scholarly journals Deep neural network-based surrogate model linked with particle swarm optimization for identification of subsurface contamination sources

2020 ◽  
Author(s):  
Aatish Anshuman ◽  
T.I. Eldho
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Surrogate Model ◽  
Deep Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Subsurface Contamination ◽  
Contamination Sources

scholarly journals A Hybrid Deep Neural Network for Electricity Theft Detection Using Intelligent Antenna-Based Smart Meters

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Ashraf Ullah ◽  
Nadeem Javaid ◽  
Adamu Sani Yahaya ◽  
Tanzeela Sultana ◽  
Fahad Ahmad Al-Zahrani ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Particle Swarm ◽  
Smart Meters ◽  
Swarm Optimization ◽  
Electricity Theft ◽  
Unit Model ◽  
Gated Recurrent Unit

This paper presents a hybrid model, named as hybrid deep neural network, which combines convolutional neural network, particle swarm optimization, and gated recurrent unit, termed as convolutional neural network-particle swarm optimization-gated recurrent unit model. The major aims of the model are to perform accurate electricity theft detection and to overcome the issues in the existing models. The issues include overfitting and inability of the models to handle imbalanced data. For this purpose, the electricity consumption data of smart meters is taken from state grid corporation of China. An electric utility company gathers the data from the intelligent antenna-based smart meters installed at the consumers’ end. The dataset contains real-time data with missing values and outliers. Therefore, it is first preprocessed to get the refined data followed by feature engineering for selection and extraction of the finest features from the dataset using convolutional neural network. The classification of electricity consumers is performed by dividing them into honest and fraudulent classes using the proposed particle swarm optimization-gated recurrent unit model. The proposed model is evaluated by performing simulations in terms of several performance measures that include accuracy, area under the curve, F 1 -score, recall, and precision. The comparison between the proposed hybrid deep neural network and benchmark models is also performed. The benchmark models include gated recurrent unit, long short term memory, logistic regression, support vector machine, and genetic algorithm-based gated recurrent unit. The results indicate that the proposed hybrid deep neural network model is more efficient in handling class imbalanced issues and performing electricity theft detection. The robustness, accuracy, and generalization of the model are also analyzed in the proposed work.

scholarly journals Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring

2018 ◽  
Vol 8 (12) ◽  
pp. 2611 ◽  
Author(s):  
Nanqi Yuan ◽  
Wenli Yang ◽  
Byeong Kang ◽  
Shuxiang Xu ◽  
Xiaolin Wang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Neural Network ◽  
Dimensional Space ◽  
Particle Swarm ◽  
Condition Assessment ◽  
Machine Condition ◽  
Swarm Optimization ◽  
Laplacian Eigenmaps ◽  
Feature Conversion

This work reports a novel method by fusing Laplacian Eigenmaps feature conversion and deep neural network (DNN) for machine condition assessment. Laplacian Eigenmaps is adopted to transform data features from original high dimension space to projected lower dimensional space, the DNN is optimized by the particle swarm optimization algorithm, and the machine run-to-failure experiment were investigated for validation studies. Through a series of comparative experiments with the original features, two other effective space transformation techniques, Principal Component Analysis (PCA) and Isometric map (Isomap), and two other artificial intelligence methods, hidden Markov model (HMM) as well as back-propagation neural network (BPNN), the present method in this paper proved to be more effective for machine operation condition assessment.

scholarly journals Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems

Photonics ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 111 ◽  
Author(s):  
Xiaoyong Sun ◽  
Shaojing Su ◽  
Junyu Wei ◽  
Xiaojun Guo ◽  
Xiaopeng Tan
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Structure ◽  
Deep Neural Network ◽  
Estimation Error ◽  
Particle Swarm ◽  
Original Data ◽  
Binary Particle Swarm Optimization ◽  
Optical Performance Monitoring ◽  
Swarm Optimization

A novel technique is proposed to implement optical signal-to-noise ratio (OSNR) estimation by using an improved binary particle swarm optimization (IBPSO) and deep neural network (DNN) based on amplitude histograms (AHs) of signals obtained after constant modulus algorithm (CMA) equalization in an optical coherent system. For existing OSNR estimation models of DNN and AHs, sparse AHs with valid features of original data are selected by IBPSO algorithm to replace the original, and the sparse sets are used as input vector to train and test the particle swarm optimization (PSO) optimized DNN (PSO-DNN) network structure. Numerical simulations have been carried out in the OSNR ranges from 10 dB to 30 dB for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16QAM signals, and results show that the proposed algorithm achieves a high OSNR estimation accuracy with the maximum estimation error is less than 0.5 dB. In addition, the simulation results with different data input into the deep neural network structure show that the mean OSNR estimation error is 0.29 dB and 0.39 dB under original data and 0.29 dB and 0.37 dB under sparse data for the two signals, respectively. In the future dynamic optical network, it is of more practical significance to reconstruct the original signal and analyze the data using sparse observation information in the face of multiple impairment and serious interference. The proposed technique has the potential to be applied for optical performance monitoring (OPM) and is helpful for better management of optical networks.

scholarly journals Particle Swarm Optimization-Based Deep Neural Network for Digital Modulation Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 104591-104600 ◽  
Author(s):  
Wenzhe Shi ◽  
Dejun Liu ◽  
Xing Cheng ◽  
Yang Li ◽  
Yang Zhao
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Neural Network ◽  
Particle Swarm ◽  
Modulation Recognition ◽  
Digital Modulation ◽  
Swarm Optimization

scholarly journals Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Tuan Anh Pham ◽  
Van Quan Tran ◽  
Huong-Lan Thi Vu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Neural Network ◽  
Soil Layer ◽  
Particle Swarm ◽  
Friction Angle ◽  
Pso Algorithm ◽  
Unit Weight ◽  
Data Set ◽  
Swarm Optimization

This study focuses on the use of deep neural network (DNN) to predict the soil friction angle, one of the crucial parameters in geotechnical design. Besides, particle swarm optimization (PSO) algorithm was used to improve the performance of DNN by selecting the best structural DNN parameters, namely, the optimal numbers of hidden layers and neurons in each hidden layer. For this aim, a database containing 245 laboratory tests collected from a project in Ho Chi Minh city, Vietnam, was used for the development of the proposed hybrid PSO-DNN model, including seven input factors (soil state, standard penetration test value, unit weight of soil, void ratio, thickness of soil layer, top elevation of soil layer, and bottom elevation of soil layer) and the friction angle was considered as the target. The data set was divided into three parts, namely, the training, validation, and testing sets for the construction, validation, and testing phases of the model. Various quality assessment criteria, namely, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were used to estimate the performance of PSO-DNN models. The PSO algorithm showed a remarkable ability to find out an optimal DNN architecture for the prediction process. The results showed that the PSO-DNN model using 10 hidden layers outperformed the DNN model, in which the average correlation improvement increased R2 by 1.83%, MAE by 5.94%, and RMSE by 8.58%. Besides, a global sensitivity analysis technique was used to detect the most important inputs, and it showed that, among the seven input variables, the elevation of top and bottom of soil played an important role in predicting the friction angle of soil.

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