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  1. Modified recurrent equation-based cubic spline interpolation for missing data recovery in phasor measurement unit (PMU)
    Thangaraj S, Goh VT, Yap TTV
    F1000Res, 2022;11:246.
    PMID: 38152076 DOI: 10.12688/f1000research.73182.3
    BACKGROUND: Smart grid systems require high-quality Phasor Measurement Unit (PMU) data for proper operation, control, and decision-making. Missing PMU data may lead to improper actions or even blackouts. While the conventional cubic interpolation methods based on the solution of a set of linear equations to solve for the cubic spline coefficients have been applied by many researchers for interpolation of missing data, the computational complexity increases non-linearly with increasing data size.

    METHODS: In this work, a modified recurrent equation-based cubic spline interpolation procedure for recovering missing PMU data is proposed. The recurrent equation-based method makes the computations of spline constants simpler. Using PMU data from the State Load Despatch Center (SLDC) in Madhya Pradesh, India, a comparison of the root mean square error (RMSE) values and time of calculation (ToC) is calculated for both methods.

    RESULTS: The modified recurrent relation method could retrieve missing values 10 times faster when compared to the conventional cubic interpolation method based on the solution of a set of linear equations. The RMSE values have shown the proposed method is effective even for special cases of missing values (edges, continuous missing values).

    CONCLUSIONS: The proposed method can retrieve any number of missing values at any location using observed data with a minimal number of calculations.

  2. Driving style recognition using machine learning and smartphones
    Bin Jamal Mohd Lokman EH, Goh VT, Yap TTV, Ng H
    F1000Res, 2022;11:57.
    PMID: 37082303 DOI: 10.12688/f1000research.73134.1
    Background: The lack of real-time monitoring is one of the reasons for the lack of awareness among drivers of their dangerous driving behavior. This work aims to develop a driver profiling system where a smartphone's built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. Methods: We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking). Results: In our preliminary studies, we encountered some difficulties in obtaining consistent driving events, which had the potential to add "noise" to the observations, thus reducing the accuracy of the classification. However, after some pre-processing, which included manual elimination of extraneous and erroneous events, and with the use of the Convolutional Neural Networks (CNN), we have been able to distinguish different driving events with an accuracy of about 95%. Conclusions: Based on the results of preliminary studies, we have determined that proposed approach is effective in classifying different driving events, which in turn will allow us to determine driver's driving behavior.
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