scholarly journals Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4270 ◽  
Author(s):  
Zilong Wu ◽  
Hong Chen ◽  
Yingke Lei
Keyword(s):  
Short Wave ◽  
Recognition Algorithm ◽  
Radio Station ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Radio Stations ◽  
Real Samples ◽  
Adversarial Network ◽  
Wave Radio ◽  
Training Samples

It is difficult to obtain many labeled Link Establishment (LE) behavior signals sent by non-cooperative short-wave radio stations. We propose a novel unidimensional Auxiliary Classifier Generative Adversarial Network (ACGAN) to get more signals and then use unidimensional DenseNet to recognize LE behaviors. Firstly, a few real samples were randomly selected from many real signals as the training set of unidimensional ACGAN. Then, the new training set was formed by combining real samples with fake samples generated by the trained ACGAN. In addition, the unidimensional convolutional auto-coder was proposed to describe the reliability of these generated samples. Finally, different LE behaviors could be recognized without the communication protocol standard by using the new training set to train unidimensional DenseNet. Experimental results revealed that unidimensional ACGAN effectively augmented the training set, thus improving the performance of recognition algorithm. When the number of original training samples was 400, 700, 1000, or 1300, the recognition accuracy of unidimensional ACGAN+DenseNet was 1.92, 6.16, 4.63, and 3.06% higher, respectively, than that of unidimensional DenseNet.

scholarly journals 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

2021 ◽  
Author(s):  
Cara Murphy ◽  
John Kerekes
Keyword(s):  
Classification Accuracy ◽  
Domain Adaptation ◽  
Real Data ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Average Classification Accuracy ◽  
Adversarial Network ◽  
Chemical Residues ◽  
Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

scholarly journals Learning to Transfer: Unsupervised Domain Translation via Meta-Learning

2020 ◽  
Vol 34 (07) ◽  
pp. 11507-11514
Author(s):  
Jianxin Lin ◽  
Yijun Wang ◽  
Zhibo Chen ◽  
Tianyu He
Keyword(s):  
Learning Experiences ◽  
Training Data ◽  
Training Procedure ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Training Samples ◽  
Meta Learning ◽  
Translation Methods ◽  
Meta Optimization ◽  
Dual Translation

Unsupervised domain translation has recently achieved impressive performance with Generative Adversarial Network (GAN) and sufficient (unpaired) training data. However, existing domain translation frameworks form in a disposable way where the learning experiences are ignored and the obtained model cannot be adapted to a new coming domain. In this work, we take on unsupervised domain translation problems from a meta-learning perspective. We propose a model called Meta-Translation GAN (MT-GAN) to find good initialization of translation models. In the meta-training procedure, MT-GAN is explicitly trained with a primary translation task and a synthesized dual translation task. A cycle-consistency meta-optimization objective is designed to ensure the generalization ability. We demonstrate effectiveness of our model on ten diverse two-domain translation tasks and multiple face identity translation tasks. We show that our proposed approach significantly outperforms the existing domain translation methods when each domain contains no more than ten training samples.

scholarly journals Analysis on the Influence of the Height of Tower on Passive Interference in shortwave

2018 ◽  
Vol 64 ◽  
pp. 05005
Author(s):  
Ying Lu ◽  
Zhibin Zhao ◽  
Jian gong Zhang ◽  
Zheyuan Gan
Keyword(s):  
Transmission Line ◽  
Method Of Moments ◽  
Transmission Lines ◽  
Electromagnetic Scattering ◽  
Electromagnetic Waves ◽  
Short Wave ◽  
Radio Station ◽  
Radio Stations ◽  
Working Frequency ◽  
Passive Interference

The passive interference of transmission lines to nearby radio stations may affect the effective reception and transmission of radio station signals. Therefore, the accurate calculation of the electromagnetic scattering of transmission lines under the condition of external electromagnetic waves is the basis for determining the reasonable avoidance spacing of the two. For passive stations operating in short-wave frequencies, passive interference is mainly generated by the tower. This paper uses the method of moments to perform passive interference calculations under normal circumstances, And elaborates the method for calculating the electromagnetic field of the transmission line, obtains the space electric field intensity of the transmission line at the same working frequency and space location of the plane wave. Uses the approximate formula to inductive the formula for calculating height of tower and the protective distance.

scholarly journals A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

2019 ◽  
Author(s):  
Oleksii Prykhodko ◽  
Simon Viet Johansson ◽  
Panagiotis-Christos Kotsias ◽  
Esben Jannik Bjerrum ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Deep Learning ◽  
De Novo ◽  
Chemical Space ◽  
Learning Method ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Structure Generation ◽  
Adversarial Network ◽  
Molecule Design ◽  
Novel Structures

<p>Recently deep learning method has been used for generating novel structures. In the current study, we proposed a new deep learning method, LatentGAN, which combine an autoencoder and a generative adversarial neural network for doing de novo molecule design. We applied the method for structure generation in two scenarios, one is to generate random drug-like compounds and the other is to generate target biased compounds. Our results show that the method works well in both cases, in which sampled compounds from the trained model can largely occupy the same chemical space of the training set and still a substantial fraction of the generated compound are novel. The distribution of drug-likeness score for compounds sampled from LatentGAN is also similar to that of the training set.</p>

scholarly journals A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

2019 ◽  
Author(s):  
Oleksii Prykhodko ◽  
Simon Viet Johansson ◽  
Panagiotis-Christos Kotsias ◽  
Josep Arús-Pous ◽  
Esben Jannik Bjerrum ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
De Novo ◽  
Molecular Design ◽  
Chemical Space ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
De Novo Molecular Design ◽  
Novel Structures

