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Ubiquitous computing in light of human phenotypes: foundations, challenges, and opportunities

Journal of Ambient Intelligence and Humanized computing 2023年第3期14卷 2341-2349页

作者： Bavaresco, Rodrigo Simon Barbosa, Jorge Luis Victória Applied Computing Graduate Program - PPGCA University of Vale do Rio dos Sinos - UNISINOS Av. Unisinos São Leopoldo Rio Grande do Sul93.022-000 Brazil

The interest in human phenotypes has leveraged interdisciplinary efforts encouraging a better understanding of the broad spectrum of psychological and behavioral disorders. Moreover, the usage of mobile and wearable devices along with unobtrusive computational capabilities provides an extensive amount of information that allows the characterization of phenotypes. This article describes the human phenotype through the lens of computational range and reviews state-of-the-art computational phenotyping. Furthermore, the article discusses computational phenotyping’s extension concerning the combination of intelligent environments and personal mobile devices, addressing technical, managerial, and ethical challenges. This combination reinforces ubiquitous computational capabilities for phenotyping as a facilitator of interdisciplinary information convergence in favor of clinical and biomedical research. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Wearable technology

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On proposing a non-intrusive device and methodology to monitor motor degradation

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Journal of King Saud university - Engineering Sciences 2023年第3期35卷 215-223页

作者： Kuhn, Henrique Cafruni da Rosa Righi, Rodrigo Crovato, Cesar David Paredes Electrical Engineering Graduate Program Universidade do Vale do Rio dos Sinos São Leopoldo Brazil Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos São Leopoldo Brazil

Monitoring the degradation of induction motors is an essential concern for industries, especially when there are high costs related to maintenance and their stops. Nowadays, there are many non-invasive ways to monitor the health status of induction motors, including sensors to measure temperature, magnetic field, vibration, current, and power. However, installing these sensors may not be a simple task since intrusive procedures such as engine disassembling, wire separation, and engine interruption are commonly observed. Thus, we propose MUCLA (Multiphase Clamp and Tracking - patent pending) as a new device and methodology for monitoring motor degradation by analyzing its stator current. MUCLA introduces a non-invasive current sensors array on a proposed clamp in the hardware perspective, while a spectral tracking methodology refers to our novelty in the software viewpoint. For end-users, the main MUCLA contribution relies on the combination of the simplicity of installation and the possibility of using the proposed device even when considering Variable-Frequency Drive powered engines. The results obtained with both simulation and practical tests show encouraging results over different input workloads and emulated degradation. Compared to the current initiatives, we can classify MUCLA as a better solution for industrial plants in terms of installation time, installation complexity, and cost. © 2021 The Authors

关键词： Induction motors

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Hybrelastic: a hybrid elasticity strategy with dynamic thresholds for microservice-based cloud applications

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International Journal of Cloud computing 2024年第2期13卷 99-123页

作者： Accorsi, José Augusto da Rosa Righi, Rodrigo Rodrigues, Vinicius Facco da Costa, Cristiano André Singh, Dhananjay Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos RS São Leopoldo Brazil Department of Electronics Engineering Hankuk University of Foreign Studies Yongin17035 Korea Republic of

Microservices-based architectures aim to divide the application’s functionality into small services so that each one of them can be scaled, managed, implemented, and updated individually. Currently, more and more microservices are used in application modelling, making them compatible with resource elasticity. In the literature, solutions employ elasticity to improve application performance;however, most of them are based on CPU utilisation metrics and only on reactive elasticity. In this context, this article proposes the hybrelastic model, which combines reactive and proactive elasticity with dynamically calculated thresholds for CPU and network metrics. The article presents three contributions in the context of microservices: 1) combination of two elasticity policies;2) use of more than one elasticity evaluation metric;3) use of dynamic thresholds to trigger elasticity. Experiments with hybrelastic demonstrate 10.31% higher performance and 20.28% lower cost compared to other executions without hybrelastic. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Elasticity

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Enhancing Synthetic Well Logs with PCA-Based GAN Models 23

Enhancing Synthetic Well Logs with PCA-Based GAN Models

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Garcia, Luciano Garim De Oliveira Ramos, Gabriel De Oliveira, José Manuel Marques Teixeira Da Silveira, Ariane Santos Universidade do Vale do Rio dos Sinos Applied Computing Graduate Program São Leopoldo Brazil Center of Excellence in Geological and Numerical Modeling Universidade do Vale do Rio dos Sinos São Leopoldo Brazil

ISBN: (纸本)9798350374889

The generation of synthetic well log data is crucial for enhancing the understanding and exploration of subsurface reservoirs. This paper introduces a novel Generative Adversarial Network (GAN) model that incorporates a Principal Component Analysis (PCA)-based loss function to improve the quality of synthetic well log data. The proposed method is distinguished by its ability to generate complete well log datasets, rather than just individual logs or completing partial logs. Traditional GANs utilize cross-entropy loss but often fail to capture the complex structural patterns inherent in well logs. By integrating PCA into the loss function, our model not only distinguishes real from synthetic data but also ensures that the synthetic data retains the intrinsic variability and relationships observed in real logs. We validate our approach using histograms, correlation heatmaps, dimensionality reduction techniques (PCA and t-SNE), and a discriminative task. Results show that synthetic data generated with PCA-based loss aligns closely with real data, demonstrating superior preservation of statistical and structural characteristics. This advancement in synthetic data generation holds promise for enriching subsurface data analysis and exploration. © 2024 IEEE.

