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Time Series to Images: Monitoring the Condition of Industrial Assets with Deep Learning Image Processing Algorithms [article]

Gabriel Rodriguez Garcia, Gabriel Michau, Mélanie Ducoffe, Jayant Sen Gupta, Olga Fink
2021 arXiv   pre-print
Inspired by the success of deep learning methods in computer vision, several studies have proposed transforming time series into image-like representations, used as inputs for deep learning models, and  ...  Essential characteristics of time series, situated outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods when no transformations have been applied to the  ...  To the best of our knowledge, however, these approaches have never been tested on anomaly detection tasks for time series, despite major successes in the use of deep learning auto-encoders in image processing  ... 
arXiv:2005.07031v3 fatcat:4f7cpnwoabb4rb7uiser6mqpxe

Time Series Analysis and Modeling to Forecast: a Survey [article]

Fatoumata Dama, Christine Sinoquet
2021 arXiv   pre-print
Time series modeling for predictive purpose has been an active research area of machine learning for many years.  ...  Beyond conventional statistical models, we highlight six categories of deep neural networks appropriate for time series forecasting in nonlinear framework.  ...  Conventional Models versus Deep Neural Models A vast field of possibilities has opened up for the modeling and prediction of time series with the emergence of deep learning.  ... 
arXiv:2104.00164v2 fatcat:zz7kaefskrhvrl7wkmbgcbkkfu

Financial Market Sequence Prediction Based on Image Processing

Han He, Weiwei Liu
2020 IEEE Access  
Then, the symbol tree of the deep learning algorithm in this study is explained, and combined with the prediction function of deep learning neural network.  ...  The development of industry promotes the development of image processing technology to a certain extent, but the correct application of image processing technology in many practical problems can often  ... 
doi:10.1109/access.2020.3020062 fatcat:gt4dopwusrgsxhbjipipmf2gkm

A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators

Hoang-Phuong Nguyen, Jie Liu, Enrico Zio
2020 Applied Soft Computing  
In this paper, we address this problem by proposing a prediction model based on Long Short-Term Memory (LSTM), a deep neural network developed for dealing with the long-term dependencies in time-series  ...  process at different time steps.  ...  Acknowledgment The authors would like to thank the PRISME department of Électricité de France (EDF) R&D for providing the data used in this paper.  ... 
doi:10.1016/j.asoc.2020.106116 fatcat:w3cgrnhxffaqbi4dyhhnnfthvu

Exploring the Response Mechanism of Remote Sensing Images in Monitoring Fixed Assets Investment Project in Terms of Building Detection

Li Wang, Wensheng Duan, Bo Yu, Qing Ying, Hanbing Yang, Yahui Lei
2019 IEEE Access  
Semantic segmentation deep learning technology is applied to detect buildings from the high spatial resolution images.  ...  INDEX TERMS Fixed assets investment project, semantic segmentation, high spatial resolution remote sensing, deep learning, continuous monitoring, supervise construction process.  ...  sensing images in indicating the corresponding fixed assets investment through time-series constructions detection.  ... 
doi:10.1109/access.2019.2948060 fatcat:32tzom7gdndevm5pffpudvgqim

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

David Verstraete, Andrés Ferrada, Enrique López Droguett, Viviana Meruane, Mohammad Modarres
2017 Shock and Vibration  
To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data.  ...  Time-frequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification  ...  Acknowledgments The authors acknowledge the partial financial support of the Chilean National Fund for Scientific and Technological Development (Fondecyt) under Grant no. 1160494.  ... 
doi:10.1155/2017/5067651 fatcat:cw3isoti3remtbkmtqlc7rjuwy

Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications [article]

Olga Fink, Qin Wang, Markus Svensén, Pierre Dersin, Wan-Jui Lee, Melanie Ducoffe
2020 arXiv   pre-print
Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting  ...  The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware.  ...  Acknowledgment The contributions of Olga Fink and Qin Wang were funded by the Swiss National Science Foundation (SNSF) Grant no. PP00P2 176878.  ... 
arXiv:2005.02144v1 fatcat:wxm3dstogjfkhbcu6aueyaddja

A Comprehensive Survey for Deep-Learning-Based Abnormality Detection in Smart Grids with Multimodal Image Data

