非确定性数据库中空值处理

尽管关系数据库有很多优势,但它缺乏一种处理非确定性数据的能力.目前,已经提出了几种将非确定性结合到关系数据库模型的方法,它们对关系数据库模型做了诸多扩展.但空值问题依旧存在,一些模型根本就没有考虑空值因素.这违背了非确定性数据库要更加真实地反应现实世界的初衷.为此,给出了一种非确定性数据库系统中空值处理方法,改进现有非确定性数据库模型中对空值处理不完善的情况.     

基于灰色RBF神经网络模型的电梯层间交通分布预测

为了有效预测电梯的层间交通分布状态,提出一种层间交通O-D矩阵的预测方法．该方法融合灰色预测和神经网络方法各自的优点,将灰色预测方法与RBF神经网络有机结合,构造灰色神经网络预测模型．利用灰色预测中的累加生成运算（accumulatedgeneratingoperation,AGO）对原始观测数据进行变换,得到规律性较强的累加数据,作为神经网络的建模和训练样本．还提出了对不良交通需求数据的修正方法,以进一步降低观测数据的随机性．所提方法既避免了灰色预测方法存在的理论误差,又提高了神经网络的训练速度和预测精度,适用于短期层间交通分布预测．仿真试验验证了该方法的有效性．     

基于关系模式和Web的工作流管理系统的研究

随着信息化发展的不断深入,传统的信息管理系统已经不能很好地满足企业的需求。传统的信息管理系统是面向功能的,在企业的业务发生变化时很难快速地做出调整。而工作流管理系统则是面向流程的,可以针对企业的流程做出相应调整。讨论了工作流管理系统对比传统信息管理系统所具有的优势,在此基础上提出了一个基于关系模式,面向Web的工作流管理系统的设计方案。给出了系统的设计思想和总体框架。并重点描述了工作流管理系统的数据库设计和系统实现的一些关键问题。     

基于灰色预测模型的个人建房分析

灰色模型具有所需数据少、预测精度高和无需先验信息的特点.本文通过建立GM(1,1)模型对某区的农村个人建房进行预测,为相关职能部门提供科学的决策依据.结果表明灰色预测模型精度较高、预测误差较小、简捷实用.     

Modalities in linear logic weaker than the exponential “of course”: Algebraic and relational semantics

We present a semantic study of a family of modal intuitionistic linear systems, providing various logics with both an algebraic semantics and a relational semantics, to obtain completeness results. We call modality a unary operator on formulas which satisfies only one rale (regularity), and we consider any subsetW of a list of axioms which defines the exponential of course of linear logic. We define an algebraic semantics by interpreting the modality as a unary operation on an IL-algebra. Then we introduce a relational semantics based on pretopologies with an additional binary relationr between information states. The interpretation of is defined in a suitable way, which differs from the traditional one in classical modal logic. We prove that such models provide a complete semantics for our minimal modal system, as well as, by requiring the suitable conditions onr (in the spirit of correspondence theory), for any of its extensions axiomatized by any subsetW as above. We also prove an embedding theorem for modal IL-algebras into complete ones and, after introducing the notion of general frame, we apply it to obtain a duality between general frames and modal IL-algebras.     

基于灰色层次分析法的有人/无人协同作战效能评估

当前和今后相当长一段时期内,战场的机械化、信息化、智能化将长期并存、并行发展。有人/无人协同作战可以通过各无人系统与有人系统之间的资源调度、态势统一、任务整合来进一步提高作战系统的作战效能。基于灰色层次分析法,结合径向基函数（Radial Basis Function,RBF）神经网络进行数据整理,对有人/无人协同作战效能进行科学评估,提出了一种新的评估模型,为优化协同作战体系结构和提高协同作战能力提供了理论依据。围绕有人/无人协同作战的作战任务和能力需求构建了相关评估指标体系,基于灰色层次分析法与RBF神经网络设计了有人/无人协同作战评估模型,并进行了仿真实验分析。仿真结果表明,该方法能够实现对有人/无人协同作战效能客观有效的评估。     

