基于ActiveX控件的InTouch控制功能扩展

针对目前组态软件InTouch控制功能不够完善,需要用户进行二次开发的情况,提出一种组态软件扩展控制功能的方法。用Matlab COM生成器生成先进控制算法COM组件,在Visual Basic中开发调用算法组件的控制器ActiveX控件,并将控件引入到组态软件InTouch中,为其扩展单神经元自适应PID控制器功能。仿真实验证明了该方法的可行性。     

基于并行遗传算法的K-means聚类研究

针对传统K-means聚类算法对初始聚类中心的选择敏感,以及聚类数K难以确定的问题,提出一种基于并行遗传算法的K-means聚类方法.该方法采用一种新型的可变长染色体编码方案,随机选择样本点作为初始聚类中心形成染色体,然后结合K-means算法的高效性和并行遗传算法的全局优化能力,通过种群内的遗传、变异和种群间的并行进化、联姻,有效地避免了局部最优解的出现,同时得到了优化的聚类数目和聚类结果.实验表明该方法是一种精确高效的聚类方法.     

基于模糊逻辑的汽车防撞控制器的设计

为了实现汽车行驶过程中与前车车距的自动控制,利用模糊推理系统定义了汽车纵向运动所需的模糊集,设计了车辆跟驰情况下汽车安全行驶自适应模糊控制器,并通过带修正因子的方法对模糊规则进行了优化,同时编写程序对控制器进行了仿真及分析,结果表明该模糊控制器具有良好的控制性能。     

基于地震灾害场景的主动配电网多维韧性评估方法

为了分析主动配电网承受破坏性扰动事件以及快速恢复重要负荷的韧性支撑能力,融合相量测量单元（PMU）高精度动态感知能力提出了一种新的以地震灾害场景为背景的主动配电网多维韧性评估方法。阐述了配电网韧性的基本概念及特征,并将地震作为极端事件代表,构建了反映配电线路故障率与地震动峰值加速度加权均值的模型,继而采用非序贯蒙特卡罗抽样和K-means++聚类算法筛选出代表性的地震场景;基于系统配置的PMU的强感知力建立反映韧性电网应变力、防御力、恢复力和协同力的评估指标,形成韧性多维特征空间,进而利用事件集点簇中心与最优韧性点的加权欧氏距离评估系统多维综合韧性;分析增强电力线路强度、提高联合系统中分布式电源可供容量这2种措施对系统韧性提升的影响,挖掘韧性电网对抗震防灾的学习力。最后,通过改进的PG&E69系统验证所提方法的有效性和准确性。     

面向三维结构视觉检测的无人机覆盖路径规划

为了高效地规划无人机在三维覆盖检测任务的飞行路径,建立了满足覆盖率要求的路径规划模型,可分为两步：第1步确定无人机巡检的视点和视线,第2步计算视点的无碰撞访问序列。首先,从巡检目标的三维点云出发,提出基于K均值聚类生成候选视点的方法,构建候选视点互连的非完全图模型;其次,提出面向排序的混合蚁群算法(sorting-oriented hybrid ant colony algorithm, S-HACO)求取无人机巡检路径,优化目标考虑了路径长度、视点数目、急转弯次数等。仿真实验结果表明,该方法得到的视点数目较偏移法和随机采样法分别减少了96.25%和42.10%,并且S-HACO算法较传统算法相比性能更优,目标函数降低了19.14%,算法的运行时间减少了25.27%,验证了模型的有效性和算法的可行性。     

Set pair three-way overlapping community discovery algorithm for weighted social internet of things

There are many problems in Social Internet of Things(IoTs), such as complex topology information, different degree of association between nodes and overlapping communities. The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure. A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things (WSIoT-SPTOCD) is proposed. In the local network structure, which fully considers the topological information between nodes, the set pair connection degree is used to analyze the identity, difference and reverse of neighbor nodes. The similarity degree of different neighbor nodes is defined from network edge weight and node degree, and the similarity measurement method of set pair between nodes based on the local information structure is proposed. According to the number of nodes' neighbors and the connection degree of adjacent edges, the clustering intensity of nodes is defined, and an improved algorithm for initial value selection of k-means is proposed. The nodes are allocated according to the set pair similarity between nodes and different communities. Three-way community structures composed of a positive domain, boundary domain and negative domain are generated iteratively. Next, the overlapping node set is generated according to the calculation results of community node membership. Finally, experiments are carried out on artificial networks and real networks. The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information, overlapping community modularity and F1.     

