scholarly journals Integration of Dimension Reduction and Uncertainty Quantification in Designing Stretchable Strain Gauge Sensor

2020 ◽  
Vol 10 (2) ◽  
pp. 643 ◽  
Author(s):  
Sungkun Hwang ◽  
Recep M. Gorguluarslan ◽  
Hae-Jin Choi ◽  
Seung-Kyum Choi
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Strain Gauge ◽  
Patch Antenna ◽  
Strain Sensor ◽  
Reduction Process ◽  
Structural Deformation ◽  
Gaussian Copula ◽  
Frequency Bandwidth ◽  
Design Variables

Interests in strain gauge sensors employing stretchable patch antenna have escalated in the area of structural health monitoring, because the malleable sensor is sensitive to capturing strain variation in any shape of structure. However, owing to the narrow frequency bandwidth of the patch antenna, the operation quality of the strain sensor is not often assured under structural deformation, which creates unpredictable frequency shifts. Geometric properties of the stretchable antenna also severely regulate the performance of the sensor. Especially rugged substrate created by printing procedure and manual fabrication derives multivariate design variables. Such design variables intensify the computational burden and uncertainties that impede reliable analysis of the strain sensor. In this research, therefore, a framework is proposed not only to comprehensively capture the sensor’s geometric design variables, but also to effectively reduce the multivariate dimensions. The geometric uncertainties are characterized based on the measurements from real specimens and a Gaussian copula is used to represent them with the correlations. A dimension reduction process with a clear decision criterion by entropy-based correlation coefficient dwindles uncertainties that inhibit precise system reliability assessment. After handling the uncertainties, an artificial neural network-based surrogate model predicts the system responses, and a probabilistic neural network derives a precise estimation of the variability of complicated system behavior. To elicit better performance of the stretchable antenna-based strain sensor, a shape optimization process is then executed by developing an optimal design of the strain sensor, which can resolve the issue of the frequency shift in the narrow bandwidth. Compared with the conventional rigid antenna-based strain sensors, the proposed design brings flexible shape adjustment that enables the resonance frequency to be maintained in reliable frequency bandwidth and antenna performance to be maximized under deformation. Hence, the efficacy of the proposed design framework that employs uncertainty characterization, dimension reduction, and machine learning-based behavior prediction is epitomized by the stretchable antenna-based strain sensor.

Optimal Design of Stretchable Electronics With the Consideration of Response Variability

2017 ◽  
Author(s):  
Sungkun Hwang ◽  
Seung-Kyum Choi
Keyword(s):  
Neural Network ◽  
Optimal Design ◽  
Patch Antenna ◽  
Probabilistic Neural Network ◽  
Decision Criterion ◽  
Reliability Estimation ◽  
Ultra Wideband ◽  
Stretchable Electronics ◽  
Computational Costs ◽  
Design Variables

Strain gauges based on the micro-strip patch antenna have been increasingly employed in structural health monitoring. However, the lower bandwidth, influenced by the antenna’s geometric properties, limits efficiency of the antenna when major strain, creating drastic variation of the resonant frequency, is applied. The performance of the antenna cannot be guaranteed without also considering the substrate’s varying thickness, caused by manual fabrication and printing procedure. However, all such considerations lead to an increase of multivariate design variables, that in turn, increase uncertainty and computational costs. Thus, the proposed research develops a framework that accurately models the geometric variables of the antenna and efficiently reduces the multivariate dimensions that draw uncertainty preventing accurate system reliability estimation. In the proposed framework, a dimension reduction method is thoroughly conducted by utilizing a critical decision criterion depending on the degree of correlation. Specifically, artificial neural network and probabilistic neural network are employed to correctly estimate the variability of complex system responses. Furthermore, an optimal design of the stretchable patch antenna is developed. This design will allow frequency shifts under tensile strain and still remain within reliable frequency ranges. The proposed approach is beneficial to the process of capturing and managing antenna design variables. The presented example clearly demonstrates the advantage of the obtained optimal design of the stretchable patch antenna compared to an ultra-wideband radar system that often requires complicated design processes and high computational costs.

scholarly journals Design and Modeling of New UWB Metamaterial Planar Cavity Antennas with Shrinking of the Physical Size for Modern Transceivers

