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Structural Damage Information Fusion Based on Soft Computing
Haoxiang He,Weiming Yan,Ailin Zhang
International Journal of Distributed Sensor Networks , 2012, DOI: 10.1155/2012/798714
Abstract: The significance of information fusion for structural health monitoring and damage detection is introduced. The three levels of information fusion for multisensors are described. For the complex in the structural health monitoring, the distributed multisensor information fusion is more suitable and the structure is discussed. In the damage information fusion for character level, the concept for structural integral support vector machine damage detection matrix, damage self-information, and damage information entropy are presented. For a complex structure, it can be divided into multiple substructures in order to simplify the difficult for health monitoring, the data acquisition and support vector machine are established for each substructure in order to form integral damage detection matrix. In the damage information fusion for decision level, the methods based on fuzzy set theory, material element theory, and fuzzy neural network are proposed. The results given by a numerical example about space structure show that all the methods are valid and effective.
Linear Decision Fusion under the Control of Constrained PSO for WSNs
Sisi Jiang,Zhiwen Zhao,Sheng Mou,Zushun Wu,Yi Luo
International Journal of Distributed Sensor Networks , 2012, DOI: 10.1155/2012/871596
Abstract: A major application of a distributed WSN (wireless sensor network) is to monitor a specific area for detecting some events such as disasters and enemies. In order to achieve this objective, each sensor in the network is required to collect local observations which are probably corrupted by noise, make a local decision regarding the presence or absence of an event, and then send its local decision to a fusion center. After that, the fusion center makes the final decision depending on these local decisions and a decision fusion rule, so an efficient decision fusion rule is extremely critical. It is obvious that the decision-making capability of each node is different owing to the dissimilar signal noise ratios and some other factors, so it is easy to understand that a specific sensor contribution to the global decision should be constrained by this sensor decision-making capability, and, based on this idea, we establish a novel linear decision fusion model for WSNs. Moreover, the constrained particle swarm optimization (constrained PSO) algorithm is creatively employed to control the parameters of this model in this paper and we also apply the typical penalty function to solve the constrained PSO problem. The emulation results indicate that our design is capable of achieving very high accuracy.
D-S Theory Based on an Improved PSO for Data Fusion  [cached]
Peiyi Zhu,Weili Xiong,Ningning Qin,Baoguo Xu
Journal of Networks , 2012, DOI: 10.4304/jnw.7.2.370-376
Abstract: The Dempster-Shafer (D-S) theory is an excellent method of information fusion. Because of the difference which is caused by the sensors, it is essential to deal with the evidence with a method of weighed D-S theory. The new method to deal with data fusion based on improved D-S theory has been proposed, and set up the concept of weight of sensor evidence itself and evidence distance based on a quantification of the similarity between sets to acquire the reliability weight of the relationship between evidences. Considering the disadvantages of the improved D-S theory, a best method of obtaining evidence weight value is presented by an improved particle swarm optimization (PSO). Compared with the compared methods, this evidence theory proves more effective and advanced by making simulation test.
Fusion of Protein Aggregates Facilitates Asymmetric Damage Segregation  [PDF]
Miguel Coelho equal contributor,Steven J. Lade equal contributor,Simon Alberti,Thilo Gross,Iva M. Toli?
PLOS Biology , 2014, DOI: 10.1371/journal.pbio.1001886
Abstract: Asymmetric segregation of damaged proteins at cell division generates a cell that retains damage and a clean cell that supports population survival. In cells that divide asymmetrically, such as Saccharomyces cerevisiae, segregation of damaged proteins is achieved by retention and active transport. We have previously shown that in the symmetrically dividing Schizosaccharomyces pombe there is a transition between symmetric and asymmetric segregation of damaged proteins. Yet how this transition and generation of damage-free cells are achieved remained unknown. Here, by combining in vivo imaging of Hsp104-associated aggregates, a form of damage, with mathematical modeling, we find that fusion of protein aggregates facilitates asymmetric segregation. Our model predicts that, after stress, the increased number of aggregates fuse into a single large unit, which is inherited asymmetrically by one daughter cell, whereas the other one is born clean. We experimentally confirmed that fusion increases segregation asymmetry, for a range of stresses, and identified Hsp16 as a fusion factor. Our work shows that fusion of protein aggregates promotes the formation of damage-free cells. Fusion of cellular factors may represent a general mechanism for their asymmetric segregation at division.
