5,291 research outputs found
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Decentralized system identification using stochastic subspace identification for wireless sensor networks
Wireless sensor networks (WSNs) facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI) technique is selected for system identification, and SSI-based decentralized system identification (SDSI) is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model. ??? 2015 by the authors; licensee MDPI, Basel, Switzerlandopen0
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Structural health monitoring (SHM) is a technique to diagnose an accurate and reliable condition of civil infrastructure by
collecting and analyzing responses from distributed sensors. In recent years, aging civil structures have been increasing and they
require further developed SHM technology for development of sustainable society. Wireless smart sensor and network technology,
which is one of the recently emerging SHM techniques, enables more effective and economic SHM system in comparison to the
existing wired systems. Researchers continue on development of the capability and extension of wireless smart sensors, and
implement performance validation in various in-laboratory and outdoor full-scale experiments. This paper presents a summary of
recent (mostly after 2010) researches on smart sensors, focused on the newly developed hardware, software, and validation
examples of the developed smart sensors.ope
Identifying set-wise differential co-expression in gene expression microarray data
<p>Abstract</p> <p>Background</p> <p>Previous differential coexpression analyses focused on identification of differentially coexpressed gene pairs, revealing many insightful biological hypotheses. However, this method could not detect coexpression relationships between pairs of gene sets. Considering the success of many set-wise analysis methods for microarray data, a coexpression analysis based on gene sets may elucidate underlying biological processes provoked by the conditional changes. Here, we propose a differentially coexpressed gene sets (dCoxS) algorithm that identifies the differentially coexpressed gene set pairs between conditions.</p> <p>Results</p> <p>dCoxS is a two-step analysis method. In each condition, dCoxS measures the interaction score (IS), which represents the expression similarity between two gene sets using Renyi relative entropy. When estimating the relative entropy, multivariate kernel density estimation was used to model gene-gene correlation structure. Statistical tests for the conditional difference between the ISs determined the significance of differential coexpression of the gene set pair. Simulation studies supported that the IS is a representative measure of similarity between gene expression matrices. Single gene coexpression analysis of two publicly available microarray datasets detected no significant results. However, the dCoxS analysis of the datasets revealed differentially coexpressed gene set pairs related to the biological conditions of the datasets.</p> <p>Conclusion</p> <p>dCoxS identified differentially coexpressed gene set pairs not found by single gene analysis. The results indicate that set-wise differential coexpression analysis is useful for understanding biological processes induced by conditional changes.</p
Correlated electronic states at domain walls of a Mott-charge-density-wave insulator 1T-TaS2
Domain walls in interacting electronic systems can have distinct localized
states, which often govern physical properties and may lead to unprecedented
functionalities and novel devices. However, electronic states within domain
walls themselves have not been clearly identified and understood for strongly
correlated electron systems. Here, we resolve the electronic states localized
on domain walls in a Mott-charge-density-wave(CDW) insulator 1T-TaS2 using
scanning tunneling spectroscopy. We establish that the domain wall state
decomposes into two nonconducting states located at the center of domain walls
and edges of domains. Theoretical calculations reveal their atomistic origin as
the local reconstruction of domain walls under the strong influence of electron
correlation. Our results introduce a concept for the domain wall electronic
property, the wall's own internal degrees of freedom, which is potentially
related to the controllability of domain wall electronic properties
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