True Inputs Research Articles

Input reconstruction in systems with unknown nonlinearities

This paper presents an approach for reconstructing the histories of inputs of known spatial distributions in systems with nonlinearities of unknown dependence on the state and unknown domain locations. The scheme treats the nonlinearities as a pseudo force extension and is thus limited to cases where the rank of the pseudo-forces plus the number of unknown inputs does not exceed the number of outputs. The approach estimates a basis for the inputs and pseudo-forces, combined, and separates them (to within an arbitrary transformation of the pseudo forces) by assuming orthogonality and enforcing the known spatial distribution of the inputs. If the assumed orthogonality is not realized in the physical problem error from spillover of the pseudo-forces to some of the inputs takes place. A guarantee that this error is always small is not given but examination under reasonable assumptions suggests that its expected value, relative to the norm of the true inputs, is acceptably small. A requirement for implementation of the approach is that the output is sufficiently long to allow formulation of multiple independent, or quasi-independent, realizations. Numerical results suggest reasonable robustness to measurement noise and to discrepancies between the reference linear model and the true tangential behavior at the origin.

False Data Injection Attacks and the Distributed Countermeasure in DC Microgrids

In this article, we consider a hierarchical control-based dc microgrid (DCmG) equipped with unknown input observer (UIO)-based detectors, where the potential false data injection (FDI) attacks and the distributed countermeasure are investigated. First, we find that the vulnerability of the UIO-based detector originates from the lack of knowledge of true inputs. Zero trace stealthy (ZTS) attacks can be launched by secretly faking the unknown inputs, under which the detection residual will not be altered, and the impact on the DCmG in terms of voltage balancing and current sharing is theoretically analyzed. Then, to mitigate the ZTS attack, we propose an automatic and timely countermeasure based on the average point of common coupling voltage obtained from the dynamic average consensus (DAC) estimator. The integrity of the communicated data utilized in DAC estimators is guaranteed via UIO-based detectors, where the DAC parameters are perturbed in a fixed period to be concealed from attackers. Finally, the detection and mitigation performance of the proposed countermeasure is rigorously investigated, and extensive simulations are conducted in Simulink/PLECS to validate the theoretical results.

Bayesian Optimisation vs. Input Uncertainty Reduction

Simulators often require calibration inputs estimated from real-world data, and the estimate can significantly affect simulation output. Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution. One remedy is to search for the solution that has the best performance on average over the uncertain range of inputs yielding an optimal compromise solution. We consider the more general setting where a user may choose between either running simulations or querying an external data source, improving the input estimate and enabling the search for a more targeted, less compromised solution. We explicitly examine the trade-off between simulation and real data collection to find the optimal solution of the simulator with the true inputs. Using a value of information procedure, we propose a novel unified simulation optimisation procedure called Bayesian Information Collection and Optimisation that, in each iteration, automatically determines which of the two actions (running simulations or data collection) is more beneficial. We theoretically prove convergence in the infinite budget limit and perform numerical experiments demonstrating that the proposed algorithm is able to automatically determine an appropriate balance between optimisation and data collection.

Simultaneous state and unknown input set‐valued observers for quadratically constrained nonlinear dynamical systems

AbstractIn this article, we propose fixed‐order set‐valued (in the form of ‐norm hyperballs) observers for several classes of quadratically constrained nonlinear dynamical systems with unknown input signals that simultaneously/jointly find bounded hyperballs of states and unknown inputs that include the true states and inputs. Necessary and sufficient conditions in the form of linear matrix inequalities (LMIs) for the stability (in the sense of quadratic stability) of the proposed observers are derived for ()‐quadratically constrained (()‐QC) systems, which includes several classes of nonlinear systems: (I) Lipschitz continuous, (II) ()‐QC* and (III) linear parameter‐varying (LPV) systems. This new quadratic constraint property is at least as general as the incremental quadratic constraint property for nonlinear systems and is proven in the paper to embody a broad range of nonlinearities. In addition, we design the optimal observer among those that satisfy the quadratic stability conditions and show that the design results in uniformly bounded‐input bounded‐state (UBIBS) estimate radii/error dynamics and uniformly bounded sequences of the estimate radii. Furthermore, we provide closed‐form upper bound sequences for the estimate radii and sufficient conditions for their convergence to steady state. Finally, the effectiveness of the proposed set‐valued observers is demonstrated through illustrative examples, where we compare the performance of our observers with some existing observers.

