Uma técnica híbrida para geração de rotas em espaço geográfico com obstáculos.

Este trabalho está inserido no campo da Geomática e se concentra, mais especificamente, no estudo de métodos para exploração e seleção de rotas em espaços geográficos sem delimitação prévia de vias trafegáveis. As atividades que poderiam se beneficiar de estudos desse tipo estão inseridas em áreas da engenharia, logística e robótica. Buscou-se, com as pesquisas realizadas nesse trabalho, elaborar um modelo computacional capaz de consultar as informações de um terreno, explorar uma grande quantidade de rotas viáveis e selecionar aquelas rotas que oferecessem as melhores condições de trajetória entre dois pontos de um mapa. Foi construído um sistema a partir do modelo computacional proposto para validar sua eficiência e aplicabilidade em diferentes casos de estudo. Para que esse sistema fosse construído, foram combinados conceitos de sistemas baseados em agentes, lógica nebulosa e planejamento de rotas em robótica. As informações de um terreno foram organizadas, consumidas e apresentadas pelo sistema criado, utilizando mapas digitais. Todas as funcionalidades do sistema foram construídas por meio de software livre. Como resultado, esse trabalho de pesquisa disponibiliza um sistema eficiente para o estudo, o planejamento ou a simulação de rotas sobre mapas digitais, a partir de um módulo de inferência nebuloso aplicado à classificação de rotas e um módulo de exploração de rotas baseado em agentes autônomos. A perspectiva para futuras aplicações utilizando o modelo computacional apresentado nesse trabalho é bastante abrangente. Acredita-se que, a partir dos resultados alcançados, esse sistema possa ajudar a reduzir custos e automatizar equipamentos em diversas atividades humanas.

Modelo de Alocação de Mamógrafos Móveis: Um estudo para a Região Serrana do Estado do Rio de Janeiro.

O Estado do Rio de Janeiro possui indicadores de produção muito baixos na realização de exames de câncer de mama. Na tentativa de melhorar o acesso aos exames, principalmente em regiões com baixa densidade populacional onde a aquisição de mamógrafos não é custo-efetiva, o uso da mamografia móvel é uma alternativa para aumentar a execução de exames de rastreamento de câncer de mama. O objetivo desta pesquisa é a construção de um modelo computacional para definir a alocação de mamógrafos móveis. O Modelo considera as variáveis associadas com os custos e prazos, indicando quando, onde e por quanto tempo, as unidades móveis de mamografia devem permanecer em cada cidade. O modelo foi construído no software de modelagem e simulação Anylogic, usando técnicas de modelagem baseada em agentes. O principal resultado é determinar o percurso de cada veículo disponível, para oferecer a cobertura desejada em cada cidade. Todas as entradas são parametrizadas, permitindo simular diferentes cenários e fornecer informações importantes para o processo de tomada de decisão. O horizonte de tempo, número de mamógrafos (fixos e móveis), a cobertura desejada da população, a capacidade de produção de cada dispositivo, a adesão da população urbana e rural, entre outras variáveis, foram consideradas no modelo. Os dados da Região Serrana do Rio de Janeiro foram usados nas simulações, onde menos de metade das cidades possuem mamógrafos fixos. Com o modelo proposto foi possível determinar a distribuição de cada dispositivo físico e o número ótimo de unidades móveis de mamografia para oferecer cobertura à totalidade da população no ciclo de dois anos. O número de mamógrafos para oferecer cobertura de toda a população da região poderia ser reduzido pela metade com o modelo de alocação proposto neste trabalho. A utilização de mamografia móvel, em conjunto com a rede existente de mamógrafos fixos, procura maximizar a disponibilização de exames de testes de diagnóstico de câncer de mama no estado do Rio de Janeiro. O desenvolvimento de um modelo de roteamento que aperfeiçoa a cobertura de rastreio do câncer de mama é apresentado como um complemento importante na tentativa de melhorar o acesso à população residente em áreas urbanas e rurais dos municípios.

