John W. Sheppard

According to our database, John W. Sheppard authored at least 128 papers between 1988 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

ScholarNodes: Applying Content-based Filtering to Recommend Interdisciplinary Communities within Scholarly Social Networks.

Md Asaduzzaman Noor, Jason A. Clark, John W. Sheppard

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

Univariate Skeleton Prediction in Multivariate Systems Using Transformers.

Giorgio Morales, John W. Sheppard

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track, 2024

Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones.

Giorgio Morales, John W. Sheppard

Proceedings of the International Joint Conference on Neural Networks, 2024

2023

A Risk-Based Approach to Prognostics and Health Management Combining Bayesian Networks and Continuous-Time Bayesian Networks.

Jordan Schupbach, Elliott Pryor, Kyle Webster, John Sheppard

IEEE Instrum. Meas. Mag., August, 2023

Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing.

Giorgio Morales, John W. Sheppard, Paul B. Hegedus, Bruce D. Maxwell

Sensors, 2023

Managing Objective Archives for Solution Set Reduction in Many-Objective Optimization.

Amy Peerlinck, John Sheppard

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

Counterfactual Explanations of Neural Network-Generated Response Curves.

Giorgio Morales, John W. Sheppard

Proceedings of the International Joint Conference on Neural Networks, 2023

Cross-Domain Similarity in Domain Adaptation for Human Activity Recognition.

Samra Kasim, John W. Sheppard

Proceedings of the International Joint Conference on Neural Networks, 2023

Factored Particle Swarm Optimization for Policy Co-training in Reinforcement Learning.

Kordel K. France, John W. Sheppard

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

Evolving Intertask Mappings for Transfer in Reinforcement Learning.

Minh Hua, John W. Sheppard

Proceedings of the IEEE Congress on Evolutionary Computation, 2023

2022

Mixing Grain to Improve Profitability in Winter Wheat Using Evolutionary Algorithms.

Md Asaduzzaman Noor, John W. Sheppard, Sean Yaw

SN Comput. Sci., 2022

Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation.

Giorgio Morales, John W. Sheppard

CoRR, 2022

Robust Spectral Based Compression of Hyperspectral Images using LSTM Autoencoders.

Kyle Webster, John Sheppard

Proceedings of the International Joint Conference on Neural Networks, 2022

Approximate Orthogonal Spectral Autoencoders for Community Analysis in Social Networks.

Scott Wahl, John Sheppard

Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

Multi-Objective Factored Evolutionary Optimization and the Multi-Objective Knapsack Problem.

Amy Peerlinck, John Sheppard

Proceedings of the IEEE Congress on Evolutionary Computation, 2022

Addressing Sustainability in Precision Agriculture via Multi-Objective Factored Evolutionary Algorithms.

Amy Peerlinck, John Sheppard

Proceedings of the Metaheuristics - 14th International Conference, 2022

2021

Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection.

Giorgio Morales, John W. Sheppard, Riley D. Logan, Joseph A. Shaw

Remote. Sens., 2021

Optimal Grain Mixing is NP-Complete.

Md Asaduzzaman Noor, Sean Yaw, Binhai Zhu, John W. Sheppard

CoRR, 2021

Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat.

Giorgio Morales, John W. Sheppard

CoRR, 2021

Using Particle Swarm Optimization to Learn a Lane Change Model for Autonomous Vehicle Merging.

Na'Shea Wiesner, John Sheppard, Brian Haberman

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

A Study in Overlapping Factor Decomposition for Cooperative Co-Evolution.

Elliott Pryor, Amy Peerlinck, John Sheppard

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Resiliency-Aware Power Management of Microgrids using Agent-based Dynamic Programming and Q-learning.

Farshina Nazrul Shimim, Mohammad Alali, M. Hashem Nehrir, John W. Sheppard, Maryam Bahramipanah, Zagros Shahooei

Proceedings of the 2021 IEEE PES Innovative Smart Grid Technologies, 2021

Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks.

Giorgio Morales, John Sheppard, Riley D. Logan, Joseph A. Shaw

Proceedings of the International Joint Conference on Neural Networks, 2021

Evolutionary Grain-Mixing to Improve Profitability in Farming Winter Wheat.

Md Asaduzzaman Noor, John W. Sheppard

Proceedings of the Applications of Evolutionary Computation, 2021

Quality Diversity Genetic Programming for Learning Decision Tree Ensembles.

