A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

A multimodel fusion engine for filtering webpages

© 2013 IEEE. Fusing multiple existing models for filtering webpages can mitigate the shortcomings of individual filtering models. To provide an engine for such fusion, we propose a multimodel fusion engine for filtering webpages for the extraction of target webpages. This engine can handle large datasets of webpages crawled from websites and supports five individual filtering models and the fusion of any two of them. There are two possible fusion methods: one is to simultaneously satisfy the conditions of both individual models, and the other is to satisfy the conditions of one of the two individual models. We present the functions, architecture, and software design of the proposed engine. We use recall ratio (RR) and precision ratio (PR) as the evaluation indices of the filtering models and propose rules describing how PR and RR change when individual models are fused. We use 200 000 webpages collected by crawling the popular online shopping website 'http://www.jd.com' as the experimental dataset to verify these rules. The experimental results show that two-model fusion can improve either PR or RR. Thus, the proposed engine has good practical value for engineering applications

Study on the application of a new multiepoxy reinforcement agent for sheep leather

Content: Leather is a kind of natural biomass composite material which is made of animal skin as material by a series of chemical and physical processing. Its main structure is Collagen fibers of three-dimensional network structure. As we all know sheep leather always exist a common problem with low strength, while the strength of leather depended on the woven degree of collagen fibers. Through the past decades, many methods have been tried to improve the properties of sheep leather. The most commonly used methods are retanning. However, the strength enhancement of sheep leather is extremely limited by retanning, although the fullness and softness may be improved. In this study, a new type of multi-epoxy reinforcement agent (IGE) and IGE with the synergistic effect of polyamine (IGE-PA) were used to enhance the strength of sheep leather in tanning and fatliquoring process. Comparing with chromium tanned leather, it was found that under the optimized conditions (dosage: 10%, pH: 8, Temperature: 35℃ for penetration and 45℃ for fixation, tanning time: 10 h) with IGE as the main tanning agent, the tearing strength was increased 56.8%. While when the polyamine as the synergetic agent for IGE, the tearing strength was significantly increased 87.9%. While IGE and IGE-PA were used in fatliquoring process, it has significant reinforcement effect for tetrakis hydroxymethyl phosphonium (THP) salt tanned leather. It was found that under the optimized conditions (Dosage: 2.5%, pH: 7-8, Temperature: 50℃, Time: 2h) with IGE in fatliquoring process, the tear strength was increased 50.24%, while the IGE-PA was used, the tear strength was increased 64.3%. Furthermore, TGA results showed that decomposition temperatures of IGE and IGE-PA enhanced leather were all higher than traditional chromium tanned leather. In addition, SEM results showed that IGE and IGE-PA enhanced leather obtained better opened-up fiber structure. Take-Away: 1. A new type of multi-epoxy tanning agent (IGE) has reinforcement effect for sheep leather especially in tear strength. 2. IGE with the synergistic effect of polyamine (IGE-PA) were used in tanning process, which has a significant enhancement for the sheep leather. 3. IGE and IGE-PA can be also used in fatliquoring process to enhance the strength of sheep leather

Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction with Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces

© 2012 IEEE. The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces