Stochastic Models for Planning VLE Moodle Environments based on Containers and Virtual Machines

Moodle Virtual Learning Environments (VLEs) represent tools of a pedagogical dimension where the teacher uses various resources to stimulate student learning. Content presented in hypertext, audio or vídeo formats can be adopted as a means to facilitate the learning. These platforms tend to produce high processing rates on servers, large volumes of data on the network and, consequently, degrade performance, increase energy consumption and costs. However, to provide eficiente sharing of computing resources and at the same time minimize financial costs, these VLE platforms typically run on virtualized infrastructures such as Virtual Machines (VM) or containers, which have advantages and disadvantages. Stochastic models, such as stochastic Petri nets (SPNs), can be used in the modeling and evaluation of such environments. Therefore, this work aims to use analytical modeling through SPNs to assess the performance, energy consumption and cost of environments based on containers and VMs. Metrics such as throughput, response time, energy consumption and cost are collected and analyzed. The results revealed that, for example, a cluster with 10 replicas, occupied at their maximum capacity, can generate a 46.54% reduction in energy consumption if containers are used. Additionally, we validate the accuracy of the analytical models by comparing their results with the results obtained in a real infrastructure

ALUPAS: Avaliação de desempenho e consumo de energia de softwares para sistemas embarcados

Com a proliferação de equipamentos portáteis operados por baterias, o projeto de sistemas embarcados de baixo consumo de energia tem despertado muito interesse nos últimos anos. Para atender aos requisitos de baixo consumo de energia,é essencial, ainda nas fases iniciais de desenvolvimento, dispor de mecanismos que auxiliem de forma rápida e exata a análise de possíveis alternativas de projeto. Este trabalho apresenta ALUPAS, um simulador estocástico baseado nas Redes de PetriColoridas (CPN) para estimar o desempenho e consumo de energia de softwares para sistemas embarcados. Resultados experimentais mostram uma exatidão, em média, de 94% utilizando o simulador proposto em comparação aos valores reais medidos no hardware

Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data

With the support of Internet of Things (IoT) devices, it is possible to acquire data from degradation phenomena and design data-driven models to perform anomaly detection in industrial equipment. This approach not only identifies potential anomalies but can also serve as a first step toward building predictive maintenance policies. In this work, we demonstrate a novel anomaly detection system on induction motors used in pumps, compressors, fans, and other industrial machines. This work evaluates a combination of pre-processing techniques and machine learning (ML) models with a low computational cost. We use a combination of pre-processing techniques such as Fast Fourier Transform (FFT), Wavelet Transform (WT), and binning, which are well-known approaches for extracting features from raw data. We also aim to guarantee an optimal balance between multiple conflicting parameters, such as anomaly detection rate, false positive rate, and inference speed of the solution. To this end, multiobjective optimization and analysis are performed on the evaluated models. Pareto-optimal solutions are presented to select which models have the best results regarding classification metrics and computational effort. Differently from most works in this field that use publicly available datasets to validate their models, we propose an end-to-end solution combining low-cost and readily available IoT sensors. The approach is validated by acquiring a custom dataset from induction motors. Also, we fuse vibration, temperature, and noise data from these sensors as the input to the proposed ML model. Therefore, we aim to propose a methodology general enough to be applied in different industrial contexts in the future

Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities

Avaliação do desempenho do processo de manufatura do café/ Performance evaluation of the coffee manufacturing process

Globalization and advanced manufacturing technologies have forced manufacturing firms to increase productivity while reducing costs. At the same time, customers are increasingly demanding better products considering tangi- ble (e.g., smell, color, taste) and intangible (e.g., mark, fair treading, and envi- ronmental responsability) attributes. Currently, Brazil consolidates a position as the largest producer and exporter of coffee, accounting for 30% of the inter- national coffee market. This paper presents a stochastic model for performance evaluation and planning of coffee manufacturing process aiming at reducing the cost and time of the production cycle. An industrial case study shows the practical usability of the proposed models and techniques

SPADE: Simulator-assisted Performability Design for UAV-based Monitoring Systems

<p>Data and models</p&gt