Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

双波段成象及融合技术研究

概括地分析了双波段成象技术的发展概况，分析了成象技术的基本原理，提出了红外双波段基本方案和涉及的关键技术，分析和计算了系统的基本性能。最后，讨论了双波段及融合技术，认为在双波段实现融合应以自主式结构为主。     

The Year 2015 Fusion Power Conversations

Assuming significant technical success in the ITER project by the year 2015, it is likely that governments will want to develop a more comprehensive plan for DT tokamak fusion power commercialization. To provide a glimpse into many of the related issues, we construct conversations between the director of the U.S. fusion program and three specialists key to commercial fusion success: an environment and safety regulator, an environmentalist, and an electric generation investor. The insights from these imagined conversations will hopefully be of value as tokamak fusion power proponents plan beyond ITER.     

Bayesian and Dempster-Shafer fusion

The Kalman Filter is traditionally viewed as a prediction-correction filtering algorithm. In this work we show that it can be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman-Kolmogorov prediction integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster-Shafer theory, which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster-Shafer theory and presenting the basic Dempster-Shafer fusion equation. Finally we compare the two methods, and discuss the relative merits and demerits using an illustrative example.     

RF antenna analysis with the ICANT code

The ICANT code computes self-consistently the surface current distribution on a 3D antenna model radiating in a plasma or vacuum and has been used to analyze the coupling properties of various antenna models. In this work it is used to assess near-fields generated by different versions of the TCABR (Tokamak Chauffage Alfvén wave heating experiment in Brazil) antenna as well as coupling properties of the TEXTOR tokamak in Forshungszentrum Jülich. Two different strap models planned for the TCABR antenna are shown to be practically equivalent in terms of maximum electric field. Textor coupling calculations are close to experimental values.     

高能超声处理Al-1Si合金凝固组织分析

采用高能超声处理Al-1Si合金熔体,借助金相显微镜和电子探针进行组织分析,并测定合金的熔化潜热,分析了熔体中超声空化效应和声流效应影响凝固过程的机理。结果表明,试样晶粒平均尺寸由超声处理前的94μm减小到超声处理后的34μm;合金试样的熔化吸热量由263.90J/g降低为159.81J/g,在熔体中施加高能超声显著改善了合金成分的均匀性并细化了晶粒。     

Background-subtraction using contour-based fusion of thermal and visible imagery

We present a new background-subtraction technique fusing contours from thermal and visible imagery for persistent object detection in urban settings. Statistical background-subtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regions-of-interest in the visible domain. Within each region, input and background gradient information are combined to form a Contour Saliency Map. The binary contour fragments, obtained from corresponding Contour Saliency Maps, are then fused into a single image. An A^* path-constrained search along watershed boundaries of the regions-of-interest is used to complete and close any broken segments in the fused contour image. Lastly, the contour image is flood-filled to produce silhouettes. Results of our approach are evaluated quantitatively and compared with other low- and high-level fusion techniques using manually segmented data.     

Comparison among feature extraction methods for HIV-1 protease cleavage site prediction

Recently, several works have approached the HIV-1 protease specificity problem by applying a number of methods from the field of machine learning. However, it is still difficult for researchers to choose the best method due to the lack of an effective comparison. For the first time we have made an extensive study on methods for feature extraction for the problem of HIV-1 protease. We show that a fusion of classifiers trained in different feature spaces permits to obtain a drastically error reduction with respect to the performance of the state-of-the-art.     

无线传感器网络簇内实时数据融合研究

现有的无线传感器网络数据融合技术存在数据延迟和数据正确性两方面的问题。针对该问题设计一种基于簇内实时数据融合的方法．该方法首先通过量化计算将数据划分为紧急数据和非紧急数据并作标记,将紧急数据迅速融合传给用户以提高实时性。在融合过程中引入传感器测量修正并结合关系矩阵法对数据进行相应的融合处理．然后对融合结果进行分析并进行反馈调整。仿真实验结果表明．该方法能够提高簇内实时数据融合正确性并且降低数据延迟．     

Online learning and fusion of orientation appearance models for robust rigid object tracking

We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.