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Mohammad Alibakhshi Kenari
Keyword(s):  
Transmission Lines ◽  
Patch Antenna ◽  
Patch Antennas ◽  
Frequency Bandwidth ◽  
Left Handed ◽  
Full Wave ◽  
Radiation Properties ◽  
Unit Cells ◽  
Constitutive Parameters ◽  
Large Frequency

A variety of antennas have been engineered with MTMs and MTM-inspired constructs to improve their performance characteristics. This report describes the theory of MTMs and its utilization for antenna's techniques. The design and modeling of two MTM structures withε-μconstitutive parameters for patch antennas are presented. The framework presents two novel ultrawideband (UWB) shrinking patch antennas filled with composite right-/left-handed transmission line (CRLH-TL) structures. The CRLH-TL is presented as a general TL possessing both left-handed (LH) and right-handed (RH) natures. The CRLH-TL structures enhance left-handed (LH) characteristics which enable size reduction and large frequency bandwidth. The large frequency bandwidth and good radiation properties can be obtained by adjusting the dimensions of the patches and CRLH-TL structures. This contribution demonstrates the possibility of reducing the size of planar antennas by using LH-transmission lines. Two different types of radiators are investigated—a planar patch antenna composed of fourO-formed unit cells and a planar patch antenna composed of sixO-shaped unit cells. A CRLH-TL model is employed to design and compare these two approaches and their realization with a varying number ofL-Cloaded unit cells. Two representative antenna configurations have been selected and subsequently optimized with full-wave electromagnetic analysis. Return loss and radiation pattern simulations of these antennas prove the developed concept.

scholarly journals Predicting tipping points in mutualistic networks through dimension reduction

2018 ◽  
Vol 115 (4) ◽  
pp. E639-E647 ◽  
Author(s):  
Junjie Jiang ◽  
Zi-Gang Huang ◽  
Thomas P. Seager ◽  
Wei Lin ◽  
Celso Grebogi ◽  
...  
Keyword(s):  
Dimension Reduction ◽  
Reduction Process ◽  
Real Data ◽  
Tipping Point ◽  
Networked Systems ◽  
Weighted Averaging ◽  
Dynamical Mechanism ◽  
Point Of No Return ◽  
2D System ◽  
Mutualistic Networks

Complex networked systems ranging from ecosystems and the climate to economic, social, and infrastructure systems can exhibit a tipping point (a “point of no return”) at which a total collapse of the system occurs. To understand the dynamical mechanism of a tipping point and to predict its occurrence as a system parameter varies are of uttermost importance, tasks that are hindered by the often extremely high dimensionality of the underlying system. Using complex mutualistic networks in ecology as a prototype class of systems, we carry out a dimension reduction process to arrive at an effective 2D system with the two dynamical variables corresponding to the average pollinator and plant abundances. We show, using 59 empirical mutualistic networks extracted from real data, that our 2D model can accurately predict the occurrence of a tipping point, even in the presence of stochastic disturbances. We also find that, because of the lack of sufficient randomness in the structure of the real networks, weighted averaging is necessary in the dimension reduction process. Our reduced model can serve as a paradigm for understanding and predicting the tipping point dynamics in real world mutualistic networks for safeguarding pollinators, and the general principle can be extended to a broad range of disciplines to address the issues of resilience and sustainability.

scholarly journals MEMS Acoustic Emission Sensors

2020 ◽  
Vol 10 (24) ◽  
pp. 8966
Author(s):  
Didem Ozevin
Keyword(s):  
Acoustic Emission ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Frequency Bandwidth ◽  
Micro Electro Mechanical Systems ◽  
Design Variables ◽  
Transduction Mechanisms ◽  
Acoustic Emission Sensors ◽  
Ae Signal ◽  
Constitutive Properties

This paper presents a review of state-of-the-art micro-electro-mechanical-systems (MEMS) acoustic emission (AE) sensors. MEMS AE sensors are designed to detect active defects in materials with the transduction mechanisms of piezoresistivity, capacitance or piezoelectricity. The majority of MEMS AE sensors are designed as resonators to improve the signal-to-noise ratio. The fundamental design variables of MEMS AE sensors include resonant frequency, bandwidth/quality factor and sensitivity. Micromachining methods have the flexibility to tune the sensor frequency to a particular range, which is important, as the frequency of AE signal depends on defect modes, constitutive properties and structural composition. This paper summarizes the properties of MEMS AE sensors, their design specifications and applications for detecting the simulated and real AE sources and discusses the future outlook.

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