Comparing and Evaluating HMM Ensemble Training Algorithms Using Train and Test and Condition Number Criteria

Hidden Markov Models have many applications in signal processing and pattern recognition, but their convergence-based training algorithms are known to suffer from over-sensitivity to the initial random model choice. This paper describes the boundary between regions in which ensemble learning is superior to Rabiner's multiplesequence Baum-Welch training method, and proposes techniques for determining the best method in any arbitrary situation. It also studies the suitability of the training methods using the condition number, a recently proposed diagnostic tool for testing the quality of the model. A new method for training Hidden Markov Models called the Viterbi Path counting algorithm is introduced and is found to produce significantly better performance than current methods in a range of trials

Improved Ensemble Training for Hidden Markov Models using Random Relative Node Permutations

Hidden Markov Models have many applications in signal processing and pattern recognition, but their convergence-based training algorithms are known to suffer from oversensitivity to the initial random model choice. This paper focuses upon the use of model averaging, ensemble thresholding, and random relative model permutations for improving average model performance. A method is described which trains by searching for the best relative permutation set for ensemble averaging. This uses the fit to the training set as an indicator. The work provides a simpler alternative to previous permutation-based ensemble averaging methods

A parametric shell analysis of the shuttle 51-L SRB AFT field joint

Following the Shuttle 51-L accident, an investigation was conducted to determine the cause of the failure. Investigators at the Langley Research Center focused attention on the structural behavior of the field joints with O-ring seals in the steel solid rocket booster (SRB) cases. The shell-of-revolution computer program BOSOR4 was used to model the aft field joint of the solid rocket booster case. The shell model consisted of the SRB wall and joint geometry present during the Shuttle 51-L flight. A parametric study of the joint was performed on the geometry, including joint clearances, contact between the joint components, and on the loads, induced and applied. In addition combinations of geometry and loads were evaluated. The analytical results from the parametric study showed that contact between the joint components was a primary contributor to allowing hot gases to blow by the O-rings. Based upon understanding the original joint behavior, various proposed joint modifications are shown and analyzed in order to provide additional insight and information. Finally, experimental results from a hydro-static pressurization of a test rocket booster case to study joint motion are presented and verified analytically

DeepQC: A Deep Learning System for Automatic Quality Control of In-situ Soil Moisture Sensor Time Series Data

Amidst changing climate, real-time soil moisture monitoring is vital for the development of in-season decision support tools to help farmers manage weather related risks. Precision Sustainable Agriculture (PSA) recently established a real-time soil moisture monitoring network across the central, Midwest, and eastern U.S., but field-scale sensor observations often come with data gaps and anomalies. To maintain the data quality needed for development of decision tools, a quality control system is necessary. The International Soil Moisture Network (ISMN) introduced the Flagit module for anomaly detection in soil moisture observations. However, under certain conditions, Flagit's quality control approaches may underperform in identifying anomalies. Recently deep learning methods have been successfully applied to detect anomalies in time series data in various disciplines. However, their use in agriculture has not been yet investigated. This study focuses on developing a Bi-directional Long Short-Term Memory (LSTM) model, referred to as DeepQC, to identify anomalies in soil moisture data. Manual flagged PSA observations were used for training, validation, and testing the model, following an 80:10:10 split. The study then compared the DeepQC and Flagit based estimates to assess their relative performance. Flagit corrected flagged 95.5% of the corrected observations and 50.3% of the anomaly observations, indicating its limitations in identifying anomalies. On the other hand, the DeepQC correctly flagged 99.7% of the correct observations and 95.6% of the anomalies in significantly less time, demonstrating its superiority over Flagit approach. Importantly, DeepQC's performance remained consistent regardless of the number of anomalies. Given the promising results obtained with the DeepQC, future studies will focus on implementing this model on national and global soil moisture networks.Comment: 9 pages, 8 figure

Improved Classification Using Hidden Markov Averaging From Multiple Observation Sequences

The enormous popularity of Hidden Markov models (HMMs) in spatio-temporal pattern recognition is largely due to the ability to 'learn' model parameters from observation sequences through the Baum-Welch and other re-estimation procedures. In this study, HMM parameters are estimated from an ensemble of models trained on individual observation sequences. The proposed methods are shown to provide superior classification performance to competing methods

Measuring Galaxy Environments with Deep Redshift Surveys

We study the applicability of several galaxy environment measures (n^th-nearest-neighbor distance, counts in an aperture, and Voronoi volume) within deep redshift surveys. Mock galaxy catalogs are employed to mimic representative photometric and spectroscopic surveys at high redshift (z ~ 1). We investigate the effects of survey edges, redshift precision, redshift-space distortions, and target selection upon each environment measure. We find that even optimistic photometric redshift errors (\sigma_z = 0.02) smear out the line-of-sight galaxy distribution irretrievably on small scales; this significantly limits the application of photometric redshift surveys to environment studies. Edges and holes in a survey field dramatically affect the estimation of environment, with the impact of edge effects depending upon the adopted environment measure. These edge effects considerably limit the usefulness of smaller survey fields (e.g. the GOODS fields) for studies of galaxy environment. In even the poorest groups and clusters, redshift-space distortions limit the effectiveness of each environment statistic; measuring density in projection (e.g. using counts in a cylindrical aperture or a projected n^th-nearest-neighbor distance measure) significantly improves the accuracy of measures in such over-dense environments. For the DEEP2 Galaxy Redshift Survey, we conclude that among the environment estimators tested the projected n^th-nearest-neighbor distance measure provides the most accurate estimate of local galaxy density over a continuous and broad range of scales.Comment: 17 pages including 16 figures, accepted to Ap

Improved estimation of hidden Markov model parameters from multiple observation sequences

The huge popularity of Hidden Markov models in pattern recognition is due to the ability to 'learn' model parameters from an observation sequence through Baum-Welch and other re-estimation procedures. In the case of HMM parameter estimation from an ensemble of observation sequences, rather than a single sequence, we require techniques for finding the parameters which maximize the likelihood of the estimated model given the entire set of observation sequences. The importance of this study is that HMMs with parameters estimated from multiple observations are shown to be many orders of magnitude more probable than HMM models learned from any single observation sequence - thus the effectiveness of HMM 'learning' is greatly enhanced. In this paper, we present techniques that usually find models significantly more likely than Rabiner's well-known method on both seen and unseen sequences

Implied Covenants in Oil and Gas Leases in the Appalachian Basin

The purpose of this Article is to examine existing case law on implied covenants in oil and gas leases in the Appalachian Basin states, identify gaps in case law, and as far as reasonably possible, to predict the issues that might be litigated in the future in light of the rush to develop the Marcellus Shale and Utica Shale formations