An alignment-free fingerprint bio-cryptosystem based on modified Voronoi neighbor structures

Bio-cryptography is an emerging security technology which combines cryptography with biometrics. A good bio-cryptosystem is required to protect the privacy of the relevant biometric data as well as achieving high recognition accuracy. Fingerprints have been widely used in bio-cryptosystem design. However, fingerprint uncertainty caused by distortion and rotation during the image capturing process makes it difficult to achieve a high recognition rate in most bio-cryptographic systems. Moreover, most existing bio-cryptosystems rely on the accurate detection of singular points for fingerprint image pre-alignment, which is very hard to achieve, and the image rotation transformation during the alignment process can cause significant singular point deviation and minutiae changes. In this paper, by taking full advantage of local Voronoi neighbor structures (VNSs), e.g. local structural stability and distortion insensitivity, we propose an alignment-free bio-cryptosystem based on fixed-length bit-string representations extracted from modified VNSs, which are rotation- and translation-invariant and distortion robust. The proposed alignment-free bio-cryptosystem is able to provide strong security while achieving good recognition performance. Experimental results in comparison with most existing alignment-free bio-cryptosystems using the publicly-available databases show the validity of the proposed scheme.     

Adaptive robust posture control of a parallel manipulator driven by pneumatic muscles

Rather severe parametric uncertainties and uncertain nonlinearities exist in the dynamic modeling of a parallel manipulator driven by pneumatic muscles. Those uncertainties not only come from the time-varying friction forces and the static force modeling errors of pneumatic muscles but also from the inherent complex nonlinearities and unknown disturbances of the parallel manipulator. In this paper, a discontinuous projection-based adaptive robust control strategy is adopted to compensate for both the parametric uncertainties and uncertain nonlinearities of a three-pneumatic-muscles-driven parallel manipulator to achieve precise posture trajectory tracking control. The resulting controller effectively handles the effects of various parameter variations and the hard-to-model nonlinearities such as the friction forces of the pneumatic muscles. Simulation and experimental results are obtained to illustrate the effectiveness of the proposed adaptive robust controller.     

Optimization of correlated multi-response quality engineering by the upside-down normal loss function

Most of the published literature on robust design is basically concerned with a single response. However, the reality is that common industrial problems usually involve several quality characteristics, which are often correlated. Traditional approaches to multidimensional quality do not offer much information on how much better or worse a process is when finding optimal settings. Köksoy and Fan [Engineering Optimization 44 (8): 935–945] pointed out that the upside-down normal loss function provides a more reasonable risk assessment to the losses of being off-target in product engineering research. However, they only consider the single-response case. This article generalizes their idea to more than one response under possible correlations and co-movement effects of responses on the process loss. The response surface methodology has been adapted, estimating the expected multivariate upside-down normal loss function of a multidimensional system to find the optimal control factor settings of a given problem. The procedure and its merits are illustrated through an example.     

Robust finite-time boundedness of multi-agent systems subject to parametric uncertainties and disturbances

In this paper, we consider finite-time control problems for linear multi-agent systems subject to exogenous constant disturbances and impulses. Some sufficient conditions are obtained to ensure the finite-time boundedness of the multi-agent systems, which could be then reduced to a feasibility problem involving linear matrix inequalities. Numerical examples are given to illustrate the results.     

健壮性学习算法

使用统计方法可以对汉英机器翻译的词性标注和句法语义分析阶段产生的歧义进行消歧,在估计过程中往往使用最大可能方法,但是并不是在所有的情况下取最大值都是正确的。为了从所有候选结果中取到正确的结果,本文使用健壮性学习算法。使用这个算法,当正确的候选结果评分不是最高时,仍能通过健壮性算法来调整正确结果的评分,使之最大,并且降低不正确候选的评分。而且,由于训练集与测试集存在不同,使训练集中的错误率最小不能保证测试集中的错误率也最小。因此当考虑训练语料库和测试语料库存在统计变化时,应该使用健壮性学习算法。     

一类不确定性广义非线性系统的鲁棒控制

考虑一类上有不确定性广义非线性系统Ｅｚ＝（Ａ＋ΔＡ）ｚ＋Ｂｖ＋ｆ（ｚ）的鲁棒控制问题,这里Ｅ是奇异的ｆ（ｚ）系统的非线性部分,在适当的假设下,得到了基于状态反馈作用的鲁棒控制存在的条件。     

遗传算法在混合H_2/H_∞优化控制设计中的应用

介绍了现已成为关键智能计算之一的一种新的全局优化搜索算法—遗传算法，并探讨了在混合Ｈ２／Ｈ∞最优指标下优化控制设计中的应用。其最大特点是：本质并行、全局优化，它不要求目标函数连续和可微，比梯度法和随机搜索算法更适合于ＰＩＤ混合Ｈ２／Ｈ∞优化问题。     

Robust Estimation of Rotation Angles from Image Sequences Using the Annealing M-Estimator

A robust method is presented for computing rotation angles of image sequences from a set of corresponding points containing outliers. Assuming known rotation axis, a least-squares (LS) solution are derived to compute the rotation angle from a clean data set of point correspondences. Since clean data is not guaranteed, we introduce a robust solution, based on the M-estimator, to deal with outliers. Then we present an enhanced robust algorithm, called the annealing M-estimator (AM-estimator), for reliable robust estimation. The AM-estimator has several attractive advantages over the traditional M-estimator: By definition, the AM-estimator involves neither scale estimator nor free parameters and hence avoids instabilities therein. Algorithmically, it uses a deterministic annealing technique to approximate the global solution regardless of the initialization. Experimental results are presented to compare the performance of the LS, M- and AM-estimators for the angle estimation. Experiments show that in the presence of outliers, the M-estimator outperforms the LS estimator and the AM-estimator outperforms the M-estimator.     

Robust Stable Fuzzy Control via Fuzzy Modeling and Feedback Linearization with Its Applications to Controlling Uncertain Single-link Flexible Joint Manipulators

In this paper, a robust stable fuzzy control design based on feedback linearization is presented. Takagi–Sugeno fuzzy model is used as representing the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. For this structured uncertainty, the closed system can be analyzed by applying the perturbation system stability analysis to the fuzzy feedback linearization systems and a sufficient condition is derived to guarantee the stability of the closed-loop system with bounded parameter uncertainties. Based on the developed analysis method, we can design a robust fuzzy controller by choosing the control parameters satisfying the robust stability condition.