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IEEE Latin American Robotics Symposium, LARS

作者： Gilmar Pereira Da Cruz Hector Azpúrua Fernando Lizarralde Gustavo Medeiros Freitas Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais - Av. Antônio Carlos 6627 Belo Horizonte MG Brazil Department of Computer Science Universidade Federal de Minas Gerais MG Brasil Electrical Engineering Program - COPPE Universidade Federal do Rio de Janeiro RJ Brasil

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

In mobile robotics, an essential requirement for the fusion of different sensors is that measurements are expressed with respect to the same reference. In this sense, the transformation between sensors and robot is necessary to ensure better sensor fusion. Therefore, this article proposes an extrinsic sensor calibration based on markers and associated to three orthogonal planes. This technique is applied to two calibration approaches, LiDAR-Robot and LiDAR-Camera. The first one calculates the transformation between a 3D LiDAR sensor and a robot, and the second system calculates the transformation between a 3D LiDAR and an embedded RGB camera. To demonstrate the efficiency of our method, we performed simulations on the CoppeliaSim simulator and experiments in the laboratory. Then, the results show that it is possible to calibrate the sensors with the methodologies.

关键词： Visualization Laser radar Three-dimensional displays Robot vision systems Sensor fusion Cameras Sensor systems Sensors Calibration Registers

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An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning

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Information Processing in Agriculture 2022年第1期9卷 38-47页

作者： Jose G.M.Esgario Pedro B.C.de Castro Lucas M.Tassis Renato A.Krohling LABCIN-Nature Inspired Computing Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil LABCIN-Nature Inspired Computing and PPGI-Graduate Program in Computer Scienceand Production Engineering DepartmentFederal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil

In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and *** the commonly used approaches,convolutional neural networks have provided results with high *** purpose of this work is to present an effective and practical system capable of seg-menting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural *** proposed approach consists of two stages:a semantic segmentation stage with severity calculation and a symptom lesion classification *** stage was tested separately,highlighting the positive and negative points of each *** obtained very good results for the severity estimation,suggesting that the model can estimate severity values very close to the real *** the biotic stress classification,the accuracy rates were greater than 97%.Due to the promising results obtained,an App for Android platform was developed and imple-mented,consisting of semantic segmentation and severity calculation,as well as symptom classification to assist both specialists and farmers to identify and quantify biotic stresses using images of coffee leaves acquired by smartphone.

关键词： Convolutional neural networks Semantic segmentation Coffee leaf Pests and diseases App.development

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Cerebral Oxygenation Dynamics in Awake Behaving Mice

Cerebral Oxygenation Dynamics in Awake Behaving Mice

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Clinical and Translational Biophotonics, Translational 2022

作者： Zhang, Qingguang Roche, Morgane Gheres, Kyle W. Chaigneau, Emmanuelle Haselden, William D. Kedarasetti, Ravi T. Charpak, Serge Drew, Patrick J. Department of Engineering Science and Mechanics The Pennsylvania State University University ParkPA United States Institut National de la Santé et de la Recherche Médicale ParisU1128 France Laboratory of Neurophysiology and New Microscopies Université Paris Descartes Paris France Graduate Program in Molecular Cellular and Integrative Biosciences The Pennsylvania State University University ParkPA United States Medical Scientist Training Program and Neuroscience Graduate Program The Pennsylvania State University University ParkPA United States Department of Neurosurgery Department of Biomedical Engineering The Pennsylvania State University University ParkPA United States

We investigated (1) how and by what mechanisms voluntary exercise impacts brain tissue oxygenation, and (2) what is the origin of the 1/f-like oxygen fluctuations, in awake behaving mice. © 2022 The Author(s).

ISBN: (纸本)9781557528209

关键词： Mammals

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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AV Navigation in 3D Urban Environments with Curriculum-based Deep Reinforcement Learning

AV Navigation in 3D Urban Environments with Curriculum-based...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kevin Braathen de Carvalho Iure Rosa L. de Oliveira Alexandre S. Brandão Department of Electrical Engineering Graduate Program in Computer Science Nucleus of Specialization in Robotics Universidade Federal de Viçosa Viçosa MG Brazil Department of Electrical Engineering Federal University of Viçosa Viçosa MG Brazil

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV’s decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments.

