Tuning Parameter Values Research Articles

Muscle synergies for multidirectional isometric force generation during maintenance of upright standing posture

Muscle synergies are defined as coordinated recruitment of groups of muscles with specific activation balances and time profiles aimed at generating task-specific motor commands. While muscle synergies in postural control have been investigated primarily in reactive balance conditions, the neuromechanical contribution of muscle synergies during voluntary control of upright standing is still unclear. In this study, muscle synergies were investigated during the generation of isometric force at the trunk during the maintenance of standing posture. Participants were asked to maintain the steady-state upright standing posture while pulling forces of different magnitudes were applied at the level at the waist in eight horizontal directions. Muscle synergies were extracted by nonnegative matrix factorization from sixteen lower limb and trunk muscles. An average of 5-6 muscle synergies were sufficient to account for a wide variety of EMG waveforms associated with changes in the magnitude and direction of pulling forces. A cluster analysis partitioned the muscle synergies of the participants into a large group of clusters according to their similarity, indicating the use of a subjective combination of muscles to generate a multidirectional force vector in standing. Furthermore, we found a participant-specific distribution in the values of cosine directional tuning parameters of synergy amplitude coefficients, suggesting the existence of individual neuromechanical strategies to stabilize the whole-body posture. Our findings provide a starting point for the development of novel diagnostic tools to assess muscle coordination in postural control and lay the foundation for potential applications of muscle synergies in rehabilitation.

Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking

BackgroundHigh-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling.ResultsOur approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants.ConclusionsOur findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques.

Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA‐Based Surrogate for Spatial Fields

AbstractGlobal Climate Model tuning (calibration) is a tedious and time‐consuming process, with high‐dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand‐picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere‐only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient‐based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly‐tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high‐dimensional output and operating in a fraction of the time.

Temporal Sequential-Artificial Neural Network Enhancements for Improved Smart Lighting Control

Several previous studies have proposed a temporal sequential-artificial neural network (TS-ANN) to convert PIR Sensor movement data into presence data and improve the performance of smart lighting control. However, such a temporal-sequential forecasting concept has a curse of dimensionality problem. This study aims to proposes the application of principal component analysis with TS-ANN (PCA-TS-ANN) as an intelligent method for controlling smart lighting with low dimensions. We have primary data directly from a smart lighting implementation that utilizes PIR sensors. We apply cross-correlation to the original dataset to find the optimum time step. Then we discover the optimum TS-ANN based on selected tuning parameter values through PCC. We then design and compare scenarios involving the combination of TS-ANN and PCA. Finally, we evaluate these scenarios using the metrics Accuracy, Precision, Recall, F1− Score, and Delay. The results of this study are the PCA-TS-ANN model with Accuracy, Precision, Recall, and F1−Score value of 0.9993, 0.9997, 0.9994, and 0.9996 respectively. The PCA method reduces the TS-ANN features from 1200 features to 36 features. The model size has also decreased from 3534kB to 807kB. Our model has a simpler complexity with TS-ANN that the µ ± σ Delay is 0.27±0.06 ms versus 0.34±0.11 ms.

Synthesis of the Control Algorithm for Third-Order Object Models with Additional Zero and Time Delay

In this paper, the tuning procedure for the standardized PID controller for third-order object models with additional zero and time delay using the maximum stability degree method with iteration is analyzed. Analytical calculation expressions for the tuning parameters of the controller were obtained as nonlinear functions of the argument - the degree of system stability, and linear functions of the known parameters of the object model. By varying the degree of stability, these functions are graphically constructed, after which, for the same argument value, sets of controller tuning parameter values are chosen, and the performances of the automatic control system for the control object are verified. The proposed method was compared with other known methods and tested for tuning the PID controller for the temperature control in the extruder of a 3D printer. The advantages of the elaborated procedure were highlighted.

Improved Pathwise Coordinate Descent for Power Penalties

Pathwise coordinate descent algorithms have been used to compute entire solution paths for lasso and other penalized regression problems quickly with great success. They improve upon cold start algorithms by solving the problems that make up the solution path sequentially for an ordered set of tuning parameter values, instead of solving each problem separately. However, extending pathwise coordinate descent algorithms to more the general bridge or power family of penalties is challenging. Faster algorithms for computing solution paths for these penalties are needed because penalized regression problems can be nonconvex and especially burdensome to solve. In this article, we show that a reparameterization of penalized regression problems is more amenable to pathwise coordinate descent algorithms. This allows us to improve computation of the mode-thresholding function for penalized regression problems in practice and introduce two separate pathwise algorithms. We show that either pathwise algorithm is faster than the corresponding cold start alternative, and demonstrate that different pathwise algorithms may be more likely to reach better solutions. Supplemental materials for this article are available online.

