Browsing by Author "Sheppard, John"

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Combining Dynamic Bayesian Networks and Continuous Time Bayesian Networks for Diagnostic and Prognostic Modeling
(IEEE, 2022-08) Schupbach, Jordan; Pryor, Elliott; Webster, Kyle; Sheppard, John
The problem of performing general prognostics and health management, especially in electronic systems, continues to present significant challenges. The low availability of failure data, makes learning generalized models difficult, and constructing generalized models during the design phase often requires a level of understanding of the failure mechanism that elude the designers. In this paper, we present a new, generalized approach to PHM based on two commonly available probabilistic models, Bayesian Networks and Continuous-Time Bayesian Networks, and pose the PHM problem from the perspective of risk mit-igation rather than failure prediction. We describe the tools and process for employing these tools in the hopes of motivating new ideas for investigating how best to advance PHM in the aerospace industry.
Counterfactual Explanations of Neural Network-Generated Response Curves
(IEEE, 2023-06) Morales, Giorgio; Sheppard, John
Response curves exhibit the magnitude of the response of a sensitive system to a varying stimulus. However, response of such systems may be sensitive to multiple stimuli (i.e., input features) that are not necessarily independent. As a consequence, the shape of response curves generated for a selected input feature (referred to as “active feature”) might depend on the values of the other input features (referred to as “passive features”). In this work we consider the case of systems whose response is approximated using regression neural networks. We propose to use counterfactual explanations (CFEs) for the identification of the features with the highest relevance on the shape of response curves generated by neural network black boxes. CFEs are generated by a genetic algorithm-based approach that solves a multi-objective optimization problem. In particular, given a response curve generated for an active feature, a CFE finds the minimum combination of passive features that need to be modified to alter the shape of the response curve. We tested our method on a synthetic dataset with 1-D inputs and two crop yield prediction datasets with 2-D inputs. The relevance ranking of features and feature combinations obtained on the synthetic dataset coincided with the analysis of the equation that was used to generate the problem. Results obtained on the yield prediction datasets revealed that the impact on fertilizer responsivity of passive features depends on the terrain characteristics of each field.
Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks
(IEEE, 2021-07) Morales, Giorgio; Sheppard, John; Logan, Riley; Shaw, Joseph
In recent years, Hyperspectral Imaging (HSI) has become a powerful source for reliable data in applications such as remote sensing, agriculture, and biomedicine. However, hyperspectral images are highly data-dense and often benefit from methods to reduce the number of spectral bands while retaining the most useful information for a specific application. We propose a novel band selection method to select a reduced set of wavelengths, obtained from an HSI system in the context of image classification. Our approach consists of two main steps: the first utilizes a filter-based approach to find relevant spectral bands based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. The second step applies a wrapper-based approach to select bands from the reduced set based on their information entropy values, and trains a compact Convolutional Neural Network (CNN) to evaluate the performance of the current selection. We present classification results obtained from our method and compare them to other feature selection methods on two hyperspectral image datasets. Additionally, we use the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager. We show that our method produces more suitable results for a multispectral sensor design.
Towards a Low-Cost Comprehensive Process for On-Farm Precision Experimentation and Analysis
(MDPI, 2023-02) Hegedus, Paul B.; Maxwell, Bruce; Sheppard, John; Loewen, Sasha; Duff, Hannah; Morales-Luna, Giorgio; Peerlinck, Amy
Few mechanisms turn field-specific ecological data into management recommendations for crop production with appropriate uncertainty. Precision agriculture is mainly deployed for machine efficiencies and soil-based zonal management, and the traditional paradigm of small plot research fails to unite agronomic research and effective management under farmers’ unique field constraints. This work assesses the use of on-farm experiments applied with precision agriculture technologies and open-source data to gain local knowledge of the spatiotemporal variability in agroeconomic performance on the subfield scale to accelerate learning and overcome the bias inherent in traditional research approaches. The on-farm precision experimentation methodology is an approach to improve farmers’ abilities to make site-specific agronomic input decisions by simulating a distribution of economic outcomes for the producer using field-specific crop response models that account for spatiotemporal uncertainty in crop responses. The methodology is the basis of a decision support system that includes a six-step cyclical process that engages precision agriculture technology to apply experiments, gather field-specific data, incorporate modern data management and analytical approaches, and generate management recommendations as probabilities of outcomes. The quantification of variability in crop response to inputs and drawing on historic knowledge about the field and economic constraints up to the time a decision is required allows for probabilistic inference that a future management scenario will outcompete another in terms of production, economics, and sustainability. The proposed methodology represents advancement over other approaches by comparing management strategies and providing the probability that each will increase producer profits over their previous input management on the field scale.