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Jiao B.,Luoyang Electronic Equipment Test Center | Wu X.,Wuhan University
Chaos | Year: 2017

One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Many of these community-based networks evolve over time, that is, we need some size-independent metrics to capture the connection relationships embedded in these clusters. One of these metrics is the average clustering coefficient, which represents the triangle relationships between all nodes of networks. However, the vast majority of network communities is composed of low-degree nodes. Thus, we should further investigate other size-independent metrics to subtly measure the triangle relationships between low-degree nodes. In this paper, we study the 3-cycle weighted spectral distribution (WSD) defined as the weighted sum of the normalized Laplacian spectral distribution with a scaling factor n, where n is the network size (i.e., the node number). Using some diachronic community-based network models and real-world networks, we demonstrate that the ratio of the 3-cycle WSD to the network size is asymptotically independent of the network size and strictly represents the triangle relationships between low-degree nodes. Additionally, we find that the ratio is a good indicator of the average clustering coefficient in evolving community-based systems.

Jiao B.,Luoyang Electronic Equipment Test Center
Computer Communications | Year: 2016

The normalized Laplacian spectrum (NLS) is a powerful tool for comparing graphs with different sizes. Recently, we showed that two NLS features, namely the weighted spectral distribution (WSD) and the multiplicity of the eigenvalue 1 (ME1), are particularly relevant to the Internet topology at the inter-domain level. In this paper, we examine the physical meaning of the two metrics for the Internet. We show that the WSD reflects the transformation from single-homed nodes to multi-homed nodes for better fault-tolerance and that the ME1 quantifies the initial star-based structure associated with node classification, both of which are critical to the robustness of the Internet structure. We then investigate the relation between the metrics and graph perturbations (i.e., small changes in a graph). We show that these two NLS metrics can be a good choice for study on the Internet optimization. Our work reveals novel insights into the Internet structure and provides useful knowledge for statistical analysis on complex networks. © 2015 Elsevier B.V.All rights reserved.

Huang H.-C.,Chung Hua University | Yang X.,Luoyang Electronic Equipment Test Center
Fundamenta Informaticae | Year: 2016

The analytic hierarchy process (AHP) is the most popular extension to the pairwise comparisons method which is based on the observation that it is much easier to rank several objects when restricted to two objects at one time. As the pairwise comparisons are subjective, the use of linguistic expressions rather than numerical values is straightforward and friendlier due to the uncertainties that are inherent in human judgments. In this paper, to handle the uncertainty and hesitancy in practical decisionmaking situations, we represent pairwise comparisons in AHP using hesitant cloud linguistic term sets (HCLTSs) which are proposed based on hesitant fuzzy linguistic term sets (HFLTSs) and normal cloud models. Then, the synthetic cloud model aggregation algorithm is proposed to transform the HCLTS pairwise comparison matrix into the positive reciprocal synthetic cloud matrix. A prioritization method using the geometric mean technique is adopted, and the ranking method based on comparing of the parameters of normal cloud models is proposed. Thus, we extend the traditional AHP method in hesitant and uncertain environment, and we call it HCLTS-AHP method. The comparative linguistic expressions of preferences become more flexible and richer and are more similar to human beings' cognitive models. Furthermore, the synthetic cloud model is consistent with objectivity and the calculations are easy to implement. An illustrated example is applied to the ranking of four alternatives to show the usefulness of the proposed HCLTS-AHP method.

Huang H.-C.,Chung Hua University | Yang X.,Luoyang Electronic Equipment Test Center
International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems | Year: 2016

Since Zadeh introduced fuzzy sets, a lot of extensions of this concept have been proposed, such as type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models, to represent higher levels of uncertainty. This paper provides a comparative investigation of type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models. Type-2 fuzzy sets study the fuzziness of the membership function (MF) using primary MF and secondary MF based on analytic mathematical methods; nonstationary fuzzy sets study the randomness of the MF using primary MF and variation function based on type-1 fuzzy sets theory; cloud models study the randomness of the distribution of samples in the universe and generate random membership grades (MGs) using two random variables based on probability and statistic mathematical methods. They all concentrate on dealing with the uncertainty of the MF or the MG which type-1 fuzzy sets do not consider, and thus have many similarities. Moreover, we find out that, the same qualitative concept "moderate amount" can be represented by an interval type-2 fuzzy set, a nonstationary fuzzy set or a normal cloud model, respectively. Then, we propose a unified mathematical expression for the interval type-2 fuzzy set, nonstationary fuzzy set and normal cloud model. On the other hand, we also find out that, the theory fundament and underlying motivations of these models are quite different. Therefore, We summarize detailed comparisons of distinctive properties of type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models. Further, we study their diverse characteristics of distributions of MGs across vertical slices. The comparative investigation shows that these models are complementary to describe the uncertainty from different points of view. Thus, this paper provides a fundamental contribution and makes a basic reference for knowledge representation and other applications with uncertainty. © 2016 World Scientific Publishing Company.

Song S.,Tsinghua University | Song S.,Luoyang Electronic Equipment Test Center | Yang J.,Tsinghua University
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2015

In order to avoid the disadvantages of CFAR detector and make full use of the polarimetric information, a novel method is proposed in this paper for detecting ships of polarimetric SAR images, based on tensor robust principle component analysis (tensor RPCA). This method is completely different from the traditional CFAR detector, and distribution model and sliding window are unnecessary. The polarimetric SAR image is firstly depicted by a tensor, then an improved version of tensor RPCA is applied to the tensor by using accelerated proximal gradient (APG) algorithm. For comparison, the polarimetric whitening filter (PWF) method is also used. Experiment results show that the proposed method has excellent performance. © 2015 IEEE.

