Input Variance Research Articles

Energy Saving in Biaxial Feed Drive Systems Using Adaptive Sliding Mode Contouring Control with a Nonlinear Sliding Surface

Energy saving and producing highly accurate products are major essential demands in computer numerical control (CNC) machines. These machines operate all day and night for a long time, therefore they largely consume energy all over the world. This paper proposes adaptive sliding mode contouring control (ASMCC) with a nonlinear sliding surface (NSS) to reduce consumed energy and further improve machining accuracy for a biaxial feed drive system. The control gain of the proposed scheme is formulated to be mainly relied on a contour error. Once the contour error is changed, the control gain is adjusted simultaneously to generate appropriate control signal. ASMCC constantly reduces consumed energy and improves machining accuracy by reducing the resultant contour error. In order to verify the effectiveness of ASMCC, simulation and experiment are carried out on a biaxial feed drive system using a circular trajectory. Results show that the adaptive algorithm significantly reduces the mean contour error by 37.62% and 34.65% compared to sliding mode control (SMCC) without any additional energy on simulation and experiment, respectively. In addition, the proposed approach reduces experimentally consumed energy and control input variance by 8.20% and 17.29%, respectively.

MV bound and MV controller for convex‐non‐linear systems with input constraints

To assess the performance of a control loop based on the minimum variance (MV) benchmark, we need to calculate MV lower bound (MVLB). Even though there is a plethora of literature available for calculating MVLB for the linear systems, these methods are not suitable for non-linear systems. Furthermore, almost all of the real-world applications have been encountered with input variance constraints. These constraints limit controllers' abilities in decreasing the output variability. Therefore, existing MVLB computation methods, which do not account for input constraints, are not realistic when applied to constrained systems. The authors propose a novel approach to estimate MVLB by employing properties of dual Lagrangian functions to address these issues simultaneously in this study. Furthermore, to design the constrained non-linear MV controller (MVC), they propose to use the recurrent neural network for accommodating non-linearities and the input constraints. Then, control loop stability, optimality with respect to MVLB as well as the global convergence of the proposed controller are analytically proved for convex-non-linear systems with input constraints. The proposed control strategy is verified through simulations performed on a non-linear quadruple-tank system. The results indicate that the proposed design provides satisfactory results in decreasing output variance while satisfying the constraints.

Multilevel Delta Modulation with Switched First-Order Prediction for Wideband Speech Coding

In this paper a delta modulation speech coding scheme based on the ITU-T G.711 standard and the switched first-order predictor is presented. The forward adaptive scheme is used, where the adaptation to the signal variance is performed on frame-by-frame basis. The classification of the frames into weakly and highly correlated was done based on the correlation coefficient calculated for each frame, providing a basis for choosing the appropriate predictor coefficient. The obtained results indicate that the proposed model significantly outperforms the scalar companding system based on the G.711 standard. The obtained experimental results were verified using the theoretical model in the wide dynamic range of the input variance. DOI: http://dx.doi.org/10.5755/j01.eie.24.1.20156

Assessing sustainability of farming systems in mountain agroecosystems of Western Himalaya, India

ABSTRACTThis paper examines sustainability of cash crops production systems as compared to traditional cereal cropping patterns in terms of their energy efficiency, ecological suitability, economic profitability, and social acceptability. The consistent supply of labor and energy are the most significant requirements for substantial growth of agricultural productivity in Himalayan region. For output–input ratio in terms of monetary efficiency, cash crops performed better (3.16–3.56) as compared to traditional crops (1.57–3.31), whereas the energy output–input ratio of traditional crops was found more efficient (1.63–4.72) as compared to cash crops (1.26–2.36). The monetary input and output analysis of Rabi crops was estimated to be 23.39% and 25.3% of Kharif crops, respectively; Similarly, variance in energy input (27.3%) and output (32.44%) analysis was observed between Rabi and Kharif crops, respectively. Energy input in the form of manure accounted >90% of total energy input, while other inputs (<10%) are contributed in the form of seeds, and animal and human power for all the crops. The study also highlighted the significance of traditional crops and factors responsible for their declining cultivation in the mountain region.

