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arXiv 2024年

作者： Budagam, Devichand Ghosh, Sayan Imanbayev, Azamat Zhanatuly Kaplun, Dmitrii Rychenkov, Artem Sinitca, Aleksandr Kumar, Ayush Shrivastav, Anuj Akhmetov, Iskander Rafailovich Antonov, Sergey Cyganov, Gleb Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Department of Chemical Engineering Indian Institute of Technology Kharagpur Kharagpur India School of IT and Engineering Kazakh-British Technical University Almaty Kazakhstan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Centre for Digital Telecommunication Technologies St. Petersburg Electrotechnical University"LETI" St. Petersburg Russia Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Architecture and Regional Planning Indian Institute of Technology Kharagpur Kharagpur India Faculty of Digital Transformation ITMO University St. Petersburg Russia

Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. © 2024, CC BY.

关键词： Object detection

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Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report

arXiv

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arXiv 2022年

作者： Ignatov, Andrey Timofte, Radu Chiang, Cheng-Ming Kuo, Hsien-Kai Xu, Yu-Syuan Lee, Man-Yu Lu, Allen Cheng, Chia-Ming Chen, Chih-Cheng Yong, Jia-Ying Shuai, Hong-Han Cheng, Wen-Huang Jia, Zhuang Xu, Tianyu Zhang, Yijian Bao, Long Sun, Heng Zhang, Diankai Gao, Si Liu, Shaoli Wu, Biao Zhang, Xiaofeng Zheng, Chengjian Lu, Kaidi Wang, Ning Sun, Xiao Wu, HaoDong Liu, Xuncheng Zhang, Weizhan Yan, Caixia Du, Haipeng Zheng, Qinghua Wang, Qi Chen, Wangdu Duan, Ran Duan, Ran Sun, Mengdi Zhu, Dan Chen, Guannan Cho, Hojin Kim, Steve Yue, Shijie Li, Chenghua Zhuge, Zhengyang Chen, Wei Wang, Wenxu Zhou, Yufeng Cai, Xiaochen Cai, Hengxing Xu, Kele Liu, Li Cheng, Zehua Lian, Wenyi Lian, Wenjing Computer Vision Lab ETH Zurich Switzerland AI Witchlabs Switzerland University of Wuerzburg Germany MediaTek Inc. Taiwan National Yang Ming Chiao Tung University Taiwan Video Algorithm Group Camera Department Xiaomi Inc. China Audio & Video Technology Platform Department ZTE Corp. China China School of Computer Science and Technology Xi'an Jiaotong University China MIGU Video Co. Ltd China BOE Technology Group Co. Ltd. China GenGenAI Korea Republic of North China University of Technology China Institute of Automation Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology China 4Paradigm Inc. Beijing China National University of Defense Technology Changsha China University of Oxford Oxford United Kingdom Uppsala University Sweden Northeastern University China

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of lowbitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt/30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

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Microphytobenthos Spatio-Temporal Dynamics Across an Intertidal Gradient in a Tropical Estuary Using Sentinel-2 Imagery

SSRN

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SSRN 2024年

作者： Haro, Sara Mucheye, Tadesse Caballero, Isabel Priego, Blanca Gonzalez, Carlos Jose Gómez-Ramírez, Eddy H. Corzo, Alfonso Papaspyrou, Sokratis Department of Biology Faculty of Marine and Environmental Sciences University of Cádiz Puerto Real Cádiz11510 Spain Department of Natural Resources Management College of Agriculture and Environmental Science University of Gondar P.O. Box 196 Gondar Ethiopia Puerto Real Cádiz11510 Spain Department of Automation Engineering Electronics and Computer Architecture and Networks School of Engineering University of Cádiz Puerto Real Cádiz11510 Spain Division of Naval Support Marine Hydrographic Institute Spanish Navy Cadiz Spain Universidad de Costa Rica Escuela de Química y CIMAR San Pedro Montes de Oca San José11501-2060 Costa Rica