Efficient Algorithm for Secure Outsourcing of Modular Exponentiation with Single Server

Outsourcing computation allows an outsourcer with limited resource to delegate the computation load to a powerful server without the exposure of true inputs and outputs. It is well known that modular exponentiation is one of the most expensive operations in public key cryptosystems. Currently, most of outsourcing algorithms for modular exponentiation are based on two untrusted servers or have small checkability with single server. In this paper, we first propose an efficient outsourcing algorithm of modular exponentiation based on two untrusted servers, where the outsourcer can detect the error based on Euler theorem with a probability of 1 if one of the servers misbehaves. We then present an outsourcing algorithm of modular exponentiation with single server, and the outsourcer can also check the failure with a probability of 1. Therefore, the proposed algorithm with single server improves efficiency and checkability simultaneously compare with the previous ones. Finally, we provide the experimental evaluations to demonstrate that the proposed two algorithms are the most efficient ones in all of the outsourcing algorithms for an outsourcer.

Output modifier adaptation with filter-based constraints

Modifier adaptation (MA) and output modifier adaptation (MAy) are iterative model-based real-time optimization (RTO) algorithms that have the proven ability to drive plants to their optimal operating condition upon convergence despite disturbances and modeling uncertainty, provided the model at hand satisfies model adequacy conditions. But there is no guarantee that constraints are satisfied before convergence. In this article, an improvement of the formulation of MA and MAy is proposed that is proven to bring significant improvements w.r.t. these two limitations – model adequacy and feasibility of iterates. While standard MA or MAy suggests to perform optimization and filtering sequentially, it is proposed to integrate the input filtering stage in the modified model-based optimization problem by means of additional filter-based constraints. The corresponding approach, labeled “KMAy”, is (i) proven to preserve constraint qualification despite additional constraints, (ii) proven to preserve the property of MA methods to converge to the true plant optimal inputs, (iii) proven to significantly relax the model adequacy condition - leading it to be independent of the constraints of the optimization problem, (iv) shown to increase the chances of converging from the safe side of the plant constraints and (v) shown to support the choice of input filtering, instead of output or modifier filtering, if the input filter is appropriately chosen. A method for the automatic selection of the largest filter gain with the five aforementioned assets, while minimizing the filter-induced conservatism, is also proposed. The performances of KMAy with and without adaptive gain are successfully illustrated by means of the optimization of a benchmark simulated chemical reactor.

Estimating Koopman Invariant Subspaces of Excited Systems Using Artificial Neural Networks

In recent years, the Koopman operator was the topic of many extensive investigations in the nonlinear system identification community. Especially, when dealing with nonlinear systems no straight forward method is available to identify systems of this class. In modern data science a standard method is using artificial neural networks to extract models from data. This method is mainly used when there is a certain function behind the measured data, but little other information is available. This paper combines the Koopman framework and artificial neural networks to achieve a linear model for nonlinear systems. The structure of the network is similar to an autoencoder. The input part is the encoder which itself consists of two different parts. The first part propagates the measurements directly to the middle part. The second encoder introduces the state-space lifting which characterizes the Koopman framework. The middle layer of the network represents an estimation of a linear state-space system that acts on a Koopman operator invariant subspace. After this layer, the extended state-space must be decoded so that the outputs of the Koopman linear system are functions of the true states. The method is evaluated with a single pendulum and a nonlinear yeast glycolysis model. Additionally, we show the advantage of considering inputs as true inputs rather than additional states.

Clock Delayed Dual Keeper Domino Logic Design with Reduced Switching

Clock Delayed Dual Keeper domino logic style with Static Switching mechanism (CDDK_SS) using delayed enabling of the keeper circuit and modified discharge path has been proposed in this paper. In CDDK domino circuit, the principle of delayed enabling of keeper circuit offers reduced contention between keeper circuit and Pull Down Network (PDN). The modified discharge path at the output node eradicates the switching at the output node for identical TRUE inputs during the pre-charge phase. This facilitates in obtaining static like output in contrast with conventional domino logic. The simulation results of Arithmetic and Logic Unit (ALU) subsystems demonstrate 17.7% reduction in dynamic power consumption while compared to conventional domino logic. Furthermore, 62% enhancement in speed performance has been achieved with good robustness. Design and simulation have been executed using Cadence® Virtuoso, with UMC 90nm technology node library.

