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We propose a novel unsupervised anomaly detection and diagnosis algorithm in power electronic networks. Since most anomaly detection and diagnosis algorithms in the literature are based on supervised methods that can hardly be generalized to broader scenarios, we propose unsupervised algorithms. Our algorithm extracts the Time-Frequency Domain (TFD) features from the three-phase currents and three-phase voltages of the point of coupling (PCC) nodes to detect anomalies and distinguish between different types of anomalies, such as cyber-attacks and physical faults. To detect anomalies through TFD features, we propose a novel Informative Leveraging for Anomaly Detection (ILAD) algorithm. The proposed unsupervised ILAD algorithm automatically extracts noise-reduced anomalous signals, resulting in more accurate anomaly detection results than other score-based methods. To assign anomaly types for anomaly diagnosis, we apply a novel Multivariate Functional Principal Component Analysis (MFPCA) clustering method. Unlike the deep learning methods, the MFPCA clustering method does not require labels for training and provides more accurate results than other deep embedding-based clustering approaches. Furthermore, it is even comparable to supervised algorithms in both offline and online experiments. To the best of our knowledge, the proposed unsupervised framework accomplishing anomaly detection and anomaly diagnosis tasks is the first of its kind in power electronic networks.

The conventional dyeing process which is practiced mostly in our textile industry to dye cotton has a high amount of water consumption and consequently releases a massive amount of wastewater which leads to economic cost, and environmental impact associated with the removal of reactive dye from the effluent. Nanocellulose gel, when combined with a reactive dye, can be applied as a coating to cotton fabric. This novel dyeing approach significantly reduces water usage, generation of wastewater, and treatment costs. The study suggests that the application of nanocellulose gel-dye coating to cotton fabrics improves the colorfastness of the fabrics. However, dyenanocellulose gel coating also lightens the fabric color. Therefore, the gel to dye ratio, in grams/milliliter, must be retained roughly under 0.40 to yield the same color as fabric dyed directly without a nanocellulose coating. [ABSTRACT FROM AUTHOR]

The paper considers the estimation of the slope parameter [image omitted]  for k ≥ 3, in a general linear model. A class of James Stein estimators is proposed and is compared with the least squares estimator under an appropriate stopping rule. It is shown that the sequential James Stein estimator dominates the sequential least squares estimator. Furthermore, under mild regularity conditions, a second order asymptotic risk expansion for the sequential James-Stein estimator is obtained.

This paper considers the problem of sequential point estimation of the autoregressive parameter in a first order autoregressive model. The sequential estimator proposed here is based on the least squares estimator and is shown to be asymtotically risk efficient as the cost of estimation error tends to infinity, under certain regularity conditions. Furthermore, nonlinear renewal theory is used to obtain a second order approximation to the expected stopping time. The asymptotic normality and uniform integrability of the standardized stopping time are also established.

The problem of estimating a linear combination,μ, of means ofp-independent, first-order autoregressive models is considered. Sequential procedures are derived (i) to estimateμpointwise using the linear combination of sample means, subject to a loss function (squared error plus cost per observation), and (ii) to arrive at a fixed-width confidence interval forμ. It is observed that in the case of point estimation we do not require a sampling scheme, where as in the case of interval estimation we do require a sampling scheme and a scheme similar to the one given in Mukhopadhyay and Liberman (1989) is proposed. All the first order efficiency properties of the sequential procedures involved here are derived. This paper is an extension of results of Sriram (1987) involving one time series to multiple time series.

Asymptotic expansions are obtained for the distribution function of a studentized estimator of the offspring mean sequence in an array branching process with immigration. The expansion result is shown to hold in a test function topology. As an application of this result, it is shown that the bootstrapping distribution of the estimator of the offspring mean in a sub-critical branching process with immigration also admits the same expansion (in probability). From these considerations, it is concluded that the bootstrapping distribution provides a better approximation asymptotically than the normal distribution.

Sequential bootstrapping for branching processes with immigration is proposed. The asymptotic validity of a sequential bootstrap estimator of the offspring mean in is estabilished for all values of m ∈ (0, 1]. This result is to be constrasted with the standard (non-sequential)bootstrapping where the bootstrap estimator estimator has been shown to asymptotically invalid (see, Sriram (1992)).