Modeling and stability analysis of social foraging swarms in multi-obstacle environment

In this article we specify an individual-based foraging swarm (i.e., group of agents) model with individuals that move in an n-dimensional multi-obstacle environment. The motion of each individual (i) is determined by three factors: i) attraction to the local object position (xˉio+ ) which is decided by the local information about the individuals’ position that individual i can find; ii) repulsion from the other individuals on short distances; and iii) attraction to the global object position (xgoal ) or repulsion from the obstacles in the environment. The emergent behavior of the swarm motion is the result of a balance between inter-individual interaction and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm based on Lyapunov stability theory. The simulations show that the swarm can converge to goal regions and diverge from obstacle regions of the environment while maintaining cohesive.     

Stable social foraging swarms in a noisy environment

Bacteria, bees, and birds often work together in groups to find food. A group of robots can be designed to coordinate their activities to search for and collect objects. Networked cooperative uninhabited autonomous vehicles are being developed for commercial and military applications. Suppose that we refer to all such groups of entities as "social foraging swarms". In order for such multiagent systems to succeed it is often critical that they can both maintain cohesive behaviors and appropriately respond to environmental stimuli (e.g., by optimizing the acquisition of nutrients in foraging for food). In this paper, we characterize swarm cohesiveness as a stability property and use a Lyapunov approach to develop conditions under which local agent actions will lead to cohesive foraging even in the presence of "noise" characterized by uncertainty on sensing other agent's position and velocity, and in sensing nutrients that each agent is foraging for. The results quantify earlier claims that social foraging is in a certain sense superior to individual foraging when noise is present, and provide clear connections between local agent-agent interactions and emergent group behavior. Moreover, the simulations show that very complicated but orderly group behaviors, reminiscent of those seen in biology, emerge in the presence of noise.     

Stability analysis of social foraging swarms.

In this article we specify an M-member "individual-based" continuous time swarm model with individuals that move in an n-dimensional space according to an attractant/repellent or a nutrient profile. The motion of each individual is determined by three factors: i) attraction to the other individuals on long distances; ii) repulsion from the other individuals on short distances; and iii) attraction to the more favorable regions (or repulsion from the unfavorable regions) of the attractant/repellent profile. The emergent behavior of the swarm motion is the result of a balance between inter-individual interactions and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm for different profiles and provide conditions for collective convergence to more favorable regions of the profile.     

大规模智能群体的建模及稳定性分析

提出一种基于个体局部信息的智能群体模型．在此模型基础上,给出了个体的运动控制方程。并基于Lyapunov稳定性理论研究了大规模智能群体动态集聚行为的稳定性．该模型的建立仅依靠相互可检测到的个体之间的局部信息。结合所提出的个体局部控制算法。便能实现群体的稳定全局集聚行为,体现出了较强的适应性、鲁棒性和可扩展性．     

Model-based analysis for the thermal management of open-cathode proton exchange membrane fuel cell systems concerning efficiency and stability

In this work we present a dynamic, control-oriented, concentrated parameter model of an open-cathode proton exchange membrane fuel cell system for the study of stability and efficiency improvement with respect to thermal management. The system model consists of two dynamic states which are the fuel cell temperature and the liquid water saturation in the cathode catalyst layer. The control action of the system is the inlet air velocity of the cathode air flow manifold, set by the cooling fan, and the system output is the stack voltage. From the model we derive the equilibrium points and eigenvalues within a set of operating conditions and subsequently discuss stability and the possibility of efficiency improvement. The model confirms the existence of a temperature-dependent maximum power in the moderate temperature region. The stability analysis shows that the maximum power line decomposes the phase plane in two parts, namely stable and unstable equilibrium points. The model is capable of predicting the temperature of a stable steady-state voltage maximum and the simulation results serve for the design of optimal thermal management strategies.     

Stability analysis of local swarms with attraction/repulsion force

This paper considers finite sense ability into swarm system. We first construct a model of local swarms with a class of attraction and repulsion. Then, we analyze aggregation motion of the swarm. It is shown that the individuals of the swarm will aggregate and eventually enter into a bounded hypeball around the swarm center in finite time and we present the range of the finite visual radius. We finally analyze their motion in convergent region.     

Modeling and stability analysis of cascade buck converters with N power stages

This paper presents a method for stability analysis of N-cell cascade step-down buck converters, which is a kind of complex nonlinear system. A nonlinear model in the form of time-variant state equations is derived, and then the loop gain describing the overall system is introduced. With the help of them, we obtain the equations for phase cross over frequency and gain margin by small-signal perturbation technique, harmonic balance method, and inverse iteration method. Based on the equations, the overall cascade system stability can be analyzed regardless of the number of converters cascaded. Finally, the cascade buck converter with three power stages is used to demonstrate the effectiveness of the proposed stability analysis method.     

