scholarly journals Reconstruction Method for Missing Measurement Data Based on Wasserstein Generative Adversarial Network

2021 ◽  
Vol 25 (2) ◽  
pp. 195-203
Author(s):  
Changfan Zhang ◽  
◽  
Hongrun Chen ◽  
Jing He ◽  
Haonan Yang
Keyword(s):  
High Speed ◽  
Measurement Data ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Distribution Law ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Context Similarity ◽  
High Speed Trains ◽  
Discrete Measurement

Focusing on the issue of missing measurement data caused by complex and changeable working conditions during the operation of high-speed trains, in this paper, a framework for the reconstruction of missing measurement data based on a generative adversarial network is proposed. Suitable parameters were set for each frame. Discrete measurement data are taken as the input of the frame for preprocessing the data dimensionality. The convolutional neural network then learns the correlation between different characteristic values of each device in an unsupervised pattern and constrains and improves the reconstruction accuracy by taking advantage of the context similarity of authenticity. It was determined experimentally that when there are different extents of missing measurement data, the model described in the present paper can still maintain a high reconstruction accuracy. In addition, the reconstruction data also conform well to the distribution law of the measurement data.

scholarly journals Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4365
Author(s):  
Kwangyong Jung ◽  
Jae-In Lee ◽  
Nammoon Kim ◽  
Sunjin Oh ◽  
Dong-Wook Seo
Keyword(s):  
Transfer Learning ◽  
Data Augmentation ◽  
Doppler Frequency ◽  
Extraction Methods ◽  
Classification Performance ◽  
Reconstruction Method ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Space Objects ◽  
Target Characteristics

Radar target classification is an important task in the missile defense system. State-of-the-art studies using micro-doppler frequency have been conducted to classify the space object targets. However, existing studies rely highly on feature extraction methods. Therefore, the generalization performance of the classifier is limited and there is room for improvement. Recently, to improve the classification performance, the popular approaches are to build a convolutional neural network (CNN) architecture with the help of transfer learning and use the generative adversarial network (GAN) to increase the training datasets. However, these methods still have drawbacks. First, they use only one feature to train the network. Therefore, the existing methods cannot guarantee that the classifier learns more robust target characteristics. Second, it is difficult to obtain large amounts of data that accurately mimic real-world target features by performing data augmentation via GAN instead of simulation. To mitigate the above problem, we propose a transfer learning-based parallel network with the spectrogram and the cadence velocity diagram (CVD) as the inputs. In addition, we obtain an EM simulation-based dataset. The radar-received signal is simulated according to a variety of dynamics using the concept of shooting and bouncing rays with relative aspect angles rather than the scattering center reconstruction method. Our proposed model is evaluated on our generated dataset. The proposed method achieved about 0.01 to 0.39% higher accuracy than the pre-trained networks with a single input feature.

scholarly journals Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3941 ◽  
Author(s):  
Li ◽  
Cai ◽  
Wang ◽  
Zhang ◽  
Tang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Imaging System ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Generative Adversarial Network ◽  
Image Domain ◽  
Adversarial Network ◽  
Domain Loss ◽  
Limited Angle

Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90°. We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms. With the development of deep learning, the generative adversarial network (GAN) performs well in image inpainting tasks and can add effective image information to restore missing parts of an image. In this study, given the characteristic of GAN to generate missing information, the sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction. We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images. Furthermore, we propose a joint projection domain and image domain loss function, in which the weighted image domain loss can be added by the back-projection operation. Then, by inputting a paired limited-angle/180° sinogram into the network for training, we can obtain the trained model, which has extracted the continuity feature of sinogram data. Finally, the classic CT reconstruction method is used to reconstruct the images after obtaining the estimated sinograms. The simulation studies and actual data experiments indicate that the proposed method performed well to reduce the serious artifacts caused by ultra-limited-angle scanning.

scholarly journals Deep Representation of a Normal Map for Screen-Space Fluid Rendering

2021 ◽  
Vol 11 (19) ◽  
pp. 9065
Author(s):  
Myungjin Choi ◽  
Jee-Hyeok Park ◽  
Qimeng Zhang ◽  
Byeung-Sun Hong ◽  
Chang-Hun Kim
Keyword(s):  
Surface Reconstruction ◽  
Computation Time ◽  
Fluid Simulation ◽  
Image Resolution ◽  
Reconstruction Method ◽  
Generative Adversarial Network ◽  
Normal Map ◽  
Adversarial Network ◽  
Reconstruction Methods ◽  
Screen Space

We propose a novel method for addressing the problem of efficiently generating a highly refined normal map for screen-space fluid rendering. Because the process of filtering the normal map is crucially important to ensure the quality of the final screen-space fluid rendering, we employ a conditional generative adversarial network (cGAN) as a filter that learns a deep normal map representation, thereby refining the low-quality normal map. In particular, we have designed a novel loss function dedicated to refining the normal map information, and we use a specific set of auxiliary features to train the cGAN generator to learn features that are more robust with respect to edge details. Additionally, we constructed a dataset of six different typical scenes to enable effective demonstrations of multitype fluid simulation. Experiments indicated that our generator was able to infer clearer and more detailed features for this dataset than a basic screen-space fluid rendering method. Moreover, in some cases, the results generated by our method were even smoother than those generated by the conventional surface reconstruction method. Our method improves the fluid rendering results via the high-quality normal map while preserving the advantages of the screen-space fluid rendering methods and the traditional surface reconstruction methods, including that of the computation time being independent of the number of simulation particles and the spatial resolution being related only to image resolution.

