A counterexample to the Alexopoulos-Griffin path planning algorithm

The planar stationary-obstacle path-planning problem for polygonal obstacles has been correctly and completely solved by T. Lozano-Perez and M. Wesley (1979), i.e., a global, optimal algorithm was provided which requires O(mu(2)logmu) computation time, where mu is the number of obstacle-faces in the scene. That algorithm is known as the VGRAPH algorithm. Two variants of VGRAPH have been developed to solve the same problem in O(mu(2)) computation time. Our paper discusses a recent algorithm proposed by C. Alexopoulos and P.M. Griffin (1992), called V*GRAPH, which also claims to provide an optimal solution. We demonstrate by counter-example that V*GRAPH is neither global nor optimal.     

Directed variation in evolution strategies

Biological evolution gives rise to self-organizing phenomena. Inspired by this theory, directed variation is added to the (/spl mu/, /spl lambda/) evolution strategies (ES) algorithm and it is called directed variation ES (DVES). In DVES, some neighboring individuals in the population mutate correlatively according to the distribution of the whole population. Experimental results showed that, with the same number of function evaluations, directed variation ES reached better optimization results for different generally used strategies under the ES framework. Experimental analysis showed that the application of directed variation could increase the expected fitness improvement and the probability of fitness improvement. From a biological perspective, directed variation can be regarded as a result of self-organizing evolution.     

Polynomial extended Kalman filter

This work presents a polynomial version of the well-known extended Kalman filter (EKF) for the state estimation of nonlinear discrete-time stochastic systems. The proposed filter, denoted polynomial EKF (PEKF), consists in the application of the optimal polynomial filter of a chosen degree /spl mu/ to the Carleman approximation of a nonlinear system. When /spl mu/=1 the PEKF algorithm coincides with the standard EKF. For the filter implementation the moments of the state and output noises up to order 2/spl mu/ are required. Numerical simulations compare the performances of the PEKF with those of some other existing filters, showing significant improvements.     

A (/spl mu/ + /spl lambda/) evolutionary algorithm for reconstruction of SPECT data

Algorithms used to reconstruct single photon emission computed tomography (SPECT) data are based on one of two principles: filtered back projection or iterative methods. In this paper, an evolution strategy (ES) was applied to reconstruct transaxial slices of SPECT data. Evolutionary algorithms are stochastic global search methods that have been used successfully for many kinds of optimization problems. The newly developed reconstruction algorithm consisting of /spl mu/ parents and /spl lambda/ children uses a random principle to readjust the voxel values, whereas other iterative reconstruction methods use the difference between measured and simulated projection data. The (/spl mu/ + /spl lambda/)-ES was validated against a test image, a heart, and a Jaszczak phantom. The resulting transaxial slices show an improvement in image quality, in comparison to both the filtered back projection method and a standard iterative reconstruction algorithm.     

Examining technology, structure and identity during an Enterprise System implementation

Abstract.  This paper presents a longitudinal study of an Enterprise System (ES) implementation by critically examining the discursive context in which an ES implementation unfolds. The findings show that users strongly supported the ES in the earlier stage of implementation when the technology was an imaginary phenomenon. However, in later stages, when the technology is in use, user support was not consistent. In this phase, the ES produces loss of control and an inability to function as an arbiter of fairness (in allocating resources associated with the system), thereby directly challenging existing professional identities and roles. These outcomes, in turn, generate acts of resistance on the part of workers. Users reach inside the technology and reshape it by devising creative workarounds that produce a sense of reskilling to counter the deskilling produced by the loss of control and power. The analysis also shows that an ES is a complex social phenomenon that is intricately linked to and complicit in shaping organizational structure and identity. In particular, this study shows how technology, structure and identity are in a mutually constitutive relationship.     

基于混合智能的航天器故障诊断系统

面向航天器测控管理,研究了一种基于专家系统（ES）、案例推理（CBR）以及故障树（FT）的混合智 能诊断技术．文中,故障树双向混合推理机制被用于实现航天器故障定位和预测．同时案例推理的k 最近邻检索 策略（KNN）采用了简单实用、易收敛特性的多感官群集算法（MSA）．基于案例推理和故障树的航天器专家系 统（SESCF）采用了2 种融合模式．案例推理和故障树采用独立运行模式,专家系统与案例推理和故障树之间则采 用了松耦合运行模式．出于改善推理效率的目的,文中提出了一种将遥测信息转化为语义信息的结合特定推理方法 的非线性转换方法．某卫星供配电分系统的测试证实了SESCF 系统诊断的有效性．测试结果表明,相对于专家系 统,SESCF 系统具有更高的诊断准确度和可靠性．SESCF 系统采用的非线性转换方法在航天器故障诊断过程中简 单实用且容错性较好．     

二元进化策略的收敛性分析

进化算法的理论研究,如收敛性、时间复杂性研究,是当前的一大热点和难点,有关的理论结果并不多。针对二元进化策略(1+1) ES建立时齐马尔科夫过程模型,利用连续状态马氏过程理论证明了与(1+1) ES相关联的马氏过程在一类连续优化问题中具有指数遍历性,在此基础上证明了(1+1)ES在求解此类优化问题时能以概率1最终找到最优解。所提出的分析方法为进化算法的理论研究提供了一条新思路。     

