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This paper aims to investigate an identification strategy for the nonlinear state-space model (SSM) in the presence of an unknown output time-delay. The equations to estimate the unknown model parameters and output time-delay are derived simultaneously in the proposed strategy.

The unknown integer-valued time-delay is processed as a latent variable which is uniformly distributed in a priori known range. The estimations of the unknown time-delay and model parameters are both realized using the Expectation-Maximization (EM) algorithm, which has a good performance in dealing with latent variable issues. Moreover, the particle filter (PF) with an unknown time-delay is introduced to calculated the Q-function of the EM algorithm.

Although amounts of effective approaches for nonlinear SSM identification have been developed in the literature, the problem of time-delay is not considered in most of them. The time-delay is commonly existed in industrial scenario and it could cause extra difficulties for industrial process modeling. The problem of unknown output time-delay is considered in this paper, and the validity of the proposed approach is demonstrated through the numerical example and a two-link manipulator system.

The novel approach to identify the nonlinear SSM in the presence of an unknown output time-delay with EM algorithm is put forward in this work.

This paper aims to investigate a probabilistic mixture model for the nonrigid point set registration problem in the computer vision tasks. The equations to estimate the mixture model parameters and the constraint items are derived simultaneously in the proposed strategy.

The problem of point set registration is expressed as Laplace mixture model (LMM) instead of Gaussian mixture model. Three constraint items, namely, distance, the transformation and the correspondence, are introduced to improve the accuracy. The expectation-maximization (EM) algorithm is used to optimize the objection function and the transformation matrix and correspondence matrix are given concurrently.

Although amounts of the researchers study the nonrigid registration problem, the LMM is not considered for most of them. The nonrigid registration problem is considered in the LMM with the constraint items in this paper. Three experiments are performed to verify the effectiveness and robustness and demonstrate the validity.

The novel method to solve the nonrigid point set registration problem in the presence of the constraint items with EM algorithm is put forward in this work.

The purpose of this paper is to analyze the spectrum influence between radon transform and log‐polar transform when rotation and scale effect is eliminated. The average retrieval performance of wavelet and NSCT with different retrieval parameters is also studied.

The authors designed a multi‐scale and multi‐orientation texture transform spectrum, as well as rotation‐invariant feature vector and its measurement criteria. Then a new two‐level coarse‐to‐fine rotation and scale‐invariant texture retrieval algorithm based on no‐parameter statistic features was proposed. Experiments on VisTex texture database show that the algorithm proposed in this paper is appropriate for main orientation capturing and detail information description.

According to the experiments results, it was found that the combination of this two‐level progressive retrieval strategy and multi‐scale analysis method can effectively improve retrieval efficiency compared with traditional algorithms and ensure a high precision as well.

The paper presents a novel algorithm for rotation and scale‐invariant texture retrieval.

The purpose of this paper is to examine the practice potential of self-administered drop-off as a survey mode for contingent valuation (CV) studies.

This paper conducts an empirical comparison of mode effects of two survey methods for improved ecological services in Beijing. Data were collected from a CV survey, which has two subsamples, one using face-to-face interviews and the other employing self-administered drop-off surveys.

There is some evidence of social desirability bias in the face-to-face interviews for the participation question; however, such effects do not carry over to subjects’ responses to the contribution decision. No difference is observed in sample demographics between modes. And satisficing effect is not observed in the drop-off survey in this study.

More well-controlled mode comparisons are warranted to test the robustness of the results; and collection time effects as well as the use of drop-off surveys for environmental valuation with different levels of complexity and familiarity are worthy of further study.

The authors find more similarities between drop-off and face-to-face surveys than differences therefore support the practice of self-administered drop-off surveys in CVM for environmental valuation.

This paper adds to the limited number of well-controlled mode comparisons in the CV surveys, and contributes to a better understanding of self-administered drop-off surveys, a potential low-cost alternative to face-to-face interviews in future CV applications.