1D convolutional neural networks and applications: A survey

During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and millions of parameters have the ability to learn complex objects and patterns providing that they can be trained on a massive size visual database with ground-truth labels. With a proper training, this unique ability makes them the primary tool for various engineering applications for 2D signals such as images and video frames. Yet, this may not be a viable option in numerous applications over 1D signals especially when the training data is scarce or application-specific. To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and motor-fault detection. Another major advantage is that a real-time and low-cost hardware implementation is feasible due to the simple and compact configuration of 1D CNNs that perform only 1D convolutions (scalar multiplications and additions). This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field. Their state-of-the-art performance is highlighted concluding with their unique properties. The benchmark datasets and the principal 1D CNN software used in those applications are also publically shared in a dedicated website.

pdf/1d-convolutional-neural-networks-and-applications-a-survey-2nsgvr9zjw.pdf

1D Convolutional Neural Networks and Applications: A Survey

In this note, robust stabilization and tracking performance of operator based nonlinear feedback control systems are studied by using robust right coprime factorization. Specifically, a new condition of robust right coprime factorization of nonlinear systems with unknown bounded perturbations is derived. Using the new condition, a broader class of nonlinear plants can be controlled robustly. When the spaces of the nonlinear plant output and the reference input are different, a space change filter is designed, and in this case this note considers tracking controller design using the exponential iteration theorem.

/pdf/operator-based-nonlinear-feedback-control-design-using-52mnbh1slg.pdf

Operator-based nonlinear feedback control design using robust right coprime factorization

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

Volterra-series-based nonlinear system modeling and its engineering applications: A state-of-the-art review

Pseudorandom signals have been widely used for system identification. Maximum length binary (MLB) signals are the best known class of pseudorandom signals, because of their ease of generation using feedback shift registers, but it is less well known that there are several other classes of binary and near-binary signals with identical, or nearly identical properties. An overview of these classes of signals is given, and the design of a new MATLAB routine incorporating all these classes of signals is described. The importance of the choice of MLB signal to use in particular applications is illustrated with the identification of a Wiener system having a quadratic nonlinearity and a cubic nonlinearity. Using correlation analysis to estimate the linear system weighting function, errors due to the nonlinearities can be reduced if an appropriate choice is made of feedback connections and of data length used for the estimation.

The generation of binary and near-binary pseudorandom signals: an overview

A survey of readily accessible perturbation signals for system identification in the frequency domain

Pseudo-random signals have been widely used for system identification. Maximum length binary signals are the best known class of pseudo-random signals, because of their ease of generation using feedback shift registers, but it is less well known that there are several other classes of binary and near-binary signals with identical, or nearly identical properties. An overview of these classes of signal is given and the design of a new MATLAB routine incorporating all these classes of signal is described. The importance of the choice of MLB signal to use in particular applications is illustrated with the identification of a Wiener system having a quadratic nonlinearity and a cubic nonlinearity. Errors in the measurements of the input-output crosscorrelation function caused by the nonlinearities can be reduced when the signal is used in the estimation of the system weighting function if an appropriate choice of feedback connections and data length used for the estimation are chosen. In the final part of the paper, three measures of signal quality are considered.

The generation of binary and near-binary pseudo-random signals: an overview

A new approach to designing sets of ternary periodic signals with different periods for multi-input multi-output system identification is described. The signals are pseudo-random signals with uniform nonzero harmonics, generated from Galois field GF(q), where q is a prime or a power of a prime. The signals are designed to be uncorrelated, so that effects of different inputs can be easily decoupled. However, correlated harmonics can be included if necessary, for applications in the identification of ill-conditioned processes. A design table is given for q les 31. An example is presented for the design of five uncorrelated signals with a common period N = 168 . Three of these signals are applied to identify the transfer function matrix as well as the singular values of a simulated distillation column. Results obtained are compared with those achieved using two alternative methods.

/pdf/design-of-ternary-signals-for-mimo-identification-in-the-48456n6kfy.pdf

Design of Ternary Signals for MIMO Identification in the Presence of Noise and Nonlinear Distortion

Direct synthesis of pseudo-random ternary perturbation signals with harmonic multiples of two and three suppressed

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