1957-2017年泾河流域降雨量及降雨侵蚀力时空变化分析

泾河流域受土壤侵蚀的影响,水土流失较为严重,为对流域水土流失风险评估和防治等提供参考依据,根据1957-2017年泾河流域5个气象站点的日降水资料,运用ArcGIS空间插值、小波分析、R/S分析等方法,分析了该时段降雨量及降雨侵蚀力时空变化规律。结果表明:泾河流域及各气象站点的降雨量与降雨侵蚀力均呈显著正相关关系;泾河流域降雨量与降雨侵蚀力年内分布不均,主要集中在夏季,分别占全年的67.80%和52.86%;泾河流域年均降雨量和降雨侵蚀力分别为496.83 mm和1 481.24 (MJ·mm)/(hm~2·h),年际总体呈波动上升趋势且未来降雨侵蚀力将延续增加趋势,两者均在1996和2009年出现突变点且第一主周期分别为27和17 a;泾河流域降雨量和降雨侵蚀力在空间上均呈从西北到东南递增的趋势。     

Semi-automated user agents in distributed change management

Flexible integration of distributed design and manufacturing activities is one of the key issues in applying just-in-time principles along the logistics chain. The potential of AI based approaches in this field is clear, but the inherent asynchrony, concurrency, and inconsistency of distributed operations has reduced enthusiasm so far.This paper discusses coordination in the context of geographically distributed manufacturing of one-of-a-kind products. Cooperation of partners is based on semi-automated user agents which operate as an integrative layer between more dedicated tools and systems. The management of order changes in elevator manufacture is used as a case example.The work reported here is part of the DIMUN project (DIMUN, 1989) in the RACE program.     

一种基于网络安全风险评估的入侵检测方法

针对传统的入侵检测系统存在报警数量大、误报率高等缺陷,提出了一种基于网络安全风险评估的入侵检测方法,该方法基于入侵检测结果,引入抗体浓度随入侵强度动态变化这一人工免疫理论的最新研究成果进行网络安全风险的计算,然后根据当前网络面临的实时安全风险动态设置报警策略。实验结果表明,该方法能够实时、定量地计算主机和网络所面临的风险,并极大地降低报警数量和误报率。     

入侵检测主流技术及发展动态

入侵检测技术是网络安全的主要技术和网络研究的热点,入侵检测方法包括基于数据挖掘、粗糙集、模式识别、支持向量机和人工免疫等主要技术,详细分析了各种检测方法在入侵检测应用中的优缺点。通过回顾研究人员近期的研究成果,提出了该技术的主要发展方向,为进一步研究提供参考。     

短波chirp探测的遗传退火频率分配算法研究

针对短波chirp探测系统的通信网络频率分配问题,建立了合理的干扰模型;利用系统所提供的测量参数,结合基本遗传算法,同时进行模拟退火操作以克服基本遗传算法易陷入局部最优解的不足,并最终趋于全局最优化;仿真结果表明,该算法在寻优性、收敛速度和稳定性等方面都要优于基本遗传算法。     

气动人工肌肉手臂的神经网络Smith预估控制

针对含时滞d的1关节气动人工肌肉(PAM)手臂,用三层递归神经网络(RNN),建立PAM手臂包含时滞的模型(即非线性Smith预估器),并超前d步预测PAM手臂的输出角度.将此超前d步的预测值作为反馈量,与设定值相比较得到的误差作为PID控制器输入量,实现Smith预估PID控制.同时每一步都用RNN模型当前时刻的输出值与PAM手臂当前时刻实际输出值之差的平方做为RNN权值的在线调整准则对RNN预测模型的权值进行在线调整,以自适应PAM手臂的不确定性和时变性.使用Matlab通过串口和研华亚当模块对实物PAM手臂进行控制,控制效果表明所提出的Smith预估PID控制算法比常规PID控制算法的性能有显著提高,证明所提出的算法是有效的和切实可行的.     

Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling

This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task.     

人工神经网络技术在电工产品可靠性实验中的应用(理论部分)

本文简要介绍了人工神经网络，BP神经网络的结构、训练和学习规则以及人工神经网络非线性时间序列预测的应用概况。     

A Novel Artificially Humanized Anti-Cripto-1 Antibody Suppressing Cancer Cell Growth

Cripto-1 is a member of the EGF-CFC/FRL1/Cryptic family and is involved in embryonic development and carcinogenesis. We designed a novel anti-Cripto-1 artificial antibody and assessed the recognition to the antigen and the potential to suppress the growth of cancer stem cells. First, single chain antibody clones were isolated by bio-panning with the affinity to recombinant Cripto-1 protein from our original phage-display library. Then, the variable regions of heavy chain VH and light chain VL in each clone were fused to constant regions of heavy chain CH and light chain CL regions respectively. These fused genes were expressed in ExpiCHO-S cells to produce artificial humanized antibodies against Cripto-1. After evaluation of the expression levels, one clone was selected and the anti-Cripto-1 antibody was produced and purified. The purified antibody showed affinity to recombinant Cripto-1 at 1.1 pmol and immunoreactivity to cancer tissues and cell lines. The antibody was available to detect the immunoreactivity in tissue microarrays of malignant tumors as well as in Cripto-1 overexpressing cells. Simultaneously, the antibody exhibited the potential to suppress the growth of human colon cancer derived GEO cells overexpressing Cripto-1 with IC₅₀ at approximately 110 nM. The artificially humanized antibody is proposed to be a good candidate to target cancer cells overexpressing Cripto-1.     

Adaptive simulated annealing particle swarm optimization algorithm

Particle swarm optimization is widely used in various fields because of the few parameters to be set and the simple calculation structure.In order to improve the optimization speed and accuracy of the PSO,and to avoid falling into the local optimal solution,an adaptive simulated annealing PSO is proposed,which uses the hyperbolic tangent function to control the inertia weight factor for nonlinear adaptive changes,uses linear change strategies to control 2 learning factors,introduces the simulation annealing operation,set a temperature according to the initial state of the population,guide the population to accept the difference solution with a certain probability according to the Metropolis criterion,and ensure the ability to jump out of the local optimal solution.To verify the effect of the algorithm proposed in this paper,7 typical test functions and 5 algorithms proposed in the literature are selected for comparison and testing.According to the average value,standard deviation and number of iterations of the optimization results,the algorithm proposed in this paper has greatly improved the iteration accuracy,convergence speed and stability so as to overcome the shortcomings of particle swarm optimization.