Exact Input Research Articles

Optimal input design for uncertain max‐plus linear systems

SummaryThis paper investigates the optimal input design for uncertain max‐plus linear systems whose parameters are only known to belong to certain intervals. The exact interval input is introduced to minimize the regulating range of the input that ensures that the system can output at any specified value in the desired interval regardless of how the parameters vary in certain ranges. The optimal input design for uncertain max‐plus linear systems is formulated as an optimal control problem that finds the maximum element of the set of exact interval inputs with respect to the order of max‐plus interval vectors. This paper also considers the optimal approximate interval input of an uncertain max‐plus linear system that has no exact interval input. A polynomial algorithm is developed to find the optimal interval input and optimal approximate interval input of an uncertain max‐plus linear system. A data transmission system with parameter perturbations is employed as an example to demonstrate how the proposed results work in practical applications.

The PSLQ algorithm for empirical data

The celebrated integer relation finding algorithm PSLQ has been successfully used in many applications. PSLQ was only analyzed theoretically for exact input data, however, when the input data are irrational numbers, they must be approximate ones due to the finite precision of the computer. When the algorithm takes empirical data (inexact data with error bounded) instead of exact real numbers as its input, how do we theoretically ensure the output of the algorithm to be an exact integer relation? In this paper, we investigate the PSLQ algorithm for empirical data as its input. Firstly, we give a termination condition for this case. Secondly, we analyze a perturbation on the hyperplane matrix constructed from the input data and hence disclose a relationship between the accuracy of the input data and the output quality (an upper bound on the absolute value of the inner product of the exact data and the computed integer relation), which naturally leads to an error control strategy for PSLQ. Further, we analyze the complexity bound of the PSLQ algorithm for empirical data. Examples on transcendental numbers and algebraic numbers show the meaningfulness of our error control strategy.

Linear Model With Exact Inputs and Interval-Valued Fuzzy Outputs

This paper considers the classical semiparametric partially linear model with exact inputs and interval-valued fuzzy outputs. For this purpose, the most commonly used classical two-phase procedure is extended to estimate an interval-valued fuzzy smooth function using nonparametric kernel methods at phase 1 and a least absolute deviation method at phase 2 to estimate the interval-valued fuzzy coefficients. A potential application of the proposed method is presented by a simulated data in hydrology study and an applied example. The proposed interval-valued semiparametric partially linear model is also examined to compare with the interval-valued fuzzy linear regression model via some extended goodness-of-fit criteria into the space of interval-valued fuzzy numbers.

Survey of duplicate detection using progressive detection techniques

Data is an important task in real world; the common data is represented and used in all the fields. The duplicate data is executed and displayed in scenario. The proposed work two types of techniques used first one Progressive Sort Neighbourhood Method (PSNM) and Progressive Blocking (PB). Progressive Sort Neighbourhood Method is used to deliver the exact input based output and the method will separate the input based keywords and check the similarity of the output data. The progressive blocking is to filter the irrelevant information, keywords based indexing and entry level filtering standard input is implemented based on user requirement.

Neural correlates of sine-wave speech intelligibility in human frontal and temporal cortex

Auditory speech comprehension is the result of neural computations that occur in a broad network that includes the temporal lobe auditory cortex and the left inferior frontal cortex. It remains unclear how representations in this network differentially contribute to speech comprehension. Here, we recorded high-density direct cortical activity during a sine-wave speech (SWS) listening task to examine detailed neural speech representations when the exact same acoustic input is comprehended versus not comprehended. Listeners heard SWS sentences (pre-exposure), followed by clear versions of the same sentences, which revealed the content of the sounds (exposure), and then the same SWS sentences again (post-exposure). Across all three task phases, high-gamma neural activity in the superior temporal gyrus was similar, distinguishing different words based on bottom-up acoustic features. In contrast, frontal regions showed a more pronounced and sudden increase in activity only when the input was comprehended, which corresponded with stronger representational separability among spatiotemporal activity patterns evoked by different words. We observed this effect only in participants who were not able to comprehend the stimuli during the pre-exposure phase, indicating a relationship between frontal high-gamma activity and speech understanding. Together, these results demonstrate that both frontal and temporal cortical networks are involved in spoken language understanding, and that under certain listening conditions, frontal regions are involved in discriminating speech sounds.