Linear stability analysis of three-dimensional compressible boundary layers

A compressible stability analysis computer code is developed. The code uses a matrix finite-difference method for local eigenvalue solution when a good guess for the eigenvalue is available and is significantly more computationally efficient than the commonly used initial-value approach. The local eigenvalue search procedure also results in eigenfunctions and, at little extra work, group velocities. A globally convergent eigenvalue procedure is also developed that may be used when no guess for the eigenvalue is available. The global problem is formulated in such a way that no unstable spurious modes appear so that the method is suitable for use in a black-box stability code. Sample stability calculations are presented for the boundary layer profiles of an LFC swept wing.     

在JADE环境下面向Agent的Gaia建模

本文介绍了当今的面向agent编程技术,然后介绍了在java环境下实现agent编程的JADE框架,随后探讨了基于JADE框架实现了对一个GIS系统的Gaia建模的过程,Gaia建模方法是一种容易使用的面向agent的软件开发模式,JADE是一种很好的支持FIPA规范的agent开发环境,两者结合起来能进行更加敏捷而且高效的开发。     

Stability analysis of the reproduction operator in bacterial foraging optimization

In his seminal paper published in 2002, Passino pointed out how individual and groups of bacteria forage for nutrients and how to model it as a distributed optimization process, which he named the Bacterial Foraging Optimization Algorithm (BFOA). One of the major operators of BFOA is the reproduction phenomenon of virtual bacteria, each of which models one trial solution of the optimization problem. During reproduction, the least healthy bacteria (with a lower accumulated value of the objective function in one chemotactic lifetime) die and the other healthier bacteria each split into two, which then starts exploring the search place from the same location. The phenomenon has a direct analogy with the selection mechanism of classical evolutionary algorithms. This paper attempts to model reproduction as a dynamics and then analyses the stability of the reproductive system very near to an equilibrium point, which in this case is an isolated optimum. It also finds conditions under which a stable reproduction event can take place, to direct a worse bacterium towards a better one. Our analysis reveals that a stable reproduction event contributes to the quick convergence of the bacterial population near optima.     

随机扰动下多源群体觅食系统建模与仿真

群集智能是指复杂的集体智能来自简单个体之间以及个体同环境之间的相互作用. 通常对群集智能的研究主要借助于群居生物行为的观察. 蚁群觅食行为是研究简单个体产生复杂行为的一个典型的例子. 首先建立群体觅食宏观序参数模型. 模型考虑了食物源的量和分布以及环境噪声对个体决策的随机影响. 给出2个食物源下系统模型的数值解,表明在较大的噪声影响下,系统有一定的概率会脱离最优解,到达次优解. 在Starlogo仿真平台下的实验结果表明,觅食蚂蚁的数量同任务完成时间以及碰撞频率之间呈现出幂指数关系. 这对自组织系统和群集智能的研究有一定的理论意义,并可以用来指导设计更加有效、适应、可靠的智能系统.     

大规模移动智能群体的建模及联合行为分析

提出一种基于个体局部信息的移动智能群体的模型,该模型的建立仅依靠相互可检测到的个体之间的局部位置信息.在此模型基础上,研究了移动智能群体联合趋向目标行为的稳定性问题.仿真实验表明,基于局部位置信息的群体模型能够实现群体的联合跟踪全局目标的行为,同时对群体的规模具有较强的可扩展性.     

Modeling and analysis of singular systems via orthogonal triangular functions

This paper presents a new approach to singular system analysis by modeling the system in terms of orthogonal triangular functions (TFs). The proposed method is more accurate compared to block pulse function-based analysis with respect to mean integral square error (MISE). A numerical example involving four states of a singular system is treated and solutions obtained thereof. Four tables and relevant curves are presented to compare the respective coefficients in block pulse function (BPF) domain as well as in TF domain. The percentage error of the samples determined via TF domain are compared with the exact samples of the states. Furthermore, MISE for both BPF and TF analysis are computed and compared to reveal the efficiency of TF-based analysis.     

Multiple integral inequalities and stability analysis of time delay systems

This paper is devoted to stability analysis of continuous-time delay systems based on a set of Lyapunov–Krasovskii functionals. New multiple integral inequalities are derived that involve the famous Jensen’s and Wirtinger’s inequalities, as well as the recently presented Bessel–Legendre inequalities of Seuret and Gouaisbaut (2015) and the Wirtinger-based multiple-integral inequalities of Park et al. (2015) and Lee et al. (2015). The present paper aims at showing that the proposed set of sufficient stability conditions can be arranged into a bidirectional hierarchy of LMIs establishing a rigorous theoretical basis for comparison of conservatism of the investigated methods. Numerical examples illustrate the efficiency of the method.     

