Parameter Selection Method Research Articles

The effect of end-point adjustments on smoothing splines used for tree-ring standardization

The cubic smoothing spline has been a popular method for detrending tree-ring data since the 1980s. The common implementation of this procedure (e.g., ARSTAN, dplR) uses a unique method for determining the smoothing parameter that is widely known as the %n criterion. However, this smoothing parameter selection method carries the assumption that end point effects are ignorable. In this paper, we complete the mathematical derivation and show how the original method differs from the complete version, both in the interpretation of the smoothing parameter and in the spline fit. Frequency response curves (FRC) demonstrate how the smoothing parameter is affected by the original assumption. For example, the FRC results indicate that a tree core of 250-year length has a 14% difference in the cut-off frequency when looking at the 67%n criterion. The FRC analysis shows that the existing approach produces a more flexible fit than anticipated, i.e., it is removing more variance than previously thought. For example, a 67%n spline under the existing approach corresponds to a 53%n spline fit. By using both simulated tree-core sequences and a dataset from a Midwest forest, we discuss which conditions result in greater differences between the spline fits and which conditions will have small differences. Tree-core sequences that have more curvature, such as a large-amplitude growth release, will lead to greater differences. Finally, we provide approximations to the end-point effect procedure. For example, using an 83%n criterion under the original approach produces a spline fit approximating the 67%n fit under the complete approach. These approximations could be easily implemented within existing programs like ARSTAN.

$N$-Dimensional Tensor Completion for Nuclear Magnetic Resonance Relaxometry

This paper deals with tensor completion for the solution of multidimensional inverse problems arising in nuclear magnetic resonance (NMR) relaxometry. We study the problem of reconstructing an approximately low-rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, nonuniform sampling strategies, and parameter selection methods are developed in this context. In particular, we derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property-based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by nonuniform sampling from a Parseval tight frame. The proposed algorithm is then applied to the setting of nuclear magnetic resonance relaxometry, for both simulated and experimental data. We compare our results with basis pursuit as well as with the state-of-the-art nonsubsampled data acquisition and reconstruction approach. Our experiments indicate that tensor recovery promises to significantly accelerate $N$-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments to be performed. Our methods could also be applied to other similar inverse problems arising in machine learning, signal and image processing, and computer vision.

Accelerated Implicit Single-Step Splitting Iteration Method for a Class of Coupled Lyapunov Equations

For the coupled Lyapunov problem derived from continuous Markov jump systems, a new implicit single-step splitting(ISS) iterative algorithm is proposed based on the idea of single step split iteration. After that, the accelerated implicit single-step splitting(AISS) iterative method, which is an accelerated algorithm of ISS iterative method, is proposed by using the idea of two-step alternate iteration. In this paper, the relevant convergence proofs of ISS and AISS iteration methods are given. Then, the selection range of the parameters of ISS and AISS iteration methods and the selection method of optimal parameters are given. Finally, this paper demonstrates the feasibility and advantage of the novel methods through actual calculations.

$ \chi^2 $ test for total variation regularization parameter selection

Total Variation (TV) is an effective method of removing noise in digital image processing while preserving edges. The scaling or regularization parameter in the TV process defines the amount of denoising, with a value of zero giving a result equivalent to the input signal. The discrepancy principle is a classical method for regularization parameter selection whereby data is fit to a specified tolerance. The tolerance is often identified based on the fact that the least squares data fit is known to follow a \begin{document}$ \chi^2 $\end{document} distribution. However, this approach fails when the number of parameters is greater than or equal to the number of data. Typically, heuristics are employed to identify the tolerance in the discrepancy principle and this leads to oversmoothing. In this work we identify a \begin{document}$ \chi^2 $\end{document} test for TV regularization parameter selection assuming the blurring matrix is full rank. In particular, we prove that the degrees of freedom in the TV regularized residual is the number of data and this is used to identify the appropriate tolerance. The importance of this work lies in the fact that the \begin{document}$ \chi^2 $\end{document} test introduced here for TV automates the choice of regularization parameter selection and can straightforwardly be incorporated into any TV algorithm. Results are given for three test images and compared to results using the discrepancy principle and MAP estimates.

