基于异类传感器的数据融合系统

运用各种有源和无源探测设备在内的异类多传感器集成提供多种观测数据,同时通过不同传感器之间的关联来提高数据精度,可以得到关于目标的更多特征信息,从而为身份识别、态势估计提供更多的依据.基于异类传感器的数据融合系统实现了雷达、ESM等异类传感器的数据关联与融合.仿真结果表明:系统能提供更高精度的数据,获得目标的更多特征信息,并可对目标实施有效识别.     

Adaptive Identification of Logging Lithology Based on VPSO-ENN Hybrid Algorithm

Particle swarm optimization (PSO) was modified by variation method of particle velocity, and a variation PSO (VPSO) algorithm was proposed to overcome the shortcomings of PSO, such as premature convergence and local optimization. The VPSO algorithm is combined with Elman neural network (ENN) to form a VPSO-ENN hybrid algorithm. Compared with the hybrid algorithm of genetic algorithm (GA) and BP neural network (GA-BP), VPSO-ENN has less adjustable parameters, faster convergence speed and higher identification precision in the numerical experiment. A system for identifying logging parameters was established based on VPSO-ENN. The results of an engineering case indicate that the intelligent identification system is effective in the lithology identification.     

基于并行组合模拟退火算法的水泵汽蚀初生故障识别

针对水泵汽蚀初始阶段故障信号微弱,检测困难的特点,提出一种基于并行组合模拟退火算法对水泵"汽蚀初生"故障进行识别.阐述了算法的原理和实现方法,通过设计的实验台模拟水泵"汽蚀初生"故障,并获取了正常与模拟故障状态下水泵壳体的振动与出口压力信号的特征参数.利用该算法将上述特征参数自组织、自生成新的特征参数,达到了识别的目的.     

基于模糊层次分析法的公路工程项目风险评价

针对公路工程项目全周期（包括投资期、施工期和运营期）的风险进行分析,将公路工程项目风险分为环境风险、过程风险和决策目标风险,在此基础上构建了公路工程项目的风险评价指标体系,在模糊数学原理基础上运用层次分析法,建立了公路工程项目风险模糊层次分析模型．通过实证项目中的模型运用,验证了此模型在公路工程项目风险评价中的有效性和可行性．     

基于支持向量回归机的船舶操纵运动在线辨识建模

采用滚动时间窗的方法实现支持向量机的在线辨识。以船舶操纵运动响应模型为研究对象,并由10°/10°和15°/15°仿真Z形试验数据构造支持向量机参数辨识所需的训练样本对,应用支持向量机进行船舶操纵运动在线辨识建模,回归操纵运动响应模型中的操纵性指数,并利用建立的响应模型进行Z形试验的数值模拟。将Z形试验数值模拟结果同仿真Z形试验数据进行比较,结果表明,在线式支持向量回归机是一种进行船舶操纵运动在线辨识建模的有效方法。     

基于主成分分析(PCA)和支持向量机(SVM)的耙吸挖泥船工况分析

土质信息对于耙吸挖泥船的挖掘与装舱过程的控制策略具有重要意义,但耙吸挖泥船的耙头无法直接感知土质信息。基于实船数据,通过主成分分析（PCA）对非线性耦合的疏浚数据进行线性可视化和特征分析,对土质信息与工况信息的关联性进行研究。采用支持向量机（SVM）构建分类器,对土质工况进行分类识别。结果表明,该方法能够有效识别不同的土质工况,实现了土质信息的间接感知。     

美国海基弹道导弹预警观测平台发展现状及思考

近年来,美国发展了海基弹道导弹预警观测平台,可对他国弹道导弹进行预警观测和精确识别,已成为美国弹道导弹预警体系的重要节点。文章主要从美国海基弹道导弹预警观测平台发展现状出发,研究分析其特点及主要功能,总结未来发展方向,为我国海基弹道导弹预警观测平台的发展提出几点思考。     

Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach

With a particular emphasis on the end-to-end travel time prediction problem, this paper proposes an information-theoretic sensor location model that aims to minimize total travel time uncertainties from a set of point, point-to-point and probe sensors in a traffic network. Based on a Kalman filtering structure, the proposed measurement and uncertainty quantification models explicitly take into account several important sources of errors in the travel time estimation/prediction process, such as the uncertainty associated with prior travel time estimates, measurement errors and sampling errors. By considering only critical paths and limited time intervals, this paper selects a path travel time uncertainty criterion to construct a joint sensor location and travel time estimation/prediction framework with a unified modeling of both recurring and non-recurring traffic conditions. An analytical determinant maximization model and heuristic beam-search algorithm are used to find an effective lower bound and solve the combinatorial sensor selection problem. A number of illustrative examples and one case study are used to demonstrate the effectiveness of the proposed methodology.     

Effects of antenna position on readability of RFID tags in a refrigerated sea container of frozen bread at 433 and 915 MHz

Perishable foods are frequently exposed to temperature abuse during transportation and distribution. The use of traditional data loggers do not permit the instantaneous data transmission that radio frequency technology offers. Temperature has a major impact on food quality and safety, particularly when long transit times are imposed. Consequently, using radio frequency identification (RFID) to track and monitor temperature in perishable shipments will bring significant benefits to the cold chain. The goal of this study was to determine the optimal RF antenna placement to achieve full RFID tag readability inside a sea container. Testing was made at two different frequencies (915 and 433 MHz) while the refrigeration unit was running at −25 °C and the container was fully loaded with frozen bread. The sea container was instrumented with eight RFID antennas, three of which were tuned for 433 MHz and five for 915 MHz. All antenna wires exited the container via the forward drain holes. The RFID readers were outside the container and connected to their respective antennas, one at a time. Thirty eight RFID tags were evenly distributed onto the pallets of frozen bread. All RFID tags were active tags capable of reading and recording temperature. Results at 915 MHz showed readability levels between 47% and 79%, with an average of 68.4%, whereas 433 MHz demonstrated 100% readability at all antenna positions. In conclusion, the 433 MHz RFID system appears suitable for real time temperature monitoring of frozen bread inside a sea container. This technology could be applied to other food items similar to frozen bread.     

小鼠肌源性干细胞的分离、培养及其分化能力检测

目的探讨小鼠肌源性干细胞(MDSCs)的原代培养方法及其分化潜能,为肌性泌尿组织的细胞修复、治疗奠定基础。方法运用中性蛋白酶(DispaseⅡ)、胰蛋白酶及Ⅰ型胶原酶联合消化法分离小鼠MDSCs,并运用差速贴壁法进行纯化。倒置显微镜下观察细胞形态,RT-PCR法鉴定MyoD及Desmin的表达,同时对分离的细胞进行成骨、成脂等分化诱导并鉴定。结果运用中性蛋白酶可很好地消化组织,差速贴壁法能得到纯度较高的MDSCs;RT-PCR检测发现MDSCs不表达MyoD以及Desmin,提示其为不同于肌卫星细胞的肌源性干细胞;成骨诱导检测碱性磷酸酶(ALP)染色(+),成脂诱导油红O染色(+);其可自体分化诱导为心肌样细胞,免疫荧光法检测心肌肌钙蛋白(cTnI)呈现阳性结果。结论利用此分离方法可获得纯度较高且具有较强分化潜能的肌源性干细胞,有助于进一步进行组织工程研究。