Mean-Variance-Skewness Portfolio Selection Model Based on RBF-GA

The classical Markowitz’s mean-variance model in modern investment science uses variance as risk measure while it ignores the asymmetry of the return distribution. This article introduces skewness, V-type transaction costs, cardinality constraint and initial investment proportion, and builds a new class of nonlinear multi-objective portfolio model (mean-variance-skewness portfolio selection model). To solve the model, we develop a genetic algorithm(GA) which contains radial basis function(RBF) neural network, called RBF-GA. The experimental results show that the proposed model is more effective and more realistic than others

An Empirical Study on the Influencing Factors of University Students’ Sense of Gain in Ideological and Political Theory Course -- Take the Course of “Ideological and Moral Cultivation and Legal Basis”as An Example

The self-made questionnaire was administered to a random sample of 1000 undergraduates, the result of data analysis shows that the “Mechanism model of influencing factors on university students’ ‘Basic Course’  gain” proposed in this paper can partly explain the influence of personal, family, school and social factors on college students’ “Basic Course” acquisition; The factors of family, school and society are the external factors which affect the students’ sense of gain of“Basic Course”, and the personal factors are the internal factors which affect the students’ sense of gain of “Basic Course”; External factors act through internal factors. Based on that, this paper puts forward some suggestions and countermeasures to enhance the sense of gain of university students’“Basic courses”

Research on characteristics of noise-perturbed M–J sets based on equipotential point algorithm

AbstractAs the classical ones among the fractal sets, Julia set (abbreviated as J set) and Mandelbrot set (abbreviated as M set) have been explored widely in recent years. In this study, J set and M set under additive noise perturbation and multiplicative noise perturbation are created by equipotential point algorithm. Changes of the J set and M set under random noise perturbation as well as the close correlation between them are studied. Experimental results show that either additive noise perturbation or multiplicative noise perturbation may cause dramatic changes on J set. On the other hand, when the M set is perturbed by additive noise, it almost changes nothing but its position; when the M set is perturbed by multiplicative noise, its inner structures change with the stabilized areas shrinking, but it keeps the symmetry with respect to X axis. In addition, the J set and the M set still share the same stabilized periodic point in spite of noise perturbation

Microstructure and mechanical behavior of TiC-reinforced Ti-Mo-Al alloys

Ti-based alloys have gained extensive attractions in high-temperature engineering applications over the past several decades because of their low density, impressive strength and wear resistance. The continuing demands for advanced structural materials in aerospace and automobile sectors encourage further exploits of Ti-based alloys. Solid-solution hardening has been confirmed as an effective way to improve the mechanical performance of Ti-based alloys. Recent studies suggest that the incorporation of fibrous or particulate reinforcements, such as SiC, TiB and TiC, is necessary to maintain their high specific strength at elevated temperatures. In this study, Ti-Mo-Al (Ti50Mo35Al15, at.%) alloys with various TiC additions (1, 5, 10 at.%) were prepared by arc melting technique. We examined the microstructure of these as-cast alloys by X-ray diffractometry (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and high-resolution TEM (HRTEM). Their mechanical properties were systematically evaluated via compression experiments at various temperatures (T=298, 1073 and 1273 K), Vickers hardness as well as four-point bending tests. According to the experimental observations, all the alloys prepared in this work were composed of two phases, Ti-Mo-Al solid solution ( phase) matrix and TiC particles. Most of the TiC particles precipitated along grain boundaries, following the N-W crystallographic relationship with the matrix. Moreover, the effect of TiC addition on the microstructure of Ti-Mo-Al alloys was mainly manifested in the reduction of average grain size, which is ~80 m in the alloy without TiC but ~30 m in the 10 at.% TiC-added one. The addition of TiC leads to an obvious enhancement of strength at both room and high temperatures, without impairing the ductility. It is worth noting that the maximum flow stress achieved in the TiC-reinforced Ti-Mo-Al alloys at 1273 K is ~400 MPa. Therefore, the reinforcement by TiC is an effective way in improving the mechanical performance of Ti-Mo-Al alloys

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN). First, with Clarifai net and VGG Net-D (16 layers), we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW