Positive observer design for positive Markovian jump systems with partly known transition rates

The paper is concerned with positive observer design for positive Markovian jump systems with partly known transition rates. By applying a linear co-positive type Lyapunov-Krasovskii function, a sufficient condition is proposed to ensure the stochastic stability of the error positive system and the existence of the positive observer, which is computed in linear programming. Finally, an example is given to demonstrate the validity of the main results.     

Spectral Properties and Positivity of Flows in Infinite Networks with Dynamic Boundary Control Nodes

In this paper we study transport processes on infinite networks with dynamic boundary control nodes. These flows can be modeled by operator semigroups on a suitable Banach space. Using functional analytical and graph theoretical methods, we investigate its spectral properties of the system and positivity of the semigroup under appropriate assumptions on the network.     

Adaptive Control of the Structural Balance for a Class of Complex Dynamical Networks

In social networks, the structural balance is a state of a group of individuals(nodes) with established mutual relationships(connection relationships) between them. It is easy to see that a social network can be described by a complex dynamical network model composed of the nodes subsystem(NS) and the connection relationships subsystem(CS), where the two subsystems are usually coupled with each other. It implies that the dynamic changes of nodes' states may cause the structural balance in CS. However, few papers have discussed the relationship between the structural balance and the specific dynamic changes of the nodes' states. This paper proposes a model of complex dynamical networks, and mainly focuses on the dynamic changes of states in NS which can lead to the structural balance in CS. It is proved that if each state in NS is doing a specific dynamic motion via the controller with the parameter adaptive law, then the CS can track a given structural balance matrix via the effective coupling and the structural balance can be achieved. Such a result can be regarded as an explanation of the relationship between the structural balance and the specific dynamic changes of the nodes' states. Finally, the simulations verify the effectiveness of the proposed method.     

基于改进智能水滴算法的动态车辆配送路径优化

针对当前车辆配送过程中存在的配送路径不合理、配送效率低和需求不确定性等问题,提出一种基于改进智能水滴算法的动态车辆配送路径优化方法。构建软时间窗惩罚函数,考虑顾客对配送时间的要求,建立顾客满意度函数。综合车辆配送过程的车速、货损成本、惩罚成本、顾客满意度等特征,建立车辆路径优化模型。采用智能水滴算法对车辆路径优化模型进行求解,使用灰狼优化算法改善智能水滴算法的搜索能力,获取最优路径。实验结果表明该方法能够提供实时优化的路径,减少调配成本。     

自动化码头运输设备充电策略优化的仿真研究

随着自动化集装箱码头成为当今码头的发展趋势,AGV(automated guided vehicle) 作为自动化码头中使用最为广泛的水平运输工具,合理制定其充电策略至关重要。针对当前自动化集装箱码头中AGV充电策略的不足,提出一种以离线充电为主在线充电为辅的充电策略,并且针对在线充电站点位置选择问题提出一种利用热区图选择有效在线充电站点的快捷方法。在使用该种充电策略情况下,通过大量仿真实验得出在码头工作任务量不变的前提下可减少AGV数量配置的结果,说明自动化集装箱码头水平搬运效率得到提高,证明该充电策略具有有效性。     

系统方法论研究的现状分析与展望

对目前系统方法论的研究进行分类：硬系统方法论、软系统方法论和批评性系统方法论，分析了不同系统方法的哲学思想、方法步骤及其局限性，探讨了未来系统方法论发展的形式。     

ISAR target recognition based on non-negative sparse coding

Aiming at technical difficulties in feature extraction for the inverse synthetic aperture radar (ISAR) target recognition, this paper imports the concept of visual perception and presents a novel method, which is based on the combination of non-negative sparse coding (NNSC) and linear discrimination optimization, to recognize targets in ISAR images. This method implements NNSC on the matrix constituted by the intensities of pixels in ISAR images for training, to obtain non-negative sparse bases which characterize sparse distribution of strong scattering centers. Then this paper chooses sparse bases via optimization criteria and calculates the corresponding non-negative sparse codes of both training and test images as the feature vectors, which are input into k neighbors classifier to realize recognition finally. The feasibility and robustness of the proposed method are proved by comparing with the template matching, principle component analysis (PCA) and non-negative matrix factorization (NMF) via simulations.     

Improved adaptive pruning algorithm for least squares support vector regression

As the solutions of the least squares support vector regression machine(LS-SVRM) are not sparse,it leads to slow prediction speed and limits its applications.The defects of the existing adaptive pruning algorithm for LS-SVRM are that the training speed is slow,and the generalization performance is not satisfactory,especially for large scale problems.Hence an improved algorithm is proposed.In order to accelerate the training speed,the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning.The novel objective function in the termination condition which involves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generalization performance.The effectiveness of the proposed algorithm is tested on six benchmark datasets.The sparse LS-SVRM model has a faster training speed and better generalization performance.     

Novel reference range selection method in InlSAR imaging

Aiming at the reference range selection for different antennas in interferometric inverse synthetic aperture radar (InISAR) systems, this paper proposes a respective focusing (RF) method. The reference ranges for echoes of different antennas are selected respectively for RF, which is different from the traditional uniform focusing (UF) with the same reference range applied to all the antennas. First, a comparison between UF and RF for InISAR signal model considering the ranging error is given. Compared with RF, UF has an advantage in overcoming the ranging error differences between different antennas. Then the influence of ranging error upon the interferometric imaging with RF is investigated particularly, and it is found that the ranging error differences between different antennas are far smaller than the wavelength, which is advantageous to imaging. By comparing the capabilities of interferometric imaging between RF and UF, it is concluded that RF is a better choice in conquering problems such as image mismatching and phase ambiguity even with ranging errors. Simulations demonstrate the validity of the proposed method.     

Modeling the congestion cost and vehicle emission within multimodal traffic network under the condition of equilibrium

Traditional system optimization models for traffic network focus on the treatment of congestion, which usually have an objective of minimizing the total travel time.However,the negative externality of congestion,such as environment pollution,is neglected in most cases.Such models fall short in taking Greenhouse Gas(GHG) emissions and its impact on climate change into consideration.In this paper,a social-cost based system optimization(SO) model is proposed for the multimodal traffic network considering both traffic congestion and corresponding vehicle emission.Firstly,a variation inequality model is developed to formulate the equilibrium problem for such network based on the analysis of travelers’ combined choices.Secondly,the computational models of traffic congestion and vehicle emission of whole multimodal network are proposed based on the equilibrium link-flows and the corresponding travel times.A bi-level programming model,in which the social-cost based SO model is treated as the upper-level problem and the combined equilibrium model is processed as the lower-level problem,is then presented with its solution algorithm.Finally,the proposed models are illustrated through a simple numerical example.The study results confirm and support the idea of giving the priority to the development of urban public transport,which is an effective way to achieve a sustainable urban transportation.