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International Conference on Machine Learning and Applications (ICMLA)

作者： Jovani Dalzochio Rafael Kunst Jorge Barbosa Edison Pignaton De Freitas Alécio Binotto Graduate Program in Applied Computing University of Vale do Rio dos Sinos São Leopoldo Brazil Institute of Informatics Federal University of Rio Grande do SuI Porto Alegre Brazil Asset and Edge Optimization IBM Consulting Munich Germany

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.

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhandarkar, Vishakha D. Rongali, Aruna Sri Shamout, Mohamed Dawood Pund, Sachin Nama, Gigih Forda Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities India University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Doctoral Program of Environtmental Science University of Lampung Department of Informatics Bandar Lampung Indonesia Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

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Identifying New Persons in the Context of the Robocup@Home Competition Using KNN

Identifying New Persons in the Context of the Robocup@Home C...

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IEEE Latin American Robotics Symposium, LARS

作者： David Segalle Gustavo Fardo Armênio Gustavo Fontes Lewin Everson De Souza Silva João Alberto Fabro Computer Engineering Course UTFPR-Federal University of Technology - Paraná Curitiba PR Brazil Computer Engineering Course - UTFPR Curitiba PR Brazil Eletronic Engineering Course - UTFPR Curitiba PR Brazil PPGCA-Graduate Program on Applied Informatics-UTFPR Curitiba PR Brazil

Robocup@Home proposes a challenge related to Person Recognition: after presented, a new ‘operator’ should become ‘immediately’ recognizable by the robot. The presentation procedure may require the operator to correctly interact with the robot, following a certain procedure, as instructed by the robot itself (for example, staying in front of the robot, so that the robot can take pictures of this person). In this paper, we propose the use of the KNN (K-Nearest Neighbor) supervised machine learning algorithm to include a new ‘operator’ in a database of persons recognizable by the robot. This algorithm uses information taken from an image segmentation of the face of the operator. The experiment evaluates how long it takes to include a new operator if the robot has from 1 to 12 current operators, evaluating also how long it takes to include this operator based on 1, 2 or more images of the new operator, taken from slightly different points of view. The results confirm that KNN can be used to ‘present to the robot’ up to 13 new operators, with up to 15 images for each operator, in less than 60 seconds.

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Application of future 6G technology to support heavy data traffic in highly mobile networks

Application of future 6G technology to support heavy data tr...

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2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH)

作者： Rafael Kunst Edison Pignaton Ting Zhou Honglin Hu Applied Computing Graduate Program University of Rio dos Sinos Valley (Unisinos) Sao Leopoldo Brazil Informatics Institute Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China

ISBN: (数字)9781728174075

ISBN: (纸本)9781728174082

SG technology is becoming a reality, with several deployments appearing around the world. Despite bringing improvements, 5G technology may not adequately support many envisaged applications that will demand large amounts of network resources. Thus, studies on the next generation, the 6G, are highly desirable. Observing this landscape, in this paper, we present a seminal study about the potential usage of 6G to support such highly demanding applications. Towards this study, we model a 6G system at the simulation level and conduct a case study exploring an application of high-definition video monitoring, applying the concepts of drone-swarm based surveillance. This scenario demands the transmission of large amounts of video data through the network. The acquired results present evidence that 6G can address these high network traffic demands.

关键词： 6G mobile communication 5G mobile communication Surveillance Telecommunication traffic Next generation networking

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An unsupervised auditory scene analysis system using incremental low-dimensional embedding

An unsupervised auditory scene analysis system using increme...

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IEEE/SICE International Symposium on System Integration

作者： Kenta Shinzato Ryosuke Kojima Undergraduate School of Informatics and Mathematical Science Undergraduate Course Program of Applied Mathematics and Physics Kyoto University 54 Kawahara-cho Shogoin Sakyo-ku Kyoto Japan Graduate School of Medicine Kyoto University 54 Kawahara-cho Shogoin Sakyo-ku Kyoto Japan

ISBN: (数字)9781728176581

ISBN: (纸本)9781728176598

This paper addresses the low-dimensional embedding of sounds towards unsupervised auditory scene analysis. Summarizing long-time recordings by mapping them into low-dimensional space is an essential task in long-time environmental monitoring. In this paper, we propose a novel low-dimensional embedding system using an incremental embedding algorithm. To analyze long-time recordings, we design an incremental system consisting of recording, feature extraction, low-dimensional embedding, and visualization. Recently, many low-dimensional embedding methods for acoustic scenes have been studied; however, applicability of these methods to the long-time recording is not adequately evaluated. Thus, this paper describes the construction of the scene analysis system and evaluates the performance of this system. In this paper, we especially focus on two important viewpoints in long-time monitoring: incremental methods and effects of noisy data. To realize an incremental system, we use Self-Organizing Nebulous Growths (SONG), which can incrementally construct a low-dimensional embedding space. Also, in our experiments, we apply our system to bird song analysis under noise conditions. By the preliminary experiments using benchmark datasets, we discover noise sensitivity of our system and applicability to environmental monitoring.

