基于密文策略属性基加密算法的云存储数据更新方法

针对云计算数据易遭非法窃取和恶意篡改问题,提出一种支持动态更新操作的密文策略的属性基加密方案（DU-CPABE）。首先利用线性分割思想将数据分成固定大小的数据块,然后采用密文策略属性基加密（CP-ABE）算法对各数据块进行加密,最后提出一种Address-Merkle Hash Tree（A-MHT）搜索树结构,借助A-MHT快速定位数据块实现云服务器中数据动态更新。经理论分析验证了方案的安全性,而且在理想信道中的仿真实验结果显示,在更新次数为5时,此方案相比CP-ABE方案的数据更新时间开销平均下降幅度为14.6%。实验结果表明：DU-CPABE方案在云计算服务中数据动态更新这一过程能够有效地减小数据更新的时间开销,同时降低系统开销。     

用大数据提升突发公共事件的应急能力

突发性公共事件,是现代社会风险治理的重要课题.党的十九届五中全会对国家治理效能提出明确要求,防范化解重大风险体制,机制不断健全,提升突发公共事件应急能力.习近平总书记在调研指导新冠肺炎疫情防控工作时强调,要健全重大疫情应急响应机制,鼓励运用大数据、人工智能及云计算等数字技术,在疫情监测分析、病毒溯源、防控救治及资源调配等方面更好地发挥支撑作用.为此,充分理解和发挥大数据优势,完善数字化应急治理体系与机制,对于提升突发公共事件应急能力具有重要现实意义.     

WebGIS动态匹配分布式设计

针对WebGIS在税务系统应用中遇到的对地图数据要求动态更新和动态匹配的问题进行了分析,给出了纳税户到街道动态匹配的方法,结合分布式对象技术,对纳税户到街道动态匹配进行了分布式设计,探讨了设计中各分布式对象间的工作过程及相关的数据分配策略。     

流式计算模型在废弃话单处理中的应用

对废弃话单进行有效管理,是保障通信系统计费可靠性的重要内容之一,然而对于废弃话单数据所呈现出的实时性、突发性等特征,现有的大数据处理模式并不能很好地应对.本文采用动态路由策略实现计算系统运行时的轻量级伸缩,应对废弃话单实时性和突发性带来的挑战.在此基础之上,本文给出了一种基于流式计算模型的废弃话单处理系统的实现,并通过实际数据验证了本文提出的动态路由策略在应对数据突发方面的有效性.     

上下文分解机的自适应更新策略

分解机模型已经被成功应用于上下文推荐系统。在分解机模型的学习算法中,交替最小二乘法是一种固定其他参数只求单一参数最优值的学习算法,其参数数目影响计算复杂度。然而当特征数目很大时,参数数目随着特征数目急剧增加,导致计算复杂度很高;即使有些参数已经达到了最优值,每次迭代仍更新所有的参数。因此,主要改进了交替最小二乘法的参数更新策略,为参数引入自适应误差指标,通过权重和参数绝对误差共同决定该参数更新与否,使得每次迭代时重点更新最近两次迭代取值变化较大的参数。这种仅更新自适应误差大于阈值的参数的策略不但减少了需要更新的参数数目,进而加快了算法收敛的速度和缩短了运行时间,而且参数权重由误差决定,又修正了误差。在Yahoo和Movielens数据集上的实验结果证明:改进的参数更新策略运行效率有明显提高。     

话题跟踪中静态和动态话题模型的核捕捉衰减

话题跟踪是一项针对新闻话题进行相关信息识别、挖掘和自组织的研究课题,其关键问题之一是如何建立符合话题形态的统计模型.话题形态的研究涉及两个问题,其一是话题的结构特性,其二是话题变形.对比分析了现有词包式、层次树式和链式这3类主流话题模型的形态特征,尤其深入探讨了静态和动态话题模型拟合话题脉络的优势和劣势,并提出一种基于特征重叠比的核捕捉衰减评价策略,专门用于衡量静态和动态话题模型追踪话题发展趋势的能力.在此基础上,分别给出突发式增量式学习方法和时序事件链的更新算法,借以提高动态话题模型的核捕捉性能.实验基于国际标准评测语料TDT4,采用NIST(National Institute of Standards and Technology)提出的最小检测错误权衡系数评测法,并结合所提出的核捕捉衰减评价方法,对各类主要话题模型进行测试.实验结果显示,结构化的动态话题模型具有最佳的跟踪性能,且突发式增量式学习和时序事件链的更新算法分别给予动态话题模型0.4％和3.3％的性能改进.     

