图 书 馆
DTW算法允许实时黄金批次监测Online Amnestic DTW to allow Real-Time Golden Batch Monitoring |
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| 课程网址: | http://videolectures.net/kdd2019_yeh_zhu_dau/ |
| 主讲教师: | Chin-Chia Michael Yeh |
| 开课单位: | 加利福尼亚大学 |
| 开课时间: | 2020-03-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In manufacturing, a golden batch is an idealized realization of the perfect process to produce the desired item, typically represented as a multidimensional time series of pressures, temperatures, flow-rates and so forth. The golden batch is sometimes produced from first-principle models, but it is typically created by recording a batch produced by the most experienced engineers on carefully cleaned and calibrated machines. In most cases, the golden batch is only used in post-mortem analysis of a product with an unexpectedly inferior quality, as plant managers attempt to understand where and when the last production attempt went wrong. In this work, we make two contributions to golden batch processing. We introduce an online algorithm that allows practitioners to understand if the process is currently deviating from the golden batch in real-time, allowing engineers to intervene and potentially save the batch. This may be done, for example, by cooling a boiler that is running unexpectedly hot. In addition, we show that our ideas can greatly expand the purview of golden batch monitoring beyond industrial manufacturing. In particular, we show that golden batch monitoring can be used for anomaly detection, attention focusing, and personalized training/skill assessment in a host of novel domains. |
| 关 键 词: | 黄金批次监控; 算法监控; 超工业制造 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-03-18：hqh |
| 最后编审: | 2022-03-18：hqh |
| 阅读次数: | 100 |