Noise-Robust, Reconfigurable Canny Edge Detection and its Hardware Realization

Edge detection is a common operation in image/video processing applications. Canny edge detection, which performs well in different conditions, is one of the most popular and widely used of these algorithms. Canny’s superior performance is due mainly to its provision of the ability to adjust the output quality by manipulating the edge detection parameters, Sigma and Threshold. Calculating values for these two parameters on-the-fly and based on the application’s circumstances requires additional preprocessing, which increases the algorithm’s computational complexity. To reduce the complexity, several proposed methods simply employ precalculated, fixed values for the Canny parameters (based on either the worst or typical conditions), which sacrifices the edge detection’s performance in favor of the computational complexity. In this paper, an adaptive parameter selection method is proposed that selects values for the Canny parameters from a configuration table (rather than calculating in run-time), based on the estimated noise intensity of the input image and the minimum output performance that can satisfy the application requirements. This adaptive implementation of the Canny algorithm ensures that, while the edge detection performance (noise robustness) is higher than state-of-the-art counterparts in different circumstances, the execution time of the proposed Canny remains lower than those of recent cutting-edge Canny realizations.

A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

Distributed Denial of Service (DDoS) attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security. Existing detection methods cannot effectively detect early attacks. In this paper, we propose a detection method of DDoS attacks based on generalized multiple kernel learning (GMKL) combining with the constructed parameter R. The super-fusion feature value (SFV) and comprehensive degree of feature (CDF) are defined to describe the characteristic of attack flow and normal flow. A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm. A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter. The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection, and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate.

An analysis on new hybrid parameter selection model performance over big data set

Parameter selection or attribute selection is one of the crucial tasks in the data analysis process. Incorrect selection of the important attribute might generate imprecise or event for a wrong decision. It is an advantage if the decision-maker could select and apply the best model that helps in identifying the best-optimized attribute set — in the decision analysis process. Recently, many data scientists from various application areas are attracted to investigate and analyze the advantages and disadvantages of big data. One of the issues is, analyzing large volumes and variety of data in a big data environment is very challenging to the data scientists when there is a lack of a suitable model or no appropriate model to be implemented and used as a guideline. Hence, this paper proposes an alternative parameterization model that is able to generate the most optimized attribute set without requiring a high cost to learn, to use, and to maintain. The model is based on two integrated models that are combined with correlation-based feature selection, best-first search algorithm, soft set, and rough set theories which were compliments to each other as a parameter selection method. Experimental have shown that the proposed model has significantly shown as an alternative model in a big data analysis process.

Self-Tuned Class-D Audio Amplifier With Post-Filter Digital Feedback Implemented on Digital Signal Controller

This article describes the designing process of a low-cost class-D audio amplifier with digitized global feedback after the output filter, using a signal microcontroller. Various approaches to the feedback-based distortions elimination of class-D amplifiers are discussed. Hardware architecture of the author’s designed class-D amplifier with digital audio input interface, output voltage feedback path relays on SAR-ADC, and output power stage in multiphase bridge tide load configuration is shown. The selection method of the output low-pass filter parameters and power stage phase count was explained. A model of class-D amplifier with PID digital compensator and control loop interfaces is presented. Efficient high-speed digital PID compensator implementation on fixed-point 16-bit DSP core is exhibited. Procedure for control loop gain and phase plots measurements and self-tuning of PID feeding under damped second-order plants is presented along with simulations of loop properties influences on tuning effects. The results of the prototype device measurements such as Bode plots or output voltage frequency spectrum are presented. A big effort has been put on a useful, complete and transparent description for the development process of the presented class-D amplifier arrangement. The described class-D amplifier is the first solution with single feedback control from the output of LC filter based on a microcontroller with embedded ADC and PWM hardware with ensuring full audio bandwidth reproduction width THD distortion level below 0.25% at 1 kHz. The amplifier has been designed with the solicitude of device cost by using low-volume inductors in the output filter and by utilization of the built-in microcontroller ADC and PWM interfaces.

