基于学习效应的项目员工调度模型研究

现代社会随着工程项目日益复杂、劳动力成本不断上升、以及多技能员工成为培养趋势,员工调度和项目管理相结合的问题已经被越来越多的学者研究,但是,将员工的学习效应结合进来的不多,建立一个非线性规划模型来对此类问题进行求解,并将员工的学习效应考虑进来,目标函数是最小化外包员工所花费用,用遗传算法来进行计算,并用多组数据进行了实验来对本文提出的算法进行验证.     

基于局部线性嵌入的随机森林算法

随机森林是一种优秀的分类算法,然而随机森林算法不能有效的判断冗余属性,因此影响了在含有冗余属性的数据集上的分类效果。针对这一问题,本文提出了一种基于局部线性嵌入的随机森林算法。该算法利用局部线性嵌入算法对冗余属性数据集进行降维,然后利用随机森林算法进行分类学习。在UCI标准数据集上的仿真实验说明,本文算法是一种优秀的含冗余属性数据集分类算法。     

求解组合优化问题的混合动力学演化算法

该文在对组合优化问题的各类求解算法的研究基础上,基于动力学演化算法的统计力学原理,将局部搜索与模拟退火的策略融入整个算法的设计框架,提出一种新的求解组合优化问题的混合动力学演化算法.该文通过最大独立集问题仿真试验证明了算法的有效性,在较少的计算量下,得到令人满意的结果.     

多基线SAR三维成像的QR分解算法

根据多基线合成孔径雷达(SAR)三维成像的信号模型,得到了利用高度向观测数据实现目标三维成像的矩阵方程,并引入QR分解算法求解矩阵方程,形成了多基线SAR三维成像的QR分解算法.使用该算法对多基线SAR仿真数据进行了三维成像实验.     

一种改进的SVM决策树文本分类算法

将SVM和二叉决策树结合起来构成SVM决策树的方法能够较好地解决多类文本分类问题,在此基础上引入了一种基于支持向量数据描述(SVDD)的类间可分性度量方法,对SVM决策树分类器进行改进,实验表明,该方法有效地提高了SVM决策树多类分类器的分类精度和速度.     

基于自适应遗传算法的混合流水车间调度研究

混合流水车间调度问题具有建模复杂性、动态多约束、计算复杂性、多目标性等特点,近些年,各种演进化算法被运用到生产调度中,特别是遗传算法的应用。本文提出了自适应遗传算法求解混合Flow-shop调度问题的方法,应用Matlab对算法进行编码,并利用实际的生产数据进行了仿真。通过仿真实验,仿真调度数据验证了自适应遗传算法用于求解大型流水车间调度的可行性和有效性。     

一种求解资源受限下项目进度问题的新方法

针对资源受限下的项目进度问题的特点,在已有对该问题的研究基础上,提出一种新方法,应用一种复合粒子群算法对问题求解,以此提高基本PSO算法的收敛速度和精度.以优先值法作为粒子表达RCRLP问题的方式:对并行进度生成机制进行处理以此扩大其搜索空间而增加找到最优解的机率,并用例子说明其有效性.最后结合PSPLIB问题集进行仿真实验,实验结果验证了本文求解RCPSP的新方法具有可行性与优越性.     

基于回归分析的信息协同过滤算法预测研究

信息过滤是图书情报领域的一个研究热点.分析了IBCF、UBCF和RBCF三种协同过滤算法,并用四种不同的数据集对三种算法进行性能测试.实验证明,不同的数据集对应的最优协同过滤算法各不相同.进而利用回归分析模型预测某一具体数据集的最优协同过滤算法,发现回归分析能够很好地进行算法预测.     

