44,998 research outputs found
A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL
Prediction of traffic-flow in Istanbul has been a great concern for planners of the city. Istanbul as being one of the most crowded cities in the Europe has a rural population of more than 10 million. The related transportation agencies ill Istanbul continuously collect data through many ways thanks to improvements in sensor technology and communication systems which allow to more closely monitor the condition of the city transportation system. Since monitoring alone cannot improve the safety or efficiency of the system, those agencies actively inform the drivers continuously through various media including television broadcasts, internet, and electronic display boards on many locations on the roads. Currently, the human expertise is employed to judge traffic-flow on the roads to inform the public. There is no reliance on past data and human experts give opinions only on the present condition without much idea on what will be the likely events in the next hours. Historical events such as school-timings, holidays and other periodic events cannot be utilized for judging the future traffic-flows. This paper makes a preliminary attempt to change scenario by using artificial neural networks (ANNs) to model the past historical data. It aims at the prediction of the traffic volume based on the historical data in each major junction in the city. ANNs have given very encouraging results with the suggested approach explained in the paper
A weather forecast model accuracy analysis and ECMWF enhancement proposal by neural network
This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people's everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time.Web of Science1923art. no. 514
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
Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems
Deep Learning is increasingly being adopted by industry for computer vision
applications running on embedded devices. While Convolutional Neural Networks'
accuracy has achieved a mature and remarkable state, inference latency and
throughput are a major concern especially when targeting low-cost and low-power
embedded platforms. CNNs' inference latency may become a bottleneck for Deep
Learning adoption by industry, as it is a crucial specification for many
real-time processes. Furthermore, deployment of CNNs across heterogeneous
platforms presents major compatibility issues due to vendor-specific technology
and acceleration libraries. In this work, we present QS-DNN, a fully automatic
search based on Reinforcement Learning which, combined with an inference engine
optimizer, efficiently explores through the design space and empirically finds
the optimal combinations of libraries and primitives to speed up the inference
of CNNs on heterogeneous embedded devices. We show that, an optimized
combination can achieve 45x speedup in inference latency on CPU compared to a
dependency-free baseline and 2x on average on GPGPU compared to the best vendor
library. Further, we demonstrate that, the quality of results and time
"to-solution" is much better than with Random Search and achieves up to 15x
better results for a short-time search
Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model
The article attempts to answer the question whether or not the latest bankruptcy prediction techniques are more reliable than traditional mathematicalâstatistical ones in Hungary. Simulation experiments carried out on the database of the first Hungarian bankruptcy prediction model clearly
prove that bankruptcy models built using artificial neural networks have higher classification accuracy than models created in the 1990s based on discriminant analysis and logistic regression analysis.
The article presents the main results, analyses the reasons for the differences and presents constructive proposals concerning the further development of Hungarian bankruptcy prediction
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
As a unique classification scheme for urban forms and functions, the local
climate zone (LCZ) system provides essential general information for any
studies related to urban environments, especially on a large scale. Remote
sensing data-based classification approaches are the key to large-scale mapping
and monitoring of LCZs. The potential of deep learning-based approaches is not
yet fully explored, even though advanced convolutional neural networks (CNNs)
continue to push the frontiers for various computer vision tasks. One reason is
that published studies are based on different datasets, usually at a regional
scale, which makes it impossible to fairly and consistently compare the
potential of different CNNs for real-world scenarios. This study is based on
the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using
this dataset, we studied a range of CNNs of varying sizes. In addition, we
proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this
base network, we propose fusing multi-level features using the extended
Sen2LCZ-Net-MF. With this proposed simple network architecture and the highly
competitive benchmark dataset, we obtain results that are better than those
obtained by the state-of-the-art CNNs, while requiring less computation with
fewer layers and parameters. Large-scale LCZ classification examples of
completely unseen areas are presented, demonstrating the potential of our
proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also
intensively investigated the influence of network depth and width and the
effectiveness of the design choices made for Sen2LCZ-Net-MF. Our work will
provide important baselines for future CNN-based algorithm developments for
both LCZ classification and other urban land cover land use classification
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