关键词： Generative adversarial networks

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Analysis of human mobility behavior in Smart Cities and Smart Environments: A systematic literature review and taxonomy

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Journal of Smart Cities and Society 2024年第PRE-PRESS期Pre-press卷 1-36页

作者： Borges, Carlos Eduardo Liedtke Aranda, Jorge Arthur Schneider Barbosa, Jorge Luis Victória Applied Computing Graduate Program (PPGCA) University of Vale do Rio dos Sinos (UNISINOS) São Leopoldo Brazil

The analysis of human mobility behavior through computational techniques finds applications in various domains and provides valuable insights for urban planning, transportation services, and a deeper understanding of human interactions in Smart Cities and Smart Environments. In this scenario, this study presents a Systematic Literature Review (SLR) with the following main question: How are computational techniques being used to analyse human mobility behavior in Smart Cities and Smart Environments? A total of 5989 articles were initially found and filtered, resulting in 56 articles reviewed. As the main contributions, this study provides responses to 19 research questions. A list of the challenges and the computational techniques identified is provided. The algorithms, machine learning techniques and data-sources used by the reviewed studies are also presented and organized through taxonomies. A comprehensive discussion of the identified techniques is conducted, finishing with a compilation of challenges, open issues and research opportunities. To the best of our knowledge, this is the first study that reviewed human mobility behavior covering a wide range of scenarios, including urban mobility, public transport, points and regions of interest, ridesharing, bike-sharing, traffic analysis, driving behavior, electric vehicle charging stations planning, mobility on demand, crowd analysis and others.

关键词： Smart City Smart Environment behavior mobility crowd analysis

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Mineralogical Classification of Sedimentary Rocks by Spectral Matching Techniques Based on Library and Field-Work References 14

Mineralogical Classification of Sedimentary Rocks by Spectra...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Zanotta, Daniel C. De Souza, Lais V. De Souza, Marcelo K. Ayres, Alysson S. Sales, Vinicius F. Racolte, Graciela R. Ibanez, Delano M. Silveira, Luiz G. Veronez, Mauricio R. UNISINOS Vizlab X-Reality and GeoInformatics Lab RS São Leopoldo Brazil Graduate Program in Applied Computing Vale do Rio dos Sinos University RS São Leopoldo Brazil RJ Brazil

ISBN: (纸本)9798331513139

Sedimentary rocks are of great importance for the oil and gas industry. Spectral matching using known references made on hyperspectral images is one of the most rapid and cost effective alternatives for the detailed analysis in these areas, since they can adequately catch the specific absorption features of the multiple minerals available. In many initiatives, scientists run the methods with standard spectral references, usually from spectral libraries or samples from other places. However, for the success of their application, they must be used along with spectral references that accurately describe the actual constituents of the surface. In this paper we scrutinize three different ways for mineralogical mapping performed by spectral matching techniques. The results showed that using reference spectra from samples collected directly on the surveyed environment can considerably improve the results, reaching high scores, pointing out that many places share common substances, but each specific material has particular differences that cannot be generalized. © 2024 IEEE.

关键词： Gas industry

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A Novel Approach for Commercial Opportunities Qualification Using the BANT Methodology under the Fuzzy Set Theory Framework. 28th

A Novel Approach for Commercial Opportunities Qualification ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Leite, Marcus Vinicius Abe, Jair Minoro Hoffmann Souza, Marcos Leandro Graduate Program in Production Engineering-Paulista University-UNIP Rua Dr. Bacelar 1212 São Paulo-SP04026002 Brazil Graduate Program in Applied Computing-Vale do Rio dos Sinos University-UNISINOS Av. Unisinos 950 - São LeopoldoRS-93022-000 Brazil

Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of experts' final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology for qualifying commercial opportunities, aiming to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed to emulate the assessments of the experts. The analysis of the ratings obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system's effectiveness in aligning with expert perceptions. While the study indicated room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance. © 2024 The Authors.

关键词： Fuzzy set theory

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A machine learning pipeline for extracting decision-support features from traffic scenes 12

A machine learning pipeline for extracting decision-support ...