Analysis of fMRI data using noise-diffusion network models: a new covariance-coding perspective.

Since the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input-output mapping-determined by EC-for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns? An application with the model fitted to experimental fMRI data-movie viewing versus resting state-illustrates that changes in local variability and changes in brain coordination go hand in hand.

Performance of Quasi-logarithmic Quantizer for Discrete Input Signal

In this paper, performance of quasi-logarithmic quantizer, designed for correlated discrete input signal is analyzed. Quantizer design is done for Laplacian source due to its both hardware and software significance, whereas experiments are done by processing test wideband speech signal sampled at 16 [kHz]. The quantizer is exploited as a second stage of two-stage quantization system, where the first step is used for continuous signal sampling, while the second stage provides additional data compression. The main goal is to provide improved design by discussing theoretical performance of two quantization models. As the traditional models for performance estimation provide estimation of average performance, we have decided to propose a novel model for performance estimation and to analyze performance in details for each random input signal variance. Finally, the experimental results have shown excellent matching with theoretical results. DOI: http://dx.doi.org/10.5755/j01.itc.46.3.16197

A Lagrangian fluctuation–dissipation relation for scalar turbulence. Part II. Wall-bounded flows

We derive here Lagrangian fluctuation–dissipation relations for advected scalars in wall-bounded flows. The relations equate the dissipation rate for either passive or active scalars to the variance of scalar inputs from the initial values, boundary values and internal sources, as those are sampled backward in time by stochastic Lagrangian trajectories. New probabilistic concepts are required to represent scalar boundary conditions at the walls: the boundary local-time density at points on the wall where scalar fluxes are imposed and the boundary first hitting time at points where scalar values are imposed. These concepts are illustrated both by analytical results for the problem of pure heat conduction and by numerical results from a database of channel-flow turbulence, which also demonstrate the scalar mixing properties of near-wall turbulence. As an application of the fluctuation–dissipation relation, we examine for wall-bounded flows the relation between anomalous scalar dissipation and Lagrangian spontaneous stochasticity, i.e. the persistent non-determinism of Lagrangian particle trajectories in the limit of vanishing viscosity and diffusivity. In Part I of this series, we showed that spontaneous stochasticity is the only possible mechanism for anomalous dissipation of passive or active scalars, away from walls. Here it is shown that this remains true when there are no scalar fluxes through walls. Simple examples show, on the other hand, that a distinct mechanism of non-vanishing scalar dissipation can be thin scalar boundary layers near the walls. Nevertheless, we prove for general wall-bounded flows that spontaneous stochasticity is another possible mechanism of anomalous scalar dissipation.

A Lagrangian fluctuation–dissipation relation for scalar turbulence. Part I. Flows with no bounding walls

An exact relation is derived between scalar dissipation due to molecular diffusivity and the randomness of stochastic Lagrangian trajectories for flows without bounding walls. This ‘Lagrangian fluctuation–dissipation relation’ equates the scalar dissipation for either passive or active scalars to the variance of scalar inputs associated with initial scalar values and internal scalar sources, as these are sampled backward in time by the stochastic Lagrangian trajectories. As an important application, we reconsider the phenomenon of ‘Lagrangian spontaneous stochasticity’ or persistent non-determinism of Lagrangian particle trajectories in the limit of vanishing viscosity and diffusivity. Previous work on the Kraichnan (Phys. Fluids, 1968, vol. 11, pp. 945–953) model of turbulent scalar advection has shown that anomalous scalar dissipation is associated in that model with Lagrangian spontaneous stochasticity. There has been controversy, however, regarding the validity of this mechanism for scalars advected by an actual turbulent flow. We here completely resolve this controversy by exploiting the fluctuation–dissipation relation. For either a passive or an active scalar advected by any divergence-free velocity field, including solutions of the incompressible Navier–Stokes equation, and away from walls, we prove that anomalous scalar dissipation requires Lagrangian spontaneous stochasticity. For passive scalars, we prove furthermore that spontaneous stochasticity yields anomalous dissipation for suitable initial scalar fields, so that the two phenomena are there completely equivalent. These points are illustrated by numerical results from a database of homogeneous isotropic turbulence, which provide both additional support to the results and physical insight into the representation of diffusive effects by stochastic Lagrangian particle trajectories.

