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The purpose of this paper considers optimal input signal design for flutter model parameters identification, as input signal is the first step during the whole identification process. According to the constructed flutter stochastic model with observed noises, separable least squares identification and set membership identification are proposed to identify those unknown model parameters for statistical noise and unknown but bounded noise, respectively. The common trace operation with respect to the asymptotic variance matrix is minimized to solve the power spectral for the optimal input signal in the framework of statistical noise. Moreover, for the unknown bout bounded noise, the radius of information, corresponding to the established parameter uncertainty interval, is minimized to give the optimal input signal.

First, model identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter statistical model with statistical noise, separable least squares identification is proposed to identify the unknown model parameters, then the optimal input signal is designed to satisfy one given performance function. Third, for aircraft flutter model with unknown but bounded noise, set membership identification is proposed to solve the parameter set for each unknown model parameter. Then, the optimal input signal is designed by applying the idea of the radius of information with unknown but bounded noise.

This aircraft flutter model corresponds to one stochastic model, whose input signal and output are corrupted by external noises. Then identification strategy and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise and unknown but bounded noise, respectively.

To the best knowledge of the authors, this problem of the model parameter identification for aircraft flutter was proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes two novel identification strategies and opens a new subject about optimal input signal design for statistical noise and unknown noise, respectively.

This paper aims to study the use of electrochemical noise (EN) technology in the corrosion continuous monitoring of stainless steel (SS) in an atmospheric environment.

An EN electrode was designed and fabricated to acquire the EN of 304 SS in the atmospheric environment. The statistical analysis and shot noise analysis were used to analyze the EN, and the surface morphology analysis of 304 SS was used to verify the EN analysis results.

The activation state, passive film formation and pitting corrosion of 304 SS can be clearly distinguished by the amplitude and frequency change of EN. The metastable pitting corrosion and steady-state pitting corrosion can be identified with the shot noise parameters q and fn. Under the existence of chloride ion, the stability of 304 SS passive film decreases and the steady-state corrosion pits of 304 SS are more likely to form with the reduction of thin electronic layer (TEL) pH. The critical TEL pH of 304 SS corrosion is a pH between 3 and 4.

In an atmospheric environment, the EN technology was used in the corrosion continuous monitoring of SS.

Since China's accession of the World Trade Organization (WTO), its construction industry has attained unprecedented growth. However, for the sources of this enormous growth, a controversy regarding the total factor productivity growth (TFPG) still remains in production practice and extant studies. In view of this, the purpose of this paper is to measure TFPG and to explore its sources in the industry post-WTO accession.

This study presents an innovative source analysis of TFPG. Stochastic frontier approach is adopted to measure TFPG and to explore its sources by decomposing TFPG into technical progress (TP), technical efficiency change (TEC), allocative efficiency change (AEC) and scale efficiency change (SEC). Although China joined WTO in 2001, to provide an effective baseline, the study period is from 2000 to 2017.

The empirical results reveal that TFPG presented an overall downward evolutionary trend, but it still maintained a high growth post-WTO accession. From the perspective of decomposition, TP was the main source of TFPG. Furthermore, as a neglected source, interaction effects among TP, TEC, AEC and SEC have been demonstrated to have a significant influence on the cumulative TFPG.

To make the results be reliable, the authors discuss the empirical findings mainly by revealing the reasons behind the evolutions of TFPG and its sources. Based on these revealed reasons, government and policy makers can further refine and summarize some more detailed and targeted policy implications to improve TFPG.

By providing many empirical evidences to solve the aforesaid TFPG controversy, this paper, therefore, enriches the body of knowledge on growth theories, especially at the level of industrial economics.

This paper aims to measure the state-level energy efficiency in Indian paper industry and simultaneously explain inter-state variation in efficiency by inefficiency effect model. Three variables, labor productivity, capital intensity and structure of paper industry, are included in inefficiency effect model to assess the likely impact on energy efficiency.

Sub-vector input distance function is derived through neo-classical production function which provides measures to estimate energy efficiency. Assuming a translog production function specification, energy efficiency is estimated by using Battese and Coelli (1995) stochastic frontier analysis (SFA). The authors also estimated four other SFA models, and energy efficiency from all the models is compared for robustness checking.

The results show the existence of a vast potential to improve energy efficiency. Inefficiency effect model reported a positive impact of structure of the industry and capital intensity on energy efficiency performance, while labor productivity does not have any significant impact on energy efficiency. There exists considerable energy efficiency variation among states. Uttarakhand, Punjab and Orissa are the best performing states while Rajasthan, Jharkhand and Goa have worst energy efficiency performance based on average efficiency. The ranks assigned to states according to inefficiency effects model are found contrary to the simple measure of energy efficiency, i.e. energy intensity. Thus, energy intensity may not always be a good proxy for underlying energy efficiency and need to be compared with a comprehensive possible measure.

To the best of the authors’ knowledge, this is the first study which measures energy efficiency of Indian paper industry through stochastic frontier model using region-level data. Instead of relying on traditional energy efficiency indicators (energy-output ratio), total-factor energy efficiency approach is used to conduct the empirical exercise. Deviations from the frontier because of factors beyond the scope of producers are also incorporated into analysis to portray the magnitude of random factors in influencing the efficiency performance.

