The microgrid is a potential solution for implementing smart distributed systems. However, controlling a microgrid is still a complex issue, and many proposed solutions are only based on locally measured signals witho...
详细信息
When developing and implementing ethical Artificial Intelligence (AI) in industrial settings, various viewpoints on building trustworthy AI emerge. This research emphasizes these differences and provides suggestions t...
详细信息
Passive components are essential in power converter circuits and significantly affect their volume and weight. Miniaturization and weight reduction of these components are crucial. Among passive components, accurate m...
详细信息
This research recognizes the pressing need for innovative research in healthcare, enabling the transition towards analytics, by explaining how previous studies utilized big data, AI, and machine learning to identify, ...
详细信息
Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian *** the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being *** ad...
详细信息
Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian *** the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being *** address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone *** deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone *** default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of *** improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV *** on the above improvements,we create a novel anomaly detection strategy *** method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)*** experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.
作者：
Ramzan, AneeqaMustafa, RafayKamran, YashfaNUTECH
Department of Electrical Engineering Islamabad Pakistan GIKI
Faculty of Computer Science and Engg. Swabi Topi Pakistan IIUI
Department of Electrical and Computer Engineering Pakistan
Providing adequate clinical and technical aid to blinds and visually impaired persons can be very challenging as it put financial strain on families due to the medical examination, treatment, surgical procedures and a...
详细信息
A new three-phase hybrid-excited multi-tooth switched reluctance motor with embedded permanent magnets is proposed, capable of achieving higher torque density for transportation electrification applications. Operating...
详细信息
Learners with a limited budget can use supervised data subset selection and active learning techniques to select a smaller training set and reduce the cost of acquiring data and training machine learning (ML) models. ...
详细信息
Learners with a limited budget can use supervised data subset selection and active learning techniques to select a smaller training set and reduce the cost of acquiring data and training machine learning (ML) models. However, the resulting high model performance, measured by a data utility function, may not be preserved when some data owners, enabled by the GDPR's right to erasure, request their data to be deleted from the ML model. This raises an important question for learners who are temporarily unable or unwilling to acquire data again: During the initial data acquisition of a training set of size k, can we proactively maximize the data utility after future unknown deletions? We propose that the learner anticipates/estimates the probability that (i) each data owner in the feasible set will independently delete its data or (ii) a number of deletions occur out of k, and justify our proposal with concrete real-world use cases. Then, instead of directly maximizing the data utility function, the learner can maximize the expected or risk-averse post-deletion utility based on the anticipated probabilities. We further propose how to construct these deletion-anticipative data selection (DADS) maximization objectives to preserve monotone submodularity and near-optimality of greedy solutions, how to optimize the objectives and empirically evaluate DADS' performance on real-world datasets. Copyright 2024 by the author(s)
The time-sensitive Internet of Things (IoT) applications within 5G and edge computing environments presents unique challenges in network resource management. Current systems struggle with efficiently managing the high...
详细信息
This paper proposes a multiport converter that integrates an auxiliary battery charger, which can be designed and controlled with simplicity for onboard wireless power transfer *** proposed converter is capable of red...
详细信息
暂无评论