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  • 1
    Online Resource
    Online Resource
    Environmental Remote Sensing in Flooding Areas : A Case Study of Ayutthaya, Thailand (2021)
    Singapore :Springer Nature Singapore :
    Details
    Keywords: Geographic information systems. ; Environmental monitoring. ; Environmental health. ; Epidemiology. ; Geographical Information System. ; Environmental Monitoring. ; Environmental Health. ; Epidemiology.
    Description / Table of Contents: Geographical characteristics of Study area -- Datasets and data preparation -- Flooding identification by vegetation index -- Flood identification by Support Vector Machine (SVM) -- Improved support vector machine classifier by Particle filter algorithm -- Flood related parameters affecting waterborne diseases -- Measure of Disease Risk -- Modeling Outbreak Risk based on Back Propagation Neural Network (BPNN) algorithm -- Application of surveillance communicable diseases risk using Expert system -- Conclusions.
    Abstract: This book introduces flood inundation area and flood risks assessment based on a comprehensive monitoring system using remote sensing and geographic information system technologies. Taking the 2011 flood disaster of Ayutthaya in Thailand as an example, it presents a flood intrusion zone identification method based on remote sensing technology, spatial information technology and geographic information system for flood disaster monitoring and early warning system. It introduces the study area and data, vegetation index, improved support vector machine and flood intrusion zone identification method. It also analyzes the flood remote sensing parameters and waterborne diseases, method of risk assessment of waterborne disease outbreak, waterborne disease outbreak risk monitoring based on backpropagation neural network and its expert system. It not only promotes a new interdisciplinary approach both in public health and space information technology, but also greatly supports decision makers in disaster reduction.
    Type of Medium: Online Resource
    Pages: XI, 148 p. 55 illus., 38 illus. in color. , online resource.
    Edition: 1st ed. 2021.
    ISBN: 9789811582028
    URL: https://doi.org/10.1007/978-981-15-8202-8
    DOI: 10.1007/978-981-15-8202-8
    DDC: 910.285
    Language: English
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  • 2
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    Vision-Based Spacecraft Pose Estimation via a Deep Convolutional Neural Network for Noncooperative Docking Operations (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-08-30
    Description: 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.
    Electronic ISSN: 2226-4310
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Molecular Diversity Preservation International
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  • 3
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    Enhancing the Capability of a Ground-Based Optical Telescope for Thai National Space objects Observation † (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-01-07
    Description: Nowadays, the space operations environment have to face with space safety problems because of the growing of space debris in resident of space objects (RSOs) that can cause a catastrophic collision. In order to prevent debris-related risks in operational orbit, ground-based passive optical telescope network were used as a primary equipment for space debris observation due to the lowest maintenance costs. Furthermore, in technical, a precise tracking (position and velocity) of space objects can be beneficial towards not only orbit determination but also estimation spacecraft collision probability especially, in Low-Earth Orbit regime. National Astronomical Research Institute of Thailand (NARIT) has long experience operate in an observatory to perform both passive & active optical instruments for astrophysics and space sciences missions. In this research, based on Thai National Space objects Observation (TNSO) project, we re-establish the basic understanding of satellite tracking, optical subsystem integration and demonstration a framework so as to enhance the capability of telescope servo control subsystem. We describe the specific solutions adopted for continuous tracking mode and the results obtained during the commissioning of an alt-azimuth mounting equipped with 0.7 meter optical aperture telescope. The observation system can be performed with negligible as continuous tracking error. This contribution will present some of the experimental results and plans for further measurement campaigns.
    Electronic ISSN: 2504-3900
    Topics: Technology
    Published by Molecular Diversity Preservation International
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  • 4
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    Outlet Temperature Prediction of Boiling Heat Transfer in Helical Coils through Artificial Neural Network (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-01-07
    Description: In the present study, deep learning neural network model has been employed in many engineering problems including heat transfer prediction. The main consideration of this document is to predict the performance of the boiling heat transfer in helical coils under terrestrial gravity conditions and compare with actual experimental data. Total of 877 data sample has been used in the present neural model. Artificial new Neural Network (ANN) model developed in Python environment with Multi-layer Perceptron (MLP) using four parameters (helical coils dimensions, mass flow rate, heating power, inlet temperature) and one parameter (outlet temperature) has been used in the input layer and output layer in order. Levenberg-Marquardt (LM) algorithm using L2 Regularization to find out the optimal model. A typical feed-forward neural network model composed of three layers, with 30 numbers of neurons in each hidden layer, has been found as optimal based on statistical error analysis. The 4-30-30-1 neural model predicts the characteristics of the helical coil with the accuracy of 98.16 percent in the training stage and 96.68 percent in the testing stage. The result indicated that the proposed ANN model successfully predicts the heat transfer performance in helical coils and can be applied for others operation concerned with heat transfer prediction for future works
    Electronic ISSN: 2504-3900
    Topics: Technology
    Published by Molecular Diversity Preservation International
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