基于协作表示Boosting 的辐射源多传感器融合识别

由于单传感器辐射源识别的局限性,在低信噪比条件下仅提高单侦测平台的识别能力无法满足实际需求,为此提出基于协作表示Boosting的辐射源多传感器融合识别算法.利用多传感器数据信息的冗余性和互补性,对多处理支路采用时频分析提取特征,并由协作表示分类器求得残差.根据Boosting在训练阶段的权重组合得到最小分类残差,实现多传感器决策域的融合识别.仿真实验结果验证了所提出方法有效性,并且在低信噪比情况下噪声鲁棒性更优异,易于实现.     

基于数据融合技术的多模型状态监测与故障预报

提出了将多传感器数据融合技术引入到工程系统的状态监测与故障预报领域,实现多模型监测,以提高故障预报的准确性和可靠性,并研究了将Bayes融合算法和自适应加权算法相结合实现多模型的决策融合。并针对一套石油化工工业的流化催化裂化装置(FCCU)仿真系统建立了多模型监测系统,融合的结果表明,对比于单一模型预报,通过使用融合算法实现多模型监测可以有效地提高预报的可靠性。     

交互式多模型异步航迹融合算法

对分布式融合系统中的异步航迹融合算法进行了研究.已有的异步航迹融合算法要求传感器所采用的状态方程与目标运动模式相同,这在实际应用中很难满足.针对已有异步航迹融合算法的不足,建立了基于状态方程集合的异步航迹融合模型.所有传感器均采用基于同一状态方程集合的交互式多模型(IMM)算法来实现航迹生成,融合中心对来自于相同状态方程的异步航迹数据进行加权平均来获得同步航迹,提出了交互式多模型异步航迹融合(IMMASTF)算法.仿真结果验证了IMMASTF算法的有效性.     

LSFR算法在多传感器分布式检测中的优化研究

在多传感器分布式检测系统中,常规融合规则算法要求传感器误差概率已知,且系统中传感器和融合中心同时优化存在一定困难.提出最小二乘融合规则(LSFR)算法,算法不依赖噪声环境稳定性以及传感器的虚警概率与检测概率,融合中心根据各个传感器的硬决策,得到全局的硬决策,并在传感器和融合中心处理达到最优时,获得最佳全局性能.仿真结果表明:对比似然比融合决策算法与Neyman Pearson融合规则(NPFR)算法,LSFR算法全局检测概率显著提高,且在不同数量规模传感器和更多类型的分布式检测系统中具有较好兼容性.     

基于决策距离的分布式多传感器动态分组算法

飞行试验中多传感器测量系统中因类型、精度以及空间位置的变化,导致在数据融合时往往不能得到最优测量精度;提出了一种新的分布式多传感器目标跟踪分组融合算法,即利用模糊理论中的决策距离(Decision Space)思想,对飞行试验目标跟踪的多传感器系统进行动态分组(Dynamic Grouping),通过定义多传感器之间的关系矩阵(Relation Matrix),依据判别门限(Threshold)判定其是否参与最终处理,据此以获得在分布式多传感器目标跟踪测量系统中目标跟踪测量的最佳融合数据精度;仿真结果证明,该算法是一种有效的分组算法.     

多传感器数据融合技术研究进展

多传感器数据融合是信息领域一个前景广阔的研究方向.由于单一的数据融合算法具有一定的局限性,将2种或2种以上的数据融合算法进行优势集成已逐渐成为数据融合领域的研究热点.介绍了数据级、特征级和决策级融合3种数据融合方式的主要特点、方法及应用,归纳了常用的数据融合方法,并重点阐述了几种多传感器数据融合集成算法的研究进展,简单介绍了多传感器数据融合技术的应用.     

