基于移动agent的WSN低能耗数据融合策略设计

为了解决无线传感器网络中LEACH协议中传感器节点需要耗费大量能量,导致节点过早死亡,在LEACH协议的基础上,将移动agent引入到数据融合策略中,提出了一种基于移动agent和LEACH协议的数据融合策略;首先,改进簇头选举机制,考虑剩余能量和与基站的距离等因素;其次,为了避免访问冗余节点而耗费能量,确定了访问节点集合,并通过改进的遗传算法来获得访问节点序列;最后,通过仿真实验并与LEACH协议、AF-LEACH协议进行比较,文中方法节点死亡时间在470s才出现,延迟了节点死亡时间,从而延长了网络的生命周期。     

机载塔康设备综合检查仪设计

使用市场上成熟的模块和技术研制机载塔康设备综合检查仪,以降低成本和提高设备的可靠性;根据设备的测试需求,选用嵌入式工业平板电脑、USB-ARINC429通讯卡、USB-数字IO卡、串行接口数字表和自研的塔康信号模拟器等构成检查仪硬件,使用CVI9.0编写了测试程序;应用表明:检查仪集成度高,使用方便,能够完成对塔康设备的自动及手动测试。     

Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

Enhancing collaborative filtering by user interest expansion via personalized ranking

Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin.     

Feature extraction using orthogonal discriminant local tangent space alignment

A novel algorithm called orthogonal discriminant local tangent space alignment (O-DLTSA) is proposed for supervised feature extraction. Derived from local tangent space alignment (LTSA), O-DLTSA not only inherits the advantages of LTSA which uses local tangent space as a representation of the local geometry so as to preserve the local structure, but also makes full use of class information and orthogonal subspace to improve discriminant power. The experimental results of applying O-DLTSA to standard face databases demonstrate the effectiveness of the proposed method.     

顶底板围岩不稳的缓倾斜中厚以上矿体的采矿新方案

密集平行束孔落矿的底盘漏斗采矿方案的特点是:用密集严行束孔落矿;凿岩与出矿水平合二为一;实行“掘井、劈漏、落矿三位一体”的回采工艺。该方案结构简单、采切比低、工人劳动强度低,适于开采缓倾斜顶底围岩不稳固的中厚以上矿体。     

《自动控制原理》课程中闭环脉冲传递函数算法研究

针对一般方法推导离散系统闭环脉冲传递函数过程繁琐费时的问题,给出一种先连续化求出闭环传递函数再离散化求解的简便方法,并给出使用本方法的单一化条件。对于满足该条件的系统,可直接求用简便方法求取系统的闭环脉冲传递函数,不满足该使用条件的系统,对其中间变量的求取可使用本方法,因而大大简化求解闭环脉冲传递函数的过程,为采样控制系统的分析和设计提供了很大的方便。     

基于数据集市的银行报表系统设计与实现

分析江苏某银行报表应用现状,探讨依据明确报表需求,运用星型模型构建数据集市,开发的新报表系统,整合了报表应用,实现电子报表、定制报表和固定报表等功能。     

计算机组装课程教学中的多媒体应用探究

因为多媒体技术具有多样的优点,为此,在当前的教学改革在全国范围内如火如荼的进行的时候,将多媒体技术引入到计算机组装课程教学活动中,必然能大大提高课堂教学效率,但是,在课堂上广泛应用多媒体技术的时候,有些问题也要引起足够的重视,避免不利影响的发生。     

As离子注入形成硅化钨的研究

大规模集成电路的发展使集成度不断提高,器件尺寸减小,线条变窄。现常用的多晶硅栅和互连,由于电阻率高,限制了集成电路的速度。近年发展起来的硅化物材料,因其电阻率低、稳定性好、自对准性能好而常被用作大规模集成电路中的欧姆接触,互连和肖特基势垒。