Performance limitations for LTI discrete-time systems in the presence of stochastic disturbance

ABSTRACTThis paper concerns the fundamental limitations of disturbance rejection for linear time-invariant discrete-time systems under the assumption that the sensor noise is ignorable. The environmental disturbance is modelled as a linear combination of a step signal, a stationary stochastic signal and several sinusoidal signals with different frequencies. Disturbance rejection is measured by the steady-state variance of the residual input of the plant. In studying this problem, it is shown that for minimum phase systems, perfect disturbance rejection can be achieved by minimising the closed-loop sensitivity function. Unlike the minimum phase systems, the performance limitations for non-minimum phase systems are lower bounded by its non-minimum phase zeros, unstable poles and characteristic quantities of the disturbance, where the typical and integral-type control are respectively addressed. Numerical examples are provided to illustrate the utility of the results by analysing the achievable performance.

Experience-dependent homeostasis of 'noise' at inhibitory synapses preserves information coding in adult visual cortex.

Synapses are intrinsically 'noisy' in that neurotransmitter is occasionally released in the absence of an action potential. At inhibitory synapses, the frequency of action potential-independent release is orders of magnitude higher than that at excitatory synapses raising speculations that it may serve a function. Here we report that the frequency of action potential-independent inhibitory synaptic 'noise' (i.e. miniature inhibitory postsynaptic currents, mIPSCs) is highly regulated by sensory experience in visual cortex. Importantly, regulation of mIPSC frequency is so far the predominant form of functional plasticity at inhibitory synapses in adults during the refractory period for plasticity and is a locus of rapid non-genomic actions of oestrogen. Models predict that regulating the frequency of mIPSCs, together with the previously characterized synaptic scaling of miniature excitatory PSCs, allows homeostatic maintenance of both the mean and variance of inputs to a neuron, a necessary feature of probabilistic population codes. Furthermore, mIPSC frequency regulation allows preservation of the temporal profile of neural responses while homeostatically regulating the overall firing rate. Our results suggest that the control of inhibitory 'noise' allows adaptive maintenance of adult cortical function in tune with the sensory environment.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.

Factors Associated with Production Input Difference of a Manufacturing Plant in Sri Lanka: A Case Study

The supply chain is a system of organizations, peoples, activities, information, and resources involved in moving a product or service from supplier to customer. As the whole supply chain is linked together, any inconsistency in one link can badly affect the overall supply chain. Each organization in the supply chain has its own internal individual supply chains. The internal supply chain is mainly based on the production demand and material supply to the production. Any inconsistency between the demand and the supply, directly affects the status of internal supply chain. Only few studies have been done on internal supply demand variance, and this study is one of the few approaches into this area. The main objective of this study is to identify the factors associated with production input difference of a manufacturing plant. This is an explanatory research, which is done using appropriate sampling methods and Vector Auto regressive (VAR) modeling. Eviews (7.0.0.1) version is used to analyze the data. First of all the data has been checked for stationary property and the related lag length has been selected. Then the VAR modeling techniques has been applied and later the diagnostic tests have been performed on the resulted models. In briefing the results, it is stated that style of the product (Style) does not impact the input variance models or downtime models. Considering input variance models, it is found that downtime at lag 1 does not have any impact on the input variance. Furthermore, the previous day input variance has a significant impact to the next day input variance. The style and previous day downtime influence the demand variance only in special cases. As heteroskedasticity is present in some of the models, exponential & power transformations have been done in order to avoid heteroskedasticity. But the results do not dramatically change due to transformations.

