Browsing by Subject "Pattern recognition"

Now showing 1 - 2 of 2
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    Algorithms for Detecting Nearby Loss of Generation Events for Decentralized Controls
    (IEEE Xplore, 2021-04) Dahal, Niraj; Rovnyak, Steven M.; Electrical and Computer Engineering, School of Engineering and Technology
    The paper describes algorithms to screen realtime frequency data for detecting nearby loss of generation events. Results from Fourier calculation are combined with other features to effectively distinguish a nearby loss of generation from similar remote disturbances. Nearby in this context usually refers to an event occurring around 50-100 miles from the measurement location. The proposed algorithm can be trained using pattern recognition tools like decision trees to enable smart devices including appliances like residential air conditioners and dryers to autonomously detect and estimate the source of large frequency disturbances. An area of application of this strategy is to actuate controls such as location targeted under frequency load shedding (UFLS) so that loads closest to a tripped generator are the most likely to shut down.
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    Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction
    (MDPI, 2023-06-19) Zhang, Qingxue; Zhou, Dian; Electrical and Computer Engineering, School of Engineering and Technology
    Background: Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals. Methods: A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations. Results: With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 ± 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements. Conclusions: Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support.