scholarly journals Affine Tensor Product Model Transformation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
József Kuti ◽  
Péter Galambos
Keyword(s):  
Tensor Product ◽  
Model Transformation ◽  
Numerical Approach ◽  
The Novel ◽  
Product Model ◽  
Linear Parameter Varying ◽  
Proposed Model ◽  
Model Size ◽  
Novel Concept ◽  
Discretization Grid

This paper introduces the novel concept of Affine Tensor Product (TP) Model and the corresponding model transformation algorithm. Affine TP Model is a unique representation of Linear Parameter Varying systems with advantageous properties that makes it very effective in convex optimization-based controller synthesis. The proposed model form describes the affine geometric structure of the parameter dependencies by a nearly minimum model size and enables a systematic way of geometric complexity reduction. The proposed method is capable of exact analytical model reconstruction and also supports the sampling-based numerical approach with arbitrary discretization grid and interpolation methods. The representation conforms with the latest polytopic model generation and manipulation algorithms. Along these advances, the paper reorganizes and extends the mathematical theory of TP Model Transformation. The practical merit of the proposed concept is demonstrated through a numerical example.

scholarly journals A model of the novel concept liquid piston engine sourced by waste heat

2018 ◽  
Vol 240 ◽  
pp. 05003
Author(s):  
Wojciech Bujalski ◽  
Kamil Futyma ◽  
Jarosław Milewski ◽  
Arkadiusz Szczęśniak
Keyword(s):  
Waste Heat ◽  
Low Grade ◽  
The Novel ◽  
Piston Engine ◽  
Aspen Hysys ◽  
Proposed Model ◽  
Power Plant Operation ◽  
Simulation Results ◽  
Novel Concept ◽  
Liquid Piston

This paper describes the model of the novel concept liquid piston engine, which is designed to convert low-grade waste heat into electricity. The proposed dynamic oriented model is implemented in Aspen Hysys that enables simulations dynamic simulation of various working agents. The simulation results were verified with experimental data obtained from the research installation. The proposed model demonstrated relatively small discrepancies with respect to experimental research, hence it could be used as a tool for research on optimization of an innovative power plant operation, i.e. various working agents, various operating pressures.

COMPLEXITY RELAXATION OF THE TENSOR PRODUCT MODEL TRANSFORMATION FOR HIGHER DIMENSIONAL PROBLEMS

2008 ◽  
Vol 9 (2) ◽  
pp. 195-200 ◽  
Author(s):  
Péter Baranyi ◽  
Zoltén Petres ◽  
Péter Korondi ◽  
Yeung Yam ◽  
Hideki Hashimoto
Keyword(s):  
Tensor Product ◽  
Model Transformation ◽  
Product Model ◽  
Higher Dimensional

scholarly journals Tensor Product Model Transformation Based Adaptive Integral-Sliding Mode Controller: Equivalent Control Method

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guoliang Zhao ◽  
Kaibiao Sun ◽  
Hongxing Li
Keyword(s):  
Sliding Mode Control ◽  
Tensor Product ◽  
Model Transformation ◽  
Control Method ◽  
Sliding Mode ◽  
Product Model ◽  
Integral Sliding Mode Control ◽  
Integral Sliding Mode ◽  
Mode Control ◽  
Equivalent Control

This paper proposes new methodologies for the design of adaptive integral-sliding mode control. A tensor product model transformation based adaptive integral-sliding mode control law with respect to uncertainties and perturbations is studied, while upper bounds on the perturbations and uncertainties are assumed to be unknown. The advantage of proposed controllers consists in having a dynamical adaptive control gain to establish a sliding mode right at the beginning of the process. Gain dynamics ensure a reasonable adaptive gain with respect to the uncertainties. Finally, efficacy of the proposed controller is verified by simulations on an uncertain nonlinear system model.

scholarly journals Knowledge Graph Transfer Network for Few-Shot Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 10575-10582
Author(s):  
Riquan Chen ◽  
Tianshui Chen ◽  
Xiaolu Hui ◽  
Hefeng Wu ◽  
Guanbin Li ◽  
...  
Keyword(s):  
Information Transfer ◽  
Knowledge Graph ◽  
Propagation Mechanism ◽  
The Novel ◽  
Main Challenge ◽  
Training Samples ◽  
Proposed Model ◽  
Significant Performance ◽  
Structured Knowledge ◽  
Novel Concept

Few-shot learning aims to learn novel categories from very few samples given some base categories with sufficient training samples. The main challenge of this task is the novel categories are prone to dominated by color, texture, shape of the object or background context (namely specificity), which are distinct for the given few training samples but not common for the corresponding categories (see Figure 1). Fortunately, we find that transferring information of the correlated based categories can help learn the novel concepts and thus avoid the novel concept being dominated by the specificity. Besides, incorporating semantic correlations among different categories can effectively regularize this information transfer. In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN). Specifically, by initializing each node with the classifier weight of the corresponding category, a propagation mechanism is learned to adaptively propagate node message through the graph to explore node interaction and transfer classifier information of the base categories to those of the novel ones. Extensive experiments on the ImageNet dataset show significant performance improvement compared with current leading competitors. Furthermore, we construct an ImageNet-6K dataset that covers larger scale categories, i.e, 6,000 categories, and experiments on this dataset further demonstrate the effectiveness of our proposed model.

Nonlinear H∞ Filtering Based on Tensor Product Model Transformation

2020 ◽  
Vol 67 (6) ◽  
pp. 1074-1078
Author(s):  
Hengheng Gong ◽  
Yin Yu ◽  
Lini Zheng ◽  
Binglei Wang ◽  
Zhen Li ◽  
...  
Keyword(s):  
Tensor Product ◽  
Model Transformation ◽  
Product Model
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