Variable output power in isolated microgrids (MGs) threatens frequency stability and may even degrade power quality. In response, intelligent control methods have been developed and applied to frequency deviation cont...
Variable output power in isolated microgrids (MGs) threatens frequency stability and may even degrade power quality. In response, intelligent control methods have been developed and applied to frequency deviation control systems with excellent results. Nevertheless, a potential problem is that the application of such advanced techniques with a large search space is not enough to deal with highly dynamic environment and real-time operations of MGs. In this light, the present study introduces a flexible artificial neural network (ANN)-based frequency deviation control solution in a constrained structure that operates as follows. First, the stable controller parameter space of the PID-based AC microgrid is derived by using the stability boundary locus method. Then, the controller parameters are tuned and updated online by searching for an optimal combination of the coefficients with consideration of output variations sensed by a constrained ANN in the derived reduced parameter space. To accomplish this step, a reinforcement learning technique is applied to train the ANN-based tuners. The performance of the proposed technique has been verified under a given scenario to demonstrate how the reduced parameter space should facilitate the optimization procedure.
A typical setup in many machine learning scenarios involves a server that holds a model and a user that possesses data, and the challenge is to perform inference while safeguarding the privacy of both parties. Private...
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Recently, generative AI has attracted much attention from both academic and industrial fields, which has shown its potential, especially in the data generation and synthesis aspects. Simultaneously, secure and privacy...
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Managing diabetes often involves monitoring blood glucose in real time to detect excursions (e.g., hypoglycemia and hyperglycemia). Continuous glucose monitors (CGMs) are generally used for this purpose, but CGMs are ...
Managing diabetes often involves monitoring blood glucose in real time to detect excursions (e.g., hypoglycemia and hyperglycemia). Continuous glucose monitors (CGMs) are generally used for this purpose, but CGMs are both expensive and invasive (they require inserting a flexible needle under the skin). To address this issue, we examine whether non-invasive devices, such as electrocardiograms (ECG), can be used to predict glucose excursions. In particular, we consider two types of cardiac information: (1) heartbeat morphology, which generally requires ECG recordings, and (2) heartbeat timing, which can be obtained from inexpensive wrist-worn devices, such as fitness trackers. We use convolutional networks to analyze beat morphology, and recurrent networks and feature engineering to analyze the inter-beat interval (IBI) time series. Then, we validate individual models and their combinations on an experimental dataset containing ECG and CGM recordings for then young adults with type 1 diabetes. We find that beat morphology outperforms beat timing in hypoglycemia prediction, but the reverse happens for hyperglycemia prediction. In both prediction problems, combining morphology and time-domain information outperforms using each source of information independently.
The safe integration of machine learning modules in decision-making processes hinges on their ability to quantify uncertainty. A popular technique to achieve this goal is conformal prediction (CP), which transforms an...
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The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potentia...
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This letter addresses the problem of flocking motion in the context of port-Hamiltonian systems. The port-Hamiltonian framework offers advantages in modeling networks, dissipation, and heterogeneous agents. In this re...
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Efficient, precise, and intuitive navigation in a Virtual Environment (VE) or in desktop Virtual Reality (VR) is critical to improving user experience. This research introduces three navigation designs for physical em...
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By the beginning of 2020, the world woke up to a global pandemic that changed people’s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean ...
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