Adversarial data-selection based work-hours estimation method on a small dataset in a logistics center

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

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Development of a Model and Estimation Method to Represent Team Search with Uncertain Detection

Proceedings of the 2020 International Technical Meeting of The Institute of Navigation ◽

10.33012/2020.17167 ◽

2020 ◽

Author(s):

Audrey Balaska ◽

Jason H. Rife

Keyword(s):

Estimation Method

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Landmark Data Selection and Unmapped Obstacle Detection in Lidar-Based Navigation

Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017) ◽

10.33012/2017.15406 ◽

2017 ◽

Cited By ~ 3

Author(s):

Mathieu Joerger ◽

Guillermo Duenas Arana ◽

Matthew Spenko ◽

Boris Pervan

Keyword(s):

Obstacle Detection ◽

Data Selection ◽

Landmark Data

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An Absolute Phase Estimation Method for Interferometry Imagery

Journal of Engineering Technology and Applied Physics ◽

10.33093/jetap.2019.1.2.4 ◽

2019 ◽

Vol 1 (2) ◽

pp. 14-19

Author(s):

Sui Ping Lee ◽

Yee Kit Chan ◽

Tien Sze Lim

Keyword(s):

Noise Suppression ◽

Estimation Method ◽

Phase Image ◽

Phase Estimation ◽

Phase Reconstruction ◽

Estimation Algorithms ◽

Absolute Phase ◽

Filtering Rate ◽

Interferometric Phase ◽

Image Performance

Accurate interpretation of interferometric image requires an extremely challenging task based on actual phase reconstruction for incomplete noise observation. In spite of the establishment of comprehensive solutions, until now, a guaranteed means of solution method is yet to exist. The initially observed interferometric image is formed by 2π-periodic phase image that wrapped within (-π, π]. Such inverse problem is further corrupted by noise distortion and leads to the degradation of interferometric image. In order to overcome this, an effective algorithm that enables noise suppression and absolute phase reconstruction of interferometric phase image is proposed. The proposed method incorporates an improved order statistical filter that is able to adjust or vary on its filtering rate by adapting to phase noise level of relevant interferometric image. Performance of proposed method is evaluated and compared with other existing phase estimation algorithms. The comparison is based on a series of computer simulated and real interferometric data images. The experiment results illustrate the effectiveness and competency of the proposed method.

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