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.

Reliability Estimation of Stretchable Electronics Using a Dimension Reduction Framework

2016 ◽  
Author(s):  
Sungkun Hwang ◽  
Recep M. Gorguluarslan ◽  
Seung-Kyum Choi ◽  
Junki Min ◽  
Jack Moon
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Computational Cost ◽  
Feature Selection Method ◽  
Principal Component ◽  
Extraction Methods ◽  
Reliability Estimation ◽  
Stretchable Electronics ◽  
Input Variables ◽  
Multidisciplinary System

The proposed study develops a framework that accurately captures and models input and output variables for multidisciplinary systems in order to mitigate the computational cost when uncertainties are involved. Under this framework, the dimension of the random input variables is reduced depending on the degree of correlation calculated by an entropy based correlation coefficient (e). According to the obtained value of e, the dimension is truncated by two different methods. First feature extraction methods, namely Principal Component Analysis and the Auto-Encoder algorithm, are utilized when the input variables are highly correlated. In contrast, the Independent Features Test is implemented as the feature selection method if the correlation is too low to select a critical subset of model features. An Artificial Neural Network, including a Probabilistic Neural Network, is integrated into the framework to correctly capture the complex response behavior of the multidisciplinary system with low computational cost. The efficacy of the proposed method is demonstrated with electro-mechanical engineering examples, including a solder joint and a stretchable patch antenna.

scholarly journals Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

2020 ◽  
Author(s):  
V. Vijayasarveswari ◽  
A.M. Andrew ◽  
M. Jusoh ◽  
T. Sabapathy ◽  
R.A.A. Raof ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Probabilistic Neural Network ◽  
The Other ◽  
Ultra Wideband ◽  
Support Vector ◽  
Data Normalization ◽  
Multi Stage

AbstractBreast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi-stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

The Most Probable Optimal Design Method for Global Optimization

1999 ◽  
Author(s):  
Qinzhong Shi ◽  
Ichiro Hagiwara ◽  
Futoshi Takashima
Keyword(s):  
Neural Network ◽  
Optimal Design ◽  
Design Method ◽  
Global Optimum ◽  
Local Optimum ◽  
Space Design ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Global Optimal ◽  
Optimal Design Method

Abstract In this study, to find the global optimum efficiently, holographic neural network is introduced to be an activate function of response surface methodology. Since the accuracy of approximation function near the global optimal design is merely important, techniques to search the region of containing the global optimal design using conditional random seeds, and techniques for finding more, accurate approximation near the global optimal design, using holographic neural network are exploited. In the study, the proposed approach called the most probable optimal design (MPOD) method to pick up one local optimum design which has the biggest probability in the design space. Design example of crash worthiness for the passenger injury with continuous and discrete design variables are shown the validity of the method.

scholarly journals Design and Optimization of Microstrip Patch Antenna using Artificial Neural Networks

2019 ◽  
Vol 8 (9S) ◽  
pp. 611-616
Keyword(s):  
Neural Network ◽  
Patch Antenna ◽  
Microstrip Patch Antenna ◽  
Simulation Software ◽  
Ultra Wideband ◽  
Rbf Network ◽  
Design And Optimization ◽  
Design Procedures ◽  
Microstrip Patch ◽  
Artificial Neural

In this paper a Neural Network model for the design of a Microstrip Patch Antenna for an Ultrawideband frequency range is presented. The reduced ground size is used to enhance bandwidth in proposed design. The results obtained from the proposed method are compared with the results of EM simulation software and are found to be in good agreement. The advantage of the proposed method lies with the fact that the various parameters required for the design of a Microstrip Patch Antenna at a particular frequency of interest can be easily extracted without going into the rigorous time consuming, iterative design procedures using a costly software package. In the paper staircase patch design is considered for ultra-wideband matching of Antenna. The results obtained from artificial neural network when compared with experimental and simulation results, found satisfactory and also it is concluded that Radial Basis Function (RBF) network is more accurate and fast for the proposed design.

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.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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