The peripheral nervous system of the ascidian tadpole larva: Types of neurons and their synaptic networks.

Physical and chemical cues from the environment are used to direct animal behavior through a complex network of connections originating in exteroceptors. In chordates, mechanosensory and chemosensory neurons of the peripheral nervous system (PNS) must signal to the motor circuits of the central nervous system (CNS) through a series of pathways that integrate and regulate the output to motor neurons (MN); ultimately these drive contraction of the tail and limb muscles. We used serial-section electron microscopy to reconstruct PNS neurons and their hitherto unknown synaptic networks in the tadpole larva of a sibling chordate, the ascidian, Ciona intestinalis. The larva has groups of neurons in its apical papillae, epidermal neurons in the rostral and apical trunk, caudal neurons in the dorsal and ventral epidermis, and a single tail tip neuron. The connectome reveals that the PNS input arises from scattered groups of these epidermal neurons, 54 in total, and has three main centers of integration in the CNS: in the anterior brain vesicle (which additionally receives input from photoreceptors of the ocellus), the motor ganglion (which contains five pairs of MN), and the tail, all of which in turn are themselves interconnected through important functional relay neurons. Some neurons have long collaterals that form autapses. Our study reveals interconnections with other sensory systems, and the exact inputs to the motor system required to regulate contractions in the tail that underlie larval swimming, or to the CNS to regulate substrate preference prior to the induction of larval settlement and metamorphosis.

Necessary and sufficient conditions for power convergence rate of approximations in Tikhonov’s scheme for solving ill-posed optimization problems

We investigate a rate of convergence of estimates for approximations generated by Tikhonov’s scheme for solving ill-posed optimization problems with smooth functionals under a structural nonlinearity condition in a Hilbert space, in the cases of exact and noisy input data. In the noise-free case, we prove that the power source representation of the desired solution is close to a necessary and sufficient condition for the power convergence estimate having the same exponent with respect to the regularization parameter. In the presence of a noise, we give a parameter choice rule that leads for Tikhonov’s scheme to a power accuracy estimate with respect to the noise level.

Semi-parametric partially logistic regression model with exact inputs and intuitionistic fuzzy outputs

Some accounting studies have focused on logistic regression relationships between exact/fuzzy inputs/outputs. However, intuitionistic fuzzy sets find application in many real studies instead of fuzzy sets. On the other hand, semi-parametric partially linear model also has attracted attentions in recent years. This study is an investigation of intuitionistic fuzzy semi-parametric partially logistic model for such cases with exact inputs, intuitionistic fuzzy outputs, intuitionistic fuzzy smooth function and intuitionistic fuzzy coefficients. For this purpose, a hybrid procedure is suggested based on curve fitting methods and least absolutes deviations to estimate the intuitionistic fuzzy smooth function and intuitionistic fuzzy coefficients. The proposed method is also compared with a common fuzzy logistic regression model as a real fuzzy data set. It is shown that the proposed intuitionistic fuzzy logistic regression model performs better and efficient results in regard to some goodness-of-fit criteria suggest that the proposed model could be successfully applied in many practical studies of intuitionistic fuzzy logistic regression model in expert systems.

Delayed unknown input observers for discrete-time linear systems with guaranteed performance

In this paper, we propose a state and unknown input observer for discrete-time linear systems with bounded unknown inputs and measurement disturbances. The design procedure is formulated using a set of linear matrix inequalities, and leverages delayed (or fixed-lag) estimates. The observer error states and/or user-defined performance outputs are guaranteed to operate at certain performance bounds. Furthermore, by employing sufficiently large delays, the observer is guaranteed to provide exact asymptotic state and input estimates for minimum-phase systems. We demonstrate, via numerical examples, that the proposed observer can be used for a wider class of systems than those satisfying matching conditions.

