利用VC++绘制XMAC各向异性图的实现方法

简要介绍了XMAC各向异性图的成图原理及算法设计。根据成图原理建立了算法的数学模型,用VC++编程具体实现了各向异性图的绘制,并给出了实现的关键步骤。通过与eXpress软件比较绘图的效果,证明了给出的绘制各向异性图的方法,绘图效果达到了工程实际应用的要求,能够满足生产实际需要,并已在GeoSpace解释平台中应用。     

C语言在化工原理离心泵特性曲线测定中的应用

离心泵特性曲线的测定是化工原理的重要内容,涉及到伯努利方程和连续性方程在流体流动中的实际应用,公式复杂,计算量大.本文利用C语言的强大结构化编程和计算功能,处理了“离心泵特性曲线的测定”实验数据,给出了程序设计的思路和方法,得到了离心泵的特性曲线.从曲线可以看出,随着流量的增大,离心泵的压头减少,轴功率升高,和教材完全一致.而离心泵的效率随流量增大而增大,并未出现先增大后减小的情况,可能是由于流量较小所导致.通过分析曲线可以得到离心泵操作相关的知识,离心泵必须在流量较小的区域工作才能获得较大的压头,大流量的离心泵需要配置大功率的电机,离心泵启动之前应关闭出口阀等.此外还发现压力表和真空表的读数随流量的增大而变小,解释了此现象.该程序结构简单,维护方便,通过简单修改可用于处理其它化工原理实验的数据,充分发挥了C语言的特长来解决化工原理计算复杂的特点.     

设计管理系统信息需求的分析与实现

对信息需求的准确分析是建立设计管理系统的基础。传统的分析方法从过程与人,过程与信息的角度出发综合分析整个机构的信息需求,但是,这种方法没有示识产生和使用信息的范围。为此,;补充分析了信息的机构和项目属性,并通过建立三维组织机构模型;机械、人员和任务模型,有效控制基本的信息存取。     

电网智能综合补偿系统的设计

电网智能综合补偿系统可根据电网的动态变化情况,对电网的电压、功能率因数、谐波及三相不平均等因素实现综合补偿,文中详细介绍了这一基于８０Ｃ１９６的智能控制系统的基本结构和软硬件设计思想,实验结果表明了该系统的可行性和智能控制方法的有效性。     

基于第二代端口VLAN技术的交换机管理

应用第二代VLAN技术,以锐捷S2126交换机为基础,设计并实现了一个解决企业内部管理的交换机网络互连方案。     

Group interaction analysis in dynamic context.

Computer understanding of human actions and interactions is one of the key research issues in human computing. In this regard, context plays an essential role in semantic understanding of human behavioral and social signals from sensor data. This paper put forward an event-based dynamic context model to address the problems of context awareness in the analysis of group interaction scenarios. Event-driven multilevel dynamic Bayesian network is correspondingly proposed to detect multilevel events, which underlies the context awareness mechanism. Online analysis can be achieved, which is superior over previous works. Experiments in our smart meeting room demonstrate the effectiveness of our approach.     

Batch Nearest Neighbor Search for Video Retrieval

To retrieve similar videos to a query clip from a large database, each video is often represented by a sequence of high- dimensional feature vectors. Typically, given a query video containing m feature vectors, an independent nearest neighbor (NN) search for each feature vector is often first performed. After completing all the NN searches, an overall similarity is then computed, i.e., a single content-based video retrieval usually involves m individual NN searches. Since normally nearby feature vectors in a video are similar, a large number of expensive random disk accesses are expected to repeatedly occur, which crucially affects the overall query performance. Batch nearest neighbor (BNN) search is stated as a batch operation that performs a number of individual NN searches. This paper presents a novel approach towards efficient high-dimensional BNN search called dynamic query ordering (DQO) for advanced optimizations of both I/O and CPU costs. Observing the overlapped candidates (or search space) of a pervious query may help to further reduce the candidate sets of subsequent queries, DQO aims at progressively finding a query order such that the common candidates among queries are fully utilized to maximally reduce the total number of candidates. Modelling the candidate set relationship of queries by a candidate overlapping graph (COG), DQO iteratively selects the next query to be executed based on its estimated pruning power to the rest of queries with the dynamically updated COG. Extensive experiments are conducted on real video datasets and show the significance of our BNN query processing strategy.     

多级故障诊断系统

介绍了为提高故障诊断的正确率,将一混合故障诊断专家系统中的基于BP神经网络诊断模块、基于自组织神经网络诊断模块和基于小波分析诊断模块设计成多级诊断系统。对多级诊断系统的性能进行了分析,给出了多级诊断系统的拒绝输出策略。     

A discriminative learning framework with pairwise constraints for video object classification

To deal with the problem of insufficient labeled data in video object classification, one solution is to utilize additional pairwise constraints that indicate the relationship between two examples, i.e., whether these examples belong to the same class or not. In this paper, we propose a discriminative learning approach which can incorporate pairwise constraints into a conventional margin-based learning framework. Different from previous work that usually attempts to learn better distance metrics or estimate the underlying data distribution, the proposed approach can directly model the decision boundary and, thus, require fewer model assumptions. Moreover, the proposed approach can handle both labeled data and pairwise constraints in a unified framework. In this work, we investigate two families of pairwise loss functions, namely, convex and nonconvex pairwise loss functions, and then derive three pairwise learning algorithms by plugging in the hinge loss and the logistic loss functions. The proposed learning algorithms were evaluated using a people identification task on two surveillance video data sets. The experiments demonstrated that the proposed pairwise learning algorithms considerably outperform the baseline classifiers using only labeled data and two other pairwise learning algorithms with the same amount of pairwise constraints.