电站仿真建模一体化环境

ＧＮＥＴ是新开发的电厂仿真图形建模一体化环境。ＧＮＥＴ建模环境完全采用图形化用主发口，使建模人员能够通过友好，方便的图形界面和数据库、模块，模型程序进行数据和信息的联系，从而支持从建模型模型验证的全过程。大大地方便了仿真建模工作。     

受控旋转弹飞行动力学建模和稳定性分析

选取带有控制系统的旋转弹为研究对象,考虑到控制环节不可避免的时滞及气动非线性效应,从理论上进一步完善了旋转弹动力学模型.从模型的特征方程出发,以时滞、控制增益为分岔参数,对系统的零平衡点稳定性进行了分析,得到平衡点失稳后发生Hopf分岔的临界参数值,并在理论预测的情况下数值模拟了攻角和侧滑角在不同情况下的失稳情况以及Hopf分岔周期解振幅随分岔参数的变化情况.数值结果表明了理论预测的正确性,时滞虽未改变旋转弹锥形运动方式,但是却大幅度的减小了稳定飞行控制增益的取值范围,因此在旋转弹姿态稳定性系统设计过程中时滞的影响不可忽略.     

复杂电磁环境雷达新型威胁信号建模与仿真

复杂电磁环境中雷达新型威胁信号相对传统威胁信号具有更强的相干性和隐蔽性,信号环境对雷达装备构成较大威胁。针对目前对该类环境信号干扰机理和数学模型欠缺系统性研究的问题,分析了环境的组成要素,并对典型的信号样式建立了数学模型。最后,研制开发了雷达信号环境全数字仿真模拟系统,在该系统上实现了信号模型的仿真验证。仿真结果表明了模型的合理性和有效性。     

Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design

As a newly-developed information exchange and management platform, Building Information Modeling (BIM) is altering the way of collaboration among multi-engineers for civil engineering projects. During the BIM implementation, a large number of event logs are automatically generated and accumulated to record details of the model evolution. For knowledge discovery from huge logs, a novel BIM event log mining approach based on the dynamic social network analysis is presented to examine designers’ performance objectively, which has been verified in BIM event logs about an ongoing year-long design project. Relying on meaningful information extracted from time-stamped logs, networks on the monthly interval are built to graphically represent information and knowledge sharing among designers. Special emphasis is put on measuring designers’ influence by a defined new metric called “impact score”, which combines the k-shell method and 1-step neighbors to achieve comparatively low computational cost and high accurate ranking. Besides, an emerging machine learning algorithm named CatBoost is utilized to predict designers’ influence intelligently by learning features from both network structure and human behavior. It has been found that twelve networks can be easily distinguished into two collaborative patterns, whose characteristics in both network structures and designers’ behaviors are significantly different. The most influential designers are similar within the same group but varied from different groups. Extensive analytical results confirm that the method can potentially serve as month-by-month feedback to monitor the complex modeling process, which further supports managers to realize data-driven decision making for better leadership and work plan towards an optimized collaborative design.     

Modeling and analysis of DNA replication

DNA replication is an important process in the life of a cell. It has to be completed with extreme accuracy in a specific phase of the cell cycle, known as the S phase. Eukaryotic DNA replication is a rather complex and uncertain process. Several mathematical models have been recently proposed in the literature to interpret experimental data from various organisms. A common concern of many of these models is the so-called random gap problem, the observation that eukaryotic DNA replication should last longer than experimental evidence suggests due to its stochastic nature. One of the biological hypotheses proposed for resolving the random gap problem postulates the presence of a limiting factor regulating the rate with which DNA replication initiates. We show how this hypothesis can be captured in the Piecewise Deterministic Markov Process modeling framework. Monte Carlo simulations allow us to analyze the proposed model and compare model predictions with independent experimental data.     

PSO算法粒子运动轨迹稳定收敛条件分析

由于随机量的作用,粒子群优化算法（ＰＳＯ）中粒子的位置迭代是一个非线性动态离散过程,单个粒子在随机量影响下的运动方程可转换为一个二阶变系数非齐次方程．为此,利用Ｌｙａｐｕｎｏｖ稳定定理对该方程的稳定性作了深入研究,分析得到了使粒子运动稳定收敛的惯性权重和随机参数取值条件．实验结果表明,按照所得到的条件选择参数取值,能使粒子运动轨迹快速稳定收敛．该结果有助于实际应用中ＰＳＯ 算法参数的选择和调整．