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2020 Science and Artificial Intelligence conference (S.A.I.ence)最新文献

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Using Computer Vision and Deep Learning for Cells Recognition 使用计算机视觉和深度学习进行细胞识别
2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303201
V. Y. Kudinov, M. Y. Mashukov, E. A. Maslova, K. Orishchenko, A. Okunev, A. V. Matveev
{"title":"Using Computer Vision and Deep Learning for Cells Recognition","authors":"V. Y. Kudinov, M. Y. Mashukov, E. A. Maslova, K. Orishchenko, A. Okunev, A. V. Matveev","doi":"10.1109/S.A.I.ence50533.2020.9303201","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303201","url":null,"abstract":"The task of the objects identification, counting, and measurement is a huge part of scientific investigations and technological applications. Automated methods using traditional processing such as segmentation, edge detection, and so on represented by available software (e.g. CellProfiler) are not flexible, can be used only with images of high-quality, and in addition require setting a part of parameters by hand. This contribution presents the applying the deep learning method for recognition of HeLa cells expressing green fluorescent protein (EGFP) automatically. We used Cascade Mask R-CNN neural networks which has a ResNeXt backbone and deformable convolutional networks layers. Training dataset contained seven pictures with 5754 labeled cells. Three images with 2469 labeled cells were used as test-dataset. The trained neural network showed mAP=0.4.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130110870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Overlapping Communities based on Relaxed Cliques 基于放松小集团的重叠社区
2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303216
M. Chernoskutov
{"title":"Overlapping Communities based on Relaxed Cliques","authors":"M. Chernoskutov","doi":"10.1109/S.A.I.ence50533.2020.9303216","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303216","url":null,"abstract":"Detection of overlapping communities is one of the most important tasks in network science that allows to simulate various real-world objects like social networks. This paper describes an algorithm for finding overlapping communities using relaxed cliques. The use of relaxed cliques makes it possible to find communities (including overlapping ones) with a denser internal structure due to the requirement for more tight connectivity of nodes inside the relaxed clique. The developed algorithm has shown its efficiency in the analysis of communities in synthetic networks built with the LFR (Lancichinetti–Fortunato–Radicchi) graph generator.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130164074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Performance Monitoring of Industrial Rotary Equipment using AI/ML Techniques 基于AI/ML技术的工业旋转设备性能监测
2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303223
M. K. Das, K. Rangarajan, Venkatakrishna Tirumala
{"title":"Performance Monitoring of Industrial Rotary Equipment using AI/ML Techniques","authors":"M. K. Das, K. Rangarajan, Venkatakrishna Tirumala","doi":"10.1109/S.A.I.ence50533.2020.9303223","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303223","url":null,"abstract":"Industrial AI is the application of Artificial Intelligence and Machine Learning techniques to optimise production, increase safety and reliability of industrial equipment and manufacturing plants. The purpose of this paper is to document and compare different AI/ML techniques and their efficacy when used with data in the industrial manufacturing context. These techniques have been compared with each other in their performance on datasets recorded from select mechanical equipment used in the factories. The paper details the use of methods such as ANN and LSTM and their applicability to this domain. The comparison of results and discussion will enable the reader to choose the appropriate method for a given scenario.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133288872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
S.A.I.ence 2020 Cover Page 《科学科学2020》封面
2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/s.a.i.ence50533.2020.9303208
{"title":"S.A.I.ence 2020 Cover Page","authors":"","doi":"10.1109/s.a.i.ence50533.2020.9303208","DOIUrl":"https://doi.org/10.1109/s.a.i.ence50533.2020.9303208","url":null,"abstract":"","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114850920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Recognition of Rocks Lithology on the Images of Core Samples 基于岩心样品图像的岩石岩性识别
2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303197
V. Panferov, Dmitry Tailakov, A. Donets
{"title":"Recognition of Rocks Lithology on the Images of Core Samples","authors":"V. Panferov, Dmitry Tailakov, A. Donets","doi":"10.1109/S.A.I.ence50533.2020.9303197","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303197","url":null,"abstract":"Oil is one of the most important resources in the modern life. When an oil well is drilled, engineers extract the samples of core to analyze it and build the model of the geological formation. Now, the core samples and rock lithology segmentation is usually implemented by people by hand. Methods for the image segmentation and possible core samples segmentation approaches are reviewed. The novel dataset consisting of 69 images of segmented core samples created specifically for the task is presented in this paper. Also, two approaches for dataset creation were tried and described in this paper. The U-Net solution of the task with the first version of the dataset consisting of 4 classes and its results are described. Also the Mask R-CNN with ResNet-50 FPN model from the library Detectron2 with the second version of the dataset consisting of 11 classes of Argillite and Sandstone and its combination is described and results of experiments are provided.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124657390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Development of water flood model for oil production enhancement 提高采收率的水驱模型开发
