Ballistic error propagation algorithm for glide trajectory based on perturbation theory

2016 ◽  
Vol 231 (9) ◽  
pp. 1574-1592 ◽  
Author(s):  
Huan Zhou ◽  
Wei Zheng ◽  
Guojian Tang
Keyword(s):  
Perturbation Theory ◽  
Error Propagation ◽  
Propagation Model ◽  
Coordinate Frame ◽  
Propagation Mechanism ◽  
State Variables ◽  
Propagation Algorithm ◽  
Proposed Model ◽  
Sufficient Precision ◽  
Perturbation Equations

A ballistic error propagation algorithm for glide trajectories of a hypersonic glide vehicle is originally proposed based on the perturbation theory. Perturbation equations that treat perturbations as external inputs and flight state deviations as state variables are established. Based on the reasonable simplification assumptions, the analytic expression of the state transition matrix is deduced and thus the ballistic error propagation model is established. A transposed coordinate frame is introduced to simplify the development of the perturbation equations and the error propagation model. By employing the gravity anomaly as the single perturbation factor, the proposed algorithm is validated and verified by numerical experiments. When compared with the traditional method, the proposed method takes only just a quarter computational costs and restrains the method errors beneath 10% of the total terminal deviations. It is an effort that initially focuses on the error propagation mechanism of the glide trajectory and the proposed model has sufficient precision for the analysis of modeling deviations, thus laying the foundation of correcting the modeling deviations and enhancing the accuracy of vehicle’s flight states.

scholarly journals Improved Label Propagation Model to Predict Drug - drug Interactions

2018 ◽  
Vol 173 ◽  
pp. 02007
Author(s):  
Zhice Yan ◽  
Lasheng Zhao ◽  
Xiaopeng Wei ◽  
Qiang Zhang
Keyword(s):  
Drug Interactions ◽  
Characteristic Curve ◽  
Clinical Stage ◽  
Real Life ◽  
Propagation Model ◽  
Label Propagation ◽  
Chi Square ◽  
Propagation Algorithm ◽  
Proposed Model ◽  
Chi Square Test

Drug-drug interactions (DDIs) is one of the most concerned issues in drug design. Accurate prediction of potential DDIs in clinical trials can reduce the occurrence of side effects in real life of drugs. Therefore, we propose a model to predict DDIs. The model integrates several methods that can improve label propagation algorithm. Firstly, the chi-square test (CHI) method is adopted to filter or select the features that contain a large amount of information. Secondly, the sample similarity calculation method is reconstructed by label similarity and feature similarity. Then the label initialization information of unlabeled samples is constructed. Finally, we use label propagation algorithm to estimate the labels of the unlabeled drugs. The results show that the proposed model can obtain higher the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPR), which provides a favorable guarantee for the discovery of DDIs in the clinical stage.

Error Propagation in Digital Additive Remanufacturing Process Planning

2019 ◽  
Author(s):  
Mojahed Alkhateeb ◽  
Jeremy L. Rickli ◽  
Nicholas J. Christoforou
Keyword(s):  
Error Propagation ◽  
Point Cloud ◽  
Point Clouds ◽  
Propagation Model ◽  
Smoothing Factor ◽  
Material Deposition ◽  
Proposed Model ◽  
Data Points ◽  
3D Scanners ◽  
The Right

Abstract A point cloud is a digital representation of a part that consists of a set of data points in space. Typically point clouds are produced by 3D scanners that hover above a part and records points in a large number that represent the external surface of a part. Additive remanufacturing offers a sustainable solution to end-of-use (EoU) core disposal and recovery and requires quantification of part damage or wear that requires reprocessing. This paper proposes an error propagation approach that models the interaction of each step of the additive remanufacturing process. This proposed model is formulated, and the results of the errors generated from the parameters of the scanner and point cloud smoothing are presented. Smoothing is an important step to reduce the noises generated from scanning, knowing the right smoothing factor is important since over smoothing results in dimensional inaccuracies and errors, especially in cores with smaller degrees of damage. It is important to know the error generated from scanning and point cloud smoothing to compensate in the following steps and generate appropriate material deposition paths. Inaccuracies in the 3D model renders can impact the remainder of the additive remanufacturing accuracy, especially because there are multiple steps in the process. Sources of error from smoothing, meshing, slicing, and material deposition are proposed in the error propagation model for additive remanufacturing. Results of efforts to quantify the scanning and smoothing steps within this model are presented.

scholarly journals Rough Set Based Affinity Propagation Model for Prediction of Future Gold Price in Indian Scenario

2019 ◽  
Vol 9 (2) ◽  
pp. 2219-2224
Keyword(s):  
Rough Set ◽  
Arima Model ◽  
Gold Price ◽  
Propagation Model ◽  
Affinity Propagation ◽  
Experimental Result ◽  
Percentage Error ◽  
Support Vector ◽  
Proposed Model ◽  
Gold Silver