Application of ELMD Entropy Feature Fusion and PSO-SVM in Gear Fault Diagnosis

- , 2019,
Abstract: 提出基于ELMD熵特征融合与PSO-SVM的齿轮故障诊断方法。该方法首先对原始信号进行总体局部均值分解(Ensemble local mean decomposition,ELMD),得到若干乘积函数(PF);其次,对ELMD分解得到的前5个PF分量进行求取能量熵和近似熵,并利用KPCA对其进行特征融合;然后,选取部分融合特征作为训练样本,其余作为测试样本;最后,利用PSO优化的支持向量机对融合特征样本进行训练与测试。实验中,将单特征和融合特征分别进行SVM和PSO-SVM识别精度的对比。实验结果证明,所提方法可有效地应用在齿轮故障诊断中。
A gear fault diagnosis method based on ELMD entropy feature fusion and PSO-SVM is proposed in this paper. Firstly, the original signal is decomposed by ensemble local mean decomposition (ELMD), and several product functions (PF) are obtained. Secondly, the energy entropy and approximate entropy of the first five PF components obtained by ELMD decomposition were obtained and characterized by KPCA. Then, some of the fusion features are selected as training samples, the rest as test samples; finally, the PSO-optimized support vector machine is used to train and test the fusion feature samples. In the experiment, the singular and fusion features are compared with the recognition accuracy of SVM and PSO-SVM respectively. Experimental results show that the proposed method can be effectively applied in gear fault diagnosis
A Hybrid Particle Swarm Optimization (PSO)-Simplex Algorithm for Damage Identification of Delaminated Beams
Xiangdong Qian,Maosen Cao,Zhongqing Su,Jiangang Chen
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/607418
Abstract: Delamination is a type of representative damage in composite structures, severely degrading structural integrity and reliability. The identification of delamination is commonly treated as an issue of nondestructive testing. Differing from existing studies, a hybrid optimization algorithm (HOA), combining particle swarm optimization (PSO) with simplex method (SM), is proposed to identify delamination in laminated beams. The objective function of the optimization problem is created using delamination variables (optimization parameters) together with actually measured modal frequencies. The HOA adopts a hierarchical and cooperative regime of global search and local search to optimize the objective function. The PSO performs global search for objective function space to achieve a preliminary solution specifying a local potential space. Initialized by this preliminary solution, the SM executes local search for the local potential space to explore the optimal solution. The HOA is validated by a series of simulated delamination scenarios, and the results show that it can identify delamination in laminated beams with decent accuracy, reliability and efficiency. The method proposed holds promise for establishing online damage detection system beneficial for health monitoring of laminated composite structures.
