A Generative Adversarial Network Based Learning Approach to The Autonomous Decision Making of High-speed Trains

2022 ◽  
pp. 1-1
Author(s):  
Xi Wang ◽  
Tianpeng Xin ◽  
Hongwei Wang ◽  
Li Zhu ◽  
Dongliang Cui
Keyword(s):  
Decision Making ◽  
High Speed ◽  
Learning Approach ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Autonomous Decision ◽  
High Speed Trains

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 Criminal Face Recognition Using GAN

2020 ◽  
Vol 5 (6) ◽  
pp. 1526-1528
Author(s):  
Anitta George ◽  
Krishnendu K A ◽  
Anusree K ◽  
Adira Suresh Nair ◽  
Hari Shree
Keyword(s):  
Face Recognition ◽  
Learning Approach ◽  
Security Measures ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Machine Learning Approach ◽  
Artificial Image ◽  
The Given ◽  
Biometric Feature

Forensics and security at present often use low technological resources. Security measures often fail to update with the upcoming technology. This project is based on implementing an automatic face recognition of criminals or specific targets using machine-learning approach. Given a set of features to a Generative Adversarial Network(GAN), the algorithm generates an image of the target with the specified feature set. The input to the machine can either be a given set of features or a set of portraits varying from frontals to side profiles from which these features can be extracted. The accuracy of the system is directly proportional to the number of epochs trained in the network. The generated output image can vary from primitive, low resolution images to high quality images where features are more recognizable. This is then compared with a predefined database of existing people. Thus, the target can immediately be recognized with the generation of an artificial image with the given biometric feature set, which will be again compared by a discriminator network to check the true identity of the target.

scholarly journals A reinforcement learning approach to autonomous decision-making in smart electricity markets

2013 ◽  
Vol 92 (1) ◽  
pp. 5-39 ◽  
Author(s):  
Markus Peters ◽  
Wolfgang Ketter ◽  
Maytal Saar-Tsechansky ◽  
John Collins
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Electricity Markets ◽  
Learning Approach ◽  
Autonomous Decision

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

scholarly journals A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Guokai Zhang ◽  
Haoping Xiao ◽  
Jingwen Jiang ◽  
Qinyuan Liu ◽  
Yimo Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Tool Wear ◽  
Imbalanced Data ◽  
Temporal Correlation ◽  
Monitoring Tool ◽  
Generative Adversarial Network ◽  
Multi Index ◽  
Machine Learning Methods ◽  
Adversarial Network

The scarcity of abnormal data leads to imbalanced data in the field of monitoring tool wear conditions. In this paper, a novel multi-index generative adversarial network (MI-GAN) is proposed to detect the tool wear conditions subject to imbalanced signal data. First, the generator in the MI-GAN is trained to produce fake normal signals, and the discriminator computes scores of testing signals and generated signals. Next, the generator detects abnormal signals based on the performance of imitating testing signals, and the discriminator will compute the scores of testing signals and generated signals. Subsequently, two indexes, i.e., L 2 -norm and temporal correlation coefficient (CORT), are put forward to measure the similarity between generated signals and testing signals. Finally, our decision-making function further combines L 2 -norm and CORT with two discriminator scores to determine the tool conditions. Experimental results show that our method obtains 97% accuracy in tool wear detection based on imbalanced data without manual feature extraction, which outperforms traditional machine learning methods.

scholarly journals High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3075 ◽  
Author(s):  
Baoqing Guo ◽  
Gan Geng ◽  
Liqiang Zhu ◽  
Hongmei Shi ◽  
Zujun Yu
Keyword(s):  
High Speed ◽  
Estimation Algorithm ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Foreign Object ◽  
Generative Adversarial Network ◽  
High Speed Railway ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Real Objects

Foreign object intrusion is a great threat to high-speed railway safety operations. Accurate foreign object intrusion detection is particularly important. As a result of the lack of intruding foreign object samples during the operational period, artificially generated ones will greatly benefit the development of the detection methods. In this paper, we propose a novel method to generate railway intruding object images based on an improved conditional deep convolutional generative adversarial network (C-DCGAN). It consists of a generator and multi-scale discriminators. Loss function is also improved so as to generate samples with a high quality and authenticity. The generator is extracted in order to generate foreign object images from input semantic labels. We synthesize the generated objects to the railway scene. To make the generated objects more similar to real objects, on scale in different positions of a railway scene, a scale estimation algorithm based on the gauge constant is proposed. The experimental results on the railway intruding object dataset show that the proposed C-DCGAN model outperforms several state-of-the-art methods and achieves a higher quality (the pixel-wise accuracy, mean intersection-over-union (mIoU), and mean average precision (mAP) are 80.46%, 0.65, and 0.69, respectively) and diversity (the Fréchet-Inception Distance (FID) score is 26.87) of generated samples. The mIoU of the real-generated pedestrian pairs reaches 0.85, and indicates a higher scale of accuracy for the generated intruding objects in the railway scene.

scholarly journals Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1418
Author(s):  
Lorenzo Ciani ◽  
Giulia Guidi ◽  
Gabriele Patrizi ◽  
Diego Galar
Keyword(s):  
Decision Making ◽  
High Speed ◽  
Failure Modes ◽  
Air Conditioning System ◽  
Suggested Approach ◽  
Reliability Centered Maintenance ◽  
High Speed Trains ◽  
Optimal Maintenance ◽  
The Cost ◽  
Maintenance Plan

Reliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method.

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