A non-Gaussian continuous state space model for asset degradation

The degradation model plays an essential role in asset life prediction and condition based maintenance. Various degradation models have been proposed. Within these models, the state space model has the ability to combine degradation data and failure event data. The state space model is also an effective approach to deal with the multiple observations and missing data issues. Using the state space degradation model, the deterioration process of assets is presented by a system state process which can be revealed by a sequence of observations. Current research largely assumes that the underlying system development process is discrete in time or states. Although some models have been developed to consider continuous time and space, these state space models are based on the Wiener process with the Gaussian assumption. This paper proposes a Gamma-based state space degradation model in order to remove the Gaussian assumption. Both condition monitoring observations and failure events are considered in the model so as to improve the accuracy of asset life prediction. A simulation study is carried out to illustrate the application procedure of the proposed model

A stochastic user-operator assignment game for microtransit service evaluation: A case study of Kussbus in Luxembourg

This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users' travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.Comment: arXiv admin note: substantial text overlap with arXiv:1912.0198

Structural reliability prediction of a steel bridge element using dynamic object oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method

Evapotranspiration Mapping for Forest Management in California's Sierra Nevada

We assessed the response of densely forested watersheds with little apparent annual water limitation to forest disturbance and climate variability, by studying how past wildfires changed forest evapotranspiration, and what past evapotranspiration patterns imply for the availability of subsurface water storage for drought resistance. We determined annual spatial patterns of evapotranspiration using a top-down statistical model, correlating measured annual evapotranspiration from eddycovariance towers across California with NDVI (Normalized Difference Vegetation Index) measured by satellite, and with annual precipitation. The study area was the Yuba and American River watersheds, two densely forested watersheds in the northern Sierra Nevada. Wildfires in the 1985-2015 period resulted in significant post-fire reductions in evapotranspiration for at least 5 years, and in some cases for more than 20 years. The levels of biomass removed in medium-intensity fires (25- 75% basal area loss), similar to magnitudes expected from forest treatments for fuels reduction and forest health, reduced evapotranspiration by as much 150-200 mm yr-1 for the first 5 years. Rates of recovery in post-wildfire evapotranspiration confirm the need for follow-up forest treatments at intervals of 5-20 years to sustain lower evapotranspiration, depending on local landscape attributes and interannual climate. Using the metric of cumulative precipitation minus evapotranspiration (P-ET) during multi-year dry periods, we found that forests in the study area showed little evidence of moisture stress during the 1985-2018 period of our analysis, owing to relatively small reliance on interannual subsurface water storage to meet dry-year evapotranspiration needs of vegetation. However, more-severe or sustained drought periods will push some lower-elevation forests in the area studied toward the cumulative P-ET thresholds previously associated with widespread forest mortality in the southern Sierra Nevada

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted

Effect of Prices, Traits and Market Structure on Corn Seeding Density

Recent agronomic research finds that economically optimal seeding densities have likely increased for many Midwestern corn farmers as a result of genetic improvements including new GM traits such as Bt corn and herbicide tolerance. We derive a per acre demand model for hybrid seed corn to examine the determinants of corn seeding densities and estimate the model using a large data set of individual farmer seed corn purchases. Current results identify factors other than prices affecting farmer corn seeding densities. Among these factors are the GM trait of the seed corn, measures of the local seed corn market structure, seed purchase source and intended end use. We interpret these effects in terms of information effects—farmers with more/better access to the latest agronomic research indicating that recommended seeding densities should be increased tend to plant corn at higher densities.hybrid seed corn, Bt corn, herbicide tolerance, Herfindahl index, corn borer, rootworm, hyperbolic yield model, Agribusiness, Crop Production/Industries, Farm Management, Industrial Organization, Production Economics, D2, D21, Q1, Q12,

Supervised Typing of Big Graphs using Semantic Embeddings

We propose a supervised algorithm for generating type embeddings in the same semantic vector space as a given set of entity embeddings. The algorithm is agnostic to the derivation of the underlying entity embeddings. It does not require any manual feature engineering, generalizes well to hundreds of types and achieves near-linear scaling on Big Graphs containing many millions of triples and instances by virtue of an incremental execution. We demonstrate the utility of the embeddings on a type recommendation task, outperforming a non-parametric feature-agnostic baseline while achieving 15x speedup and near-constant memory usage on a full partition of DBpedia. Using state-of-the-art visualization, we illustrate the agreement of our extensionally derived DBpedia type embeddings with the manually curated domain ontology. Finally, we use the embeddings to probabilistically cluster about 4 million DBpedia instances into 415 types in the DBpedia ontology.Comment: 6 pages, to be published in Semantic Big Data Workshop at ACM, SIGMOD 2017; extended version in preparation for Open Journal of Semantic Web (OJSW