关键帧样条插值优化位置偏移的虚拟教学动作识别

针对目前人体动作识别算法精确度不高的问题,提出了一种关键帧样条插值优化位置偏移的虚拟教学动作识别策略。首先对运动数据进行去均值的预处理并转化为矩阵形式,然后通过对运动数据矩阵进行非线性降维处理,构建人体姿态空间集,接着在末端约束条件下对集合进行逆向运动学求解,最后对其进行关键帧样条插值优化。算法仿真实验结果表明,本文提出的改进算法具有较高的识别精度,可以精准识别虚拟教学动作,方便人机交互。     

基于单张二维图像的三维建模研究

对基于单张二维图像重建三维模型问题进行深入研究,对目前使用的方法进行比较分析,提出了基于紧水平支集的S FS三维模型重建方法。首先改进了基于快速匹配算法求解的紧水平支集方法,以提高紧水平支集求解的精度和效率,并将此方法应用到求解S FS问题,提出基于水平集快速求解S FS问题的方法;接下来为解决S FS固有的凹凸二义性问题,基于分水岭的思想寻找凹凸分界线,以此确定产生凹凸二义性的区域用以进行重建;最后利用拓扑变换重构凹陷区域,得到正确的三维模型。     

Towards cost efficient mobile service and information management in ubiquitous environment with cloud resource scheduling

The past few years have witnessed an explosive popularity of mobile services, especially in the form of smart phone applications. To cope with the limited batteries and computational capacities of mobile devices, prior studies suggest to deploy service instances in clouds for accomplishing most of the computation-intensive tasks. Service composition, which compensates for the simplicity of single service, is an effective way to utilize the plentiful services on the clouds all over the world. In this paper, we focus on the problem of service instance selection with service instance replica limitation constraint. The objective is to select the optimal set of service instances, which composes the integrated service and brings out the optimal QoS (quality of service), in terms of service response time. To characterize the problem, we establish a new QoS model, which considers the comprehensive quality over all users, not just for any single user or service instance. We prove that the problem is NP-hard, since many functionally equivalent service instances spread all over the distributed clouds. To address the problem, we classify the problem into three cases, including two special cases and the general case. We present two effective heuristic algorithms to determine the service instances selection for the two special cases, which are still NP-hard. The two special cases provide empirical bounds for the general case. We propose an algorithm that simulates a vote procedure for the users in the general case. The selected service instances, which come from the vote procedure, can satisfy a majority of users. We conduct extensive simulations for all of the algorithms. The simulation results show that our algorithms work efficiently on service response time reduction.     

边界元法中角点问题的一种计算方法

在分析边界元法中多种角点处理方法的基础上,提出了一种新的方法-单角点法.该方法在继承双角点与二重节点法优点的基础上,不仅提高了解的精度且有效解决了边界变形的问题.     

XML data exchange with target constraints

Data exchange is the problem of taking data structured under a source schema and creating an instance of a target schema, by following a mapping between the two schemas. There is a rich literature on problems related to data exchange, e.g., the design of a schema mapping language, the consistency of schema mappings, operations on mappings, and query answering over mappings. Data exchange is extensively studied on relational model, and is also recently discussed for XML data. This article investigates the construction of target instance for XML data exchange, which has received far less attention. We first present a rich language for the definition of schema mappings, which allow one to use various forms of document navigation and specify conditions on data values. Given a schema mapping, we then provide an algorithm to construct a canonical target instance. The schema mapping alone is not adequate for expressing target semantics, and hence, the canonical instance is in general not optimal. We recognize that target constraints play a crucial role in the generation of good solutions. In light of this, we employ a general XML constraint model to define target constraints. Structural constraints and keys are used to identify a certain entity, as rules for data merging. Moreover, we develop techniques to enforce non-key constraints on the canonical target instance, by providing a chase method to reason about data. Experimental results show that our algorithms scale well, and are effective in producing target instances of good quality.     

