A set of canonical problems in sloshing. Part 0: Experimental setup and data processing

In a series of attempts to research and document relevant sloshing type phenomena, a series of experiments have been conducted. The aim of this paper is to describe the setup and data processing of such experiments. A sloshing tank is subjected to angular motion. As a result pressure registers are obtained at several locations, together with the motion data, torque and a collection of image and video information. The experimental rig and the data acquisition systems are described. Useful information for experimental sloshing research practitioners is provided. This information is related to the liquids used in the experiments, the dying techniques, tank building processes, synchronization of acquisition systems, etc. A new procedure for reconstructing experimental data, that takes into account experimental uncertainties, is presented. This procedure is based on a least squares spline approximation of the data. Based on a deterministic approach to the first sloshing wave impact event in a sloshing experiment, an uncertainty analysis procedure of the associated first pressure peak value is described.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50–100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Data Processing Modeling in Decision Support Systems.

Due to the advancement of both, information technology in general, and databases in particular; data storage devices are becoming cheaper and data processing speed is increasing. As result of this, organizations tend to store large volumes of data holding great potential information. Decision Support Systems, DSS try to use the stored data to obtain valuable information for organizations. In this paper, we use both data models and use cases to represent the functionality of data processing in DSS following Software Engineering processes. We propose a methodology to develop DSS in the Analysis phase, respective of data processing modeling. We have used, as a starting point, a data model adapted to the semantics involved in multidimensional databases or data warehouses, DW. Also, we have taken an algorithm that provides us with all the possible ways to automatically cross check multidimensional model data. Using the aforementioned, we propose diagrams and descriptions of use cases, which can be considered as patterns representing the DSS functionality, in regard to DW data processing, DW on which DSS are based. We highlight the reusability and automation benefits that this can be achieved, and we think this study can serve as a guide in the development of DSS.

Integration of embedded data processing algorithms inside PAMELA devices

PAMELA (Phased Array Monitoring for Enhanced Life Assessment) SHMTM System is an integrated embedded ultrasonic guided waves based system consisting of several electronic devices and one system manager controller. The data collected by all PAMELA devices in the system must be transmitted to the controller, who will be responsible for carrying out the advanced signal processing to obtain SHM maps. PAMELA devices consist of hardware based on a Virtex 5 FPGA with a PowerPC 440 running an embedded Linux distribution. Therefore, PAMELA devices, in addition to the capability of performing tests and transmitting the collected data to the controller, have the capability of perform local data processing or pre-processing (reduction, normalization, pattern recognition, feature extraction, etc.). Local data processing decreases the data traffic over the network and allows CPU load of the external computer to be reduced. Even it is possible that PAMELA devices are running autonomously performing scheduled tests, and only communicates with the controller in case of detection of structural damages or when programmed. Each PAMELA device integrates a software management application (SMA) that allows to the developer downloading his own algorithm code and adding the new data processing algorithm to the device. The development of the SMA is done in a virtual machine with an Ubuntu Linux distribution including all necessary software tools to perform the entire cycle of development. Eclipse IDE (Integrated Development Environment) is used to develop the SMA project and to write the code of each data processing algorithm. This paper presents the developed software architecture and describes the necessary steps to add new data processing algorithms to SMA in order to increase the processing capabilities of PAMELA devices.An example of basic damage index estimation using delay and sum algorithm is provided.

Algorithms hardware implementation for ultrasonic data processing in SHM system

Nowadays, devices that monitor the health of structures consume a lot of power and need a lot of time to acquire, process, and send the information about the structure to the main processing unit. To decrease this time, fast electronic devices are starting to be used to accelerate this processing. In this paper some hardware algorithms implemented in an electronic logic programming device are described. The goal of this implementation is accelerate the process and diminish the information that has to be send. By reaching this goal, the time the processor needs for treating all the information is reduced and so the power consumption is reduced too.

Assessment of insurance coverage and claims in rainfall related risksin processing tomato in Western Spain

An extension of guarantees related to rainfall-related risks in the insurance of processing tomato crops hasbeen accompanied with a large increase in claims in Western Spain, suggesting that damages may havebeen underestimated in previous years. A database was built by linking agricultural insurance records,meteorological data from local weather stations, and topographic data. The risk of rainfall-related dam-ages in processing tomato in the Extremenian Guadiana river basin (W Spain) was studied using a logisticmodel. Risks during the growth of the crop and at harvesting were modelled separately. First, the riskrelated to rainfall was modelled as a function of meteorological, terrain and management variables. Theresulting models were used to identify the variables responsible for rainfall-related damages, with a viewto assess the potential impact of extending insurance coverage, and to develop an index to express thesuitability of the cropping system for insurance. The analyses reveal that damages at different stages ofcrop development correspond to different hazards. The geographic dependence of the risk influences the scale at which the model might have validity, which together with the year dependency, hampers the possibilityof implementing index based insurances is questioned.