A Third-Order Super-Twisting Extended State Observer for Dynamic Performance Enhancement of Sensorless IPMSM Drives

In practice, sensorless drive systems of interior permanent magnet synchronous motors (IPMSMs) are usually working in highly utilized conditions where fast changing uncertain loads, inverter losses, magnetic saturation, and other disturbance exist. These issues can reduce the performance and stability, which become the main vulnerability of sensorless drives. Linear extended state observer (LESO) or quadrature-phase-locked loop is conventionally adopted to estimate the speed and position. Without compensation or adaptive methods, fast changing uncertainties cannot be observed thoroughly. In this article, a third-order super-twisting extended state observer (STESO) is proposed to enhance the dynamic performance of position and speed estimation for IPMSM. Utilizing the high-order extended state and super-twisting algorithm, fast convergence and disturbance estimation can be achieved in STESO. The effectiveness and stability of STESO are analyzed and an optimized parameter selection method is presented. Comparative experimental results between the LESO and STESO verify the effectiveness and improvement of the proposed STESO against rapid speed and load variations.

Group variable selection in the Andersen–Gill model for recurrent event data

In many scientific applications, such as biological studies, the predictors or covariates are naturally grouped. In this research, we consider the Andersen–Gill regression model for the analysis of recurrent event data with group covariates. In order to study the effects of the covariates on the occurrence of recurrent events, a bi-level penalized group selection method is introduced to address the group selection problem. A general group-bridge penalty function with varying weights is invoked to achieve the goal. It is shown that the performance of the bi-level selection depends on the weights. In order to select covariates more efficiently, especially for identifying the important covariates in important groups, adaptive weights are required. The asymptotic oracle properties of these methods are investigated. Three methods of tuning parameter selection are proposed. Our simulation studies show that the proposed method performs well in selecting important groups and important individual covariates in these groups simultaneously, and outperforms other popular group selection methods and the traditional unpenalized Wald testing method. We illustrate the method using a real life data set from medicine.

Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method

Trading strategies intended for high frequency trading in Forex markets are executed by cutting-edge automated trading systems. Such systems implement algorithmic trading strategies and are configured with predefined optimized parameters in order to generate entry and exit orders and execute trades on trading platforms. Three high-frequency automated trading systems were developed in the current research, using the MACD (oscillator), the SMA (moving average) and the PIVOT points (price crossover) technical indicators. The systems traded on hourly time frames, employing historical data of closing prices and the parameter optimization for each system was done using the d-Backtest PS method over weekly periods. With this work we intend to extend the methods of parameter selection for automated trading systems in high frequency trading. Through this research and the interpretation and evaluation of its results, we conclude that backtesting parameters’ optimization, especially through the d-Backtest PS method, is much more profitable than the default values of the parameters and that the optimization of parameters yields the highest profits through the implementation of restrictive relationships among them. It is also observed that the selection of the most profitable parameters of a trading system can be unrestricted, rendering the validation of the minor divergence occurring among slightly varying prices redundant. Meanwhile, other conclusions that can be drawn are that the most profitable classification system employed by the d-Backtest PS method is calibrated by means of two validation periods and that the most efficient profitability ratio between historical data period and validation period is 6:1 (in- and out-of-the-sample ratio).