入出力誤差の分散と共分散の推定を用いた一入力一出力システムに対するパラメータ同定法

A new algorithm is proposed to estimate parameters of SISO (Single-Input Single-Output) system. The algorithm is an expansion of the previously proposed algorithms by the author with iterative estimation of the variances of noises. The previously proposed algorithms are based on the assumption that input and output noises have no correlation. Whereas, the method proposed in this paper is available even if input and output noises have correlation. This algorithm uses an iterative calculation and it consists of two parts. One part is the estimation of the system parameters by use of the variance and covariance matrix of input and output noises. The estimation of the system parameters is one of the least squares identification algorithms using eigenvector. The other part is the estimation of the variances and covariances of input and output noises using the system parameters. The variances and covariance of noises are estimated by solving linear simultaneous equations derived from the system parameters of transfer functions. The simulation results show the effectiveness of the proposed method.

Laser resurfacing of Al–Zn–Mg–Si coatings

The feasibility of refinement of Al–Zn–Mg–Si hot dip coatings by diode laser resurfacing has been demonstrated. The microstructure of the laser resurfaced coatings was characterised mainly by transmission electron microscopy and also by scanning electron microscopy. The variance of heat input results in different cooling rates and varying degrees of refinement; that is, the higher the power density, the lower the cooling rate and the larger the secondary dendrite arm spacing of α-Al dendrites. In addition, the inherent variation of chemical composition in coatings also affects the microstructure as well as phase distribution. This laser resurfacing technique has not only reduced the secondary dendrite arm spacing/α-Al dendrites but also changed the solidification path and produce different phases and microstructure in the interdendritic areas.

Methods for global sensitivity analysis in life cycle assessment

PurposeInput parameters required to quantify environmental impact in life cycle assessment (LCA) can be uncertain due to e.g. temporal variability or unknowns about the true value of emission factors. Uncertainty of environmental impact can be analysed by means of a global sensitivity analysis to gain more insight into output variance. This study aimed to (1) give insight into and (2) compare methods for global sensitivity analysis in life cycle assessment, with a focus on the inventory stage.MethodsFive methods that quantify the contribution to output variance were evaluated: squared standardized regression coefficient, squared Spearman correlation coefficient, key issue analysis, Sobol’ indices and random balance design. To be able to compare the performance of global sensitivity methods, two case studies were constructed: one small hypothetical case study describing electricity production that is sensitive to a small change in the input parameters and a large case study describing a production system of a northeast Atlantic fishery. Input parameters with relative small and large input uncertainties were constructed. The comparison of the sensitivity methods was based on four aspects: (I) sampling design, (II) output variance, (III) explained variance and (IV) contribution to output variance of individual input parameters.Results and discussionThe evaluation of the sampling design (I) relates to the computational effort of a sensitivity method. Key issue analysis does not make use of sampling and was fastest, whereas the Sobol’ method had to generate two sampling matrices and, therefore, was slowest. The total output variance (II) resulted in approximately the same output variance for each method, except for key issue analysis, which underestimated the variance especially for high input uncertainties. The explained variance (III) and contribution to variance (IV) for small input uncertainties were optimally quantified by the squared standardized regression coefficients and the main Sobol’ index. For large input uncertainties, Spearman correlation coefficients and the Sobol’ indices performed best. The comparison, however, was based on two case studies only.ConclusionsMost methods for global sensitivity analysis performed equally well, especially for relatively small input uncertainties. When restricted to the assumptions that quantification of environmental impact in LCAs behaves linearly, squared standardized regression coefficients, squared Spearman correlation coefficients, Sobol’ indices or key issue analysis can be used for global sensitivity analysis. The choice for one of the methods depends on the available data, the magnitude of the uncertainties of data and the aim of the study.

