大型原油蒸馏过程能量全局优化控制策略

本文研究了大型原油蒸馏过程能量全局优化控制问题。通过对原油蒸馏装置备用能环节能量传递关系的分析，将全装置能量系统分解为四个能量子系统。在对常压塔、减压塔、换热网络和加热炉等四个能量子系统优化控制问题求解的基础上，运用大系统理论分解协调的原理，求解了原油蒸馏过程的能量全局优化控制问题。对某炼厂大型原油蒸馏装置现场操作数据优化计算结果表明，本文提出的能量全局优化控制策略将使装置的总能耗降低8.46％

原油蒸馏过程换热网络的最优操作

本文研究了原油蒸馏过程换热网络的最优操作。针对上海高桥石化公司炼油厂号 原油蒸馏装置换热硫程的特点, 通过建立加热炉冷进料段的热平衡模型, 提出了考虑加 热炉冷进料影响的整个换热过程最优操作问题。这是一个具有个优化变量的非线性规 划间题。操作优化间题的求解是在对换热过程进行综合模拟的基础上进行的。对现场操 作数据的优化计算结果表明, 换热网络的终温可以提高3℃左右

原油蒸馏过程换热网络——加热炉联合系统的最优操作

〕本文研究了原油蒸馏过程换热网络一加热炉联合系统的最优控制问题。首先分别讨论换热网络和 加热炉的最优操作间题, 在此基础上, 考虑到这两个系统之间存在的关联, 提出了换热网络一加热炉联合系 统的最优操作问题。在应用大系统分解协调理论求解该问题时, 针对系统之间的特殊性, 提出了一种新的协 调算法—预估平衡协调法。由某炼油厂大型原油蒸馏过程换热网络一加热炉联合系统的仿真结果表明, 本文 提出的最优操作方法将带来显著的节能效果

Application of space spectrum estimation technique based on MUSIC algorithm in TD-LTE uplink timing

空间谱估计算法因其在参数估计方面具有分辨性能高、稳健性好等优点被广泛应用。为提高Td-lTE低信噪比时定时的准确度,提出一种基于空间谱估计的定时偏移估计算法,利用Td-lTE上行特殊的导频结构,构造出与空间谱估计相类似的模型,利用MuSIC算法进行定时偏移估计。仿真结果表明,即使在信噪比很低的情况下,基于MuSIC的定时偏移估计算法的估计性能依然很好。与传统的算法相比,存在多径的情况下,基于空间谱估计技术的定时准确度大大提高。The spatial spectrum estimation algorithm was widely used at the aspect of the parameter estimation because of its many advantages such as higher resolution,greater robustness and so on.In order to improve the timing accuracy at low sig nal-to-noise ratio(SNR)of TD-LTE,a kind of timing offset estimation algorithm based on the spatial spectral sstimation was presented.By the special pilot structure of TD-LTE uplink,a model which is similar to patial spectrum estimation can be built and then use MUSIC algorithm to get the timing offset estimation.The simulations reveal that,the estimation performance is very good even in the case of low signal-to-noise ratio.Compared with the traditional algorithm,the timing accuracy will greatly improve when use a technology which is based on spatial spectral estimation algorithm.国家自然科学基金项目(61201192):基于时频域耦合的分布式MIMO信道建模; 重大专项项目(20122X03003007-004)支持:基带集中处理的RAN构架研究; 973项目(2012CB316000); 无线移动通信国家重点实验室(电信科学技术研究院)合作项目(20122000153):认知无线电中的动态频谱分配技术研

