动态抗饱和平滑切换控制器设计方法

针对一类受饱和约束的切换系统,提出了一种动态抗饱和平滑切换控制器的设计方法。首先,基于模型依赖平均驻留时间的方法设计了平滑切换控制器,能够确保系统全局一致渐进稳定并具有一定的鲁棒性能。同时,通过引入链式切换律降低控制器设计的保守性。在此基础上,给出动态抗饱和补偿器的设计方法。将动态抗饱和补偿器设计问题转化为受线性矩阵不等式组约束的优化问题。以航天器在轨加注过程姿态控制系统为例,建立了该系统的链式平滑切换模型,上述模型能够更加准确地反映系统转动惯量的渐变特性。最后,数值仿真算例表明了所提出的控制器设计方法的有效性。     

A DEA-TOPSIS method for multiple criteria decision analysis in emergency management

A hybrid approach of DEA (data envelopment analysis) and TOPSIS (technique for order performance (preference) by similarity to ideal solution) is proposed for multiple criteria decision analysis in emergency management. Two DEA-based optimization models are constructed to facilitate identifying parameter information regarding criterion weights and quantifying qualitative criteria in TOPSIS. An emergency management case study utilizing data from the Emergency Management Australia (EMA) Disasters Database is provided to demonstrate the feasibility of the proposed analysis procedure.     

DDFS spurious signals due to amplitude quantization in absence of phase-accumulator truncation

Spurious signals in direct digital frequency synthesizers (DDFSs) are partly caused by amplitude quantization and phase truncation, which affect their application to many wireless telecommunication systems. Thee signals are deterministic and periodic in the time domain, so they appear as line spectra in the frequency domain. Two types of spurious signals due to amplitude quantization are exactly formulated and compared in thetime and frequency domains respectively. Then the frequency spectra and power levels of the spurious signals due to amplitude quantization in the absence of phase-accumulator truncation are emphatically analyzed, and the effects of the DDFS parameter variations on the spurious signals are thoroughly studied by computer simulation. And several important conclusions are derived which can provide theoretical support for parameter choice and spurious performance evaluation in the application of DDFSs.     

基于非线性内电位构造函数的抗伪状态联想记忆

针对传统Hopfield神经网络在联想记忆中存在的大量伪状态,设计出一种以非线性内电位函数构造为核心的抗伪状态方案,以取代传统的Hebb规则及其外积法,建立了一种抗伪状态的联想记忆网络(ASS-SSNN),并通过吸引域的变化研究抗伪状态方案的正确性和有效性.仿真结果表明:ASS-SSNN比采用传统Hebb规则及其外积法的Hopfield联想记忆网络(HNN)在容错性上更优,记忆状态的吸引域明显增大,存储容量也有所增加.     

A multiyear lags input-holding-output model on education with excluding idle capital

This paper develops a multi-year lag Input-Holding-Output (I-H-O) Model on education with exclusion of the idle capital to address the reasonable education structure in support of a sustainable development strategy in China. First, the model considers the multiyear lag of human capital because the lag time of human capital is even longer and more important than that of fixed capital. Second, it considers the idle capital resulting from the output decline in education, for example, student decrease in primary school. The new generalized Leonitief dynamic inverse is deduced to obtain a positive solution on education when output declines as well as expands. After compiling the 2000 I-H-O table on education, the authors adopt modifications-by-step method to treat nonlinear coefficients, and calculate education scale, the requirement of human capital, and education expenditure from 2005 to 2020. It is found that structural imbalance of human capital is a serious problem for Chinese economic development. This research was supporting by the Key Project of National Natural Science Foundation of China under Grant No. 60874119, Innovation Funds of Chinese Academy of Sciences, Research Fund for Humanities and Social Sciences in colleges under Grant No. 06BJY102, Key project in Jiangxi Soft Science Project under Grant No. [2006]188, Jiangxi Research Fund for Humanities and Social Sciences in colleges under Grant No. 03JJ01, and Henan Research Fund for Humanities and Social Sciences in colleges under Grant No. 2008-ZD-002.     

基于多策略搜索的出租车服务仿真模型

为更细致、精确地描述出租车服务规律,提出一种依据历史信息和路网供求信息模拟出租车空车搜索行为的动态仿真模型,出租车根据以往的驾驶经验和对路网供求信息的掌握程度决定空车时如何寻找新乘客.仿真结果表明,向出租车提供路网供求信息不能有效提高出租车的空车效率,但有助于缩短乘客平均等待时间;依据平均历史信息的搜索策略能够有效提高空车效率.模型有助于城市交通管理部门更有效地规划和管理出租车运营系统,为其决策提供参考依据.     

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.     

Spiking neural P systems with anti-spikes and without annihilating priority as number acceptors

Spiking neural P systems with anti-spikes （ASN P systems） are variant forms of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, spikes and anti-spikes wil immediately annihilate each other in a maximal way. In this paper, a restricted variant of ASN P systems, cal ed ASN P systems without anni-hilating priority, is considered, where the annihilating rule is used as the standard rule, i.e., it is not obligatory to use in the neuron associated with both spikes and anti-spikes. If the annihilating rule is used in a neuron, the annihilation wil consume one time unit. As a result, such systems using two categories of spiking rules （identified by （a, a） and （a,a^-）） can achieve Turing completeness as number accepting devices.     

Memetic algorithm for multi-mode resource-constrained project scheduling problems

A memetic algorithm （MA） for a multi-mode resourceconstrained project scheduling problem （MRCPSP） is proposed. We use a new fitness function and two very effective local search procedures in the proposed MA. The fitness function makes use of a mechanism called ＂strategic oscillation＂ to make the search process have a higher probability to visit solutions around a ＂feasible boundary＂. One of the local search procedures aims at improving the lower bound of project makespan to be less than a known upper bound, and another aims at improving a solution of an MRCPSP instance accepting infeasible solutions based on the new fitness function in the search process. A detailed computational experiment is set up using instances from the problem instance library PSPLIB. Computational results show that the proposed MA is very competitive with the state-of-the-art algorithms. The MA obtains improved solutions for one instance of set J30.