关键词： 模式识别   职业成熟度   SWOT分析法   实习护生  

摘要： 介绍高职护生目前的就业现状、职业成熟度和模式识别的基本知识,阐述模式识别作为交叉学科在教育学领域“职业成熟度”中应用的实际意义,利用SWOT分析探索将“模式识别”应用于“95后”高职实习护生职业成熟度研究的优势、劣势、外部机遇及威胁。

关键词： OpenCV   模式识别   Blob算法   距离变换   图像处理  

摘要： 针对大量物体计数依靠人工会漏数,或者重复,生产效率相对较低的问题,系统提出将OpenCV距离变换与Blob算法相结合实现对物体计数,首先以一定数量硬币的图片为实验对象,对图像进行滤波,阈值处理,采用数学形态学中的方法开运算,来调整分割区域的形状,其次基于欧几里得算法距离变换等分割出物体,后采用Blob算法识别目标物体,进而进行数量统计。此次硬币测试目标共24个,最终本系统计数结果也是24个。系统计数准确,有良好的图形界面,具有一定的使用价值。

关键词： 模式识别   数据库   管理系统   人工智能  

摘要： 在计算机技术发展迅速的时代,模式识别技术的应用越来越广泛,并得到了人们的重视。模式识别技术是信息技术和人工智能结合的产物,具有很大的发展空间。将模式识别技术应用到数据库管理系统中,是信息技术发展的一种新的领域。模式识别技术能够对数据库系统中的各种信息进行识别、组合,并对信息进行处理,满足人们的需求。模式识别技术的应用,改变了企事业单位中数据信息不集中、不规范和不安全等情况,有效地实现了系统和数据的整合,具有高时效性、高数据吞吐量的特点。随着人们活动范围的逐渐扩大,模式识别技术的应用领域也不断扩大。在生产管理、财务管理、仓库管理方面建立众多的这种“数据库”,使其可以利用计算机实现财务管理、仓库管理的自动化,有效地将底层数据与上层管理系统结合起来,促进了对信息的管理,提升了应用的技术水平。基于此,本文分析了模式识别技术的相关内涵,概括了数据库管理系统中模式识别技术的主要方法,并探讨了模式识别技术在数据库管理系统中的应用情况。

关键词： 理论线损计算   模式识别   K-Means聚类算法   C&R分类回归树   线损预测  

摘要： 在工程实践中,需要在计算条件成熟的线路上计算线损,分析线路的特征量,建立高损耗线路模式识别体系,从而对计算条件不成熟线路的线损率指标进行预测。从理论线损的计算方法出发,分析不同电压等级下的计算条件和适用的计算方法;提出了通过K-Means聚类算法对线路特征进行初步分类,在各个类中使用C&R分类回归树的方法,最终形成模式识别的判别树。分析结果表明,该方法能有效跟踪某一线路高损耗产生的原因,并对线损进行预测。

关键词： 脑-机接口   脑控下肢外骨骼   脑电信号处理   模式识别   神经网络  

摘要： 由中风、脊髓损伤或其他相关疾病引起的行动困难的人数正在迅速增加。为了改善这些人的生活质量,帮助他们重新获得行走能力,需要大量的辅助行走设备。近来研究的脑控下肢外骨骼可以释放治疗师负担,并提供有效且重复的步态训练。在过去的10年中,外骨骼等机器人辅助器械取得了巨大的进步,一些产品已经商业化。脑控下肢外骨骼是可穿戴的机器人系统,集成了人类智能和机器人的力量。本文作者介绍了脑控下肢外骨骼的基本概念,并总结了几种脑控下肢外骨骼在步态康复方面的主要应用,并对获得穿戴者的动作意图和脑电信号处理技术做出评价,讨论了目前开发的下肢外骨骼的广泛应用、局限性、未来研究和发展方向。

关键词： 智慧教育   深度学习   模式识别   专家系统  

摘要： 【目的/意义】互联网、大数据时代为我们提供了丰富的信息资源,已经引起社会、经济的重大变革,尤其在教育领域的应用备受关注,而把人工智能引入教育领域会使教育更加智能化、个性化、精准化、多元化,也为教育带来了前所未有的机遇和挑战。【方法/过程】本文首先阐述了大数据、人工智能技术的概念以及人工智能与教育的深度融合,然后分析了人工智能对未来教育的影响,深刻探讨了推动个性化服务、构建人工智能生态环境因素。【结果/结论】大数据下人工智能有效助力智慧教育,其在教育人工智能和教育数据挖掘方而发挥的作用也是其他技术无法取代的,其与教育领域的融合定会不断推动教育创新。

关键词： 典型干扰信号   特征参数   模式识别   决策树算法  

摘要： 深入分析并综合了单音干扰、宽带噪声干扰、多音干扰、扫频干扰等典型干扰信号时域、频域、变换域等多维域特征参数的提取方法,并提出了一种基于决策树算法的干扰识别方法,且该方法所需的特征参数较少,有助于减少运算量。仿真结果表明,对于典型干扰样式,所提干扰识别算法比传统的K最近邻算法具有更好的识别性能。

关键词： water distribution systems   pipe bursts   hydraulic transients   real-time control   machine learning  

摘要： A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.

关键词： Feature extraction   Pattern recognition   Noise reduction   Shape   Semiconductor device modeling   Moon   Manufacturing   Semiconductor wafer map   defect recognition   feature extraction   rotation invariance  

摘要： In semiconductor manufacturing, the patterns on the wafer map provide important information for engineers to identify the root causes of production problems. The detection and recognition of wafer map patterns is thus an important issue in semiconductor industry. Automatic techniques are required to cut down on cost and to improve accuracy. In this study, we propose an approach to recognize patterns in the wafer maps which uses the extracted features based on the proposed weight masks. The proposed masks contain three types, namely, polar masks, line masks and arc masks. Polar masks aim to extract features of concentric patterns, while line and arc masks are designed to mainly deal with eccentric patterns like scratches. These masks can be applied to extract rotation-invariant features for the classification of the defect patterns. To demonstrate the effectiveness of our model, we apply the method to a real-world wafer map dataset. Comparisons with alternative methods show superiority of our method in the task of wafer map defect pattern recognition.

关键词： Structural health monitoring   damage diagnosis   AutoRegressive modeling   online learning   high-dimensional data   Kullback-Leibler divergence  

摘要： Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article is to propose an innovative residual-based feature extraction approach based on AutoRegressive modeling and a novel statistical distance method named as Partition-based Kullback-Leibler Divergence for damage detection and localization by using randomly high-dimensional damage-sensitive features under environmental and operational variability. The key novel element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms for extracting the residuals of AutoRegressive model as the main damage-sensitive features. This technique brings the great benefit of reducing the computational time and storage space for feature extraction in long-term monitoring conditions. The major contribution of Partition-based Kullback-Leibler Divergence method is to exploit a partitioning strategy for dividing random features into individual partitions and utilize numerical information of partitioning in distance calculation rather than directly applying random samples. Dealing with the major challenging issue of using the high-dimensional features in decision making and applicability to both correlated and uncorrelated random datasets are the main advantages of Partition-based Kullback-Leibler Divergence method. The accuracy and reliability of the proposed approaches are experimentally validated by two well-known benchmark structures. The stationarity and linearity of measured vibration responses for using in AutoRegressive modeling are evaluated by two hypothesis tests. Comparative studies are also conducted to demonstrate the superiority of the proposed methods over some exciting state-of-the-art techniques. Results show that the methods presented here succeed in detecting and locating damage and ma