An analytic method for sensitivity analysis of complex systems

Sensitivity analysis is concerned with understanding how the model output depends on uncertainties (variances) in inputs and then identifies which inputs are important in contributing to the prediction imprecision. Uncertainty determination in output is the most crucial step in sensitivity analysis. In the present paper, an analytic expression, which can exactly evaluate the uncertainty in output as a function of the output's derivatives and inputs' central moments, is firstly deduced for general multivariate models with given relationship between output and inputs in terms of Taylor series expansion. A $\gamma$-order relative uncertainty for output, denoted by $\mathrm{R^{\gamma}_v}$, is introduced to quantify the contributions of input uncertainty of different orders. On this basis, it is shown that the widely used approximation considering the first order contribution from the variance of input variable can satisfactorily express the output uncertainty only when the input variance is very small or the input-output function is almost linear. Two applications of the analytic formula are performed to the power grid and economic systems where the sensitivity of both actual power output and Economic Order Quantity models are analyzed. The importance of each input variable in response to the model outputs is quantified by the analytic formula.

Brightness in human rod vision depends on neural adaptation to the quantum statistics of light

In cone-mediated vision, light adaptation obeys Weber's law (constant visual response to constant contrast). Weber adaptation begins in the cones themselves and is well suited to support color constancy. In rod-mediated vision, the visual input consists of a retinal rain of discrete photons. Individual rods do not receive a sufficient number of light quanta within their integration time to light adapt. Instead, light adaptation occurs in post-receptoral circuitry. The nature of this adaptation is still poorly understood. Since the pioneering work of Barlow (1956, 1958), it has been known that psychophysical rod thresholds, when measured with small, brief test probes, increase in proportion to the square-root of adapting intensity, rather than in direct proportion to intensity (Weber's law). A bedrock idea of classical psychophysics ascribes the square-root law to the masking of dim threshold flashes by the naturally occurring variability in the photon rain from the adapting field (Shapley & Enroth-Cugell, 1975). Importantly, this classical explanation does not require actual physiological adaptation and, classically, none was assumed. Here, we present the results of three brightness matching experiments that together demonstrate that the square-root law arises from a neural adaptation (monocular gain control), driven by statistical photon inputs, that influences brightness as well as threshold. The experiments were performed in a haploscope with one eye's adaptive state manipulated and the other eye used as a reference. Our results show that: 1) brightness gain for supra-threshold flashes varies inversely with the standard deviation of the quantal fluctuations from the adapting field; 2) brightness gain is controlled by both the mean and variance of time-integrated photon inputs and changes only slowly (adaptation takes a few minutes). These properties suggest that brightness at low light levels evolved to encode the reliability (signal-to-noise ratio) of sparse statistical light inputs, rather than either luminance or contrast. Meeting abstract presented at VSS 2016

Testing the Sampling-Based NUSS-RF Tool for the Nuclear Data—Related Global Sensitivity Analysis with Monte Carlo Neutronics Calculations

NUSS-RF is a tool for nuclear data uncertainty propagation through neutronics calculations with continuous-energy Monte Carlo codes and ACE-formatted nuclear data libraries. Many existing codes, including the original version of NUSS (Nuclear data Uncertainty Stochastic Sampling), are based on simple random sampling algorithms. The NUSS-RF extension now uses a frequency-based sampling algorithm, called the random balance design (RBD), to analyze individual nuclear data uncertainty contributions in regard to the total output (e.g., keff) uncertainty. The implementation of the RBD method into NUSS-RF is initially verified by comparing the computed individual input variance contributions with analytical solutions for two analytical test cases. As well, it is assessed against the alternative approach based on the use of correlation coefficients. NUSS-RF is then used for an analysis of the Jezebel and Godiva fast-spectrum criticality benchmarks: in a first step, the overall effect of the 239Pu(n,f) and 235U(n,f) cross-section uncertainties on keff is evaluated, while in a second step, the contributions from the individual energy groups are quantified. As an additional verification, the NUSS-RF results are assessed against sensitivity and uncertainty analysis based on perturbation theory, showing good agreement between the two solutions. Finally, the capability of NUSS-RF is demonstrated for ranking the input parameters with respect to their influence on the total uncertainty of the output parameters, taking into account possible correlations between input parameters. Possible future improvements for the current computational scheme are discussed in the conclusions.

Impact of clinical input variable uncertainties on ten-year atherosclerotic cardiovascular disease risk using new pooled cohort equations.

