Necessary and sufficient convergence conditions of the instrumental variable method for identification

The consistency of the instrumental variable method is considered for a system driven by independent identically distributed input signals with zero mean. Necessary and sufficient conditions for convergence are developed with respect to the given model structure. The sufficient and necessary conditions are established for cases with and without data prefiltering. In this paper we extend on existing results such that the requirements on the degree of the instrumental variables can be relaxed. Numerical examples supporting the theoretical results are provided.     

Environmental sustainability assessment of bioeconomy value chains

Bioeconomy has gained political momentum since 2012 when the European Commission adopted the strategy “Innovating for Sustainable Growth: A Bioeconomy for Europe”. Assessing the environmental performance of different bioeconomy value chains (divided in three pillars: food and feed, bio-based products and bioenergy) is key to facilitate solid and evidence-based policy making. The objectives of this work were: (1) to map and analyse accessible LCA data related to bioeconomy value chains in order to identify knowledge gaps; (2) provide a more robust and complete picture of the environmental performance of three bioeconomy value chains (i.e. one per each bioeconomy pillar). This analysis reveals that apart from few products (such as liquid biofuels, some biopolymers and food crops) the environmental assessment of bioeconomy value chains is still incipient and limited to few indicators (e.g. Global Warming Potential and energy efficiency). In this study, a harmonised procedure – the Product Environmental Footprint (PEF), which includes fourteen impact categories – is used to estimate the environmental performance of three exemplary case studies which are inter-related due to the use of sugar as feedstock: sugar (food and feed), bio-based ethanol (bioenergy) and polyhydroxyalkanoates (bio-based product). Results highlight the strong need for methodological harmonisation and coherence for LCA of bioeconomy value chains.     

Source cell-phone recognition from recorded speech using non-speech segments

In a recent study, we have introduced the problem of identifying cell-phones using recorded speech and shown that speech signals convey information about the source device, making it possible to identify the source with some accuracy. In this paper, we consider recognizing source cell-phone microphones using non-speech segments of recorded speech. Taking an information-theoretic approach, we use Gaussian Mixture Model (GMM) trained with maximum mutual information (MMI) to represent device-specific features. Experimental results using Mel-frequency and linear frequency cepstral coefficients (MFCC and LFCC) show that features extracted from the non-speech segments of speech contain higher mutual information and yield higher recognition rates than those from speech portions or the whole utterance. Identification rate improves from 96.42% to 98.39% and equal error rate (EER) reduces from 1.20% to 0.47% when non-speech parts are used to extract features. Recognition results are provided with classical GMM trained both with maximum likelihood (ML) and maximum mutual information (MMI) criteria, as well as support vector machines (SVMs). Identification under additive noise case is also considered and it is shown that identification rates reduces dramatically in case of additive noise.     

基于非线性GA算法的MFF透射模型的参数辨识与优化

基于磁性流体(MF)磁链理论,建立了磁流体薄膜(MFF)传感模型,并通过Monte Carlo法分析了MF透射特性,建立了MFF透射模型。采用非线性遗传算法(GA)对MFF透射模型进行了参数辨识,分析了种群规模、进化代数、交叉率、变异率等参数选值对算法运行结果的影响,并选取了最佳参数组合,搭建了MFF电流传感器实验平台,分析了MFF厚度和粒子浓度对MFF透射性的影响,运用MFF透射模型对MFF电流传感器进行了仿真预测。实验及仿真结果表明该模型具有较好的预测性,预测误差在2.29%以内,所设计的MFF电流传感器的测量灵敏度达到11μW/A。     

A star pattern recognition algorithm for cameras with large FOV

In most large field of view (FOV) observations, the distortion problem is inevitably and significantly more serious than in small FOV ones. In the circumstances, many traditional star identification approaches are not able to efficiently identify stars any more. In order to deal with this problem, we put forward a star identification method that is less sensitive to distortion. The method first processes stars in the central area of the image, using traditional identification logic, and then applies the region growing strategy to enlarge the identified regions iteratively until the entire image is covered. The performance of the new scheme is analysed in the presence of both simulated data and real data. The results show that the proposed algorithm has the advantage of speed, and the strategy of regional extension can efficiently identify stars in large FOV images compared with other existing algorithms.     