Determination of geometric parameters for rational load-bearing unit of bridge cylindrical column pier

This paper deals with a bridge pier energywise uniform strength cylindrical steel-concrete load-bearing unit having a mesh case, which is made of an expanded metal mesh, as the most rational and the least resource-demanding one, using the nonwaste technology. The rational parameter selection method, which guarantees cross section uniform strength for the suggested load-bearing unit, is provided. The concrete core of the given element is a hollow cylindrical element that varies in height and whose centre of gravity coincides with an external loading point for any horizontal cross section. In addition to uniform strength, the hollow enables you to control the centre of gravity of the given complicated compound cross section of a cylindrical steel-concrete load-bearing unit. The calculation method to determine the main geometric parameters of the uniform strength load-bearing unit of a bridge cylindrical column pier having the lateral cross section area that varies in height is provided. The geometric parameters are the centre of gravity and its coordinates, central inertia moments and the radius of inertia. To exclude an impact of random eccentricity on a complicated compound cross section of a pier uniform strength steel-concrete load-bearing unit, the method to design a cross section core is provided.

How to manipulate droplet jetting from needle type jet dispensers

The needle-type inkjet dispenser has been widely used for various research and industrial purposes. The droplet jetting from the dispenser is closely related to the needle motion, which strikes against the nozzle seat. The strike of the needle on the nozzle seat often cause additional impact due to the bounce back, which may produce multiple droplets per jetting trigger. However, the needle motion is difficult to measure, and the actual behaviors have been known little. In this study, we measured the needle motion using an accelerometer and visualized jetting images to understand jetting behavior in relation to the needle motion. Then, we investigated various parameter effects on needle motion and jetting behaviors based on our proposed measurement methods. From the experimental results, we found that needle travel distance should be in the optimal range in order to produce single droplet per jetting trigger. In conclusion, we proposed an effective parameter selection method for the optimal jetting based on understanding of the jetting physics.

Discrete Element Numerical Simulation Method for Permeability Stress Sensitivity of Persistent Fractured Coal Samples

The stress sensitivity of the persistent fracture and primary joint directly affect the stress and permeability of elastic and shear fracture coal samples in fluid–solid coupling simulation. Therefore, the method to select the parameters of the primary joints and persistent fracture affects the accuracy and reliability of the numerical simulation. This paper introduces the discrete element flow coupling simulation program based on the cubic law. The research on the relationship between flow rates and model parameters, including the mesh number, practice length, aperture, model size, and pressure difference, were performed. The fitting formulas of the flow rates and model parameters were obtained by numerical simulation. Based on the experimental study on the stress permeability of the elastic coal sample and the persistent fracture coal sample, the fluid–solid coupling simulation parameters selection method of the initial joint and persistent fracture is presented. The numerical simulation results with the calculation parameters under triaxial equi-compressive and deviatoric loads are in good agreement with the experimental results, indicating that the method herein can well simulate the relationship between the stress and permeability of the coal samples.

Cross-Validation With Confidence

Cross-validation is one of the most popular model and tuning parameter selection methods in statistics and machine learning. Despite its wide applicability, traditional cross-validation methods tend to overfit, due to the ignorance of the uncertainty in the testing sample. We develop a novel statistically principled inference tool based on cross-validation that takes into account the uncertainty in the testing sample. This method outputs a set of highly competitive candidate models containing the optimal one with guaranteed probability. As a consequence, our method can achieve consistent variable selection in a classical linear regression setting, for which existing cross-validation methods require unconventional split ratios. When used for tuning parameter selection, the method can provide an alternative trade-off between prediction accuracy and model interpretability than existing variants of cross-validation. We demonstrate the performance of the proposed method in several simulated and real data examples. Supplemental materials for this article can be found online.

Tuning Parameter Selection Based on Blocked $$3\times 2$$ Cross-Validation for High-Dimensional Linear Regression Model

In high-dimensional linear regression, selecting an appropriate tuning parameter is essential for the penalized linear models. From the perspective of the expected prediction error of the model, cross-validation methods are commonly used to select the tuning parameter in machine learning. In this paper, blocked \(3\times 2\) cross-validation (\(3\times 2\) BCV) is proposed as the tuning parameter selection method because of its small variance for the prediction error estimation. Under some weaker conditions than leave-\(n_v\)-out cross-validation, the tuning parameter selection method based on \(3\times 2\) BCV is proved to be consistent for the high-dimensional linear regression model. Furthermore, simulated and real data experiments support the theoretical results and demonstrate that the proposed method works well in several criteria about selecting the true model.