Properties of ladder tournaments

Ladder tournaments use work trials to screen workers for positions in organizations. Despite being common in many industries, their properties remain poorly understood, especially in organizations characterized by complex complementarities and externalities in their production process. In this study, we formalize the ranking rule generated by a ladder tournament and derive measures of relative performance that can be used to compare workers when their true inputs cannot be observed. We show that this rule is reflexive but not generally transitive, unless the organization has fewer than three layers of hierarchy or is complete. It follows that complete organizations have an embedded merit-based hierarchical structure. We also study the relationship between a worker’s tournament rank and his utility or usefulness as measured by the frequency with which he plays a pivotal role. We show that a worker’s likelihood of being pivotal is a weakly increasing function of his rank. This finding implies that in complete organizations, ladder tournaments discriminate more effectively between workers than do mechanisms based on pivotability or marginal productivity.

Optimal air route flight conflict resolution based on receding horizon control

To deal with the air route flight conflicts caused by abnormal situations during four-dimensional based air traffic operation, the air route flight conflict resolution problem for two aircrafts was addressed. Based on the optimized static single heading angle or ground speed adjustment strategy, we proposed an optimized dynamic mixed conflict resolution strategy based on Receding Horizon Control (RHC), which considered the possibility of one aircraft's ground speed variation. In particular, the wind speed vector disturbance during conflict resolution might lead to a model mismatch, so the maximum likelihood estimation method and the Newton–Raphson iterative algorithm were employed to identify the wind speed vector using the aircraft true airspeed inputs and ground based trajectory measurements. Moreover, we considered the convergence of conflict resolution method based on RHC and proposed the pre-condition that local and global optimization resolution can be achieved. We compared three situations, i.e., static single strategy optimization, dynamic mixed strategy optimization using RHC with one aircraft's ground speed variation, and dynamic mixed strategy optimization using RHC with the wind speed vector disturbance. We demonstrated that the dynamic mixed strategy responds to one aircraft's ground speed disturbance quickly, and resolved conflict by track angle and ground speed adjustments in an effective manner after the wind speed vector being identified accurately.

Cooperative Multi-Cell MIMO Downlink Precoding With Finite-Alphabet Inputs

This work investigates the design of linear precoders in cooperative multi-cell MIMO downlink coverage for finite-alphabet source signals. Traditional design of multi-cell MIMO downlink precoder relies on Gaussian input assumption, which may lead to performance loss when true data inputs consist of discrete non-Gaussian symbols. This work presents optimized precoders for finite-alphabet input by maximizing the sum rate under per-base station power constraints. Specifically, we propose two distributed algorithms: a finite-alphabet signal Gaussian interference gradient projection algorithm and a block diagonalization alternating optimization algorithm while supporting interference cancellation. Our numerical results demonstrate considerable performance gain in terms of approximate transmission data rate as well as decoded bit error rate over precoding schemes designed by using the non-realistic Gaussian input assumption.

Neurovascular deconvolution of optical signals as a proxy for the true neuronal inputs

Since the Kalman filter and Monte Carlo techniques, much theoretical work has been put into the development of signal deconvolution tools. Among recent developments taking place in neuroscience are Dynamic Expectation Maximization, Generalized Filtering and the Cubature Kalman Filter. While there are exciting prospects to use these tools for Dynamic Causal Modeling and other analyses of networks, there has been comparatively little work to validate the algorithms on controlled experimental data. In this work, the latest evolution of these tools, the square-root cubature Kalman smoother (SCKS), is tested for its effectiveness on multimodal neurovascular data. Multispectral intrinsic optical imaging and electrophysiological measurements of Wistar rats are used in combination with somatosensory stimulation. The Buxton–Friston (B–F) balloon model is then deconvolved with the SCKS algorithm to obtain the estimated neuronal inputs u(t) from the hemodynamic measurements (flow, oxy- and deoxygenated hemoglobin). ResultsThe estimated neuronal inputs are compared to the stimulation protocol and a sensitivity and specificity analysis is carried out. SCKS succeeds in recovering most of the stimulations. Next, the estimated inputs are compared to actual measures of neuronal activity: local field potentials (LFPs) and multiunit activity (MUA). Good sensitivity of the technique is obtained with both LFPs and MUA over the whole recordings, with the area of the ROC curves favoring LFPs. A weak correlation between SCKS estimated inputs and LFPs is found outside stimulation periods, significant at one standard deviation. Finally, the accuracy of state reconstructions is studied and SCKS reconstructed states are highly concordant with measured states.