Stephen Boisvert, John W. Sheppard

Proceedings of the Genetic Programming - 24th European Conference, 2021

Tournament Topology Particle Swarm Optimization.

Jason Kuo, John W. Sheppard

Proceedings of the IEEE Congress on Evolutionary Computation, 2021

2020

Autonomous Vehicle Control Using Particle Swarm optimization in a Mixed Control Environment.

Na'Shea Wiesner, John Sheppard, Brian Haberman

Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

Evaluating Explanations of Convolutional Neural Network Image Classifications.

Sumeet S. Shah, John W. Sheppard

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Quantifying Uncertainty in Neural Network Ensembles using U-Statistics.

Jordan Schupbach, John W. Sheppard, Tyler Forrester

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Enhancing Neural Networks with Locality-Sensitive Clustering of Internal Representations.

Richard A. McAllister, John W. Sheppard

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019

Compact structures for continuous time Bayesian networks.

Logan Perreault, John W. Sheppard

Int. J. Approx. Reason., 2019

Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images.

Peter Lawson, Jordan Schupbach, Brittany Terese Fasy, John W. Sheppard

Proceedings of the Medical Imaging 2019: Digital Pathology, 2019

Using Winning Lottery Tickets in Transfer Learning for Convolutional Neural Networks.

Ryan Van Soelen, John W. Sheppard

Proceedings of the International Joint Conference on Neural Networks, 2019

Efficient Convolutional Neural Networks for Multi-Spectral Image Classification.

Jacob J. Senecal, John W. Sheppard, Joseph A. Shaw

Proceedings of the International Joint Conference on Neural Networks, 2019

AdaBoost with Neural Networks for Yield and Protein Prediction in Precision Agriculture.

Amy Peerlinck, John Sheppard, Jacob J. Senecal

Proceedings of the International Joint Conference on Neural Networks, 2019

Exploring Transferability in Deep Neural Networks with Functional Data Analysis and Spatial Statistics.

Richard McAllister, John Sheppard

Proceedings of the International Joint Conference on Neural Networks, 2019

Legislative Vote Prediction using Campaign Donations and Fuzzy Hierarchical Communities.

Scott Wahl, John Sheppard, Elizabeth Shanahan

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

Using a genetic algorithm with histogram-based feature selection in hyperspectral image classification.

Neil S. Walton, John W. Sheppard, Joseph A. Shaw

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Spatially Biased Random Forests.

Benjamin Mitchell, John Sheppard

Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference, 2019

Optimal Design of Experiments for Precision Agriculture Using a Genetic Algorithm.

Amy Peerlinck, John Sheppard, Julie Pastorino, Bruce D. Maxwell

Proceedings of the IEEE Congress on Evolutionary Computation, 2019

2018

Agent-Based Modeling of Retail Electrical Energy Markets With Demand Response.

Kaveh Dehghanpour, M. Hashem Nehrir, John W. Sheppard, Nathan C. Kelly

IEEE Trans. Smart Grid, 2018

Multiple fault diagnosis using factored evolutionary algorithms.

John W. Sheppard, Shane Strasser

IEEE Instrum. Meas. Mag., 2018

Association Rule Mining in Fuzzy Political Donor Communities.

Scott Wahl, John Sheppard

Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2018

Information sharing and conflict resolution in distributed factored evolutionary algorithms.

Stephyn G. W. Butcher, John W. Sheppard, Shane Strasser

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

Comparative performance and scaling of the pareto improving particle swarm optimization algorithm.

Stephyn G. W. Butcher, John W. Sheppard, Brian K. Haberman

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

An actor model implementation of distributed factored evolutionary algorithms.

Stephyn G. W. Butcher, John W. Sheppard

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

Evaluating Spatial Generalization of Stacked Autoencoders in Wind Vector Determination.

Richard McAllister, John Sheppard

Proceedings of the Thirty-First International Florida Artificial Intelligence Research Society Conference, 2018

Pareto Improving Selection of the Global Best in Particle Swarm Optimization.

Stephyn G. W. Butcher, John W. Sheppard, Shane Strasser

Proceedings of the 2018 IEEE Congress on Evolutionary Computation, 2018

2017

Factored Evolutionary Algorithms.

Shane Strasser, John W. Sheppard, Nathan Fortier, Rollie Goodman

IEEE Trans. Evol. Comput., 2017

Factored performance functions and decision making in continuous time Bayesian networks.

