Applications of Data Mining Theory in Electrical Engineering

In this paper, we adopt a novel applied approach to fault analysis based on data mining theory. In our researches, global information will be introduced into the electric power system, we are using mainly cluster analysis technology of data mining theory to resolve quickly and exactly detection of fault components and fault sections, and finally accomplish fault analysis. The main technical contributions and innovations in this paper include, introducing global information into electrical engineering, developing a new application to fault analysis in electrical engineering. Data mining theory is defined as the process of automatically extracting valid, novel, potentially useful and ultimately comprehensive information from large databases. It has been widely utilized in both academic and applied scientific researches in which the data sets are generated by experiments. Data mining theory will contribute a lot in the study of electrical engineering.

Mobile Data Mining-Based Services on the Base of Mobile Device Management (MDM) System

Client software on mobile devices that can cause the remote control perform data mining tasks and show production results is significantly added the value for the nomadic users and organizations that need to perform data analysis stored in the repository, far away from the site, where users work, allowing them to generate knowledge regardless of their physical location. This paper presents new data analysis methods and new ways to detect people work location via mobile computing technology. The growing number of applications, content, and data can be accessed from a wide range of devices. It becomes necessary to introduce a centralized mobile device management. MDM is a KDE software package working with enterprise systems using mobile devices. The paper discussed the design system in detail.

Data Processing System （DPS） software with experimental design, statistical analysis and data mining developed for use in entomological research

A comprehensive but simple-to-use software package called DPS （Data Pro- cessing System） has been developed to execute a range of standard numerical analyses and operations used in experimental design, statistics and data mining. This program runs on standard Windows computers. Many of the functions are specific to entomological and other biological research and are not found in standard statistical sottware. This paper presents applications of DPS to experimental design, statistical analysis and data mining in entomology.

Hyperparameter optimization for cardiovascular disease data-driven prognostic system

Prediction and diagnosis of cardiovascular diseases(CVDs)based,among other things,on medical examinations and patient symptoms are the biggest challenges in medicine.About 17.9 million people die from CVDs annually,accounting for 31%of all deaths worldwide.With a timely prognosis and thorough consideration of the patient’s medical history and lifestyle,it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease.In this study,we used various patient datasets from a major hospital in the United States as prognostic factors for CVD.The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old.In this study,we present a data mining modeling approach to analyze the performance,classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning(ML)using the Orange data mining software.Various techniques are then used to classify the model parameters,such as k-nearest neighbors,support vector machine,random forest,artificial neural network(ANN),naïve bayes,logistic regression,stochastic gradient descent(SGD),and AdaBoost.To determine the number of clusters,various unsupervised ML clustering methods were used,such as k-means,hierarchical,and density-based spatial clustering of applications with noise clustering.The results showed that the best model performance analysis and classification accuracy were SGD and ANN,both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets.Based on the results of most clustering methods,such as k-means and hierarchical clustering,Cardiovascular Disease Prognostic datasets can be divided into two clusters.The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model.The more accurate the model,the better it can predict which patients are at risk for CVD.

Classification analysis of microarray data based on ontological engineering

Background knowledge is important for data mining, especially in complicated situation. Ontological engineering is the successor of knowledge engineering. The sharable knowledge bases built on ontology can be used to provide background knowledge to direct the process of data mining. This paper gives a common introduction to the method and presents a practical analysis example using SVM (support vector machine) as the classifier. Gene Ontology and the accompanying annotations compose a big knowledge base, on which many researches have been carried out. Microarray dataset is the output of DNA chip. With the help of Gene Ontology we present a more elaborate analysis on microarray data than former researchers. The method can also be used in other fields with similar scenario.

基于Data Mining技术的平顶山矿区不同赋存深度采动煤岩体巷道稳定性研究

为探究不同赋存深度采动煤岩体巷道稳定性及其差异性,选择Data Mining(数据挖掘)技术,基于平顶山矿区不同赋存深度(700 m,850 m,1 050 m)巷道的现场监测数据,选择多元线性回归和神经网络模型对顶板离层进行公式拟合和影响因素的权重分析,并开展顶板离层及锚杆应力的时序预测研究,初步揭示不同赋存深度开采扰动影响范围、巷道变形及应力变化特征。研究结果表明:(1)距工作面距离及锚杆应力对顶板离层变化影响最大,但随着赋存深度的增加,其所占权重降低近50%。(2)顶板离层及锚杆应力的时序预测分析发现,随工作面推进,赋存深度1 050 m巷道顶板离层位移及锚杆应力将出现激增现象,且其最大锚杆应力预测值达15 MPa,为千米以浅两巷道的2~3.5倍;离层预测值高达80 mm,为千米以浅两巷道的6~8倍。表明随着赋存深度增加,煤岩巷道变形及应力变化受开采扰动的影响越来越剧烈,对千米以深巷道应及时加强开采过程中的稳定性监测及控制工作。以上技术路线和研究结果对不同赋存深度煤炭资源安全高效开采具有一定指导作用。

Events Sourcing and Command Query Responsibility Segregation Based Fast Data Architecture

With the advent of Big Data, the fields of Statistics and Computer Science coexist in current information systems. In addition to this, technological advances in embedded systems, in particular Internet of Things technologies, make it possible to develop real-time applications. These technological developments are disrupting Software Engineering because the use of large amounts of real-time data requires advanced thinking in terms of software architecture. The purpose of this article is to propose an architecture unifying not only Software Engineering and Big Data activities, but also batch and streaming architectures for the exploitation of massive data. This architecture has the advantage of making possible the development of applications and digital services exploiting very large volumes of data in real time;both for management needs and for analytical purposes. This architecture was tested on COVID-19 data as part of the development of an application for real-time monitoring of the evolution of the pandemic in Côte d’Ivoire using PostgreSQL, ELasticsearch, Kafka, Kafka Connect, NiFi, Spark, Node-Red and MoleculerJS to operationalize the architecture.

KERNEL SOFTWARE ENGINEERING ENVIRONMENT BETA-85

BETA-85 is the kernel of an integrated software engineering environment, hosted by UNIX operating system. It is general-purposed and open-ended, using programming language C as its base language and supporting a variety of software development and maintenance methodologies.BETA-85 is organized as a hierarchical structure of environment work bench which, corresponds to a multi-base facility for organizing and managing information entities in the environment. A general-purposed interactive editing system is designed as its user interface. The technical and managerial supports at different levels are specially provided for programming in the small, in the large, and in the many. Therefore, the visibility and traceability of software engineering project are greatly increased, the software productivity is significantly raised, the quality of software products is effectively improved, and the cost of software development and maintenance is strictly controlled.

A Functional Data Modeling Tool for Engineering Applications

The fact that most engineering applications are developed by engineers themselves rather than computer professionals calls for the data modeling methods to be powerful enough to represent complex engineering phenomena, but simple enough to use. A data modeling method which can help engineers to write C++ code with high quality is introduced.

Analytical Engineering for Data Stream

The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources. The data sources are varied and can generate a huge amount of data, which can be processed in batch or stream settings. The stream setting corresponds to the treatment of a continuous sequence of data that arrives in real-time flow and needs to be processed in real-time. The models, tools, methods and algorithms for generating intelligence from data stream culminate in the approaches of Data Stream Mining and Data Stream Learning. The activities of such approaches can be organized and structured according to Engineering principles, thus allowing the principles of Analytical Engineering, or more specifically, Analytical Engineering for Data Stream (AEDS). Thus, this article presents the AEDS conceptual framework composed of four pillars (Data, Model, Tool, People) and three processes (Acquisition, Retention, Review). The definition of these pillars and processes is carried out based on the main components of data stream setting, corresponding to four pillars, and also on the necessity to operationalize the activities of an Analytical Organization (AO) in the use of AEDS four pillars, which determines the three proposed processes. The AEDS framework favors the projects carried out in an AO, that is, its Analytical Projects (AP), to favor the delivery of results, or Analytical Deliverables (AD), carried out by the Analytical Teams (AT) in order to provide intelligence from stream data.

Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity(MDI) method. To establish productivity prediction models, linear regression(LR), random forest regression(RF), support vector regression(SVR), backpropagation(BP), extreme gradient boosting(XGBoost), and light gradient boosting machine(Light GBM) algorithms are used. Their performance is evaluated using the coefficient of determination(R^(2)), explained variance score(EV), mean squared error(MSE), and mean absolute error(MAE) metrics. The Light GBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query,oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.

矿井探地雷达背景杂波抑制方法综述

随着中国煤炭安全高效智能开采需求的不断提升,矿井探地雷达(ground-penetrating radar, GPR)作为一种高精度物探方法,在煤矿井下探测中有广阔的应用前景。由于探地雷达目标回波信号受系统内部噪声、外部电磁干扰、巷道回波、地下介质不均匀等干扰影响,虚警率较高。尤其对于小尺寸目标,其回波信号为弱信号,容易淹没在强烈的背景杂波中,成为目前制约探地雷达探测性能的主要因素之一,进一步实现井下煤层、构造探测、提高探测精度的关键在于对背景杂波的抑制。从对消法、滤波法、分解法和网络法出发,综述这些杂波抑制方法的基本原理、优缺点及其在探地雷达干扰抑制领域的研究进展,并探讨各种方法的适用范围和发展潜力。杂波抑制技术的不断创新将进一步提高探地雷达的信噪比与灵敏度,使其在煤矿井下探测中发挥更大的应用价值。

基于频繁序列挖掘的出租车轨迹特性分析

为进一步厘清不同出租车路径选择行为的差异性,采用频繁序列挖掘方法提取了同一个OD对间的频繁路径,构建路径选择集,分别从静态和动态两个角度分析路径集的相似特性。以西安市出租车的轨迹数据为研究对象,通过栅格划分与路网匹配,获得了不同OD对之间的路径集合。重新定义了频繁路径,采用PrefixSpan演变算法,在得到频繁子序列的基础上引入动态阈值和频繁度指标挖掘频繁路径,提取了最短路径和其他路径,完成了3类有效路径集的构建,并分析了路径集的一般属性。其后,将路径上二维时间序列(轨迹)间的相似度表示为动态相似度,将一维有向序列(路段)间的相似度表示为静态相似度,基于改进的最长公共子序列和动态时间规整算法对3类路径进行了相似性分析。结果表明:频繁路径与最短路径的相似度较高,意味着大多数出租车仍然选择具有最低出行时间的路段,但不一定会选择最短路径;时间和距离仍是出行者选择路径时主要考虑的因素,但出行者并不完全追求时间最短或距离最短;试验得到的动态相似度计算结果显著高于静态相似度计算结果,说明路径上的二维时序相似度高于一维形状相似度;两种方法下频繁路径和最短路径的相似度均最高,最短路径和其他路径的相似度均最低,比较结果的一致性说明可以用动态轨迹的相似度来大致度量静态路径的相似度。文中的频繁路径挖掘算法具有一定的可靠性,可为城市交通管理者进行路径推荐、道路规划等提供支持。

基于数据挖掘技术的拖拉机发动机故障诊断

拖拉机是农业生产的重要工具,发动机是其核心部件,如发动机出现故障,将会直接影响农业生产效率和产量。为此,提出了一种使用数据挖掘技术进行拖拉机发动机故障诊断的方法。利用了机器学习技术和统计学,首先针对拖拉机田间运行信号噪音较大的问题,引入小波阈值去噪的方法;其次,基于卷积神经网络模型,引入一种注意力机制,提高故障诊断准确率,并通过对拖拉机传感器数据进行分析,可以帮助诊断和预测发动机故障;最后,通过实验结果验证了算法的有效性。研究结果不仅可以提高故障的准确性和效率,还能够节约维修成本和提高机器的利用率,具有较高的应用价值。

基于R软件的某中医院治疗骨折延迟愈合中药方剂数据挖掘

目的:探讨我院应用中药方剂治疗骨折延迟愈合的效果,并总结其治疗规律,为临床提供新的中医诊疗思路。方法:采用回顾性研究方法,收集我院HIS系统中符合纳入标准的骨折延迟愈合患者的临床资料。使用R软件进行数据分析,包括频次分析、关联规则、phi相关性分析和聚类分析,以探索中药方剂的组方模式及其中的诊疗思路。结果:共纳入215例病例,包括药物179种,药物类型42种,药物功效308种,药物主治病症566种,四气以温、平为主,五味以甘、苦、辛为主,归经以肝、肾、肺为主,强关联对药21个、角药262个,药物共现网络有73个节点、806条边。其中“骨碎补、杜仲、自然铜、淫羊藿、狗脊、土鳖虫、龙骨、续断、合欢皮、黄芪、红花”药物之间phi相关性最高;可能的新方剂组合4个,包括“狗脊、龙骨、自然铜、杜仲、黄芪、淫羊藿、土鳖虫”“合欢皮、骨碎补、牛膝、续断、当归、川芎、红花”“青风藤、蜈蚣、地龙、桂枝、粉葛”“麦芽、山楂、薏苡仁、北沙参、白术、远志、苍术、酸枣仁、茯苓、甘草、珍珠母”。结论:我院治疗骨折延迟愈合采用的中药方剂为补益肝肾、续筋接骨为主的药物,形成了治疗骨折延迟愈合的新方剂组合,为未来的中医药治疗策略提供了创新思路。

基于学习的源代码漏洞检测研究与进展

源代码漏洞自动检测是源代码漏洞修复的前提和基础,对于保障软件安全具有重要意义.传统的方法通常是基于安全专家人工制定的规则检测漏洞,但是人工制定规则的难度较大,且可检测的漏洞类型依赖于安全专家预定义的规则.近年来,人工智能技术的快速发展为实现基于学习的源代码漏洞自动检测提供了机遇.基于学习的漏洞检测方法是指使用基于机器学习或深度学习技术来进行漏洞检测的方法,其中基于深度学习的漏洞检测方法由于能够自动提取代码中漏洞相关的语法和语义特征,避免特征工程,在漏洞检测领域表现出了巨大的潜力,并成为近年来的研究热点.本文主要回顾和总结了现有的基于学习的源代码漏洞检测技术,对其研究和进展进行了系统的分析和综述,重点对漏洞数据挖掘与数据集构建、面向漏洞检测任务的程序表示方法、基于机器学习和深度学习的源代码漏洞检测方法、源代码漏洞检测的可解释方法、细粒度的源代码漏洞检测方法等五个方面的研究工作进行了系统的分析和总结.在此基础上,给出了一种结合层次化语义感知、多粒度漏洞分类和辅助漏洞理解的漏洞检测参考框架.最后对基于学习的源代码漏洞检测技术的未来研究方向进行了展望.

基于注意力机制的双向长短期记忆网络的在线工程实践评价框架

在虚拟学习环境下,工程实践课程通常以小组方式在线协同完成一个实践项目,但现有在线教学平台缺少基于学习行为数据的深度挖掘,教师难以像线下实践那样感知学习者的学习状态,从而无法进行十分客观公正的评价。为此,提出基于注意力机制的双向长短期记忆网络的在线工程实践评价框架,通过在线实践行为数据构建注意力机制的双向长短期记忆网络模型(GEP-BiLSTM)预测学生能否通过未来的实践考核。实践表明,所提方法可在学生出现学业预警前进行更有针对性的帮扶,从而提升工程实践的教学效果。

工程教育认证背景下数据挖掘课程群的构建与实践研究

以工程教育认证为引领,根据数据挖掘分析在专业认证申请中制定的毕业生应该具备的知识和能力要求,以培养高质量数据分析、数据挖掘人才为宗旨,制定课程群素质、能力、知识三位一体的培养目标,结合学校应用型本科定位、专业培养目标及课程群培养目标构建课程目标能力点与毕业指标点关系结构体系,整合教学资源、建立课程群关联,创新实践教学体系,改革教学模式与方法、考核模式等,从而持续有效地提升学生的数据分析挖掘能力和综合素养,更好地达到行业对人才的认可标准。

一种农业认知智能服务构建框架及其应用实践

随着大数据、人工智能、物联网、云计算等现代信息技术与农业领域的深度融合,现代农业正朝着智能化方向迈进.知识工程在整合、管理、挖掘和利用农业知识方面发挥了至关重要的作用,为实现个性化、精准化的农业认知智能服务提供了强有力的技术支持.探讨了当前农业知识工程及认知智能服务面临的主要挑战,综述了国内外农业认知智能服务领域的研究现状,提出了集成数据层、算法层和认知服务层的基础研究框架.在此基础上,创新性地设计了基于主动元学习思想,通过软件智能体与科学大数据双向偶联自指循环方式完成农业大数据整合和知识建模、知识抽取、知识融合以及知识推理的农业认知智能服务构建框架,梳理了各环节涉及的关键技术和服务应用.最后,对农业认知智能服务领域的未来发展趋势和对策建议进行总结与展望.

一种基于词法特征和数据挖掘的无意义变量名检测方法

标识符是代码的重要组成部分,也是人们理解代码语义的关键元素之一。变量名是最常见的标识符之一,其质量对于代码的可读性和可理解性有着重要的意义。然而,因为各种原因程序员经常使用一些毫无意义的变量名,如“a”和“var”等。这些无意义的变量名严重降低了代码的可理解性,需要进行检测并重构(重命名)。为此,提出了一种基于词法特征和数据挖掘的自动化方法,以检测代码中无意义的变量名。首先,对开源代码中的无意义变量名进行了实证分析,发现无意义变量名通常比较短且不包含任何有意义的单词,因此可以利用词法特征筛选出名称较短且不包含有意义单词的可疑变量名。如果可疑变量名包含缩写词,则使用缩写词扩展算法进行扩展,以获得完整的变量名。然后,基于数据挖掘算法判断可疑变量名是否为约定俗成的常用变量名。有些常用的变量名,如“i”和“e”,虽然字面上没有明确的语义,但是通过约定俗成的表示规范,程序员可以理解该变量的语义,因此不算是无意义的变量名,也不需要进行重构。如果可疑变量名称不是约定俗成的常用变量名,则断定该变量名为无意义的变量名,并提醒程序员进行重命名。在开源数据集上进行实验,结果表明,该方法具有较高的准确率,其平均查准率为85%,平均查全率为91.5%。