoviridae </it>and exists as 24 distinct serotypes. BTV can infect all ruminant species and causes severe sickness in sheep. Recently, it was reported that BTV can infect some human cancer cells selectively. Because of the important oncolysis of this virus, we developed a novel purifying method for large-scale production. The purifying logic is simple, which is picking out all the components unwanted and the left is what we want. The process can be summarized in 4 steps: centrifugation, pulling down cell debrises and soluble proteins by co-immunoprecipitation with agarose Protein A, dialysis and filtration sterilization after concentration.</p> <p>Results</p> <p>The result of transmission electron microscope (TEM) observation showed that the sample of purified virus has a very clear background and the virions still kept intact. The result of 50% tissue culture infective dose (TCID₅₀) assay showed that the bioactivity of purified virus is relatively high.</p> <p>Conclusions</p> <p>This method can purify BTV-10 with high quality and high biological activity on large-scale production. It also can be used for purifying other BTV serotypes.</p