8,448 research outputs found
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field
Quantum-inspired Machine Learning on high-energy physics data
Tensor Networks, a numerical tool originally designed for simulating quantum
many-body systems, have recently been applied to solve Machine Learning
problems. Exploiting a tree tensor network, we apply a quantum-inspired machine
learning technique to a very important and challenging big data problem in high
energy physics: the analysis and classification of data produced by the Large
Hadron Collider at CERN. In particular, we present how to effectively classify
so-called b-jets, jets originating from b-quarks from proton-proton collisions
in the LHCb experiment, and how to interpret the classification results. We
exploit the Tensor Network approach to select important features and adapt the
network geometry based on information acquired in the learning process.
Finally, we show how to adapt the tree tensor network to achieve optimal
precision or fast response in time without the need of repeating the learning
process. These results pave the way to the implementation of high-frequency
real-time applications, a key ingredient needed among others for current and
future LHCb event classification able to trigger events at the tens of MHz
scale.Comment: 13 pages, 4 figure
Fraud detection in energy consumption: a supervised approach
Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter anomalies and customer fraudulent behavior (meter tampering). Our system detects anomalous meter readings on the basis of models built using machine learning techniques on past data. Unlike most previous work, it can incrementally incorporate the result of field checks to grow the database of fraud and non-fraud patterns, therefore increasing model precision over time and potentially adapting to emerging fraud patterns. The full system has been developed with a company providing electricity and gas and already used to carry out several field checks, with large improvements in fraud detection over the previous checks which used simpler techniques.Peer ReviewedPostprint (author's final draft
CONGRATS - Convolutional Networks in GPU-based Reliability Assessment of Transmission Systems
Monte Carlo Simulation (MCS) is a powerful method frequently used for composite power system adequacy assessment. However it requires a considerable amount of time to provide accurate estimates for the reliability indexes. In the last years, mathematical approaches have been developed, for instance variance reduction techniques, with the aim to speed up this process.
More recently, the MCS method has been implemented in parallel using a Graphics Processing Unit (GPU) to take advantage of the fast calculations
provided by these computing platforms, resulting in reduction of the simulation time.
In this dissertation, a new approach is developed to shrink simulation time by apllying Convolutional Neural Networks (CNN), trained on a GPU
Smart Grid for the Smart City
Modern cities are embracing cutting-edge technologies to improve the services they offer to the citizens from traffic control to the reduction of greenhouse gases and energy provisioning. In this chapter, we look at the energy sector advocating how Information and Communication Technologies (ICT) and signal processing techniques can be integrated into next generation power grids for an increased effectiveness in terms of: electrical stability, distribution, improved communication security, energy production, and utilization. In particular, we deliberate about the use of these techniques within new demand response paradigms, where communities of prosumers (e.g., households, generating part of their electricity consumption) contribute to the satisfaction of the energy demand through load balancing and peak shaving. Our discussion also covers the use of big data analytics for demand response and serious games as a tool to promote energy-efficient behaviors from end users
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