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The Effects of Growth Environments on Ws2 in Electrocatalytic Hydrogen Evolution Reaction

SSRN

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SSRN 2023年

作者： Chang, Yung-Huang Huang, Chien-Sheng Chen, Yuan-Tsung Bachelor Program in Industrial Technology National Yunlin University of Science and Technology Yunlin Taiwan Department of Electronic Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Materials Science National Yunlin University of Science and Technology Yunlin Taiwan

Transition metal dichalcogenides (TMDs) recently have attracted much attention because of their outstanding catalytic activity, low cost, and earth abundance in the hydrogen evolution reaction (HER). Among these TMDs, MoS2 and WS2 are recognized as promising HER electrocatalysts and have been explored to be one of the candidates for replacing precious metals. In this work, the electrocatalytic activity of WS2 grown in Ar-only, Ar + H2, and ambient environments were studied, where the WS2 was synthesized by thermolysis from (NH4)2WS4 precursor. Different surface morphologies are shown under the different growth atmospheres. The structures of pure WS2, WS2 and WO3 mixed, and almost WO3 grow under the growth atmospheres of Ar-only, Ar + H2, and ambient environment, respectively. All the materials prepared in these different environments exhibit high crystalline characteristic. Especially, the pure WS2 grown in an Ar-only environment presents the preferred (103) lattice plane. The pure WS2 performs the best H2 evolution efficiency, indicating that high crystallinity and preferred (103) lattice plane are relative to the electrocatalytic activity. The studies provide fundamental insight for further design and preparation of electrocatalysts, useful in the development of clean energy. © 2023, The Authors. All rights reserved.

关键词： Electrocatalysts

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FincGAN: A Gan Framework of Imbalanced Node Classification on Heterogeneous Graph Neural Network

FincGAN: A Gan Framework of Imbalanced Node Classification o...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hung Chun Hsu Ting-Le Lin Bo-Jun Wu Ming-Yi Hong Che Lin Chih-Yu Wang Graduate Program of Data Science National Taiwan University and Academia Sinica Taiwan Department of Accounting and Statistics National ChengChi University Taiwan Department of Risk Management and Insurance National ChengChi University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan

Graph Neural Networks (GNNs) frequently face class imbalance issues, especially in heterogeneous graphs. Existing GNNs often assume balanced class sizes, which isn’t true in many cases. Applying them directly to imbalanced data can lead to sub-optimal performance. Traditional oversampling methods, while effective, risk overfitting and face difficulties in reintegrating synthetic samples into the original graph. In this study, we introduce Framework of Imbalanced Node Classification on heterogeneous graph neural network with GAN (FincGAN), a new framework that utilizes oversampling techniques to address class imbalance in heterogeneous graphs. Instead of duplicating existing samples, FincGAN employs a Generative Adversarial Network (GAN) to create synthetic samples and uses deep learning-based edge generators to connect them back to the original graph. Our evaluations on spam user detection in the Amazon and Yelp Review datasets show that FincGAN outperforms baseline models in all essential metrics, including F-score and AUC-PRC score, showing its effectiveness in addressing class imbalance.

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Anisotropic Oldroyd-type models for non-colloidal suspensions of viscoelastic particles in Newtonian and yield-stress fluids via homogenization

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science Talks 2022年 4卷

作者： Ponte Castañeda, Pedro Department of Mechanical Engineering and Applied Mechanics Graduate Group of Applied Math and Computational Science University of Pennsylvania Philadelphia United States

This talk presents new rheological models, derived by means of homogenization techniques, for non-colloidal suspensions of particles undergoing finite deformations and rotations under Stokes flow conditions. The particles can be nonlinear viscoelastic, or elasto-viscoplastic, while the suspending fluid can be Newtonian or viscoplastic. The microstructure is described by two microstructural ellipsoids, which are allowed to evolve with the flow, characterizing the shape and orientation of the inclusions and the angular dependence of their distribution in space. The models account for particle interactions through the two-point correlation function for the particle centers, in a way that reproduces exactly classical and more recent estimates for dilute suspensions. While special cases of the models have been considered in earlier publications, novel features of this work include the possibility of multiple inclusion families, physically motivated evolutions laws for the particle distributions and more general rheological behaviors for the matrix and inclusions. The resulting models can be viewed as anisotropic generalizations of Oldroyd's invariant models accounting for statistical measures of the microstructure and their evolution. © 2022 The Author