Simultaneous tuning of multiple PID controllers for multivariable systems using deep reinforcement learning

Traditionally, tuning of PID controllers is based on linear approximation of the dynamics between the manipulated input and the controlled output. The tuning is performed one loop at a time and interaction effects between the multiple single-input-single-output (SISO) feedback control loops is ignored. It is also well-known that if the plant operates over a wide operating range, the dynamic behaviour changes thereby rendering the performance of an initially tuned PID controller unacceptable. The design of PID controllers, in general, is based on linear models that are obtained by linearizing a nonlinear system around a steady state operating point. For example, in peak seeking control, the sign of the process gain changes around the peak value, thereby invalidating the linear model obtained at the other side of the peak. Similarly, at other operating points, the multivariable plant may exhibit new dynamic features such as inverse response. This work proposes to use deep reinforcement learning (DRL) strategies to simultaneously tune multiple SISO PID controllers using a single DRL agent while enforcing interval constraints on the tuning parameter values. This ensures that interaction effects between the loops are directly factored in the tuning. Interval constraints also ensure safety of the plant during training by ensuring that the tuning parameter values are bounded in a stable region. Moreover, a trained agent when deployed, provides operating condition based PID parameters on the fly ensuring nonlinear compensation in the PID design. The methodology is demonstrated on a quadruple tank benchmark system via simulations by simultaneously tuning two PI level controllers. The same methodology is then adopted to tune PI controllers for the operating condition under which the plant exhibits a right half plane multivariable direction zero. Comparisons with PI controllers tuned with standard methods suggest that the proposed method is a viable approach, particularly when simulators are available for the plant dynamics.

Effects of Personalization on Gait-State Tracking Performance Using Extended Kalman Filters.

Emerging partial-assistance exoskeletons can enhance able-bodied performance and aid people with pathological gait or age-related immobility. However, every person walks differently, which makes it difficult to directly compute assistance torques from joint kinematics. Gait-state estimation-based controllers use phase (normalized stride time) and task variables (e.g., stride length and ground inclination) to parameterize the joint torques. Using kinematic models that depend on the gait-state, prior work has used an Extended Kalman filter (EKF) to estimate the gait-state online. However, this EKF suffered from kinematic errors since it used a subject-independent measurement model, and it is still unknown how personalization of this measurement model would reduce gait-state tracking error. This paper quantifies how much gait-state tracking improvement a personalized measurement model can have over a subject-independent measurement model when using an EKF-based gait-state estimator. Since the EKF performance depends on the measurement model covariance matrix, we tested on multiple different tuning parameters. Across reasonable values of tuning parameters that resulted in good performance, personalization improved estimation error on average by 8.5 ± 13.8% for phase (mean ± standard deviation), 27.2 ± 8.1% for stride length, and 10.5 ± 13.5% for ground inclination. These findings support the hypothesis that personalization of the measurement model significantly improves gait-state estimation performance in EKF based gait-state tracking (), which could ultimately enable reliable responses to faster human gait changes.

Application of step-response lambda tuning to proportional-integral controllers in water resource recovery facilities

Abstract Proportional-integral-derivative (PID) controllers in water resource recovery facilities (WRRFs) feedback control loops are commonplace. While simple to implement, such control loops are rarely tuned optimally or systematically. Heuristic tuning approaches are commonly applied with varying degrees of success using trial-and-error, ad hoc tuning rules, or duplication of tuning values from a similar system. However, there are effective methods, such as lambda tuning, produce acceptable tuning with limited effort. These are based on the step-response method, where a manual process perturbation is used to define the relationship between the manipulated and controlled variables. Based on such an experiment, a simple process model is constructed and used to determine the controller tuning values. In this work, we used the step-response method and lambda tuning for two control systems in full-scale WRRFs. This led to responsive and stable behavior of the controlled system as defined by the absolute average error of the controlled variable to setpoint and standard deviation of the manipulated variable. Tuning of feedback control loops can be completed successfully through a systematic approach, and this work suggests that tuning tools, like lambda, should be part of all wastewater treatment control engineers' toolbox.