Field programmable gate array based predictive control system for spacecraft rendezvous in elliptical orbits

Copyright © 2014 John Wiley & Sons, Ltd. Copyright © 2014 John Wiley & Sons, Ltd. Summary A field programmable gate array (FPGA) based model predictive controller for two phases of spacecraft rendezvous is presented. Linear time-varying prediction models are used to accommodate elliptical orbits, and a variable prediction horizon is used to facilitate finite time completion of the longer range manoeuvres, whilst a fixed and receding prediction horizon is used for fine-grained tracking at close range. The resulting constrained optimisation problems are solved using a primal-dual interior point algorithm. The majority of the computational demand is in solving a system of simultaneous linear equations at each iteration of this algorithm. To accelerate these operations, a custom circuit is implemented, using a combination of Mathworks HDL Coder and Xilinx System Generator for DSP, and used as a peripheral to a MicroBlaze soft-core processor on the FPGA, on which the remainder of the system is implemented. Certain logic that can be hard-coded for fixed sized problems is implemented to be configurable online, in order to accommodate the varying problem sizes associated with the variable prediction horizon. The system is demonstrated in closed-loop by linking the FPGA with a simulation of the spacecraft dynamics running in Simulink on a PC, using Ethernet. Timing comparisons indicate that the custom implementation is substantially faster than pure embedded software-based interior point methods running on the same MicroBlaze and could be competitive with a pure custom hardware implementation.

一种基于Agent的自适应的分布式入侵检测系统

针对传统分布式入侵检测系统组件之间依赖程度大、系统不够健壮且入侵检测系统自身结构固定不能适应入侵的变化的问题,提出了一种基于Agent的自适应的分布式入侵检测系统(简称AAA-DIDS)·AAADIDS采用Agent概念重新构造系统的组件,改进了分布式入侵检测系统由于高层节点单一无冗余而产生的可靠性差的缺陷,从构造上克服了分布式入侵检测系统的脆弱性·同时,AAADIDS系统采用智能技术构建了自适应的入侵检测系统模型,增加了系统应对入侵行为变化的智能性·AAA-DIDS系统相对于传统的分布式入侵检测系统有效地提高了系统自身的可靠性和针对外界变化的适应能力·

基于MDA的TRISO-Model模型管理方法及应用

TRISO-Model(tridimensional integrated software development model)是为处理软件开发的复杂性和动态性而提出的三维集成软件开发方法学,其中,多维模型之间的语义一致性维护以及对模型应用中公共操作部分的重用,提出了基于一致语义进行模型管理的需求.给出了基于MDA(model driven architecture)进行模型管理的方法MDA-MMMethod(MDA based model management method),应用MDA的4层模型管理结构,基于MDA核心标准MOF(meta object facility)所提供的公共语义基础管理模型和元模型,MDA-MMMethod支持各种MDA模型操作标准实现在TRSIO-model应用中的重用.开发了相应的支持系统MDA-MMSystem(MDA based model management system),应用于SoftPM的项目实践中.与传统方法相比,模型应用的开发效率得到了显著提高,同时降低了开发成本.最后,给出了模型融合的应用实例介绍.

基于Agent的智能交通体系及仿真

交通问题已成为全世界所共同关注的主要问题 ,如何运用现代的科技手段来缓解日益严峻的交通压力 ,是目前研究的重点。该文基于目前交通问题及交通系统发展的现状 ,结合计算机软件技术的最新成果———Agent技术 ,提出了基于Agent技术的智能交通控制的体系结构 ,论述了该结构的优点 ;并根据Agent的特点 ,介绍了运用Agent技术进行交通仿真的优势 ,探讨了具体采用Agent技术进行交通仿真的方法。

基于Rete算法的多Agent的实时协同推理

1 引言多Agent系统是由若干具有一个或多个目标的Agent按照一定的信息关系和控制关系以及问题求解能力的分布模式组成的系统,它主要研究一组在逻辑上或物理上分离的Agent之间行为的协调。目前,多Agent系统已应用于诸如空中交通控制、电子商务、通讯网络管理和作业调度等生产实际领域。Agent技术应用到实际领域时映射的对象一般有