基于样本扩充和特征优选的IGWO优化SVM的变压器故障诊断技术

为了增强变压器故障诊断模型对不平衡样本的学习能力从而提高少数类故障样本的识别精度,提出了一种基于样本扩充和特征优选的融合多策略改进灰狼算法(improved grey wolf optimizer with multi-strategy, IGWO)优化支持向量机(support vector machine, SVM)的变压器故障诊断技术。首先,使用基于K最近邻过采样方法及核密度估计自适应样本合成算法的混合过采样技术对少数类样本进行扩充得到均衡数据集,并在此基础上采用方差分析对变压器候选比值征兆进行特征优选。然后,通过改进灰狼优化算法(grey wolf optimizer, GWO)初始化策略、参数及位置更新公式,并引入差分进化策略调整种群,提出了融合多策略的改进灰狼算法。最后,构建了一种基于混合过采样技术的IGWO优化SVM的变压器故障诊断模型,并通过多组对比实验验证了所提方法能够有效增强模型对少数类故障样本的识别能力,并提升模型的整体分类性能。     

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies. Early diagnosis of many diseases will improve the patient life. The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things (IoT), Wireless Sensor Networks (WSN), Embedded systems, Deep learning approaches and Optimization and aggregation methods. The data generated through these technologies will demand the bandwidth, data rate, latency of the network. In this proposed work, efficient discrete grey wolf optimization (DGWO) based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code (ECEMAC) has been used to aggregate the parameters generated from the wearable sensor devices of the patient. The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO. Aggregation scheme will reduce the number of transmissions over the network. The aggregated data are preprocessed at edge node to remove the noise for better diagnosis. Edge node will reduce the overhead of cloud server. The aggregated data are forward to cloud server for central storage and diagnosis. This proposed smart diagnosis will reduce the transmission cost through aggregation scheme which will reduce the energy of the system. Energy cost for proposed system for 300 nodes is 0.34μJ. Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme (SPPDA), concealed data aggregation scheme for multiple application (CDAMA) and secure aggregation scheme (ASAS) are 1.3 μJ, 0.81 μJ and 0.51 μJ respectively. The optimization approaches and encryption method will ensure the data privacy.     

Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

Cloud storage is essential for managing user data to store and retrieve from the distributed data centre. The storage service is distributed as pay a service for accessing the size to collect the data. Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy, duplication leads to increase storage space. The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis. It creates a complex nature to increase the storage consumption under cost. To resolve this problem, this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication (HIBD) based on Segmented Bind Linkage (SBL) Methods for reducing storage in a cloud environment. Initially, preprocessing is done using the sparse augmentation technique. Further, the preprocessed files are segmented into blocks to make Hash-Index. The block of the contents is compared with other files through Semantic Content Source Deduplication (SCSD), which identifies the similar content presence between the file. Based on the content presence count, the Distance Vector Weightage Correlation (DVWC) estimates the document similarity weight, and related files are grouped into a cluster. Finally, the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case. This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.     

基于参数联合优化VMD-SVM的工业机器人旋转部件故障诊断方法

针对因工业机器人旋转部件故障诊断模型最优参数难以自适应确定导致故障识别率低的问题,提出了一种参数联合优化的VMD-SVM的工业机器人旋转部件故障诊断方法;提出了一种基于遗传变异的改进灰狼算法,该算法采用Logistic混沌映射进行种群初始化,将非线性因子引入位置更新公式,并利用遗传变异策略解决算法陷入局部最优时的停滞现象;基于该算法对VMD和SVM进行参数联合优化;利用参数优化的VMD对故障信号进行分解,对所得的本征模态函数计算改进样本熵以构成特征向量,再输入至参数优化的SVM完成工业机器人旋转部件的故障诊断;仿真和实验结果表明,本文方法能够准确地进行故障诊断,在信号无噪和含噪的条件下准确率最高均达100%,较EMD、LMD、DTCWT、VMD等四种方法具有更优的指标。