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Event-triggered Natural Hazard Monitoring with Convolutional Neural Networks on the Edge [article]

Matthias Meyer, Timo Farei-Campagna, Akos Pasztor, Reto Da Forno, Tonio Gsell, Jérome Faillettaz, Andreas Vieli, Samuel Weber, Jan Beutel, Lothar Thiele
2019 arXiv   pre-print
In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization  ...  The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network.  ...  We would like to thank the the PermaSense and TEC team for integration support.  ... 
arXiv:1810.09409v2 fatcat:upcyi6p3gfhztkff2q4hlwfjbm

Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding

Panagiotis Kasnesis, Nicolaos-Alexandros Tatlas, Stelios Mitilineos, Charalampos Patrikakis, Stelios Potirakis
2019 Sensors  
The extracted information is presented exploiting the designed STORM Audio Signal ontology and then fused with spatiotemporal information using semantic rules.  ...  To this end, advanced monitoring systems harnessing the power of sensors are deployed near the sites to collect data which can fuel systems and processes aimed at protection and preservation.  ...  Acknowledgments: We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan-X GPU that was used for this research.  ... 
doi:10.3390/s19071629 fatcat:dkpwlkq24bhzjp2dkdvhkfvjlm

Review of the Application of Social Media Data in Disaster Research [chapter]

Jiting Tang, Saini Yang, Weiping Wang
2020 Frontiers in Artificial Intelligence and Applications  
So far, the research in this area is focused on the types of hazard, but rarely considers the relationship between the technical methods and applicable tasks.  ...  Social media data (SMD) is a new data source in disaster research, which can be used in hazard identification, disaster analysis, risk assessment and emergency rescue.  ...  (SVM), random forest or deep learning neural network algorithms including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Encoder Representation from Transformers (BERT  ... 
doi:10.3233/faia200642 fatcat:kvlrreau2fdxjgwz6qgmwaabyi

A YOLO based Technique for Early Forest Fire Detection

The drones will be equipped with sensors, Raspberry pi 3, neural stick, APM 2.5, GPS, Wifi.  ...  And as soon as forest fire is detected the UAV will send an alert message to the concerned authority on the mobile App along with location coordinates of the fire, image and the amount of area in which  ...  [12] suggested two deep Convolutional Neural Network(CNN) joined together to detect fire in images of the forest.  ... 
doi:10.35940/ijitee.f4106.049620 fatcat:b6snajrl6zcnvpnqoe6vtdjw34

Introducing the Architecture of FASTER: A Digital Ecosystem for First Responder Teams

Evangelos Katsadouros, Dimitrios G. Kogias, Charalampos Z. Patrikakis, Gabriele Giunta, Anastasios Dimou, Petros Daras
2022 Information  
Thus, it is important to provide them with technology that will maximize their performance and their safety on the field of action.  ...  monitoring, privacy protection and smart detection mechanisms.  ...  Acknowledgments: The work presented in this paper has received funding from the European Union's Horizon 2020 research and innovation program under the FASTER project, grant agreement no. 833507.  ... 
doi:10.3390/info13030115 fatcat:rphwbbzcyfc7lj4zebsie4vim4

Driver Drowsiness Detection System

Manjunath S, Banashree P, Shreya M, Sneha Manjunath Hegde, Nischal H P
2022 International Journal for Research in Applied Science and Engineering Technology  
Therefore, based on the relationship between facial features and a driver's drowsy state, variables that reflect facial features have been established.  ...  Recent developments in video processing using machine learning have enabled images obtained from cameras to be analysed with high accuracy.  ...  . 2 8 13 Distracted Detection with Deep Driver Convolutional Neural Network, O.  ... 
doi:10.22214/ijraset.2022.42109 fatcat:tczyrxfcsjfnbmnyvbwuqdoxui

Safety Distance Identification for Crane Drivers Based on Mask R-CNN

Zhen Yang, Yongbo Yuan, Mingyuan Zhang, Xuefeng Zhao, Yang Zhang, Boquan Tian
2019 Sensors  
In order to address crane issue, this research recorded video data by a tower crane camera, labeled the pictures, and operated image recognition with the MASK R-CNN method.  ...  Tower cranes are the most commonly used large-scale equipment on construction site.  ...  The feedforward architecture of the convolutional neural network could be extended by horizontal and feedback connections in the neural abstraction pyramid and the resulting recurring convolutional network  ... 
doi:10.3390/s19122789 fatcat:lm2ahzqw75fxhcbaqckh7nr2ja

Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring

Francesco Barchi, Luca Zanatta, Emanuele Parisi, Alessio Burrello, Davide Brunelli, Andrea Bartolini, Andrea Acquaviva
2021 Future Internet  
The proposed solution exploits recurrent spiking neural networks (LSNNs), which are emerging for their theoretical energy efficiency and compactness, to recognise damage conditions by processing data from  ...  Moving this coding on the sensor can remove this limitation leading to an overall more energy-efficient monitoring system.  ...  In this context, hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing is proposed, exploiting a Convolutional Neural Network (CNN)  ... 
doi:10.3390/fi13080219 fatcat:2pmrqwysanebloasddvcyluf4q

