The Artificial Power System Networks Stability Control Using the Technology of Neural Network

In the present work, the electric voltage stability at Muharda station in Syria was studied during the normal and up to normal loading states. The results were obtained using artificial neural network, which consists of three layers (input-hidden-output). This network is characterized by the speed and accuracy in processing before failure and supply turn-off, which may lead to economical problems. This study was carried out using two different generating schemes in this station (single double). The performance of this network consists of two stages: training stage (off-line) and testing stage (on-line), and a comparison between these stages is carried out, which leads to optimization the load in testing cases depending on the training data.

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CNN for Image Identification of Hiragana Based on Pattern Recognition using CNN

Journal of Applied Intelligent System ◽

10.33633/jais.v6i2.4586 ◽

2021 ◽

Vol 6 (2) ◽

pp. 62-71

Author(s):

Chaerul Umam ◽

Andi Danang Krismawan ◽

Rabei Raad Ali

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Convolutional Neural Network ◽

Training Data ◽

Training Process ◽

Image Identification ◽

Training Stage ◽

Testing Stage ◽

Testing Data ◽

Network Method

Hiragana is one of the letters in Japanese. In this study, CNN (Convolutional Neural Network) method used as identication method, while he preprocessing used thresholding. Then carry out the normalization stage and the filtering stage to remove noise in the image. At the training stage use maxpooling and danse methods as a liaison in the training process, wherea in testing stage using the Adam Optimizer method. Here, we use 1000 images from 50 hiragana characters with a ratio of 950: 50, 950 as training data and 50 data as testing data. Our experiment yield accuracy in 95%.

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DANNP: an efficient artificial neural network pruning tool

PeerJ Computer Science ◽

10.7717/peerj-cs.137 ◽

2017 ◽

Vol 3 ◽

pp. e137 ◽

Cited By ~ 7

Author(s):

Mona Alshahrani ◽

Othman Soufan ◽

Arturo Magana-Mora ◽

Vladimir B. Bajic

Keyword(s):

Neural Network ◽

State Of The Art ◽

Model Performance ◽

Training Data ◽

Classification Problems ◽

Link Type ◽

On Line ◽

Pruning Algorithms ◽

Artificial Neural ◽

The Impact

Background Artificial neural networks (ANNs) are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links) may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs, these algorithms are not able to efficiently cope with intricate ANN structures required for complex classification problems. Methods We developed DANNP, a web-based tool, that implements parallelized versions of several ANN pruning algorithms. The DANNP tool uses a modified version of the Fast Compressed Neural Network software implemented in C++ to considerably enhance the running time of the ANN pruning algorithms we implemented. In addition to the performance evaluation of the pruned ANNs, we systematically compared the set of features that remained in the pruned ANN with those obtained by different state-of-the-art feature selection (FS) methods. Results Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge, DANNP (publicly available at www.cbrc.kaust.edu.sa/dannp) is the only available and on-line accessible tool that provides multiple parallelized ANN pruning options. Datasets and DANNP code can be obtained at www.cbrc.kaust.edu.sa/dannp/data.php and https://doi.org/10.5281/zenodo.1001086.

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A K Nearest Neighborhood-Based Wind Estimation for Rotary-Wing VTOL UAVs

Drones ◽

10.3390/drones3020031 ◽

2019 ◽

Vol 3 (2) ◽

pp. 31 ◽

Cited By ~ 2

Author(s):

Liyang Wang ◽

Gaurav Misra ◽

Xiaoli Bai

Keyword(s):

Speed Estimation ◽

Wind Disturbance ◽

Wind Estimation ◽

Estimation Algorithms ◽

Aerodynamic Modeling ◽

Training Stage ◽

On Line ◽

Two Stages ◽

Nearest Neighborhood ◽

Rotary Wing

Wind speed estimation for rotary-wing vertical take-off and landing (VTOL) UAVs is challenging due to the low accuracy of airspeed sensors, which can be severely affected by the rotor’s down-wash effect. Unlike traditional aerodynamic modeling solutions, in this paper, we present a K Nearest Neighborhood learning-based method which does not require the details of the aerodynamic information. The proposed method includes two stages: an off-line training stage and an on-line wind estimation stage. Only flight data is used for the on-line estimation stage, without direct airspeed measurements. We use Parrot AR.Drone as the testing quadrotor, and a commercial fan is used to generate wind disturbance. Experimental results demonstrate the accuracy and robustness of the developed wind estimation algorithms under hovering conditions.