信息融合技术在伺服控制系统中的应用

对一种基于多传感器信息融台方案的伺服控制系统进行研究，采用信息融合方法，对扭角、扭矩及抗压等信号进行数据采样，提取它们的特征量，并且对采集的数据进行信息融合，采用一种新的融合算法来实现对被测试件的实时监测以及预测控制；经过对融合算法的Matlab仿真和现场参数的调试，都取得了比较好的效果，这套控制系统具有很强的实时性，很高的控制精度，能够满足伺服控制系统的控制要求。     

分布式贝叶斯检测融合优化算法及其计算机仿真

该文研究了利用分布式多传感器获得全局决策的分布式信号检测问题。在这种检测系统中各传感器将其各自关于观测对象的决策传送至融合中心,融合中心根据融合规则给出全局决策。研究重点是基于贝叶斯准则的分布式并联检测融合系统的数据融合理论,给出了使系统全局最优的融合规则和传感器决策规则,提出了对融合规则和传感器决策规则进行优化计算的非线性高斯一赛德尔算法,具体讨论了两相同传感器、两个不同传感器和三个相同传感器在具有独立观测时的数据融合问题。给出了利用本文所提算法对上述几种情况进行计算机仿真的仿真实例。仿真结果表明：融合系统的性能相对传感器有显著改善,采用三个相同传感器的融合系统,其贝叶斯风险下降了26．5％。     

基于改进D-S证据理论的室内环境控制决策系统

针对室内环境因子多且相互作用关系复杂,影响室内环境舒适度的控制精准决策,设计了一种基于改进D-S证据理论的室内环境控制决策系统。首先采用箱线图法和均值替代法检测修复异常采集数据,然后利用距离自适应加权融合算法实现同类传感器数据一级融合,最后利用改进D-S证据理论算法,实现全局融合决策。实验结果表明,改进D-S证据理论算法能够有效避免冲突证据带来的融合决策误差,系统可以实现室内环境控制的精准决策,融合决策精度高,具有一定的推广应用价值。     

航迹融合算法在多传感器融合中的应用

研究寻的制导优化控制问题,针对传统单一传感器导引不能满足性能要求,提出采用多传感器复合制导.航迹融合是多传感器数据融合中一个非常重要的方面.由于公共过程噪声的原因,使在应用状态估计融合系统中,来自不同传感器的航迹估计误差未必有独立性,为了使航迹与航迹关联和融合,提出自适应航迹和协方差加权航迹融合的算法.通过仿真研究说明自适应航迹融合和协方差加权航迹融合的算法对多传感器数据融合技术有很明显的作用,数据融合效果好,为复合寻的制导优化设计提供了依据.     

Thermal comfort control using a non-linear MPC strategy: A real case of study in a bioclimatic building

The aim to maintain thermal comfort conditions in confined environments may require complex regulation procedures and the proper management of an HVAC (heating, ventilation and air conditioning) system. This problem is being widely analyzed, since it has a direct effect on users’ productivity, and an indirect effect on energy saving. This paper presents a hierarchical thermal comfort control system with two layers. The upper layer includes a non-linear model predictive controller that allows to obtain a high thermal comfort level by optimizing the use of an HVAC system in order to reduce, as much as possible, the energy consumption. On the other hand, the lower layer is formed by a PID (proportional, integrative and derivative) controller with anti-windup function which is in charge of reach the setpoints calculated by the non-linear model predictive controller. In order to probe the effectiveness of the proposed control system, suitable real results obtained in a bioclimatic building are included and commented.     

Recursive Approximation of Complex Behaviours With IoT-Data Imperfections

This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect(noisy and incomplete) measurements in the internet of things(IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality(l0) optimization problem, known to be NP-hard.To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe(mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning(HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method.     

基于STM32F107的暖通空调混合通风自适应控制系统设计

针对暖通空调混合通风自适应控制系统受到室内空气环境的影响,导致暖通空调的通风控制效果变差,增加了空调能耗的问题,提出了基于STM32F107的暖通空调混合通风自适应控制系统设计。在STM32F107控制平台上,通过暖通空调混合通风自适应控制电源柜设计和暖通空调混合通风自适应控制器设计,完成了系统的硬件设计,结合暖通空调混合通风换气量的计算和暖通空调混合通风控制算法设计,完成了系统的软件设计,实现了暖通空调混合通风的自适应控制。测试结果表明,基于STM32F107的暖通空调混合通风自适应控制系统不仅可以控制室内的温度环境,还可以控制室内的湿度环境,有效提高了暖通空调混合通风的控制效果。     

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.      