Land-use-accessibility model: A theoretical approach to capturing land-use influence on vehicular flows through configurational measures of spatial networks

Conventional traffic models are cost intensive, and they occasionally suffer for not availing true trip-distribution inputs. At the same time, space syntax traffic models are less cost intensive, but they do not typically factor the influence of land use on movement in any systematic way. In this paper, a theoretical rationale has been proposed showing how the influence of land use on movement can be captured through configurational measures of spatial networks. Using the unit-segment approach to modeling vehicular movement networks (a recent development in space syntax transportation research, that offers a fairly strong post-diction of vehicular flows), the paper shows that roadway segments with high configurational measures get polarized in certain areas of an urban structure, where the trip-generation rates of land uses are also high. This new configurational measure of land-use accessibility is termed trip-integration. Conclusions suggest that, using the algorithm of trip-integration, the unit-segment model is expected to produce vehicular travel demands even with higher accuracy than its previous outcomes.

Managing Structural Uncertainty in Health Economic Decision Models: A Discrepancy Approach

SummaryHealthcare resource allocation decisions are commonly informed by computer model predictions of population mean costs and health effects. It is common to quantify the uncertainty in the prediction due to uncertain model inputs, but methods for quantifying uncertainty due to inadequacies in model structure are less well developed. We introduce an example of a model that aims to predict the costs and health effects of a physical activity promoting intervention. Our goal is to develop a framework in which we can manage our uncertainty about the costs and health effects due to deficiencies in the model structure. We describe the concept of ‘model discrepancy’: the difference between the model evaluated at its true inputs, and the true costs and health effects. We then propose a method for quantifying discrepancy based on decomposing the cost-effectiveness model into a series of subfunctions, and considering potential error at each subfunction. We use a variance-based sensitivity analysis to locate important sources of discrepancy within the model to guide model refinement. The resulting improved model is judged to contain less structural error, and the distribution on the model output better reflects our true uncertainty about the costs and effects of the intervention.

Construction of direction selectivity in V1: from simple to complex cells

AbstractDespite detailed knowledge about the anatomy and physiology of the primary visual cortex (V1), the immense number of feed-forward and recurrent connections onto a given V1 neuron make it difficult to understand how the physiological details relate to a given neuron’s functional properties. Here, we focus on a well-known functional property of many V1 complex cells: phase-invariant direction selectivity (DS). While the energy model explains its construction at the conceptual level, it remains unclear how the mathematical operations described in this model are implemented by cortical circuits. To understand how DS of complex cells is constructed in cortex, we apply a nonlinear modeling framework to extracellular data from macaque V1. We use a modification of spike-triggered covariance (STC) analysis to identify multiple biologically plausible "spatiotemporal features" that either excite or suppress a cell. We demonstrate that these features represent the true inputs to the neuron more accurately, and the resulting nonlinear model compactly describes how these inputs are combined to result in the functional properties of the cell. In a population of 59 neurons, we find that both simple and complex V1 cells are selective to combinations of excitatory and suppressive motion features. Because the strength of DS and simple/complex classification is well predicted by our models, we can use simulations with inputs matching thalamic and simple cells to assess how individual model components contribute to these measures. Our results unify experimental observations regarding the construction of DS from thalamic feed-forward inputs to V1: based on the differences between excitatory and inhibitory inputs, they suggest a connectivity diagram for simple and complex cells that sheds light on the mechanism underlying the DS of cortical cells. More generally, they illustrate how stage-wise nonlinear combination of multiple features gives rise to the processing of more abstract visual information.

Influence of biomass, throughput and true electric power input on the separation efficiency of a 60 mL acoustic filter

A close-to-production separation system was used to assess the influence of certain process parameters on the separation performance of an acoustic filter based on the Ultrasonically Enhanced Settling technique. Measurements of the cell concentration of the first 80 mL of yeast suspension that left the filter were conducted at various throughputs and true electrical power inputs. Moreover, the number of cells/mL was ranging over two orders of magnitude. Results delivered an upper limit for the true electrical power input from which an increase does not improve the separation efficiency and a lower threshold of cells/mL below which too less cells were available for the principle to work.

