32,827 research outputs found
Use of rod compactors for high voltage overhead power lines magnetic field mitigation
In the last decades, strengthening the high voltage transmission system through the installation of new overhead power lines has become critical, especially in highly developed areas. Present laws concerning the human exposure to electric and magnetic fields introduce constraints to be considered in both new line construction and existing systems. In the paper, a technique for passive magnetic field mitigation in areas close to overhead power lines is introduced, fully modelled and discussed through a parametric analysis. The investigated solution, which basically consists in approaching line conductors along the span making use of rod insulators, is applicable on both existing and under-design overhead lines as an alternative to other mitigating actions. Making use of a 3-dimensional representation, the procedure computes both positions of phase conductors and forces acting on insulators, towers, conductors and compactors, with the aim of evaluating the additional mechanical stress introduced by the compactors. Finally, a real case study is reported to demonstrate and quantify the benefits in terms of ground magnetic field reduction achievable by applying the proposed solution, in comparison to a traditional configuration. Furthermore, using compactors to passively reduce the magnetic field is simple to be applied, minimally invasive and quite inexpensive as regards to alternative mitigating actions
Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat
A crop can be viewed as a complex system with outputs (e.g. yield) that are
affected by inputs of genetic, physiology, pedo-climatic and management
information. Application of numerical methods for model exploration assist in
evaluating the major most influential inputs, providing the simulation model is
a credible description of the biological system. A sensitivity analysis was
used to assess the simulated impact on yield of a suite of traits involved in
major processes of crop growth and development, and to evaluate how the
simulated value of such traits varies across environments and in relation to
other traits (which can be interpreted as a virtual change in genetic
background). The study focused on wheat in Australia, with an emphasis on
adaptation to low rainfall conditions. A large set of traits (90) was evaluated
in a wide target population of environments (4 sites x 125 years), management
practices (3 sowing dates x 2 N fertilization) and (2 levels). The
Morris sensitivity analysis method was used to sample the parameter space and
reduce computational requirements, while maintaining a realistic representation
of the targeted trait x environment x management landscape ( 82 million
individual simulations in total). The patterns of parameter x environment x
management interactions were investigated for the most influential parameters,
considering a potential genetic range of +/- 20% compared to a reference. Main
(i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity
indices calculated for most of APSIM-Wheat parameters allowed the identifcation
of 42 parameters substantially impacting yield in most target environments.
Among these, a subset of parameters related to phenology, resource acquisition,
resource use efficiency and biomass allocation were identified as potential
candidates for crop (and model) improvement.Comment: 22 pages, 8 figures. This work has been submitted to PLoS On
Coherency in space of lake and river temperature and water quality records
Environmental time series observed over 100’s of monitoring locations usually possess some spatial structure in terms of common patterns throughout time, commonly described as temporal coherence. This paper will apply, develop and compare two methods for clustering time series on the basis of their patterns over time. The first approach treats the time series as functional data and applies hierarchical clustering while the second uses a state-space model based clustering approach. Both methods are developed to incorporate spatial correlation and stopping criteria are investigated to identify an appropriate number of clusters. The methods are applied to Total Organic Carbon data from river sites across Scotland
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things
The Internet of Things (IoT) is part of the Internet of the future and will
comprise billions of intelligent communicating "things" or Internet Connected
Objects (ICO) which will have sensing, actuating, and data processing
capabilities. Each ICO will have one or more embedded sensors that will capture
potentially enormous amounts of data. The sensors and related data streams can
be clustered physically or virtually, which raises the challenge of searching
and selecting the right sensors for a query in an efficient and effective way.
This paper proposes a context-aware sensor search, selection and ranking model,
called CASSARAM, to address the challenge of efficiently selecting a subset of
relevant sensors out of a large set of sensors with similar functionality and
capabilities. CASSARAM takes into account user preferences and considers a
broad range of sensor characteristics, such as reliability, accuracy, location,
battery life, and many more. The paper highlights the importance of sensor
search, selection and ranking for the IoT, identifies important characteristics
of both sensors and data capture processes, and discusses how semantic and
quantitative reasoning can be combined together. This work also addresses
challenges such as efficient distributed sensor search and
relational-expression based filtering. CASSARAM testing and performance
evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with
arXiv:1303.244
Target Tracking in Confined Environments with Uncertain Sensor Positions
To ensure safety in confined environments such as mines or subway tunnels, a
(wireless) sensor network can be deployed to monitor various environmental
conditions. One of its most important applications is to track personnel,
mobile equipment and vehicles. However, the state-of-the-art algorithms assume
that the positions of the sensors are perfectly known, which is not necessarily
true due to imprecise placement and/or dropping of sensors. Therefore, we
propose an automatic approach for simultaneous refinement of sensors' positions
and target tracking. We divide the considered area in a finite number of cells,
define dynamic and measurement models, and apply a discrete variant of belief
propagation which can efficiently solve this high-dimensional problem, and
handle all non-Gaussian uncertainties expected in this kind of environments.
Finally, we use ray-tracing simulation to generate an artificial mine-like
environment and generate synthetic measurement data. According to our extensive
simulation study, the proposed approach performs significantly better than
standard Bayesian target tracking and localization algorithms, and provides
robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201
Data mining as a tool for environmental scientists
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
Ecological Modelling with the Calculus of Wrapped Compartments
The Calculus of Wrapped Compartments is a framework based on stochastic
multiset rewriting in a compartmentalised setting originally developed for the
modelling and analysis of biological interactions. In this paper, we propose to
use this calculus for the description of ecological systems and we provide the
modelling guidelines to encode within the calculus some of the main
interactions leading ecosystems evolution. As a case study, we model the
distribution of height of Croton wagneri, a shrub constituting the endemic
predominant species of the dry ecosystem in southern Ecuador. In particular, we
consider the plant at different altitude gradients (i.e. at different
temperature conditions), to study how it adapts under the effects of global
climate change.Comment: A preliminary version of this paper has been presented in CMC13 (LNCS
7762, pp 358-377, 2013
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