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A novel principal components analysis (PCA) method for energy absorbing structural design enhanced by data mining
تجزیه و تحلیل اجزای اصلی جدید (PCA) برای جذب انرژی در طراحی ساختاری افزایش یافته توسط داده کاوی-2019 A PCA based structural design methodology is proposed and applied to the vehicle structure crashworthiness
design. The aim of this approach is to develop structures with complex geometry to satisfy the energy absorbing
design requirements using reduced number of design variables. This method is assisted by data mining technique
to discover the implicit interrelationship of these variables and generate corresponding design rules. In this
approach, the surface of structure is described by the control points, in the form of non-uniform rational basis
spline (NURBS). The large control point dataset is compressed using PCA technique, which is able to reduce
high-dimensional data by expressing them with a set of linearly uncorrelated orthogonal basis, i.e. Principal
Components (PCs), together with corresponding Principal Components Scores (PCSs). By changing the value of
the PCSs, the geometry of the part can be modified. Through this process, instead of directly handling a huge
number of geometry control points, one can perform the design by adjusting the values of a small number of
PCSs, and thus the computational cost is significantly reduced. As a case study, the vehicle frontal side rail
(known as an S-shaped beam) is designed using this method. After the design is complete, a data mining process
is performed, to explore the implicit interrelationship between design variables (i.e. PCSs), and generate design
rules to guide the design procedure. The results suggest that the PCA approach can be used to design a complicated
structure with irregular shape effectively and efficiently, and overcome the weakness of conventional
design methods, i.e. limited capability of handling high-dimensional design variables and big design datasets.
The subsequent data mining process enhances the design procedure by revealing some critical interrelations
between parameters and generating design rules for practical applications. Keywords: Principal component analysis (PCA) | Structural design | Data mining | Decision tree | Energy absorption |
مقاله انگلیسی |
2 |
Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets
ترکیب تجزیه و تحلیل مؤلفه های اصلی ، تبدیل موجک گسسته و XGBoost برای تجارت در بازارهای مالی-2019 When investing in financial markets it is crucial to determine a trading signal that can provide the in- vestor with the best entry and exit points of the financial market, however this is a difficult task and has become a very popular research topic in the financial area. This paper presents an expert system in the financial area that combines Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT), Extreme Gradient Boosting (XGBoost) and a Multi-Objective Optimization Genetic Algorithm (MOO-GA) in order to achieve high returns with a low level of risk. PCA is used to reduce the dimensionality of the financial input data set and the DWT is used to perform a noise reduction to every feature. The re- sultant data set is then fed to an XGBoost binary classifier that has its hyperparameters optimized by a MOO-GA. The importance of the PCA is analyzed and the results obtained show that it greatly improves the performance of the system. In order to improve even more the results obtained in the system using PCA, the PCA and the DWT are then applied together in one system and the results obtained show that this system is capable of outperforming the Buy and Hold (B&H) strategy in three of the five analyzed financial markets, achieving an average rate of return of 49.26% in the portfolio, while the B&H achieves on average 32.41%. Keywords: Financial markets | Principal Component Analysis (PCA) | Discrete Wavelet Transform (DWT) | Extreme Gradient Boosting (XGBoost) | Multi-Objective Optimization Genetic | Algorithm (MOO-GA) |
مقاله انگلیسی |