网上订票系统分析与设计

基于Web的订票系统是通过网络查询信息,进行火车票的预订电子商务系统。订票在购票交易过程中扮演着十分重要的角色,设计和实现一个完整的基于Web的订票系统能够为旅客提供一个快捷、便利的购票渠道,对促进交通旅游票务事业的发展具有十分重要的意义。     

拓扑距离特征指数对多氯联苯的QSPR研究

根据分子拓扑理论,提出1种新的多氯联苯(PCBs)化合物分子中,顶点原子(基团)点价值δ_i的计算方法,据以构建分子的结构参数——拓扑距离特征指数(TDEI);同时考虑分子体积以及氯原子在苯环上的位置和数目对PCBs性质的影响,引入分子体积参数(V_R)和基团定位指数(D),然后,采用多元线性回归法,分别建立了PCBs的正辛醇-水分配系数(lgK_(OW))和水溶解度(-lgS_w)的定量结构-性质相关模型,其相关系数分别达到0.9972和0.9730,均优于前人的工作。利用模型预测另外7个PCBs分子的lgK_(OW)和-lgS_W,预测值与实验值的一致性令人满意。     

液晶显示技术及其应用

通过对液晶显示的基本原理、液晶显示常见模块及其几个应用实例的介绍,使人们清楚地认识到液晶显示的应用相当广泛,本文能帮助人们在实际工程应用中根据需要有选择地应用液晶显示技术。     

一种基于PSIM的脉宽调制芯片SG3525的建模与仿真

SG3525是一种应用非常广泛的脉宽调制芯片,在开关电源设计中得到了非常广泛的应用。本文以其为研究对象,对其功能详细研究,并根据资料手册在PSIM中对其各个模块进行建模。最后,将所搭建的各个模块相结合并利用PSIM现有的模块封装功能将其封装起来,用于控制一个全桥DC—DC变换,并通过仿真来验证其功能。     

谱聚类算法综述

谱聚类算法是近年来国际上机器学习领域的一个新的研究热点.谱聚类算法建立在谱图理论基础上,与传统的聚类算法相比,它具有能在任意形状的样本空间上聚类且收敛于全局最优解的优点.本文首先介绍了图论方法用于聚类的基本理论,然后根据图划分准则对谱聚类算法进行分类,着重阐述了各类中的典型算法,并对算法进行了比较分析,最后进行总结并提出了几个有价值的研究方向.     

用FPGA实现基于PC／104的ARINC429接口模块

针对分离元件设计的总线接口电路复杂、可靠性差等缺点,采用FPGA技术结合高性能的HS-3282/HS-3182芯片组实现了基于PC/104的ARINC429总线接口模块。硬件上,该接口模块具有模块化强、电路简单、器件少、可靠性高等特点,软件上采用简洁方便的C语言实现多种工作方式下数据的收发功能。     

基于运动区域划分的H.264低复杂度模式选择

为降低H.264编码器的复杂度,提出基于运动区域划分的模式选择算法.首先利用已编码宏块的运动信息识别运动对象的内部区域和边界区域,然后利用已编码宏块的模式信息对边界区域的运动剧烈程度和细节复杂程度进行进一步的划分,从而将视频内容划分为运动性质不同的区域,并选择合适的模式对其进行编码.实验结果表明,该算法平均节省编码时间达47.95%,有利于实时应用;同时PSNR平均下降约0.06 dB,不影响主观视觉效果.     

Shape disassembly using generating merging probability

Computer integrated manufacturing uses computer technology to integrate a manufacturing system through a man-machine interface that fills the gap between manual operation and machine processes. It is clear that a computer vision-based man-machine interface makes a fully automated system possible. The basic challenge of a vision-based interface is how to extract information from digitized images and convert it to machine-friendly knowledge. To extract information, then, it often end up to the problem of shape decomposition. This paper proposes an new approach in decomposing compound shapes without prior knowledge of the scene. The proposed algorithm exploits the fact that planar shapes can be completely described by contour segments, and can be decomposed at their maximum concavity into simpler objects. To reduce spurious decomposition, the decomposed segments are merged into groups by analyzing and utilizing the merging hypotheses. The algorithm calculates the linking possibility by weighting the angular differentiation between two segments. The techniques are implemented and are applied to other partial shape matching problems for clustering purposes.     

Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.     

基于IGD+指标的两阶段选择高维多目标进化算法

针对在高维空间下多目标进化算法难以维持种群收敛性和多样性平衡的问题, 本文提出一个基于IGD+指标的两阶段选择高维多目标进化算法(MaOEA–ITS). 在第1阶段, 算法基于IGD+指标选择收敛性良好的精英个体, 其所需的参考点通过引入切割平面截距法构建. 在第2阶段, MaOEA–ITS使用模糊c均值算法对参考向量进行聚类, 聚类后的参考向量引导种群分解策略对剩余个体进行环境选择, 从而维持种群的多样性. 另外, 为了保护能够提高种群多样性的极值解, 本文提出一个参考点分布自适应策略. 最后, 通过仿真实验来验证MaOEA–ITS的有效性和优越性.