Linear Filter Model Research Articles

Probabilistic Amplitude Shaping and Nonlinearity Tolerance: Analysis and Sequence Selection Method

Probabilistic amplitude shaping (PAS) is a practical means to achieve a shaping gain in optical fiber communication. However, PAS and shaping in general also affect the signal-dependent generation of nonlinear interference. This provides an opportunity for nonlinearity mitigation through PAS, which is also referred to as a nonlinear shaping gain. In this paper, we introduce a linear lowpass filter model that relates transmitted symbol-energy sequences and nonlinear distortion experienced in an optical fiber channel. Based on this model, we conduct a nonlinearity analysis of PAS with respect to shaping blocklength and mapping strategy. Our model explains results and relationships found in literature and can be used as a design tool for PAS with improved nonlinearity tolerance. We use the model to introduce a new metric for PAS with sequence selection. We perform simulations of selection-based PAS with various amplitude shapers and mapping strategies to demonstrate the effectiveness of the new metric in different optical fiber system scenarios.

An adaptive linear filter model of procedural category learning.

We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.

Markov (Set) chains application to predict mortality rates using extended Milevsky–Promislov generalized mortality models

The mortality rates () measure the frequency of deaths in a fixed: population and time interval. The ability to model and forecast allows determining, among others, fundamental characteristics of life expectancy tables, e.g. used to determine the amount of premium in life insurance, adequate to the risk of death. The article proposes a new method of modelling and forecasting , using the class of stochastic Milevsky–Promislov switch models with excitations. The excitations are modelled by second, fourth and sixth order polynomials of outputs from the non-Gaussian Linear Scalar Filter (nGLSF) model and taking into account the Markov (Set) chain. The Markov (Set) chain state space is defined based on even orders of the nGLSF polynomial. The model order determines the theoretical values of the death rates. The obtained results usually provide a more precise forecast of the mortality rates than the commonly used Lee–Carter model.

A Deep Learning-Based Vision System Combining Detection and Tracking for Fast On-Line Citrus Sorting.

Defective citrus fruits are manually sorted at the moment, which is a time-consuming and cost-expensive process with unsatisfactory accuracy. In this paper, we introduce a deep learning-based vision system implemented on a citrus processing line for fast on-line sorting. For the citrus fruits rotating randomly on the conveyor, a convolutional neural network-based detector was developed to detect and temporarily classify the defective ones, and a SORT algorithm-based tracker was adopted to record the classification information along their paths. The true categories of the citrus fruits were identified through the tracked historical information, resulting in high detection precision of 93.6%. Moreover, the linear Kalman filter model was applied to predict the future path of the fruits, which can be used to guide the robot arms to pick out the defective ones. Ultimately, this research presents a practical solution to realize on-line citrus sorting featuring low costs, high efficiency, and accuracy.

A model of confocal microscopy measurements combining empirical and physical properties

Virtual measuring instruments are useful tools in surface topography measurement and can be applied for the prediction of measuring results and the determination of measurement uncertainty. For optical measuring instruments, often computationally expensive ray-tracing algorithms are applied. Other models provide an approximation of the transfer behavior based on techniques from signal processing or physical considerations of the instrument setup. For confocal microscopy, we suggest the application of a hybrid model which combines a linear filter model with limitations caused by physical properties of the measuring instrument. The application of the comprehensive model and the estimation of measurement uncertainty based on the instrument characteristics are presented. The results are in good compliance with experimental data and the model requires very few computing effort. An easy-to-use approach to implement the corresponding models for a specific measuring instrument is presented.

Improved Kalman filter and its application in initial alignment

In order to use linear filtering algorithm, many linear Kalman filter models are based on linear hypothesis and assumption of small quantities. In order to improve the robustness of the Kalman filter algorithm in the initial alignment, the influence of the feedback coefficient on the initial alignment based on the state feedback Kalman filter algorithm is analyzed and the recommended values of feedback coefficients are given in this paper. In order to improve the accuracy of the measurement noise covariance matrix of Kalman filter, an improved algorithm based on adaptive and fading schemes for the matrix is proposed in this paper, and the matrix is diagonalized during the filtering process. The improved algorithms are verified by initial alignment simulation and turntable experiment, and the error precisions of misalignment angles are improved by one order of magnitude compared with traditional Kalman filter.

