Optimal reference sequence selection for genome assembly using minimum description length principle

Reference assisted assembly requires the use of a reference sequence, as a model, to assist in the assembly of the novel genome. The standard method for identifying the best reference sequence for the assembly of a novel genome aims at counting the number of reads that align to the reference sequence, and then choosing the reference sequence which has the highest number of reads aligning to it. This article explores the use of minimum description length (MDL) principle and its two variants, the two-part MDL and Sophisticated MDL, in identifying the optimal reference sequence for genome assembly. The article compares the MDL based proposed scheme with the standard method coming to the conclusion that “counting the number of reads of the novel genome present in the reference sequence” is not a sufficient condition. Therefore, the proposed MDL scheme includes within itself the standard method of “counting the number of reads that align to the reference sequence” and also moves forward towards looking at the model, the reference sequence, as well, in identifying the optimal reference sequence. The proposed MDL based scheme not only becomes the sufficient criterion for identifying the optimal reference sequence for genome assembly but also improves the reference sequence so that it becomes more suitable for the assembly of the novel genome

A novel leak detection approach in water distribution networks

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper proposes a novel leak monitoring framework aims to improve the operation of water distribution network (WDN). To do that, an online statistical hypothesis test based leak detection is proposed. The main advantages of the developed method are first to deal with the higher required computational time for detecting leaks and then, to update the KPCA model according to the dynamic change of the process. Thus, this can be performed to massive and online datasets. Simulation results obtained from simulated WDN data demonstrate the effectiveness of the proposed technique.Peer ReviewedPostprint (author's final draft

Fault Detection of Single and Interval Valued Data Using Statistical Process Monitoring Techniques

Principal component analysis (PCA) is a linear data analysis technique widely used for fault detection and isolation, data modeling, and noise filtration. PCA may be combined with statistical hypothesis testing methods, such as the generalized likelihood ratio (GLR) technique in order to detect faults. GLR functions by using the concept of maximum likelihood estimation (MLE) in order to maximize the detection rate for a fixed false alarm rate. The benchmark Tennessee Eastman Process (TEP) is used to examine the performance of the different techniques, and the results show that for processes that experience both shifts in the mean and/or variance, the best performance is achieved by independently monitoring the mean and variance using two separate GLR charts, rather than simultaneously monitoring them using a single chart. Moreover, single-valued data can be aggregated into interval form in order to provide a more robust model with improved fault detection performance using PCA and GLR. The TEP example is used once more in order to demonstrate the effectiveness of using of interval-valued data over single-valued data

Process Monitoring Using Data-Based Fault Detection Techniques: Comparative Studies

Data based monitoring methods are often utilized to carry out fault detection (FD) when process models may not necessarily be available. The partial least square (PLS) and principle component analysis (PCA) are two basic types of multivariate FD methods, however, both of them can only be used to monitor linear processes. Among these extended data based methods, the kernel PCA (KPCA) and kernel PLS (KPLS) are the most well-known and widely adopted. KPCA and KPLS models have several advantages, since, they do not require nonlinear optimization, and only the solution of an eigenvalue problem is required. Also, they provide a better understanding of what kind of nonlinear features are extracted: the number of the principal components (PCs) in a feature space is fixed a priori by selecting the appropriate kernel function. Therefore, the objective of this work is to use KPCA and KPLS techniques to monitor nonlinear data. The improved FD performance of KPCA and KPLS is illustrated through two simulated examples, one using synthetic data and the other using simulated continuously stirred tank reactor (CSTR) data. The results demonstrate that both KPCA and KPLS methods are able to provide better detection compared to the linear versions

Online statistical hypothesis test for leak detection in water distribution networks

This paper aims at improving the operation of the water distribution networks (WDN) by developing a leak monitoring framework. To do that, an online statistical hypothesis test based on leak detection is proposed. The developed technique, the so-called exponentially weighted online reduced kernel generalized likelihood ratio test (EW-ORKGLRT), is addressed so that the modeling phase is performed using the reduced kernel principal component analysis (KPCA) model, which is capable of dealing with the higher computational cost. Then the computed model is fed to EW-ORKGLRT chart for leak detection purposes. The proposed approach extends the ORKGLRT method to the one that uses exponential weights for the residuals in the moving window. It might be able to further enhance leak detection performance by detecting small and moderate leaks. The developed method’s main advantages are first dealing with the higher required computational time for detecting leaks and then updating the KPCA model according to the dynamic change of the process. The developed method’s performance is evaluated and compared to the conventional techniques using simulated WDN data. The selected performance criteria are the excellent detection rate, false alarm rate, and CPU time.Peer ReviewedPostprint (author's final draft

Nonlinear State and Parameter Estimation Using Iterated Sigma Point Kalman Filter: Comparative Studies

In this chapter, iterated sigma‐point Kalman filter (ISPKF) methods are used for nonlinear state variable and model parameter estimation. Different conventional state estimation methods, namely the unscented Kalman filter (UKF), the central difference Kalman filter (CDKF), the square‐root unscented Kalman filter (SRUKF), the square‐root central difference Kalman filter (SRCDKF), the iterated unscented Kalman filter (IUKF), the iterated central difference Kalman filter (ICDKF), the iterated square‐root unscented Kalman filter (ISRUKF) and the iterated square‐root central difference Kalman filter (ISRCDKF) are evaluated through a simulation example with two comparative studies in terms of state accuracies, estimation errors and convergence. The state variables are estimated in the first comparative study, from noisy measurements with the several estimation methods. Then, in the next comparative study, both of states and parameters are estimated, and are compared by calculating the estimation root mean square error (RMSE) with the noise‐free data. The impacts of the practical challenges (measurement noise and number of estimated states/parameters) on the performances of the estimation techniques are investigated. The results of both comparative studies reveal that the ISRCDKF method provides better estimation accuracy than the IUKF, ICDKF and ISRUKF. Also the previous methods provide better accuracy than the UKF, CDKF, SRUKF and SRCDKF techniques. The ISRCDKF method provides accuracy over the other different estimation techniques; by iterating maximum a posteriori estimate around the updated state, it re‐linearizes the measurement equation instead of depending on the predicted state. The results also represent that estimating more parameters impacts the estimation accuracy as well as the convergence of the estimated parameters and states. The ISRCDKF provides improved state accuracies than the other techniques even with abrupt changes in estimated states

Enhanced Multiscale Principal Component Analysis for Improved Sensor Fault Detection and Isolation

Multiscale PCA (MSPCA) is a well-established fault-detection and isolation (FDI) technique. It utilizes wavelet analysis and PCA to extract important features from process data. This study demonstrates limitations in the conventional MSPCA fault detection algorithm, thereby proposing an enhanced MSPCA (EMSPCA) FDI algorithm that uses a new wavelet thresholding criterion. As such, it improves the projection of faults in the residual space and the threshold estimation of the fault detection statistic. When tested with a synthetic model, EMSPCA resulted in a 30% improvement in detection rate with equal false alarm rates. The EMSPCA algorithm also relies on the novel application of reconstruction-based fault isolation at multiple scales. The proposed algorithm reduces fault smearing and consequently improves fault isolation performance. The paper will further investigate the use of soft vs. hard wavelet thresholding, decimated vs. undecimated wavelet transforms, the choice of wavelet decomposition depth, and their implications on FDI performance.The FDI performance of the developed EMSPCA method was illustrated for sensor faults. This undertaking considered synthetic data, the simulated data of a continuously stirred reactor (CSTR), and experimental data from a packed-bed pilot plant. The results of these examples show the advantages of EMSPCA over existing techniques