A Model-Based Methodology to Formalize Specifications of Railway Systems

Exploring Intercity Trip Patterns of Railway Systems on National Holidays Using Deep Auto-Encoder

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120917385 ◽

2020 ◽

Vol 2674 (5) ◽

pp. 662-672

Author(s):

Wen-Yu Lee ◽

Yu-Ting Hsu ◽

Chian-Shan Suen ◽

Ming-Hsuan Wu ◽

Ying-Chuan Ni

Keyword(s):

Logistic Regression ◽

Pattern Generation ◽

External Factors ◽

Analysis Framework ◽

System Operation ◽

Railway Systems ◽

Model Based ◽

Intercity Railway ◽

Railway System ◽

Key Features

Intercity railway system operation on national holidays can be challenging because of possible surging demand. This study proposes an analysis framework to investigate railway system ridership data on national holidays, seeking to attain better understanding of relevant intercity trip patterns, so as to enable enhanced preparation and response before and during national holidays. The ridership data are analyzed in the form of Origin–Destination (O-D) tables and regarded as pictures of N × N pixels, where N is the number of the considered stations/cities in a railway system. The framework primarily adopts a deep auto-encoder to process these pictures to reduce data dimensions and abstracting key features within these pictorial data. Based on the abstracted features, k-means clustering is then conducted to categorize the O-D tables with similar trip patterns into the same group. Further, a discrete outcome model based on logistic regression is developed on the clustering results to enhance the interpretation of the trip pattern in each group and identify the significant holiday-related characteristics and external factors that can affect the trip pattern generation. The ridership data of Taiwan Railways Administration associated with 38 national holidays from January 2014 to August 2018 are analyzed. The analysis results highlight insightful interpretation in relation to clustered trip patterns and relevant trip characterization relative to various national holidays. The proposed framework and developed discrete outcome model are also validated, showing 85% correct assignments of O-D tables to the groups of relevant trip patterns.

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Model-Based Design of Operational Management System for Integrated Wireless Communication Network of Korean Railway Systems

Journal of the Korea Academia-Industrial cooperation Society ◽

10.5762/kais.2015.16.5.3071 ◽

2015 ◽

Vol 16 (5) ◽

pp. 3071-3080

Author(s):

Changwon Kim ◽

Kyung-Hee Kim ◽

Young-Hoon Lee ◽

Jae-Chon Lee

Keyword(s):

Communication Network ◽

Wireless Communication ◽

Management System ◽

Railway Systems ◽

Model Based ◽

Wireless Communication Network ◽

Operational Management

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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