Multi-Scale Temporal-Spatial Feature-Based Hybrid Deep Neural Network for Remaining Useful Life Prediction of Aero-Engine.

Remaining useful life (RUL) prediction is crucial for simplifying maintenance procedures and extending the lifespan of aero-engines. Therefore, research on RUL prediction methods for aero-engines is increasingly gaining attention. In particular, some existing deep neural networks based on multiscale features extraction have achieved certain results in RUL predictions for aero-engines.

A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks.

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR).

Event-trigger-based adaptive fuzzy hierarchical sliding mode control of uncertain under-actuated switched nonlinear systems.

In this technical note, we present an adaptive fuzzy hierarchical sliding mode control method to deal with the control problem of under-actuated switched nonlinear systems. For the system under consideration, both the issues of unknown uncertain functions and aperiodically updating input are taken into account, which are of practical importance. A bounded time-varying function is employed to make a linear transformation of the control input, leading to a transformed system that can be applied to the control design.

Regularized correntropy criterion based semi-supervised ELM.

Along with the explosive growing of data, semi-supervised learning attracts increasing attention in the past years due to its powerful capability in labeling unlabeled data and knowledge mining. As an emerging method, the semi-supervised extreme learning machine (SSELM), that builds on ELM, has been developed for data classification and shown superiorities in learning efficiency and accuracy. However, the optimization of SSELM as well as most of the other ELMs is generally based on the mean square error (MSE) criterion, which has been shown less effective in dealing with non-Gaussian noises.

Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and Uncertain Measurements.

This paper addresses the problem of state estimation for a class of discrete-time stochastic complex networks with a constrained and randomly varying coupling and uncertain measurements. The randomly varying coupling is governed by a Markov chain, and the capacity constraint is handled by introducing a logarithmic quantizer. The uncertainty of measurements is modeled by a multiplicative noise.

Node Deployment Algorithm for Underwater Sensor Networks Based on Connected Dominating Set.

Existing node deployment algorithms for underwater sensor networks are nearly unable to improve the network coverage rate under the premise of ensuring the full network connectivity and do not optimize the communication and move energy consumption during the deployment. Hence, a node deployment algorithm based on connected dominating set (CDS) is proposed. After randomly sowing the nodes in 3D monitoring underwater space, disconnected nodes move to the sink node until the network achieves full connectivity.

Synchronization on complex networks of networks.

In this paper, pinning synchronization on complex networks of networks is investigated, where there are many subnetworks with the interactions among them. The subnetworks and their connections can be regarded as the nodes and interactions of the networks, respectively, which form the networks of networks. In this new setting, the aim is to design pinning controllers on the chosen nodes of each subnetwork so as to reach synchronization behavior.

Penalty dynamic programming algorithm for dim targets detection in sensor systems.

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods.

Adaptive extended state space predictive control for a kind of nonlinear systems.

This paper presents an adaptive nonlinear predictive control design strategy for a kind of nonlinear systems with output feedback coupling and results in the improvement of regulatory capacity for reference tracking, robustness and disturbance rejection. The nonlinear system is first transformed into an equal time-variant system by analyzing the nonlinear part. Then an extended state space predictive controller with a similar structure of a PI optimal regulator and with P-step setpoint feedforward control is designed.