邻接矩阵在高校图书馆借阅数据挖掘中的应用

Apriori算法是关联规则挖掘的一个经典算法,针对Apriori算法的不足,提出了基于邻接矩阵的算法,该算法首先用邻接矩阵将事务数据库表示出来,然后基于邻接矩阵生成频繁k_项集。以高校图书馆借阅历史数据的挖掘为例,详细描述了事务数据库相应的邻接矩阵生成算法、k_项集生成算法以及相应的数据结构设计,算法均采用C语言描述。     

基于ASP.NET的交叉表的—种新的实现方法

对数据库的交叉表的实现有很多的实现方案，但是以前的实现方法不能在作为字段名称的原表中的字段数据未如时实现交叉表，本文提出的这种实现方法能解决这一情况，并结合实例介绍这种方案的具体实现。     

Data-driven attacks and data recovery with noise on state estimation of smart grid

In this paper, we focus on the false data injection attacks (FDIAs) on state estimation and corresponding countermeasures for data recovery in smart grid. Without the information about the topology and parameters of systems, two data-driven attacks (DDAs) with noisy measurements are constructed, which can escape the detection from the residue-based bad data detection (BDD) in state estimator. Moreover, in view of the limited energy of adversaries, the feasibility of proposed DDAs is improved, such as more sparse and low-cost DDAs than existing work. In addition, a new algorithm for measurement data recovery is introduced, which converts the data recovery problem against the DDAs into the problem of the low rank approximation with corrupted and noisy measurements. Especially, the online low rank approximate algorithm is employed to improve the real-time performance. Finally, the information on the 14-bus power system is employed to complete the simulation experiments. The results show that the constructed DDAs are stealthy under BBD but can be eliminated by the proposed data recovery algorithms, which improve the resilience of the state estimator against the attacks.     

基于栅格数据的降雨汇流模型的计算机模拟实现

本文基于降雨地表径流汇流模型,用计算机模拟实现了基于DEM的降雨汇流情况,并开发了基于GIS软件Supermap的汇水模块,对城市的规划可以起到辅助决策的作用,并已应用到实际中。     

Instance-prototype similarity consistency for unsupervised 2D image-based 3D model retrieval

The unsupervised 3D model retrieval is designed to joint the information of well-labeled 2D domain and unlabeled 3D domain to learn collaborative representations. Most existing methods adopted semantic alignment, but were inevitably affected by false pseudo-label. In this paper, we design a novel Instance-Prototype Similarity Consistency Network (IPSC) to guide domain alignment with similarity consistency, which can simultaneously suppress the impact of false pseudo-label information and well reduce the domain discrepancy. IPSC contains two similarity strategies, named Single instance vs Multiple prototypes and Instance-pair vs Single prototype. The first strategy utilizes a single instance as an anchor, and measures the similarities between the anchor and multiple prototypes with the same category but from different domains. The minimization between these similarities can better align the cross-domain prototypes with Kullback–Leibler (KL) divergence than traditional Euclidean similarities. The second strategy utilizes a single prototype as an anchor, and measures the similarities between this anchor and an instance-pair with the same category but from different domains. The minimization between these similarities can conduct the instance-level alignment with KL divergence, which can better suppress the negative effect of noisy pseudo-labels. We conduct various experiments on two datasets, MI3DOR-1 (21000 2D images and 7690 3D models) and MI3DOR-2 (19694 2D images and 3982 3D models), to verify the superiority of our algorithm.     

聚类分析在入侵检测系统中的改进

本文针对传统的聚类算法在入侵检测系统中的不足,提出一种基于密度的初始聚类中心的选择方法,可克服普通K-Means中的需人工确定K值的问题,用此算法改进的入侵检测模型能够获得很好的聚类效果。对比实验结果,发现使用改进后的算法与传统的K-Means相比可以获得更高的检测率和较低的误报率。     

基于隐性社会网络社团划分的推荐方法研究

结合社会网络分析的推荐方法研究已成为热点。电子商务中用户的动态行为异常丰富,隐含了用户的关联关系,利用这些信息进行商品推荐是个新研究思路。分析电子商务系统中用户动态行为关联关系及用户间明确好友关系形成复杂隐性社会网络,将社团划分算法应用到该网络中,则社团内部用户联系紧密且具有更相似的消费偏好,据此设计了电子商务中社团内部的推荐方法,应用R语言进行了算法的验证并与传统的协同过滤算法进行比较。实验表明,该推荐算法提高了推荐的质量,缓解了传统推荐算法中数据稀疏性及冷启动问题等。