Stochastic Dynamic Analysis of an Offshore Wind Turbine Structure by the Path Integration Method

Stochastic dynamic analysis of an offshore wind turbine (OWT) structure plays an important role in the structural safety evaluation and reliability assessment of the structure. In this paper, the OWT structure is simplified as a linear single-degree-of-freedom (SDOF) system and the corresponding joint probability density function (PDF) of the dynamic response is calculated by the implementation of the path integration (PI) method. Filtered Gaussian white noise, which is obtained from the utilization of a second-order filter, is considered as horizontal wind excitation and used to excite the SDOF system. Thus, the SDOF model and the second-order linear filter model constitute a four-dimensional dynamic system. Further, a detailed three-dimensional finite element model is applied to obtain the natural frequency of the OWT and the efficient PI method, which is modified based on the fast Fourier transform (FFT) convolution method, is also utilized to reduce the execution time to obtain the PDF of the response. Two important parameters of wind conditions, i.e., horizontal mean wind speed and turbulence standard deviation, are investigated to highlight the influences on the PDF of the dynamic response and the reliability of the OWT.

Application of the LPCF Model for Real-Time Contamination Warning in the Water Supply System

Currently, various water quality parameters are monitored for real-time contamination warning in the water supply system of South Korea. If the measured values of water quality parameters exceed the threshold, contamination warnings are issued. However, the U.S. Environmental Protection Agency (EPA) reported the following limitations of the contamination warning system based on these thresholds. First, irregular and sudden hydraulic changes in water supply system caused by pump or valve malfunction may cause measurement error of the water quality sensors, which may cause nuisance and unnecessary false-positive alarms. Second, in the case of long-term outflow of micropollutants, water quality changes are slightly within the thresholds, which causes a serious monitoring error of false-negatives that cannot be detected even in actual contamination. Therefore, the U.S. EPA applied a linear prediction–correction filter (LPCF) model for real-time contamination warning, which is based on the autoregressive (AR) model. The main purpose of this study is to develop a contamination warning technique to be applied to water supply system of South Korea. For the development of a real-time contamination warning technique, the LPCF model was applied and Testing with water quality data collected from G_water supply system of South Korea. Key words: Water Supply System, Real-Time Contamination Warning, Autoregressive Model, Linear Prediction-Correction Filter Model

Blind separation of temporally correlated noncircular sources using complex matrix joint diagonalization

In this paper, we introduce a new blind source separation (BSS) method for temporal correlated noncircular sources that uses widely linear filter (WLF) model to efficiently describe the temporal structure. The algorithm consists of a WLF coefficients estimator followed by complex matrix joint diagonalization. In the derivation of the new BSS algorithm, an emerging matrix joint diagonalization issue needs to be addressed. Subsequently, a complex matrix joint diagonalization algorithm based on successive Shear and Givens rotations (SGR) is proposed to deal with the new joint diagonalization problem. Simulations of SGR show that it converges fast and converges to excellent value, and it shows a satisfactory anti-perturbation performance of matrix for the new BSS algorithm. We compare the performance of the new BSS algorithm with several competing algorithms, and the simulation results demonstrate the superior performance of the proposed BSS algorithm for both noncircular complex Gaussian and non-Gaussian sources.

An adaptive scale Gaussian filter to explain White's illusion from the viewpoint of lightness assimilation for a large range of variation in spatial frequency of the grating and aspect ratio of the targets.

