954 research outputs found
Resilience enhancement for urban distribution network via risk-based emergency response plan amendment for ice disasters
No full textHigh-impact low-probability (HILP) events, such as ice disasters, result in large losses of critical loads (CLs) in the urban distribution network (UDN). Thus, emergency response plans are usually made against various HILP events to improve the UDN resilience. However, these emergency plans are usually made under incomplete and imperfect information; thus, the execution of these plans may not be effective and even risky under the real situation. Therefore, in this paper, a multi-stage UDN resilience enhancement framework is proposed for tackling this challenge. At the first stage, the distribution system operator (DSO) forms typical failure scenarios based on historical data of damage to electric components under ice disasters. Under each scenario, the DSO designs a response plan to minimize the CLs' loss and associated costs. Thanks to the updated information on the ice disaster, at the second stage, DSO makes a risk assessment on the planned emergency response. If the risk is unacceptable for any period of the ice disaster, at the third stage, DSO amends the response plan to alleviate the "second-order " impacts on CLs, distribution lines, and distributed generations (DGs). Finally, simulations on the modified IEEE-69 system, including 10 CLs and some DGs, show that the proposed framework can effectively reduce second-order impacts due to both the ice disaster and its impact on the execution of originally planned emergency response
Co-Assembly of Heparin and Polypeptide Hybrid Nanoparticles for Biomimetic Delivery and Anti-Thrombus Therapy
No full text
New Color Features for Pattern Recognition
No full textThis chapter presents a pattern recognition framework that applies new color features, which are derived from both the primary color (the red component) and the subtraction of the primary colors (the red minus green component, the blue minus green component). In particular, feature extraction from the three color components consists of the following processes: Discrete Cosine Transform (DCT) for dimensionality reduction for each of the three color components, concatenation of the DCT features to form an augmented feature vector, and discriminant analysis of the augmented feature vector with enhanced generalization performance. A new similarity measure is presented to further improve pattern recognition performance of the pattern recognition framework. Experiments using a large scale, grand challenge pattern recognition problem, the Face Recognition Grand Challenge (FRGC), show the feasibility of the proposed framework. Specifically, the experimental results on the most challenging FRGC version 2 Experiment 4 with 36,818 color images reveal that the proposed framework helps improve face recognition performance, and the proposed new similarity measure consistently performs better than other popular similarity measures. © Springer-Verlag Berlin Heidelberg 2012
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No full textThis paper presents a kernel Fisher Linear Discriminant (FLD) method for face recognition. The kernel FLD method is extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semi-definite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semi-definite Gram matrix, either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. The feasibility of the kernel FLD method with fractional power polynomial models has been successfully tested on face recognition using a FERET data set that contains 600 frontal face images corresponding to 200 subjects. These images are acquired under variable illumination and facial expression. Experimental results show that the kernel FLD method with fractional power polynomial models achieves better face recognition performance than the Principal Component Analysis (PCA) method using various similarity measures, the FLD method, and the kernel FLD method with polynomial kernels
Mixture of Classifiers for Face Recognition across Pose
No full textA two dimensional Mixture of Classifiers (MoC) method is presented in this chapter for face recognition across pose. The 2D MoC method performs first pose classification with predefined pose categories and then face recognition within each individual pose class. The main contributions of the paper come from (i) proposing an effective pose classification method by addressing the scales problem of images in different pose classes, and (ii) applying pose-specific classifiers for face recognition. Comparing with the 3D methods for face recognition across pose, the 2D MoC method does not require a large number of manual annotations or a complex and expensive procedure of 3D modeling and fitting. Experimental results using a data set from the CMU PIE database show the feasibility of the 2D MoC method. © Springer-Verlag Berlin Heidelberg 2012
Functional architectures based on self-assembly of bio-inspired dipeptides: Structure modulation and its photoelectronic applications
No full textGetting inspiration from nature and further developing functional architectures provides an effective way to design innovative materials and systems. Among bio-inspired materials, dipeptides and its self-assembled architectures with functionalities have recently been the subject of intensive studies. However, there is still a great challenge to explore its applications likely due to the lack of effective adaptation of their self-assembled structures as well as a lack of understanding of the self-assembly mechanisms. In this context, taking diphenylalanine (FF, a core recognition motif for molecular self-assembly of the Alzheimer's p-amyloid polypeptides) as a model of bio-inspired dipeptides, recent strategies on modulation of dipeptide-based architectures were introduced with regard to both covalent (architectures modulation by coupling functional groups) and non-covalent ways (controlled architectures by different assembly pathways). Then, applications are highlighted in some newly emerging fields of innovative photoelectronic devices and materials, such as artificial photosynthetic systems for renewable solar energy storage and renewable optical waveguiding materials for optoelectronic devices. At last, the challenges and future perspectives of these bio-inspired dipeptides are also addressed. (C) 2015 Elsevier B.V. All rights reserved
Minutiae-Based Fingerprint Matching
No full textAt today, thanks to the high discriminability of minutiae and the availability of standard formats, minutia-based fingerprint matching algorithms are the most widely adopted methods in fingerprint recognition systems. Many minutiae matching algorithms employ a local minutiae matching stage followed by a consolidation stage. In the local matching stage, local minutiae descriptors are used, since they are discriminant and robust against typical perturbations (e.g., skin and non-linear distortion, partial overlap, rotation, displacement, noise). Minutiae Cylinder-Code representation (MCC), recently proposed by the authors, obtained remarkable performance
with respect to state-of-the-art local minutiae descriptors. In this chapter, the basic principles of minutiae-based techniques and local minutiae descriptors are discussed, then the MCC approach is described in detail. Experimental results on standard benchmarks such as FVC2006 and FVC-onGoing are reported to show the great accuracy and efficiency of MCC
Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance
No full textAbstract—This paper presents a novel pattern recognition framework by capitalizing on dimensionality increasing techniques. In particular, the framework integrates Gabor image representation, a novel multiclass Kernel Fisher Analysis (KFA) method, and fractional power polynomial models for improving pattern recognition performance. Gabor image representation, which increases dimensionality by incorporating Gabor filters with different scales and orientations, is characterized by spatial frequency, spatial locality, and orientational selectivity for coping with image variabilities such as illumination variations. The KFA method first performs nonlinear mapping from the input space to a high-dimensional feature space, and then implements the multiclass Fisher discriminant analysis in the feature space. The significance of the nonlinear mapping is that it increases the discriminating power of the KFA method, which is linear in the feature space but nonlinear in the input space. The novelty of the KFA method comes from the fact that 1) it extends the two-class kernel Fisher methods by addressing multiclass pattern classification problems and 2) it improves upon the traditional Generalized Discriminant Analysis (GDA) method by deriving a unique solution (compared to the GDA solution, which is not unique). The fractional power polynomial models further improve performance of the proposed pattern recognition framework. Experiments on face recognition using both the FERET database and the FRGC (Face Recognition Grand Challenge) databases show the feasibility of the proposed framework. In particular, experimental results using the FERET database show that the KFA method performs better than the GDA method and the fractional power polynomial models help both the KFA method and the GDA method improve their face recognition performance. Experimental results using the FRGC databases show that the proposed pattern recognition framework improves fac
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