Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Photonic crystal fiber based surface plasmon resonance chemical sensors

Abstract: Research developments of the photonic crystal fiber based surface plasmon resonance (PCF-SPR) chemical sensors were intensively reviewed Photonic crystal fibers, such as the microstructured optical fiber, the photonic bandgap fiber and the Bragg fiber with various structures were applied to the SPR sensors, including fuse-tapered fiber structure, D-type fiber structure and cladding-off fiber structure Those sensors were classified as three kinds of configurations which were respectively based on the inner metal layer, the metallic nanowire and the outer metal film What's more, the principles, superiorities and problems of the PCF-SPR sensors were also discussed in detail

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

Abstract: Accurate and timely prediction of remaining useful life (RUL) of a machine enables the machine to have an appropriate operation and maintenance decision. Data-driven RUL prediction methods are more attractive to researchers because they can be deployed quicker and cheaper compared to other approaches. The existing deep neural network (DNN) models proposed for the applications of RUL prediction are mostly single-path and top-down propagation. In order to improve the prognostic accuracy of the network, this paper proposes a directed acyclic graph (DAG) network that combines long short term memory (LSTM) and a convolutional neural network (CNN) to predict the RUL. Different from the existing prediction models combined with CNN and LSTM, the method proposed in this paper combines CNN and LSTM organically instead of just using CNN for feature extraction. Moreover, when a single timestamp is used as an input, padding the signals in the same training batch would affect the prediction ability of the developed model. To overcome this drawback, the proposed method generates a short-term sequence by sliding the time window (TW) with one step size. In addition, based on the degradation mechanism, the piece-wise RUL function is used instead of the traditional linear function. In the experimental test, the turbofan engine degradation simulation dataset provided by NASA is used to validate the proposed RUL prediction model. By comparing with the existing methods using the same dataset, it can be concluded that the prediction method proposed in this paper has better prediction capability.

Fractional calculus in image processing: a review

Abstract: Over the last decade, it has been demonstrated that many systems in science and engineering can be modeled more accurately by fractional-order than integer-order derivatives, and many methods are developed to solve the problem of fractional systems. Due to the extra free parameter order, fractional-order based methods provide additional degree of freedom in optimization performance. Not surprisingly, many fractional-order based methods have been used in image processing field. Herein recent studies are reviewed in ten sub-fields, which include image enhancement, image denoising, image edge detection, image segmentation, image registration, image recognition, image fusion, image encryption, image compression and image restoration. In sum, it is well proved that as a fundamental mathematic tool, fractional-order derivative shows great success in image processing.

Genetic algorithms for solving shortest path problems

Abstract: In this study, we investigated the possibility of using genetic algorithms to solve shortest path problems. The most thorny and critical task for developing a genetic algorithm to this problem is how to encode a path in a graph into a chromosome. A priority-based encoding method is proposed which can potentially represent all possible paths in a graph. Because a variety of network optimization problems may be solved, either exactly or approximately, by identifying shortest path, this studies will provide a base for constructing efficient solution procedures for shortest path-based network optimization problems. The proposed approach has been tested on three randomly generated problems with different size from 6 nodes to 70 nodes and from 10 edges to 211 edges. The experiment results are very encouraging: it can find the known optimum very rapidly with very high probability. It can be believed that genetic algorithms may hopefully be a new approach for such kinds of difficult-to-solve problems.

Adaptive Actor–Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances

Abstract: This paper is concerned with the problem of integral sliding-mode control for a class of nonlinear systems with input disturbances and unknown nonlinear terms through the adaptive actor–critic (AC) control method. The main objective is to design a sliding-mode control methodology based on the adaptive dynamic programming (ADP) method, so that the closed-loop system with time-varying disturbances is stable and the nearly optimal performance of the sliding-mode dynamics can be guaranteed. In the first step, a neural network (NN)-based observer and a disturbance observer are designed to approximate the unknown nonlinear terms and estimate the input disturbances, respectively. Based on the NN approximations and disturbance estimations, the discontinuous part of the sliding-mode control is constructed to eliminate the effect of the disturbances and attain the expected equivalent sliding-mode dynamics. Then, the ADP method with AC structure is presented to learn the optimal control for the sliding-mode dynamics online. Reconstructed tuning laws are developed to guarantee the stability of the sliding-mode dynamics and the convergence of the weights of critic and actor NNs. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.