关键词： Training Image analysis Feature extraction Birds Real-time systems Environmental monitoring Task analysis

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Retraction Note: Toward a novel method to support decision-making process in health and behavioral factors analysis for the composition of IT projects teams

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Neural Computing and Applications 2024年第24期36卷 15221-15221页

作者： Nunes, Luciano Comin Pinheiro, Plácido Rogério Pinheiro, Mirian Caliope Dantas Filho, Marum Simão Nunes, Rafael Espíndola Comin Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil Federal University of Ceara Fortaleza Brazil

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Developing self-adaptive microservice systems: Challenges and directions

arXiv

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arXiv 2019年

作者： Mendonça, Nabor C. Jamshidi, Pooyan Garlan, David Pahl, Claus Post Graduate Program in Applied Informatics University of Fortaleza FortalezaCE Brazil Computer Science and Engineering Department University of South Carolina ColumbiaSC United States School of Computer Science Carnegie Mellon University PittsburghPA United States Faculty of Computer Science Free University of Bozen-Bolzano Bozen-Bolzano Italy

A self-adaptive system can dynamically monitor and adapt its behavior to preserve or enhance its quality attributes under uncertain operating conditions. This article identifies key challenges for the development of microservice applications as self-adaptive systems, using a cloud-based intelligent video surveillance application as a motivating example. It also suggests potential new directions for addressing most of the identified challenges by leveraging existing microservice practices and technologies. Copyright © 2019, The Authors. All rights reserved.

关键词： Adaptive systems

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Design and Deployment of an Efficient Communication Service for Multi-UAV Systems

Design and Deployment of an Efficient Communication Service ...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Pedro H. M. Pereira Faezeh Pasandideh Maik Basso Joao Paulo J. da Costa Edison Pignaton de Freitas Graduate Program in Electrical Engineering Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Hamm-Lippstadt University of Applied Sciences (HSHL) Hamm Germany Institute of Informatics Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Department of "Technology and Systems" PK NRW Germany

Recent advances in the areas of microelectronics, information technology, and communication protocols have made possible the development of smaller devices, with increasing processing capacity and low energy consumption. This context contributed to the growth of applications based on the use of one or multiple Unmanned Aerial Vehicles (UAVs). Networks composed of multiple UAVs are being used as a matter of improving the effectiveness, accuracy, and minoring the time of missions. However, these applications demand a high rate of data exchange, such as the localization information of each UAV, which can be a challenge, due to the limited transmission power of certain drone platforms. This article proposes a communication service for multi-UAV systems based on dividing the UAV-fleet into groups using the communication protocol IEEE 802.11 ac. Each group has its local network, whose participants can be chosen based on the UAV’s localization or task assignment. UAVs/Drones within the same group constantly communicate, exchanging pose information and specific mission-related data. On the other hand, communication between different groups is only established by messenger drones in pre-set times. The communication service from its detailed implementation to its simulated and field validation experiments is presented in this paper. The results of three different network topologies provide evidence that the proposed communication service for multi-UAV is efficient and can be used for drone cooperative missions.

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Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals

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Sensors 2023年第22期23卷 9199-9199页

作者： Chen, Shuo-Tsung Ye, Ren-Jie Wu, Tsung-Hsien Cheng, Chun-Wen Zhan, Po-You Chen, Kuan-Ming Zhong, Wan-Yu Department of Medical Informatics Chung Shan Medical University Taichung40201 Taiwan Department of Information Center Chung Shan Medical University Hospital Taichung40201 Taiwan Graduate School of Applied Chinese Studies National Yunlin University of Science and Technology Yunlin 64002 Taiwan Bachelor’s Program in Business Management Fu Jen Catholic University New Taipei City242062 Taiwan

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30. © 2023 by the authors.

关键词： Electrocardiograms

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Retraction Note: A proposal for Internet of Smart Home Things based on BCI system to aid patients with amyotrophic lateral sclerosis

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Neural Computing and Applications 2024年第28期36卷 17761-17761页

作者： de Oliveira Júnior, Wilson G. de Oliveira, Juliana M. Munoz, Roberto de Albuquerque, Victor Hugo C. Graduate Program in Applied Informatics Laboratory of Bioinformatics University of Fortaleza Fortaleza Brazil School of Informatics Engineering Universidad de Valparaíso Valparaíso Chile

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