Joint tracking coefficients and the time delay of a nonstationary linear system,preceded by a static nonlinearity

The paper considers a block-oriented nonlinear Hammerstein system that consists of a linear block with real-valued time-varying unknown coefficients and a pure time delay, preceded by the static nonlinearity. The joint tracking of linear block coefficients and the time delay while processing observation samples is under consideration. The difficulty of the tracking process consists in the initial nonconvexity of the objective function to be minimized, in respect of the time delay. To obtain current estimates of the true coefficients and the time delay it is necessary to overcome the multiextremality of the objective function. A global optimization problem could be solved by changing a form of the nonconvex objective function into a convex one. The optimization problem has been subdivided into two problems that are analyzed in this article, as well. The first one consists of removal of the objective function multiextremality in respect of the time delay, while hunting for the global extremum. Therefore, the well-known approach based on the method of corrective operators, which is used to transform the multiextremal criterion into a unimodal function for the nonstationary Wiener system, is developed here for the Hammerstein one. The other problem consists of the applicability of unified adaptive algorithms, used for Wiener systems in the case of Hammerstein systems. The recursive parametric identification technique, based on the adaptive algorithm procedures, is developed here for the nonstationary block-oriented Hammerstein systems, too. The applicability of algorithms is supported by various simulation tests solved by computer for distinct inputs and for two static nonlinearities.     

消防监督检查中对火灾隐患的认定探析

文章将重点阐述消防监督检查中对火灾隐患的认定,并从实际角度出发,充分的考虑可能存在的问题,具体问题具体分析,制定相应的解决对策,以保证消防监督检查工作能够顺利的开展.     

Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.     

Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

It is essential to develop an accurate model of proton exchange membrane fuel cell (PEMFC) for a reliable operation and analysis, in which unknown parameters usually need to be determined. The inherent nonlinear, strong coupling, and diversification of PEMFC model seriously hinder traditional methods to identify the parameters. For the sake of overcoming these thorny obstacles, Levenberg-Marquardt backpropagation (LMBP) algorithm based on artificial neural networks (ANNs) is proposed for PEMFC parameter identification. Furthermore, the performance of LMBP is thoroughly evaluated and compared with four typical meta-heuristic algorithms under three cases. Simulation results indicate that LMBP performs a higher accuracy and faster speed for parameter identification. In particular, accuracy and convergence speed can achieve as much as 99.8% and 95.9% growth via LMBP, respectively.     

A robust locating multi-optima approach for damage identification of plate-like structures

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances.     

Bioprospecting for brewers: Exploiting natural diversity for naturally diverse beers

The burgeoning interest in archaic, traditional, and novel beer styles has coincided with a growing appreciation of the role of yeasts in determining beer character as well as a better understanding of the ecology and biogeography of yeasts. Multiple studies in recent years have highlighted the potential of wild Saccharomyces and non-Saccharomyces yeasts for production of beers with novel flavour profiles and other desirable properties. Yeasts isolated from spontaneously fermented beers as well as from other food systems (wine, bread, and kombucha) have shown promise for brewing application, and there is evidence that such cross-system transfers have occurred naturally in the past. We review here the available literature pertaining to the use of nonconventional yeasts in brewing, with a focus on the origins of these yeasts, including methods of isolation. Practical aspects of utilizing nondomesticated yeasts are discussed, and modern methods to facilitate discovery of yeasts with brewing potential are highlighted.     

Nuclear DNA barcodes for cod identification in mildly-treated and processed food products

Gadoids are a group of fish with historical importance in the fishing industry. The high demand for cod is one of the reasons why cod products are often mislabelled, and numerous observations have been made on the replacement of Atlantic cod (Gadus morhua) by cheaper species or its illegal capture in contravention of fish quotas. Fish species identification is traditionally based on morphological features, but this may be difficult in case of heat-treated or processed products, or where the species look similar, as in the Gadoid group. DNA-based approaches (using either nuclear or mitochondrial DNA) are most commonly used in this case, due to their high specificity and to the high resilience of the target molecules to food processing techniques. In this article, we identified, using an automated screening approach, novel barcode regions and their associated primers in the nuclear genome, to be used for the efficient identification of Gadoids. The barcode regions were tested on official and commercial samples, raw or mildly treated products, like frozen, or salted, as well as pre-cooked complex mixtures and processed samples, using next-generation sequencing (NGS) technique. The method proposed could complement existing fish identification strategies in establishing an efficient framework to detect and prevent frauds along the food chain.