APPLICATION OF QUANTITATIVE ANALYSIS OF GEAR FAULT IN μ-SVD NOISE REDUCTION ALGORITHM DIAGNOSIS

In order to extract the fault features of mechanical equipment submerged by strong background noise, a general subspace denoising algorithm based on Singular value decomposition (SVD) is used to process the signal, that is, μ-SVD denoising algorithm. This algorithm overcomes the disadvantage of traditional wavelet threshold denoising algorithm, which only deals with the wavelet coefficients point by point and ignores the whole structure of the wavelet coefficients. The traditional SVD denoising algorithm is a special case when Lagrange multiplier μ = 0 in μ-SVD denoising algorithm. μ-SVD denoising algorithm includes filter factor, which can suppress the contribution of singular value dominated by noise contribution to the signal after denoising. The parameter selection method of μ-SVD denoising algorithm is discussed, and the influence of denoising order and Lagrange multiplier on denoising effect is emphatically studied. The test results of gear fault simulation signal and gear fault vibration signal at the early stage show that μ-SVD denoising algorithm is better than traditional SVD denoising algorithm in denoising effect. It can extract gear fault features better under strong background noise.

Compressed and Penalized Linear Regression

Modern applications require methods that are computationally feasible on large datasets while retaining good statistical properties. Recent work has focused on developing fast and randomized approximations for solving least squares problems when the data are too large to fit into memory easily or when computations are at a premium. Many of these techniques rely on data-driven subsampling or random compression. In this article, we provide new approximate algorithms for solving penalized least squares problems which have improved statistical performance relative to existing methods. We provide the first efficient methods for tuning parameter selection, compare our methods with current approaches via simulation and application, and provide theoretical intuition which makes explicit the impact of approximation on statistical efficiency and demonstrates the necessity of careful parameter tuning. Supplementary materials for this article are available online.

Alternating direction method of multiplier for the unilateral contact problem with an automatic penalty parameter selection

We propose an alternating direction method of multiplier (ADMM) for the unilateral (frictionless) contact problem with an optimal parameter selection. We first introduce an auxiliary unknown to seprate the linear elasticity subproblem from the unilateral contact condition. Then an alternating direction is applied to the corresponding augmented Lagrangian. By eliminating the primal and auxiliary unknowns, at the discrete level, we derive a pure dual algorithm, starting point for the convergence analysis and the optimal parameter approximation. Numerical experiments are proposed to illustrate the efficiency of the proposed (optimal) penalty parameter selection method.

Variance-Preserving Estimation of Intensity Values Obtained From Omics Experiments.

Faced with the lack of reliability and reproducibility in omics studies, more careful and robust methods are needed to overcome the existing challenges in the multi-omics analysis. In conventional omics data analysis, signal intensity values (denoted by M and values) are estimated neglecting pixel-level uncertainties, which may reflect noise and systematic artifacts. For example, intensity values from two-color microarray data are estimated by taking the mean or median of the pixel intensities within the spot and then subjected to a within-slide normalization by LOWESS. Thus, focusing on estimation and normalization of gene expression profiles, we propose a spot quantification method that takes into account pixel-level variability. Also, to preserve relevant variation that may be removed in LOWESS normalization with poorly chosen parameters, we propose a parameter selection method that is parsimonious and considers intrinsic characteristics of microarray data, such as heteroskedasticity. The usefulness of the proposed methods is illustrated by an application to real intestinal metaplasia data. Compared with the conventional approaches, the analysis is more robust and conservative, identifying fewer but more reliable differentially expressed genes. Also, the variability preservation allowed the identification of new differentially expressed genes. Using the proposed approach, we have identified differentially expressed genes involved in pathways in cancer and confirmed some molecular markers already reported in the literature.