Today, it is inclusive of one of the cornerstones in the realization of equal access and potential. However, despite great progress, primary learning opportunities still show great disparities, especially in communiti...
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Structural simulation models using pre-defined assumptions and values of material properties usually produce results that differ from the real structures with varying degree of accuracy. This is commonly attributed to...
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ISBN:
(数字)9780784485231
ISBN:
(纸本)9780784485231
Structural simulation models using pre-defined assumptions and values of material properties usually produce results that differ from the real structures with varying degree of accuracy. This is commonly attributed to two broad types of uncertainties, namely aleatory (related to inherent randomness) and epistemic (related to lack of knowledge). Sources of such uncertainties include material properties, construction techniques, aging, and natural or man-made hazard-induced damage. Accurate computational models with on-time model updating capabilities are important goals in engineering research and practice for monitoring the structural health during the operation stage and for making rapid and well-informed decisions following extreme events, for example, major earthquakes. Moreover, the advances and recent adoption of artificial intelligence technologies bring effective and innovative solutions for the structural model updating endeavors. In this paper, a novel model updating method is proposed using two deep reinforcement learning algorithms, namely, Advantage Actor-Critic and Asynchronous Advantage Actor-Critic. In addition, transfer learning is adopted, which generalizes the trained model to various scenarios and enhances the computational efficiency. Through several computer experiments, the results demonstrate the high accuracy and computational efficiency of the proposed approach, which brings about its promising potential for practical engineering applications.
Speech is the most common means of communication between people. In the field of human-computer interaction, speech is an indispensable medium for information transmission. However, due to the time-varying and unstabl...
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deep learning Frameworks are being increasingly used for computerized multimodal clinical diagnostics. Mainly they help automate the procedure of inferring the prognosis from an expansion of found organic alerts. For ...
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Federated learning(FL), a cutting-edge method of distributed learning, enables multiple users to share training results while maintaining the privacy of their personal data. Collecting data from different data owners ...
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In this work, we present a novel approach to colorectal cancer tissue classification using Swin-Transformer V2 on the NCT-CRC-HE-100K dataset. This study is the first to apply Swin-Transformer V2 in this domain, lever...
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One of the biggest threats that malicious software, or malware, poses to cybersecurity is exploiting vulnerabilities to infect computer systems. This research is based on malware classification using ensemble-based an...
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Epilepsy, characterized by recurrent seizures, necessitates timely and accurate detection for effective management. This study presents EpiCNN-LSTMDetect, a hybrid deep learning framework combining Convolutional Neura...
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Road obstacle detection is one of the crucial tasks for the safe and efficient operation of autonomous vehicles. Existing detection methods often try to detect all objects in the image (i.e. a frame in a video stream)...
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ISBN:
(纸本)9798331539856
Road obstacle detection is one of the crucial tasks for the safe and efficient operation of autonomous vehicles. Existing detection methods often try to detect all objects in the image (i.e. a frame in a video stream). However, it is important to note that not all objects are equally dangerous and that the detection system should not react equally to them. To reduce computation costs (e.g. time and power consumption) in a mobile device, this study introduces a cost-sensitive obstacle detection method to leverage the performance of YOLIC (You Only Look at Interested Cells), a method proposed by us previously. The new method allows YOLIC to pay more attention to important areas for driving. In the experiments, we assign high weights to areas close to the vehicle, and the weights vary depending on the detection distance, direction, and relation to driving. Experimental results on two road obstacle datasets demonstrate that the proposed cost-sensitive detection method can effectively reduce the costs in the most dangerous areas of a given image compared with the baseline YOLIC model. Moreover, our fastest model can achieve real-time performance on a Raspberry Pi 4B, making it possible to deploy the proposed method in low-cost vehicles such as scooters and delivery robots.
Stress, a ubiquitous part of modern life, greatly affects an individual's mental and physical well- being. This work addresses the need for an efficient non-invasive system that can accurately detect stress levels...
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