Methodologies to Predict Service Lives of Pavement Marking Materials

This study focuses on developing methodologies to predict the service life of a pavement marking material based on its retroreflectivity. Data from the 2002 National Transportation Product Evaluation Program (NTPEP) pavement marking material test deck in Mississippi are used for model development and model validation. The smoothing spline method and time series modeling are applied to estimate the service lives of different types of materials based on the assumption of a required minimum retroreflectivity value. The same models can also predict the retroreflectivity values at future times for a pavement marking product based on its retroreflectivity values in the past. The validation of the models shows satisfactory accuracy. As demonstrated in a case study, the predicted service lives of the marking materials can be used in life-cycle cost comparisons for selecting pavement marking material

CGPE: A user-friendly gene and pathway explore webserver for public cancer transcriptional data

Digitized for IUPUI ScholarWorks inclusion in 2021.High throughput technology has been widely used by researchers to understand diseases at the molecular level. Database and servers for downloading and analyzing these publicly data is available as well. But there is still lacking tools for facilitating researchers to study the function of genes in pathways views by integrated public omics data

System Model Bias Processing Approach for Regional Coordinated States Information Involved Filtering

In the Kalman filtering applications, the conventional dynamic model which connects the states information of two consecutive epochs by state transition matrix is usually predefined and assumed to be invariant. Aiming to improve the adaptability and accuracy of dynamic model, we propose multiple historical states involved filtering algorithm. An autoregressive model is used as the dynamic model which is subsequently combined with observation model for deriving the optimal window-recursive filter formulae in the sense of minimum mean square error principle. The corresponding test statistics characteristics of system residuals are discussed in details. The test statistics of regional predicted residuals are then constructed in a time-window for model bias testing with two hypotheses, that is, the null and alternative hypotheses. Based on the innovations test statistics, we develop a model bias processing procedure including bias detection, location identification, and state correction. Finally, the minimum detectable bias and bias-tonoise ratio are both computed for evaluating the internal and external reliability of overall system, respectively

Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing

Autonomous drones can operate in remote and unstructured environments, enabling various real-world applications. However, the lack of effective vision-based algorithms has been a stumbling block to achieving this goal. Existing systems often require hand-engineered components for state estimation, planning, and control. Such a sequential design involves laborious tuning, human heuristics, and compounding delays and errors. This paper tackles the vision-based autonomous-drone-racing problem by learning deep sensorimotor policies. We use contrastive learning to extract robust feature representations from the input images and leverage a two-stage learning-by-cheating framework for training a neural network policy. The resulting policy directly infers control commands with feature representations learned from raw images, forgoing the need for globally-consistent state estimation, trajectory planning, and handcrafted control design. Our experimental results indicate that our vision-based policy can achieve the same level of racing performance as the state-based policy while being robust against different visual disturbances and distractors. We believe this work serves as a stepping-stone toward developing intelligent vision-based autonomous systems that control the drone purely from image inputs, like human pilots