KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Delahaye, Daniel

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Structural Equation Modeling for Airspace Optimization: The Analysis of Causal Factors Influencing Aviation Safety
    (2026-05-01)
    Yenpiem, Siriporn
    ;
    Yooyen, Soemsak
    ;
    Delahaye, Daniel
    ;
    Yoneyama, Keito R.
    Increased flight volumes necessitate urgent reforms in Airspace Management (ASM) to mitigate risks of fatalities and near-misses. In order to enhance aviation system safety, the International Civil Aviation Organization (ICAO) mandates that state parties must conduct the Universal Safety Oversight Audit Program (USOAP) to continuously monitor civil aviation. This research aims to identify critical factors influencing Thailand’s ASM by employing experimental design and Structural Equation Modeling (SEM) to analyze influences and relationships among communication, surveillance, navigation, Air Traffic Management (ATM), and ASM. The methodology includes stimulation and a questionnaire-based survey conducted with aviation professionals and mapping out their answers to find the influences, relationships, and importance of the different factors. The results were validated using various statistical tools. The findings indicate signi1ficant direct and indirect effects on ASM, emphasizing that effective communication and robust surveillance are essential for safety and operational efficiency. This study highlights the need to increase the ASM framework, providing actionable insights for optimizing air traffic control in response to the growing air traffic demand. Furthermore, SEM for Airspace optimization can be applied internationally to significantly reduce accidents and incidents in the future.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Vision-based spacecraft pose estimation via a deep convolutional neural network for noncooperative docking operations
    (2020-09-01)
    Phisannupawong, Thaweerath
    ;
    Kamsing, Patcharin
    ;
    Torteeka, Peerapong
    ;
    Channumsin, Sittiporn
    ;
    Sawangwit, Utane
    The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Aircraft trajectory recognition via statistical analysis clustering for Suvarnabhumi International Airport
    (2020-02-01)
    Kamsing, Patcharin
    ;
    Torteeka, Peerapong
    ;
    Yooyen, Soemsak
    ;
    Yenpiem, Siriporn
    ;
    Delahaye, Daniel
    Since Suvarnabhumi International Airport is considered to be the biggest airport in Thailand, a big travelling-hub of southeast Asia and plays a significant part to the economy of Thailand relying on the tourism industry, an aircraft trajectory recognition is essential to support the high traffic management system from around the world. The first and essential stage of airport capacity enhancement is descriptive-analytic in several sections of the airport, including flight trajectory behaviors in order to plan an improvement procedure in the future. This experiment deploys K-mean and Gaussian Mixture clustering to compare results by using available automatic dependent surveillance-broadcast (ADS-B) dataset provided by the bigdata system from various websites. The test varies the number of clustering from three to ten and measures how similar an object is to its cluster by using the Silhouette score. Gaussian Mixture clustering produces at least three unique flight trajectories when setting the number of clustering equal to four, giving the Silhouette score of 0.43. K-mean clustering with the number of clustering equal to ten gives the highest Silhouette score of 0.45. However, its routes are not clearly recognized when compared with the Gaussian Mixture clustering. Although the overall results are not clearly shown in the pattern, it is enough to describe the trajectory patterns of the aircrafts taking off or landing over Suvarnabhumi International Airport.