Advanced Probabilistic Neural Network for Reliability Estimation via Semi-supervised Learning

2012 ◽  
Author(s):  
Jiten Patel ◽  
Seung-kyum Choi
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Probabilistic Neural Network ◽  
Reliability Estimation

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.

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.

scholarly journals Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 403
Author(s):  
Xun Zhang ◽  
Lanyan Yang ◽  
Bin Zhang ◽  
Ying Liu ◽  
Dong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Structured Data ◽  
Similarity Matrix ◽  
Scale Feature ◽  
Multi Scale ◽  
Adjacent Node ◽  
Feature Similarity ◽  
The Mean ◽  
Graph Neural Networks

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.

scholarly journals Study on channel prediction for automated guided vehicle using a probabilistic neural network

2019 ◽  
Vol 8 (8) ◽  
pp. 311-317 ◽  
Author(s):  
Julian Webber ◽  
Norisato Suga ◽  
Abolfazl Mehbodniya ◽  
Kazuto Yano ◽  
Yoshinori Suzuki
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Automated Guided Vehicle ◽  
Channel Prediction

An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection

2018 ◽  
Vol 108 ◽  
pp. 339-354 ◽  
Author(s):  
Nivethitha Somu ◽  
Gauthama Raman M.R. ◽  
Kalpana V. ◽  
Kannan Kirthivasan ◽  
Shankar Sriram V.S.
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Service Selection ◽  
Cloud Service ◽  
Trust Prediction ◽  
Prediction Approach
Sign in / Sign up

Export Citation Format

Share Document