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| 基于能量约束的多传感器故障非线性最优化规划决策机制 | |
| 引用本文： | 李如平,王勇,徐珍玉.基于能量约束的多传感器故障非线性最优化规划决策机制[J].传感器与微系统,2014,33(11):48-51. |
| 作者姓名： | 李如平 王勇 徐珍玉 |
| 作者单位： | 1. 合肥工业大学计算机与信息学院,安徽合肥230009;安徽工商职业学院电子信息系,安徽合肥231131;中国科学技术大学管理学院,安徽合肥230022 2. 合肥工业大学机械与汽车工程学院,安徽合肥,230009 3. 农业部农业物联网技术集成与应用重点实验室,安徽合肥,230088 |
| 基金项目： | 国家自然科学基金资助项目，国家科技部创新基金资助项目，安徽省科技攻关计划资助项目 |
| 摘 要： | 针对多传感器网络系统的稳定性和可靠性的保障问题,采用能量约束的故障检测技术,通过均衡非线性系统资源,提高故障检测效率和检测精度。同时,结合非线性多传感器系统资源情况和应用需求计算得到故障均衡权重系数,对故障提取特征并映射,建立通过多目标约束的最优化故障处理规划决策机制。实验结果表明：所提算法与传统故障检测处理算法相比,对故障节点定位准确,检测高效。另外,在故障处理时,通过非线性最优化决策降低了能耗,有效保障了多传感器系统的鲁棒性。 |
| 关 键 词： | 能量约束 多传感器故障 非线性规划 最优化决策 |
Nonlinear optimization planning decision-making mechanism of multi-sensor failure based on energy constrained |
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| LI Ru-ping,WANG Yong,XU Zhen-yu.Nonlinear optimization planning decision-making mechanism of multi-sensor failure based on energy constrained[J].Transducer and Microsystem Technology,2014,33(11):48-51. | |
| Authors: | LI Ru-ping WANG Yong XU Zhen-yu |
| Affiliation: | LI Ru-ping, WANG Yong , XU Zhen-yu ( 1. School of Computer and Information, Hefei University of Technology, Hefei 230009, China ; 2. Department of Electronic Information, Anhui Business Vocational College, Hefei 231131, China; 3. School of Management, University of Science ＆ Technology China, Anhui 230022, China; 4. School of Mechanical and Automotive Engineering, Hefei University of Technology, Hefei 230009, China; 5. Key Laboratory of Agriculture IoT Technology Integration and Application, Ministry of Agriculture, Hefei 230088, China) |
| Abstract: | Aiming at problem of stability and reliability guarantee of muhi-sensor network system, use energy constrained fault detection technology,through balance nonlinear system resources and improve efficiency of fault detection and detection precision. Meanwhile, combine multi-sensor system resources and application requirements,fauh balanced weighting parameter is obtained by calculation, extract fault feature and mapping, then establish optimal troubleshooting plan decision-making mechanism through multi-objective constraints. Experimental results show that the proposed algorithm is superior to traditional fault detection algorithm, failed node positioning is accurate, detection is efficient. On the other hand, when troubleshooting, nonlinear optimization decisions is able to reduce energy consumption, effectively guarantee robustness of multi-sensor system. |
| Keywords: | energy constrained multi-sensor failure nonlinear planning optimization decision-making |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |