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关键词： 大数据   自动批改   百万同题   人机协作  

摘要： 随着大数据和人工智能的发展,自动批改技术在英语写作批改和评价的运用也日益深入,其产生的海量数据十分具有研究价值。以批改网为代表的基于语料库的交互式批改技术是自动批改技术的最新研发成果,对于当前写作教学与写作批改有着日益显著的影响,以年度百万同题英文写作大赛为基础的大数据报告为写作教学研究者提供了各维度数据的对比分析。通过对近三年年度写作数据的分析,发现了高校学生在校际差异,自主学习效能所出现的学情变化,并体现了当前该技术侧重微观语言错误,对不同水平学习者的使用差异以及评语的程序化等技术局限因素。在未来,自动批改技术将在研发上应融入多学科视角,教师个性化指导以及更深层次的人机交互。

关键词： 运动鞋   大数据分析   维多利亚蓝   凉鞋  

摘要： 占比分析在2月的运动鞋TOP100榜单数据中可以发现,此次的维多利亚蓝以及灰绿色较为新颖且色彩靓丽,占比达到了12%和10%。而在风格与单品数据中,随着全国气温的逐渐上升,人们想要春游的心蠢蠢欲动。随之而来的靴款数据的下降也是在意料之中的,估计下月的重点在单鞋上,同时也会有一些凉鞋单品的出现。

关键词： 大数据   煤炭分选   原煤   商品精煤  

摘要： 煤炭企业将矿井采掘出的原煤通过洗选加工为商品精煤的过程中,为实现对产品质量的稳定控制,运用大数据处理分析技术对生产运行中的各阶段煤质指标和对主要设备运行参数的数据建议数学模型的方法,实现了以预警和反馈机制等进行实时质量信息对比和对生产中产品的质量数据进行分析,起到超前预防主要分选设备运行故障和原煤分选过程中煤质变化的作用,并可以随着使用时间的延长进行自主的修正和学习优化预测的数据模型,提高了洗选精度和选煤的数量效率,稳定了商品精煤的质量。

关键词： big data   data science   driving data   NVH   correlation analysis   sensitivity analysis  

摘要： A new development process for the noise, vibration, and harshness (NVH) of a vehicle is presented using data analysis and machine learning with long-term NVH driving data. The process includes exploratory data analysis (EDA), variable importance analysis, correlation analysis, sensitivity analysis, and development target selection. In this paper, to dramatically reduce the development period and cost related to vehicle NVH, we propose a technique that can accurately identify the precise connectivity and relationship between vehicle systems and NVH factors. This new technique uses whole big data and reflects the nonlinearity of dynamic characteristics, which was not considered in existing methods, and no data are discarded. Through the proposed method, it is possible to quickly find areas that need improvement through correlation analysis and variable importance analysis, understand how much room noise increases when the NVH level of the system changes through sensitivity analysis, and reduce vehicle development time by improving efficiency. The method could be used in the development process and the validation of other deep learning and machine learning models. It could be an essential step in applying artificial intelligence, big data, and data analysis in the vehicle and mobility industry as a future vehicle development process.

关键词： machine learning   association rule mining   ant colony optimization   incremental classifier   big data analytics   IoT  

摘要： Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demon

关键词： Students  

摘要： With the rapid development of information technology, the traditional single classroom teaching and passive learning methods of students can no longer meet the needs of all-round development of college students, and its urgent need to integrate with information technology. This article is aimed at the problem of lagging feedback on training results in the traditional teaching model, teachers' active control, students' passive obedience, ignoring the development of students' personality in college football classrooms, and the inability to carry out personalized tracking and quantitative improvement of the training process of students' related abilities. We constructed a college football classroom practice teaching system model based on big data analysis from the perspectives of establishing big data teaching resources, and implementing personalized resource recommendation, optimizing the traditional teaching process, integrating quantitative training, measurement and recording, implementing quantitative intervention, etc. Colleges and universities have carried out experimental observations. Through continuous observation and comparison, it is found that college football classroom practice teaching under big data is more conducive to improving students' football skills and theoretical level than traditional teaching. This model makes full use of the advantages of big data and the combination of practical teaching methods, which can bring students a different learning experience and obtain good teaching effects. It has guiding and reference significance for college football practical teaching.

关键词： Big data  

摘要： Based on the news and public opinion multioutput Internet of Things architecture, this article analyzes and predicts its popularity with big data. Firstly, the model adopts a three-tier architecture, in which the bottom layer is the data layer. It is mainly responsible for the collection of the terminal sensor data of the Internet of Things, and it uses intelligent big data as the data warehouse. Secondly, the computing layer on the data layer mainly provides the computing framework. Using the open-source SQL query engine, a cluster environment based on memory computing is constructed to realize the parallelization of data computing. It is used for interactive operations between the system and users. It receives and forwards the query requests submitted by the client browser, transmits them to the server cluster for execution, and displays the results in the browser. The end is displayed to the user. After that, combined with the needs of the development and application of news and public opinion big data, the data collection process was analyzed and designed, and the distributed data collection architecture was built. The intelligent Internet of Things was adopted for data storage, the data storage structure was analyzed and designed, and the data storage structure was designed to avoid catching. The repeat check algorithm is used to repeatedlystore the obtained page data. At the same time, according to the analysis of the business needs of the news and public opinion information platform, the overall functional structure of the platform was designed. The database and platform interface were designed in detail. The simulation results show that the model realizes the statistical query of the collected sensor alarm information and historical data on the user system, combines the historical operating data to analyze the relationship between the supply/return water temperature of the heat exchange station and the outdoor temperature, and realizes chart visualization of