The variation between the actual and perceived lightness of a stimulus has strong dependency on its background, a phenomena commonly known as lightness induction in the literature of visual neuroscience and psychology. For instance, a gray patch may perceptually appear to be darker in a background while it looks brighter when the background is reversed. In the literature it is further reported that such variation can take place in two possible ways. In case of stimulus like the Simultaneous Brightness Contrast (SBC), the apparent lightness changes in the direction opposite to that of the background lightness, a phenomenon often referred to as lightness contrast, while in the others like neon colour spreading or checkerboard illusion it occurs opposite to that, and known as lightness assimilation. The White’s illusion is a typical one which according to many, does not completely conform to any of these two processes. This paper presents the result of quantification of the perceptual strength of the White’s illusion as a function of the width of the background square grating as well as the length of the gray patch. A linear filter model is further proposed to simulate the possible neurophysiological phenomena responsible for this particular visual experience. The model assumes that for the White’s illusion, where the edges are strong and quite a few, i.e., the spectrum is rich in high frequency components, the inhibitory surround in the classical Difference-of-Gaussians (DoG) filter gets suppressed, and the filter essentially reduces to an adaptive scale Gaussian kernel that brings about lightness assimilation. The linear filter model with a Gaussian kernel is used to simulate the White’s illusion phenomena with wide variation of spatial frequency of the background grating as well as the length of the gray patch. The appropriateness of the model is presented through simulation results, which are highly tuned to the present as well as earlier psychometric results.

Computational model-based assessment of baroreflex function from response to Valsalva maneuver.

Functional metrics of autonomic control of heart rate, including baroreflex sensitivity, have been shown to be strongly associated with cardiovascular risk. A decrease in baroreflex sensitivity with aging is hypothesized to represent a contributing causal factor in the etiology of primary hypertension. To assess baroreflex function in human subjects, two complementary methods to simulate the response in heart rate elicited by the Valsalva maneuver were developed and applied to data obtained from a cohort of healthy normal volunteers. The first method is based on representing the baroreflex arc as a simple linear filter, transforming changes in arterial pressure to changes in R-R interval. The second method invokes a physiologically based model for arterial mechanics, afferent baroreceptor strain-dependent firing, and control of heart rate via central autonomic response to changes in afferent inputs from aortic and carotid sensors. Analysis based on the linear filter model reveals that the effective response time of the baroreflex arc tends to increase with age in healthy subjects and that the response time/response rate is a predictor of resting systolic pressure. Similar trends were obtained based on the physiologically based model. Analysis of the Valsalva response using the physiologically based model further reveals that different afferent inputs from the carotid sinus and the aortic arch baroreceptors govern different parts of the heart rate response. The observed relationship between baroreflex sensitivity and systolic pressure is surprising because hypertensive subjects were excluded from the study, and there was no observed relationship between arterial pressure and age. NEW & NOTEWORTHY We introduce two methods to assess baroreflex function from data recorded from human subjects performing the Valsalva maneuver. Results demonstrate that the baroreflex response time tends to increase with age in healthy subjects, that response time represents a predictor of resting systolic pressure, and that the Valsalva response reveals different effects mediated by baroreceptors in the carotid sinus compared with those in the aortic arch.

A proposition of generalized stochastic Milevsky–Promislov mortality models

The aim of this article is to propose a new approach to the estimation of the mortality rates based on two extended Milevsky and Promislov models: the first one with colored excitations modeled by Gaussian linear filters and the second one with excitations modeled by a continuous non-Gaussian process. The exact analytical formulas for theoretical mortality rates based on Gaussian linear scalar filter models have been derived. The theoretical values obtained in both cases were compared with theoretical mortality rates based on a classical Lee–Carter model, and verified on the basis of empirical Polish mortality data. The obtained results confirm the usefulness of the switched model based on the continuous non-Gaussian process for modeling mortality rates.

Efficient Monte Carlo simulation and Grim effective wave model for predicting the extreme response of a vessel rolling in random head seas

In this paper, a computationally efficient Monte Carlo simulation (MCS) approach is introduced in order to estimate the extreme response statistics of parametric rolling in random longitudinal seas. Basically, parametric roll is caused by sufficiently large oscillations of the roll restoring moment occurring within certain frequencies of wave encounter (approximately twice the natural roll frequency). The concept of the Grim effective wave is applied herein in order to approximate the variation of the restoring moment in random waves. A fourth order linear filter is introduced to model the random effective wave amplitude process, which is assumed to be the driving process for the variation of the restoring term as well as for the stochastic nonlinear system of the roll motion by application of the Grim effective wave approximation. For the stochastic dynamical system, the roll response is a random process and an extrapolation procedure is developed for estimating the extreme values of the response statistics by assuming regular behavior in the tail region of the mean upcrossing rate. The rationality of the linear filter model and the feasibility of an efficient MCS method based on extrapolation techniques for predicting the extreme roll response are illustrated. Furthermore, the phenomenon of parametric roll in random seas as well as the effect of vessel speed on the stochastic roll response are investigated.

