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Deep Learning for Multi-Facility Location Mechanism Design

Noah Golowich, Harikrishna Narasimhan, David C. Parkes
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
Our goal is to design strategy-proof, multi-facility mechanisms that minimize expected social cost.  ...  We first give a PAC learnability result for the class of multi-facility generalized median rules, and utilize neural networks to learn mechanisms from this class.  ...  Acknowledgments We thank Dimitris Fotakis for a useful discussion, Zhe Feng for helpful suggestions, and the anonymous reviewers for their comments.  ... 
doi:10.24963/ijcai.2018/36 dblp:conf/ijcai/GolowichNP18 fatcat:gq2nra2jhzhnbcgbey4xo6xjeq

Large excavations and multi-disciplinary studies in deep underground laboratories

Joseph S.Y. Wang, P. Febvre, E. Pozzo di Borgo, K. Coulié-Castellani
2014 E3S Web of Conferences  
In this review, we presented what we learned on both the large excavations and on multi-disciplinary studies.  ...  Some existing physics laboratories are interested to use available spaces for geo-sciences studies, including microbiological research for deep life. Summary of recent progress will be discussed.  ...  We present examples indicating that there are spaces planned for large excavations for next generation of physics rare-event experiments and there are also interests in multi-disciplinary studies at depths  ... 
doi:10.1051/e3sconf/20140400002 fatcat:2tqjchi4tfgdlfgafspci4gi3m

Survival study on cyclone prediction methods with remote sensing images

B. Suresh Kumar, D. Jayaraj
2022 International Journal of Health Sciences  
Many existing works have been designed in cyclone prediction for attaining better prediction accuracy. But, it is difficult to enhance the cyclone prediction accuracy with minimum time complexity.  ...  In order to address these issues, cyclone prediction can be carried out using deep leaning methods.  ...  A two-step scheme was designed for locating the TC centre for deep learning based object detection and complete decision.  ... 
doi:10.53730/ijhs.v6ns1.6668 fatcat:g4gdygi7l5el5eynoqgvebvmze

A Vehicle-Borne Mobile Mapping System Based Framework for Semantic Segmentation and Modeling on Overhead Catenary System Using Deep Learning

Lei Xu, Shunyi Zheng, Jiaming Na, Yuanwei Yang, Chunlin Mu, Debin Shi
2021 Remote Sensing  
Then, a deep learning network based on multi-scale feature fusion and an attention mechanism (MFF_A) is trained for semantic segmentation on a catenary facility.  ...  Firstly, an enhanced VMMS is designed for accurate data generation. Secondly, an automatic searching method based on a two-level stereo frame is designed to filter the irrelevant non-OCS point cloud.  ...  Then, a deep learning network based on multi-scale feature System Based Framework for fusion and an attention mechanism (MFF_A) is trained for semantic segmentation on a catenary Semantic  ... 
doi:10.3390/rs13234939 fatcat:ookfz7r3qbgjvhoxtzzfdu3y3i

Power Plant Classification from Remote Imaging with Deep Learning [article]

Michael Mommert, Linus Scheibenreif, Joëlle Hanna, Damian Borth
2021 arXiv   pre-print
Using a ResNet-50 deep learning model, we are able to achieve a mean accuracy of 90.0% in distinguishing 10 different power plant types and a background class.  ...  Furthermore, we are able to identify the cooling mechanisms utilized in thermal power plants with a mean accuracy of 87.5%.  ...  Our deep learning methods are implemented utilizing PyTorch [7] .  ... 
arXiv:2107.10894v1 fatcat:vuutjiylind7zduao72esluv3e

Power Plant Classification from Remote Imaging with Deep Learning

Michael Mommert, Linus Scheibenreif, Joelle Hanna, Damian Borth
2021 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS  
Using a ResNet-50 deep learning model, we are able to achieve a mean accuracy of 90.0% in distinguishing 10 different power plant types and a background class.  ...  Furthermore, we are able to identify the cooling mechanisms utilized in thermal power plants with a mean accuracy of 87.5%.  ...  Our deep learning methods are implemented utilizing PyTorch [7] .  ... 
doi:10.1109/igarss47720.2021.9553219 fatcat:ypseo655zfccdc6bjlc52aswmy

BIO-INSPIRED MULTIPLE SCALES PLACE RECOGNITION FOR ELECTRIC SUBSTATIONS

G. Wen, F. Zhou, H. Zhang, H. Pan, J. Cao, Z. Gao, Y. Liu, Z. Sun, L. Pei
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In the recent years, novel computer vision and deep neural network have got a lot of attention in many fields because of mimicking mammalian cognitive mechanism as much as possible.  ...  With the in-depth of mammalian cognitive and motor mechanisms research, people trend to adopt these reliable and efficient methods for power grid management and maintenance.For utilizing computing resources  ...  The reason why deep learning can achieve such achievements depends on the powerful features of deep learning Representation ability, deep learning features have better robustness than traditional handcrafted  ... 
doi:10.5194/isprs-archives-xlvi-3-w1-2022-315-2022 fatcat:h5osjsfb4rczlazmtetxg4cdby

