Hopf bifurcation analysis in a stage-structure predator–prey model with two time delay

In this paper, we consider a predator-prey system with two time delays, which describes a prey–predator model with parental care for predators. The local stability of the positive equilibrium is analysed. By choosing the two time delays as the bifurcation parameter, the existence of Hopf bifurcation is studied. Numerical simulations show the positive equilibrium loses its stability via the Hopf bifurcation when the time delay increases beyond a threshold.

A Novel Fuzzy PI Control Method for Variable Frequency Brushless Synchronous Generators

With the application of more electric aircraft (MEA) technology, variable frequencies and high power ratings become import features of aero-generators. The brushless synchronous generator, which has a three-stage structure, is the most commonly used type of aero-generator. Due to the variation of operating conditions, the implementation of generator controllers becomes more and more difficult. In this paper, a state space model of a generator is derived and the influence of different operating conditions on the frequency response characteristics of the generator is revealed. Based on a fuzzy PI controller, an additional fuzzy logic controller is applied to modify the PI parameters of the voltage loop by introducing the generator speed to cope with the speed variation. Finally, the results of the simulations and experiments demonstrate that the dual fuzzy PI controller can improve both the steady-state and dynamic performance of the brushless synchronous generator, verifying the previous theoretical study.

Lung cancer subtype classification using histopathological images based on weakly supervised multi-instance learning

Subtype classification plays a guiding role in the clinical diagnosis and treatment of non-small-cell lung cancer (NSCLC). However, due to the gigapixel of whole slide images (WSIs) and the absence of definitive morphological features, most automatic subtype classification methods for NSCLC require manually delineating the regions of interest (ROIs) on WSIs. In this paper, a weakly supervised framework is proposed for accurate subtype classification while freeing pathologists from pixel-level annotation. With respect to the characteristics of histopathological images, we design a two-stage structure with ROI localization and subtype classification. We first develop a method called MR-EM-CNN (multi-resolution expectation-maximization convolutional neural network) to locate ROIs for subsequent subtype classification. The EM algorithm is introduced to select the discriminative image patches for training a patch-wise network, with only WSI-wise labels available. A multi-resolution mechanism is designed for fine localization, similar to the coarse-to-fine process of manual pathological analysis. In the second stage, we build a novel hierarchical attention multi-scale network (HMS) for subtype classification. HMS can capture multi-scale features flexibly driven by the attention module and implement hierarchical features interaction. Experimental results on the 1002-patient Cancer Genome Atlas dataset achieved an AUC of 0.9602 in the ROI localization and an AUC of 0.9671 for subtype classification. The proposed method shows superiority compared with other algorithms in the subtype classification of NSCLC. The proposed framework can also be extended to other classification tasks with WSIs.