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Nonlinear principal component analysis—Based on principal curves and neural networks

Computers & chemical engineering, 1996, Vol.20 (1), p.65-78

1996

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A multiyear year study of three plant communities with purple loosestrife and biological control agents in Virginia

Biological control, 2016-03, Vol.94, p.62-73

2016

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Field-Cage Evaluation of Survival, Reproduction, and Feeding Behavior of Adult Scymnus coniferarum (Coleoptera: Coccinellidae), a Predator of Adelges tsugae (Hemiptera: Adelgidae)

Environmental entomology, 2016-12, Vol.45 (6), p.1527-1535

2016

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Host-Range Testing of Eucryptorrhynchus brandti (Coleoptera: Curculionidae), a Candidate for Biological Control of Tree-of-Heaven, Ailanthus altissima

Environmental entomology, 2012-02, Vol.41 (1), p.118-124

2012

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Field-Cage Evaluation of Survival, Reproduction, and Feeding Behavior of Adult Scymnus coniferarum (Coleoptera: Coccinellidae), a Predator of Adelges tsugae (Hemiptera: Adelgidae)

Environmental entomology, 2016-12, Vol.45 (6), p.1527

2016

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Occurrence and development of Eucryptorrhynchus brandti and E. chinensis (Coleoptera: Curculionidae) on Ailanthus altissima trees subjected to different levels of mechanical damage

Biocontrol science and technology, 2014-01, Vol.24 (1), p.65-79

2014

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TU‐G‐BRA‐08: Understanding the Performance of Control Limit‐Based Monitoring of Respiratory Surrogate Tumor Motion Models

Medical Physics, 2012-06, Vol.39 (6), p.3923-3923

2012

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Life History, Development, and Rearing of Eucryptorrhynchus brandti (Coleoptera: Curculionidae) in Quarantine

Annals of the Entomological Society of America, 2011-07, Vol.104 (4), p.718-725

2011

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Host Range of Laricobius osakensis (Coleoptera: Derodontidae), A New Biological Control Agent of Hemlock Woolly Adelgid (Hemiptera: Adelgidae)

Environmental entomology, 2011-04, Vol.40 (2), p.324-332

2011

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Base control for the Tennessee Eastman problem

Computers & chemical engineering, 1994-05, Vol.18 (5), p.383-413

1994

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SU‐E‐T‐512: Targeting Mobile Tumors Precisely with an Integrated Robotic Target Tracking and Dynamic Couch‐Based Motion Compensation System for a Conventional Linear Accelerator

Medical Physics, 2011-06, Vol.38 (6), p.3606-3607

2011

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Use of neural nets for dynamic modeling and control of chemical process systems

Computers & chemical engineering, 1990, Vol.14 (4), p.573-582

1990

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Nonlinear PLS modeling using neural networks

Computers & chemical engineering, 1992, Vol.16 (4), p.379-391

1992

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TU‐G‐BRC‐08: Maintaining Tumor Targeting Accuracy in Gating and Tracking Systems for Respiration‐Induced Tumor Motion

Medical Physics, 2011-06, Vol.38 (6), p.3782-3782

2011

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TU‐E‐BRC‐10: Selecting Training Data to Optimize Accuracy of Respiratory Surrogate‐Based Tumor Displacement Models

Medical Physics, 2011-06, Vol.38 (6), p.3771-3771

2011

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Identification of faulty sensors using principal component analysis

AIChE journal, 1996-10, Vol.42 (10), p.2797-2812

1996

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SU‐FF‐T‐657: Performance Characteristics of An Experimental Couch for Real‐Time Tumor Motion Tracking

Medical Physics, 2009-06, Vol.36 (6), p.2676-2676

2009

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MO‐EE‐A3‐02: Inferring Nodal Volume and Primary Tumor Positions From Multiple Anatomical Surrogates Using 4D CT in Stage III Lung Cancer

Medical Physics, 2009-06, Vol.36 (6), p.2704-2704

2009

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MO‐EE‐A3‐03: Ordinary Least Squares and Partial Least Squares for Intra‐Fraction Lung Tumor Motion Modeling

Medical Physics, 2009-06, Vol.36 (6), p.2704-2704

2009

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Use of Hybrid Models in Wastewater Systems