The purpose of this paper is to review recent applications of functional magnetic resonance imaging (fMRI) and other neuroimaging techniques in marketing and advertising, and to present some methodological and statistical considerations that should be taken into consideration when applying fMRI to study consumers’ cognitive behavior related to marketing phenomena.

A critical approach to investigate three methodological issues related to fMRI applications in marketing is adopted. These issues deal mainly with brain activation regions, event-related fMRI and signal-to-noise ratio. Statistical issues related to fMRI data pre-processing, analyzing and reporting are also investigated.

Neuroimaging cognitive techniques have great potential in marketing and advertising. This is because, unlike conventional marketing research methods, neuroimaging data are much less susceptible to social desirability and “interviewer’s” effect. Thus, it is expected that using neuroimaging methods to investigate which areas in a consumer’s brain are activated in response to a specific marketing stimulus can provide a much more honest indicator of their cognition compared to traditional marketing research tools such as focus groups and questionnaires.

By merging disparate fields, such as marketing, neuroscience and cognitive psychology, this research presents a comprehensive critical review of how neuroscientific methods can be used to test existing marketing theories.

The purpose of this paper is to extend the authors’ previous contributions on aircraft flutter model parameters identification. Because closed-loop condition is more widely used in today’s practice, a closed-loop stochastic model of the aircraft flutter test is constructed to model the aircraft flutter process, whose input–output signals are all corrupted by the observed noises. Through using a rational transfer function, the equivalent property between the aircraft flutter model parameters and polynomial coefficients is established, and then the problem of aircraft flutter model parameters identification is turned to one closed-loop identification problem. An iterative identification algorithm is proposed to identify the unknown polynomial coefficients, being benefit for the latter flutter model parameter identification. Furthermore, as the closed-loop output corresponds to the flutter amplitude, so from the point of the minimization with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.

First, model parameter identification for aircraft flutter is reviewed as one problem of parameter identification and this aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Second, for aircraft flutter closed-loop statistical model with statistical noise, an iterative identification algorithm is proposed to identify the unknown model parameters. Third, from the point of minimizing with respect to the variance of the closed-loop output, the optimal input signal and optimal feedback controller are all derived to achieve the zero flutter, respectively, for example, the optimal input spectrum and the detailed form for optimal feedback controller.

This aircraft flutter model corresponds to one closed-loop stochastic model, whose input signal and output are corrupted by external noises. Then, identification algorithm and optimal input signal design are studied for aircraft flutter model parameter identification with statistical noise, respectively. It means the optimal input signal and optimal feedback controller are useful for the aircraft flutter model parameter identification within the constructed new closed-loop stochastic model.

To the best of the authors’ knowledge, this problem of the model parameter identification for aircraft flutter is proposed by their previous work, and they proposed many identification strategies to identify these model parameters. This paper proposes a new closed-loop stochastic model to construct the aircraft flutter test, and some related topics are considered about this closed-loop identification for aircraft flutter model parameter identification in the framework of closed-loop condition.

The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown parameters; a more general nonlinear dynamical model for each UAV is considered to include two terms. Due to an unknown parameter corresponding to the normal or abnormal state for each UAV, the bias-compensated approach is proposed to obtain the unbiased parameter estimation. Meanwhile, the biased error and accuracy analysis are also given in case of strict statistical description of the uncertainty or white noise. To relax this strict statistical description on external noise, an analytic center approach is proposed to identify the unknown parameters in presence of bounded noise, such that two inner and outer ellipsoidal approximations are constructed to include the membership set. To be precise, this paper is regarded as one extension and summary for the author’s previous research on the anomaly detection in multi-UAV formation. Finally, one simulation example is given to confirm the theoretical results.

Firstly, one extended nonlinear relation is constructed to embody the mutual relationship of UAVs. Secondly, to obtain the unbiased parameter estimations, the bias-compensated approach is applied to achieve it under the condition of white noise. Thirdly, in case of unknown but bounded noise, an analytic center approach is proposed to deal with this special case. Without loss of generality, the author thinks this paper can be used as one extension and summary for research on multi-UAVs formation anomaly detection.

An anomaly detection problem in multi-UAVs formation can be transformed into a problem of nonlinear system identification, and in modeling the nonlinear dynamical model for each UAV, two terms are considered simultaneously to embody the mutual relationships with other nearest UAV.

To the best knowledge of the authors, this problem of the anomaly detection problem in multi-UAVs formation is proposed by the authors’ previous work, and the problem of multi-UAVs formation anomaly detection can be transferred into one problem of parameter identification. In case of unknown but bounded noise, an analytic center approach is proposed to identify the unknown parameters, which correspond to achieve the goal of the anomaly detection.

The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The covariance of the measurement noise may deviate from its nominal value in practical environment, and the filtering performance may decline because of the statistical uncertainty. Although the adaptive EKF (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics.

Aiming at this problem, this paper develops a parallel adaptive EKF (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value.

The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty and ensure the estimation accuracy in the normal case. The simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF.

The presented algorithm is applicable for spacecraft relative attitude and position estimation.

The PAEKF is presented for a kind of nonlinear uncertain systems. Stability analysis is provided to show that the error of the estimator is bounded under certain assumptions.

Flight Operational Noise Internationally, noise due to flight operations in the vicinity of airports. Each nation has developed an individual system of assessment and criteria for judging acceptability or probable extents of annoyance arising from the community based on local conditions and experience.