基于Agent的分布式多机器人系统建模及分析

进行一个多装配机器人系统 DAMAS的模型研究 ,介绍离散事件动态系统的重要建模工具 Petri网 ,提出解决模型复杂性的有效方法 ,建立了系统中各个 Agent及整个系统的 Petri网模型 ,对这些模型及相互间的交互模型进行了重要的活性和有界性分析 ,对多机器人系统的协作机制进行了验证分析。

网络环境下基于Agent的多机器人协调与路径规划

本文针对基于Ａｇｅｎｔ的分布协作式多机器人装配系统——ＤＡＭＡＳ的特点，在原有工作的基础上，提出了网络环境下基于Ａｇｅｎｔ的路径规划思想，重新定义Ａｇｅｎｔ各功能模块的内容，建立系统中的通讯机制．同时，介绍了系统进行路径规划的工作过程，给出了路径规划器的规划算法

基于Multi-Agent的生产过程建模与仿真技术研究

某些流程行业由于采用按配方进行分组加工的模式组织生产,在排产时存在多条路径调度优化的问题,应用一般的优化算法对于现场在线调度难以给出满意结果,而基于Agent的过程仿真在解决离散、非线性系统模拟方面有显著的优势,本文采用Agent的方法对生产过程建模,然后对方案组内的备选方案进行仿真,通过对比各方案的仿真结果找到最优的方案作为执行方案,为现场的优化排产提供决策支持。

View-Based Models of 3D Object Recognition and Class-Specific Invariances

This paper describes the main features of a view-based model of object recognition. The model tries to capture general properties to be expected in a biological architecture for object recognition. The basic module is a regularization network in which each of the hidden units is broadly tuned to a specific view of the object to be recognized.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.

An evaluation of remifentanil-sevoflurane response surface models in patients emerging from anesthesia: model improvement using effect-site sevoflurane concentrations.

INTRODUCTION: We previously reported models that characterized the synergistic interaction between remifentanil and sevoflurane in blunting responses to verbal and painful stimuli. This preliminary study evaluated the ability of these models to predict a return of responsiveness during emergence from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non-steady-state conditions, accounting for the lag time between sevoflurane effect-site concentration (Ce) and end-tidal (ET) concentration would improve predictions. METHODS: Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET-based and a Ce-based prediction. Similarly, 2 predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared with observations with graphical and temporal analyses. RESULTS: While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (> or = 99%). However, after termination of the anesthetic, models exhibited a wide range of predictions at emergence (1%-97%). Although wide, the Ce-based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET-based predictions. For the ET-based model, 45% of the patients awoke within 2 min of the 50% model predicted probability of unresponsiveness and 65% awoke within 4 min. For the Ce-based model, 45% of the patients awoke within 1 min of the 50% model predicted probability of unresponsiveness and 85% awoke within 3.2 min. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce- and ET-based models. DISCUSSION: Results confirmed, in part, our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large-scale evaluations with numerous patients are needed to better characterize model performance.

Investigating the use of elevators for high-rise building evacuation through computer simulation

This paper presents a description of a new agent based elevator sub-model developed as part of the buildingEXODUS software intended for both evacuation and circulation applications. A description of each component of the newly developed model is presented, including the elevator kinematics and associated pedestrian behaviour. The elevator model is then used to investigate a series of full building evacuation scenarios based on a hypothetical 50 floor building with four staircases and a population of 7,840 agents. The analysis explores the relative merits of using up to 32 elevators (arranged in four banks) and various egress strategies to evacuate the entire building population. Findings from the investigation suggest that the most efficient evacuation strategy utilises a combination of elevators and stairs to empty the building and clear the upper half of the building in minimum time. Combined stair elevator evacuation times have been shown to be as much as 50% faster than stair only evacuation times.