Liessman Sturlaugson, Logan Perreault, John W. Sheppard

J. Appl. Log., 2017

Disjunctive interaction in continuous time Bayesian networks.

Logan Perreault, Monica Thornton, John W. Sheppard, Joseph DeBruycker

Int. J. Approx. Reason., 2017

A formal approach to deriving factored evolutionary algorithm architectures.

Shane Strasser, John W. Sheppard, Stephyn Butcher

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Evaluating factored evolutionary algorithm performance on binary deceptive functions.

Shane Strasser, John W. Sheppard

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Deep learning for wind vector determination.

Richard McAllister, John Sheppard

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Convergence of Factored Evolutionary Algorithms.

Shane Strasser, John W. Sheppard

Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2017

Fuzzy Spectral Hierarchical Communities in Evolving Political Contribution Networks.

Scott Wahl, John Sheppard

Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, 2017

2016

Uncertain and negative evidence in continuous time Bayesian networks.

Liessman Sturlaugson, John W. Sheppard

Int. J. Approx. Reason., 2016

MICPSO: A method for incorporating dependencies into discrete particle swarm optimization.

Rollie Goodman, Monica Thornton, Shane Strasser, John W. Sheppard

Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016

Fast classifier learning under bounded computational resources using Partitioned Restricted Boltzmann Machines.

Hasari Tosun, John W. Sheppard

Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Assessing diffusion of spatial features in Deep Belief Networks.

Hasari Tosun, Ben Mitchell, John W. Sheppard

Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

A New Discrete Particle Swarm Optimization Algorithm.

Shane Strasser, Rollie Goodman, John W. Sheppard, Stephyn Butcher

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

Relaxing Consensus in Distributed Factored Evolutionary Algorithms.

Stephyn Butcher, Shane Strasser, Jenna Hoole, Benjamin Demeo, John W. Sheppard

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

A Noisy-OR Model for Continuous Time Bayesian Networks.

Logan Perreault, Shane Strasser, Monica Thornton, John W. Sheppard

Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, 2016

Dynamic sampling in training artificial neural networks with overlapping swarm intelligence.

Shehzad Qureshi, John W. Sheppard

Proceedings of the IEEE Congress on Evolutionary Computation, 2016

2015

A load profile management integrated power dispatch using a Newton-like particle swarm optimization method.

Caisheng Wang, Carol J. Miller, M. Hashem Nehrir, John W. Sheppard, Shawn P. McElmurry

Sustain. Comput. Informatics Syst., 2015

Abductive inference in Bayesian networks using distributed overlapping swarm intelligence.

Nathan Fortier, John W. Sheppard, Shane Strasser

Soft Comput., 2015

Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models.

Liessman Sturlaugson, John W. Sheppard

SIAM/ASA J. Uncertain. Quantification, 2015

An AI-ESTATE conformant interface for net-centric diagnostic and prognostic reasoning.

Houston King, Nathan Fortier, John W. Sheppard

IEEE Instrum. Meas. Mag., 2015

The Long-Run Behavior of Continuous Time Bayesian Networks.

Liessman Sturlaugson, John W. Sheppard

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

A Swarm-Based Approach to Learning Phase-Type Distributions for Continuous Time Bayesian Networks.

Logan Perreault, Monica Thornton, Rollie Goodman, John W. Sheppard

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015

Deep learning using partitioned data vectors.

Ben Mitchell, Hasari Tosun, John W. Sheppard

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Cross-Dataset Validation of Feature Sets in Musical Instrument Classification.

Patrick J. Donnelly, John W. Sheppard

Proceedings of the IEEE International Conference on Data Mining Workshop, 2015

Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence.

Nathan Fortier, John W. Sheppard, Shane Strasser

Proceedings of the Genetic and Evolutionary Computation Conference, 2015

Hierarchical Fuzzy Spectral Clustering in Social Networks using Spectral Characterization.

Scott Wahl, John Sheppard

Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, 2015

2014

Inference Complexity in Continuous Time Bayesian Networks.

Liessman Sturlaugson, John W. Sheppard

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Communication-aware distributed PSO for dynamic robotic search.

Logan Perreault, Mike P. Wittie, John W. Sheppard

Proceedings of the 2014 IEEE Symposium on Swarm Intelligence, 2014

Learning Bayesian classifiers using overlapping swarm intelligence.