Simultaneous Optimization of 20 Key Parameters of the Integrated Forecasting System of ECMWF Using OpenIFS. Part I: Effect on Deterministic Forecasts

Abstract Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. It is thus desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. Traditionally, the parameter tuning has been done manually, which can lead to the tuning process being a maze of subjective choices. In this paper we present how to optimize 20 key physical parameters in the atmospheric model Open Integrated Forecasting System (OpenIFS) that have a strong impact on forecast quality. The results show that simultaneous optimization of O(20) parameters is possible with O(100) algorithm steps using an ensemble of O(20) members; the results also show that the optimized parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimized model. We find that the optimization process is dependent on the starting values of the parameters that are optimized (starting from better-suited values results in a better model). The results show also that the applicability of the tuned parameter values across different model resolutions is somewhat limited because of resolution-dependent model biases, and we also found that the parameter covariances provided by the tuning algorithm seem to be uninformative. Significance Statement The purpose of this work is to show how to use algorithmic methods to optimize a weather model in a computationally efficient manner. Traditional manual model tuning is an extremely laborious and time-consuming process, so algorithmic methods have strong potential for saving the model developers’ time and accelerating development. This paper shows that algorithmic optimization is possible and that weather forecasts can be improved. However, potential issues related to the use of the optimized parameter values across different model resolutions are discussed as well as other shortcomings related to the tuning process.

Squirrel search algorithm based intelligent controller for interconnected power system

ABSTRACT In this paper, a novel controller, namely fractional order fuzzy proportional-integral-derivative (FOFPID) has been proposed for the dual area power system of hydro-thermal-gas units (DAHTG). The squirrel search algorithm (SSA) is implemented to achieve the optimal values of the controller tuning parameters. Further, various non-linearity constraints, such as governor dead band (GDB), generation rate constraint (GRC) and communication time delays (CTDs) have also been considered to test the effectiveness of the proposed controller in a real-time environment. The step load of 10% is provided as the disturbance under the regulation. In addition, the coordinated strategy of a static synchronous series compensator (SSSC) device and superconducting magnetic energy storage (SMES) is employed in the proposed system to enhance the system’s performance. Further, the accuracy, effectiveness and robustness of the proposed controller are revealed through the comparative analysis with classical PID and traditional fuzzy PID controllers. Furthermore, presented controller efficacy is revealed with the other recently reported control techniques by implementing it on the two-area thermal system with GDB. The comparative analysis shows the better performance of the proposed controller than these two. Finally, the sensitivity analysis is performed to check the controller robustness.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

Experimental investigations and modeling for multi-pass laser micro-milling by soft computing-physics informed machine learning on PMMA sheet using CO2 laser

Laser micro-machining has gained significant attraction from industries and researchers due to the wide range of processability and material flexibility with micro-scale accuracy. However, only a few process variables and soft computing approaches are taken into consideration in micro-scale sectors. This provides the opportunity to explore a new and relatively unexplored area of physics-informed advanced soft computing techniques for laser beam machining. This study aims to develop a framework to investigate the micro-milling capabilities of 10 mm thick poly-methyl-methacrylate (PMMA) using various input parameters such as laser power (8–16 W), scanning speed (25–50 mm/s), number of passes (2–6), and incident energy (0.107–0.64 J/mm). A soft computing technique to build depth, width and surface roughness prediction models on a CO2 laser machine, is presented. Advanced soft computing approaches such as random forest, gradient boost, ridge regression, linear regression, support vector regression and gaussian process regression are evaluated to predict the microchannel's depth, surface roughness, and kerf width. The proposed work is validated experimentally and compared with the different prediction/regression models available in the literature. The values of hyper tuning parameters are optimized using by grid search method. Based on the 5-fold cross-validation analysis, the most accurate predictions of the depth, surface roughness and kerf width could be achieved through the gaussian process regression (GPR) model with highest accuracy of 98.34 %, 97.68 % and 96.38 % for depth, surface roughness and kerf width respectively.

