13: Industrie 4.0, Vorlesung, WS 2018/19, 25.01.2019
Author
Editor
Participating institute
Institut für Anthropomatik und Robotik (IAR)
Genre
Description
- 0:00:00 Start
- 0:00:07 Syllabus - Winter term 2018/19
- 0:01:48 Literature for Today's class
- 0:02:41 Video: Simple Outlier Detection
- 0:06:25 Motivation: Predictive Maintenance
- 0:09:51 Machine Learning Algorithms for Preditictive Maintenance
- 0:16:27 Anomaly Detection
- 0:18:26 Related Problems
- 0:20:18 Relationship Among Data Instances
- 0:21:21 Rule-based Anomaly Detection
- 0:29:01 Representation Matters
- 0:31:02 Anomalies in a Single Time Series Signal
- 0:32:28 Anomalies in a Multiple Time Series Signals
- 0:34:20 Criteria for Anomaly Detection
- 0:39:42 Characteristics of Univariate, Multivariate and Hybrid Anomaly Detection Methods
- 0:49:31 Characteristics of Univariate, Multivariate and Hybrid Anomaly Detection Methods
- 0:54:12 Type of Anomalies
- 0:55:23 Contextual Anomalies
- 0:57:13 Collective Anomalies
- 0:58:42 Output of Anomaly Detection
- 0:59:22 Evaluation of Anomaly Detection - F-value
- 1:01:33 Accuracy of Anomaly Detection - F-value
- 1:03:26 Evaluation of Outlier Detection - ROC & AUC
- 1:07:04 General Scheme for Anomaly Detection
- 1:08:48 Taxonomy
- 1:12:11 Variants of Anomaly Detection Problem
Duration (hh:mm:ss)
01:15:35
Series
Industrie 4.0, Vorlesung, WS 2018/19
Published on
29.01.2019
Subject area
License
| Resolution | 1280 x 720 Pixel |
| Aspect ratio | 16:9 |
| Audio bitrate | 128000 bps |
| Audio channels | 2 |
| Audio Codec | aac |
| Audio Sample Rate | 48000 Hz |
| Total Bitrate | 934106 bps |
| Color Space | yuv420p |
| Container | mov,mp4,m4a,3gp,3g2,mj2 |
| Media Type | video/mp4 |
| Duration | 4535 s |
| Filename | DIVA-2019-92_hd.mp4 |
| File Size | 529.515.322 byte |
| Frame Rate | 25 |
| Video Bitrate | 800020 bps |
| Video Codec | h264 |
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Industrie 4.0, Vorlesung, WS 2018/19
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