Throughfall fluxes in a secondary spruce ( Picea abies), a beech ( Fagus sylvatica) and a mixed spruce–beech stand

Bulk precipitation, throughfall (TF) and stemflow (SF, for beech only) were monitored from May 02 to October 03 in adjacent stands of pure spruce, pure beech and mixed spruce–beech in Lower Austria. The main objective of this study was to specify regulating factors of TF and to address the impact of tree species composition on atmospheric input. We were especially interested, whether components of TF in the mixed stand indicate non-linear effects that would not be obvious in the single species stands. The mean annual nitrogen (NH 4 + NO 3) and sulfur (SO 4) TF (+SF) fluxes were in the low to moderate range, increasing from the beech (N: 9.1 versus S: 5.5 kg ha −1) over the mixed (N: 11.6 versus S: 6.1 kg ha −1) to the spruce stand (N: 16.6 versus S: 9.2 kg ha −1). Two different methods were used to separate net throughfall (NTF) into the dry deposition (DD) and canopy exchange (CE) components. The regression analysis method on the event-to-event variation in NTF was useful for explaining the regulating factors for H + and K but failed for other substances, since we used multiple rain event collections, which mixed up both components. According to these results it is concluded that cation leaching was mainly driven by organic anions and bicarbonate but insignificantly by a cation exchange reaction with H +, what may be considered a natural process in the absence of heavy acidic deposition. The filtering approach method, applying sulfate instead of widely used sodium as tracer for calculation of DD, yielded reasonable estimates. This model revealed that during the dry summer 03 DD rates were much higher than during the wet summer 02 for all studied elements. However, canopy leaching rates of K, Ca and Mg showed the opposite trend, since this process is mainly controlled by the amount of precipitation. Calculated total deposition (TD) fluxes as estimates of true inputs to the system were different from measured TF fluxes and justify the conclusion that the mixed spruce–beech stand showed an approximately linear response for H +, K, NH 4, NO 3, SO 4, Cl and PO 4, if compared to the TD fluxes of the single species stands, but an antagonist response for Ca and Mg, since TD fluxes of these cations were almost identical for the beech and the mixed stand.

Understanding the Characteristics of Mass Spectrometry Data through the use of Simulation

BackgroundMass spectrometry is actively being used to discover disease-related proteomic patterns in complex mixtures of proteins derived from tissue samples or from easily obtained biological fluids. The potential importance of these clinical applications has made the development of better methods for processing and analyzing the data an active area of research. It is, however, difficult to determine which methods are better without knowing the true biochemical composition of the samples used in the experiments.MethodsWe developed a mathematical model based on the physics of a simple MALDI-TOF mass spectrometer with time-lag focusing. Using this model, we implemented a statistical simulation of mass spectra. We used the simulation to explore some of the basic operating characteristics of MALDI or SELDI instruments.ResultsThe simulation reproduced several characteristics of actual instruments. We found that the relative mass error is affected by the time discretization of the detector (about 0.01%) and the spread of initial velocities (about 0.1%). The accuracy of calibration based on external standards decays rapidly outside the range spanned by the calibrants. Natural isotope distributions play a major role in broadening peaks associated with individual proteins. The area of a peak is a more accurate measure of its size than the height.ConclusionsThe model described here is capable of simulating realistic mass spectra. The simulation should become a useful tool for generating spectra where the true inputs are known, allowing researchers to evaluate the performance of new methods for processing and analyzing mass spectra.Availabilityhttp://bioinformatics.mdanderson.org/cromwell.html

Fuzzy Three-valued Switching Functions Using Choquet Integral

Fuzzy three-valued switching functions have true, false and unknown inputs and outputs values. The functions are calculated by Choquet integral whose fuzzy measure values are assigned from normal three-valued switching functions. The functions are extension of normal three-valued functions and satisfy the monotonicity with respect to vagueness.

Cochlear and Neural Delays for Coincidence Detection in Owls

The auditory system uses delay lines and coincidence detection to measure the interaural time difference (ITD). Both axons and the cochlea could provide such delays. The stereausis theory assumes that differences in wave propagation time along the basilar membrane can provide the necessary delays, if the coincidence detectors receive input from fibers innervating different loci on the left and right basilar membranes. If this hypothesis were true, the left and right inputs to coincidence detectors should differ in their frequency tuning. The owl's nucleus laminaris contains coincidence detector neurons that receive input from the left and right cochlear nuclei. Monaural frequency-tuning curves of nucleus laminaris neurons showed small interaural differences. In addition, their preferred ITDs were not correlated with the interaural frequency mismatches. Instead, the preferred ITD of the neuron agrees with that predicted from the distribution of axonal delays. Thus, there is no need to invoke mechanisms other than neural delays to explain the detection of ITDs by the barn owl's laminaris neurons.