德国老年人药物管理系统设计研究

目的针对德国老年人用药问题进行分析,运用智能药物管理系统,提出解决方案。方法重点分析了德国人口老龄化产生的健康问题,阐述了老年用药方面的问题,提出了用药过程中针对患者、医生、药店有机结合的智能药物管理系统的设计原则和案例。结论人口的老龄化和老年人的健康是全球面临的问题,在"互联网+"健康的基础上,设计人性化的智能药物管理系统,可以为老年人健康生活带来更多的便利。     

La1-xNdxB6单晶的制备及热电子发射性能

以LaB_6和NdB_6粉末为原料,采用区域熔炼法成功制备了大尺寸、高质量的多元稀土六硼化物La_(1-x)Nd_xB_6(x=0.1~0.3)单晶,并测试了该系列单晶(100)晶面的热电子发射性能。本实验制备的单晶长度大于40mm,直径大于4mm。单晶衍射、劳埃照片、晶面摇摆曲线、单晶断口扫描等检测结果表明单晶质量较高,热发射测试结果表明在1 873K、4 000V下,单晶零场电流发射密度随着Nd含量增加从6.01A/cm~2降为4.83A/cm~2,有效逸出功从2.88eV增大到2.95eV。     

Fractional Order Nonlinear Bone Remodeling Dynamics Using the Supervised Neural Network

This study aims to solve the nonlinear fractional-order mathematical model (FOMM) by using the normal and dysregulated bone remodeling of the myeloma bone disease (MBD). For the more precise performance of the model, fractional-order derivatives have been used to solve the disease model numerically. The FOMM is preliminarily designed to focus on the critical interactions between bone resorption or osteoclasts (OC) and bone formation or osteoblasts (OB). The connections of OC and OB are represented by a nonlinear differential system based on the cellular components, which depict stable fluctuation in the usual bone case and unstable fluctuation through the MBD. Untreated myeloma causes by increasing the OC and reducing the osteoblasts, resulting in net bone waste the tumor growth. The solutions of the FOMM will be provided by using the stochastic framework based on the Levenberg-Marquardt backpropagation (LVMBP) neural networks (NN), i.e., LVMBPNN. The mathematical performances of three variations of the fractional-order derivative based on the nonlinear disease model using the LVMPNN. The static structural performances are 82% for investigation and 9% for both learning and certification. The performances of the LVMBPNN are authenticated by using the results of the Adams-Bashforth-Moulton mechanism. To accomplish the capability, steadiness, accuracy, and ability of the LVMBPNN, the performances of the error histograms (EHs), mean square error (MSE), recurrence, and state transitions (STs) will be provided.     

基于强化学习的多策略选择遗传算法

为解决传统遗传算法早熟收敛和收敛速度慢的问题,提出一种基于强化学习的多策略选择遗传算法MPSGA。通过使用不同的选择策略将整个种群划分为3个子种群并分别进化,能提高种群的多样性,有效避免遗传算法的早熟收敛问题。将种群的多样性和算法的运行机制相结合,根据种群多样性的变化运用强化学习算法动态地优化各子种群间的比例参数,从而将种群多样性保持在合适的范围,一定程度上解决了收敛速度和全局收敛性之间的矛盾。实验结果表明,该算法在收敛精度和搜索效率上都表现出较好的性能。     

Low-complexity joint transmit and receive antenna selection for MIMO systems

In this paper, we present a low-complexity algorithm for real-time joint transmit and receive antenna selection in MIMO systems. The computational complexity of exhaustive search in this problem grows exponentially with the number of transmit and receive antennas. We apply Binary Particle Swarm Optimization (BPSO) to the joint transmit and receive antenna selection problem. In addition, applying the general BPSO to joint antenna selection, we also present a specific improvement to this population-based heuristic algorithm, namely, we feed cyclically shifted initial population so that the average convergence time (the number of iterations until reaching an acceptable solution) is reduced. The proposed BPSO for joint antenna selection problem has a low computational complexity, and its effectiveness is verified through simulation results.     

Random linear-quadratic mathematical models: Computing explicit solutions and applications

This paper deals with the study of linear random population models and with a random logistic model (where parameters are random variables). Assuming appropriate conditions, the stochastic processes solutions are obtained under closed form using mean square calculus. Expectation and variance expressions for the stochastic processes solutions are given and illustrative examples are included.     

Replica Exchange Light Transport

We solve the light transport problem by introducing a novel unbiased Monte Carlo algorithm called replica exchange light transport, inspired by the replica exchange Monte Carlo method in the fields of computational physics and statistical information processing. The replica exchange Monte Carlo method is a sampling technique whose operation resembles simulated annealing in optimization algorithms using a set of sampling distributions. We apply it to the solution of light transport integration by extending the probability density function of an integrand of the integration to a set of distributions. That set of distributions is composed of combinations of the path densities of different path generation types: uniform distributions in the integral domain, explicit and implicit paths in light (particle/photon) tracing, indirect paths in bidirectional path tracing, explicit and implicit paths in path tracing, and implicit caustics paths seen through specular surfaces including the delta function in path tracing. The replica‐exchange light transport algorithm generates a sequence of path samples from each distribution and samples the simultaneous distribution of those distributions as a stationary distribution by using the Markov chain Monte Carlo method. Then the algorithm combines the obtained path samples from each distribution using multiple importance sampling. We compare the images generated with our algorithm to those generated with bidirectional path tracing and Metropolis light transport based on the primary sample space. Our proposing algorithm has better convergence property than bidirectional path tracing and the Metropolis light transport, and it is easy to implement by extending the Metropolis light transport.     

基于分级策略的演化算法

为了避免演化算法过早收敛,保持种群多样性,增加算法的搜索能力,本文提出基于分级策略的演化算法．即通过对种群进行分级,来度量种群的多样性,衡量算法是否陷入局部最优,协调种群多样性和精英策略之间的矛盾,再根据种群分布的多样性设计一种有效的半一致交叉算子与单重均匀变异算子。     

燃气报警器报警点漂移模型研究

根据燃气报警器报警点年漂移试验 ,探讨了报警点漂移原因及改进措施 ,建立了报警点漂移的正态分布模型 ,并经用 χ2 检验法进行了检验。依据行业标准CJ- 30 5 7,结合燃气报警器的加严试验结果 ,提出了报警器最佳标定点。