Machine Learning

The dataset provided contains the invariants in the form of antecedent and consequent mined using Association Rule Mining with implicit measures such as Confidence, Support, Lift, etc. from an EPIC Plant (Electrical Power and Intelligent Control System).

Dataset generated with Unreal Engine 4 and Nvidia NDDS. Contains 1500 images of each object: Forklift, pallet, shipping container, barrel, human, paper box, crate, and fence. These 1500 images are split into 500 images from each environment: HDRI and distractors, HDRI with no distractors, and a randomized environment with distractors.

This work aims to identify anomalous patterns that could be associated with performance degradation and failures in datacenter nodes, such as Virtual Machines or Virtual Machines clusters. The early detection of anomalies can enable early remediation measures, such as Virtual Machines migration and resource reallocation before losses occur. One way to detect anomalous patterns in datacenter nodes is using monitoring data from the nodes, such as CPU and memory utilization.

This dataset has 2 different RFID manufacturer tags  (2 of each) with different EPC content. Signal Strength data of all 4 tags were taken in the frequency spectrum in the US UHF (900-910 MHz) range. There are 100 data readings per tag, so 400 files total.