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On-line training neural network for process control detects availability of new training data and constructs training set by retrieving corresponding input data

Food Control ◽

10.1016/0956-7135(93)90132-8 ◽

1993 ◽

Vol 4 (1) ◽

pp. 50 ◽

Cited By ~ 1

Keyword(s):

Neural Network ◽

Process Control ◽

Input Data ◽

Training Data ◽

Training Set ◽

On Line ◽

Training Neural Network

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Few-shot Classification of Aerial Scene Images via Meta-learning

10.20944/preprints202010.0033.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Pei Zhang ◽

YIng Li ◽

Dong Wang ◽

Yunpeng Bai

Keyword(s):

Classification Problem ◽

Training Data ◽

Scene Classification ◽

Shot Classification ◽

Training Stage ◽

Testing Stage ◽

Meta Learning ◽

Cosine Distance ◽

Query Sample

CNN-based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale and support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

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A LOCAL-PATCH BASED MULTI-STAGE ARTIFICIAL-NEURAL-NETWORK TRAINING PROCEDURE AND ITS APPLICATION TO MATERIAL CHARACTERIZATION

International Journal of Computational Methods ◽

10.1142/s0219876207001266 ◽

2007 ◽

Vol 04 (03) ◽

pp. 439-458 ◽

Cited By ~ 1

Author(s):

YUNHUA LUO ◽

ARVIND SHAH

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Finite Element ◽

Training Data ◽

Training Procedure ◽

Training Time ◽

Multi Stage ◽

Training Stage ◽

Local Patch ◽

Artificial Neural

A local-patch based multi-stage artificial-neural-network (ANN) training procedure is proposed in this paper, to improve the accuracy of an ANN trained by a backpropagation (BP) algorithm and, at the same time, to reduce the overall training time. In the proposed procedure a conventional one-stage training procedure is split into multiple stages: an initial training stage and subsequent re-training stage(s). In the initial stage the training data are so selected that the trained ANN has adequate ability of generalization, that is, if provided with a set of new input, the ANN can predict the right region where the output is located, but the accuracy of the solution is not necessarily high. In the following re-training stage(s), local patches of training data, either selected from an existing data pool or generated by numerical methods such as finite element method, are used to re-train the ANN to improve the accuracy. Several factors that may have significant effects on the proposed procedure were investigated based on function approximation. As an example of application, the procedure was then used to train an ANN with finite element data to characterize material parameters.

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Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks

Mathematical Problems in Engineering ◽

10.1155/2021/8268249 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Pengfei Zhang ◽

Xiaoming Ju

Keyword(s):

Neural Network ◽

Gaussian Mixture ◽

Physical World ◽

Training Data ◽

Generative Adversarial Networks ◽

Support Vector ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Testing Stage

It is important to detect adversarial samples in the physical world that are far away from the training data distribution. Some adversarial samples can make a machine learning model generate a highly overconfident distribution in the testing stage. Thus, we proposed a mechanism for detecting adversarial samples based on semisupervised generative adversarial networks (GANs) with an encoder-decoder structure; this mechanism can be applied to any pretrained neural network without changing the network’s structure. The semisupervised GANs also give us insight into the behavior of adversarial samples and their flow through the layers of a deep neural network. In the supervised scenario, the latent feature of the semisupervised GAN and the target network’s logit information are used as the input of the external classifier support vector machine to detect the adversarial samples. In the unsupervised scenario, first, we proposed a one-class classier based on the semisupervised Gaussian mixture conditional generative adversarial network (GM-CGAN) to fit the joint feature information of the normal data, and then, we used a discriminator network to detect normal data and adversarial samples. In both supervised scenarios and unsupervised scenarios, experimental results show that our method outperforms latest methods.

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Few-Shot Classification of Aerial Scene Images via Meta-Learning

Remote Sensing ◽

10.3390/rs13010108 ◽

2020 ◽

Vol 13 (1) ◽

pp. 108

Author(s):

Pei Zhang ◽

Yunpeng Bai ◽

Dong Wang ◽

Bendu Bai ◽

Ying Li

Keyword(s):

Classification Problem ◽

Training Data ◽

Scene Classification ◽

Shot Classification ◽

Training Stage ◽

Testing Stage ◽

Meta Learning ◽

Cosine Distance ◽

The Impact

Convolutional neural network (CNN) based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method yields state-of-the-art performance. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale, the impact of different metrics and the number of support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

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