Using an evolutionary heterogeneous ensemble of artificial neural network and multivariate adaptive regression splines to predict bearing capacity in axial piles

Accurately estimating the bearing capacity of piles is an onerous task in structural design task that requires a powerful computation model able to elucidate nonlinear impacts of geotechnical factors and the dimension and shape of piles. This study develops a novel heterogeneous ensemble artificial intelligence model, named intelligence multivariate neural network inference model (IMNNIM), to accurately and quickly predict bearing capacity of piles. The IMNNIM was created by integrating the equilibrium optimization algorithm (EO) into a combination of multivariate adaptive regression splines (MARS) and radial basis neural network (RBFNN). The predictive values of the IMNNIM were qualified by dynamically merging prediction information generated by MARS and RBFNN and then adjusting the associated weights and assigned tuning parameter values of the learner members. The performance of the IMNNIM was evaluated using data from 472 driven pile static load test reports. The experimental results using a 10-fold cross-validation method demonstrated the IMNNIM to be the most reliable model for predicting pile-bearing-capacity by achieving the greatest values of MAPE (7.24%), RMSE (90.92kN), MAE (67.98kN), and R2 (0.930) and the lowest standard deviation values. A t-test analysis method confirmed the IMNNIM as a superior tool for pile-bearing-capacity estimation.

Four Circuit Transmission Line Location for Inter Circuit Faults Using Fuzzy Expert System

The inter circuit faults in four circuit power transmission line (FCPTL) have a significant influence on the conventional relay performance. Due to the special nature of faults, the protective relaying has been a challenging work. This paper investigates a fuzzy expert system (FES) approach for FCPTL to locate the inter circuit faults and to improve the accuracy of shunt faults location. This approach adopts the fundament component of current (FCC) of the FCPTL at only one end. With the goal of attaining improved inputs–outputs mapping capacity of FES for datasets, an optimization approach, i.e., Mamdani type, has been used for finding the optimal values of tuning parameters. The FES with If-Then rules has been framed for inter circuit faults location. The reported studies are performed in the LabVIEW platform using a 200 km, 500 kV, 50 Hz, FCPTL MATLAB test system. The MATLAB and LabVIEW simulation conform that the proposed approach can correctly locate faults considering various fault types and fault distances within the FCPTL. The proposed approach is easy to design and low cost for existing and new FCPTL installations.

Model-based clustering of high-dimensional longitudinal data via regularization.

We propose a model-based clustering method for high-dimensional longitudinal data via regularization in this paper. This study was motivated by the Trial of Activity in Adolescent Girls (TAAG), which aimed to examine multilevel factors related to the change of physical activity by following up a cohort of 783 girls over 10 years from adolescence to early adulthood. Our goal is to identify the intrinsic grouping of subjects with similar patterns of physical activity trajectories and the most relevant predictors within each group. The previous analyses conducted clustering and variable selection in two steps, while our new method can perform the tasks simultaneously. Within each cluster, a linear mixed-effects model (LMM) is fitted with a doubly penalized likelihood to induce sparsity for parameter estimation and effect selection. The large-sample joint properties are established, allowing the dimensions of both fixed and random effects to increase at an exponential rate of the sample size, with a general class of penalty functions. Assuming subjects are drawn from a Gaussian mixture distribution, model effects and cluster labels are estimated via a coordinate descent algorithm nested inside the Expectation-Maximization (EM) algorithm. Bayesian Information Criterion (BIC) is used to determine the optimal number of clusters and the values of tuning parameters. Our numerical studies show that the new method has satisfactory performance and is able to accommodate complex data with multilevel and/or longitudinaleffects.

Adaptive hybrid screening for efficient lasso optimization

Lasso-type models are a popular approach for analysing high-dimensional data. The size of modern data sets can be quite large, so developing efficient algorithms is important. Feature screening techniques have proven to be effective at increasing efficiency, as they allow for considerable dimension reduction during the optimization process. In this paper, we develop an adaptive hybrid screening framework where screening is carried out adaptively along the path of tuning parameter values, reusing previous solutions to reduce heavy screening computations if they fail to significantly reduce dimensionality. We focus on the standard lasso model and sparse logistic model, but the proposed framework is flexible and can be easily extended to different types of lasso models. Through experiments involving a wide variety of simulated and real data sets, we show that the adaptive hybrid methods significantly outperform other state-of-the-art methods, with the greatest speedup occurring in the most challenging scenarios.

