An Adaptive Nonlinear Control for Gyro Stabilized Platform Based on Neural Networks and Disturbance Observer

In order to improve the tracking performance of gyro stabilized platform with disturbances and uncertainties, an adaptive nonlinear control based on neural networks and reduced-order disturbance observer for disturbance compensation is developed. First the reduced-order disturbance observer estimates the disturbance directly. The error of the estimated disturbance caused by parameter variation and measurement noise is then approximated by neural networks. The phase compensation is also introduced to the proposed control law for the desired sinusoidal tracking. The stability of the proposed scheme is analyzed by the Lyapunov criterion. Experimental results show the validity of the proposed control approach

ERSVC: An Efficient Routing Scheme for Satellite Constellation Adapting Vector Composition

AbstractCompared with GEO and MEO satellites, LEO satellite constellation is able to provide low-latency, broadband communications which is difficult to be provided by the GEO or MEO satellites. However, one of the challenges in LEO constellation is the development of an efficient and specialized routing scheme. This paper takes transmission rate and data transmission time into consideration, and proposes ERSVC, an efficient routing scheme for satellite constellation adapting vector composition. ERSVC reduces routing table computation complexity, and saves restricted satellite resources. By adapting vector composition method, the amount of data flowing into satellite constellation is maximized while the data traffic is well controlled. Correlative and comprehensive simulation indicates that ERSVC is superior to existing schemes for LEO satellite constellation, especially in balancing data flow

Complex-Coefficient Frequency Domain Stability Analysis Method for a Class of Cross-Coupled Antisymmetrical Systems and Its Extension in MSR Systems

This paper develops a complex-coefficient frequency domain stability analysis method for a class of cross-coupled two-dimensional antisymmetrical systems, which can greatly simplify the stability analysis of the multiple-input multiple-output (MIMO) system. Through variable reconstruction, the multiple-input multiple-output (MIMO) system is converted into a single-input single-output (SISO) system with complex coefficients. The pole locations law of the closed-loop system after the variable reconstruction has been revealed, and the controllability as well as observability of the controlled plants before and after the variable reconstruction has been studied too, and then the classical Nyquist stability criterion is extended to the complex-coefficient frequency domain. Combined with the rigid magnetically suspended rotor (MSR) system with heavy gyroscopic effects, corresponding stability criterion has been further developed. Compared with the existing methods, the developed criterion for the rigid MSR system not only accurately predicts the absolute stability of the different whirling modes, but also directly demonstrates their relative stability, which greatly simplifies the analysis, design, and debugging of the control system

Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

Radiomics analysis has achieved great success in recent years. However, conventional Radiomics analysis suffers from insufficiently expressive hand-crafted features. Recently, emerging deep learning techniques, e.g., convolutional neural networks (CNNs), dominate recent research in Computer-Aided Diagnosis (CADx). Unfortunately, as black-box predictors, we argue that CNNs are "diagnosing" voxels (or pixels), rather than lesions; in other words, visual saliency from a trained CNN is not necessarily concentrated on the lesions. On the other hand, classification in clinical applications suffers from inherent ambiguities: radiologists may produce diverse diagnosis on challenging cases. To this end, we propose a controllable and explainable {\em Probabilistic Radiomics} framework, by combining the Radiomics analysis and probabilistic deep learning. In our framework, 3D CNN feature is extracted upon lesion region only, then encoded into lesion representation, by a controllable Non-local Shape Analysis Module (NSAM) based on self-attention. Inspired from variational auto-encoders (VAEs), an Ambiguity PriorNet is used to approximate the ambiguity distribution over human experts. The final diagnosis is obtained by combining the ambiguity prior sample and lesion representation, and the whole network named $DenseSharp^{+}$ is end-to-end trainable. We apply the proposed method on lung nodule diagnosis on LIDC-IDRI database to validate its effectiveness.Comment: MICCAI 2019 (early accept), with supplementary material

Evaluation of applying retaining shield rotor for high-speed interior permanent magnet motors

This paper proposes a novel rotor structure for high-speed interior permanent magnet motors to overcome huge centrifugal forces under high-speed operation. Instead of the conventional axial stacking of silicon-steel laminations, the retaining shield rotor is inter-stacked by high-strength stainless-steel plates to enhance the rotor strength against the huge centrifugal force. Both mechanical characteristics and electromagnetic behaviors of the retaining shield rotor are analyzed using finite-element method in this paper. Prototypes and experimental results are demonstrated to evaluate the performance. The analysis and test results show that the proposed retaining shield rotor could effectively enhance the rotor strength without a significant impact on the electromagnetic performance, while some design constraints should be compromised