12,667 research outputs found
Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps
Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which
have the potential for causing a great impact on the effectiveness and performance of the overall process and the
sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this
impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations
before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the
combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal
modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating
condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of
recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of
performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version
Forecasting bus passenger flows by using a clustering-based support vector regression approach
As a significant component of the intelligent transportation system, forecasting bus passenger
flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains
challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to
varied destinations and departure times. For this reason, a novel forecasting model named as affinity
propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear
simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based
intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each
cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally,
the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model
is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate
that the proposed model performs better than other peer models in terms of absolute percentage error and
mean absolute percentage error. It is recommended that the deterministic clustering technique with stable
cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio
Using Non-Parametric Tests to Evaluate Traffic Forecasting Performance.
This paper proposes the use of a number of nonparametric comparison methods for evaluating traffic flow forecasting techniques. The advantage to these methods is that they are free of any distributional assumptions and can be legitimately used on small datasets. To demonstrate the applicability of these tests, a number of models for the forecasting of traffic flows are developed. The one-step-ahead forecasts produced are then assessed using nonparametric methods. Consideration is given as to whether a method is universally good or good at reproducing a particular aspect of the original series. That choice will be dictated, to a degree, by the user’s purpose for assessing traffic flow
Infering Air Quality from Traffic Data using Transferable Neural Network Models
This work presents a neural network based model for inferring air quality from traffic measurements.
It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial.
The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK.
Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks.
This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time.
By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
Fast Predictive Simple Geodesic Regression
Deformable image registration and regression are important tasks in medical
image analysis. However, they are computationally expensive, especially when
analyzing large-scale datasets that contain thousands of images. Hence, cluster
computing is typically used, making the approaches dependent on such
computational infrastructure. Even larger computational resources are required
as study sizes increase. This limits the use of deformable image registration
and regression for clinical applications and as component algorithms for other
image analysis approaches. We therefore propose using a fast predictive
approach to perform image registrations. In particular, we employ these fast
registration predictions to approximate a simplified geodesic regression model
to capture longitudinal brain changes. The resulting method is orders of
magnitude faster than the standard optimization-based regression model and
hence facilitates large-scale analysis on a single graphics processing unit
(GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from
the ADNI datasets.Comment: 19 pages, 10 figures, 13 table
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Within the field of soft computing, intelligent optimization modelling techniques include
various major techniques in artificial intelligence. These techniques pretend to generate new business
knowledge transforming sets of "raw data" into business value. One of the principal applications of
these techniques is related to the design of predictive analytics for the improvement of advanced
CBM (condition-based maintenance) strategies and energy production forecasting. These advanced
techniques can be used to transform control system data, operational data and maintenance event data
to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation.
One of the systems where these techniques can be applied with massive potential impact are the
legacy monitoring systems existing in solar PV energy generation plants. These systems produce a
great amount of data over time, while at the same time they demand an important e ort in order to
increase their performance through the use of more accurate predictive analytics to reduce production
losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of
the problems to address. This paper presents a review and a comparative analysis of six intelligent
optimization modelling techniques, which have been applied on a PV plant case study, using the
energy production forecast as the decision variable. The methodology proposed not only pretends
to elicit the most accurate solution but also validates the results, in comparison with the di erent
outputs for the di erent techniques
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