Channel Capacity Analysis of Distributed MIMO Systems in Cabin

采用自主搭建的信道测量平台测得了机舱环境下分布式MIMO系统的信道冲击响应矩阵。根据实测的信道矩阵分别计算了4种具有不同收发天线数目的分布式MIMO系统的信道容量。为了便于比较,SISO系统的信道容量也通过实测数据进行了计算。计算结果表明:在机舱环境下,采用分布式MIMO系统和采用SISO系统相比能够显著提高系统信道容量,说明分布式MIMO系统可以充分满足未来机舱内无线通信高速率数据传输的需求。With the self-built channel measurement platform,the channel matrix of in-cabin distributed MIMO system is measured.The channel capacity of 4 different distributed MIMO systems and also the channel capacity of one SISO system are calculated from the measured data.The calculation results show that as compared with one SISO system,the distributed MIMO system could significantly raise the capacity gain in the aircraft cabin environment,and thus could fully satisfy the requirements of high data-rate delivery for future in-cabin wireless communication.国家科技重大专项课题(No.2009ZX03002-002);国家重点基础研究发展规划项目资助(No.2007CB310608);国家863项目资助(No.2009AA011501);国家科技合作项目(No.2010DFB10410);清华-高通联合研究计划资助项目;长江学者和创新团队发展计划资助项目;中国博士后科学基

a time-division qos routing protocol for three-layered satellite networks

提出了一种基于低轨/中轨/高轨(LEO/MEO/GEO)卫星网络体系结构(TLSN)和时空分割思想的QoS路由协议(TDRP),详细阐述了时空分割思想和时隙优化方法,重点研究了QoS路由的实现框架和路由算法(BDA)的实现原理.在此基础上,卫星网络体系结构和路由协议的性能得到了详细分析和仿真验证.相同负载情况下,TLSN中端到端时延和时延抖动性能明显好于LEO卫星网络;同Dijkstra算法相比,BDA算法在端到端时延、时延抖动、丢包率、吞吐量方面具有更好的QoS保证能力

Fault Detection of AUV Resource Exploration System Based on Multi-block Information Extraction

针对&ldquo;潜龙二号&rdquo;AUV在实际航行过程中,资源勘查系统传感器数据有着多重变量相关性、故障类型多样、受运行状态和环境变化影响数值变化大以及噪声强等问题,提出一种新的基于多块信息提取的主元分析(PCA)故障检测方法.首先,针对变量之间的多重相关性,通过滑窗和相关系数的方法提取变量间相关性信息;然后,根据变化率在不同运行状态和环境下基本稳定的特点,对于不同类型故障,分别提取变化率信息和变化率信息的各阶统计量累积误差信息;最后,基于提取的特征信息建立3个子块,对每个子块分别建立PCA模型并进行检测,将检测的结果通过中值滤波去噪后,用贝叶斯推断进行融合.通过对&ldquo;潜龙二号&rdquo;实际运行数据进行检测,验证了该方法的有效性.</p

Fault Detection of AUV Resource Exploration System Based on Multi-block Information Extraction

针对&ldquo;潜龙二号&rdquo;AUV在实际航行过程中,资源勘查系统传感器数据有着多重变量相关性、故障类型多样、受运行状态和环境变化影响数值变化大以及噪声强等问题,提出一种新的基于多块信息提取的主元分析(PCA)故障检测方法.首先,针对变量之间的多重相关性,通过滑窗和相关系数的方法提取变量间相关性信息;然后,根据变化率在不同运行状态和环境下基本稳定的特点,对于不同类型故障,分别提取变化率信息和变化率信息的各阶统计量累积误差信息;最后,基于提取的特征信息建立3个子块,对每个子块分别建立PCA模型并进行检测,将检测的结果通过中值滤波去噪后,用贝叶斯推断进行融合.通过对&ldquo;潜龙二号&rdquo;实际运行数据进行检测,验证了该方法的有效性.</p

A fault diagnosis method based on attention mechanism with application in Qianlong-2 autonomous underwater vehicle

This study proposed a fault diagnosis method based on deep learning and attention mechanism for autonomous underwater vehicle (AUV). Firstly, a data attention mechanism is proposed to introduce dynamic weighting coefficients of monitoring variables to realize dynamic decorrelation. Then, the automatic feature engineering is realized by a bi-directional gated recurrent unit (GRU) network to acquire the time dynamic characteristics of monitoring variables. Finally, fault detection is implemented via multi-layer perceptron (MLP). With respect to fault identification, this study embeds a spatial attention mechanism in the fault detection network to capture the semantic relationship between monitoring variables and faults, and fault identification result can be obtained by parsing this semantic relationship. We present a new loss function and training strategy for cooperation between the fault detection and identification tasks. The proposed method is validated on the monitoring data of Qianlong-2 AUV obtained during the mission in the South China Sea, which shows the effectiveness and superiority of the method.</p