Table of Contents

2022 IEEE Transactions on Smart Grid  
Catalão 3049 Routing and Charging Facility Location for EVs Under Nodal Pricing of Electricity: A Bilevel Model Solved Using Special Ordered Set . . . . . . . . . . . . . . . . . . . . . . . S.  ...  Dujić 2547 Deep Reinforcement Learning-Based Model-Free On-Line Dynamic Multi-Microgrid Formation to Enhance Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tsg.2022.3181387 fatcat:ilrslvdy7vac5olawh4mhp56im

Emergency response facility location in transportation networks: A literature review

Yang Liu, Yun Yuan, Jieyi Shen, Wei Gao
2021 Journal of Traffic and Transportation Engineering (English ed. Online)  
h i g h l i g h t s Emergency response facility location problem in transportation networks is reviewed for taxonomy purpose.  ...  Emergency facility location interacts with transportation networks clearly. This review is aimed to provide a combined framework for emergency facility location in transportation networks.  ...  All of the deep reinforcement learning algorithm for VRP is based on attention mechanism and encoder-decoder formulation to date.  ... 
doi:10.1016/j.jtte.2021.03.001 fatcat:nyyzzu5hpvgglmzaa7st7upfn4

Front Matter: Volume 10195

2017 Unmanned Systems Technology XIX  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  trust estimation in multi-agent systems [10195-37] 10195 11 Fast reinforcement learning based distributed optimal flocking control and network co-design for uncertain networked multi-UAV system [10195  ...  Introduction ROBOTICS CTA 02 Assessment of RCTA research 10195 03 Using deep learning to bridge the gap between perception and intelligence 10195 04 Gait design and optimization for efficient running  ... 
doi:10.1117/12.2281256 fatcat:z2ujancvajcpld4nhgis6eok4u

WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM

F. Chen, C. Jing, H. Zhang, X. Lv
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Student behavior research can improve learning efficiency, provide decision evidences for infrastructure management.  ...  Furthermore, a deep learning model based on LSTNet to predict student behaviour. Our work takes WiFi log data of a university in Beijing as dataset.  ...  The system adopts the design idea of geospatial dashboard and integrates the multi-view and multi-dimensional spatiotemporal mining method and the behavior prediction framework based on deep learning LSTNet  ... 
doi:10.5194/isprs-archives-xliii-b4-2022-493-2022 fatcat:yl4waktiwrgarlxh5hpytumoaa

Wayfinding Techniques at Common First Year King Saud University - An Indoor Localization Approach for Navigation

Dr. Wael Mohammad Alenazy
2021 Turkish Journal of Computer and Mathematics Education  
This presented an opportunity for indoor navigation to develop a system for indoor positioning systems, such as GPS, that can assist any beneficiary in navigating inside the building and locating themselves  ...  His current area of research interests includes Smart Learning, Enhanced Smart Education, Image Processing and Augmented Reality.  ...  Deep learning is used in the offline training phase to train all the weights of a deep network as fingerprints, and a greedy learning algorithm is incorporated layer by layer for weight training to reduce  ... 
doi:10.17762/turcomat.v12i3.1686 fatcat:hnouwig3lzaazco6zkwud5llcq

Research on Chengdu Ma Goat Recognition Based on Computer Vison

Jingyu Pu, Chengjun Yu, Xiaoyan Chen, Yu Zhang, Xiao Yang, Jun Li
2022 Animals  
In this paper, an automatic individual recognition method for Chengdu ma goats is proposed, which saves labor costs and does not depend on large-scale mechanized facilities.  ...  Chengdu ma goats based on TPH-YOLOv5 is proposed, which is able to accurately localize goats in high-density scenes with severe scale variance of targets; (3) a classifier incorporating a self-supervised learning  ...  Acknowledgments: Thanks to Xiaoli Yao for the help in data annotation. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ani12141746 pmid:35883293 pmcid:PMC9312181 fatcat:57t7a5yelva4dbpwuudmsc7nla

Editorial for the special issue on "Research on methods of multimodal information fusion in emotion recognition"

Kaijian Xia, Tao Hu, Wen Si
2019 Personal and Ubiquitous Computing  
The paper "A multiobjective evolutionary algorithm based on surrogate individual selection mechanism", proposed a  ...  In this paper, "Multi-source heterogeneous data fusion based on perceptual semantics in narrow-band Internet of Things", a multi-source heterogeneous data fusion based on perceptual semantics in NB-IoT  ...  In the paper "Feature recognition of motor imaging EEG signals based on deep learning", designs the acquisition experiment of EEG signals.  ... 
doi:10.1007/s00779-019-01260-x fatcat:6e7btyoj7zbtlahdghv23qkrye

Hybrid Technique for Medical Data Classification using Multi-Layer Perceptron with NB Classifier

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
For to analyze such heterogeneous data traditional data analysis mechanisms are inefficient. To handle this heterogeneous data deep learning is obvious choice.  ...  In this paper we proposed a deep learning based multilayer perceptron to analysis medical data.  ...  Deep learning techniques have been used in a wide assortment of utilization spaces, for example, Visa misrepresentation location, manually written character acknowledgment, discourse acknowledgment, showcasing  ... 
doi:10.35940/ijitee.k2179.1081219 fatcat:2s7sa326ljda3a7vugfrnvnnca
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