基于多源信息空间的遥感影像自动解译研究

通过分析遥感影像自动解译的研究现状和存在的问题,提出了基于多源信息空间的遥感影像自动解译的研究方法,将遥感影像分为光谱信息空间、形状信息空间、区域地学信息空间、干扰信息空间等四个信息空间,通过对多源信息空间的数学描述和计算机处理实现了遥感影像的自动解译.     

DCT域双重图像数字水印算法

在简要分析DCT域数字水印算法的基础上,提出了双重图像数字水印算法,嵌入一个低频单向水印和一个基于人类视觉系统分析的中频盲水印.实验证明,该算法满足水印图像客观评价质量,能够实现两种水印的鲁棒性互补,具有方便灵活的实用价值.     

面向仿真数据的电网运行方式可视分析

对真实电网提出故障发生的假设并通过计算检验,观察故障对真实电网的影响。电网仿真能有效发现电网运行中的问题,被广泛应用于电网规划与电网运行方式调整。然而,在实际仿真计算过程中,往往需要依据经验调整电网配置和计算参数,以得到稳定、收敛的电网运行方式。大规模的仿真数据、调整的未知性加之分析的片面性会影响传统人工调整的准确性,为此提出了基于可视化的仿真计算调整方法。分析人员通过与可视分析系统的交互,可以快速认识电网的整体状态,发现问题元件,从而实现高效的仿真调整与计算。最后通过案例分析验证了本可视分析系统的有效性。     

Morphine-induced conditioned place preference in mice: Metabolomic profiling of brain tissue to find “molecular switch” of drug abuse by gas chromatography/mass spectrometry

Conditioned place preference (CPP) is a widely used model to explore the mechanism of context-dependent learning. In this work, we developed a GC–MS method to investigate the metabolites in mice brain which was used to study the mechanism of context-dependent learning associated with rewarding effect of morphine. Metabolites were extracted from brain tissues and derivatized followed by analysis by gas chromatography/mass spectrometry (GC–MS). In total, 69 peaks were identified as known compounds. By a Wilcoxon ran sum test with p value ≤0.05, 21 metabolites were selected and considered as the potential biomarkers of morphine in mice brain. Using principal component analysis (PCA) and receiver-operator characteristic (ROC) curves, a model was constructed with a combination of these 21 metabolic markers. Multivariate statistics of the model yielded separation between the two groups with an area under the curve value of 0.947. Some metabolites were further discussed in detail about their pathway. Results showed that our technique can be successfully applied to profile for biomarkers and in understanding molecular mechanisms of drug abuse.     

The Influence of Visual and Spatial Reasoning in Interpreting Simulated 3D Worlds

This paper explores the way in which young children (6 year olds) made sense of screen-based images on the computer. The participants were required to interpret 3D-like representations and relate these images to objects in their environment. Both static and relatively dynamic software programs were used in the investigation. Some of the children could not make links between the screen representation and the intended three-dimensional (3D) objects. It is argued that young children should be exposed to activities that establish explicit links between 2D and 3D objects ``away' from the computer before attempting the more difficult links required to interpret and represent 3D objects in ICT contexts. This revised version was published online in July 2006 with corrections to the Cover Date.     

基于顾客偏好的瓶装水外观感性质量评价

从感性质量评价的角度提取13个感性质量评价词对,用于全面且针对性地评价顾客对瓶装水的偏好.以想购买为综合感性提取瓶装水的感性质量评价要素,结合聚类分析等方法确定7种代表性样本.基于语义差异法构建瓶装水感性质量评价量表,运用多元回归模型分析建立顾客偏好与感性评价词对之间的关系模型.关系模型解决了如何识别顾客偏好和感性质量需求,以及如何将这些偏好和需求整合到产品设计过程中的问题.     

基于标志点识别的自主车视觉导航

提出了一种基于室内标志点识别的单目视觉导航的方法。在地面上有规律地旋转一些标志点,采用辅助先验知识对标志点进行识别和分割,利用透视投影变换得到标志点与摄像机之间的相对距离,达到通过单目摄像机获得景物深度信息的目的。整个算法结构简单,易于实现,对由照明不均匀和地面反光所引起的各种干扰有较好地抑制作用。     

Multicriteria analysis in decision making under information uncertainty

This paper presents results of research related to multicriteria decision making under information uncertainty. The Bellman–Zadeh approach to decision making in a fuzzy environment is utilized for analyzing multicriteria optimization models (X,M models) under deterministic information. Its application conforms to the principle of guaranteed result and provides constructive lines in obtaining harmonious solutions on the basis of analyzing associated maxmin problems. This circumstance permits one to generalize the classic approach to considering the uncertainty of quantitative information (based on constructing and analyzing payoff matrices reflecting effects which can be obtained for different combinations of solution alternatives and the so-called states of nature) in monocriteria decision making to multicriteria problems. Considering that the uncertainty of information can produce considerable decision uncertainty regions, the resolving capacity of this generalization does not always permit one to obtain unique solutions. Taking this into account, a proposed general scheme of multicriteria decision making under information uncertainty also includes the construction and analysis of the so-called X,R models (which contain fuzzy preference relations as criteria of optimality) as a means for the subsequent contraction of the decision uncertainty regions. The paper results are of a universal character and are illustrated by a simple example.     

A correlative classifiers approach based on particle filter and sample set for tracking occluded target

Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haar-like feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally, we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers.     

基于权重调节和用户偏好的协同过滤算法

针对传统相似度计算方法只利用用户的评分信息这一显性反馈行为进行推荐, 导致推荐效果不理想的问题, 提出一种新的相似度计算方法, 通过引入权重调节机制及用户行为偏好等隐性反馈信息, 提升推荐的准确度. 首先, 根据负采样的反用户频率, 降低流行物品全局软件工程的影响程度, 并使用共同评分行为的最小权重, 调节因共同评分数过少而导致的推荐准确度偏差. 其次, 提出项目偏好词定义, 根据项目偏好词矩阵计算出在项目特征上具有共同偏好的用户. 最后, 在MovieLens数据集上进行实验对比分析, 实验结果表明, 改进后的相似度计算有较优的MAE值, 且有更高的推荐准确性.