Dye Sensitization Offers a Brighter Afterglow Nanoparticle Future for in vivo Recharged Luminescent Imaging

While concerns about improving recharged afterglow intensity in vivo still motivate further exploration, afterglow nanoparticles (AGNP) offer unique optical merit for autofluorescence-free biological imaging. Apart from efforts enhancing the afterglow emission properties of AGNP, improving afterglow excitation response to visible or near infrared light is important but has lacked success. Dye sensitization has been used to improve the optical response of photovoltaic nanomaterials and to enhance upconversion luminescence efficiency. This concept has recently been expanded and applied to AGNPs. As a new multifunctional nanoprobe, such dye-sensitized AGNP takes advantage of both high spatial resolution fluorescence imaging and sensitive afterglow imaging. This Concept introduces the background, the concept, mechanism, and related imaging application, as well as reviewing existing challenges and proposing future developmental directions for the dye-sensitized AGNPs.     

基于CA和LS-SVM的TE过程多模型建模

针对TE化工过程高度非线性、复杂性的特点,本文提出了一种基于相关分析和最小二乘支持向量机对TE过程进行多模型建模方法,以提高模型性能。首先对TE过程采用相关分析法划分为3个子系统,对每个子系统分别采用基于C-均值聚类的最小二乘支持向量机建模和基于k均值聚类的最小二乘支持向量机多模型建模。实验表明,基于K-均值聚类的多模型建模能简化计算、提高模型精度、并且能更好的预测模型输出。     

多模跟踪技术在轮式侦察车图像处理器的应用

为解决目标旋转形变、遮挡、光照变化等目标跟踪的难题,对粒子滤波和尺度不变特征变换（SIFT）算法进行了改进,结合两种算法提出了决策主导模式的多模跟踪技术。该技术采用粒子滤波预测目标位置进行粗定位,SIFT特征匹配进行精定位的方法,在解决上述难题上有很好的鲁棒性。将该技术应用于轮式侦察车图像处理器,并进行了各种实验验证,结果证明了提出算法的有效性。     

Multi-modal calculations of prompt fission neutrons from 238U（n,f） at low induced energy

Properties of prompt fission neutrons from ²³⁸U(n, f) are calculated for incident neutron energies below 6 MeV using the multi-modal model, including the prompt fission neutron spectrum, the average prompt fission neutron multiplicity, and the prompt fission neutron multiplicity as a function of the fission fragment mass υ(A) (usually named “sawtooth” data) The three most dominant fission modes are taken into account. The model parameters are determined on the basis of experimental fission fragment data. The predicted results are in good agreement with the experimental data.     

一种基于并行化方法的自适应光学闭环预测控制器

自适应光学系统的性能受限于伺服系统的延迟误差和波前传感器的光电子噪声。提出了一种多模型单变量预测模型,该模型采用基于Levenberg-Marquardt学习算法的前馈型神经网络。利用计算机多核处理器,设计了一个具有并行处理能力的预测控制器,来实现对自适应光学闭环控制电压的预测,以消除延迟误差的影响。通过数值仿真实验,研究了预测控制器对控制电压和远场斯特雷尔比的影响,与未采用预测控制器的系统进行了比较,并对预测算法的并行性能进行了分析。实验结果表明,使用并行化方法的预测控制器可以有效缩短系统的预测时间,提高预测算法的加速比,与经典比例积分(PI)控制算法相比可以更有效地降低系统由于伺服延迟引起的误差,远场的斯特雷尔比有明显地提高。     

出行量预测不确定下基于灵敏度分析的拥挤收费定价模型

拥挤收费是改善城市交通的有效手段.对拥挤收费定价的研究方法之一,就是针对固定的出行量进行收费设计.由于出行量预测不确定的因素,应针对长期出行量预测不确定的情况,利用灵敏度分析的方法,对拥挤收费定价的问题进行研究.首先研究了出行者出行的广义费用,然后考虑出行者的出行决策及出行量预测不确定的因素,给出一个拥挤收费定价模型,为交通管理者提供理论依据.     

Multi-model predictive control of Hammerstein-Wiener systems based on balanced multi-model partition

ABSTRACT

Model analysis of Hammerstein-Wiener systems has been made, and it is found that the included angle is applicable to such systems to measure the non-linearity. Then, a dichotomy gridding algorithm is proposed based on the included angle. Supporting by the gridding algorithm, a balanced multi-model partition method is put forward to partition a Hammerstein-Wiener system into a set of local linear models. For each linear model, a linear model predictive controller (MPC) is designed. After that, a multi-MPC is composed of the linear MPCs via soft switching. Thus, a complex non-linear control problem is transformed into a set of linear control problems, which simplifies the original control problem and improves the control performance. Two non-linear systems are built into Hammerstein-Wiener models and investigated using the proposed methods. Simulations demonstrate that the proposed gridding and partition methods are effective, and the resulted multi-MPC controller has satisfactory performance in both set-point tracking and disturbance rejection control.     

多模跟踪技术在轮式侦察车图像处理器的应用

为解决目标旋转形变、遮挡、光照变化等目标跟踪的难题,对粒子滤波和尺度不变特征变换(SIFT)算法进行了改进,结合两种算法提出了决策主导模式的多模跟踪技术。该技术采用粒子滤波预测目标位置进行粗定位,SIFT特征匹配进行精定位的方法,在解决上述难题上有很好的鲁棒性。将该技术应用于轮式侦察车图像处理器,并进行了各种实验验证,结果证明了提出算法的有效性。     

新型可燃性气体报警仪的研制

介绍新型可燃性气体报警仪的设计,可用于对丙烷、丁烷、氢气及其他可燃性气体的检测,用于检测可燃气体爆炸性;采用C8051F020单片机,通过2M007型电阻式气敏传感器采集信号,经过放大电路放大,传入单片机,进行模数转换,采用算术平均滤波对数据过滤,再通过一系列的数据处理,显示可燃性气体浓度,通讯方式采用以sim300通讯模块的GPRS通讯的新型可燃性气体报警系统;软件设计采用C语言编程,灵活方便、具有强大的移植性;如空气中可燃性气体浓度超标,开始报警,显示屏上显示出大于阀值的可燃性气体浓度,且通过无线通讯使手机也正常收到了报警短信;该系统功能齐全,操作简便,具有很高的应用价值。     

一种基于自适应去偏转换滤波的机动目标跟踪

针对于机动目标的跟踪问题,提出了一种基于交互式多模型的自适应去偏转换卡尔曼滤波器.该算法利用交互多模型算法来完成不同跟踪模型的相互切换;根据自适应去偏转换测量卡尔曼滤波算法来推导跟踪目标状态,同时自适应因子可以确保不正常测量时的鲁棒性.与传统的去偏转换卡尔曼滤波算法对比,该算法可以很好地改善所获量测信息在雷达被干扰时的目标跟踪精度.仿真结果表明了算法的有效性和可行性,且跟踪精度相对传统的去偏转换卡尔曼滤波算法减少9.38％的位置误差.