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DAMAGE ASSESSMENT OF CIVIL STRUCTURES AFTER NATURAL DISASTERS USING DEEP LEARNING AND SATELLITE IMAGERY
Title
DAMAGE ASSESSMENT OF CIVIL STRUCTURES AFTER NATURAL DISASTERS USING DEEP LEARNING AND SATELLITE IMAGERY
Creator
Jones, Scott F
Date
2019
Description
Since 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars....
Show moreSince 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars. After a natural disaster has occurred, the creation of maps that identify the damage to buildings and infrastructure is imperative. Currently, many organizations perform this task manually, using pre- and post-disaster images and well-trained professionals to determine the degree and extent of damage. This manual task can take days to complete. I propose to do this task automatically using post-disaster satellite imagery. I use a pre-trained neural network, SegNet, and replaced its last layer with a simple damage classification scheme. This final layer of the network is re-trained using cropped segments of the satellite image of the disaster. The data were obtained from a publicly accessible source, the Copernicus EMS system. They provided three channel (RGB) reference and damage grading maps that were used to create the images of the ground truth and the damaged terrain. I then retrained the final layer of the network to identify civil structures that had been damaged. The resulting network was 85% accurate at labelling the pixels in an image of the disaster from typhoon Haiyan. The test results show that it is possible to create these maps quickly and efficiently.
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SOLID-STATE SMART PLUG DEVICE
Title
SOLID-STATE SMART PLUG DEVICE
Creator
Deng, Zhixi
Date
2022
Description
Electrical faults are a leading cause of residential fire, and flexible power cords are particularly susceptible to metal or insulation...
Show moreElectrical faults are a leading cause of residential fire, and flexible power cords are particularly susceptible to metal or insulation degradation that may lead to a variety of electrical faults. Smart Plugs are a type of plug-in device controlling electrical loads via wireless communication for consumer market. However, there is lack of circuit protection features in existing Smart Plug products. Moreover, there is no previous product or research on Smart Plug with circuit protection features. This thesis introduces a new Smart Plug 2.0 concept which offers all-in-one protection against over-current, arc, and ground faults in addition to the smart features in Smart Plug products. It aims at preventing fire and shock hazards caused by degraded or damaged power cords and electrical connections in homes and offices. It offers microsecond-scale time resolution to detect and respond to a fault condition, and significantly reduces the electrothermal stress on household electrical wires and loads. A new arc fault detection method is developed using machine learning models based on load current di/dt events. The Smart Plug 2.0 concept has been validated experimentally. A 120V/10A solid-state Smart Plug 2.0 prototype using power MOSEFTs is designed and tested. It has experimentally demonstrated the comprehensive protection features against all types of electrical faults.
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Exploiting contextual information for deep learning based object detection
Title
Exploiting contextual information for deep learning based object detection
Creator
Zhang, Chen
Date
2020
Description
Object detection has long been an important research topic in computer vision area. It forms the basis of many applications. Despite the great...
Show moreObject detection has long been an important research topic in computer vision area. It forms the basis of many applications. Despite the great progress made in recent years, object detection is still a challenging task. One of the keys to improving the performance of object detection is to utilize the contextual information from the image itself or from a video sequence. Contextual information is defined as the interrelated condition in which something exists or occurs. In object detection, such interrelated condition can be related background/surroundings, support from image segmentation task, and the existence of the object in the temporal domain for video-based object detection. In this thesis, we propose multiple methods to exploit contextual information to improve the performance of object detection from images and videos.First, we focus on exploiting spatial contextual information in still-image based object detection, where each image is treated independently. Our research focuses on extracting contextual information using different approaches, which includes recurrent convolutional layer with feature concatenation (RCL-FC), 3-D recurrent neural networks (3-D RNN), and location-aware deformable convolution. Second, we focus on exploiting pixel-level contextual information from a related computer vision task, namely image segmentation. Our research focuses on applying a weakly-supervised auxiliary multi-label segmentation network to improve the performance of object detection without increasing the inference time. Finally, we focus on video object detection, where the temporal contextual information between video frames are exploited. Our first research involves modeling short-term temporal contextual information using optical flow and modeling long-term temporal contextual information using convLSTM. Another research focuses on creating a two-path convLSTM pyramid to handle multi-scale temporal contextual information for dealing with the change in object's scale. Our last work is the event-aware convLSTM that forces convLSTM to learn about the event that causes the performance to drop in a video sequence.
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