A New Deep-Learning-based Approach for Earthquake-triggered Landslide Detection from Single-temporal RapidEye Satellite Imagery

Yaning Yi, Wanchang Zhang
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Comparative studies on the feasibility and robustness of the proposed approach with ResUNet and DeepUNet demonstrated its strong application potentials in the emergency response of natural disasters.  ...  Finally, the identified landslide maps were further optimized with morphological processing.  ...  During the past several years, convolution neural network (CNN) has achieved significant advances in natural image processing [32] [33] [34] .  ... 
doi:10.1109/jstars.2020.3028855 fatcat:iem62o7nfzcmbhlnnan4ku6bxu

Classification of Video Observation Data for Volcanic Activity Monitoring Using Computer Vision and Modern Neural NetWorks (on Klyuchevskoy Volcano Example)

Sergey Korolev, Aleksei Sorokin, Igor Urmanov, Aleksandr Kamaev, Olga Girina
2021 Remote Sensing  
The tests show the high efficiency of the use of convolutional neural networks in volcano image classification, and the accuracy of classification achieved 91%.  ...  It is a problem to set it in advance and keep it up to date, especially for an observation network with multiple cameras.  ...  Acknowledgments: The studies were carried out using the resources of the Center for Shared Use of Scientific Equipment "Center for Processing and Storage of Scientific Data of the Far Eastern Branch of  ... 
doi:10.3390/rs13234747 fatcat:lu3srasw4ff4vcog2ey472cdoq

Spatiotemporal event detection: a review

Manzhu Yu, Myra Bambacus, Guido Cervone, Keith Clarke, Daniel Duffy, Qunying Huang, Jing Li, Wenwen Li, Zhenlong Li, Qian Liu, Bernd Resch, Jingchao Yang (+1 others)
2020 International Journal of Digital Earth  
processes based on the extracted events) as an agenda for future event detection research.  ...  Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and  ...  Yu and Yang designed the paper structure. Yu wrote the paper with assistant from other authors. Yang revised the paper with other authors.  ... 
doi:10.1080/17538947.2020.1738569 fatcat:urbuc2zii5bajjmmkzu6idyrg4

Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection [article]

Omid Ghorbanzadeh, Yonghao Xu, Pedram Ghamisi, Michael Kopp, David Kreil
2022 arXiv   pre-print
All models were trained from scratch on patches from one quarter of each study area and tested on independent patches from the other three quarters.  ...  The repository features 3,799 image patches fusing optical layers from Sentinel-2 sensors with the digital elevation model and slope layer derived from ALOS PALSAR.  ...  The advent of the era of DL models like deep convolutional neural networks (DCNN) has provided unique opportunities for landslide detection from satellite imagery, mainly on a large scale [5] , [29]  ... 
arXiv:2206.00515v2 fatcat:ytnxczt2yfexhlvolu4rj2yiom

Using Neural Networks with data Quantization for time Series Analysis in LHC Superconducting Magnets

Maciej Wielgosz, Andrzej Skoczeń
2019 International Journal of Applied Mathematics and Computer Science  
The aim of this paper is to present a model based on the recurrent neural network (RNN) architecture, the long short-term memory (LSTM) in particular, for modeling the work parameters of Large Hadron Collider  ...  A novel approach to signal level quantization allowed reducing the size of the model, simplifying the tuning of the magnet monitoring system and making the process scalable.  ...  within the subsidy of the Polish Ministry of Science and Higher Education.  ... 
doi:10.2478/amcs-2019-0037 fatcat:aaoaupbd4rgr3epj7gupi5eqqq

Displacement Prediction of the Muyubao Landslide Based on a GPS Time-Series Analysis and Temporal Convolutional Network Model

Da Huang, Jun He, Yixiang Song, Zizheng Guo, Xiaocheng Huang, Yingquan Guo
2022 Remote Sensing  
To achieve accuracy prediction of landslide displacement, a displacement prediction model based on a salp-swarm-algorithm-optimized temporal convolutional network (SSA-TCN) is proposed.  ...  This research also compares the proposed approach results with the other popular machine learning and deep learning models.  ...  Methodologies Convolutional Neural Network (CNN) Convolutional neural networks are widely applied in extracting features from multiple dimensions data with Euclidean space [33, 34] .  ... 
doi:10.3390/rs14112656 fatcat:ytuoitb7xner7cqmqy4avfdc6q

Structural Health Monitoring using Neural Networks in IoT and CPS paradigm- A Review

Partha Sarathi Pal, Sucharita Khuntia
2020 Zenodo  
the usage of Neural Networks (NN) in damage detection problems.  ...  In this paper exhaustive review has been done on wireless systems of SHM along with the usage of many emerging technologies such as the Internet of Things (IoT), Cyber-Physical System (CPS), along with  ...  Convolution Neural Network is one of the finest things that has come up which is inspired by biological neurons and networks in the nervous system in animals.  ... 
doi:10.5281/zenodo.4739186 fatcat:p77nezk4sfeeba7uhe5t3caxru
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