基于专家系统的高级持续性威胁云端检测博弈

云计算系统是高级持续性威胁(advanced persistent threats, APT)的重要攻击目标.自动化的APT检测器很难准确发现APT攻击,用专家系统对可疑行为进行二次检测可以减少检测错误.但是专家系统完成二次检测需要花费一段额外的时间,可能导致防御响应延迟,而且专家系统本身也会产生误判.在综合考虑APT检测器和专家系统的虚警率和漏报率的基础上,用博弈论方法讨论在云计算系统的APT检测和防御中,利用专家系统进行二次检测的必要性.设计了一个基于专家系统的APT检测方案,并提出一个ES-APT检测博弈模型,推导其纳什均衡,据此研究了专家系统对云计算系统安全性能的改善作用.此外,当无法获得APT攻击模型时,提出了一种利用强化学习算法获取最优防御策略的方案.仿真结果表明：基于WoLF-PHC算法的动态ES-APT检测方案较之其他对照方案能够提高防御者的效用和云计算系统的安全性.     

基于神经网络的控制专家系统开发环境的实现

为了充分发挥专家系统与人工神经网络技术两者的优势，支持高效实用的结合型控制专家系统的开发，本文首次提出并在微机上实现了一个以神经网络为核心的控制专家系统开发环境（ＮＥＳＤＥＶ）。可辅助控制领域的工程技术人员，方便、快速、高效地开发基于神经网络的实时控制专家系统。本文将叙述该开发环境的系统结构，规则库－神经网络翻译算法，人机接口技术等内容。     

Expert system for clustering prokaryotic species by their metabolic features

Studying the communities of microbial species is highly important since many natural and artificial processes are mediated by groups of microbes rather than by single entities. One way of studying them is the search of common metabolic characteristics among microbial species, which is not only a potential measure for the differentiation and classification of closely-related organisms but also their study allows the finding of common functional properties that may describe the way of life of entire organisms or species. In this work we propose an expert system (ES), making the main contribution, to cluster a complex data set of 365 prokaryotic species by 114 metabolic features, information which may be incomplete for some species. Inspired on the human expert reasoning and based on hierarchical clustering strategies, our proposed ES estimates the optimal number of clusters adequate to divide the dataset and afterwards it starts an iterative process of clustering, based on the Self-organizing Maps (SOM) approach, where it finds relevant clusters at different steps by means of a new validity index inspired on the well-known Davies Bouldin (DB) index. In order to monitor the process and assess the behavior of the ES the partition obtained at each step is validated with the DB validity index. The resulting clusters prove that the use of metabolic features combined with the ES is able to handle a complex dataset that can help in the extraction of underlying information, gaining advantage over other existing approaches, that may relate metabolism with phenotypic, environmental or evolutionary characteristics in prokaryotic species.     

A new edge-detected lift force flow sensor

A lift force gas flow sensor which uses the force normal to the fluid flow to measure the flow velocity has recently been introduced. Two thin plates mounted at an angle are deflected when they are subjected to fluid flow. For most mechanical flow sensors the flow sensitivity is closely connected to the time response. A weaker structure gives higher flow sensitivity but a lower natural frequency, i.e., a slower response time. The lift force sensor is designed for measurements of respiratory gas flow in ventilators, where, in addition to low flow restriction, both high sensitivity and fast response are required. A new type of suspension has now been realized for the lift force flow sensor. The detection is separated from the suspension of the airfoil plate with the strain gauges placed on separate detector beams. This leads to separate parameters for optimization of the lift force concept with "independent" control of flow sensitivity and natural frequency. This paper presents an analytical model, simulations and measurements on the new structure. The new edge-detected sensor has been experimentally evaluated for different lengths (100-600 /spl mu/m), widths (20-100 /spl mu/m) and thicknesses (8-20 /spl mu/m) of the detector beams. In accordance with the theory, the measurements show that the new structure has approximately three times the natural frequency of the old, center detected structure and similar or improved flow sensitivity. The evaluation has also resulted in a design scheme for optimal performance. A flow sensitivity of 0.65 /spl mu/V/V/(l/min)/sup 2/ has been obtained for the best edge-detected sensor with a natural frequency of 3.2 kHz.     

基于OpenGL ES的3D图形绘制管线优化问题

目前,在嵌入式设备上（如手机）的3D游戏开发总是基于一定的图形API来进行的。OpenGL ES是为嵌入式系统而开发的3D图形绘制编程接口。在基于嵌入式的3D游戏开发过程中,由于硬件资源的相对不足,要得到绘制效果较理想的图形,就需要优化3D图形绘制过程。采用最新的图形管线理论,将OpenGL ES的3D图形管线绘制过程划分为七个管线绘制部分（这七个部分归属于三个绘制阶段）,分析每个绘制部分的主要工作,在此基础上针对各个阶段提出了相应的优化方法。     

控制系统计算机辅助设计的专家系统AI技术与CADCS的结合

本文研究并实现了一种用于控制系统进行建模、分析、设计和仿真的专家系统.介绍了作者提出的一种分层专家系统的总体结构.详细阐述了两种知识库的内容及构成,列举了极点配置法校正的二级规则树.并通过例子说明了该专家系统的主要功能和特点.     