In international market, trading of metals has played a vital role. Metal cost might affect the nation’s economy. There are so many base metals available which have been utilized in world trading for construction and manufacturing of goods. Among them gold, silver, platinum, palladium have been treated as precious metals which has economic values. Therefore today’s researchers have concentrated their investigation on metal prediction using diversified algorithms like Auto Regressive Integrated Moving Average (ARIMA), KNN (K-Nearest Neighbor),Artificial Neural Network (ANN) and Support Vector Machine (SVM) etc. In this paper our foremost objective is to predict gold price, so we put our research on this metal. In this work we have employed rough set based affinity propagation algorithm for predicting future gold price and we compared our proposed model with rough set and ARIMA model basing upon the performance measures such as root mean square error (RMSE) and mean absolute percentage error (MAPE). The experimental result shows that the proposed model outperforms rough set and ARIMA model

scholarly journals Propagation Model of Panic Buying Under the Sudden Epidemic

2021 ◽  
Vol 9 ◽  
Author(s):  
Peihua Fu ◽  
Bailu Jing ◽  
Tinggui Chen ◽  
Chonghuan Xu ◽  
Jianjun Yang ◽  
...  
Keyword(s):  
Online Social Networks ◽  
Group Behavior ◽  
Propagation Model ◽  
Release Time ◽  
External Information ◽  
Buying Behavior ◽  
Factors Affecting ◽  
Proposed Model ◽  
Huge Impact ◽  
Number Of Individuals

The sudden outbreak of COVID-19 at the end of 2019 has had a huge impact on people's lives all over the world, and the overwhelmingly negative information about the epidemic has made people panic for the future. This kind of panic spreads and develops through online social networks, and further spreads to the offline environment, which triggers panic buying behavior and has a serious impact on social stability. In order to quantitatively study this behavior, a two-layer propagation model of panic buying behavior under the sudden epidemic is constructed. The model first analyzes the formation process of individual panic from a micro perspective, and then combines the Susceptible-Infected-Recovered (SIR) Model to simulate the spread of group behavior. Then, through simulation experiments, the main factors affecting the spread of panic buying behavior are discussed. The experimental results show that: (1) the dissipating speed of individual panics is related to the number of interactions and there is a threshold. When the number of individuals involved in interacting is equal to this threshold, the panic of the group dissipates the fastest, while the dissipation speed is slower when it is far from the threshold; (2) The reasonable external information release time will affect the occurrence of the second panic buying, meaning providing information about the availability of supplies when an escalation of epidemic is announced will help prevent a second panic buying. In addition, when the first panic buying is about to end, if the scale of the second panic buying is to be suppressed, it is better to release positive information after the end of the first panic buying, rather than ahead of the end; and (3) Higher conformity among people escalates panic, resulting in panic buying. Finally, two cases are used to verify the effectiveness and feasibility of the proposed model.

scholarly journals The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Zhang ◽  
Bin Chen ◽  
Liang Ma ◽  
Zhen Li ◽  
Zhichao Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Ebola Virus ◽  
Virus Disease ◽  
Propagation Model ◽  
Propagation Mechanism ◽  
Chinese Government ◽  
Emergency Plans ◽  
Artificial Society ◽  
The City

Ebola virus disease (EVD) distinguishes its feature as high infectivity and mortality. Thus, it is urgent for governments to draw up emergency plans against Ebola. However, it is hard to predict the possible epidemic situations in practice. Luckily, in recent years, computational experiments based on artificial society appeared, providing a new approach to study the propagation of EVD and analyze the corresponding interventions. Therefore, the rationality of artificial society is the key to the accuracy and reliability of experiment results. Individuals’ behaviors along with travel mode directly affect the propagation among individuals. Firstly, artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals’ behaviors. Meanwhile, Ebola course model and propagation model are built, according to the parameters in West Africa. Subsequently, propagation mechanism of EVD is analyzed, epidemic scenario is predicted, and corresponding interventions are presented. Finally, by simulating the emergency responses of Chinese government, the conclusion is finally drawn that Ebola is impossible to outbreak in large scale in the city of Beijing.

On the state independency and log-linearity of error propagation for discrete group affine systems with application to attitude estimation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiaolong Wang ◽  
Chengxi Zhang ◽  
Jin Wu
Keyword(s):  
Error Propagation ◽  
Discrete Group ◽  
Attitude Estimation ◽  
The State ◽  
Propagation Model ◽  
Rigorous Proof ◽  
Affine Systems ◽  
Content Type ◽  
Physical Systems ◽  
Propagation Properties

Purpose This paper aims to propose a general and rigorous study on the propagation property of invariant errors for the model conversion of state estimation problems with discrete group affine systems. Design/methodology/approach The evolution and operation properties of error propagation model of discrete group affine physical systems are investigated in detail. The general expressions of the propagation properties are proposed together with the rigorous proof and analysis which provide a deeper insight and are beneficial to the control and estimation of discrete group affine systems. Findings The investigation on the state independency and log-linearity of invariant errors for discrete group affine systems are presented in this work, and it is pivotal for the convergence and stability of estimation and control of physical systems in engineering practice. The general expressions of the propagation properties are proposed together with the rigorous proof and analysis. Practical implications An example application to the attitude dynamics of a rigid body together with the attitude estimation problem is used to illustrate the theoretical results. Originality/value The mathematical proof and analysis of the state independency and log-linearity property are the unique and original contributions of this work.

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