基于差异路径权重的二部图网络推荐算法

针对二部图网络结构推荐算法中资源分配不合理的现象,同时为了丰富推荐结果多样性和促进冷门物品的推荐,提出了一种利用差异路径权重改变资源传递的二部图网络结构算法。利用用户相似性构造路径权重改变第一阶段资源传递规则,使资源较多地流向与目标用户相似的用户节点。通过物品属性相似的构造路径权重,使第二阶段资源更多地流向与目标用户已购物品具有相似属性的物品。实验结果表明,本算法相对于其他流行网络结构算法提高了推荐的综合性能,并且更好地解决推荐中的相关问题。     

工业PTA氧化过程4-CBA浓度的模糊神经网络模型

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.     

Attitudes towards mobile banking: are there any differences between users and non-users?

Extant research has focused on monitoring the behaviour of people who use mobile banking (MB) but it has paid little attention to understanding the impact of information technology usage behaviour due to cultural differences. Humans are the weakest link in information technology adoption; past research has shown that not all users are predisposed to change their behaviour radically and adopt new channels of banking. This paper examines the demographic patterns of users and non-users of MB. The paper also investigates the attitudinal influences of users and non-users of MB based on innovation attributes. Using empirical research, the study identifies constructs of innovation attributes that were perceived to be significantly different among the users and non-users of MB. The study provides valuable insights into MB in Saudi Arabia that have not been previously investigated. From a practical point, findings of this study will be particularly useful to banks, financial institutions and telecommunication service providers.     

New results on PWARX model identification based on clustering approach

This paper deals with the problem of piecewise auto regressive systems with exogenous input(PWARX) model identification based on clustering solution. This problem involves both the estimation of the parameters of the affine sub-models and the hyper planes defining the partitions of the state-input regression. The existing identification methods present three main drawbacks which limit its effectiveness. First, most of them may converge to local minima in the case of poor initializations because they are based on the optimization using nonlinear criteria. Second, they use simple and ineffective techniques to remove outliers. Third, most of them assume that the number of sub-models is known a priori. To overcome these drawbacks, we suggest the use of the density-based spatial clustering of applications with noise(DBSCAN) algorithm. The results presented in this paper illustrate the performance of our methods in comparison with the existing approach. An application of the developed approach to an olive oil esterification reactor is also proposed in order to validate the simulation results.     

Robust Unknown Input Observer Design for Linear Uncertain Time Delay Systems with Application to Fault Detection

In this paper, a novel approach is proposed to design a robust fault detection observer for uncertain linear time delay systems. The system is composed of both norm‐bounded uncertainties and exogenous signals (noise, disturbance, and fault) which are considered to be unknown. The main contribution of this paper is to present unknown input observer (UIO)‐based fault detection system which shows the maximum sensitivity to fault signals and the minimum sensitivity to other signals. Since the system contains uncertainty terms, an H_∞ model‐matching approach is used in design procedure. The reference residual signal generator system is designed so that the fault signal has maximum sensitivity while the exogenous signals have minimum sensitivity on the residual signal. Then, the fault detection system is designed by minimizing the estimation error between the reference residual signal and the UIO residual signal in the sense of H_∞ norm. A sufficient condition for the existence of such a filter is exploited in terms of certain linear matrix inequalities (LMIs). Application of the proposed method in a numerical example and an engineering process are simulated to demonstrate the effectiveness of the proposed algorithm. Simulation results show the validity of the proposed approach to detect the occurrence of faults in the presence of modeling errors, disturbances, and noise.     

Observability Analysis of Continuous‐Time LTI Systems with Limited Derivative Data

We consider continuous‐time LTI systems with either unknown‐input or with lack of information about the input and output derivatives. We compute the unknown‐input observability subspace and the observability subspace with unknown derivatives of input and output. We first formulate the unknown‐input observability subspace via projection matrices, then show that through having the unknown‐input observability subspace, one can easily evaluate the effect of known input and output signals but unknown derivatives on the observability subspace. Our method is demonstrated on the dynamics of a longitudinal aircraft in steady‐state flight.     

Robust state estimation for uncertain linear systems with deterministic input signals

In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.     

对象代理数据库的虚属性查询优化方法磁

为对象代理数据库的虚属性查询提出两种优化方法---表达式提升和虚属性合并。表达式提升将虚属性查询转换为对源类中实属性的查询;虚属性合并是对查询涉及的属于相同源对象的多个虚属性,一次性获取相应源对象的属性值用于虚属性值的计算,避免多次访问同一源对象。通过实验分析了这两种方案对虚属性查询的性能影响,实验结果表明,对于虚属性和代理层数较多的查询,表达式提升和虚属性合并的方法极大提升了查询效率。     

基于手势识别的智能输入

针对在人机交互中键盘远远不能满足人们的要求,提出了一种手势识别算法,并结合键盘的按键实现智能输入．该算法采用肤色分割提取出手部区域,根据手形检测出大拇指和伸出的手指数目及位置,把手势转化为计算机所能接受的二进制语言．根据二进制数,结合键盘的按键,把不同手势定义为不同的按键,实现对计算机的智能输入．实验结果表明该算法具有一定的鲁棒性,并能达到较高的成功率．     

约束条件下的舵鳍联合减摇控制研究

舵鳍联合减摇是一种新型减摇方法。在已有研究文献的基础上,基于输入输出线性化的鲁棒控制器设计方法,针对某集装箱船设计舵鳍联合减摇控制器,并重点研究舵速、鳍速限制对减摇效果与航向保持效果的影响,探讨舵速与鳍速之间的制约关系。仿真结果表明鳍速越低、舵速越高则减摇效果越好,但舵速、鳍速均有一上限值,超过上限值后,舵速、鳍速变化对减摇效果及航向保持效果的进一步改善不再明显。