Syntactic Pattern Recognition For Seismic Oil Exploration
Syntactic Pattern Recognition For Seismic Oil Exploration

Author: Kou-yuan Huang

Publisher: World Scientific

Published: 2002-05-08

Total Pages: 149

ISBN-13: 9814491195

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The use of pattern recognition has become more and more important in seismic oil exploration. Interpreting a large volume of seismic data is a challenging problem. Seismic reflection data in the one-shot seismogram and stacked seismogram may contain some structural information from the response of the subsurface. Syntactic/structural pattern recognition techniques can recognize the structural seismic patterns and improve seismic interpretations.The syntactic analysis methods include: (1) the error-correcting finite-state parsing, (2) the modified error-correcting Earley's parsing, (3) the parsing using the match primitive measure, (4) the Levenshtein distance computation, (5) the likelihood ratio test, (6) the error-correcting tree automata, and (7) a hierarchical system.Syntactic seismic pattern recognition can be one of the milestones of a geophysical intelligent interpretation system. The syntactic methods in this book can be applied to other areas, such as the medical diagnosis system. The book will benefit geophysicists, computer scientists and electrical engineers.

Advanced Intelligent Computing Theories and Applications
Advanced Intelligent Computing Theories and Applications

Author: De-Shuang Huang

Publisher: Springer

Published: 2007-08-10

Total Pages: 1400

ISBN-13: 3540742824

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This volume, in conjunction with the two volumes LNCS 4681 and LNAI 4682, constitutes the refereed proceedings of the Third International Conference on Intelligent Computing held in Qingdao, China, in August 2007. The conference sought to establish contemporary intelligent computing techniques as an integral method that underscores trends in advanced computational intelligence and links theoretical research with applications.

Underwater Robots
Underwater Robots

Author: Junku Yuh

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 251

ISBN-13: 1461314194

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All life came from sea but all robots were born on land. The vast majority of both industrial and mobile robots operate on land, since the technology to allow them to operate in and under the ocean has only become available in recent years. A number of complex issues due to the unstructured, hazardous undersea environment, makes it difficult to travel in the ocean while today's technologies allow humans to land on the moon and robots to travel to Mars . . Clearly, the obstacles to allowing robots to operate in a saline, aqueous, and pressurized environment are formidable. Mobile robots operating on land work under nearly constant atmospheric pressure; their legs (or wheels or tracks) can operate on a firm footing; their bearings are not subjected to moisture and corrosion; they can use simple visual sensing and be observed by their creators working in simple environments. In contrast, consider the environment where undersea robots must operate. The pressure they are subjected to can be enormous, thus requiring extremely rugged designs. The deep oceans range between 19,000 to 36,000 ft. At a mere 33-foot depth, the pressure will be twice the normal one atmosphere pressure of 29. 4 psi. The chemical environment of the sea is highly corrosive, thus requiring the use of special materials. Lubrication of moving parts in water is also difficult, and may require special sealed, waterproof joints.

Computational Texture and Patterns
Computational Texture and Patterns

Author: Kristin J. Dana

Publisher: Synthesis Lectures on Computer

Published: 2018-09-13

Total Pages: 113

ISBN-13: 9781681730110

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Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance-to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.