碳黑水分离技术的研究

脱油沥青作为谢尔气化工艺的气化原料生产化肥在国内尚属首次,由于沥青原料中的灰分和重金属比渣油原料大得多,若仍采用石脑油萃取碳黑技术,灰分和重金属随碳黑返回气化装置,将造成设备的堵塞、磨损,无法保证设备安稳长期运行.通过对碳黑水分离技术进行多方面的研究,寻找到适宜的工艺路线与解决方案.     

全生物降解PBS的扩链改性研究

采用新型扩链剂2,2′-双(2-噁唑啉)(BOZ)对聚丁二酸丁二醇酯(PBS)进行扩链,研究了BOZ加入量、稳定剂加入量、时间、温度对扩链反应的影响。结果表明,选择最佳条件,扩链后的PBS的相对分子质量较未扩链PBS的相对分子质量大为增加,而且扩链PBS相对分子质量分布与未扩链PBS基本一致,说明PBS扩链后的线性度与起始PBS相同,没有支化或交联反应发生。     

高转化率SBR1712的合成和性能

分子量调节剂叔十二碳硫醇(TDM)采用分批加入方式,TDM补加量为0.35～0.45份;选用歧化松香酸钾和脂肪酸钠复合乳化剂,采用一次加入方式;在聚合速率为(7～8)%/h时,可制备出转化率达70%±2%的充油丁苯橡胶SBR 1712(充油量27.3%).在10L釜中进行扩大试验,所得试样的性能与JSR公司同类产品的性能相当.     

H∞ filtering for discrete-time systems subject to stochastic missing measurements: a decomposition approach

This paper deals with the problem of H_∞ filtering for discrete-time systems with stochastic missing measurements. A new missing measurement model is developed by decomposing the interval of the missing rate into several segments. The probability of the missing rate in each subsegment is governed by its corresponding random variables. We aim to design a linear full-order filter such that the estimation error converges to zero exponentially in the mean square with a less conservatism while the disturbance rejection attenuation is constrained to a given level by means of an H_∞ performance index. Based on Lyapunov theory, the reliable filter parameters are characterised in terms of the feasibility of a set of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach.     

Stochastically resilient extended Kalman filtering for discrete-time nonlinear systems with sensor failures

Missing sensor data is a common problem, which severely influences the overall performance of modern data-intensive control and computing applications. In order to address this important issue, a novel resilient extended Kalman filter is proposed for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations. The failure mechanisms of multiple sensors are assumed to be independent of each other with different failure rates. The locally unbiased robust minimum mean square filter is designed for state estimation under these conditions. The performance of the proposed estimation method is verified by means of numerical Monte Carlo simulation of two different nonlinear stochastic systems, involving a sinusoidal system and a Lorenz oscillator system.     

A trustworthy QoS-based collaborative filtering approach for web service discovery

Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services’ Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.     

一种用于目标跟踪的波形自适应选择算法

提出了一种用于雷达目标跟踪的自适应广义调频波形设计算法。该算法根据跟踪器的动态需求,以广义调频信号为样板波形自适应设计下一时刻的发射波形,其目的是最小化预测的目标跟踪均方误差,并假定高信噪比条件,且目标跟踪运动模型和观测模型均为线性,利用与某一波形相对应的克拉美--罗下限（CRLB）以及卡尔曼滤波器,通过最小化预测的跟踪均方误差来实现广义调频波形的自适应设计。仿真结果表明：在信噪比相同的情况下,与使用固定参数、自适应参数的线性调频波形设计算法相比,所提出的算法能够获得更低的目标跟踪均方误差。     

A New Derivation of the Cubature Kalman Filters

This paper investigates the cubature Kalman filtering (CKF) for nonlinear dynamic systems. This third‐degree rule based filter employs a spherical‐radial cubature rule to numerically compute the integrals encountered in nonlinear filtering problems, thereby removing the requirements of explicitly computing the Jacobians. The cubature rule, however, requires computing the intractable integrals over a high‐dimensional spherical region for multidimensional applications. Moreover, the cubature formula that has been used to construct the spherical cubature formula has some demerits, most notably its inconvenient properties in computation and low estimation accuracy. Aimed at these issues, a general class of CKFs that uses only cubature rules is derived in this paper. It can be shown that the conventional CKF is a special case of the proposed algorithm. The paper also includes higher‐degree CKFs, especially two representative types of the fifth‐degree CKFs. Performance of the proposed algorithms is demonstrated via two target tracking problems. The experimental results, presented herein, illustrate the superior performance of higher‐degree CKFs to conventional nonlinear filters.     

基于中位数的用户信誉度排名算法

针对推荐系统易受Spammer攻击的影响,从而导致对象的实际得分不准确的问题,提出基于中位数的用户信誉度排名算法。通过衡量用户信誉度调整用户打分权重,根据中位数具有不易受极端打分影响的特性,选取用户打分与对象得分差距的中位数作为降低用户信誉度的标准,不断迭代调整用户信誉度以及最终得分直至收敛。在多个真实数据集上的运行结果证明,相比现有排名算法,该算法具有更合理的信誉度分布和更高的排名结果准确度,通过该算法预处理后的数据集在SVD++上运行可以得到更低的均方根误差。     

一种卡尔曼增益约束滤波算法

在滤波过程中,将卡尔曼增益结合约束条件,可以有效地提高滤波精度.论文推导了含有卡尔曼增益约束条件的代价函数,根据Karush-Kuhn-Tucker（KKT）最小化的一阶必要条件,使用高斯牛顿法求出约束卡尔曼增益的最优约束解.最后对具有约束条件的目标跟踪问题进行了仿真实验,结果表明该算法的跟踪精度要高于普通卡尔曼滤波算法.