话题跟踪中静态和动态话题模型的核捕捉衰减?

话题跟踪是一项针对新闻话题进行相关信息识别、挖掘和自组织的研究课题,其关键问题之一是如何建立符合话题形态的统计模型.话题形态的研究涉及两个问题,其一是话题的结构特性,其二是话题变形.对比分析了现有词包式、层次树式和链式这3类主流话题模型的形态特征,尤其深入探讨了静态和动态话题模型拟合话题脉络的优势和劣势,并提出一种基于特征重叠比的核捕捉衰减评价策略,专门用于衡量静态和动态话题模型追踪话题发展趋势的能力.在此基础上,分别给出突发式增量式学习方法和时序事件链的更新算法,借以提高动态话题模型的核捕捉性能.实验基于国际标准评测语料TDT4,采用NIST(National Institute of Standards and Technology)提出的最小检测错误权衡系数评测法,并结合所提出的核捕捉衰减评价方法,对各类主要话题模型进行测试.实验结果显示,结构化的动态话题模型具有最佳的跟踪性能,且突发式增量式学习和时序事件链的更新算法分别给予动态话题模型0.4%和3.3%的性能改进.     

基于AdaBoost集成加权宽度学习系统的不平衡数据分类

宽度学习系统（BLS）是一种浅层的神经网络结构,具有快速训练、增量学习等特征,在处理类别不平衡数据时提取到的少数类别特征较少,导致识别结果不理想。提出一种基于AdaBoost集成加权宽度学习系统（AdaBoost-WBLS）的不平衡数据分类方法,通过迭代实现权重的动态更新,获得更符合数据特征的权重,提升集成模型对少数类的识别能力。基于KKT条件,对加权宽度学习系统的加权优化过程进行推导,验证了对角权重对BLS模型误差的抑制作用。在AdaBoost-WBLS模型集成初始化时,采用基于类别信息的初始化权值策略,使模型具有更高的集成训练效率。在集成权重更新时,不同数据类别采用不同的正则化更新方式,保留数据的类内特征并增加类间区分度。在实验过程中,对AdaBoost-WBLS模型的不同参数进行寻优,得到相关参数在有限范围内的最优取值。实验结果表明,AdaBoost-WBLS模型相比AdaBoost和BLS类相关模型能有效改善少数类别特征的提取能力,并且在Satimage数据集上相比加权过采样的深度自编码器模型的G-mean高出4.36个百分点,明显提升了不平衡数据的识别能力。     

一种采用抽样策略的PSO 算法

原始粒子群优化算法(PSO) 和各种改进方法存在着参数取值固定、收敛精度低等问题. 为此, 提出一种采用抽样策略的粒子群优化算法(SS-PSO). 通过拉丁超立方抽样(LHS) 策略更新粒子速度和位置, 以加快收敛速度; 提出一种基于随机采样的最优位置修正方法, 以微调全局最优; 提出“双抽样”LHS 局部搜索方法, 以提高收敛精度. 与其他新近提出的两个算法进行对比, 结果显示SS-PSO 在一定程度上提高了算法的性能.

     

Ad Hoc网络中基于贝叶斯博弈的节点激励策略

Ad Hoc网络中的节点在转发数据时易出现自私行为,为激励自私节点参与数据转发,提出一种节点激励策略IMTFT。根据贝叶斯博弈理论建立节点转发博弈模型,在该模型中引入增加激励因子的改进TFT策略,以均衡激励自私节点。在IMTFT策略下对节点的纳什均衡条件进行推理分析,并确定激励因子相关参数的最优取值。仿真结果表明,该策略能有效激励自私节点参与数据转发,提升网络整体性能。     

Structure optimization based on memetic algorithm for adjusting epidemic threshold on complex networks

Many social spreading phenomena can be modeled as epidemic spreading models over networks, and the studies of these phenomena are important to avoid epidemic outbreaks. Epidemic threshold of the network, which fundamentally depends on the network structure itself, is a critical measure to judge whether the epidemic dies out or results in an epidemic breakout. In this study, epidemic threshold is regarded as the objective function to control the spreading process. In addition, an efficient structure optimization strategy based on memetic algorithm is proposed to adjust the spreading threshold without changing the degree of each node. Lowering the threshold can promote the spreading process whereas heightening the threshold can prevent the spreading process. In the proposed algorithm, genetic algorithm is adopted as the global search strategy and a modified simulated annealing algorithm combined with the properties of networks is proposed as the local search strategy. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm has superior performances for both the threshold minimization and maximization problems.     