Intertidal mudflats are among the most productive coastal ecosystems, largely because of the activity of the photosynthetic microbial community on the sediment surface, known as microphytobenthos (MPB). While the dynamics of MPB have been extensively studied in temperate estuaries, there is limited research in tropical estuaries. To address this knowledge gap, we investigated the spatio-temporal dynamics of MPB in the Nicoya Gulf (Costa Rica), one of the world's most productive tropical estuaries, using Sentinel-2 images at 10 m spatial resolution from 2018 to 2022. We used Normalized Difference Vegetation Index (NDVI) values ranging from 0 to 0.4 to monitor MPB cover and growth rates (µNDVI, days-1). Analysis of data using Generalized Additive Models (GAM) showed that up to 62% of the temporal variability in average MPB NDVI, peaking in late wet and early dry seasons (November-December), can be explained by years and months. The high temperatures and irradiance during the dry season (December to April) in the Nicoya estuary may inhibit MPB growth, contrasting with patterns observed in temperate estuaries. We observed higher MPB NDVI in the upper intertidal zone (mean sea level > 0.5 m) as usually occurs in temperate estuaries. This research highlights the importance of high-resolution satellite imagery for long-term monitoring of MPB dynamics in tropical tidal flats, crucial for accurate evaluation of global carbon cycling and ecosystem services. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Application of proper orthogonal decomposition and radial basis functions for crack size estimation using particle swarm optimization

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Journal of Physics: Conference Series 2017年第1期842卷

作者： B Benaissa M Köppen M Abdel Wahab S Khatir LEMI Department of Mechanical Engineering University M'hamed Bougara of Boumerdes. Algeria. Faculty of Computer Science and Systems Engineering Department of Creative Informatics Kyushu Institute of Technology. Japan. Department of Electrical Energy Systems and Automation Faculty of Engineering and Architecture Ghent University. Belgium.

Complex engineering problems require simulations, which are computationally expensive in cases of inverse identification tasks since they commonly requires hundreds of thousands of simulations. This paper propose a method based on model reduction for crack size estimation, combining the proper orthogonal decomposition method with radial basis functions. The reduced model is validated by comparing the obtained boundary displacements with the corresponding results from a finite element model. This inverse procedure is formulated as the minimization of the difference between the measured and computed values of displacement at selected boundary nodes, called sensor points, using particle swarm optimization algorithm. Convex and a non-convex specimens have been considered for investigations of crack presence, and identification of its size, different crack sizes have been tested to demonstrate the efficiency of the proposed approach.

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Windfix: Harnessing the Power of Self-Supervised Learning for Versatile Imputation of Offshore Wind Speed Time Series

SSRN

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SSRN 2023年

作者： Chen, Yaoran Zhang, Dan Cao, Leilei Cai, Candong Kuang, Limin Peng, Yan Pu, Huayan Wu, Chuhan Zhou, Dai Cao, Yong Institute of Artificial Intelligence Collaborative Innovation Center for the Marine Artificial Intelligence Shanghai University Shanghai200444 China School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Engineering Research Center of Unmanned Intelligent Marine Equipment Ministry of Education Shanghai200444 China School of Software Northwestern Polytechnical University Shannxi Xi’an710072 China School of Computer Engineering and Science Shanghai University 200444 China Department of Electronic Engineering Tsinghua University Beijing100084 China School of Naval Architecture Ocean & Civil Engineering Shanghai Jiao Tong University Shanghai200240 China

The accurate prediction of offshore wind speed is crucial for effective wind energy management. Deep learning models, utilizing large-scale wind speed time series, have emerged as a prominent approach for this task. However, the availability of wind history and other oceanic data are often capriciously incomplete, with intermittent gaps in the time series due to transmission difficulties or measurement device malfunctions. This presents a significant challenge for both data preparation and model training. In this paper, a self-supervised model, WindFix, is proposed for versatile imputation of offshore wind time series using meteorological features from both the self-spot and neighboring spots. The numerical results validated on wind dataset from Pacific Ocean show that the model can serve for various missing types with a mean squared error around 0.002 for the simplest continue-missing case. The comprehensive analysis also provided deep explanations on the different patterns occurring in various imputation types, attributing to the allocation of attention weights on features. © 2023, The Authors. All rights reserved.