BackgroundRecently released American College of Cardiology/American Heart Association (ACC/AHA) guideline recommends the Pooled Cohort equations for evaluating atherosclerotic cardiovascular risk of individuals. The impact of the clinical input variable uncertainties on the estimates of ten-year cardiovascular risk based on ACC/AHA guidelines is not known.MethodsUsing a publicly available the National Health and Nutrition Examination Survey dataset (2005–2010), we computed maximum and minimum ten-year cardiovascular risks by assuming clinically relevant variations/uncertainties in input of age (0–1 year) and ±10 % variation in total-cholesterol, high density lipoprotein- cholesterol, and systolic blood pressure and by assuming uniform distribution of the variance of each variable. We analyzed the changes in risk category compared to the actual inputs at 5 % and 7.5 % risk limits as these limits define the thresholds for consideration of drug therapy in the new guidelines. The new-pooled cohort equations for risk estimation were implemented in a custom software package.ResultsBased on our input variances, changes in risk category were possible in up to 24 % of the population cohort at both 5 % and 7.5 % risk boundary limits. This trend was consistently noted across all subgroups except in African American males where most of the cohort had ≥7.5 % baseline risk regardless of the variation in the variables.ConclusionsThe uncertainties in the input variables can alter the risk categorization. The impact of these variances on the ten-year risk needs to be incorporated into the patient/clinician discussion and clinical decision making. Incorporating good clinical practices for the measurement of critical clinical variables and robust standardization of laboratory parameters to more stringent reference standards is extremely important for successful implementation of the new guidelines. Furthermore, ability to customize the risk calculator inputs to better represent unique clinical circumstances specific to individual needs would be highly desirable in the future versions of the risk calculator.Electronic supplementary materialThe online version of this article (doi:10.1186/s12872-016-0352-x) contains supplementary material, which is available to authorized users.

Inverse Stochastic Resonance in Cerebellar Purkinje Cells.

Purkinje neurons play an important role in cerebellar computation since their axons are the only projection from the cerebellar cortex to deeper cerebellar structures. They have complex internal dynamics, which allow them to fire spontaneously, display bistability, and also to be involved in network phenomena such as high frequency oscillations and travelling waves. Purkinje cells exhibit type II excitability, which can be revealed by a discontinuity in their f-I curves. We show that this excitability mechanism allows Purkinje cells to be efficiently inhibited by noise of a particular variance, a phenomenon known as inverse stochastic resonance (ISR). While ISR has been described in theoretical models of single neurons, here we provide the first experimental evidence for this effect. We find that an adaptive exponential integrate-and-fire model fitted to the basic Purkinje cell characteristics using a modified dynamic IV method displays ISR and bistability between the resting state and a repetitive activity limit cycle. ISR allows the Purkinje cell to operate in different functional regimes: the all-or-none toggle or the linear filter mode, depending on the variance of the synaptic input. We propose that synaptic noise allows Purkinje cells to quickly switch between these functional regimes. Using mutual information analysis, we demonstrate that ISR can lead to a locally optimal information transfer between the input and output spike train of the Purkinje cell. These results provide the first experimental evidence for ISR and suggest a functional role for ISR in cerebellar information processing.

Gaussian Process Regression for Fingerprinting based Localization

In this paper, Gaussian process regression (GPR) for fingerprinting based localization is presented. In contrast to general regression techniques, the GPR not only infers the posterior received signal strength (RSS) mean but also the variance at each fingerprint location. The GPR does take into account the variance of input i.e., noisy RSS data. The hyper-parameters of GPR are estimated using trust-region-reflective algorithm. The Cramér-Rao bound is analysed to highlight the performance of the parameter estimator. The posterior mean and variance of RSS data is utilized in fingerprinting based localization. The principal component analysis is employed to choose the k strongest wi-fi access points (APs). The performance of the proposed algorithm is validated using using real field deployments. Accuracy improvements of 10% and 30% are observed in two sites compared to the Horus fingerprinting approach.