Tuberculosis (TB) is a highly contagious and life-threatening infectious disease that affects millions of people worldwide. Early diagnosis of TB is essential for prompt treatment and control of the spread of the disease. In this paper, a new deep learning model called CBAMWDnet is proposed for the detection of TB in chest X-ray (CXR) images. The model is based on the Convolutional Block Attention Module (CBAM) and the Wide Dense Net (WDnet) architecture, which has been designed to effectively capture spatial and contextual information in the images. The performance of the proposed model is evaluated based on a large dataset of chest X-ray images and it is compared to several state-of-the-art models. The results show that the proposed model outperforms the other models in terms of accuracy (98.80%), sensitivity (94.28%), precision (98.50%), specificity (95.7%) and F1 score (96.35%). Additionally, our model demonstrates excellent generalization ability, with consistent performance on different datasets. In conclusion, the proposed CBAMWDnet model is a promising tool for the early diagnosis of TB, with superior performance compared to other state-of-the-art models, as evidenced by the evaluation metrics of accuracy, sensitivity, and specificity.

This paper presents a self-organizing control system based on cerebellar model articulation controller (CMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding-mode control (SMC), so the input space dimension of CMAC can be simplified. The structure of CMAC will be self-organized; that is, the layers of CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The control system consists of a self-organizing CMAC (SOCM) and a robust controller. SOCM containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient-descent method is used to online tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. A simulation study of inverted double pendulums system and an experimental result of linear ultrasonic motor motion control show that favorable tracking performance can be achieved by using the proposed control system.

This paper presents a novel fuzzy neural network structure for uncertain nonlinear systems based on the framework of cerebellar model neural network (CMNN) and the Takagi -Sugeno-Kang (TSK) fuzzy knowledge base. To solve the uncertainty problem of nonlinear systems better, the wavelet functions are used in the consequent parts of each TSK fuzzy rule instead of the linear combination of the input variables, and the Gaussian-type functions are used as the membership functions in the antecedent parts. Combining the advantages of the wavelet function, the CMNN and the TSK fuzzy system, this new model is more suitable for uncertain nonlinear systems. To provide rapid training, parameter update rules of the proposed model are derived based on the gradient descent method, in which the learning rates are online adapted. Furthermore the Lyapunov function is used to analyze the convergence of the considered systems. Finally, four different types of applications are applied to demonstrate the performance of the proposed model. The comparison simulation results with other models verify the effectiveness of this new model.

In this article, a new idea of chaos synchronization and chaos-based secure communication is developed. First, the chaotic master system is used as a transmitter in chaos-based secure communication, then a drive signal is constructed, and the information message is encrypted into the drive signal to form a transmitted signal for secure communication. Second, in the receiver, a recurrent Takagi–Sugeno–Kang (TSK) fuzzy brain emotional learning cerebellar model articulation controller (RTFBECAC) is developed to control the slave system to follow the master system in the transmitter. Third, after descripting the chaotic signal, the embedded information message can be recovered. Besides, the stability problem is analyzed in detail based on the stability theory. Finally, two simulation examples, including audio signal and image, are introduced to illustrate the effectiveness and the advantages of the proposed method.

A hybrid control system, integrating principal and compensation controllers, is developed for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. This hybrid control system is based on sliding-mode technique and uses a recurrent cerebellar model articulation controller (RCMAC) as an uncertainty observer. The principal controller containing an RCMAC uncertainty observer is the main controller, and the compensation controller is a compensator for the approximation error of the system uncertainty. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. The Taylor linearization technique is employed to increase the learning ability of RCMAC and the adaptive laws of the control system are derived based on Lyapunov stability theorem and Barbalat's lemma so that the asymptotical stability of the system can be guaranteed. Finally, the proposed design method is applied to control a biped robot. Simulation results demonstrate the effectiveness of the proposed control scheme for the MIMO uncertain nonlinear system