Industrial & engineering chemistry research, 2000-06, Vol.39 (6), p.1694-1704

2000

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Fecundity and feeding of Galerucella calmariensis and G. pusilla on Lythrum salicaria

BioControl (Dordrecht, Netherlands), 2007-06, Vol.52 (3), p.351-363

2007

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WE‐E‐ValA‐05: Real‐Time Tumor Tracking with a Feedback‐Controlled Treatment Couch

Medical Physics, 2006-06, Vol.33 (6), p.2254-2254

2006

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SU‐FF‐J‐107: Predicting Dosimetric Errors From Real‐Time Tumor Tracking Using a Treatment Couch During IMRT Delivery

Medical Physics, 2006-06, Vol.33 (6), p.2045-2045

2006

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Synthesis of Plantwide Control Systems Using a Dynamic Model and Optimization

Industrial & engineering chemistry research, 2001-11, Vol.40 (24), p.5732-5742

2001

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Nonlinear FIR modeling via a neural net PLS approach

Computers & chemical engineering, 1996, Vol.20 (2), p.147-159

1996

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Nonlinear principal component analysis—Based on principal curves and neural networks

A multiyear year study of three plant communities with purple loosestrife and biological control agents in Virginia

Field-Cage Evaluation of Survival, Reproduction, and Feeding Behavior of Adult Scymnus coniferarum (Coleoptera: Coccinellidae), a Predator of Adelges tsugae (Hemiptera: Adelgidae)

Host-Range Testing of Eucryptorrhynchus brandti (Coleoptera: Curculionidae), a Candidate for Biological Control of Tree-of-Heaven, Ailanthus altissima

Field-Cage Evaluation of Survival, Reproduction, and Feeding Behavior of Adult Scymnus coniferarum (Coleoptera: Coccinellidae), a Predator of Adelges tsugae (Hemiptera: Adelgidae)

Occurrence and development of Eucryptorrhynchus brandti and E. chinensis (Coleoptera: Curculionidae) on Ailanthus altissima trees subjected to different levels of mechanical damage

TU‐G‐BRA‐08: Understanding the Performance of Control Limit‐Based Monitoring of Respiratory Surrogate Tumor Motion Models

Life History, Development, and Rearing of Eucryptorrhynchus brandti (Coleoptera: Curculionidae) in Quarantine

Host Range of Laricobius osakensis (Coleoptera: Derodontidae), A New Biological Control Agent of Hemlock Woolly Adelgid (Hemiptera: Adelgidae)

Base control for the Tennessee Eastman problem

SU‐E‐T‐512: Targeting Mobile Tumors Precisely with an Integrated Robotic Target Tracking and Dynamic Couch‐Based Motion Compensation System for a Conventional Linear Accelerator

Use of neural nets for dynamic modeling and control of chemical process systems

Nonlinear PLS modeling using neural networks

TU‐G‐BRC‐08: Maintaining Tumor Targeting Accuracy in Gating and Tracking Systems for Respiration‐Induced Tumor Motion

TU‐E‐BRC‐10: Selecting Training Data to Optimize Accuracy of Respiratory Surrogate‐Based Tumor Displacement Models

Identification of faulty sensors using principal component analysis

SU‐FF‐T‐657: Performance Characteristics of An Experimental Couch for Real‐Time Tumor Motion Tracking

MO‐EE‐A3‐02: Inferring Nodal Volume and Primary Tumor Positions From Multiple Anatomical Surrogates Using 4D CT in Stage III Lung Cancer

MO‐EE‐A3‐03: Ordinary Least Squares and Partial Least Squares for Intra‐Fraction Lung Tumor Motion Modeling

Use of Hybrid Models in Wastewater Systems

Fecundity and feeding of Galerucella calmariensis and G. pusilla on Lythrum salicaria

WE‐E‐ValA‐05: Real‐Time Tumor Tracking with a Feedback‐Controlled Treatment Couch

SU‐FF‐J‐107: Predicting Dosimetric Errors From Real‐Time Tumor Tracking Using a Treatment Couch During IMRT Delivery

Synthesis of Plantwide Control Systems Using a Dynamic Model and Optimization

Nonlinear FIR modeling via a neural net PLS approach

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