2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303200
Jetina J. Tsvaki, Dmitry Tailakov, Evgeny Nikolaevich Pavlovskiy
{"title":"Development of water flood model for oil production enhancement","authors":"Jetina J. Tsvaki, Dmitry Tailakov, Evgeny Nikolaevich Pavlovskiy","doi":"10.1109/S.A.I.ence50533.2020.9303200","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303200","url":null,"abstract":"Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to develop a water flood model for oil production enhancement using artificial neural networks and provide a model that maximizes oil production for a given water injection that in turn will extend mature fields life and decrease operational costs. Using the data comprising of daily water injection rates, oil production rates, water production, and gas production from the year 2004 to 2016 for 577 injection wells, 1344 production wells, and 36 events which had occurred during the course. Comparative analysis on the deep neural models such as Multi-Layer Perception, Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Neural Networks are used, and Gated Recurrent Neural Networks outperformed them. To minimize the loss and improve the performance of the water flood model tabular data mix-up was adopted on all the models above. The results showed that the data mixed up Gated Recurrent Neural Network outperformed all the other models. To maximize the oil production Nelder-Mead optimization method was adopted to find appropriate water injection rates. A simple two-layered multi-layer perceptron was used in modeling the nonlinear relationship between water injection and oil production to avoid function complexity.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133355524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Implementation of Convolutional Neural Network to Enhance Turbulence Models for Channel Flows 卷积神经网络在河道湍流模型中的应用
2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303178
O. Razizadeh, S. Yakovenko
{"title":"Implementation of Convolutional Neural Network to Enhance Turbulence Models for Channel Flows","authors":"O. Razizadeh, S. Yakovenko","doi":"10.1109/S.A.I.ence50533.2020.9303178","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303178","url":null,"abstract":"The convolutional neural network (CNN) is implemented to enhance a turbulence model which is needed to close the Reynolds-averaged Navier–Stokes (RANS) equations. The machine-learning technique uses the available data sets of high fidelity for canonical flow test cases. These data have been produced from large-eddy simulations or direct numerical simulations, which require huge computing resources. At the first stage, the widely used k-ω model is taken as a baseline RANS model, and computations are performed by means of OpenFOAM for turbulent flows in the plane channel having the periodic hills on the lower wall and in the converging-diverging channel. Then, the CNN algorithm is applied to these cases. The prediction of the Reynolds-stress anisotropy tensor components is shown to be improved after the application of CNN with the mean square error loss function in comparison with that for the baseline RANS model in the investigated canonical turbulent flows in channels with walls of different geometry.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123773325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Using Computer Vision and Deep Learning for Nanoparticle Recognition on Scanning Probe Microscopy Images: Modified U-net Approach 利用计算机视觉和深度学习对扫描探针显微镜图像进行纳米粒子识别:改进的U-net方法
2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/S.A.I.ence50533.2020.9303184
Mikhail F. Liz, A. Nartova, A. V. Matveev, A. Okunev
{"title":"Using Computer Vision and Deep Learning for Nanoparticle Recognition on Scanning Probe Microscopy Images: Modified U-net Approach","authors":"Mikhail F. Liz, A. Nartova, A. V. Matveev, A. Okunev","doi":"10.1109/S.A.I.ence50533.2020.9303184","DOIUrl":"https://doi.org/10.1109/S.A.I.ence50533.2020.9303184","url":null,"abstract":"Particles characterization is a significant part of numerous studies in material sciences and engineering technologies. Microscopy images of materials containing particles are usually analyzed by operator with manual counting and measuring of particle sizing by a software ruler. Traditional automated image analyzing methods such as edge detection, segmentation, etc. are not universal, giving poor results on noisy pictures and need empirical fitted parameters. To realize automatic method of particles recognition on scanning tunneling microscopy (STM) data we used U-net and modified U-net neural networks, which was trained on ten STM images contained 1918 particles. Verification on 3 pictures with 695 particles showed mAP=0.12 for modified U-net neural network.","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115877105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
[Copyright notice] (版权)
2020 Science and Artificial Intelligence conference (S.A.I.ence) Pub Date : 2020-11-14 DOI: 10.1109/s.a.i.ence50533.2020.9303202
{"title":"[Copyright notice]","authors":"","doi":"10.1109/s.a.i.ence50533.2020.9303202","DOIUrl":"https://doi.org/10.1109/s.a.i.ence50533.2020.9303202","url":null,"abstract":"","PeriodicalId":201402,"journal":{"name":"2020 Science and Artificial Intelligence conference (S.A.I.ence)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133138582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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