基于FDTD的三维全波电磁分析软件及工程应用

介绍三维全波电磁分析软件的算法实现和主要功能,软件包含电磁建模和FDTD数值求解两大模块,可用于射频微波等系统的电磁场分析和设计。自动进行满足FDTD算法条件的均匀和非均匀网格划分,生成描述介质、网格、边界条件、激励源设置等所需的数据文件。通过对目标物体进行平移、旋转、缩放等操作可以进行实时观察与修改,具有较强的电磁建模效率和三维可视化效果,实现了计算区间内电磁场的动态变化演示。通过在实际工程中的应用,以及和商用软件HFSS的对比,验证了软件的功能和应用价值。     

SAIL: Structure-aware indexing for effective and progressive top-k keyword search over XML documents

Keyword search in XML documents has recently gained a lot of research attention. Given a keyword query, existing approaches first compute the lowest common ancestors (LCAs) or their variants of XML elements that contain the input keywords, and then identify the subtrees rooted at the LCAs as the answer. In this the paper we study how to use the rich structural relationships embedded in XML documents to facilitate the processing of keyword queries. We develop a novel method, called SAIL, to index such structural relationships for efficient XML keyword search. We propose the concept of minimal-cost trees to answer keyword queries and devise structure-aware indices to maintain the structural relationships for efficiently identifying the minimal-cost trees. For effectively and progressively identifying the top-k answers, we develop techniques using link-based relevance ranking and keyword-pair-based ranking. To reduce the index size, we incorporate a numbering scheme, namely schema-aware dewey code, into our structure-aware indices. Experimental results on real data sets show that our method outperforms state-of-the-art approaches significantly, in both answer quality and search efficiency.     

Effects of three‐dimensional virtual reality and traditional training methods on mental workload and training performance

Industries will implement effective training programs to improve training performance, and an ideal training performance occurs under proper mental workload (MWL). Virtual reality (VR) has recently been widely utilized in training; however, only a few studies have investigated its effects on MWL and training performance simultaneously. The purpose of this study is to investigate the effects of VR training and traditional training methods, such as technical manuals (TM) and multimedia films (MF), on training performance and MWL. The results of the performance measurement show that VR training is considered the best training method compared to TM and MF, particularly in the case of complex tasks. The results of physiological measurements (GSR [galvanic skin response], LF% [low frequency], and LF/HF [high frequency] ratio) show a significant difference between reading TM and using computer (MF and VR), wherein the latter has a lower MWL. However, no significant difference in subjective MWL assessment (NASA‐TLX [task load index]) and HF% measurement is found.     

Empirical correction of multiple flightline hyperspectral aerial image mosaics

Aerial survey provides the user with great flexibility in terms of the geometry of sensing and the timing of measurements, but mosaicking individual aerial images to produce an extensive coverage remains a problem. Empirical methods based on normalising individual images to a common standard are used widely to create visually acceptable mosaics. However, the effect of these methods on quantitative estimation of land surface properties is unknown. An existing method for atmospherically correcting an aerial image mosaic involves fitting a regression model using pixels from the overlapping edges of adjacent flightlines. Here, we demonstrate a new method of atmospherically correcting an aerial image mosaic, based on use of an additional orthogonal flightline. The two methods were compared by using the two image mosaics to calculate vegetation indices (NDVI, SAVI, ARVI), which were then used to predict leaf area index, which was known in detail from ground survey. The second method was found to have lower uncertainty for all three vegetation indices tested. ARVI was found to be the most robust of the three when applied across multiple flightlines, regardless of the method of atmospheric correction.     

Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R² greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R² greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.     

多通道Wiener 滤波器设计新方法

应用现代时间序列分析方法,提出了多通道Ｗｉｅｎｅｒ滤波器设计的一种新的时域方法,它可统一处理滤波、平滑和预报问题,且可处理非平稳信号和噪声,仿真例子说明了该方法的有效性。     

Effective indices to characterize short sequences of human random generations

Brain impediments such as dementia are a serious problem today. It would be very useful if software for private diagnosis were available. In this paper, we show the effectiveness of the human random generation test (HRG) for such software, and propose a set of four indices to be used for classifying the HRG data. Human-generated random numbers have strong characteristics compared to computer-generated random numbers, and these are known to be correlated to the individual characters of the subjects. However, analysis using the correlation dimension or HMM requires a long data sequence, and thus is not suitable for diagnoses. We therefore focus on short sequences of HRG and search for effective indices to detect signs of brain disability hidden in the HRG data. We studied data from subjects of different age groups, and successfully differentiated the data from the different groups. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

全分布式光纤温度传感器研究的进展及趋势

综述了基于光时域反射和光频域反射的分布式温度光纤传感器的原理、技术以及最新的研究进展 ,并在此基础上分析了温度光纤传感器的发展趋势。     

利用演化神经网络研究有机污染物色谱保留值

利用分子连接性指数作为分子的描述性参数,定量刻画两类主要环境污染物的分子结构,通过演化神经网络分别建立了多环芳烃（PAH）和酚两类有机污染物的结构与色谱保留值之间的定量关系。计算过程中,采用留十法（Ieave-10-out）对所有化合物进行了计算,并将计算值与实验值进行了比较。     

Monitoring access to water in rural areas based on the human right to water framework: a local level case study in Nicaragua

Much effort has gone into the recognition of the human right to water. Without doubt, this milestone influences governance and decision making processes at many scales so it is essential now to shift the discussion from the legal and conceptual framework to practice. Along this line, the article proposes a methodology for monitoring access to water in rural areas using the framework of this human right. The practicality of the approach is demonstrated by a case study carried out in Nicaragua. Different criteria of the right to water were included in surveys and structured interviews that were conducted in rural households and water committees, respectively. A discussion analyzes the advantages and challenges of using this framework. Finally, the approach provides elements for policy making that can be used by different stakeholders in the development and human rights sectors.