Robust Fusion Filtering for Multisensor Time-Varying Uncertain Systems: The Finite Horizon Case

The robust H∞ fusion filtering problem is considered for linear time-varying uncertain systems observed by multiple sensors. A performance index function for this problem is defined as an indefinite quadratic inequality which is solved by the projection method in Krein space. On this basis, a robust centralized finite horizon H∞ fusion filtering algorithm is proposed. However, this centralized fusion method is with poor real time property, as the number of sensors increases. To resolve this difficulty, within the sequential fusion framework, the performance index function is described as a set of quadratic inequalities including an indefinite quadratic inequality. And a sequential robust finite horizon H∞ fusion filtering algorithm is given by solving this quadratic inequality group. Finally, two simulation examples for time-varying/time-invariant multisensor systems are exploited to demonstrate the effectiveness of the proposed methods in the respect of the real time property and filtering accuracy

Analysis on Strong Tracking Filtering for Linear Dynamic Systems

Strong tracking filtering (STF) is a popular adaptive estimation method to effectively deal with state estimation for linear and nonlinear dynamic systems with inaccurate models or sudden change of state. The key of the STF is to use a time-variant fading factor, which can be evaluated based on the current measurement innovation in real time, to forcefully correct one step state prediction error covariance. The strong tracking filtering technology has been extensively applied in many practical systems, but the theoretical analysis is highly lacking. In an effort to better understand STF, a novel analysis framework is developed for the strong tracking filtering and some new problems are discussed for the first time. For this, we propose a new perspective that correcting the state prediction error covariance by using the fading factor can be thought of directly modifying the state model by correcting the covariance of the process noise. Based on this proposed point of view, the conditions for the STF function to be effective are deeply analyzed in a certain linear dynamic system. Meanwhile, issues of false alarm and alarm failure are also briefly discussed for the strong tracking filtering function. Some numerical simulation examples are demonstrated to validate the results

Traction Power Substation Load Analysis with Various Train Operating Styles and Substation Fault Modes

Simulation of railway systems plays a key role in designing the traction power supply 13 network, managing the train operation and making changes of timetables. Various simulation 14 technologies have been developed to study the railway traction power network and train operation 15 independently. However, the inter-action among load performance, train operation and fault 16 conditions have been fully understood. This paper proposes a mathematical modeling method to 17 simulate the railway traction power network with consideration of multi-train operation, driving 18 controls, under-voltage traction, and substation fault modes. The network voltage, power load 19 demands, energy consumption according to the existing operation are studied. The hotspots of the 20 power supply network are identified based on the evaluation of train operation and power demand. 21 The impact of traction power substation (TPSS) outage and short circuit on the power supply 22 network have been simulated and analyzed. The simulation results have been analyzed and 23 compared with the normal operation. A case study based on a practical metro line in Singapore 24 Metro is developed to illustrate the power network evaluation performance

Unified Finite Horizon H

This paper addresses the H∞ fusion filtering problem for networked dynamical systems, where measurements may arrive at fusion center in four different scenes and the fusion center could receive none, one, or multiple measurements in a fusion period. A unified H∞ performance criterion function, which is suitable for different measurement arrival scenes, is designed for the filtering process of networked dynamical systems. Then, the H∞ performance criterion function is described as an indefinite quadratic inequality and solved by a novel noise projection method in Krein space. On this basis, a unified finite horizon H∞ filtering method is proposed for networked dynamical systems. Simulation results are provided to illustrate the correctness and the effectiveness of the theoretical analysis

Local Bifurcations for a Delay Differential Model of Plankton Allelopathy

Abstract: This paper is concerned with a two-species competitive system of plankton allelopathy with delay. A modified differential equation model of plankton allelopathy having stimulatory effects on each other is investigated in this paper. By regarding the delay τ as the bifurcation parameter, firstly, the stability of the positive equilibrium and the existence of Hopf bifurcation are investigated. Furthermore, the direction of Hopf bifurcation and the stability of the bifurcating periodic solutions are determined by the normal form theory and the center manifold theorem for functional differential equations. Finally, some numerical simulations are carried out for illustrating the theoretical results

TSViT: A Time Series Vision Transformer for Fault Diagnosis

Traditional fault diagnosis methods using Convolutional Neural Networks (CNNs) face limitations in capturing temporal features (i.e., the variation of vibration signals over time). To address this issue, this paper introduces a novel model, the Time Series Vision Transformer (TSViT), specifically designed for fault diagnosis. On one hand, TSViT model integrates a convolutional layer to segment vibration signals and capture local features. On the other hand, it employs a transformer encoder to learn long-term temporal information. The experimental results with other methods on two distinct datasets validate the effectiveness and generalizability of TSViT with a comparative analysis of its hyperparameters' impact on model performance, computational complexity, and overall parameter quantity. TSViT reaches average accuracies of 100% and 99.99% on two test sets, correspondingly