Support Vector Machine Based Trajectory Metamodel for Conceptual Design of Multi-stage Space Launch Vehicle

2005 ◽  
pp. 528-535 ◽  
Author(s):  
Saqlain Akhtar ◽  
He Linshu
Keyword(s):  
Support Vector Machine ◽  
Conceptual Design ◽  
Launch Vehicle ◽  
Support Vector ◽  
Multi Stage ◽  
Space Launch

scholarly journals Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

2020 ◽  
Author(s):  
V. Vijayasarveswari ◽  
A.M. Andrew ◽  
M. Jusoh ◽  
T. Sabapathy ◽  
R.A.A. Raof ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Probabilistic Neural Network ◽  
The Other ◽  
Ultra Wideband ◽  
Support Vector ◽  
Data Normalization ◽  
Multi Stage

AbstractBreast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi-stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

scholarly journals Selecting Design Objectives for an Integrated Guidance System of a Commercial Launch Vehicle with Application of GPS Technologies

2013 ◽  
Vol 6 (1) ◽  
pp. 6-19
Author(s):  
Nickolay Zosimovych
Keyword(s):  
Conceptual Design ◽  
Navigation System ◽  
Technical Problem ◽  
Launch Vehicle ◽  
Guidance System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Space Launch ◽  
Artificial Earth

In this article have been proposed the design objectives for an integrated guidance system of a commercial launch vehicle with application of GPS technologies and has been set a technical problem of the conceptual design of an integrated navigation system for the space launch vehicle qualified to inject small artificial Earth satellites into low and medium circular orbits. The conceptual design of the integrated navigation system based on GPS technology involves determination of its structure, models and algorithms, providing the required accuracy and reliability in injecting payloads with due regard to restrictions on weight and dimensions of the system.

An approach for space launch vehicle conceptual design and multi-attribute evaluation

2013 ◽  
Vol 25 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Rizwan Ullah ◽  
De-Qun Zhou ◽  
Peng Zhou ◽  
Mukarrum Hussain ◽  
M. Amjad Sohail
Keyword(s):  
Conceptual Design ◽  
Launch Vehicle ◽  
Space Launch ◽  
Attribute Evaluation

Analysis of targeting method for closed-loop guidance of a multi-stage space launch vehicle

2016 ◽  
Vol 57 (7) ◽  
pp. 1494-1507 ◽  
Author(s):  
Eun-Jung Song ◽  
Sangbum Cho ◽  
Woong-Rae Roh
Keyword(s):  
Closed Loop ◽  
Launch Vehicle ◽  
Multi Stage ◽  
Space Launch
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