考虑舒适度的制造环境中HVAC节能优化控制研究

针对制造系统中HVAC高能耗的问题,通过分析制造环境中的热量对HVAC与温度的影响,建立了考虑舒适度的HVAC节能优化模型,并运用模拟退火算法优化目标函数。实验结果表明,HVAC节能优化模型不仅降低了5.9%的能耗,而且室内温度范围在28~29℃,符合节能与舒适的双标准。     

Fuzzy-genetic algorithm for automatic fault detection in HVAC systems

Detecting fault before it deteriorates the system performance is crucial for the reliability and safety of many engineering systems. This paper develops an intelligent technique based on fuzzy-genetic algorithm (FGA) for automatically detecting faults on HVAC system. Many researchers have proposed only using fuzzy systems to effect fault detection and diagnosis. Other applications of the FGA are mainly focused on the synthesis of fuzzy control rules. The proposed automatic fault detection system (AFD) monitors the HVAC system states continuously by fuzzy system. The optimization capability of genetic algorithms allows the generation of optimal fuzzy rules. Faults are represented as different fault levels in the AFD system and are distinguished by fuzzy system after tuning its rule table. Simulation studies are conducted to verify the proposed AFD system for the single zone air handler system.     

基于人体热舒适性指标PMV的暖通空调控制器

分析了人类对暖通空调系统的要求,介绍了暖通空调控制的现状,提出了一种新的基于人体热舒适性指标PMV的暖通空调控制器,该控制器能满足人类对暖通空调系统健康、舒适和节能的要求,是一种理想的暖通空调控制器。     

The effects of service delays on a BACnet-based HVAC control system

Building automation and control network (BACnet) is a standard data communication protocol for building automation and control systems. This study investigated the relationship between network-induced delay and control performance of a BACnet-based heating, ventilating, and air-conditioning (HVAC) control system using a simulation. The model consisted of the HVAC control system model and a BACnet communication protocol model. The BACnet communication model delivered the HVAC control data using the communication services provided by BACnet. The simulation results were analysed to develop recommendations for designing and operating BACnet-based HVAC control systems.     

Data-driven Fault Detection and Diagnosis for HVAC water chillers

Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the users, energy wastage, system unreliability and shorter equipment life. Faults need to be early diagnosed to prevent further deterioration of the system behaviour and energy losses. Since it is not a common practice to collect historical data regarding unforeseen phenomena and abnormal behaviours for HVAC installations, in this paper, a semi-supervised data-driven approach is employed for fault detection and isolation that makes no use of a priori knowledge about abnormal phenomena. The proposed method exploits Principal Component Analysis (PCA) to distinguish anomalies from normal operation variability and a reconstruction-based contribution approach to isolate variables related to faults. The diagnosis task is then tackled by means of a decision table that associates the influence of faults to certain characteristic features. The Fault Detection and Diagnosis (FDD) algorithm performance is assessed by exploiting experimental datasets from two types of water chiller systems.     

基于热舒适度的办公建筑HVAC系统节能控制

建筑节能控制是一个满足舒适需求条件下的多目标优化问题,然而对于缺失运行数据的新建建筑,如何控制供暖、通风和空调(HVAC)系统达到既舒适又节能的效果是一个控制难题.针对这个问题,本文首先建立了新建建筑空间模型,然后对该模型进行能耗仿真分析,在此基础上,提出基于人员热舒适度的模糊控制算法,得出最优控制区间,从而在较低能耗水平情况下获得更长的热舒适天数,达到既节能又舒适的目标.基于人员热舒适度的节能控制对建筑HVAC系统绿色运行具有促进作用.