无线传感器网络节点的数据存储管理系统

设计一种无线传感器网络节点本地数据存储系统,通过建立合理的逻辑结构,对存储资源进行分区划分,使之达到最大化利用。给出数据分组存储的方法,在存储过程中进行快速的址定位,有效地减少对存储介质的访问频率和访问存储介质带来的能量损耗,增加安全性检测机制,保障数据的一致性。     

基于相位编码的音频信息隐藏方法

针对听觉感知无差异时的局部相位失真条件,在宽带音频信号实验的基础上,提出一种利用局部相位分区实现信息隐藏的新算法。相对于经典的相位调制算法,利用局部相位信息,在保证听觉相似性的同时,能提高信息的嵌入量、改善误码率。理论分析和实验结果表明了该算法的有效性。     

太空帆板振动形态感知与主动控制的软件环境

作为试验研究与方法验证的重要环节,测控平台及其软件环境的开发与设计占据重要的地位。该文基于太空帆板振动形态监测与主动控制的研究背景,以Visual C++环境、OpenGL技术和嵌入式测控单元为实现手段,阐述光纤机敏帆板结构振动形态感知、现场可视化与振动主动控制软件环境的开发与设计,包括设计思路、关键技术、主要功能与核心单元,并通过试验结果验证了测控软件环境开发设计的有效性,为试验平台及其测控系统提供了环境友好、功效强大的软件平台支撑。     

基于交互的Agent行为选择动态性分析

在动态不可预知环境下,Agent行为选择过程表现出动态性。该文基于人工生命和MAS框架,建立Agent行为选择动态系统模型,运用动态系统观点研究Agent感知、认知交互和适应,分析Agent行为选择适应外部环境和满足内部需求的动态平衡过程。在仿真试验中,通过21个Agent局部感知和交互,实现了全局平衡、稳定的群体运动行为。     

基于国家信息安全风险评估指南的量化模型研究

以2005年颁布的国标草案《信息安全风险评估指南》为主要指导,并参考NISTSP800—30的管理控制思想、评估方法以及威胁发生可能性的计算方法,给出了一个具有可操作性的信息安全风险评估量化模型。     

基于属性识别理论的网络威胁评估方法

针对网络威胁评估问题,提出一种基于属性识别理论的网络威胁评估方法。通过提取一段时间内网络攻击事件的指标,采用熵值确定权重并建立模型,对攻击事件进行评估、分级,进而量化出该段时间内网络的威胁程度。实例分析结果表明,该方法简单、实用,能够有效进行网络威胁的评估。     

基于攻击路径图的威胁评估方法

为解决复杂系统安全威胁评估问题,研究并建立了基于攻击路径图的系统安全威胁模型。该模型首先提出了漏洞利用可能性及利用结果的评估标准,进而在分析漏洞之间利用关系的基础上生成目标系统的攻击路径图,并以图论、概率论等理论作为基本方法对将复杂攻击路径分解为以串、并联形式为基本构成单元的简单路径,从而降低了安全威胁评估问题的规模和难度,实现了对漏洞威胁以及攻击路径威胁的量化评估。     

A learning classifier system for mazes with aliasing clones

Maze problems represent a simplified virtual model of the real environment and can be used for developing core algorithms of many real-world application related to the problem of navigation. Learning Classifier Systems (LCS) are the most widely used class of algorithms for reinforcement learning in mazes. However, LCSs best achievements in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons for failure. Moreover, there is a lack of knowledge of what makes a maze problem hard to solve by a learning agent. To overcome this restriction we try to improve our understanding of the nature and structure of maze environments. In this paper we describe a new LCS agent that has a simpler and more transparent performance mechanism. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability to Associative Perception, adopted from psychology. We then assess the new LCS with Associative Perception on an extensive set of mazes and analyse the results to discover which features of the environments play the most significant role in the learning process. We identify a particularly hard feature for learning in mazes, aliasing clones, which arise when groups of aliasing cells occur in similar patterns in different parts of the maze. We discuss the impact of aliasing clones and other types of aliasing on learning algorithms.     

Discrimination of visual and haptic rendering delays in networked environments

Many networked human-machine interface systems have a distributed structure for certain purposes such as more computational power, tele-presence, collaboration, and portability. However, network delays are inevitable in the distributed structure, and often make sensory information delivered behind time to the user. In the literature, the effect of network delays on the quality of information presentation has been considered with respect to task performances in most cases. In this paper, we pay attention to a more stringent criterion, namely whether perceptual artifacts caused by network delays are perceptible by the user. We examined minimum perceptible visual and/or haptic rendering delays by measuring their discrimination thresholds between normal and delayed virtual environments with and without a task, and report the results in this paper. We also provide a simple guideline for determining whether active delay compensation algorithms are required in a networked human-machine interface system by comparing representative network delays to the measured discrimination thresholds. Recommended by Guest Editor Phill Kyu Rhee. This work was supported in parts by a grant R01-2006-000-10808-0 and a NRL grant R0A-2008-000-20087-0 both from KOSEF and by a ITRC support program C1090-0804-0002 from IITA, all funded by the Korea government. In Lee is a Ph.D student in Computer Science and Engineering at POSTECH, of Korea. He received the B.S. degree in Information and Communication Engineering from Sungkyunkwan University in 2006. His research interests include haptics, virtual reality, and human-computer interaction. Seungmoon Choi is an Assistant Professor in Computer Science and engineering at POSTECH, Korea. He received the B.S. and M.S. degrees in Control and Instrumentation Engineering from Seoul National University in 1995 and 1997, respectively, and the Ph.D. degree in Electrical and Computer Engineering from Purdue University in 2003. His research areas include haptics, virtual reality, data perceptualization, and applied perception.     

感性消费需求下的设计艺术

科技进步带来丰盛的商品,我们的生活方式、消费需求发生极大转变,社会进入感性消费时代,本文论述了消费心理变化的主观和客观因素,探索感性消费需求心理变化趋势,呼吁企业和设计师关注消费需求的变化,探索感性消费需求下的个性化设计艺术。