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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Senoo, Taku Konno, Atsushi Kawahara, Hiromichi Ishii, Idaku Course of Systems Science and Informatic Graduate School of Information Science and Technology Hokkaido University Kita 14 Nishi 9 Kita-ku Sapporo Hokkaido060-0814 Japan Smart Innovation Program Graduate School of Advanced Science and Engineering Hiroshima University 1-4-1 Kagamiyama Higashi-Hiroshima Hiroshima739-8527 Japan

ISBN: (纸本)9781665481090

In this study, two-finger grasping based on plastic deformation control for human-robot interaction is designed and implemented. This control strategy is derived based on the concept that balances both retention of grasping force on objects and plastic behavior in response to external forces. The control law consists of a set of constraints and offsets with respect to the springs that appear in the Maxwell model. In particular, the inner loop that is robust against parameter errors is implemented for actual machine experiments. Simulation and actual experiments with a robotic hand are executed to validate the proposed control law. © 2024 IEEE.

关键词： Human robot interaction

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Using LQG Control as the Inner Loop Controller in Quadrotor Navigation

Using LQG Control as the Inner Loop Controller in Quadrotor ...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Luiz Miguel Monteiro Nascimento Pessotti Tavares Rafael De Angelis Cordeiro Mário Sarcinelli-Filho Daniel Khéde Dourado Villa Graduate Program on Electrical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

LQG (linear Quadratic Gaussian) control is an optimizing control technique able to consider the noise included in the control input and in the output measurement as well. In this paper it is discussed its usage to close the inner control loop of an inner-outer control scheme applied to a quadrotor. The focus is to show that such a controller is efficient in dealing with measurement noise, which degrades the feedback quality, thus affecting the performance of the control system. The proposed control structure adopts an outer control loop based on the inverse kinematics, which produces velocity references for position control, and an inner control loop based on LQG, which controls the attitude and thrust of the vehicle, ensuring that the vehicle velocity converges asymptotically to the velocity reference while keeping the control signals within physical constraints. Experimental results of applying such a control structure to a micro aerial vehicle, Crazyflie 2.1, are presented and discussed, which validate the proposed control system.

关键词： Tracking loops Trajectory tracking Noise Kinematics Tutorials Control systems Robustness

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Predicting Factors Influencing the Actual Use of E-learning Platform Among Medical Students in the Philippines

Predicting Factors Influencing the Actual Use of E-learning ...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Benito, Omar Paolo Prasetyo, Yogi Tri Roque, Ralph Andre C. Flores, Fernan Patrick Cahigas, Maela Madel L. Nadlifatin, Reny Ayuwati, Irene Dyah International Bachelor Program in Engineering Yuan Ze University Taoyuan Taiwan Department of Industrial Engineering and Management Yuan Ze University Taoyuan Taiwan School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines School of Graduate Studies Mapúa University Manila Philippines Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Rectorate University of Surabaya Surabaya Indonesia

ISBN: (纸本)9798350386097

This study aimed to examine the factors influencing the acceptance of eLearning platforms in medical education during the COVID-19 pandemic in the Philippines, using the latent variables from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and two additional variables derived from relevant literature. A total of 360 medical students participated by completing an online survey comprising 40 questions. The analysis, conducted using stepwise multiple linear regression, revealed that Performance Expectancy (PE), Habit (HB), and Instructor Characteristics (IC) were significant predictors of Actual Use (AU) with an accuracy of 54.22%. Higher levels of PE, HB, and IC were found to positively influence the acceptance and utilization of medical eLearning platforms. The findings from this study can provide valuable insights for the Commission on Higher Education in the Philippines, aiding in the enhancement of eLearning platforms for medical education. Although this research primarily focused on UTAUT2 and two additional variables-Learning Value and Instructor Characteristics-the results offer substantial implications for the broader development of eLearning platforms in the medical field. © 2024 IEEE.