<p> </p><p>Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: one to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases: sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.</p><p> </p>

scholarly journals Recognizing Automatic Link Establishment Behaviors of a Short-Wave Radio Station by an Improved Unidimensional DenseNet

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96055-96064 ◽  
Author(s):  
Zilong Wu ◽  
Hong Chen ◽  
Yingke Lei ◽  
Hao Xiong
Keyword(s):  
Short Wave ◽  
Radio Station ◽  
Wave Radio

A Generative Adversarial Network Model for Simulating Various Types of Human-Induced Loads

2019 ◽  
Vol 19 (08) ◽  
pp. 1950092 ◽  
Author(s):  
Jiecheng Xiong ◽  
Jun Chen
Keyword(s):  
Network Model ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Real Samples ◽  
Load Model ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Power Spectral ◽  
Physical Features ◽  
Fully Connected

Severe vibrations may occur on slender structures like footbridges and cantilever stands due to human-induced loads such as walking, jumping or bouncing. Currently, to develop a load model for structural design, the main features, such as periodicity and stationarity of experimental load records, are artificially extracted and then mathematically modeled. Different physical features have been included in different load models, i.e. no unified load model exists for different individual activities. The recently emerged generative adversarial networks can be used to model high-dimensional random variables. The probability distribution of these variables learned from real samples can be used to generate new samples, avoiding extracting features artificially. In this paper, a new model is proposed which combines the conditional generative adversarial networks and Wasserstein generative adversarial networks with gradient penalty to generate individual walking, jumping and bouncing loads. The generator of the model has five fully connected layers and a one-dimensional convolutional layer, and the discriminator has five fully connected layers. After one million training steps using the experimental records, the generator can generate high-quality samples similar to real samples in waveform. Finally, by comparing the power spectral densities and single degree of freedom system’s responses of the generated samples with real samples, it is further proved that the proposed generative adversarial network model can be used to simulate various human-induced loads. Source code of the model along with its trained weights is provided to the readers to further analysis and application.

Data Augmentation based on Sequence Generative Adversarial Network for Chinese Clinical Named Entity Recognition (Preprint)

2020 ◽  
Author(s):  
蓬辉 王
Keyword(s):  
Data Augmentation ◽  
Named Entity Recognition ◽  
Basic Element ◽  
Entity Recognition ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Named Entity ◽  
Adversarial Network ◽  
External Resource ◽  
Medical Domain

BACKGROUND Chinese clinical named entity recognition, as a fundamental task of Chinese medical information extraction, plays an important role in recognizing medical entities contained in Chinese electronic medical records. Limited to lack of large annotated data, existing methods concentrate on employing external resources to improve the performance of clinical named entity recognition, which require lots of time and efficient rules to add external resources. OBJECTIVE To solve the problem of lack of large annotated data, we employ data augmentation without external resource to automatically generate more medical data depending on entities and non-entities in the training set, and enlarge training dataset to improve the performance of named entity recognition. METHODS In this paper, we propose a method of data augmentation, based on sequence generative adversarial network, to enlarge the training set. Different from other sequence generative adversarial networks, where the basic element is character or word, the basic element of our generated sequence is entity or non-entity. In our model, the generator can generate new sentences composed of entities and non-entities based on the learned hidden relationship between the entities and non-entities in the training set and the discriminator can judge if the generated sentences are positive and give rewards to help train the generator. The generated data from sequence adversarial network is used to enlarge the training set and improve the performance of named entity recognition in medical records. RESULTS Without external resource, we employ our data augmentation method in three datasets, both in general domains and medical domain. Experiments show that when we use generated data from data augmentation to expand training set, named entity recognition system has achieved competitive performance compared with existing methods, which shows the effectiveness of our data augmentation method. In general domains, our method achieves an overall F1-score of 59.42% in Weibo NER dataset and a F1-score of 95.28% in Resume. In medical domain, our method achieves 83.40%. CONCLUSIONS Our data augmentation method can expand training set based on the hidden relationship between entities and non-entities in the dataset, which can alleviate the problem of lack of labeled data while avoid using external resource. At the same time, our method can improve the performance of named entity recognition not only in general domains but also medical domain.

scholarly journals Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks

2019 ◽  
Vol 11 (9) ◽  
pp. 1017 ◽  
Author(s):  
Yang Zhang ◽  
Zhangyue Xiong ◽  
Yu Zang ◽  
Cheng Wang ◽  
Jonathan Li ◽  
...  
Keyword(s):  
Road Network ◽  
Quality Measurement ◽  
Human Interaction ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
The Road ◽  
Adversarial Network ◽  
Road Network Extraction ◽  
Training Samples ◽  
Public Datasets

Road network extraction from remote sensing images has played an important role in various areas. However, due to complex imaging conditions and terrain factors, such as occlusion and shades, it is very challenging to extract road networks with complete topology structures. In this paper, we propose a learning-based road network extraction framework via a Multi-supervised Generative Adversarial Network (MsGAN), which is jointly trained by the spectral and topology features of the road network. Such a design makes the network capable of learning how to “guess” the aberrant road cases, which is caused by occlusion and shadow, based on the relationship between the road region and centerline; thus, it is able to provide a road network with integrated topology. Additionally, we also present a sample quality measurement to efficiently generate a large number of training samples with a little human interaction. Through the experiments on images from various satellites and the comprehensive comparisons to state-of-the-art approaches on the public datasets, it is demonstrated that the proposed method is able to provide high-quality results, especially for the completeness of the road network.

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