Non-Fermi liquid behavior below the Néel temperature in the frustrated heavy fermion magnet UAu2

The term Fermi liquid is almost synonymous with the metallic state. The association is known to break down at quantum critical points (QCPs), but these require precise values of tuning parameters, such as pressure and applied magnetic field, to exactly suppress a continuous phase transition temperature to the absolute zero. Three-dimensional non-Fermi liquid states, apart from superconductivity, that are unshackled from a QCP are much rarer and are not currently well understood. Here, we report that the triangular lattice system uranium diauride (UAu2) forms such a state with a non-Fermi liquid low-temperature heat capacity [Formula: see text] and electrical resistivity [Formula: see text] far below its Néel temperature. The magnetic order itself has a novel structure and is accompanied by weak charge modulation that is not simply due to magnetostriction. The charge modulation continues to grow in amplitude with decreasing temperature, suggesting that charge degrees of freedom play an important role in the non-Fermi liquid behavior. In contrast with QCPs, the heat capacity and resistivity we find are unusually resilient in magnetic field. Our results suggest that a combination of magnetic frustration and Kondo physics may result in the emergence of this novel state.

Analysis of the Impact of Randomization of Search-Control Parameters in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) has been applied successfully in many domains, including games. However, its performance is not uniform on all domains, and it also depends on how parameters that control the search are set. Parameter values that are optimal for a task might be sub-optimal for another. In a domain that tackles many games with different characteristics, like general game playing (GGP), selecting appropriate parameter settings is not a trivial task. Games are unknown to the player, thus, finding optimal parameters for a given game in advance is not feasible. Previous work has looked into tuning parameter values online, while the game is being played, showing some promising results. This tuning approach looks for optimal parameter values, balancing exploitation of values that performed well so far in the search and exploration of less sampled values. Continuously changing parameter values while performing the search, combined also with exploration of multiple values, introduces some randomization in the process. In addition, previous research indicates that adding randomization to certain components of MCTS might increase the diversification of the search and improve the performance. Therefore, this article investigates the effect of randomly selecting values for MCTS search-control parameters online among predefined sets of reasonable values. For the GGP domain, this article evaluates four different online parameter randomization strategies by comparing them with other methods to set parameter values: online parameter tuning, offline parameter tuning and sub-optimal parameter choices. Results on a set of 14 heterogeneous abstract games show that randomizing parameter values before each simulation has a positive effect on the search in some of the tested games, with respect to using fixed offline-tuned parameters. Moreover, results show a clear distinction between games for which online parameter tuning works best and games for which online randomization works best. In addition, the overall performance of online parameter randomization is closer to the one of online parameter turning than the one of sub-optimal parameter values, showing that online randomization is a reasonable parameter selection strategy. When analyzing the structure of the search trees generated by agents that use the different parameters selection strategies, it is clear that randomization causes MCTS to become more explorative, which is helpful for alignment games that present many winning paths in their trees. Online parameter tuning, instead, seems more suitable for games that present narrow winning paths and many losing paths.

Reliable Model Selection without Reference Values by Utilizing Model Diversity with Prediction Similarity.

Predictive modeling (calibration or training) with various data formats, such as near-infrared (NIR) spectra and quantitative structure-activity relationship (QSAR) data, provides essential information if a proper model is selected. Similarly, with a general model selection approach, spectral model maintenance (updating) from original modeling conditions to new conditions can be performed for dynamic modeling. Fundamental modeling (partial least-squares (PLS) and others) and maintenance processes (domain adaptation or transfer learning and others) require selection of tuning parameter(s) values to isolate models that can accurately predict new samples or molecules, e.g., number of PLS latent variables to predict analyte concentration. Regardless of the modeling task, model selection is complex and without a reliable protocol. Tuning parameter selection typically depends on only one model quality measure assessing model bias using prediction accuracy. Developed in this paper is a generic model selection process using concepts from consensus modeling and QSAR activity landscapes. It is a consensus filtering approach that prioritizes model diversity (MD) while conserving prediction similarity (PS) fused with a common bias-variance trade-off measure. A significant feature of MDPS is that a cross-validation scheme is not needed because models are selected relative to predicting new samples or molecules, i.e., model selection uses unlabeled samples (without reference values) for active predictions. The versatility and reliability of MDPS model selection is shown using four NIR data sets and a QSAR data set. The study also substantiates the Rashomon effect where there is not one best model tuning parameter value that provides accurate predictions.