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Computer Science Society (SCCC) International Conference Chilean FLAGGED

作者： Gustavo Rostirolla Rodrigo da Rosa Righi Cristiano André da Costa Jorge Luis Victória Barbosa Applied Computing Graduate Program - Unisinos Av. Unisinos 950 - São Leopoldo RS Brazil

ISBN: (纸本)9781509033409

As a result of rural and suburban migration to the cities, urban life has become a significant challenge for citizens and mayors, imposing a huge concern in the sustainable use of resources like energy, water, transportation and housing. Smart cities are the biggest bet to address these challenges efficiently through a real-time monitoring approach which aims to assist in intelligent planning and sustainable urban development. However, to accomplish this monitoring efficiently and to enable the sustainable use of resources, it is necessary an integration between citizens, city devices and the platform where the data is stored and processed. In this context, we are proposing the 3LES model (3 Levels of Energy Saving for Smart Cities), a model that combines data from citizens, city devices and nodes from the processing plataform to offer a multilevel energy saving proposal. 3LES must act transparently without affecting the quality of the services already offered in the city. This paper presents the components of management and monitoring of power consumption at various levels, as well as a model that monitors the energy consumption of elastic cloud applications. The results are promising, with a model that allows the estimation of the energy consumption of elastic applications based on CPU and memory traces with an average and median precision of 97.15% and 97.72%. In addition, we obtained a reduction of more than 90% in the energy spent in public lighting in the city of Rome when analyzing the citizens' location.

关键词： Portable document format IEEE Xplore

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Elastic Management of Physical Spaces and Objects in Multi-Hospital Environments

Elastic Management of Physical Spaces and Objects in Multi-H...

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IEEE International Conference on Internet of Things

作者： Rodrigo da Rosa Righi Gustavo Rostirolla Cristiano André da Costa Murillo Goulart érico Rocha Applied Computing Graduate Program-Unisinos-Av. Unisinos 950-S?o Leopoldo RS Brazil

ISBN: (纸本)9781509058815

The fast growing and the aging of the population are some of the major problems today in terms of medical treatment in public or private hospitals. In this context, particularly talking about the health equipments belonging to each hospital, we also observe that governments and administrators still face difficulties in overseeing the tracking, allocation and management of such equipments in real-time. The gap of acquiring a global overview about what each hospital has or not invariably leads to resource wasting in some places, while there is a lacking of resources in others, so providing a deficient service at regional or national scale. Here, we are proposing the EMH (Elastic Multi-hospital Management) model, which uses the cloud elasticity facility to provide a distributed framework for managing things including physical spaces and objects at real-time within the context of multiple healthcare institutions. Our solution consists in offering a centralized management control center that works with cloud elasticity to enlarge or reduce the number of virtual machines to support the variable incoming demands from the hospitals. Thus, the idea is to subsidize real-time decision making in favor of managers who can, for instance, plan expansions, new health policies or equipment exchanges between hospitals. EMH is based on the RFID (Radio-frequency identification) sensor tracking technology, combined with the client-server Web Service concept, to detect motion and usability of objects. Finally, we developed a prototype of the EMH model which emphasized the benefits on providing a unique administration point and handling incoming workload with cloud elasticity to address user requests inside an acceptable response time (here denoted as lower than 200 ms).

关键词： Hospitals Performance Cloud Elasticity

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Congestion control with ECN support in poll-based multicast protocols

Congestion control with ECN support in poll-based multicast ...

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IEEE Symposium on Computers and Communications (ISCC)

作者： M.P. Barcellos A. Detsch Post-graduate Program on Applied Computing PIPCA/UNISINOS Sao Leopoldo Rio Grande do Sul Brazil

Most of the traffic in the Internet nowadays is transmitted using TCP, or transport control protocol. The stability of the Internet depends on the congestion control being performed by this protocol, as well as equivalent mechanisms employed by other protocols. The ECN technique (explicit congestion notification), in which packets forwarded by routers are marked whenever congestion is about to occur (or already occurring), allows a transmitter to reduce the sending rate accordingly without relying on packet drops. ECN has not been fully explored, particularly regarding multicast protocols. This paper presents models of poll-based reliable multicast protocols and extends them with a new single-rate congestion control mechanism that harnesses ECN. Simulation results show that it provides fairness between flows from multiple sources, and is TCP-friendly. Further, they demonstrate the substantial efficiency gain obtained with the use of ECN in multicast.

关键词： Multicast protocols Sliding mode control Internet Transport protocols Multicast algorithms Scalability Signal generators Stability Transmitters Grid computing

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Digital field book for geosciences 37

Digital field book for geosciences

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37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017

作者： Cagliari, Joice Veronez, Mauricio R. Heinen, Farlei Gonzaga, Luiz Tognoli, Francisco M. W. Gallon, Debora P. Marson, Fernando P. Graduate Program in Geology Vale Do Rio Dos Sinos University São Leopoldo RS Brazil Vale Do Rio Dos Sinos University São Leopoldo RS Brazil Graduate Program in Applied Computing Vale Do Rio Dos Sinos University Av. UNISINOS 950 São Leopoldo RS93022-000 Brazil

ISBN: (纸本)9781509049516

In this study we present a mobile application for geoscience. It refers to a digital field book for automating data collection and outcrop/core description, and optimizing the final data processing. Sensors were developed for semi-automatic data collection, real time calculations, measurements of dip angles and dip directions, geographic location, among others. Field tests were performed comparing the traditional method with the proposed digital method. The preliminary results show a good acceptance of the mobile application by geoscientists and an improvement in the time required to perform data collection. Field tests are still going on and the complete results will be used to improve the development of this important tool in geoscience field work. © 2017 IEEE.

关键词： Data collection Sensors Software Real-time systems Rocks Mobile applications

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GreenHPC: A novel framework to measure energy consumption on HPC applications 4

GreenHPC: A novel framework to measure energy consumption on...

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4th International Conference on Sustainable Internet and ICT for Sustainability, SustainIT 2015

作者： Rostirolla, Gustavo Da Rosa Righi, Rodrigo Rodrigues, Vinicius Facco Velho, Pedro Padoin, Edson Luiz Applied Computing Graduate Program Unisinos - Av. Unisinos 950 São Leopoldo RS Brazil INRIA - Univates 2004 route de Lucioles BP93 Sophia Antipolis CEDEX06902 France Ijui RS Brazil

ISBN: (纸本)9783901882708

Energy consumption on systems that have a continuous power source is tightly-related to both the computing time of an application and its required CPU load. Considering the scope of HPC applications which commonly have a time precision in nano or milliseconds, we observe a lack of systems that combine appropriate sampling rate, low intrusiveness and low cost. In this context, this article presents a model called GreenHPC that uses a hall effect sensor to precisely capture current with an arbitrary timeslice on HPC applications. Its scientific contribution relies on analyzing the energy consumption at a cluster scale, without application intrusiveness, showing the impact of maintaining idle nodes or turning them off for energy saving. Furthermore, considering the use of GreenHPC over the execution of a seismic wave application, we also present the number of employed processors which present the best energy consumption index. Finally, we have used the obtained results to infer a model to estimate energy consumption of HPC applications. All the developed work has a special concern on reproducibility, so all data and hardware schematics are available for download at 1. © 2015 IFIP.

关键词： Energy utilization

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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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Context-oriented exception handling

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International Journal of High Performance Systems Architecture 2009年第1期2卷 16-25页

作者： Dillenburg, Fabiane Cristine Barbosa, Jorge Luis Victoria Porto Alegre Brazil Interdisciplinary Graduate Program in Applied Computing University of Vale do Rio Dos Sinos São Leopoldo Brazil

The growing availability of small, faster computational devices has made mobile computing more popular in our daily life. The development of new applications demands an adoption of programming languages to this new reality, in order to take advantage of new available technologies. In this context, this paper presents a proposal for the specification of context-oriented exception handling features, aiming the development of mobile and ubiquitous applications. These features were implemented in the Holoparadigm mobile development and execution platform. Also, these features were tested using specially crafted applications that triggered exception handling. Copyright © 2009 Inderscience Enterprises Ltd.

关键词： Mobile computing

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Evaluation of convolutional neural networks for COVID-19 classification on chest X-rays

arXiv

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arXiv 2021年

作者： Zeiser, Felipe André Da Costa, Cristiano André De Oliveira Ramos, Gabriel Bohn, Henrique Santos, Ismael Da Rosa Righi, Rodrigo Graduate Program in Applied Computing Universidade Do Vale Do Rio Dos Sinos São Leopoldo Brazil

Early identification of patients with COVID-19 is essential to enable adequate treatment and to reduce the burden on the health system. The gold standard for COVID-19 detection is the use of RT-PCR tests. However, due to the high demand for tests, these can take days or even weeks in some regions of Brazil. Thus, an alternative for detecting COVID-19 is the analysis of Digital Chest X-rays (XR). Changes due to COVID-19 can be detected in XR, even in asymptomatic patients. In this context, models based on deep learning have great potential to be used as support systems for diagnosis or as screening tools. In this paper, we propose the evaluation of convolutional neural networks to identify pneumonia due to COVID-19 in XR. The proposed methodology consists of a preprocessing step of the XR, data augmentation, and classification by the convolutional architectures DenseNet121, InceptionResNetV2, InceptionV3, MovileNetV2, ResNet50, and VGG16 pre-trained with the ImageNet dataset. The obtained results demonstrate that the VGG16 architecture obtained superior performance in the classification of XR for the evaluation metrics using the methodology proposed in this article. The obtained results for our methodology demonstrate that the VGG16 architecture presented a superior performance in the classification of XR, with an Accuracy of 85.11%, Sensitivity of 85.25%, Specificity of 85.16%, F1-score of 85.03%, and an AUC of 0.9758. © 2021, CC BY-NC-SA.

关键词： Convolutional neural networks

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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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Artificial Neural Network Model for Steel Strip Tandem Cold Mill Power Prediction 3rd

Artificial Neural Network Model for Steel Strip Tandem Cold ...

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3rd International Conference on applied Informatics, ICAI 2020

作者： de Oliveira, Danilo G. da Silva, Eliton M. Miranda, Fabiano J. F. Filho, José F. S. Parpinelli, Rafael S. Graduate Program in Applied Computing Santa Catarina State University – UDESC Joinville Brazil Department of Computer Science Santa Catarina State University – UDESC Joinville Brazil ArcelorMittal – Global Research and Development Group São Francisco do Sul Brazil

ISBN: (纸本)9783030617011

In the cold rolling of flat steel strips, electric energy consumption is one of the highest expenses. Predicting the power requirements according to the line and product conditions can significantly impact the energy cost and, thus, on the business’s profitability. This paper proposes predicting the power requirements of a tandem cold rolling mill of steel strips on a coil-to-coil base applying Artificial Neural Networks (ANN) as a uni-variate regression problem. The tests yielded an MSE of 300.39 kW or 2.4% and are better than the acceptable 5% error margin for this project, indicating that the ANN presented satisfactory results. The application of six full-month worth of data in the trained ANN model showed the excellent correlation of the ANN predictions with the measured data, leading to the conclusion that the system is ready for the deployment for daily use for line engineers. Overall, the results obtained show that the steel industry can highly benefit from Industry 4.0 and Artificial Intelligence technologies. © 2020, Springer Nature Switzerland AG.

关键词： Automotive industry

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