关键词： Multiple linear regression

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An Expanded Latent Space Autoencoder for Land Cover Classification of Remote Sensing Images from EuroSAT

An Expanded Latent Space Autoencoder for Land Cover Classifi...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： Emerson Vilar Oliveira Luiz Marcos Garcia Gonçalves Graduate Program in Electrical and Computer Engineering Universidade Federal do Rio Grande do Norte Natal Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

Image classification has been instrumental in the interpretation and labeling of images in the field of remote sensing, computer vision, and in robotics applications. Machine learning and artificial intelligence algorithms, particularly artificial neural networks, are extensively utilized for this purpose. In this work we propose the Expanded Latent Space Autoencoder (ELSA) with a case use application to classify land cover data. The main idea on the ELSA network structure is to utilize the latent spaces of multiple internal autoencoders in order to create an expanded latent space. This expanded latent space extracts more information from the input data, and serves as input features for a more simpler classifier network. In order to evaluate the proposed network's ability to extract features and classify complex and multispectral images we employed it to the EuroSAT dataset. The results demonstrate a remarkable capacity for feature extraction using the ELSA network, with lower complexity, trained with a reduced number of images. The classifier network achieved a final accuracy of 98.7%, matching or exceeding the performance of more complex state-of-the-art models.

关键词： Machine learning algorithms Instruments Autoencoders Land surface Machine learning Feature extraction Robot sensing systems Labeling Remote sensing Image classification

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Game-Theoretic Lightweight Autoencoder Design for Intrusion Detection

Game-Theoretic Lightweight Autoencoder Design for Intrusion ...

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IEEE Conference on Wireless Communications and Networking

作者： Joohong Rheey Hyunggon Park Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In response to the security threats in wireless networks with concurrent device connections, deploying Intrusion Detection Systems (IDS) at the network edge is a promising strategy. However, this approach must take into account the resource constraints incurred by power-limited edge devices, requiring a lightweight solution to IDS. At the same time, the lightweight IDS solution has to minimize performance degradation as higher detection performance is also a key requirement of IDS. In this paper, we design a lightweight autoencoder with explainability, employing the Shapley value to measure unit importance and link importance. This approach can selectively activate only critical components, thereby reducing the complexity for IDS while effectively lowering its performance degradation. We confirm that the proposed algorithm is robust against the harsh sparsity of the autoencoder. Moreover, the sparsity of the proposed lightweight autoencoder can be easily manageable, such that it can be controlled to satisfy the potential constraints of power-limited edge devices. Therefore, the solution is a suitable algorithm for IDS that can be deployed on edge devices in wireless networks.

关键词： Performance evaluation Degradation Measurement units Accuracy Image edge detection Wireless networks Intrusion detection

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Improving Control Theory Education Through Face-to-Face Implementation of a Mobile Robotics Methodology

Improving Control Theory Education Through Face-to-Face Impl...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： M. A. Pastrana William Humberto Cuéllar Sánchez Jose Mendoza-Peñaloza M. S. Santana Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Federal District UDF Brasilia DF Brazil Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

Advances in information and communication technologies have significantly transformed engineering education. Virtual laboratories are increasingly adopted to enhance student interaction with control system simulations. The improved visualization and interaction capabilities of modern computers offer a more organic way to teach theoretical foundations. This paper describes the implementation of a methodology for teaching control theory that until now was entirely online in a face-to-face environment at the University of the Federal District in Brasilia, Brazil. This methodology utilizes virtual laboratories with CoppeliaSim, MATLAB, and the EVA mobile robot to teach control theory focused on single-input, single-output (SISO) systems for mobile robot tracking and obstacle avoidance applications. The results of the face-to-face implementation are compared to those of the fully online methodology, revealing that in-person teaching significantly enhances the quality of instruction.

关键词： Visualization Mechatronics Educational robots SISO Education Laboratories Software Control theory Mobile robots MATLAB

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Morphological and Permeability Fraction of Mixed Matrix Polysulfone Membrane on Batik Palembang Wastewater Treatment 1

Morphological and Permeability Fraction of Mixed Matrix Poly...