Modeling for soft sensor systems and parameters updating online

Soft sensor technology is an important means to estimate important process variables in real-time. Modeling for soft sensor system is the core of this technology. Most nonlinear dynamic modeling methods integrate the processes of building the dynamic and static relationships between secondary and primary variables, which limits the estimation accuracy for primary variables. To avoid the problem, a kind of soft sensor model consisting of a dynamic model in cascade with a static one is proposed. The model identification and update online are conducted in substep way. In order to improve the model update efficiency, two improved Gauss–Newton recursive algorithms, which avoid nonsingular covariance matrix, are proposed for time-invariant and time-variant soft sensor systems. The uniform convergence for dynamic model parameter and the existence of estimation deviations for static model parameters are proved for time-invariant soft sensor system. The parameters of time-variant soft sensor system would be boundedly convergent. Case study confirms that, on the basis of the proposed model and recursive algorithms, the dynamic and static characteristics of soft sensor system can be described efficiently, and the primary variables are ensured to be estimated accurately.     

基于1‑bit压缩感知的高效无线联邦学习算法

在无线联邦学习（FL）的架构中,用户端与服务器端之间需要持续交换模型参数数据来实现模型的更新,因此会对用户端造成较大的通信开销和功率消耗。目前已经有多种通过数据量化以及数据稀疏化来降低通信开销的方法。为了进一步降低通信开销,提出了一种基于1?bit压缩感知的无线FL算法。在无线FL架构的上行链路中,这种算法首先在用户端记录其本地模型数据的更新参数,包括更新幅值和趋势;接着对幅值和趋势信息进行稀疏化,并确定更新所需的阈值;最后对更新趋势信息进行1?bit压缩感知,从而压缩上行数据。在此基础上,通过设置动态阈值的方法进一步压缩数据大小。在MNIST数据集上的实验结果表明：引入动态阈值的1?bit压缩感知过程能够获得与无损传输过程相同的效果,在FL应用的上行通信过程中能将用户端需要传输的模型参数数据量降低至不采用该方法的标准FL过程的1/25;而在全局模型训练到相同水平时,能将用户上传数据总大小降低至原来的2/11,将传输能耗降低至原来的1/10。     

基于隔离策略的蠕虫传播模型及分析

近几年,蠕虫频繁爆发,而且传播愈来愈快,破坏力也增大,已成为互联网安全的主要威胁。基于经典的Kermack-Mckendrick模型,本文提出了一个采用动态隔离策略、动态传染率和恢复率的蠕虫传播模型。分析表明此模型能更有效降低蠕虫的传播速度,为防御蠕虫赢得更多宝贵的时间,减缓和降低了蠕虫造成的负面影响,仿真试验证明了此模型的有效性。     

An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers

This study provides evidence supporting the use of the update strategies for the support vector regression (SVR) model. Firstly, the fitting and interpolation method (FIM) is presented to select SVR parameters, and three infill strategies are adopted to search for update points. Secondly, the infill strategy and parameter selection method are illustrated by test functions that illustrate their dependability. The distribution of update points, the sample density and the proportion of update points are discussed. Finally, the adaptive SVR surrogate model is applied to optimize the protective effect of railway wind barriers. The result shows that the parameter selection method has high stability. On the whole, the accuracy of the adaptive SVR model using a suitable infill strategy will be improved with an increasing proportion of update points if the final number of training points is identical. The optimization result shows an optimal porosity of 0.117 when the height of the railway wind barrier is 2.05 m (full scale).

