Robust fault detection and diagnosis for uncertain nonlinear systems

This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensitive to the uncertainty as much as possible. Then the paper solves the proposed criterion by maximizing the smallest singular value of the transformation from faults to fault detection residuals while minimizing the largest singular value of the transformation from input uncertainty to the fault detection residuals. This method is applied to an aircraft which has a fault in the left elevator or rudder. The simulation results show the proposed method can detect the control surface failures rapidly and efficiently.     

Online blind source separation based on joint diagonalization

A now algorithm is proposed for joint diagonalization. With a modified objective function, the now algorithm not only excludes trivial and unbalanced solutions successfully, but is also easily optimized. In addition, with the new objective function, the proposed algorithm can work well in online blind source separation (BSS) for the first time, although this family of algorithms is always thought to be valid only in batch-mode BSS by far. Simulations show that it is a very competitive joint diagonalization algorithm.     

Closed circle DNA algorithm of change positive-weighted Hamilton circuit problem

Chain length of closed circle DNA is equal. The same closed circle DNA's position corresponds to different recognition sequence, and the same recognition sequence corresponds to different foreign DNA segment, so closed circle DNA computing model is generalized. For change positive-weighted Hamilton circuit problem, closed circle DNA algorithm is put forward. First, three groups of DNA encoding are encoded for all arcs, and deck groups are designed for all vertices. All possible solutions axe composed. Then, the feasible solutions axe filtered out by using group detect experiment, and the optimization solutions are obtained by using group insert experiment and electrophoresis experiment. Finally, all optimization solutions are found by using detect experiment. Complexity of algorithm is concluded and validity of DNA algorithm is explained by an example. Three dominances of the closed circle DNA algorithm are analyzed, and characteristics and dominances of group delete experiment axe discussed.     

High efficient moving object extraction and classification in traffic video surveillance

Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is used in the stable region with gradual changes, and a nonparametric model is used in the variable region with jumping changes. A generalized agglomerative scheme is used to merge the pixels in the variable region and fill in the small interspaces. A two-threshold sequential algorithmic scheme is used to group the background samples of the variable region into distinct Gaussian distributions to accelerate the kernel density computation speed of the nonparametric model. In the feature-based object classification phase, the surveillance scene is first partitioned according to the road boundaries of different traffic directions and then re-segmented according to their scene localities. The method improves the discriminability of the features in each partition. AdaBoost method is applied to evaluate the relative importance of the features in each partition respectively and distinguish whether an object is a vehicle, a single human, a human group, or a bike. Experimental results show that the proposed method achieves higher performance in comparison with the existing method.     

控制污染物排放的支线机场跑滑结构优化仿真

民航运输在LTO(Landing and take-off)循环阶段的大气污染物排放对地面人员和近地环境危害较大,应主要通过减少地面滑行时间进行控制。考虑支线机场常见的单跑滑结构给出飞机地面滑行时间计算方法,基于该方法提出了污染物评价指标并建立了支线机场航空器污染物排放评估模型。以某支线机场为例,使用Matlab建模计算得出污染物评价指数最小滑行道口位置。利用AirTOp仿真验证了模型计算结果有效性,优化后的跑道出口位置将使该机场污染物总指数较原位置降低21.7%。     

基于仿真的无人机空袭沙特油田事件探索性分析

以2019 年9 月也门空袭沙特油田事件为例,提出一种基于仿真的危机事件快速分析方法,将仿真与探索性分析相结合,对危机事件进行全面的分析与处理。根据沙特、也门关于事件细节的公开资料,基于仿真推演复盘此次空袭的作战情况,校验各方在事件声明中细节的合理性和真实性,同时可为我国防御“低、小、慢”无人机的发展和作战研究提供借鉴意义。     

An Evolutionary Real-Time 3D Route Planner for Aircraft

Abstract: A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candi-dates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By usingproblem-specific chromosome structure and genetic operators, the routes are generated in real time, with different missionconstraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving an-gle and route distance constraint taken into account.     

中国社会稳定与安全预警系统的理论设计

简要概述了中国社会稳定与安全预警系统的功能与作用 ;重点介绍了该预警系统设计的理论依据 ,即在预警系统设计过程中 ,综合、集成、借鉴了不同学科的理论与方法 ,其中 ,创造性地应用了社会物理学的思想、理论与方法 ,以此作为研制预警系统的总体理论框架 ;依据研究组独创的社会稳定与安全五大支持系统理论 ,设计出了“四级叠加 ,逐层收敛 ,系统耦合 ,规范权重 ,识别阈值 ,统一排序”的预警指标体系 ;最后 ,就预警系统内部模块的结构、功能与系统运行流程作了一定程度的说明。     

Pre-processing for time domain image formation in SS-BSAR system

A pre-processing procedure is designed for a space-surface bistatic synthetic aperture radar (SS-BSAR) system when a time domain image formation algorithm is employed. Three crucial technical issues relating to the procedure are fully discussed. Firstly, unlike image formation algorithms operating in the frequency domain, a time domain algorithm requires the accurate global navigation satellite system (GNSS) time and position. This paper proposes acquisition of this information using a time-and-spatial transfer with precise ephemeris and interpolation. Secondly, synchronization errors and compensation methods in SS-BSAR are analyzed. Finally, taking the non-ideal factors in the echo and the compatibility of image formation algorithms into account, a matched filter based on the minimum delay is constructed. Experimental result using real data suggest the pre-processing is functioning properly.     

Enhanced self-adaptive evolutionary algorithm for numerical optimization

There are many population-based stochastic search algorithms for solving optimization problems. However, the universality and robustness of these algorithms are still unsatisfactory. This paper proposes an enhanced self-adaptiveevolutionary algorithm (ESEA) to overcome the demerits above. In the ESEA, four evolutionary operators are designed to enhance the evolutionary structure. Besides, the ESEA employs four effective search strategies under the framework of the self-adaptive learning. Four groups of the experiments are done to find out the most suitable parameter values for the ESEA. In order to verify the performance of the proposed algorithm, 26 state-of-the-art test functions are solved by the ESEA and its competitors. The experimental results demonstrate that the universality and robustness of the ESEA out-perform its competitors.