计算DES监控中优于闭可识别语言的闭可观子语言

本文给出一种求闭能观子语言的算法，该算法经ｍ步（系统中离散事件个数）收敛；并且证明无论对系统中事件如何排序，由该算法求出的闭能观子语言总包含最大闭可识别子语言。     

满足电压暂降与故障位置均可观的监测装置二阶段配置

为获得系统内暂降水平和暂降原因等信息,以暂降可观测区域法和故障定位法为基础,提出一种满足全网电压暂降和导致暂降的故障位置均可观的监测装置二阶段配置法。基于暂降可观测区域法,确定第1阶段的配置方案,实现全网电压暂降可观。基于故障定位法,引入虚拟监测的概念,以短路计算和对称分量法确定的不同故障类型引起的母线三相电压幅值和相位信息,确定导致被监测暂降事件的故障位置,建立全网故障定位矩阵;通过在剩余母线中逐步增加监测装置,消除所有伪故障点,确定第2阶段的配置方案,实现全网故障位置可观。对IEEE-30节点系统进行仿真,验证了方法的正确性和适用性。     

基于有效实例的改进U树算法

传统U-Tree算法对于部分观测马尔可夫决策过程POMDP问题的解决已取得较为显著的成效,但是由于边缘节点生长过于随意,所以仍存在树的规模庞大、内存需求比较大、计算复杂度过高的问题。在原U-Tree算法的基础上,通过得到下一步观测值,来划分同一个叶子节点中做相同动作的实例,提出了一种基于有效实例来扩展边缘节点的EIU-Tree算法,大大缩减了计算规模,以此来帮助智能体更好更快地学习,并且在4×3经典栅格问题中做了仿真实验,对比于原有的U-Tree算法,该算法运行效果更好。     

物理可观测下DES的安全性研究

利用物理观测效应进行的旁路攻击,是通过对密码设备工作时泄漏的时间、功耗等信息的分析,获取密码系统的密钥或相关秘密信息.已有大量防护对策但并没有从根本上阻止攻击.本文在AT89C52上加载了DES算法,并在该平台上对差分功耗旁路攻击与防护方法进行了实验和验证.根据Micali和Reyzin建立的物理观测密码术理论模型,将该模型具体化,对可以抵抗黑盒攻击的密码要素进行修正以抵抗基于物理泄漏的旁路攻击,将RO(random oracle)模型用于物理观测现实世界的安全性证明,给出了对称加密方案物理可观测下安全性定义,并对DES定义了在DPA攻击下的安全性.     

冷却剂欠热度对蒸汽爆炸的影响研究

为了研究蒸汽爆炸的爆炸机理,设计建造了低温熔融金属入水碎化的可视化实验装置,采用高速摄像仪拍摄记录熔融金属液柱入水爆炸或碎化的过程。实验研究了冷却水温度对蒸汽爆炸的影响,实验结果表明：冷却水温度对蒸汽爆炸起抑制作用。为了验证该结论,进行了碎化实验。结果表明：冷却水温度对碎裂后颗粒直径范围影响明显,熔融金属液柱在温度高的冷却水中遇到的阻力小,碎裂的颗粒体表比越大,换热能力越弱。后者实验结果与前者相符。     

A Human–Robot Cooperative Learning System for Easy Installation of Assistant Robots in New Working Environments

One of the applications of service robots with a greater social impact is the assistance to elderly or disabled people. In these applications, assistant robots must robustly navigate in structured indoor environments such as hospitals, nursing homes or houses, heading from room to room to carry out different nursing or service tasks. Among the main requirements of these robotic aids, one that will determine its future commercial feasibility, is the easy installation of the robot in new working domains without long, tedious or complex configuration steps. This paper describes the navigation system of the assistant robot called SIRA, developed in the Electronics Department of the University of Alcalá, focusing on the learning module, specially designed to make the installation of the robot easier and faster in new environments. To cope with robustness and reliability requirements, the navigation system uses probabilistic reasoning (POMDPs) to globally localize the robot and to direct its goal-oriented actions. The proposed learning module fast learns the Markov model of a new environment by means of an exploration stage that takes advantage of human–robot interfaces (basically speech) and user–robot cooperation to accelerate model acquisition. The proposed learning method, based on a modification of the EM algorithm, is able to robustly explore new environments with a low number of corridor traversals, as shown in some experiments carried out with SIRA.     

Shaping multi-agent systems with gradient reinforcement learning

An original reinforcement learning (RL) methodology is proposed for the design of multi-agent systems. In the realistic setting of situated agents with local perception, the task of automatically building a coordinated system is of crucial importance. To that end, we design simple reactive agents in a decentralized way as independent learners. But to cope with the difficulties inherent to RL used in that framework, we have developed an incremental learning algorithm where agents face a sequence of progressively more complex tasks. We illustrate this general framework by computer experiments where agents have to coordinate to reach a global goal. This work has been conducted in part in NICTA’s Canberra laboratory.     

基于服务本体的可观测协同模型*

为得到在通用抽象级别上的可观测协同模型,采用描述逻辑对构件服务予以概念化而构成服务本体并将其作为构件可观测的基准,在软件体系结构的抽象级别上利用服务本体扩展了软件体系结构连接件。基于上述方法,设计了协同介质的基本结构并规范了基于脚本语言的协同规则,提出了一种通用的可观测协同模型。研究结果表明,该协同模型具有与应用无关的通用性。     

What is decidable about partially observable Markov decision processes with ω-regular objectives

We consider partially observable Markov decision processes (POMDPs) with ω-regular conditions specified as parity objectives. The class of ω-regular languages provides a robust specification language to express properties in verification, and parity objectives are canonical forms to express them. The qualitative analysis problem given a POMDP and a parity objective asks whether there is a strategy to ensure that the objective is satisfied with probability 1 (resp. positive probability). While the qualitative analysis problems are undecidable even for special cases of parity objectives, we establish decidability (with optimal complexity) for POMDPs with all parity objectives under finite-memory strategies. We establish optimal (exponential) memory bounds and EXPTIME-completeness of the qualitative analysis problems under finite-memory strategies for POMDPs with parity objectives. We also present a practical approach, where we design heuristics to deal with the exponential complexity, and have applied our implementation on a number of POMDP examples.     

User-adapted plan recognition and user-adapted shared control: A Bayesian approach to semi-autonomous wheelchair driving

Many elderly and physically impaired people experience difficulties when maneuvering a powered wheelchair. In order to ease maneuvering, powered wheelchairs have been equipped with sensors, additional computing power and intelligence by various research groups. This paper presents a Bayesian approach to maneuvering assistance for wheelchair driving, which can be adapted to a specific user. The proposed framework is able to model and estimate even complex user intents, i.e. wheelchair maneuvers that the driver has in mind. Furthermore, it explicitly takes the uncertainty on the user’s intent into account. Besides during intent estimation, user-specific properties and uncertainty on the user’s intent are incorporated when taking assistive actions, such that assistance is tailored to the user’s driving skills. This decision making is modeled as a greedy Partially Observable Markov Decision Process (POMDP). Benefits of this approach are shown using experimental results in simulation and on our wheelchair platform Sharioto.