Fangrong Zhou, Gang Wen, Yi Ma, Hao Geng, Ran Huang, Ling Pei, Wenxian Yu, Lei Chu, Robert Qiu
2022 Applied Sciences  
Traditional approaches are also summarized together with their performance comparison with deep-learning-based approaches, based on which the necessity, seen in the surveyed literature, of adopting image-data-based  ...  In addition, several key methodologies and conditions for applying these techniques to abnormality detection are identified to help determine whether to use deep learning and which kind of learning techniques  ...  YNKJXM20191246), which focuses on the construction of satellite remote-sensing technology for power applications and wide-area intelligent monitoring of environments.  ... 
doi:10.3390/app12115336 fatcat:ot7ptci7bzafng3ytwhkk53ole

Potential, challenges and future directions for deep learning in prognostics and health management applications

Olga Fink, Qin Wang, Markus Svensén, Pierre Dersin, Wan-Jui Lee, Melanie Ducoffe
2020 Engineering applications of artificial intelligence  
Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting  ...  The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware.  ...  Acknowledgements The contributions of Olga Fink and Qin Wang were funded by the Swiss National Science Foundation (SNSF) Grant no. PP00P2_176878.  ... 
doi:10.1016/j.engappai.2020.103678 fatcat:6heplbhuozautml5p7pzwrlfvq

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Abouzar Estebsari, Roozbeh Rajabi
2020 Electronics  
We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.  ...  Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved.  ...  At first, time series data are encoded into an image, then the generated image is fed into deep CNN to predict the next value of the time series data.  ... 
doi:10.3390/electronics9010068 fatcat:z6tvtmgyczhcnhhvjnkdbo5l5q

Semantic Image Segmentation Based Cable Vibration Frequency Visual Monitoring Using Modified Convolutional Neural Network with Pixel-wise Weighting Strategy

Han Yang, Hong-Cheng Xu, Shuang-Jian Jiao, Feng-De Yin
2021 Remote Sensing  
intersection over union (IoU) reached 0.8226 when utilizing images with the optimal C-T ratio of 0.04 as testing set.  ...  Attributed to the explosive adoption of large-span spatial structures and infrastructures as a critical damage-sensitive element, there is a pressing need to monitor cable vibration frequency to inspect  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13081466 fatcat:nwlft33tdjcszgpfa2uys6uf6e

Overview of Image Inpainting and Forensic Technology

Kai Liu, Junke Li, Syed Sabahat Hussain Bukhari
2022 Security and Communication Networks  
With the progress of image processing theory, digital image inpainting technology has also developed rapidly. It not only enhances the image expression but also provides tools for image forgery.  ...  To eliminate the negative impact of image forgery, many scholars have conducted in-depth research on it and proposed a series of detection methods called image forensics for institutions or communities  ...  of having to learn all scales at the same time.  ... 
doi:10.1155/2022/9291971 doaj:9085f6d15bc64d13acd70f636181bf23 fatcat:pg5ncxzngjblzl42bstp3ykxpi

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of Paragorgia arborea in Relation to Hydrographic Conditions

Ander Zuazo, Jordi Grinyó, Vanesa López-Vázquez, Erik Rodríguez, Corrado Costa, Luciano Ortenzi, Sascha Flögel, Javier Valencia, Simone Marini, Guosong Zhang, Henning Wehde, Jacopo Aguzzi
2020 Sensors  
Our study shows that it is possible to establish a model for the development of automated pipelines that are able to extract biological information from times series of images.  ...  They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20216281 pmid:33158174 fatcat:4vksck7mabdennt5oyll7mty5m

Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model

Zhangxi Ye, Jiahao Wei, Yuwei Lin, Qian Guo, Jian Zhang, Houxi Zhang, Hui Deng, Kaijie Yang
2022 Remote Sensing  
The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown.  ...  Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and  ...  Acknowledgments: The authors would like to thank the developers in the GitHub community for their open-source U 2 -Net deep learning projects.  ... 
doi:10.3390/rs14061523 fatcat:3nyopgd2fbcxvpgfvq3ifv2ep4

Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

Paloma Merodio Gómez, Olivia Jimena Juarez Carrillo, Monika Kuffer, Dana R. Thomson, Jose Luis Olarte Quiroz, Elio Villaseñor García, Sabine Vanhuysse, Ángela Abascal, Isaac Oluoch, Michael Nagenborg, Claudio Persello, Patricia Lustosa Brito
2021 Sustainability  
We provide solutions to key challenges, including the provision of multi-scale data, the reduction in data costs, and the mapping of socio-economic conditions.  ...  EO data have the ability to provide information at detailed spatial and temporal scales so as to support monitoring transformations.  ...  The EO industry is undeniably exploding because EO presents an opportunity to build consistent time series that can fill data gaps.  ... 
doi:10.3390/su132212640 fatcat:wgxb5lv5mrgsrbyhd7ybmsoqsq
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