Two-Stage Procrustes Rotation with Sparse Target Matrix and Least Squares Criterion with Regularization and Generalized Weighting

In factor analysis, a factor loading matrix is often rotated to a simple target matrix for its simplicity. For the purpose, Procrustes rotation minimizes the discrepancy between the target and rotated loadings using two types of approximation: 1) approximate the zeros in the target by the non-zeros in the loadings, and 2) approximate the non-zeros in the target by the non-zeros in the loadings. The central issue of Procrustes rotation considered in the article is that it equally treats the two types of approximation, while the former is more important for simplifying the loading matrix. Furthermore, a well-known issue of Simplimax is the computational inefficiency in estimating the sparse target matrix, which yields a considerable number of local minima. The research proposes a new rotation procedure that consists of the following two stages. The first stage estimates sparse target matrix with lesser computational cost by regularization technique. In the second stage, a loading matrix is rotated to the target, emphasizing on the approximation of non-zeros to zeros in the target by least squares criterion with generalized weighing that is newly proposed by the study. The simulation study and real data examples revealed that the proposed method surely simplifies loading matrices.

Analysis of effect factors on crack rotation factor

Crack rotation factor r and plastic crack rotation factor rp are analyzed by means of simulated test using finite element methods. The results indicate that r and rp are influenced by material and geometry parameters in three-point bend specimen and r and rp are variable value in small scope of yield. Therefore, it has some great error that r and rp are regarded as constants in analyzing CTOD crack driving force.

The optimal fractional Gabor transform based on the adaptive window function and its application

We designed the window function of the optimal Gabor transform based on the time-frequency rotation property of the fractional Fourier transform. Thus, we obtained the adaptive optimal Gabor transform in the fractional domain and improved the time-frequency concentration of the Gabor transform. The algorithm first searches for the optimal rotation factor, then performs the p-th FrFT of the signal and, finally, performs time and frequency analysis of the FrFT result. Finally, the algorithm rotates the plane in the fractional domain back to the normal time-frequency plane. This promotes the application of FrFT in the field of high-resolution reservoir prediction. Additionally, we proposed an adaptive search method for the optimal rotation factor using the Parseval principle in the fractional domain, which simplifies the algorithm. We carried out spectrum decomposition of the seismic signal, which showed that the instantaneous frequency slices obtained by the proposed algorithm are superior to the ones obtained by the traditional Gabor transform. The adaptive time frequency analysis is of great significance to seismic signal processing.

DETERMINATION OF SOME PARAMETERS OF CRACK BASED ON FRASTA SIMULATION

A new method, using the fracture-surface topography analysis （FRASTA）, of determining the parameters of rotation factor and CTOA is provided. The fracture surfaces are scanned by laser microscope and the height data is recorded to analyze. By comparing the crack extension, plastic rotation factor and COA obtained by the method of double clip gauge and by this new method respectively, the validity of it is verified. It is then used to determine the CTOA. .The critical CTOA is well determined in this paper. Also by the new method, the variation of crack opening deformation （COA and CTOA） through specimen thickness can be obtained which can＇t be obtained by the usual methods. It also can provide much information of crack extension such as direction and ratio.

A Novel Method for Thermoelectric Generator Based on Neural Network

The growing need for renewable energy and zero carbon dioxide emissions has fueled the development of thermoelectric generators with improved power generating capability.Along with the endeavor to develop thermoelectric materials with greater figures of merit,the geometrical and structural optimization of thermoelectric generators is equally critical for maximum power output and efficiency.Green energy strategies that are constantly updated are a viable option for addressing the global energy issue while also protecting the environment.There have been significant focuses on the development of thermoelectric modules for a range of solar,automotive,military,and aerospace applications in recent years due to various advantages including as low vibration,great reliability and durability,and the absence of moving components.In order to enhance the system performance of the thermoelectric generator,an artificial neural network(ANN)based algorithm is proposed.Furthermore,to achieve high efficiency and system stability,a buck converter is designed and deployed.Simulation and experimental findings demonstrate that the suggested method is viable and available,and that it is almost similar to the real value in the steady state with the least power losses,making it ideal for vehicle exhaust thermoelectric generator applications.Furthermore,the proposed hybrid algorithm has a high reference value for the development of a dependable and efficient car exhaust thermoelectric generating system.

Mixing of two-quasineutron and two-quasiproton K~π=6^+ configurations in the vicinity of ^(174)Yb

In the framework of the projected shell model, we investigate the competition between the two-quasineutron and two-quasiproton K~π=6~＋ states in the ytterbium isotopes and N =104 isotones adjacent to ^（174）Yb. The ^（174）Yb results are compared with the experimental data.The K~π =6~＋ isomer observed in ^（174） Yb is assigned as an admixture of the ν7/2^-[514] ν5/2^-[512] and π7/2~＋ [404]π5/2~＋ [402] intrinsic structure, which explains the experimental ｜g K-g R ｜ value. Similar mixing would appear in ^（174） Yb, ^（176） Hf,and ^（178） W. The low-lying K~π=6~＋ states are also predicted in ^（170-178） Yb.