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1st International Conference on Industrial Automation, Smart Grid and its Application, ICIASGA 2020

作者： Yuliwati, E. Elfidiah Melani, A. Porawati, H. Graduate Program Study Program of Chemical Engineering Universitas Muhammadiyah Palembang Indonesia Program Study of Chemical Engineering Faculty of Engineering Universitas Muhammadiyah Palembang Indonesia Program Study of Mechanical Politeknik Jambi Indonesia

High performance polysulfone (PSf) membrane has been prepared for treating of batik palembang wastewater. Many investigations attempted the use of polysulfone (PSf) membranes to filtrate dilute contaminant in textile wastewater. The matrix membrane is a mixture of PSf which is produced by adding inorganic materials to obtain a polymer that functions to manufacture the membrane by increasing hydrophilicity and impurity resistance. MMM were fabricated from a dope solution containing PSf/LiCl/DMF at different additives composition. Antifouling properties of membranes could be enhance with 0.0 - 10 wt.% LiCl that has been added into dope solution. Pure water permeation flux analysis (PWP), AFM and FESEM. From the results of research on FESEM, the PSf / LiCL membrane showed a fairly symmetrical cross-sectional structure containing two layers. The layers of the tiny finger-like structure on the edge of the perforated fibers and the holes like the larger fingers are mixed with the macrovoid layer in the middle section. PWP flux is not proportional to mean rough outer surface but higher water permeation. Therefore, PSf/LiCl exhibited a better performance that resulted a much lower fouling tendency than the naked membrane in batik Palembang wastewater treatment. © Published under licence by IOP Publishing Ltd.

关键词： Chlorine compounds

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The technological potential of agro-industrial residue from grape pulping(Vitis spp.)for application in meat products:A review

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Food Bioscience 2022年第5期49卷 40-49页

作者： Marcelo Edvan dos Santos Silva Cristiani Viegas Brandao Grisi Suzana Pedroza da Silva Marta Suely Madruga Fabio Anderson Pereira da Silva Post-Graduate Program in Food Science and Technology Department of Food EngineeringTechnology CentreFederal University of ParaibaJoao PessoaParaiba58051-900Brazil Post-Graduate Program in Agrifood Technology Federal University of ParaibaCampus Universitario IIIBananeirasPBBrazil Federal University of Pernambuco’s Agreste Av.Bom PastorBoa Vista55296-901GaranhunsPEBrazil

This review aimed to investigate the technological potential of grape pomace for application in the meat industry,approaching the compounds of interest and presenting the challenges of its application in meat *** pomace contains bioactive and nutritional compounds with beneficial properties for maintaining the physical-chemical,sensory,and nutritional quality of meat and its *** studies have reported the presence of fiber,amino acids,essential fatty acids and minerals capable of increasing the nutritional value of meat products and promoting a nutrient supply when associated with animal ***,the bioactive composition of grape pomace has anthocyanins,flavanols and phenolic acids capable of increasing the shelf life of meat products,inhibiting microbial development,and delaying the oxidative processes responsible for the deterioration of sensory and nutritional *** use of nano and microencapsulation technologies and the use of active packaging as carriers of grape pomace extracts enhance their application in meat and meat *** technologies,applied individually or together,promote the controlled release of bioactive compounds during the storage and mask undesirable flavors promoted by phenolic *** addition to the technological potential,the use of grape pomace by the meat industry meets the current demand for the use of natural additives and is in accordance with the sustainable development objectives established by the United Nations Organization,reducing environmental impacts and allowing the obtaining molecules with high added value from industrial waste.

关键词： Natural antioxidant Grape bagasse Meat products Bioactive compounds Oxidation Waste reuse

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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Bringing Bearing Fault Detection to Embedded Systems Using Low-Cost Machine Learning Techniques

Bringing Bearing Fault Detection to Embedded Systems Using L...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Natalia S. Teixeira M. A. Pastrana Henrique G. Moura Daniel M. Muñoz Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Automotive Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Vibration analysis is crucial for predictive maintenance of bearing systems, aiming to reduce costs and prevent failures. This paper investigates signal processing techniques such as FFTs and Hilbert Transforms for fault diagnosis, alongside decision tree models, particularly the J48 algorithm within the WEKA environment, for fault classification. The study emphasizes the integration of signal processing and machine learning for effective fault diagnosis and maintenance. Notably, a classification model with low computational cost has been chosen, facilitating implementation in embedded systems. This model also offers interpretability of outputs, as it is based on decision trees of frequency components obtained through preprocessing.

关键词： Vibrations Accuracy Computational modeling Transforms Machine learning Signal processing Robustness Mathematical models Field programmable gate arrays Predictive maintenance

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