关键词： Multiphase and particle-laden flows Non-Newtonian flows Particle/fluid flow Rheology Suspensions Yield-stress fluids

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Additive Poisson regression via forced categorical covariates and generalized fused Lasso 27

Additive Poisson regression via forced categorical covariate...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Yamamura, Mariko Ohishi, Mineaki Yanagihara, Hirokazu Department of Statistics Radiation Effects Research Foundation 5-2 Hijiyama Park Minami-ku Hiroshima732-0815 Japan Center for Data-driven Science and Artificial Intelligence Tohoku University 41 Kawauchi Aoba-ku Miyagi Sendai980-8576 Japan Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8526 Japan

In this study, we use the log-linear link function and propose a generalized fused Lasso (GFL) Poisson regression model in which the nonlinear trend is discretely represented by categorical covariates in the additive model. We use the coordinate descent algorithm for the estimation and show that the optimal solution in a coordinate axis can be found explicitly. To demonstrate the proposed approach, we analyze Japanese crime data. Simulation results showed a fitness ratio for true fusion to be more than 90% in total, demonstrating the reliability of the estimates. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Additive Poisson regression Coordinate descent algorithm Generalized fused Lasso Spatio-temporal analysis

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Microencapsulated phenolic-rich extract from juice processing grape pomace(Vitis *** Var):Effects on oxidative stability of raw and pre-cooked bovine burger

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Food Bioscience 2022年第6期50卷 2120-2129页

作者： Marcelo Edvan dos Santos Silva Rodrigo Lira de Oliveira Thamyres Cesar de Albuquerque Sousa Cristiani Viegas Brandão Grisi Valquíria Cardoso da Silva Ferreira Tatiana Souza Porto Marta Suely Madruga Suzana Pedroza da Silva Fábio Anderson Pereira da Silva Post-Graduate Program in Food Science and Technology Department of Food EngineeringTechnology CentreFederal University of ParaibaJoão PessoaParaiba58051-900Brazil Northeast Biotechnology Network/RENORBIO Federal Rural University of Pernambuco (UFRPE)Dom Manoel de MedeirosDois Irmãos52171-900RecifePEBrazil Federal University of Pernambuco's Agreste (UFAPE) GaranhunsAv.Bom PastorBoa Vista55296-901 GaranhunsPEBrazil Post-Graduate Program in Agrifood Technology Federal University of ParaibaCampus Universitario IIIBananeirasPBBrazil

Grape processing generates residues rich in bioactive compounds with potential application in the meat ***,these substances are unstable and needs to be treated to allow their action during the processing and storage of meat *** this sense,a phenolic-rich extract from grape pomace was microencapsulated and evaluated for its potential to inhibit the oxidative process in raw and precooked beef burgers during cold *** hamburger formulations containing lyophilized extract(LEB),microencapsulated extract(MEB)and synthetic antioxidant sodium erythorbate(SEB)were prepared,pre-cooked,and evaluated for lipid and protein *** microencapsulation,with 95.0%efficiency,provided a material with a crystalline structure,rich in phenolics and with high antioxidant potential,showing 377.4±0.01,1768.1±0.07 and 1185.0±0.20μmol TEAC.100 g^(−1) for DPPH,ABTS and FRAP,*** values of malonaldehyde were found in LEB and MEB(2.9±0.3 and 1.4±0.5 mg ***^(−1),respectively)at the end of storage,with emphasis on for the hamburgers that received the microencapsulated *** behavior was observed for the analysis of carbonyl compounds,with values of 11.1±1.1,8.7±1.4 and 6.8±1.0 nM ***^(−1) in SEB,LEB and MEB,***,the freeze-dried and microencapsulated grape extracts showed superior results when compared to the synthetic antioxidant,increasing the oxidative stability of hamburgers,and avoiding significant color changes during the cold *** results indicate the high potential of the microencapsulated material to be used as a natural antioxidant in the meat industry.

关键词： Lipid oxidation Protein oxidation Meat products Natural antioxidant Encapsulation

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