一种嵌入式实时数据库系统查询优化算法

针对嵌入式实时数据库系统的特点,提出了一种新的连接顺序优化算法 (Greedy Iterative Improvement, GII)。该算法结合了贪婪算法和迭代改进算法的优点,能满足系统的实时要求,可以控制查询优化时间,比传统查询优化策略更好地适应不同类型事务的需要。模拟实验结果表明,该算法在穷举式搜索适用时,能以比它小得多的时间和内存开销找到最优解;在穷举式搜索无法实现优化时,GII算法生成解的质量也明显优于贪婪算法和迭代改进。     

On three new approaches to handle constraints within evolution strategies

Evolutionary algorithms with a self-adaptive step control mechanism like evolution strategies (ES) often suffer from premature fitness stagnation on constrained numerical optimization problems. When the optimum lies on the constraint boundary or even in a vertex of the feasible search space, a disadvantageous success probability results in premature step size reduction. We introduce three new constraint-handling methods for ES on constrained continuous search spaces. The death penalty step control evolution strategy (DSES) is based on the controlled reduction of a minimum step size depending on the distance to the infeasible search space. The two sexes evolution strategy (TSES) is inspired by the biological concept of sexual selection and pairing. At last, the nested angle evolution strategy (NAES) is an approach in which the angles of the correlated mutation of the inner ES are adapted by the outer ES. All methods are experimentally evaluated on four selected test problems and compared with existing penalty-based constraint-handling methods.     

求解约束优化问题的改进自适应mu约束处理技术

针对当前的约束处理技术存在易陷入局部最优解、难以满足等式约束和多控制参数的问题,在mu约束处理技术的基础上,以梯度下降法和多目标拥挤距离为理论依据,设计反映种群约束违反度分布信息的omega参数,它可以自适应地调节约束违反度阈值mu的松弛进而有效地解决约束问题.此外,改进了mu阈值比较准则以提高种群的多样性.经对CEC2017的标准约束优化问题(Constraint optimization problems,COP)进行求解,并与其他先进算法相比较,结果表明,改进的mu约束处理技术能够高效地处理含等式约束的COP.     

Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics

The emerging field of vehicular ad hoc networks (VANETs) deals with a set of communicating vehicles which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, it is crucial to make an optimal configuration of the communication protocols previously to the final network deployment. This way, a human designer can obtain an optimal QoS of the network beforehand. The problem we consider in this work lies in configuring the File Transfer protocol Configuration (FTC) with the aim of optimizing the transmission time, the number of lost packets, and the amount of data transferred in realistic VANET scenarios. We face the FTC with five representative state-of-the-art optimization techniques and compare their performance. These algorithms are: Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Evolutionary Strategy (ES), and Simulated Annealing (SA). For our tests, two typical environment instances of VANETs for Urban and Highway scenarios have been defined. The experiments using ns- 2 (a well-known realistic VANET simulator) reveal that PSO outperforms all the compared algorithms for both studied VANET instances.     

An experimental study on the optimization of controller gains for an electro-hydraulic servo system using evolution strategies

This paper deals with an experimental optimization problem of the controller gains for an electro-hydraulic position control system through evolution strategies (ESs)-based method. The optimal controller gains for the control system are obtained by maximizing fitness function designed specially to evaluate the system performance. In this paper, for an electro-hydraulic position control system which would represent a hydraulic mill stand for the roll-gap control in plate hot-rollings, the time delay controller (TDC) is designed, and three control parameters of this controller are directly optimized through a series of experiments using this method. It is shown that the near-optimal value of the controller gains is obtained in about 5th generation, which corresponds to approximately 150 experiments. The optimal controller gains are experimentally confirmed by inspecting the fitness function topologies that represent system performance in the gain spaces. It is found that there are some local optimums on a fitness function topology so that the optimization of the three control parameters of a TDC by manual tuning could be a task of great difficulty. The optimized results via the ES coincide with the maximum peak point in topologies. It is also shown that the proposed method is an efficient scheme giving economy of time and labor in optimizing the controller gains of fluid power systems experimentally.     

基于反馈和混沌变异的自适应进化策略*

为了提高进化策略的搜索精度和全局搜索能力,提出了一种基于反馈和混沌变异的改进进化策略,将各代当前最优搜索结果反馈到变异步长的更新公式中,通过对变异算子中随机数方差的调整使进化策略的变异步长随搜索过程自适应地变化,同时根据混沌运动具有遍历性的特点,利用混沌变异产生个体,保证种群中的部分个体在搜索后期仍保持较大的跳出局部极小的能力,从而达到提高算法全局搜索能力和搜索精度。为了对比改进后进化策略与常规进化策略的优化效果,利用三个测试函数对两种进化策略进行了仿真测试,测试结果表明,与常规进化策略相比,提出的基于反