Che J.,Luoyang Electronic Equipment Test Center | Che J.,Key Laboratory of Electro Optical Countermeasures Test and Evaluation Technology | Zhang J.,Luoyang Electronic Equipment Test Center
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

In the jamming effectiveness experiments, in which the thermal infrared imager was interfered by the CO2 Laser, in order to evaluate the jamming effect of the thermal infrared imager by the CO2 Laser, it was needed to analyses the obtained infrared imagery of laser spot. Because the laser spot pictures obtained from the thermal infrared imager are irregular, the edge detection is an important process. The image edge is one of the most basic characteristics of the image, and it contains most of the information of the image. Generally, because of the thermal balance effect, the partly temperature of objective is no quite difference; therefore the infrared imagery's ability of reflecting the local detail of object is obvious week. At the same time, when the information of heat distribution of the thermal imagery was combined with the basic information of target, such as the object size, the relative position of field of view, shape and outline, and so on, the information just has more value. Hence, it is an important step for making image processing to extract the objective edge of the infrared imagery. Meanwhile it is an important part of image processing procedure and it is the premise of many subsequent processing. So as to extract outline information of the target from the original thermal imagery, and overcome the disadvantage, such as the low image contrast of the image and serious noise interference, and so on, the edge of thermal imagery needs detecting and processing. The principles of the Roberts, Sobel, Prewitt and Canny operator were analyzed, and then they were used to making edge detection on the thermal imageries of laser spot, which were obtained from the jamming effect experiments of CO2 laser jamming the thermal infrared imager. On the basis of the detection result, their performances were compared. At the end, the characteristics of the operators were summarized, which provide reference for the choice of edge detection operators in thermal imagery processing in future. © 2016 SPIE.

Zhang W.-A.,Luoyang Electronic Equipment Test Center | Chen B.-L.,Changchun University of Science and Technology
Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves | Year: 2014

We derived object temperature from emissivity spectrum using the emissivity normalized method and discussed the feasibility of recognizing objects using ground object thermal infrared spectra. The results show that the emissivity normalized method based on thermal multispectral data can invert object temperature and emissivity spectrum efficiently. It means that inversion of emissivity spectra can be effectively applied in feature recognition, especially for different soil properties.

Xiaojun H.,State Key Laboratory of Complex Electromagnetic | Jinghai Y.,Luoyang Electronic Equipment Test Center
Proceedings - 2014 International Conference on Information Science, Electronics and Electrical Engineering, ISEEE 2014 | Year: 2014

Because of multipath transmission, the quality of communication has been affected greatly. In this paper, based on the time reversal technique, iterative arithmetic for multipath signal transmission is proposed. By the time reversal iterative technique, the influence of multipath can be reduced greatly, and S/N can be also improved. By performing the injecting test based on the test bed for multipath communications, this arithmetic is proved to be effective. © 2014 IEEE.

Yang X.,Luoyang Electronic Equipment Test Center | Yan L.,Luoyang Electronic Equipment Test Center | Peng H.,Luoyang Electronic Equipment Test Center | Gao X.,Luoyang Electronic Equipment Test Center
Knowledge-Based Systems | Year: 2014

When constructing the model of a word by collecting interval-valued data from a group of individuals, both interpersonal and intrapersonal uncertainties coexist. Similar to the interval type-2 fuzzy set (IT2 FS) used in the enhanced interval approach (EIA), the Cloud model characterized by only three parameters can manage both uncertainties. Thus, based on the Cloud model, this paper proposes a new representation model for a word from interval-valued data. In our proposed method, firstly, the collected data intervals are preprocessed to remove the bad ones. Secondly, the fuzzy statistical method is used to compute the histogram of the surviving intervals. Then, the generated histogram is fitted by a Gaussian curve function. Finally, the fitted results are mapped into the parameters of a Cloud model to obtain the parametric model for a word. Compared with eight or nine parameters needed by an IT2 FS, only three parameters are needed to represent a Cloud model. Therefore, we develop a much more parsimonious parametric model for a word based on the Cloud model. Generally a simpler representation model with less parameters usually means less computations and memory requirements in applications. Moreover, the comparison experiments with the recent EIA show that, our proposed method can not only obtain much thinner footprints of uncertainty (FOUs) but also capture sufficient uncertainties of words. © 2013 Elsevier B.V. All rights reserved.

Wang X.Z.,Luoyang Electronic Equipment Test Center | Xiao J.B.,Luoyang Electronic Equipment Test Center
Applied Mechanics and Materials | Year: 2013

The particle swarm optimization algorithm was improved in this paper, a novel self-adaption dynamic sub-swarms hybrid particle swarm optimization algorithm is proposed, in this algorithm, subgroup partition method based on dynamic clustering of particle adaptive value is adopted to divide particle group to different capability sub-group, then execute different optimize strategy to different subgroup, simultaneity, inertial weight and accelerating coefficient are adaptive set, through contacting adjustment of parameter with sub-group capability, the particle mode method and intersect and aberrance strategy of double deck are designed, The experimental results show that the algorithm has simple programming, good robust capability and strong optimizing capability which established the foundation of task planning of multi-UAV Cooperative. © (2013)Trans Tech Publications, Switzerland.

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