The Role of Peripheral Vision in Static Balancing

Abstract

A study was made to determine the role of peripheral vision in static balancing. Three groups of subjects were tested under sighted and blindfold conditions, and also under a minimal cues condition.

One of these groups was trained in the use of peripheral vision and after five days achieved sighted competence under the minimal cues condition. A second group had no training, but practised for a similar period and showed no such improvement. The third group was tested with progressively decreasing amounts of peripheral vision, and the stage at which deterioration in performance occurred was noted. The results are discussed in relation to the use of peripheral vision, and in the relevance of this work to the training of the blind.     

Non‐minimum phase switched systems: HOSM‐based fault detection and fault identification via Volterra integral equation

In this paper, the problem of continuous and discrete state estimation for a class of linear switched systems with additive faults is studied. The class of systems under study can contain non‐minimum phase zeroes in some of their ‘operating modes’. The conditions for exact reconstruction of the discrete state are given using structural properties of the switched system. The state space is decomposed into the strongly observable part, the non‐strongly observable part, and the unobservable part, to analyze the effect of the unknown inputs. State observers based on high‐order sliding mode to exactly estimate the strongly observable part and Luenberger‐like observers to estimate the remaining parts are proposed. For the case when the exact estimation of the state cannot be achieved, the ultimate bounds on the estimation errors are provided. The proposed strategy includes a high‐order sliding‐mode‐based fault detection and a fault identification scheme via the solution of a Volterra integral equation. The feasibility of the proposed method is illustrated by simulations. Copyright © 2013 John Wiley & Sons, Ltd.     

System identification of a composite plate using hybrid response surface methodology and particle swarm optimization in time domain

Material properties of composites are identified using a novel hybrid RSM–PSO method in this paper. Different response surface methodology (RSM) methods and particle swarm optimization (PSO) methods are studied initially on a 4 degrees-of-freedom (4DOF) dynamic system on their performance in terms of speed and accuracy. The best combination is used as a hybrid RSM–PSO method to evaluate the performance on system identification of an orthotropic plate along with a 4DOF dynamic system and an isotropic plate. The novelty of the present paper is to identify the composite plate material properties using RSM methods based on time domain signals, which is not hitherto reported in the literature. Also, whereas previous papers have used full factorial design for system identification, here CCDI is used. The input factors (design variables) are the system parameters which are to be identified and the response (objective function) is error sum-of-square of acceleration response with respect to new test system. The performance of the proposed method is also evaluated with the addition of 5% Gaussian noise to simulate the experimental errors. The system parameters of the orthotropic plate were identified with 0% and 0.25% average prediction error with zero and 5% addition of noise respectively by the proposed hybrid RSM–PSO method. It is also showed a much better performance and robustness to noise addition when compared to the other RSM methods in the literature.     

A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding

Mass detection is a very important process for breast cancer diagnosis and computer aided systems. It can be very complex when the mass is small or invisible because of dense breast tissue. Therefore, the extraction of suspicious mass region can be very challenging. This paper proposes a novel segmentation algorithm to identify mass candidate regions in mammograms. The proposed system includes three parts: breast region and pectoral muscle segmentation, image enhancement and suspicious mass regions identification. The first two parts have been examined in previous studies. In this study, we focused on suspicious mass regions identification using a combination of Havrda & Charvat entropy method and Otsu's N thresholding method. An open access Mammographic Image Analysis Society (MIAS) database, which contains 59 masses, was used for the study. The proposed system obtained a 93% sensitivity rate for suspicious mass regions identification in 56 abnormal and 40 normal images.