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Tarek Berghout, Mohamed-Djamel Mouss, Leïla-Hayet Mouss and Mohamed Benbouzid
Machine learning prognosis for condition monitoring of safety-critical systems, such as aircraft engines, continually faces challenges of data unavailability, complexity, and drift. Consequently, this paper overcomes these challenges by introducing adapt...
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Abishek Manikandaraja, Peter Aaby and Nikolaos Pitropakis
Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices. Because machine learning and artificial intelligence can detect malware better than traditional sig...
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Kun Li, Xinke Zhu, Huawei Qin and Fei Hou
Mobile Earthquake Recording in Marine Areas by Independent Divers (MERMAID) provides a possibility for long-term and large-scale observation of natural seismic P waves, but it does not have mobility and can only drift with ocean currents, resulting in ob...
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Yajie Han, Guangqing Xia, Bin Sun, Junjun Zhang, Liuwei Chen and Chang Lu
This paper presents the development, analysis, and performance evaluation of a novel transversal-feed Electron Cyclotron Resonance Plasma Thruster (ECRPT). The ECRPT operates based on the transversal-feed principle and incorporates optimized structural d...
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Yankai Lv, Haiyan Ding, Hao Wu, Yiji Zhao and Lei Zhang
Federated learning (FL) is an emerging decentralized machine learning framework enabling private global model training by collaboratively leveraging local client data without transferring it centrally. Unlike traditional distributed optimization, FL trai...
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