Growth and physical properties of ZnO rod arrays that integrally match the geometries of photoresists with hole sizes larger than rods' habit diameters

Bottom-up and top-down methods can be combined to create well-designed two-dimensional rod arrays for use in optoelectronics. Toward this end, the bottom-up material growth should follow the exact input of the top-down surface patterning, for example, the growth morphologies should integrally match to the geometries of the patterning. Through the repeated use of appropriate amounts of sodium citrate and polyethylenimine, ZnO rod arrays, in which each rod integrally matched with restriction holes in the surfaces, were formed even when the diameters of restriction holes were much larger than the original habit diameters of the ZnO rods. The diameters and lengths of the ZnO rods were varied by adjusting the diameters of photoresist holes, the concentrations of precursors in solution, and the reaction time. This method was successfully extended the diameter of integrally matching growth of ZnO rods to micrometer scale. The effects of the restriction diameters, the concentration of adjuvants, and the crystallinity of the seeds on integrally matching growth of ZnO rods are discussed. The photoluminescence spectra and crystallinities of the ZnO rods produced in different environments were characterized, revealing that a smaller photoresist aperture improved crystallization quality. The photoluminescence spectra of ZnO rods with different growth times and adjuvant concentrations were also explored. Fibrous ZnO/Cu2O heterostructures were successfully synthesized via the growth of ZnO rods. The electrical properties of ZnO/Cu2O and ZnO rods/Si heterojunctions were investigated, and the crystallinities of the ZnO rods were shown to change based on the different synthesis conditions used. This method for the integrally matching growth of ZnO using photoresist patterns was also generalized to ZnO tubes and rings, which individually match to single restriction hole.

Reconstruction of a source domain from boundary measurements

Inverse and ill-posed problems which consist of reconstructing the unknown support of a source from a single pair of exterior boundary Cauchy data are investigated. The underlying dependent variable, e.g. potential, temperature or pressure, may satisfy the Laplace, Poisson, Helmholtz or modified Helmholtz partial differential equations (PDEs). For constant coefficients, the solutions of these elliptic PDEs are sought as linear combinations of explicitly available fundamental solutions (free-space Greens functions), as in the method of fundamental solutions (MFS). Prior to this application of the MFS, the free-term inhomogeneity represented by the intensity of the source is removed by the method of particular solutions. The resulting transmission problem then recasts as that of determining the interface between composite materials. In order to ensure a unique solution, the unknown source domain is assumed to be star-shaped. This in turn enables its boundary to be parametrized by the radial coordinate, as a function of the polar or, spherical angles. The problem is nonlinear and the numerical solution which minimizes the gap between the measured and the computed data is achieved using the Matlab toolbox routine lsqnonlin which is designed to minimize a sum of squares starting from an initial guess and with no gradient required to be supplied by the user. Simple bounds on the variables can also be prescribed. Since the inverse problem is still ill-posed with respect to small errors in the data and possibly additional ill-conditioning introduced by the spectral feature of the MFS approximation, the least-squares functional which is minimized needs to be augmented with regularizing penalty terms on the MFS coefficients and on the radial function for a stable estimation of these couple of unknowns. Thorough numerical investigations are undertaken for retrieving regular and irregular shapes of the source support from both exact and noisy input data.

HFES: A Height Function Method with Explicit Input and Signed Output for High-Order Estimations of Curvature and unit Vectors of Planar Curves

The author proposes a height function (HF) method for estimating curvature and unit vectors of planar curves. The new method is named HFES since its signature consists of explicit input parameters (instead of implicit volume fractions) and signed output estimates. Due to this signature and its formal description, HFES has a number of appealing advantages. First, the convergence rate of HFES can be 4, 6, or a greater even integer. Second, local arcs can be rotated to further reduce leading truncation errors. Third, the sampling size of estimation is completely independent from the Eulerian grid size of the main flow. Fourth, signs of the signed output can be conveniently chosen by orientations of input Jordan curves, e.g., surface tension of a tracked material can be determined in a simple and worry-free manner. Last, HFES is applicable both to numerical simulation of multiphase flows and shape analysis in computer imaging. Furthermore, the author analyzes the effect of input perturbations upon output estimates, derives expressions of the best attainable accuracy afforded by a given input on uniform grids, and explains how to achieve this best accuracy with a concrete example. Results of numerical experiments with both exact and inexact input demonstrate that HFES can be much more accurate than previous HF methods. For the vortex-shear test of a circular disk, the eighth-order HFES coupled with the recent cubic iPAM method is millions of times more accurate than a fourth-order HF method coupled with volume-of-fluid methods! This drastic accuracy improvement is mainly due to the purely explicit spline representation of the curve and the rotation of local arcs.