Nathan Fortier, John W. Sheppard, Shane Strasser

Proceedings of the 2014 IEEE Symposium on Swarm Intelligence, 2014

Training Restricted Boltzmann Machines with Overlapping Partitions.

Hasari Tosun, John W. Sheppard

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Factored Performance Functions with Structural Representation in Continuous Time Bayesian Networks.

Liessman Sturlaugson, John W. Sheppard

Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference, 2014

Clustering Spectral Filters for Extensible Feature Extraction in Musical Instrument Classification.

Patrick J. Donnelly, John W. Sheppard

Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference, 2014

2013

Classification of Musical Timbre Using Bayesian Networks.

Patrick J. Donnelly, John W. Sheppard

Comput. Music. J., 2013

Bayesian abductive inference using overlapping swarm intelligence.

Nathan Fortier, John W. Sheppard, Karthik Ganesan Pillai

Proceedings of the 2013 IEEE Symposium on Swarm Intelligence, 2013

Principal component analysis preprocessing with Bayesian networks for battery capacity estimation.

Liessman E. Sturlaugson, John W. Sheppard

Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, 2013

Cluster Analysis for Optimal Indexing.

Tim Wylie, Michael A. Schuh, John W. Sheppard, Rafal A. Angryk

Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference, 2013

Comparing Frequency- and Style-Based Features for Twitter Author Identification.

Rachel M. Green, John W. Sheppard

Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference, 2013

2012

Overlapping particle swarms for energy-efficient routing in sensor networks.

Brian Haberman, John W. Sheppard

Wirel. Networks, 2012

Using machine learning to predict catastrophes in dynamical systems.

Jesse Berwald, Tomás Gedeon, John W. Sheppard

J. Comput. Appl. Math., 2012

Abductive inference in Bayesian belief networks using swarm intelligence.

Karthik Ganesan Pillai, John W. Sheppard

Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems (SCIS), 2012

DOSI: Training artificial neural networks using overlapping swarm intelligence with local credit assignment.

Nathan Fortier, John W. Sheppard, Karthik Ganesan Pillai

Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems (SCIS), 2012

Deep Structure Learning: Beyond Connectionist Approaches.

Ben Mitchell, John W. Sheppard

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

Taxonomic Dimensionality Reduction in Bayesian Text Classification.

Richard McAllister, John Sheppard

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

Evolving Kernel Functions with Particle Swarms and Genetic Programming.

Michael A. Schuh, Rafal A. Angryk, John W. Sheppard

Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference, 2012

Automated Weather Sensor Quality Control.

Douglas E. Galarus, Rafal A. Angryk, John W. Sheppard

Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference, 2012

2011

Overlapping swarm intelligence for training artificial neural networks.

Karthik Ganesan Pillai, John W. Sheppard

Proceedings of the 2011 IEEE Symposium on Swarm Intelligence, 2011

Incorporating evidence into trust propagation models using Markov Random Fields.

Hasari Tosun, John W. Sheppard

Proceedings of the Ninth Annual IEEE International Conference on Pervasive Computing and Communications, 2011

Evolving Four-Part Harmony Using Genetic Algorithms.

Patrick J. Donnelly, John W. Sheppard

Proceedings of the Applications of Evolutionary Computation, 2011

2009

Special Section on the 2007 IEEE AUTOTESTCON.

John W. Sheppard

IEEE Trans. Instrum. Meas., 2009

Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems.

Kihoon Choi, Satnam Singh, Anuradha Kodali, Krishna R. Pattipati, John W. Sheppard, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov, Liu Qiao

IEEE Trans. Instrum. Meas., 2009

Distributional Smoothing in Bayesian Fault Diagnosis.

Stephyn G. W. Butcher, John W. Sheppard

IEEE Trans. Instrum. Meas., 2009

2008

Image-based tracking with Particle Swarms and Probabilistic Data Association.

Edward Kao, Peter VanMaasdam, John W. Sheppard

Proceedings of the 2008 IEEE Swarm Intelligence Symposium, 2008

2007

A Formal Analysis of Fault Diagnosis with <i>D</i>-matrices.

John W. Sheppard, Stephyn G. W. Butcher

J. Electron. Test., 2007

Offline and Online Evolutionary Bi-Directional RRT Algorithms for Efficient Re-Planning in Dynamic Environments.

Sean R. Martin, Steve E. Wright, John W. Sheppard

Proceedings of the IEEE Conference on Automation Science and Engineering, 2007

2005

A Bayesian approach to diagnosis and prognosis using built-in test.