Modeling of surface metrology of state-of-the-art x-ray mirrors as a result of stochastic polishing process

The design and evaluation of the expected performance of optical systems requires sophisticated and reliable information about the surface topography of planned optical elements before they are fabricated. The problem is especially severe in the case of x-ray optics for modern diffraction-limited-electron-ring and free-electron-laser x-ray facilities, as well as x-ray astrophysics missions, such as the X-ray Surveyor under development. Modern x-ray source facilities are reliant upon the availability of optics of unprecedented quality, with surface slope accuracy <0.1 μrad. The unprecedented high angular resolution and throughput of future x-ray space observatories require high-quality optics of 100 m2 in total area. The uniqueness of the optics and limited number of proficient vendors make the fabrication extremely time-consuming and expensive, mostly due to the limitations in accuracy and measurement rate of metrology used in fabrication. We continue investigating the possibility of improving metrology efficiency via comprehensive statistical treatment of a compact volume of metrology of surface topography, which is considered the result of a stochastic polishing process. We suggest, verify, and discuss an analytical algorithm for identification of an optimal symmetric time-invariant linear filter model with a minimum number of parameters and smallest residual error. If successful, the modeling could provide feedback to deterministic polishing processes, avoiding time-consuming, whole-scale metrology measurements over the entire optical surface with the resolution required to cover the entire desired spatial frequency range. The modeling also allows forecasting of metrology data for optics made by the same vendor and technology. The forecast data are vital for reliable specification for optical fabrication, evaluated from numerical simulation to be exactly adequate for the required system performance, avoiding both over- and underspecification.

EXPERIMENTAL TESTS ON LOCAL DAMAGE DETECTION USING OPTICAL FBG SENSORS

In this study, the fiber Bragg grating (FBG)-sensor based local damage detection method is proposed under circumstances with temperature and external loading variations. To compensate the environmental effects, principal component analysis (PCA) is utilized and also the performance of PCA is compared with that of the conventional linear adaptive filter (LF) model. Laboratory tests with a 1/20 scale model of a jacket-type offshore structure with six jacket-legs and a heavy super structure have been carried out for investigating the performance of the proposed damage detection method. From the experimental tests, it is observed that the local damage feature is mostly hidden and difficult to identify due to the environmental effects. By utilizing the conventional LF and PCA models, the effects of the undesirable environmental effects can be efficiently eliminated, and it is also found that the performances of the LF and PCA models are very similar and competitive to each other. However PCA model does not require the information on the temperature and external load variations, hence it can be concluded that the PCA-based local damage detection can be more efficiently applied for FBG-based local damage detection under temperature and external loading variations.

Stochastic nonlinear ship rolling in random beam seas by the path integration method

Nonlinear ship rolling in random seas is a serious threat to ship stability. In this work, the dynamic stability of the vessel in random seas is evaluated by means of probabilistic methods. Specifically, the Markov diffusion theory is applied in order to study the stochastic aspects of the roll motion driven by random wave loads. The roll motion is modeled as a single-degree-of-freedom (SDOF) model in which the nonlinear damping and restoring terms as well as the random wave excitation are all incorporated. The stationary wave excitation moment in the SDOF model is represented as a filtered white noise by employing a second order linear filter. Therefore, a four-dimensional (4D) Markov dynamic system is established by combing the SDOF model with the linear filter model. Because the probabilistic property of the 4D Markov system is governed by the Fokker–Planck (FP) equation, the response statistics of roll motion can be obtained by solving the FP equation via an efficient 4D path integration (PI) method, which is based on the Markov property of the coupled dynamic system. Furthermore, the random wave excitation is approximated by an equivalent Gaussian white noise, and a two-dimensional (2D) PI technique is applied in order to obtain the response statistics of the dynamic system driven by this Gaussian white noise. The rationality and accuracy of applying the equivalent Gaussian white noise to simulate nonlinear ship rolling in random seas is studied. Moreover, the accuracy of the response statistics computed by the 4D and 2D PI techniques is verified by means of the versatile Monte Carlo simulation technique.

