663 research outputs found
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
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
Reference-Free Displacement Estimation of Bridges Using Kalman Filter-Based Multimetric Data Fusion
Displacement responses of a bridge as a result of external loadings provide crucial information regarding structural integrity and current conditions. Due to the relative characteristic of displacement, the conventional measurement approach requires reference points to firmly install the transducers, while the points are often unavailable for bridges. In this paper, a displacement estimation approach using Kalman filter-based data fusion is proposed to provide a practical means for displacement measurement. The proposed method enables accurate displacement estimation by optimally utilizing acceleration and strain in combination that have high availability and are free from reference points for sensor installation. The Kalman filter is formulated using a state-space model representing the double integration of acceleration and model-based strain-displacement relationship. The validation of the proposed method is conducted successfully by a numerical simulation and a field experiment, which shows the efficacy and accuracy of the proposed approach in bridge displacement measurement.ope
An Active and Soft Hydrogel Actuator to Stimulate Live Cell Clusters by Self-folding
The hydrogels are widely used in various applications, and their successful uses depend on controlling the mechanical properties. In this study, we present an advanced strategy to develop hydrogel actuator designed to stimulate live cell clusters by self-folding. The hydrogel actuator consisting of two layers with different expansion ratios were fabricated to have various curvatures in self-folding. The expansion ratio of the hydrogel tuned with the molecular weight and concentration of gel-forming polymers, and temperature-sensitive molecules in a controlled manner. As a result, the hydrogel actuator could stimulate live cell clusters by compression and tension repeatedly, in response to temperature. The cell clusters were compressed in the 0.7-fold decreases of the radius of curvature with 1.0 mm in room temperature, as compared to that of 1.4 mm in 37 degrees C. Interestingly, the vascular endothelial growth factor (VEGF) and insulin-like growth factor-binding protein-2 (IGFBP-2) in MCF-7 tumor cells exposed by mechanical stimulation was expressed more than in those without stimulation. Overall, this new strategy to prepare the active and soft hydrogel actuator would be actively used in tissue engineering, drug delivery, and micro-scale actuators
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters
Graph pooling is a crucial operation for encoding hierarchical structures
within graphs. Most existing graph pooling approaches formulate the problem as
a node clustering task which effectively captures the graph topology.
Conventional methods ask users to specify an appropriate number of clusters as
a hyperparameter, then assume that all input graphs share the same number of
clusters. In inductive settings where the number of clusters can vary, however,
the model should be able to represent this variation in its pooling layers in
order to learn suitable clusters. Thus we propose GMPool, a novel
differentiable graph pooling architecture that automatically determines the
appropriate number of clusters based on the input data. The main intuition
involves a grouping matrix defined as a quadratic form of the pooling operator,
which induces use of binary classification probabilities of pairwise
combinations of nodes. GMPool obtains the pooling operator by first computing
the grouping matrix, then decomposing it. Extensive evaluations on molecular
property prediction tasks demonstrate that our method outperforms conventional
methods.Comment: 10 pages, 3 figure
Detecting Bladder Biomarkers for Closed-Loop Neuromodulation: A Technological Review
Neuromodulation was introduced for patients with poor outcomes from the existing traditional treatment approaches. It is well-established as an alternative, novel treatment option for voiding dysfunction. The current system of neuromodulation uses an open-loop system that only delivers continuous stimulation without considering the patient’s state changes. Though the conventional open-loop system has shown positive clinical results, it can cause problems such as decreased efficacy over time due to neural habituation, higher risk of tissue damage, and lower battery life. Therefore, there is a need for a closed-loop system to overcome the disadvantages of existing systems. The closed-loop neuromodulation includes a system to monitor and stimulate micturition reflex pathways from the lower urinary tract, as well as the central nervous system. In this paper, we reviewed the current technological status to measure biomarker for closed-loop neuromodulation systems for voiding dysfunction
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
Pretraining molecular representations from large unlabeled data is essential
for molecular property prediction due to the high cost of obtaining
ground-truth labels. While there exist various 2D graph-based molecular
pretraining approaches, these methods struggle to show statistically
significant gains in predictive performance. Recent work have thus instead
proposed 3D conformer-based pretraining under the task of denoising, which led
to promising results. During downstream finetuning, however, models trained
with 3D conformers require accurate atom-coordinates of previously unseen
molecules, which are computationally expensive to acquire at scale. In light of
this limitation, we propose D&D, a self-supervised molecular representation
learning framework that pretrains a 2D graph encoder by distilling
representations from a 3D denoiser. With denoising followed by cross-modal
knowledge distillation, our approach enjoys use of knowledge obtained from
denoising as well as painless application to downstream tasks with no access to
accurate conformers. Experiments on real-world molecular property prediction
datasets show that the graph encoder trained via D&D can infer 3D information
based on the 2D graph and shows superior performance and label-efficiency
against other baselines.Comment: 16 pages, 5 figure
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