大型场景海量传感信息弹性感知机制及验证

项目名称： 大型场景海量传感信息弹性感知机制及验证

项目编号： No.61272523

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 胡小鹏

作者单位： 大连理工大学

项目金额： 77万元

中文摘要： 大型传感系统通常具有多源海量数据、复杂连接、大空间尺度等特点，在处理资源受限的条件下实现高效场景感知是一个严峻挑战。本项目探索学习人类感知智能，通过融合自底向上传感信息和自顶向下用户信息，研究面向大型场景感知的贝叶斯注意力选择方法，建立海量传感数据条件下弹性感知机制。主要研究内容包括：（1）利用注意力选择机制指导数据表达、资源分配、人机交互等方法的设计与实现，使大型复杂场景感知建立在一个统一的理论体系之上；（2）研究空间域和时间域中信息局部处理机制，建立分散式信息管理框架，提高海量信息处理效率；（3）将事件检测分解为面向已知确定事件和面向未知异常突发事件检测过程，利用贝叶斯注意力选择机制实现两者的融合，使系统能够在重点完成预定的检测任务的同时，兼顾对未知异常突发事件的处理；（4）研制实验设备，在实际大规模的工业生产监控项目中，验证理论成果和关键技术。

中文关键词： 数据融合；贝叶斯方法；注意力选择；传感网络；事件检测

英文摘要： Large-scale sensor networks (LSSNs) are usually characterized by a huge amount of sensor data, complicated connectivity, wide coverage areas and increasingly stringent response-time requirements. These requirements present a significant challenge to sensor data processing and perception. This is especially so when the computational resources available are limited. This project aims to tackle this challenge by using attention-like mechanisms, inspired by the human-sensory system. This attention process will be based on the integration of both user-defined (top-down) preference and sensory (bottom-up) information. By approaching the large scale and complexity problem through Bayesian attention modeling, we will provide an elastic framework for sensor data management in LSSNs. The theoretical and practical value lies on the facts that (1) it provides a unified theoretical system where the design of several major components including data representation, resource allocation and scheduling and human-machine interface control can be guided by the attention mechanism. (2) A decentralized framework will be developed based on a mechanism that processes sensor data locally in both spatial and temporal domains. (3) The events to be detected are divided into two groups: user pre-specified events and unpredictable abnormal e

英文关键词： Data Fusion；Bayesian Method；Attention Selection；Sensor Network；Event Detection