Thermally Responsive Fibers for Versatile Thermoactivated Protective Fabrics

Smart textiles with good mechanical adaptability play an important role in personal protection, health monitoring, and aerospace applications. However, most of the reported thermally responsive polymers has long response time and poor processability, comfort, and wearability. Skin-core structures of thermally responsive fibers with multiple commercial fiber cores and temperature-responsive hydrogel skins are designed and fabricated, which exhibit rapid mechanical adaptability, good thermohardening, and thermal insulation. This universal method enables tight bonding between various commercial fiber cores and hydrogel skins via specific covalently anchored networks. At room temperature, prepared fibers show softness, flexibility, and skin compatibility similar to those of ordinary fibers. As temperature rises, smart fibers become hard, rigid, and self-supporting. The modulus of hydrogel skin increases from 304% to 30883%, showing good mechanoadaptability and impact resistance owing to the synergy between hydrophobic interactions and ionic bonding. Moreover, this synergistic effect leads to an increase in heat absorption, and fibers exhibit good thermal insulation, which reduces the contact temperature of the body surface by ≈25 °C under the external temperature of 95 °C, effectively preventing thermal burns. Notably, the active mechanoadaptability of these smart fibers using conductive fibers as cores is demonstrated. This study provides feasibility for fabricating environmentally adaptive intelligent textiles.     

Association Rule Mining Frequent-Pattern-Based Intrusion Detection in Network

In the network security system, intrusion detection plays a significant role. The network security system detects the malicious actions in the network and also conforms the availability, integrity and confidentiality of data information resources. Intrusion identification system can easily detect the false positive alerts. If large number of false positive alerts are created then it makes intrusion detection system as difficult to differentiate the false positive alerts from genuine attacks. Many research works have been done. The issues in the existing algorithms are more memory space and need more time to execute the transactions of records. This paper proposes a novel framework of network security Intrusion Detection System (IDS) using Modified Frequent Pattern (MFP-Tree) via K-means algorithm. The accuracy rate of Modified Frequent Pattern Tree (MFPT)-K means method in finding the various attacks are Normal 94.89%, for DoS based attack 98.34%, for User to Root (U2R) attacks got 96.73%, Remote to Local (R2L) got 95.89% and Probe attack got 92.67% and is optimal when it is compared with other existing algorithms of K-Means and APRIORI.     

结合半监督聚类的地质图像多条件生成方法

近年来深度学习技术在地质学的应用越来越广泛.地质学科中的一个重要课题是根据稀疏的空间观测数据建立合理的地下模型.最近的工作通过条件生成对抗网络来探索条件化地质建模,产生了逼真且符合空间观测数据的地质图像.然而,多数方法只关注将空间观测数据作为硬条件,忽视了对生成图像中地质属性的调节.本文引入地质属性标签调节地质图像中具体的地质属性表现,将表征地质属性类别的标签数据作为生成条件之一,扩展一个属性分类器与该标签配合,从而实现更可控的图像生成.针对属性标签的人工标注成本大的问题,本文采用半监督聚类利用少量的标注数据为无标签数据自动分配标签.此外,聚类可能产生噪声标签影响建模结果,此方法使用对称交叉熵损失改进分类网络以提高网络对于噪声标签的鲁棒性.本文在黄河地区的河流地质数据集上进行大量实验,结果表明所提出的方法对于不同的属性标签生成了地质模式不同且符合空间观测数据的逼真地质图像,证明了本方法的有效性.     

基于灰度-梯度共生矩阵的车轮踏面缺陷聚类分析北大核心CSCD

车轮作为列车走行部的关键部件之一,其踏面产生缺陷后会直接影响到列车的运行安全。为了能够在检测时准确识别车轮踏面缺陷不同类型,提出一种基于灰度-梯度共生矩阵的纹理特征提取方法,对踏面图像的灰度和梯度特征分析之后,根据灰度-梯度共生矩阵提取踏面图像纹理特征矢量,再结合K-均值(K-means)聚类优化算法对踏面缺陷特征量进行聚类,从而将踏面缺陷类型进行分类,并将分类结果用可视化数据显示。实验结果表明,采用上述所提方法,对车轮踏面缺陷不同类型的分类识别精度达96%以上。