John W. Sheppard, Mark A. Kaufman

IEEE Trans. Instrum. Meas., 2005

2004

Multi-agent Simulation of Airline Travel Markets.

Rashad L. Moore, Ashley Williams, John W. Sheppard

Proceedings of the Genetic and Evolutionary Computation, 2004

The Royal Road Not Taken: A Re-examination of the Reasons for GA Failure on R1.

Brian Howard, John W. Sheppard

Proceedings of the Genetic and Evolutionary Computation, 2004

1999

Genetic programming and co-evolution with exogenous fitness in an artificial life environment.

Michael Waters, John Sheppard

Proceedings of the 1999 Congress on Evolutionary Computation, 1999

1998

Colearning in Differential Games.

John W. Sheppard

Mach. Learn., 1998

A Behavior Model for Next Generation Test Systems.

Lee A. Shombert, John W. Sheppard

J. Electron. Test., 1998

Managing Conflict in System Diagnosis.

John W. Sheppard, William R. Simpson

Computer, 1998

Standard representations of diagnostic models.

William R. Simpson, John W. Sheppard

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1998

1997

A Teaching Strategy for Memory-Based Control.

John W. Sheppard, Steven Salzberg

Artif. Intell. Rev., 1997

Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)-A New Standard for System Diagnostics.

John W. Sheppard, Leslie A. Orlidge

Proceedings of the Proceedings IEEE International Test Conference 1997, 1997

1996

SCC20 attracts IEC participation.

John W. Sheppard

IEEE Des. Test Comput., 1996

Improving the accuracy of diagnostics provided by fault dictionaries.

John W. Sheppard, William R. Simpson

Proceedings of the 14th IEEE VLSI Test Symposium (VTS'96), April 28, 1996

Hardware-Software Co-Design for Test: It's the Last Straw!

J. El-Ziq, Najmi T. Jarwala, Niraj K. Jha, Peter Marwedel, Christos A. Papachristou, Janusz Rajski, John W. Sheppard

Proceedings of the 14th IEEE VLSI Test Symposium (VTS'96), April 28, 1996

1995

Combining Genetic Algorithms with Memory Based Reasoning.

John W. Sheppard, Steven Salzberg

Proceedings of the 6th International Conference on Genetic Algorithms, 1995

1993

Fault Isolation in an Integrated Diagnostic Environment.

William R. Simpson, John W. Sheppard

IEEE Des. Test Comput., 1993

Performing Effective Fault Isolation in Integrated Diagnostics.

John W. Sheppard, William R. Simpson

IEEE Des. Test Comput., 1993

The Impact of Commercial Off-The-Shelf (COTS) Equipment on System Test and Diagnosis.

William R. Simpson, John W. Sheppard

Proceedings of the Proceedings IEEE International Test Conference 1993, Designing, Testing, and Diagnostics, 1993

Testing Fully Testable Systems: A Case Study.

John W. Sheppard

Proceedings of the Proceedings IEEE International Test Conference 1993, Designing, Testing, and Diagnostics, 1993

1992

System Testability Assessment for Integrated Diagnostics.

William R. Simpson, John W. Sheppard

IEEE Des. Test Comput., 1992

Applying Testability Analysis for Integrated Diagnostics.

John W. Sheppard, William R. Simpson

IEEE Des. Test Comput., 1992

System Perspective on Diagnostic Testing.

William R. Simpson, John W. Sheppard

Proceedings of the Proceedings IEEE International Test Conference 1992, 1992

1991

System Complexity and Integrated Diagnostics.

William R. Simpson, John W. Sheppard

IEEE Des. Test Comput., 1991

A Mathematical Model for Integrated Diagnostics.

John W. Sheppard, William R. Simpson

IEEE Des. Test Comput., 1991

An Intelligent Approach to Automatic Test Equipment.

William R. Simpson, John W. Sheppard

Proceedings of the Proceedings IEEE International Test Conference 1991, 1991

1990

Using a Competitive Learning Neural Network to Evaluate Software Complexity.

John W. Sheppard, William R. Simpson

Proceedings of the 1990 ACM SIGSMALL/PC Symposium on Small Systems, 1990

1988

Functional path analysis: an approach to software verification.

John W. Sheppard, William R. Simpson

Proceedings of the Sixteenth ACM Annual Conference on Computer Science, 1988