Shape Reconstruction with A Priori Knowledge Based on Integral Invariants

2012 ◽  
Vol 5 (2) ◽  
pp. 726-745 ◽  
Author(s):  
Thomas Fidler ◽  
Markus Grasmair ◽  
Otmar Scherzer
Keyword(s):  
A Priori ◽  
Shape Reconstruction ◽  
A Priori Knowledge ◽  
Integral Invariants ◽  
Knowledge Based ◽  
Priori Knowledge

A priori knowledge based particle filter for estimating 3-D pose position of implanted knee

2010 ◽  
Author(s):  
Yusuke Nakajima ◽  
Syoji Kobashi ◽  
Yohei Tsumori ◽  
Nao Shibanuma ◽  
Fumiaki Imamura ◽  
...  
Keyword(s):  
Particle Filter ◽  
A Priori ◽  
A Priori Knowledge ◽  
Knowledge Based ◽  
Priori Knowledge

A Priori knowledge based secure payload estimation

2017 ◽  
Vol 77 (14) ◽  
pp. 17889-17911 ◽  
Author(s):  
Sai Ma ◽  
Xianfeng Zhao ◽  
Qingxiao Guan ◽  
Zhoujun Xu ◽  
Yi Ma
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Knowledge Based ◽  
Priori Knowledge

The A Priori Knowledge Based Secure Payload Estimation for Additive Model

2017 ◽  
Vol 2017 (7) ◽  
pp. 16-21
Author(s):  
Sai Ma ◽  
Xianfeng Zhao ◽  
Qingxiao Guan ◽  
Chengduo Zhao
Keyword(s):  
A Priori ◽  
Additive Model ◽  
A Priori Knowledge ◽  
Knowledge Based ◽  
Priori Knowledge

The Linguistic View of a Priori Knowledge

Philosophy ◽  
2008 ◽  
Vol 83 (1) ◽  
pp. 89-111 ◽  
Author(s):  
M. Giaquinto
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Knowledge Based ◽  
Implicit Definitions ◽  
Priori Knowledge

AbstractThis paper presents considerations against the linguistic view of a priori knowledge. The paper has two parts. In the first part I argue that problems about the individuation of lexical meanings provide evidence for a moderate indeterminacy, as distinct from the radical indeterminacy of meaning claimed by Quine, and that this undermines the idea of a priori knowledge based on knowledge of synonymies. In the second part of the paper I argue against the idea that a priori knowledge not based on knowledge of synonymies can be explained in terms of implicit definitions.1

Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS

2013 ◽  
Vol 40 (17) ◽  
pp. 6863-6876 ◽  
Author(s):  
Bindi Chen ◽  
Peter C. Matthews ◽  
Peter J. Tavner
Keyword(s):  
Wind Turbine ◽  
A Priori ◽  
A Priori Knowledge ◽  
Knowledge Based ◽  
Priori Knowledge

Efficient Dynamic Object Search in Home Environment by Mobile Robot: A Priori Knowledge-Based Approach

2019 ◽  
Vol 68 (10) ◽  
pp. 9466-9477 ◽  
Author(s):  
Ying Zhang ◽  
Guohui Tian ◽  
Jiaxing Lu ◽  
Mengyang Zhang ◽  
Senyan Zhang
Keyword(s):  
Mobile Robot ◽  
Home Environment ◽  
A Priori ◽  
A Priori Knowledge ◽  
Dynamic Object ◽  
Knowledge Based ◽  
Object Search ◽  
Priori Knowledge

scholarly journals Experimental Assessment of Linear Sampling and Factorization Methods for Microwave Imaging of Concealed Targets

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
M. N. Akıncı ◽  
T. Çağlayan ◽  
S. Özgür ◽  
U. Alkaşı ◽  
M. Abbak ◽  
...  
Keyword(s):  
A Priori ◽  
Surrounding Medium ◽  
Shape Reconstruction ◽  
A Priori Knowledge ◽  
Reconstruction Algorithms ◽  
Electrical Parameters ◽  
Reconstruction Methods ◽  
Factorization Methods ◽  
Priori Knowledge ◽  
Linear Sampling

Shape reconstruction methods are particularly well suited for imaging of concealed targets. Yet, these methods are rarely employed in real nondestructive testing applications, since they generally require the electrical parameters of outer object as a priori knowledge. In this regard, we propose an approach to relieve two well known shape reconstruction algorithms, which are the linear sampling and the factorization methods, from the requirement of the a priori knowledge on electrical parameters of the surrounding medium. The idea behind this paper is that if a measurement of the reference medium (a medium which can approximate the material, except the inclusion) can be supplied to these methods, reconstructions with very high qualities can be obtained even when there is no information about the electrical parameters of the surrounding medium. Taking the advantage of this idea, we consider that it is possible to use shape reconstruction methods in buried object detection. To this end, we perform several experiments inside an anechoic chamber to verify the approach against real measurements. Accuracy and stability of the obtained results show that both the linear sampling and the factorization methods can be quite useful for various buried obstacle imaging problems.

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