In this study, we report a novel monolithically integrated GaN-based light-emitting diode (LED) with metal-oxide-semiconductor field-effect transistor (MOSFET). Without additionally introducing complicated epitaxial structures for transistors, the MOSFET is directly fabricated on the exposed n-type GaN layer of the LED after dry etching, and serially connected to the LED through standard semiconductor-manufacturing technologies. Such monolithically integrated LED/MOSFET device is able to circumvent undesirable issues that might be faced by other kinds of integration schemes by growing a transistor on an LED or vice versa. For the performances of resulting device, our monolithically integrated LED/MOSFET device exhibits good characteristics in the modulation of gate voltage and good capability of driving injected current, which are essential for the important applications such as smart lighting, interconnection, and optical communication.
The detailed monitoring of jointed plain concrete pavement (JPCP) slab condition is essential for cost-effective JPCP maintenance and rehabilitation. However, existing visual inspection practices for detailed slab condition classification are time-consuming and labor-intensive. In this paper, we proposed an automated JPCP slab condition classification model based on convolutional neural networks (ConvNets), which is the first to perform multi-label classification on the JPCP slab condition based on both crack types and severity levels. To handle the different scales between JPCP slab condition states, the model includes a novel global context block with atrous spatial pyramid pooling, denoted as a GC-ASPP block.The block can be flexibly applied to any ConvNets to effectively model the global context of images with the extraction of multiscale image features. The proposed model was evaluated using real-world 3D JPCP surface data. With the GC-ASPP block, our best model achieved an average precision of 85.42% on multi-label slab condition classification.
Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-ofthe-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We proposed a perturbation-based visual explanation method to inspect the models' performance visually. By examining the video attention saliency, we found that existing models could not precisely capture the causes (e.g., traffic light) of the specific action (e.g., stopping). Therefore, the Temporal Reasoning Block (TRB) was proposed and introduced to the models. With the TRB models, we achieved the accuracy of 86.3%, which outperform the state-of-the-art 3D CNNs from previous works. The attention saliency also demonstrated that TRB helped models focus on the causes more precisely. With both numerical and visual evaluations, we concluded that our proposed TRB models were able to provide accurate driving behavior prediction by learning the causal reasoning of the behaviors.
Raveling is one of the most common asphalt pavement distresses. The survey of its condition is required for transportation agencies to ensure roadway safety and appropriately apply preservation and rehabilitation treatments. However, the traditional raveling condition survey, including the determination of the raveling severity, is typically manually conducted by in-field visual inspection methods that are time consuming, labor intensive, and error prone. Although automated raveling detection and severity classification models have been developed, these existing models have shortcomings. Therefore, there is an urgent need to develop a more accurate and reliable model to automatically detect and classify raveling. This study proposes the first convolutional neural network (CNN)-based model for automated raveling detection and classification. Compared with general CNNs, the proposed model combines the data-driven features learned from training data and macrotexture features of 3D pavement surface data to achieve better performance. The proposed model was evaluated and compared with existing machine learning models using real-world 3D pavement surface data collected from the state of Georgia, U.S. By combining data-driven features with macrotexture features, the proposed model achieved the highest accuracy of 90.8% on raveling classification. The proposed model also achieved classification precision and recall higher than 85% for all raveling severity levels, which is more accurate and robust than existing models. It is concluded that, with multi-type features extraction and proper model design, the proposed model can provide more accurate and reliable predictions for raveling detection and classification.
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