IDEAL: a Vector-Raster Hybrid Model for Efficient Spatial Queries over Complex Polygons

Abstract In the paper, the authors describe the method of reduction of a model of rotor system. The proposed approach makes it possible to obtain a low order model including e.g. non-proportional damping or the gyroscopic effect. This method is illustrated using an example of a rotor system. First, a model of the system is built without gyroscopic and damping effects by using the rigid finite element method. Next, this model is reduced. Finally, two identical, low order, reduced models in two perpendicular planes are coupled together by means of gyroscopic and damping interaction to form one model of the system. Thus a hybrid model is obtained. The advantage of the presented method is that the number of gyroscopic and damping interactions does not affect the model range

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Electricity demand forecasting with a novel hybrid model

Automation, Mechanical and Electrical Engineering ◽

10.2495/amee140832 ◽

2014 ◽

Author(s):

L.P. Wu ◽

Q.S. Yang ◽

G.Q. Du ◽

J.H. Wang

Keyword(s):

Hybrid Model ◽

Demand Forecasting ◽

Electricity Demand ◽

Electricity Demand Forecasting

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Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/573 ◽

2018 ◽

Cited By ~ 1

Author(s):

Prachi Jain ◽

Shikhar Murty ◽

Mausam . ◽

Soumen Chakrabarti

Keyword(s):

Knowledge Base ◽

Hybrid Model ◽

Matrix Factorization ◽

Relation Extraction ◽

Tensor Factorization ◽

Evaluation Protocol

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.

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