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: Microstructure & Control theory. 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).

Adaptive Neural Network Output Feedback Control for Stochastic Nonlinear Systems With Unknown Dead-Zone and Unmodeled Dynamics

Abstract: This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Lexicographic Multiobjective Scatter Search for the Optimization of Sequence-Dependent Selective Disassembly Subject to Multiresource Constraints

Abstract: Industrial products’ reuse, recovery, and recycling are very important because of their environmental and economic benefits. Effective product disassembly planning methods can improve their recovery efficiency and reduce their bad environmental impact. However, the existing approaches pay little attention to sequence-dependent disassembly with resource constraints, such as limited disassembly operators and tools, which makes the current planning methods ineffective in practice. This paper considers a multiobjective resource-constrained and sequence-dependent disassembly optimization problem with disassembly precedence constraints. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with traditional optimization criterion leads to a novel multiobjective optimization model such that the energy consumption and disassembly time are minimized while disassembly profit is maximized. Since the problem complexity increases with the number of components in a product, a lexicographic multiobjective scatter search (SS) method is proposed to solve the proposed multiobjective optimization problem. Its effectiveness is verified by comparing the results of linear weight SS and genetic algorithms. The results show that it is able to provide a better solution in a short execution time and fulfills the precedence requirement in a product structure and resource constraints.

CAS(ME) $^2$ : A Database for Spontaneous Macro-Expression and Micro-Expression Spotting and Recognition

Abstract: Deception is a very common phenomenon and its detection can be beneficial to our daily lives. Compared with other deception cues, micro-expression has shown great potential as a promising cue for deception detection. The spotting and recognition of micro-expression from long videos may significantly aid both law enforcement officers and researchers. However, database that contains both micro-expression and macro-expression in long videos is still not publicly available. To facilitate development in this field, we present a new database, Chinese Academy of Sciences Macro-Expressions and Micro-Expressions (CAS(ME) $^2$ ), which provides both macro-expressions and micro-expressions in two parts (A and B). Part A contains 87 long videos that contain spontaneous macro-expressions and micro-expressions. Part B includes 300 cropped spontaneous macro-expression samples and 57 micro-expression samples. The emotion labels are based on a combination of action units (AUs), self-reported emotion for every facial movement, and the emotion types of emotion-evoking videos. Local Binary Pattern (LBP) was employed for the spotting and recognition of macro-expressions and micro-expressions and the results were reported as a baseline evaluation. The CAS(ME) $^2$ database offers both long videos and cropped expression samples, which may aid researchers in developing efficient algorithms for the spotting and recognition of macro-expressions and micro-expressions.