A Study on the Statistical Predictability of Drinking Water Qualities for Contamination Warning System

This study have been conducted to analyze the feasibility of establishing Contamination Warning System(CWS) that is capable of monitoring early natural or intentional water quality accidents, and providing active and quick responses for domestic C_water supply system. In order to evaluate the water quality data set, pH, turbidity and free residual chlorine concentration data were collected and each statistical value(mean, variation, range) was calculated, then the seasonal variability of those were analyzed using the independent t-test. From the results of analyzing the distribution of outliers in the measurement data using a high-pass filter, it could be confirmed that a lot of lower outliers appeared due to data missing. In addition, linear filter model based on autoregressive model(AR(1) and AR(2)) was applied for the state estimation of each water quality data set. From the results of analyzing the variability of the autocorrelation coefficient structure according to the change of window size(6hours~48hours), at least the window size longer than 12hours should be necessary for estimating the state of water quality data satisfactorily.

Evaluation of a wind speed estimator for effective hub-height and shear components

Estimates of the effective wind speed disturbances acting on a wind turbine are useful in a variety of control applications. With some simplifications, it is shown that for zero yaw error, any wind field interacting with a turbine can be equivalently described using a hub-height (uniform) component as well as linear horizontal and vertical shear components. A Kalman filter-based wind speed estimator is presented for estimation of these effective hub-height and shear components. The wind speed estimator is evaluated in the frequency domain using the FAST aeroelastic simulator with the National Renewable Energy Laboratory's 5 MW reference wind turbine model and realistic hub-height and shear disturbances. In addition, the impact of the inflow model, used to simulate the rotor aerodynamics, on the Kalman filter performance is investigated. It is found that the estimator accuracy strongly depends on the inflow model used. In general, the estimator performs well up to a bandwidth of 1 Hz when the inflow model used for simulation matches the model used to create the linear Kalman filter model and blade pitch angle remains close to the linearization operating point. However, inaccuracies in the linear model of the turbine when dynamic inflow is used for simulation as well as nonlinearities in the turbine dynamics due to blade pitch actuation cause performance to degrade. Finally, the improvement gained by employing a non-causal wind speed estimator is assessed, showing a minor increase in performance. Copyright © 2014 John Wiley & Sons, Ltd.

A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting

2-D linear prediction filter approach is a well-known method in the literature. Such an approach that uses past samples to predict present values is also applicable to solar irradiance data. Therefore, such models have limited accuracy levels. In this study, a new approach for hourly solar radiation forecasting is developed. First, the data measured hourly throughout a year, i.e., ambient temperature and extraterrestrial irradiance are converted into 2-D image forms. Then, the data points are evaluated as pixels of the images. Next, different M-D (Multi-Dimensional) linear prediction filter models are designed. The novelty of these models is that they utilize not only the solar irradiance image but also different images that correlate with solar irradiance data. These filters are employed both to link the images with each other and to predict solar irradiance data. It is shown that, to incorporate the temperature, extraterrestrial irradiance and the derivatives of these data with proposed M-D linear prediction filters, it is possible to improve the prediction accuracy. The results are compared with previously developed linear prediction filter models and conventional methods. Experimental results illustrates that proposed approach gives better prediction accuracies at a range 1%–30% for different M-D models compared with 2-D ones.

Identification of Nonlinear Systems Using Parallel Laguerre-NN Model

In this paper, a nonlinear system identification framework using parallel linear-plus-neural networks model is developed. The framework is established by combining a linear Laguerre filter model and a nonlinear neural networks (NN) model in a parallel structure. The main advantage of the proposed parallel model is that by having a linear model as the backbone of the overall structure, reasonable models will always be obtained. In addition, such structure provides great potential for further study on extrapolation benefits and control. Similar performance of proposed method with other conventional nonlinear models has been observed and reported, indicating the effectiveness of the proposed model in identifying nonlinear systems.