Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.     

参数自适应的条件随机场视频分割方法

针对已有算法需要采用一系列参数经验值得到最优视频分割结果的问题,提出根据视频特征自适应地计算视频邻域关系的特征强度函数,构造参数自适应的条件随机场视频分割模型。算法的核心思想是采用视频中像素之间的邻域关系自适应计算各个模型的特征函数,通过条件随机场模型对这些特征能量函数进行约束并利用Gibbs采样算法对该模型进行求解,获得全局优化的分割结果。针对不同环境下的视频分割实验表明,该算法能够很好地逼近最优经验参数所得到的视频分割结果,从而避免定义经验值所导致的算法局限性问题。     

基于Irrlicht引擎的实时浅水效果模拟

本文在Irrlicht引擎的基础上结合GLSL语言,对浅水的水面波动、反射与折射特性及菲涅尔现象进行了实时模拟。将基于Gestner波的水面波动与基于纹理波的动态法线贴图相结合,真实地模拟了水面的波动与波纹效果。同时,采用渲染到纹理技术实时生成反射贴图,准确地模拟了水面的反射效果,解决了采用环境贴图渲染时的反射失真问题。在不考虑水深的情况下,本文将菲涅尔权值与材质的alpha通道相结合,去掉了实时渲染中折射贴图的生成,在满足实时浅水效果渲染的视觉需求条件下,减小了CPU与GPU的计算量。     

多级变长异或实时加解密及校验方法

为了实现银行实时业务数据加密提出的速度、安全性以及自动化管理的加密要求,本文提出以密钥和实时明文互结合的方式对明文进行多级变长异或加密,实现数据的实时加解密、数据完整性验证以及密钥的自动化管理。     

用于计算机系统的铁电随机存储器的研究进展

综述了铁电存储器的存储原理、电路结构及应用、尚未解决的主要铁电材料的环保问题、疲劳失效问题、信息保持力和破坏性读出等问题.     

C语言中有关内存概念的教学研究

为使学生能快速掌握C语言的相关概念,为今后进行程序设计打下良好的基础,针对在教学过程中学生难以理解的几个问题,结合实例探讨了C语言中有关内存概念的教学方法,着重分析了内存与数组、函数参数、变量的作用域和生成期之间的关系。     

无线传感器网络中目标跟踪的马尔可夫模型与预测方法

针对目标运动是一个包含许多不确定因素的非线性非高斯随机过程,提出基于马尔可夫随机场模型与粒子滤波的WSN分布式目标跟踪方法(MRF-PF)。把目标跟踪过程看作是一个马尔可夫过程,基于贝叶斯规则,建立目标状态分布函数,用粒子滤波估计目标状态,实现目标跟踪。实验结果:对于泊松白噪声,MRF-PF方法的跟踪均方根误差RMSE相比卡尔曼滤波(KF)和扩展卡尔曼滤波(EKF)方法分别降低52.6%、49.2%;对于方差σ2由0.3→3的高斯噪声,GM-PF方法的RMSE相比KF、EKF分别降低54.5%～77.2%和23.5%～54.2%。这表明MRF-PF方法在非线性非高斯噪声或高斯噪声变化较大时具有较好的抗噪能力及跟踪性能。     

航空发动机几何位置调节器转换性能验证

研究某型航空发动机几何位置的调节时,采用了具有全功能液压备份控制器,是全权限数字电子控制器,若全权限数字电子控制器出现故障,系统应立即转换到液压机械备份控制方式,以保证系统工作正常。控制器转换性能的研究对系统的改进及故障检测有很大帮助。为保证发动机的可靠性,对数字电子控制器在系统稳态和过渡态工作点出现故障的情况,利用AMES im软件平台对控制器转换过程进行了动静态仿真,仿真结果验证了所设计控制器转换性能优良,控制器正常转换不会对系统正常工作造成较大影响。     

Using local transition probability models in Markov random fields for forest change detection

Change detection based on the comparison of independently classified images (i.e. post-classification comparison) is well-known to be negatively affected by classification errors of individual maps. Incorporating spatial-temporal contextual information in the classification helps to reduce the classification errors, thus improving change detection results. In this paper, spatial-temporal Markov Random Fields (MRF) models were used to integrate spatial-temporal information with spectral information for multi-temporal classification in an attempt to mitigate the impacts of classification errors on change detection. One important component in spatial-temporal MRF models is the specification of transition probabilities. Traditionally, a global transition probability model is used that assumes spatial stationarity of transition probabilities across an image scene, which may be invalid if areas have varying transition probabilities. By relaxing the stationarity assumption, we developed two local transition probability models to make the transition model locally adaptive to spatially varying transition probabilities. The first model called locally adjusted global transition model adapts to the local variation by multiplying a pixel-wise probability of change with the global transition model. The second model called pixel-wise transition model was developed as a fully local model based on the estimation of the pixel-wise joint probabilities. When applied to the forest change detection in Paraguay, the two local models showed significant improvements in the accuracy of identifying the change from forest to non-forest compared with traditional models. This indicates that the local transition probability models can present temporal information more accurately in change detection algorithms based on spatial-temporal classification of multi-temporal images. The comparison between the two local transition models showed that the fully local model better captured the spatial heterogeneity of the transition probabilities and achieved more stable and consistent results over different regions of a large image scene.     

Distributing test cases more evenly in adaptive random testing

Adaptive random testing (ART) has recently been proposed to enhance the failure-detection capability of random testing. In ART, test cases are not only randomly generated, but also evenly spread over the input domain. Various ART algorithms have been developed to evenly spread test cases in different ways. Previous studies have shown that some ART algorithms prefer to select test cases from the edge part of the input domain rather than from the centre part, that is, inputs do not have equal chance to be selected as test cases. Since we do not know where the failure-causing inputs are prior to testing, it is not desirable for inputs to have different chances of being selected as test cases. Therefore, in this paper, we investigate how to enhance some ART algorithms by offsetting the edge preference, and propose a new family of ART algorithms. A series of simulations have been conducted and it is shown that these new algorithms not only select test cases more evenly, but also have better failure detection capabilities.