关键词： Time series

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Correction to: Gyral-sulcal contrast in intrinsic functional brain networks across task performances

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Brain imaging and behavior 2021年第4期15卷 2235页

作者： Lin Zhao Tuo Zhang Lei Guo Tianming Liu Xi Jiang School of Automation Northwestern Polytechnical University Xi'an China. Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia Athens GA USA. School of Automation Northwestern Polytechnical University Xi'an China. tuozhang@***. School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China. xijiang@***.

A Correction to this paper has been published: https://***/10.1007/s11682-020-00426-z

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The electric surface interaction in the soil-slag-biological solids system

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IOP Conference Series: Materials Science and Engineering 2019年第1期708卷

作者： L V Trykoz S V Panchenko D O Bondarenko A A Plugin O S Borziak Building Materials and Structures Department Ukrainian State University of Railway Transport Feuerbach sq.7 61050 Kharkiv Ukraine Department of Automation and Computer Train Operation Remote Control Ukrainian State University of Railway Transport Feuerbach sq.7 61050 Kharkiv Ukraine Computer Science and Information Technologies Department Kharkiv National University of Civil Engineering and Architecture Sumska str. 40 61002 Kharkiv Ukraine

The issue of wastes reclaiming in large cities is really a pressing problem today. First of all, the wastes which occupy wide areas of land need to be reclaimed. The metal production slag and waste water sediments are typical examples of such waste products. They create a wide area of ecological and social discomfort when collected and accumulated in large quantities after treatment. The purpose of this study is development of new soil materials which include waste products. The material properties can be influenced in a level of electric-surface interaction of the studied system components. The phase boundary presence and high degree of dispersion of the components require considering the sign of the charge of the particle surface and the particle size, influence of the charge on the nature of interaction between the surfaces and thus allows defining possible methods of modifying the surface properties in order to obtain materials needed. This includes modification of the surface of the aggregates and filling agents, activation of the functional groups, dispersed system stability, coagulation control, etc.

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Synthesis of La0.5Ca0.5-xxMnO3 nanocrystalline manganites by sucrose assisted auto combustion route and study of their structural, magnetic and magnetocaloric properties

arXiv

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arXiv 2019年

作者： Moumen, S. Ben Gagou, Y. Chettab, M. Mezzane, D. Amjoud, M. Fourcade, S. Hajji, L. Kutnjak, Z. Marssi, M.E. Amraoui, Y.E. Kopelevich, Y. Lukýanchuk, Igor A. LMCN F.S.T.G. Université Cadi Ayyad BP 549 Marrakech Morocco LPMC Université de Picardie Jules Verne 33 rue Saint-Leu Amiens Cédex80039 France LCMCB Université de Bordeaux Pessac33600 France Jozef Stefan Institute Jamova cesta 39 Ljubljana1000 Slovenia LaMCScI Faculté des Sciences Université Mohammed V Rabat Morocco Instituto de Fisica Gleb Wataghin Universidade Estadual de Campinas Unicamp 13083-859 Campinas Sao Paulo Brazil Department of Cybersecurity and Computer Engineering Faculty of Automation and Information Technologies Kyiv National University of Construction and Architecture Kyiv Ukraine

