Matthew J. Ernst - North Oaks MN, US Matthew T. Haberman - Mahtomedi MN, US
Assignee:
CARTER DAY INTERNATIONAL, INC. - Minneapolis MN
International Classification:
A01F 12/44 F16J 15/38
US Classification:
460 79, 277390
Abstract:
The invention provides for a seal assembly for a grain separator. The grain separator may include a housing, a rotatable grain separation cylinder within the housing, the cylinder having an end surface, a grain inlet having an inlet spout extending through the end surface for conveying grain into the cylinder in a feed zone, an auxiliary end piece or seal cone functionally connected to the end surface of the cylinder. The auxiliary end piece may have an annular external surface spaced laterally from the feed zone, and a seal interfacing with the annular external surface, thereby defining a seal zone spaced laterally from the feed zone. Such grain separators have increased capacity compared to separators with traditional seal assemblies.
Matthew T. Haberman - Mahtomedi MN, US Kevin Weiss - West Lakeland MN, US
Assignee:
CARTER DAY INTERNATIONAL, INC - Minneapolis MN
International Classification:
B07B 1/22 B07B 1/46
US Classification:
209288
Abstract:
The invention includes a grain separator with a seal system. The seal system includes an elastomeric portion and an inelastomeric portion. The seal system interfaces with an inlet spout at at least two locations. The seal system is rotatable about an inlet spout. A sealing pressure is increased as grain interacts with the seal system.
Predictive Anomaly Detection Using Defined Interaction Level Anomaly Scores
- Minnetonka MN, US Matthew James Haberman - Golden Valley MN, US Hadi D. Halim - Kendall Park NJ, US
International Classification:
G06N 5/02
Abstract:
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive anomaly detection. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive anomaly detection by utilizing at least one of defined interaction level anomaly scores, such as defined interaction level anomaly scores for non-constant defined interaction levels that are determined using weighted feature tuple anomaly scores for feature tuple values that are associated with the non-constant defined interaction levels, as well as defined interaction level anomaly scores for constant defined interaction levels that are determined using an anomaly distribution measure for an anomaly quantization metric across a plurality of inferred predictive entities.