Estimating Fast Neural Input Using Anatomical and Functional Connectivity

In the last 20 years there has been an increased interest in estimating signals that are sent between neurons and brain areas. During this time many new methods have appeared for measuring those signals. Here we review a wide range of methods for which connected neurons can be identified anatomically, by tracing axons that run between the cells, or functionally, by detecting if the activity of two neurons are correlated with a short lag. The signals that are sent between the neurons are represented by the activity in the neurons that are connected to the target population or by the activity at the corresponding synapses. The different methods not only differ in the accuracy of the signal measurement but they also differ in the type of signal being measured. For example, unselective recording of all neurons in the source population encompasses more indirect pathways to the target population than if one selectively record from the neurons that project to the target population. Infact, this degree of selectivity is similar to that of optogenetic perturbations; one can perturb selectively or unselectively. Thus it becomes possible to match a given signal measurement method with a signal perturbation method, something that allows for an exact input control to any neuronal population.

An effective series expansion to the equation of state of unitary Fermi gases

Using universal properties and a basic statistical mechanical approach, we propose a general equation of state for unitary Fermi gases. The universal equation of state is written as a series solution to a self consistent integral equation where the general solution is a linear combination of Fermi functions. First, by truncating our series solution to four terms with already known exact theoretical inputs at limiting cases, namely the first three virial coefficients and using the Bertsch parameter as a free parameter, we find a good agreement with experimental measurements in the entire temperature region in the normal state. This analytical equation of state agrees with experimental data up to the fugacity z = 18, which is a vast improvement over the other analytical equations of state available where the agreements is only up to . Second, by truncating our series solution to four terms again using first four virial coefficients, we find the Bertsch parameter , which is in good agreement with the direct experimental measurement of . This second form of equation of state shows a good agreement with self-consistent T-matrix calculations in the normal phase.

스크린도어(PSD)의 개폐율에 따른 피난소요시간 분석에 관한 연구

Platform Screen Door System (PSD)는 지하철 승객의 편의 및 안전을 위해 설치되기 시작하였고, 현재 전국 9개 운영기관이 관리하고 있는 824개 역사 중 전체의 71.8%에 해당하는 592개 역이 PSD를 운영 중에 있다. 본 연구에서는 전동차 정차 위치에 따른 PSD와 전동차 가동문 사이의 개폐율 변화를 분석하였고, 수도권 지하철을 운행 중인 기관에서 제공받은 재차인원 자료 분석 및 피난시뮬레이션을 통해 각 역사별 피난소요시간을 산정하였다. 연구결과 승객이 매우 혼잡하게 밀집되는 일부 고밀도 역사에서는 PSD 개폐율에 따라 매우 위험한 상황이 초래될 수 있음을 알 수 있었다. 특히 정위치에서 7 m 벗어난 곳에 정차할 경우 PSD의 고정 안전보호벽과 맞물려 열차 출입문 절반 이상이 폐쇄되는 것으로 분석되었다. 이러한 문제점을 개선하기 위해 기(旣) 설치된 PSD 고정안전보호벽을 모두 개폐 가능한 비상문으로 교체 및 개량하는 것을 제안한다. The PSD (Platform Screen Door System) has been installed to enhance the passengers' safety. A total of 592 stations operate the PSD system, which is almost 71.8% of all stations in South Korea. This study compared the opening rate between the PSD and train door, and calculated the exact amount of passengers at peak time. In addition, the evacuation time was simulated by Pathfinder 2015 with the exact input data. Some of the high density stations have extremely high dangerous points about the passengers' evacuation at some situations by the PSD door opening rate. In particular, due to the interference of a fixed door, when it stops at 7 m less than the regular position, its opening rate becomes less than half of the normal state. To solve this problem, it should be made possible to open the fixed door by changing it to an emergency door or improving the PSD module system.

