338 research outputs found
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Railway Network Delay Evolution: A Heterogeneous Graph Neural Network Approach
Railway operations involve different types of entities (stations, trains,
etc.), making the existing graph/network models with homogenous nodes (i.e.,
the same kind of nodes) incapable of capturing the interactions between the
entities. This paper aims to develop a heterogeneous graph neural network
(HetGNN) model, which can address different types of nodes (i.e., heterogeneous
nodes), to investigate the train delay evolution on railway networks. To this
end, a graph architecture combining the HetGNN model and the GraphSAGE
homogeneous GNN (HomoGNN), called SAGE-Het, is proposed. The aim is to capture
the interactions between trains, trains and stations, and stations and other
stations on delay evolution based on different edges. In contrast to the
traditional methods that require the inputs to have constant dimensions (e.g.,
in rectangular or grid-like arrays) or only allow homogeneous nodes in the
graph, SAGE-Het allows for flexible inputs and heterogeneous nodes. The data
from two sub-networks of the China railway network are applied to test the
performance and robustness of the proposed SAGE-Het model. The experimental
results show that SAGE-Het exhibits better performance than the existing delay
prediction methods and some advanced HetGNNs used for other prediction tasks;
the predictive performances of SAGE-Het under different prediction time
horizons (10/20/30 min ahead) all outperform other baseline methods;
Specifically, the influences of train interactions on delay propagation are
investigated based on the proposed model. The results show that train
interactions become subtle when the train headways increase . This finding
directly contributes to decision-making in the situation where
conflict-resolution or train-canceling actions are needed.Comment: 29 pages; 8 figures; 7 table
Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making
A large amount of data is generated during the operation of a railcar fleet,
which can easily lead to dimensional disaster and reduce the resiliency of the
railcar network. To solve these issues and offer predictive maintenance, this
research introduces a hybrid fault diagnosis expert system method that combines
density-based spatial clustering of applications with noise (DBSCAN) and
principal component analysis (PCA). Firstly, the DBSCAN method is used to
cluster categorical data that are similar to one another within the same group.
Secondly, PCA algorithm is applied to reduce the dimensionality of the data and
eliminate redundancy in order to improve the accuracy of fault diagnosis.
Finally, we explain the engineered features and evaluate the selected models by
using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid
expert system model to enhance maintenance planning decisions by assigning a
health score to the railcar system of the North American Railcar Owner (NARO).
According to the experimental results, our expert model can detect 96.4% of
failures within 50% of the sample. This suggests that our method is effective
at diagnosing failures in railcars fleet.Comment: 21 pages, 7 figures, 3 table
Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance
The pervasive nature of wireless telecommunication has made it the foundation
for mainstream technologies like automation, smart vehicles, virtual reality,
and unmanned aerial vehicles. As these technologies experience widespread
adoption in our daily lives, ensuring the reliable performance of cellular
networks in mobile scenarios has become a paramount challenge. Beamforming, an
integral component of modern mobile networks, enables spatial selectivity and
improves network quality. However, many beamforming techniques are iterative,
introducing unwanted latency to the system. In recent times, there has been a
growing interest in leveraging mobile users' location information to expedite
beamforming processes. This paper explores the concept of contextual
beamforming, discussing its advantages, disadvantages and implications.
Notably, the study presents an impressive 53% improvement in signal-to-noise
ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared
to scenarios without beamforming. It further elucidates how MRT contributes to
contextual beamforming. The importance of localization in implementing
contextual beamforming is also examined. Additionally, the paper delves into
the use of artificial intelligence schemes, including machine learning and deep
learning, in implementing contextual beamforming techniques that leverage user
location information. Based on the comprehensive review, the results suggest
that the combination of MRT and Zero forcing (ZF) techniques, alongside deep
neural networks (DNN) employing Bayesian Optimization (BO), represents the most
promising approach for contextual beamforming. Furthermore, the study discusses
the future potential of programmable switches, such as Tofino, in enabling
location-aware beamforming
An intelligent surveillance platform for large metropolitan areas with dense sensor deployment
ProducciĂłn CientĂficaThis paper presents an intelligent surveillance platform based on the usage of
large numbers of inexpensive sensors designed and developed inside the European Eureka
Celtic project HuSIMS. With the aim of maximizing the number of deployable units while
keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is
based on the usage of inexpensive visual sensors which apply efficient motion detection
and tracking algorithms to transform the video signal in a set of motion parameters. In
order to automate the analysis of the myriad of data streams generated by the visual
sensors, the platform’s control center includes an alarm detection engine which comprises
three components applying three different Artificial Intelligence strategies in parallel.
These strategies are generic, domain-independent approaches which are able to operate in
several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The
architecture is completed with a versatile communication network which facilitates data
collection from the visual sensors and alarm and video stream distribution towards the
emergency teams. The resulting surveillance system is extremely suitable for its
deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap
visual sensors and autonomous alarm detection facilitate dense sensor network deployments
for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)
Video trajectory analysis using unsupervised clustering and multi-criteria ranking
Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead
Operational Research: Methods and Applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
Advances in Reliability, Risk and Safety Analysis with Big Data: Proceedings of the 57th ESReDA Seminar: Hosted by the Technical University of Valencia, 23-24 October, 2019, Valencia, Spain
The publication presents 57th Seminar organized by ESReDA that took place at the Polytechnic University of
Valencia/Universitat Politècnica de Valencia, Spain. The Seminar was jointly organized by ESReDA and CMT Motores Termicos, a research
unit at the Polytechnic University of Valencia. In accordance with the theme proposed for the Seminar, communications were presented
that made it possible to discuss and better understand the role of the latest big data, machine learning and artificial intelligence technologies in the development of reliability, risk and safety analyses for industrial systems. The world is moving fast towards wide applications of big data techniques and artificial intelligence is considered to be the future of our societies. Rapid development of 5G telecommunications infrastructure would only speed up deployment of big data analytic tools. However, despite the recent advances in the these fields, there is still a long way to go for integrated applications of big data, machine learning and artificial intelligence tools in business practice.
We would like to express our gratitude to the authors and key note speakers in particular and to all those who shared with us these moments of discussion on subjects of great importance and topicality for the members of ESReDA. The editorial work for this volume was supported by the Joint Research Centre of the European Commission in the frame of JRC support to ESReDA activities.JRC.C.3-Energy Security, Distribution and Market
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