Effect of Long-Term Bridge Deformations on Safe Operation of High-Speed Railway and Vibration of Vehicle–Bridge Coupled System

2019 ◽  
Vol 19 (09) ◽  
pp. 1950111 ◽  
Author(s):  
Hongye Gou ◽  
Longcheng Yang ◽  
Zhixiang Mo ◽  
Wei Guo ◽  
Xiaoyu Shi ◽  
...  
Keyword(s):  
High Speed ◽  
Measurement Data ◽  
Coupled System ◽  
Bridge Pier ◽  
Railway Track ◽  
Vertical Acceleration ◽  
High Speed Train ◽  
High Speed Trains ◽  
Track Deformation

Operation safety of high-speed trains is dependent on their vibration characteristics, which vary with bridge deformation. This paper studies the influence of bridge pier settlement and girder creep camber, which are two typical types of long-term bridge deformation, on the vibration of high-speed trains. To this end, an analytical approach is presented to link the bridge deformation with railway track deformation; the track deformation is used to analyze the vibration of the CRH2 high-speed train in China. The vibration analysis results are validated using the in-situ measurement data. The present study shows that bridge pier settlement greatly affects the vertical acceleration, derailment coefficient and wheel unloading rate of the high-speed train; incorporating bridge girder camber aggravates the vibration of the train–bridge system. The threshold of bridge pier settlement is suggested to be 11.1[Formula: see text]mm for trains moving at 350[Formula: see text]km/h with regard to the code-specified vibration limit. This study has significant implications for the design and operation of high-speed railways.

Thru-the-wall Eavesdropping on Loudspeakers via RFID by Capturing Sub-mm Level Vibration

2021 ◽  
Vol 5 (4) ◽  
pp. 1-25
Author(s):  
Chuyu Wang ◽  
Lei Xie ◽  
Yuancan Lin ◽  
Wei Wang ◽  
Yingying Chen ◽  
...  
Keyword(s):  
Frequency Band ◽  
Free Space ◽  
High Speed ◽  
Complex Frequency ◽  
Brick Wall ◽  
User Privacy ◽  
Generative Adversarial Network ◽  
Vibration Effect ◽  
Human Voice ◽  
Adversarial Network

The unprecedented success of speech recognition methods has stimulated the wide usage of intelligent audio systems, which provides new attack opportunities for stealing the user privacy through eavesdropping on the loudspeakers. Effective eavesdropping methods employ a high-speed camera, relying on LOS to measure object vibrations, or utilize WiFi MIMO antenna array, requiring to eavesdrop in quiet environments. In this paper, we explore the possibility of eavesdropping on the loudspeaker based on COTS RFID tags, which are prevalently deployed in many corners of our daily lives. We propose Tag-Bug that focuses on the human voice with complex frequency bands and performs the thru-the-wall eavesdropping on the loudspeaker by capturing sub-mm level vibration. Tag-Bug extracts sound characteristics through two means: (1) Vibration effect, where a tag directly vibrates caused by sounds; (2) Reflection effect, where a tag does not vibrate but senses the reflection signals from nearby vibrating objects. To amplify the influence of vibration signals, we design a new signal feature referred as Modulated Signal Difference (MSD) to reconstruct the sound from RF-signals. To improve the quality of the reconstructed sound for human voice recognition, we apply a Conditional Generative Adversarial Network (CGAN) to recover the full-frequency band from the partial-frequency band of the reconstructed sound. Extensive experiments on the USRP platform show that Tag-Bug can successfully capture the monotone sound when the loudness is larger than 60dB. Tag-Bug can efficiently recognize the numbers of human voice with 95.3%, 85.3% and 87.5% precision in the free-space eavesdropping, thru-the-brick-wall eavesdropping and thru-the-insulating-glass eavesdropping, respectively. Tag-Bug can also accurately recognize the letters with 87% precision in the free-space eavesdropping.

Generative Adversarial Network-based Channel Estimation in High-Speed Mobile Scenarios

2021 ◽  
Author(s):  
Danyang Zhang ◽  
Junhui Zhao ◽  
Lihua Yang ◽  
Yiwen Nie ◽  
Xiangcheng Lin
Keyword(s):  
Channel Estimation ◽  
High Speed ◽  
Generative Adversarial Network ◽  
Adversarial Network
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