Multi-objective Optimization of Welding Parameters in MMAW for Nano-structured Hardfacing Material Using GRA Coupled with PCA

Hardfacing is one of the most productive and practical approaches to cut down operating expense on the maintenance front and at the same time to improve performance and reliability of the equipment. Presence of nano-particles in hard facing materials significantly enhances surface area to volume ratio and accordingly it improves conductivity, hardness, heat and wear resistant properties. The main objective of this paper is to efficiently apply manual metal arc welding process for hardfacing of nano-structure based electrode. The most important process variables that have been considered in conducting the experiments are welding current, arc voltage and welding speed; while the response parameters include weld bead width, reinforcement and bead hardness, respectively. Taguchi’s (L25) orthogonal array has been used to perform the experimental runs. A combination of grey relational analysis coupled with principal component analysis has been applied to identify optimal settings of the input process parameters. Moreover, the exact input and output welding parameters have been examined with the help of genetic algorithm. Finally, confirmation test has also been carried out with the optimal welding process parameters to validate the experiment result.

Relating observability and compressed sensing of time-varying signals in recurrent linear networks

In this paper, we study how the dynamics of recurrent networks, formulated as general dynamical systems, mediate the recovery of sparse, time-varying signals. Our formulation resembles the well-described problem of compressed sensing, but in a dynamic setting. We specifically consider the problem of recovering a high-dimensional network input, over time, from observation of only a subset of the network states (i.e., the network output). Our goal is to ascertain how the network dynamics may enable recovery, even if classical methods fail at each time instant. We are particularly interested in understanding performance in scenarios where both the input and output are corrupted by disturbance and noise, respectively. Our main results consist of the development of analytical conditions, including a generalized observability criterion, that ensure exact and stable input recovery in a dynamic, recurrent network setting.

Flatness-based feedforward control of polymer electrolyte membrane fuel cell gas conditioning system

Manufacturers of automotive applications rely on high-performance testing environments for the development of polymer electrolyte membrane fuel cell (PEMFC) technologies. The main component of a PEMFC testbed is the gas conditioning system, which controls the inlet gas temperature, stack pressure, relative humidity and mass flow of the gas at the inlet of the PEMFC stack. This paper presents a control concept for a highly dynamic PEMFC gas conditioning system. The main control challenge lies in the decoupling of the governing thermodynamic quantities. Therefore, the nonlinear control concept of exact input–output linearisation with an extended PID control structure is applied. Constraints on the actuators are incorporated by formulating the resulting control law as an optimisation problem. The robustness with respect to parameter uncertainties for the model is shown by investigating the trajectory tracking error of the disturbed system. Simulation results show an overall good performance for the trajectory tracking and demonstrate the robustness of the model against parameter uncertainties.

A Robustness Study of State-of-the-Art Surrogate Weights for MCDM

A vast number of methods for solving multi-criteria decision problems have been suggested for assessing criteria weights requiring more exact input data than users normally are able to provide. In particular, the selection of adequate criteria weights is difficult and in order to be realistic, other methods must be introduced. One class of such methods is to introduce so called surrogate weights, where numerical weights are assigned to each criterion based on a cardinal or ordinal rank ordering, assumed to represent the information extracted from the user. One essential problem is the robustness of such methods. In this article, we compare state-of-the-art methods based on surrogate weights from the literature and, utilising a simulation approach, discuss underlying assumptions and robustness properties. This results in a quantitative measurement of these weighting methods and a methodology applicable also to forthcoming methods.

Conservative reliability-based design optimization method with insufficient input data

Reliability analysis and reliability-based design optimization (RBDO) require an exact input probabilistic model to obtain accurate probability of failure (PoF) and RBDO optimum design. However, often only limited input data is available to generate the input probabilistic model in practical engineering problems. The insufficient input data induces uncertainty in the input probabilistic model, and this uncertainty forces the PoF to be uncertain. Therefore, it is necessary to consider the PoF to follow a probability distribution. In this paper, the probability of the PoF is obtained with consecutive conditional probabilities of input distribution types and parameters using the Bayesian approach. The approximate conditional probabilities are obtained under reasonable assumptions, and Monte Carlo simulation is applied to calculate the probability of the PoF. The probability of the PoF at a user-specified target PoF is defined as the conservativeness level of the PoF. The conservativeness level, in addition to the target PoF, will be used as a probabilistic constraint in an RBDO process to obtain a conservative optimum design, for limited input data. Thus, the design sensitivity of the conservativeness level is derived to support an efficient optimization process. Using numerical examples, it is demonstrated that the conservativeness level should be involved in RBDO when input data is limited. The accuracy and efficiency of the proposed design sensitivity method is verified. Finally, conservative RBDO optimum designs are obtained using the developed methods for limited input data problems.