Incremental-based Nonlinear Detection Algorithm for MIMO System

A novel incremental nonlinear detection algorithm is presented for Multiple-Input Multiple-Output （MIMO） system. In this algorithm, the received data at multiple receiver antennas are nonlinearly mapped and then summed with weights. The weight coefficients are incrementally computed to avoid direct computation of the inverse of a matrix, which greatly reduce the computational complexity. Simulation and comparison show that the proposed algorithm can obtain better performance of Bit Error Rate （BER） than linear Minimum Mean Square Error （MMSE）.

Locating Sources of Oscillations Induced by Control of Voltage Source Converters Based on Energy Structure and Nonlinearity Detection

The oscillation phenomena associated with the control of voltage source converters(VSCs)are concerning,making it crucial to locate the sources of such oscillations and suppress the oscillations.Therefore,this paper presents a location scheme based on the energy structure and nonlinearity detection.The energy structure,which conforms to the principle of the energy-based method and dissipativity theory,is developed to describe the transient energy flow for VSCs,based on which a defined characteristic quantity is implemented to narrow the scope for locating the sources of oscillations.Moreover,based on the self-sustained oscillation characteristics of VsCs,an index for nonlinearity detection is applied to locate the VSCs that produce the oscillation energy.The combination of the energy structure and nonlinearity detection distinguishes the contribu-tions of different VSCs to the oscillation.The results of a case study implemented by the PSCAD/EMTDC simulation validate theproposed scheme.

Detection of Nonlinear Behavior Induced by Hard Limiting in Voltage Source Converters in Wind Farms Based on Higher-order Spectral Analysis

In recent years, sub-synchronous oscillation accidents caused by wind power integration have received extensive attention. The recorded constant-amplitude waveforms can be induced by either linear or nonlinear oscillation mechanisms. Hence, the nonlinear behavior needs to be distinguished prior to choosing the analysis method. Since the 1960s, the higher-order statistics(HOS) theory has become a powerful tool for the detection of nonlinear behavior(DNB) in production quality control wherein it has mainly been applied to mechanical condition monitoring and fault diagnosis. This study focuses on the hard limiters of the voltage source converter(VSC) control systems in the wind farms and attempts to detect the nonlinear behavior caused by bi-or uni-lateral saturation hard limiting using the HOS analysis. First, the conventional describing function is extended to obtain the detailed frequency domain information on the bi-and uni-lateral saturation hard limiting. Furthermore, the bi-and tri-spectra are introduced as the HOS, which are extended into bi-and tri-coherence spectra to eliminate the effects of the linear parts on the harmonic characteristics of hard limiting in the VSC control system, respectively. The effectiveness of the HOS in the DNB and the classification of the hard-limiting types is proven, and its detailed derivation and estimation procedure is presented. Finally, the quadratic and cubic phase coupling in the signals is illustrated, and the performance of the proposed method is evaluated and discussed.

Nonlinear Analysis of Physiological Time Series

The heart rate variability could be explained by a low-dimensional governing mechanism. There has been increasing interest in verifying and understanding the coupling between the respiration and the heart rate. In this paper we use the nonlinear detection method to detect the nonlinear deterministic component in the physiological time series by a single variable series and two variables series respectively, and use the conditional information entropy to analyze the correlation between the heart rate, the respiration and the blood oxygen concentration. The conclusions are that there is the nonlinear deterministic component in the heart rate data and respiration data, and the heart rate and the respiration are two variables originating from the same underlying dynamics.

THE ACCOMPANIED SLOWLY-VARIANT-SYSTEM OF NONLINEAR DYNAMIC SYSTEMS

The slowly-variant-system is defined and analyzed in this paper and the nonlinear relationship between its instantaneous parameters and the instantaneous amplitude and frequency of its free vibration response is established. By defining the band-pass mapping, a slowly-variant-system which we call the accompanied slowly-variant-system is extracted from the nonlinear system; and the relationship between the two systems is discussed. Also, the skeleton curves that can illustrate the nonlinearity and the main properties of the nonlinear system directly and concisely are defined. Work done in this paper opens a new way for nonlinearity detection and identification for nonlinear systems.

Effective model based fault detection scheme for rudder servo system

The inherent nonlinearities of the rudder servo system(RSS) and the unknown external disturbances bring great challenges to the practical application of fault detection technology. Modeling of whole rudder system is a challenging and difficult task. Quite often, models are too inaccurate, especially in transient stages. In model based fault detection, these inaccuracies might cause wrong actions. An effective approach, which combines nonlinear unknown input observer(NUIO) with an adaptive threshold, is proposed. NUIO can estimate the states of RSS asymptotically without any knowledge of external disturbance. An adaptive threshold is used for decision making which helps to reduce the influence of model uncertainty. Actuator and sensor faults that occur in RSS are considered both by simulation and experimental tests. The observer performance, robustness and fault detection capability are verified. Simulation and experimental results show that the proposed fault detection scheme is efficient and can be used for on-line fault detection.

Direct detection of the transient superresolution effect of nonlinear saturation absorption thin films

Using a strong nonlinear saturation absorption effect is one technique for breaking through the diffraction limit. In this technique, formation of a dynamic and reversible optical pinhole channel and transient superresolution is critical. In this work, a pump–probe transient detection and observation–experimental setup is constructed to explore the formation process directly. A Ge2Sb2Te5 thin film with strong nonlinear saturation absorption is investigated. The dynamic evolution of the optical pinhole channel is detected and imaged, and the transient superresolution spot is directly captured experimentally. Results verify that the superresolution effect originates from the generation of an optical pinhole channel and that the formation of the optical pinhole channel is dynamic and reversible. A good method is provided for direct detection and observation of the transient process of the superresolution effect of nonlinear thin films.

Step-size selection for split-step based nonlinear compensation with coherent detection in 112-Gb/s 16-QAM transmission

Non-uniform step-size distribution is implemented for split-step based nonlinear compensation in singlechannel 112-Gb/s 16 quadrature amplitude modulation （QAM） transmission. Numerical simulations of the system including a 20 × 80 km uncompensated link are performed using logarithmic step size distribution to compensate signal distortions. 50% of reduction in number of steps with respect to using constant step sizes is observed. The performance is further improved by optimizing nonlinear calculating position （NLCP） in case of using constant step sizes while NLCP optimization becomes unnecessary when using logarithmic step sizes, which reduces the computational effort due to uniformly distributed nonlinear phase for all successive steps.

Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods

Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management.Current studies in this area often have relied on psychology-driven linear models,which frequently exhibited limitations in practice.The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors,compared to existing models that mainly rely on psychological factors.An enhanced logistic regression model(that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions(that is,univariate and bivariate threshold effects).Specifically,nonlinearity and interaction detection were enabled by low-depth decision trees,which offer transparent model structure and robustness.A survey dataset collected in the aftermath of Hurricanes Katrina and Rita,two of the most intense tropical storms of the last two decades,was employed to test the new methodology.The findings show that,when predicting the households’ evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability.This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.