Large Class Iconic Pattern Recognition An OCR Case Study

1987 ◽  
Author(s):  
Abhijit Mahalanobis ◽  
David Casasent
Keyword(s):  
Pattern Recognition ◽  
Large Class

scholarly journals Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 383
Author(s):  
Guanggang Song ◽  
Bin Li ◽  
Yuqing He
Keyword(s):  
Pattern Recognition ◽  
Performance Evaluation ◽  
Container Terminal ◽  
Container Terminals ◽  
Computing Architecture ◽  
Supply Chain Logistics ◽  
Green Production ◽  
And Performance ◽  
Container Terminal Logistics

Container terminals are the typical representatives of complex supply chain logistics hubs with multiple compound attributes and multiple coupling constraints, and their operations are provided with the strong characteristics of dynamicity, nonlinearity, coupling, and complexity (DNCC). From the perspective of computational logistics, we propose the container terminal logistics generalized computing architecture (CTL-GCA) by the migration, integration, and fusion of the abstract hierarchy, design philosophy, execution mechanism, and automatic principles of computer organization, computing architecture, and operating system. The CTL-GCA is supposed to provide the problem-oriented exploration and exploitation elementary frameworks for the abstraction, automation, and analysis of green production at container terminals. The CTL-GCA is intended to construct, evaluate, and improve the solution to planning, scheduling, and decision at container terminals, which all are nondeterministic polynomial hard problems. Subsequently, the logistics generalized computational pattern recognition and performance evaluation of a practical container terminal service case study is launched by the qualitative and quantitative approach from the sustainable perspective of green production. The case study demonstrates the application, utilization, exploitation, and exploration of CTL-GCA preliminarily, and finds the unsustainable patterns of production at the container terminal. From the above, we can draw the following conclusions. For one thing, the CTL-GCA makes a definition of the abstract and automatic running architecture of logistics generalized computation for container terminals (LGC-CT), which provides an original framework for the design and implementation of control and decision mechanism and algorithm. For another, the CTL-GCA can help us to investigate the roots of DNCC thoroughly, and then the CTL-GCA makes for conducting the efficient and sustainable running pattern recognition of LGC-CT. It is supposed to provide a favorable guidance and supplement to define, design, and implement the agile, efficient, sustainable, and robust task scheduling and resource allocation for container terminals by computational logistics whether in the strategy level or the tactical one.

Pattern Recognition Techniques for the Evaluation of Spatial and Temporal Variations in Water Quality. A Case Study:

2001 ◽  
Vol 35 (12) ◽  
pp. 2881-2894 ◽  
Author(s):  
Wunderlin Daniel Alberto ◽  
Dı́az Marı́a del Pilar ◽  
Amé Marı́a Valeria ◽  
Pesce Silvia Fabiana ◽  
Hued Andrea Cecilia ◽  
...  
Keyword(s):  
Water Quality ◽  
Pattern Recognition ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Pattern Recognition Techniques

scholarly journals Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia

2013 ◽  
Vol 15 (9) ◽  
pp. 1717 ◽  
Author(s):  
Sharifah Norsukhairin Syed Abdul Mutalib ◽  
Hafizan Juahir ◽  
Azman Azid ◽  
Sharifah Mohd Sharif ◽  
Mohd Talib Latif ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Air Quality

scholarly journals PATTERN RECOGNITION OF LONGITUDINAL TRIAL DATA WITH NONIGNORABLE MISSINGNESS: AN EMPIRICAL CASE STUDY

2009 ◽  
Vol 08 (03) ◽  
pp. 491-513 ◽  
Author(s):  
HUA FANG ◽  
KIMBERLY ANDREWS ESPY ◽  
MARIA L. RIZZO ◽  
CHRISTIAN STOPP ◽  
SANDRA A. WIEBE ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Missing Data ◽  
Growth Pattern ◽  
Growth Patterns ◽  
Trial Data ◽  
Parameter Estimates ◽  
Nonignorable Missing Data ◽  
Fuzzy Clustering Method ◽  
Nonignorable Missing

Methods for identifying meaningful growth patterns of longitudinal trial data with both nonignorable intermittent and drop-out missingness are rare. In this study, a combined approach with statistical and data mining techniques is utilized to address the nonignorable missing data issue in growth pattern recognition. First, a parallel mixture model is proposed to model the nonignorable missing information from a real-world patient-oriented study and concurrently to estimate the growth trajectories of participants. Then, based on individual growth parameter estimates and their auxiliary feature attributes, a fuzzy clustering method is incorporated to identify the growth patterns. This case study demonstrates that the combined multi-step approach can achieve both statistical generality and computational efficiency for growth pattern recognition in longitudinal studies with nonignorable missing data.

Fixture Failure Diagnosis for Autobody Assembly Using Pattern Recognition

1996 ◽  
Vol 118 (1) ◽  
pp. 55-66 ◽  
Author(s):  
D. Ceglarek ◽  
J. Shi
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Diagnostic Method ◽  
Measurement Data ◽  
Principal Component ◽  
Failure Diagnosis ◽  
Variation Pattern ◽  
Mapping Procedure ◽  
Pattern Recognition Approach

In this paper, a fault diagnostic method is proposed for autobody assembly fixtures. This method uses measurement data to detect and isolate dimensional faults of part caused by fixture. The proposed method includes a predetermined variation pattern model and a fault mapping procedure. The variation pattern model is based on CAD information about the fixture geometry and location of the measurement points. This fault mapping procedure combines Principal Component Analysis with pattern recognition approach. Simulations and one case study illustrate the proposed method.

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