Browsing by Subject "visibility"

Now showing 1 - 2 of 2
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    Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach
    (MDPI, 2020-11) Kang, Kyubyung; Chen, Donghui; Peng, Cheng; Koo, Dan; Kang, Taewook; Kim, Jonghoon; Computer and Information Science, School of Science
    Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challenging due to the lack of available human resources. This study was conducted to develop an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S. It was found that the detection model of the framework was very precise, which means most of the identified pavement markings were correctly classified. In addition, in the proposed framework, visibility was confirmed as an important factor of driver safety and maintenance, and visibility standards for pavement markings were defined.
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    Using Reservoir Sampling and Parallelization to Improve Dynamic Binary Instrumentation
    (IEEE, 2022-05-16) Upp, Brandon; Meruga, Sai Pavan Kumar; Hill, James H.; Human-Centered Computing, School of Informatics and Computing
    This paper investigates two aspects of using dynamic binary instrumentation for real-time instrumentation of a distributed software systems. First, this paper investigates techniques for achieving different levels of visibility (i.e., ensuring all parts of a system are represented, or visible, in final results) into a software system without compromising software system performance. Secondly, this paper investigates how using reservoir sampling can be used to further reduce instrumentation overhead. The results of the research show that reservoir sampling can be used to reduce instrumentation overhead when compared to regular sampling methods like Constant, Percentage and Exhaustive sampling while also providing the desired system visibility.