An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification

Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals.     

A genetic algorithm with gene rearrangement for K-means clustering

In this paper, a new clustering algorithm based on genetic algorithm (GA) with gene rearrangement (GAGR) is proposed, which in application may effectively remove the degeneracy for the purpose of a more efficient search. A new crossover operator that exploits a measure of similarity between chromosomes in a population is also presented. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GAGR to a local optimum. Using the real-world data sets, we compare the performance of our GAGR clustering algorithm with K-means algorithm and other GA methods. An application of the GAGR clustering algorithm in unsupervised classification of multispectral remote sensing images is also provided. Experiment results demonstrate that the GAGR clustering algorithm has high performance, effectiveness and flexibility.     

Learning a color distance metric for region-based image segmentation

In this paper we describe an experiment where we studied empirically the application of a learned distance metric to be used as discrimination function for an established color image segmentation algorithm. For this purpose we chose the Mumford–Shah energy functional and the Mahalanobis distance metric. The objective was to test our approach in an objective and quantifiable way on this specific algorithm employing this particular distance model, without making generalization claims. The empirical validation of the results was performed in two experiments: one applying the resulting segmentation method on a subset of the Berkeley Image Database, an exemplar image set possessing ground-truths and validating the results against the ground-truths using two well-known inter-cluster validation methods, namely, the Rand and BGM indexes, and another experiment using images of the same context divided into training and testing set, where the distance metric is learned from the training set and then applied to segment all the images. The obtained results suggest that the use of the specified learned distance metric provides better and more robust segmentations, even if no other modification of the segmentation algorithm is performed.     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

基于贝叶斯最大化后验估计方法的图片合成模型研究

在图像处理应用中,常常需要根据一些列相关的输入图片生成一张新的图片。现有的研究大都设定一些启发式规则用于图片的合成过程。为了提高图片合成的性能,提出了一种基于改进的贝叶斯方法的图片合成模型。在给定理想的图片合成模型后,对传感器误差和图片误差进行了分析。由于图片误差和几何误差之间是相关的,因此分析了它们之间的关系。在根据已有数据对模型进行后验估计时,通过最小化能量来得到模型的先验参数。在目标函数的优化过程中,基于现有研究通过重新赋权值的迭代方法进行优化问题的求解。最后,通过大量的实验表明,所提出的图片合成模型与相关方法相比具有更好的图片合成和渲染效果。     

Moving-in-pulse duration model-based target integration method for HSV-borne high-resolution radar

Hypersonic vehicles (HSVs) offer great advantages over airplanes and satellites, owing to their hypersonic speeds and flexible trajectories. However, HSV-borne radar may suffer from performance degradation if traditional target integration methods with stop-and-go (SAG) approximation are applied; this is especially true for high resolution radar and applications that utilize long-time coherent integration. In this paper, we consider the effects of moving-in-pulse duration (MPD) to derive an HSV-borne radar signal model without SAG approximation by specifically characterizing platform motion during the transmission and reception of a pulse. An explicit formula for wavenumber domain echo is derived using the 2-D joint stationary phase method. To mitigate the pulse-dependent echo distortion induced by the MPD effect, a target integration method based on the omega-K algorithm is proposed; this method employs revised filters for bulk focusing and revised Stolt interpolation for differential focusing to improve the overall focusing quality. The paper discusses the ambiguity functions of in-range MPD echo models, and describes the performance metrics for the integration results in both dimensions, including impulse response width (IRW), peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR). Simulation results have verified the effectiveness of the proposed method in MPD circumstances.     

基于FDI算法在软件测试实践中的应用

软件测试对于软件系统的性能以及功能都有很重要的意义,是保证软件可靠运行的关键.在软件测试的实践应用中,对于软件故障定位时,提出了基于谓词统计故障定位的算法,也就是FDI频率差异积分算法.在软件测试实践中采用基于FDI频率差异积分算法,可以很好地监控软件程序,测试软件的覆盖率以及执行时间等内存占用情况,并找到软件的故障瓶颈,以便采取更好的方法改进软件性能.探讨了实践中基于FDI频率差异积分算法在软件测试内的应用.     

基于一次密钥的数据加密算法优化

介绍了关于DES算法的一些研究情况, 分析了DES了算法的加密原理以及实现过程, 通过内容分析, 简单介绍了DES算法的几种改进方案, 通过分析它的加密过程以及几种改进方案, 进而提出了自己的一个改进思想即一次密钥的加密方案, 通过这个方案为DES算法的优化作一个参考.     

Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints

In this paper, we introduce a mathematical model for analyzing the dynamics of the univariate marginal distribution algorithm (UMDA) for a class of parametric functions with isolated global optima. We prove a number of results that are used to model the evolution of UMDA probability distributions for this class of functions. We show that a theoretical analysis can assess the effect of the function parameters on the convergence and rate of convergence of UMDA. We also introduce for the first time a long string limit analysis of UMDA. Finally, we relate the results to ongoing research on the application of the estimation of distribution algorithms for problems with unitation constraints.