     

树形网络中的副本更新策略及算法

树形网络中的副本放置和更新是网络通讯中值得研究的重要问题之一。面对网络中数据访问需求的动态变化,好的副本放置和更新策略可以在保证服务质量的前提下有效减少网络运行及副本更新成本。针对此问题提出了两种贪心的动态副本更新策略,最大重用策略和请求覆盖策略。通过算法复杂度分析和仿真实验可以看出,所提出的两种算法的最坏时间复杂度为O(nlog n),远低于现有的使用动态规划求最优解的最坏时间复杂度O(n5),而网络运行及副本更新成本与最优解相差不超过11%。在极大地缩短了运算时间的同时,保持了尽可能低的网络运行及副本更新成本。     

A parameter selection strategy for particle swarm optimization based on particle positions

In this study, we found that engineering experience can be used to determine the parameters of an optimization algorithm. We came to this conclusion by analyzing the dynamic characteristics of PSO through a large number of experiments. We constructed a relationship between the dynamic process of particle swarm optimization and the transition process of a control system. A novel parameter strategy for PSO was proven in this paper using the overshoot and the peak time of a transition process. This strategy not only provides a series of flexible parameters for PSO but it also provides a new way to analyze particle trajectories that incorporates engineering practices. In order to validate the new strategy, we compared it with published results from three previous reports, which are consistent or approximately consistent with our new strategy, using a suite of well-known benchmark optimization functions. The experimental results show that the proposed strategy is effective and easy to implement. Moreover, the new strategy was applied to equally spaced linear array synthesis examples and compared with other optimization methods. Experimental results show that it performed well in pattern synthesis.     

基于Wiener结构的软测量模型及辨识算法

Wiener模型结构能有效地表征系统的动态和静态特性, 因此这里首先基于这一结构建立软测量模型, 利用动态与静态子模型分别建立辅助变量与主导变量间的动态与静态关系, 并说明该软测量模型的可行性, 给出模型具体表达式. 其次, 针对所提模型, 提出分步辨识方式获得最优模型参数, 说明其可行性. 再次, 为了减少计算和实现模型在线更新, 这里提出参数辨识递推算法, 并给出软测量模型参数的收敛性结论. 通过实例仿真, 可以看出本文提出模型的可行性, 以及分步辨识方式与递推算法的有效性.     

Online moving horizon estimation of fluxes in metabolic reaction networks

Using online state and parameter estimation, concentrations and fluxes in bioprocesses can be estimated for use in monitoring, optimization and control applications. Existing methodologies, however, either ignore the dynamic nature of the problem, or focus on the extracellular concentration states and pay less attention to accurate flux estimates. These estimates are useful for online monitoring of the flux state of an organism, or for developing novel flux-based strategies for online control of bioreactors.In this contribution, the dynamic metabolic flux analysis model structure is combined with two kinetic flux models: a linear flux model and a nonlinear, more mechanistic flux model. The parameters of these models are estimated online through a moving horizon estimation strategy. The resulting algorithm is illustrated on two simulated case studies: a small-scale network, to assess the influence of important algorithm parameters on the final estimates, and a medium-scale network for Escherichia coli, to empirically test the performance of the methodology in a more realistic situation.An important parameter in this estimation strategy is the chosen noise level on the estimated parameters. This choice is not trivial, but is observed to have a significant influence on the resulting estimates. Furthermore, also the effect of the choice of the null space basis for the stoichiometric matrix of the metabolic reaction network was assessed. In the small-scale case study, it was found that a linear flux model with a specific parameter noise level was performing well for both state and flux estimation. The influence of the choice of the null space basis matrix on the estimation performance was much lower. The resulting scenario was evaluated in the medium-scale case study and found to be performing very well also in that case.     

Constrained nonlinear multivariable control of a fluid catalytic cracking process

In this paper, a nonlinear constrained optimization strategy is proposed and applied to the reactor-regenerator section of a fluid catalytic cracking (FCC) unit. A nonlinear dynamic model of the fluid catalytic cracking process was used for the dynamic analysis of the plant and nonlinear multivariable control system. The model realistically simulates the riser-reactor and the one stage regenerator by assembling the mass and energy balances on the system of reactions. The model results were tested in a real-time application and the results were used to provide the initial values for the nonlinear control system design. A dynamic parameter update algorithm was used to reduce the effect of large modelling errors by regularly updating the model parameters. The constrained nonlinear optimization algorithm and strategies were tested in real-time on the fluid catalytic cracking reactor-regenerator. The results compared favourably to those from a linear multivariable controller.