Histopathological Imaging Classification of Breast Tissue for Cancer Diagnosis Support Using Deep Learning Models

According to some medical imaging techniques, breast histopathology images called Hematoxylin and Eosin are considered as the gold standard for cancer diagnoses. Based on the idea of dividing the pathologic image (WSI) into multiple patches, we used the window [512,512] sliding from left to right and sliding from top to bottom, each sliding step overlapping by 50% to augmented data on a dataset of 400 images which were gathered from the ICIAR 2018 Grand Challenge. Then use the EffficientNet model to classify and identify the histopathological images of breast cancer into 4 types: Normal, Benign, Carcinoma, Invasive Carcinoma. The EffficientNet model is a recently developed model that uniformly scales the width, depth, and resolution of the network with a set of fixed scaling factors that are well suited for training images with high resolution. And the results of this model give a rather competitive classification efficiency, achieving 98% accuracy on the training set and 93% on the evaluation set.Comment: International Conference on Industrial Networks and Intelligent Systems (INISCOM-2022), Springer, Vol. 444, pp. 152-16

Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM) drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method

An Improved Adaptive Tracking Controller of Permanent Magnet Synchronous Motor

This paper proposes a new adaptive fuzzy neural control to suppress chaos and also to achieve the speed tracking control in a permanent magnet synchronous motor (PMSM) drive system with unknown parameters and uncertainties. The control scheme consists of fuzzy neural and compensatory controllers. The fuzzy neural controller with online parameter tuning is used to estimate the unknown nonlinear models and construct linearization feedback control law, while the compensatory controller is employed to attenuate the estimation error effects of the fuzzy neural network and ensure the robustness of the controlled system. Moreover, due to improvement in controller design, the singularity problem is surely avoided. Finally, numerical simulations are carried out to demonstrate that the proposed control scheme can successfully remove chaotic oscillations and allow the speed to follow the desired trajectory in a chaotic PMSM despite the existence of unknown models and uncertainties

An Optimal Cluter Head Parameter in Leach Protocol of Wireless Sensor Networks

In wireless sensor networks, LEACH is often used as an energy saving protocol and extends the network life. However, there are many parameters that affect the performance of the LEACH protocol, one of which is the number of cluster heads. This paper proposes a simple and efficient solution to determine the optimal number of cluster heads in the LEACH protocol. For the proposed solution, the system can achieve the optimal performance between the longest lifetime in the constraint as the largest amount of data transmitted in the network.  &nbsp

Sliding Mode Based Adaptive Control of Chaos for Permanent Magnet Synchronous Motors

The paper presents the sliding mode based adaptive control (SMAC) of chaos for a permanent magnet synchronous motor (PMSM) subjected to parameter uncertainties and an external disturbance. A PMSM faces the chaos phenomenon when its parameters fall into a certain area. The sliding mode based adaptive control is developed to eliminate chaos and ensure the robust stability even when the system parameters are in the chaotic area and the external disturbance affects system dynamics. Finally, under the control actions, the chaos phenomenon can be driven to zero. The numerical simulation is carried out to demonstrate the perfect performance of the proposed control approach