Perovskite manganite La0.5Ca0.5-xxMnO3 (LCMO) nanomaterials were elaborated using the sucrose modified auto combustion method. Rietveld refinements of the X-ray diffraction patterns of the crystalline structure confirm a single-phase orthorhombic state with Pbnm space group (No. 62). The Ca-vacancies were voluntarily created in the LCMO structure in order to study their influence on the magnetic behaviour in the system. The magnetic susceptibility was found to be highly enhanced in the sample with Ca-vacancies. Paramagnetic-to-ferromagnetic phase transition was evidenced in both samples around 254 K. This transition is, characterized by a drastic jump of the susceptibility in the sample with Ca-vacancies. The maximum of entropy change, observed for both compounds at magnetic field of 6T was 2.30 J kg-1K-1 and 2.70 J kg-1K-1 for the parent compound and the lacunar one respectively. The magnetocaloric adiabatic temperature change value calculated by indirect method was 5.6 K and 5.2 K for the non-lacunar and Ca-vacancy compound, respectively. The Ca-lacunar La0.5Ca0.5-xxMnO3 (x=0.05) reported in this work demonstrated overall enhancement of the magnetocaloric effect over the LCMO. The technique used to elaborate LCMO materials was beneficial to enhance the magnetocaloric effect and magnetic behaviour. Therefore, we conclude that this less costly environmentally friendly system can be considered as more advantageous candidate for magnetic refrigeration applications then the commonly Gd-based compounds. Copyright © 2019, The Authors. All rights reserved.

关键词： Sugar (sucrose)

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Mathematical Control Model of Secondary Coextrusion Composition Preparation

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IOP Conference Series: Earth and Environmental Science 2020年第3期459卷

作者： V V Dyadichev A V Kolesnikov A V Dyadichev V.I. Vernadsky Crimean Federal University Academy of Construction and Architecture Engineering Center 'Bio-positive Construction and Resource-saving' 3 Pavlenko Str. Simferopol Crimea 295000 Russia Lugansk National University named after Vladimir Dal Department of Automation and Computer-Integrated Technologies 20-A Molodezhnyi Sq. Lugansk 91034 Ukraine

The control of polymer waste recycling process is an important modern problem. A partial solution to the problem can be got by using coextrusion technology. As a result of its application with respect to polymer waste, we can get a multilayer qualitative product, containing waste and safe for usage. Because of economic causes, the problem of using this technology is the correct preparation of secondary coextrusion composition based on the requirements to the quality of the obtained product. The scientific paper presents the mathematical control model of secondary polymer composition preparation. The developed model allows carrying out an operational control of composition components consumption, determining and correcting the consumption of the most expensive astringent component in order to guarantee the required quality of the product. The mathematical model is the basis for construction of intellectual automated systems for controlling polymer waste recycling process by coextrusion technology.

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Improvement of extrusion equipment and technologies for processing secondary combined polymer materials and mixtures

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Journal of Physics: Conference Series 2019年第1期1210卷

作者： V V Dyadichev A V Kolesnikov S G Menyuk A V Dyadichev V.I. Vernadsky Crimean Federal University Academy of Construction and Architecture Engineering Center 'Bio-positive Construction and Resource-Saving' 3 Pavlenko Str. Simferopol Crimea 295000 Russia Lugansk National University named after Vladimir Dal Department of Automation and Computer-Integrated Technologies 20-A Molodezhnyi Sq. Lugansk 91034 Ukraine

The article is devoted to the solution of scientific and technical problem of perfecting the extrusion equipment and technology for processing secondary combined polymeric materials and polymeric mixtures including wastes, by the method of extrusion with use of multi-section screw that allows to manufacture new high-quality products and reduce the environment pollution. A new screw design is proposed consisting of several functional sections. A mathematical model for operation of the extruder with the multi-section screw has been developed to analyse the operation parameters and produce new designs of the extruder screw. Based on the studies of the functional purpose of the extruder zones and features of processing the polymeric mixtures with secondary raw materials, a functional diagram of the screw is proposed. The thickness values of the strips of the polymer mixture components at the outlet of the extruder sections for analysing the mixing capacities have been obtained. It has been established that the extruder with the multi-section screw can be used with effective combination of two stages: mixing and basic processing. With the help of mathematical modelling the diagrams of linear velocities and shear rates for each extruder section have been obtained.

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