Decision Fusion for Structural Damage Detection: Numerical and Experimental Studies  [PDF]
Yong Chen,Senyuan Tian,Bingnan Sun
Advances in Civil Engineering , 2010, DOI: 10.1155/2010/820762
Abstract: This paper describes a decision fusion strategy that can integrate multiple individual damage detection measures to form a new measure, and the new measure has higher probability of correct detection than any individual measure. The method to compute the probability of correct selection is presented to measure the system performance of the fusion system that includes the presented fusion strategy. And parametric sensitive studies on system performance are also conducted. The superiority of the fusion strategy herein is that it can be extended to deal with the multiresolution subdecision or blind adaptive detection, and corresponding methodologies are also provided. Finally, an experimental setup was fabricated, whereby the vibration properties of damaged and undamaged structures were measured. The experimental results with the undamaged structural model provide information for producing an improved theoretical and numerical model via model updating techniques. Three existing vibration-based damage detection methods with varied resolutions were utilized to identify the damage that occurred in the structure, based on the experimental results. Then the decision fusion strategy was implemented to join the subdecisions from these three methods. The fused results are shown to be superior to those from single method. 1. Introduction In the structural health monitoring (SHM) for civil engineering, the monitoring system that consists of numerous sensors is usually employed to achieve more accurate damage detection results, and a number of detection methods have been developed to identify the damages in accordance with structural vibration information (see, e.g., [1–5]). However, there is no perfect damage detection method that is capable of dealing with all kinds of structures, sensors or damages. Focusing on achieving a perfect single damage detection method to solve all damage detection problems does not sound feasible especially for civil engineering structures. Consequently, combining many detection methods together and fusing the sub-results to obtain more accurate detection results would be reasonable. Damage detection-oriented decision fusion that has this ability attracted increasing attentions of researchers studying SHM-based damage detection. Actually, the initial data fusion researches were predominantly in the defense systems (see, e.g., [6]). It can be traced back to Tenney and Sandell’s achievements [7], and it has been developed quickly in recent years. In early research, Chair and Varshney concentrated on binary decision fusion [8]. Demirbas
Radiation damage of the ILC positron source target  [PDF]
Andriy Ushakov,Sabine Riemann
Physics , 2007,
Abstract: The radiation damage of the positron source target for the International Linear Collider (ILC) has been studied. The displacement damage in target material due to multi-MeV photons has been calculated by combining FLUKA simulations for secondary particle production, SPECTER data for neutron displacement cross-sections and the Lindhard model for estimations of displacement damage by ions. The radiation damage of a stationary Ti6Al4V target in units of displacements per atom (dpa) has been estimated for photons from an undulator with strength 0.92 and period 1.15 cm. The calculated damage is 7 dpa. Approximately 12.5% of displacement damage result from neutrons.
Uniformity of fuel target implosion in Heavy Ion Fusion  [PDF]
S. Kawata,K. Noguchi,T. Suzuki,T. Karino,D. Barada,A. I. Ogoyski,Y. Y. Ma
Physics , 2014,
Abstract: In inertial confinement fusion the target implosion non-uniformity is introduced by a driver beams' illumination non-uniformity, a fuel target alignment error in a fusion reactor, the target fabrication defect, et al. For a steady operation of a fusion power plant the target implosion should be robust against the implosion non-uniformities. In this paper the requirement for the implosion uniformity is first discussed. The implosion uniformity should be less than a few percent. A study on the fuel hotspot dynamics is also presented and shows that the stagnating plasma fluid provides a significant enhancement of vorticity at the final stage of the fuel stagnation. Then non-uniformity mitigation mechanisms of the heavy ion beam (HIB) illumination are also briefly discussed in heavy ion inertial fusion (HIF). A density valley appears in the energy absorber, and the large-scale density valley also works as a radiation energy confinement layer, which contributes to a radiation energy smoothing. In HIF a wobbling heavy ion beam illumination was also introduced to realize a uniform implosion. In the wobbling HIBs illumination, the illumination non-uniformity oscillates in time and space on a HIF target. The oscillating-HIB energy deposition may contribute to the reduction of the HIBs' illumination non-uniformity by its smoothing effect on the HIB illumination non-uniformity and also by a growth mitigation effect on the Rayleigh-Taylor instability.
Target threat assessment based on PSO-BP algorithm

CHEN Hu,ZHANG Ke,CAO Jian-shu,

计算机应用研究 , 2012,
Abstract: This paper established a target thread model based on the factors of space situation and air combat capacity of targets, such as altitude, distance, speed and angle, and proposed a target threat level assessment method based on PSO (particle swarm optimization) and BP(back-propagation) algorithm to estimate the threat level of aerial targets. Through predicting threat level of 8 aerial targets, the result shows that this method is effective to solve the problem of air threat assessment target, greatly improving the objectivity of decision-making.
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