Quality Prediction in Interlinked Manufacturing Processes based on Supervised & Unsupervised Machine Learning

AbstractIn the context of a rolling mill case study, this paper presents a methodical framework based on data mining for predicting the physical quality of intermediate products in interlinked manufacturing processes. In the first part, implemented data preprocessing and feature extraction components of the Inline Quality Prediction System are introduced. The second part shows how the combination of supervised and unsupervised data mining methods can be applied to identify most striking operational patterns, promising quality-related features and production parameters. The results indicate how sustainable and energy-efficient interlinked manufacturing processes can be achieved by the application of data mining

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Literature Review on Big Data Analytics Methods

Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

SOME APPROACHES TO TEXT MINING AND THEIR POTENTIAL FOR SEMANTIC WEB APPLICATIONS

In this paper we describe some approaches to text mining, which are supported by an original software system developed in Java for support of information retrieval and text mining (JBowl), as well as its possible use in a distributed environment. The system JBowl1 is being developed as an open source software with the intention to provide an easily extensible, modular framework for pre-processing, indexing and further exploration of large text collections. The overall architecture of the system is described, followed by some typical use case scenarios, which have been used in some previous projects. Then, basic principles and technologies used for service-oriented computing, web services and semantic web services are presented. We further discuss how the JBowl system can be adopted into a distributed environment via technologies available already and what benefits can bring such an adaptation. This is in particular important in the context of a new integrated EU-funded project KP-Lab2 (Knowledge Practices Laboratory) that is briefly presented as well as the role of the proposed text mining services, which are currently being designed and developed there

PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE

Um estudo sobre métodos de determinação de estados e parâmetros de máquinas síncronas de polos salientes

Orientador: Mateus GiesbrechtDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: As máquinas síncronas de polos salientes desempenham um papel fundamental na análise de estabilidade de sistemas elétricos de potência, especialmente em países cuja maior parte da energia gerada provém de fontes hidráulicas. Os modelos elétricos equivalentes que descrevem o comportamento dessas máquinas são compostos por diversos parâmetros, os quais são utilizados em uma ampla gama de estudos. No presente trabalho, estudam-se e propõem-se técnicas de estimação de estados e parâmetros de máquinas síncronas de polos salientes. A princípio, as equações de tensão, de fluxos concatenados, de potência e de movimento são desenvolvidas com as devidas unidades de medida, tanto em variáveis de máquina quanto em variáveis projetadas sobre um plano ortogonal que gira na velocidade elétrica do rotor. Na maior parte da literatura, essas unidades não são explicitadas no equacionamento. Dentre os parâmetros elétricos dos modelos das máquinas síncronas de polos salientes, as reatâncias de magnetização são os que mais influenciam o comportamento da máquina em condições de regime permanente senoidal. Desta forma, apresenta-se uma nova abordagem à estimação do ângulo de carga dessas máquinas e o subsequente cálculo das reatâncias de magnetização a partir de condições de carga específicas -- o desempenho do método proposto é avaliado em dados de simulação e em dados reais de operação de um gerador síncrono de grande porte. Algumas abordagens à determinação de parâmetros requerem que a máquina seja posta fora de operação para que ensaios específicos possam ser realizados. Dentre eles, um dos mais empregados na determinação de parâmetros transitórios e de regime permanente é o ensaio de rejeição de carga; assim, este ensaio também é analisado e aperfeiçoado por um método automatizado de separação de soma de exponenciais baseado em projeção de variáveis. Por tratar-se de um sistema multivariável e altamente não linear, diferentes observadores de estado também são utilizados para se determinarem estados e parâmetros de máquinas síncronas em tempo hábil e com precisão satisfatória. Este trabalho apresenta uma abordagem não linear recursivamente aplicável à estimação de fluxos concatenados, correntes de enrolamentos amortecedores, ângulo de carga e reatâncias de magnetização de máquinas síncronas de polos salientes por meio da filtragem de partículas. Um modelo não linear de oitava ordem é considerado e apenas as medições realizadas nos terminais da armadura e do campo durante regime permanente se fazem necessárias para estimar as referidas grandezasAbstract: Salient-pole synchronous machines play a fundamental role in the stability analysis of electrical power systems, especially in countries where most of the generated energy comes from hydraulic sources. The electrical equivalent models that describe the behavior of these machines are composed of several electrical parameters, which are used in a wide range of studies. In the present work, techniques for estimating states and parameters of salient-pole synchronous machines are studied and proposed. A priori, the voltage, flux linkage, power, and motion equations are developed with the appropriate units included, both in machine variables and in variables projected on an orthogonal plane rotating in the rotor's electrical speed. In most of the literature, these units are not explained in the equation process. Among the electrical parameters, the magnetizing reactances are the ones that most influence the machine behavior under transient and steady-state conditions. In this way, a new approach to estimate the load angle of these machines and the subsequent calculation of the magnetizing reactances from specific load conditions are presented -- the performance of the proposed method is evaluated by means of simulation data and by operating data of a large synchronous generator. Some approaches to determine parameters require the machine to be taken out of operation, so that specific tests may be performed. Among them, one of the most used to determine transient and steady-state parameters is the load rejection test; thus, this test is also analyzed and refined by an automated method based on variable projection for separating the resulting sum-of-exponentials. Since the machines are highly nonlinear, multivariate, dynamic systems, different state observers seek to solve the state estimation problem in a timely manner and with satisfactory accuracy. This work presents a nonlinear and recursive approach for the estimation of flux linkages per second, amortisseur winding currents, load angle, and magnetizing reactances of salient-pole synchronous machines by means of the particle filtering. An eighth-order nonlinear model is considered, and only measurements taken at the machine terminals are necessary to estimate these quantitiesMestradoAutomaçãoMestre em Engenharia Elétrica162015/2018-6CNPq