Using the Modified FPE Criteria Forecasting the Realized Variance: a Statistical Analysis

Numéro de référence interne originel : a1.1 g 112

Explosion gravitation field algorithm with dust sampling for unconstrained optimization

This research was funded by the National Natural Science Foundation of China (Nos. 61572227, 61772227, 61702214), the Development Project of Jilin Province of China (Nos 20170101006JC, 20180414012GH, 20170203002GX, 20190201293JC), Zhuhai Premier-Discipline Enhancement Scheme, China (Grant 2015YXXK02) and Guangdong Premier Key-Discipline Enhancement Scheme, China (Grant 2016GDYSZDXK036). This work was also supported by Jilin Provincial Key Laboratory of Big Date Intelligent Computing, China (No. 20180622002JC).Peer reviewedPostprin

EGFAFS:A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data

Accuracy improvement on fatigue test of megawatt wind turbine blades by adaptive fuzzy control

A single-point fatigue loading system of wind turbine blade driven by an unbalanced shaft was designed. To determine whether the vibrating performance of the loading system satisfied the fatigue test demanding, the blade was driven by different frequency under open-loop control mode in on-site flapwise fatigue test. The results showed that the more the driven frequency close to the blade’s natural frequency, the more the amplitude of the blade increase. In resonance mode, the amplitude of the blade can reach the maximum value certainly. However, the peak values of the vibration have some fluctuation, which will influence the accuracy of fatigue test. To solve the unstable problem of blade’s amplitude, the amplitude of blade’s loading point obtained by laser range meter was taken as the control index, the deviation of the amplitude and its variation tendency were taken as the input and the loading frequency as the output, then an adaptive fuzzy control system based on multistage network was built to realize blade’s constant amplitude vibrating. The on-site test showed the adaptive fuzzy control algorithm put forward in this paper could maintain the error of the peak value of vibration less than 5 mm, which satisfied the fatigue test requirement

Coupling mechanism of dual-excitation fatigue loading system of wind turbine blades

A new dual-excitation fatigue loading system of wind turbine blades was designed in this paper. However, the two excitations and blade constituted a complicated non-liner energy transferring system in which the vibration coupling effect would influence the sequent accurate control of fatigue test. To study the mechanism of the coupling system mentioned above, the electromechanical coupling mathematical model was established by simplifying the loading system rationally and the factors affecting the vibration coupling were obtained accordingly. Then the simulation model of the system was built in Matlab/Simulink environment to mainly analyze the basic influence laws of the motor speed and the initial phase difference of two excitations. Finally, a small dual-excitation fatigue loading system was established to verify the correctness of the mathematical and simulation model. It could be concluded that the results of on-site test were consistent with the results of simulation

APETALA2 antagonizes the transcriptional activity of AGAMOUS in regulating floral stem cells in Arabidopsis thaliana.

APETALA2 (AP2) is best known for its function in the outer two floral whorls, where it specifies the identities of sepals and petals by restricting the expression of AGAMOUS (AG) to the inner two whorls in Arabidopsis thaliana. Here, we describe a role of AP2 in promoting the maintenance of floral stem cell fate, not by repressing AG transcription, but by antagonizing AG activity in the center of the flower. We performed a genetic screen with ag-10 plants, which exhibit a weak floral determinacy defect, and isolated a mutant with a strong floral determinacy defect. This mutant was found to harbor another mutation in AG and was named ag-11. We performed a genetic screen in the ag-11 background to isolate mutations that suppress the floral determinacy defect. Two suppressor mutants were found to harbor mutations in AP2. While AG is known to shut down the expression of the stem cell maintenance gene WUSCHEL (WUS) to terminate floral stem cell fate, AP2 promotes the expression of WUS. AP2 does not repress the transcription of AG in the inner two whorls, but instead counteracts AG activity

ADRC Method for Noncascaded Integral System Based on the Total Derivative of Composite Functions of Several Variables

The standard ADRC controller usually selects the canonical plant in the form of cascaded integrators. However, the condition variables of practical system do not necessarily have the cascaded integral relationship. Therefore, this paper proposes a method of total derivative of composite functions of several variables and a structure, which can convert the state space system of noncascaded integral form into the cascaded integral form. In this way, the converted system can be directly controlled with ADRC. Meanwhile, the control of Chen chaotic system is discussed in detail to show the conversion and the controller design. The control performances under different levels of complication and different strengths of disturbance are comparably researched. The converted system achieves significantly better control effects under ADRC than that of the PID. This converting method solves the control problem of some noncascaded integral systems in both theory and application and greatly expands the application scope of the standard ADRC method