|
|
|
|
|
Yeon Moon Choo, Deok Jun Jo, Gwan Seon Yun and Eui Hoon Lee
Frequent localized torrential rains, excessive population density in urban areas, and increased impervious areas have led to massive flood damage that has been causing overloading of drainage systems (watersheds, reservoirs, drainage pump sites, etc.). F...
ver más
|
|
|
|
|
|
|
|
|
|
|
Pin-Hung Juan and Ja-Ling Wu
In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non...
ver más
|
|
|
|
|
|
|
|
|
|
|
Alya Alshammari and Khalil El Hindi
The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource r...
ver más
|
|
|
|
|
|
|
|
|
|
|
Nan Lao Ywet, Aye Aye Maw, Tuan Anh Nguyen and Jae-Woo Lee
Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial r...
ver más
|
|
|
|
|
|
|
|
|
|
|
Justyna Dydek, Katarzyna Blizniak, Hugo Sarmento, Andreas Ihle, Élvio Rúbio Gouveia, Janusz Iskra and Krzysztof Przednowek
Tensiomyography (TMG) has been described as an effective method for identifying differences in muscle response to athletic training stimuli. However, there is a lack of studies that evaluated changes in muscle contractile properties in response to anaero...
ver más
|
|
|
|
|
|
|
|
|
|
|
Bohdan Petryshyn, Serhii Postupaiev, Soufiane Ben Bari and Armantas Ostreika
The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Hyeon-Kyu Noh and Hong-June Park
A convolutional neural network (CNN) transducer decoder was proposed to reduce the decoding time of an end-to-end automatic speech recognition (ASR) system while maintaining accuracy. The CNN of 177 k parameters and a kernel size of 6 generates the proba...
ver más
|
|
|
|
|
|
|
|
|
|
|
Reenu Mohandas, Mark Southern, Eoin O?Connell and Martin Hayes
Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedure...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jianlei Qiao, Yonglu Lv, Yucai Feng, Chang Liu, Yi Zhang, Jinying Li, Shuang Liu and Xiaohui Weng
At present, the electronic nose has became a new technology for the rapid detection of pesticides. However, the technique may misidentify them for samples that have not been involved in training. Therefore, a hybrid model based on unsupervised and superv...
ver más
|
|
|
|
|
|
|
|
|
|
|
MohammadHossein Reshadi, Wen Li, Wenjie Xu, Precious Omashor, Albert Dinh, Scott Dick, Yuntong She and Michael Lipsett
Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially ...
ver más
|
|
|
|
|
|