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12th International Workshop on Agents in Traffic and Transportation, ATT 2022

作者： Fraga, Vitor A. Schreiber, Lincoln V. Kunst, Rafael Barbosa, Jorge Luis V. Ramos, Gabriel de O. Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos São Leopoldo Brazil

Traffic systems play a key role in modern society. However, these systems are increasingly suffering from problems, such as congestions. A well-known way to efficiently reduce this kind of problem is to perform traffic light control intelligently through reinforcement learning (RL) algorithms. In this context, extracting relevant features from the traffic environment to support decision-making becomes a central concern. Examples of such features include vehicle counting on each queue, identification of vehicles’ origins and destinations, among others. Recently, the advent of deep learning has paved to way to efficient methods for extracting some of the aforementioned features. However, the problem of identifying vehicles and their origins and destinations within an intersection has not been fully addressed in the literature, even though such information has shown to play a role in RL-based traffic signal control. Building against this background, in this work we propose a deep learning pipeline for extracting relevant features from intersections based on traffic scenes. Our pipeline comprises three main steps: (i) a YOLO-based object detector fine-tuned using the UAVDT dataset, (ii) a tracking algorithm to keep track of vehicles along their trajectories, and (iii) an origin-destination identification algorithm. Using this pipeline, it is possible to identify vehicles as well as their origins and destinations within a given intersection. In order to assess our pipeline, we evaluated each of its modules separately as well as the pipeline as a whole. The object detector model obtained 98.2% recall and 79.5% accuracy. The tracking algorithm obtained a MOTA of 72.6% and a MOTP of 74.4%. Finally, the complete pipeline obtained an error of 7.53% in terms of origin and destination counts. © 2021 Copyright for this paper by its authors.

关键词： Object detection

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MILPdM: A Predictive Maintenance Architecture for the Military domain 22

MILPdM: A Predictive Maintenance Architecture for the Milita...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Dalzochio, Jovani Kunst, Rafael Barbosa, Jorge Pignaton De Freitas, Edison Binotto, Alecio University of Vale Do Rio Dos Sinos Graduate Program in Applied Computing São Leopoldo Brazil Institute of Informatics Federal University of Rio Grande Do sui Porto Alegre Brazil Ibm Consulting Asset and Edge Optimization Munich Germany

ISBN: (纸本)9798350345346

Equipment monitoring for failure prediction is receiving attention from different sectors of society, such as industry, healthcare, and defense. In the defense domain, assets like military vehicles generate data that one can use to identify behavior changes and anticipate possible real-time failures, avoiding unnecessary maintenance interventions. Failure anticipation is crucial, as assets operated in the military domain perform critical tasks in which unexpected equipment failures result in high material and human costs. Approaches found in the literature typically deal with failure generation, aiming at analyzing the equipment's behavior. This paper proposes a broader approach called MILPdM. This proposal is a failure prediction architecture covering the whole failure prediction and predictive maintenance procedures. We evaluate MILPdM architecture by analyzing an engine-failure scenario where we train models to predict time series by collecting vibration data that describes the degradation of the vehicle's health. Considering the implementation of an LSTM-based neural network and Random Forest, the acquired results lead to a root mean square error of 0.15015 in the best case, which allows to predict the failure status two minutes in advance with only 3 hours of data history. This result shows that MILPdM is capable of anticipating failures with high assertiveness. © 2023 IEEE.

关键词： IoBT machine learning neural networks predictive maintenance random forest

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4G/5G performance of a multi-RAT UAV for medical parcel delivery 19

4G/5G performance of a multi-RAT UAV for medical parcel deli...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： De Resende, Henrique Carvalho Pinheiro, Joao Francisco Nunes Reiter, Philippe Both, Cristiano Bonato Marquez-Barja, Johann M. University of Antwerp - Imec IDLab Faculty of Applied Engineering Antwerp Belgium Universidade Do Vale Do Rio Dos Sinos Applied Computing Graduate Program RS Porto Alegre Brazil

ISBN: (纸本)9781665431620

Unmanned Aerial Vehicles (UAVs) are being developed and researched to be used in different areas and diverse use cases. The delivery of medical parcels is an emergency service that avails from the air traffic for the fast delivery of critical health content. Besides 5G networks being the target communication technology for the UAV parcel transport, the infrastructure is still not optimized for ground-level User Equipments (UEs). Using multiple Radio Access Technology (RAT) by UAVs is an alternative solution for scenarios where the 5G network is unstable or not yet fully deployed. In this paper, we present bench-marking of 5G and 4G networks on unideal radio scenarios to understand the real latency and throughput delivered by these technologies in worst-case scenarios. The results show that the 5G network can provide the necessary Quality of Service (QoS) for UAV operations even with Reference Signal Received Power (RSRP) in the order of -90. In comparison, the preliminary data for the 4G network validates that the 4G network could not provide the required latency. © 2022 IEEE.

关键词： 5G mobile communication systems

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