Using Provenance for Quality Assessment and Repair in Linked Open Data

As the number of data sources publishing their data on the Web of Data is growing, we are experiencing an immense growth of the Linked Open Data cloud. The lack of control on the published sources, which could be untrustworthy or unreliable, along with their dynamic nature that often invalidates links and causes conflicts or other discrepancies, could lead to poor quality data. In order to judge data quality, a number of quality indicators have been proposed, coupled with quality metrics that quantify the “quality level” of a dataset. In addition to the above, some approaches address how to improve the quality of the datasets through a repair process that focuses on how to correct invalidities caused by constraint violations by either removing or adding triples. In this paper we argue that provenance is a critical factor that should be taken into account during repairs to ensure that the most reliable data is kept. Based on this idea, we propose quality metrics that take into account provenance and evaluate their applicability as repair guidelines in a particular data fusion setting.

Physical characterization of laser interaction and shock generation in laser shock processing: coupled theoretical-experimental analysis

Laser Shock Processing is developing as a key technology for the improvement of surface mechanical and corrosion resistance properties of metals due to its ability to introduce intense compressive residual stresses fields into high elastic limit materials by means of an intense laser driven shock wave generated by laser with intensities exceeding the 109 W/cm2 threshold, pulse energies in the range of 1 Joule and interaction times in the range of several ns. However, because of the relatively difficult-to-describe physics of shock wave formation in plasma following laser-matter interaction in solid state, only limited knowledge is available in the way of full comprehension and predictive assessment of the characteristic physical processes and material transformations with a specific consideration of real material properties. In the present paper, an account of the physical issues dominating the development of LSP processes from a moderately high intensity laser-matter interaction point of view is presented along with the theoretical and computational methods developed by the authors for their predictive assessment and new experimental contrast results obtained at laboratory scale.

A model of amacrine and ganglion cells for optical information processing

Based on a previously reported logic cell structure (see SPIE, vol. 2038, p. 67-77, 1993), the two types of cells present at the inner and ganglion cell layers of the vertebrate retina and their intracellular response, as well as their connections with each other, have been simulated. These cells are amacrines and ganglion cells. The main scheme of the authors' configuration is shown in a figure. These two types of cells, as well as some of their possible interconnections, have been implemented with the authors' previously reported optical-processing element. As it has been shown, the authors' logic structure is able to process two optical input binary signals, being the output two logical functions. Moreover, if a delayed feedback from one of the two possible outputs to one or both of the inputs is introduced, a very different behaviour is obtained. Depending on the value of the time delay, an oscillatory output can be obtained from a constant optical input signal. Period and length pulses are dependent on delay values, both external and internal, as well as on other control signals. Moreover, a chaotic behaviour can be obtained too under certain conditions

Neighborhood mobility: A Long-Gone Pardigm? Approaching proximity and land use mix using data from home based surveys.

El WCTR es un congreso de reconocido prestigio internacional en el ámbito de la investigación del transporte y aunque las actas publicadas están en formato digital y sin ISSN ni ISBN, lo consideramos lo suficientemente importante como para que se considere en los indicadores. Policies trying to increase walking within urban mobility modal split usually highlight the importance of the functional patterns and the environmental quality of the urban space as major drivers of citizens modal choices. Functional characteristics would be mainly associated to an appropriate mix of land uses within neighbourhoods, whereas environmental quality would be associated to the characteristics of urban spaces. The purpose of this research is threefold: first, to identify relevant proxy indicators, which could characterize pedestrian-friendly land use mix and environmental quality. Second, to assess, for both traits, existing disparities among neighbourhoods in a major metropolitan area. And finally, to explore the association between both indicators and children mobility patterns: according to their built environment, which neighbourhoods have a greater proportion of children and, how is their mobility? Using data from the 2004 household mobility survey in the 128 neighbourhoods of the municipality of Madrid, this paper concludes that potentially favourable conditions at the neighbourhood level seem to have only a modest influence in,mobility patterns , in terms of both, selection of closer destinations and a higher share of walking within modal split. The citys policy choices, with intensive investment in road and public transport infrastructure may explain why short-distance mobility is not as important as it could have been expected in those neighbourhoods with more pedestrian-friendly conditions. The metropolitan transport system is providing mobility conditions, which make far-away destinations attractive to most citizens.

Neighbourhood mobility: a longgone Paradigm? Approaching "proximity" and "land use mix" using data from home-based surveys

Policies trying to increase walking within urban mobility modal split usually highlight the importance of the functional patterns and the environmental quality of the urban space as major drivers of citizens modal choices. Functional characteristics would be mainly associated to an appropriate mix of land uses within neighbourhoods, whereas environmental quality would be associated to the characteristics of urban spaces. The purpose of this research is threefold: first, to identify relevant proxy indicators, which could characterize pedestrian-friendly land use mix and environmental quality. Second, to assess, for both traits, existing disparities among neighbourhoods in a major metropolitan area. And finally, to explore the association between both indicators and children mobility patterns: according to their built environment, which neighbourhoods have a greater proportion of children and, how is their mobility? Using data from the 2004 household mobility survey in the 128 neighbourhoods of the municipality of Madrid, this paper concludes that potentially favourable conditions at the neighbourhood level seem to have only a modest influence in,mobility patterns , in terms of both, selection of closer destinations and a higher share of walking within modal split. The city s policy choices, with intensive investment in road and public transport infrastructure may explain why short-distance mobility is not as important as it could have been expected in those neighbourhoods with more pedestrian-friendly conditions. The metropolitan transport system is providing mobility conditions, which make far-away destinations attractive to most citizens.

Identifying wind power ramp causes from multivariate datasets: a methodological proposal and its application to reanalysis data.

Forecasting abrupt variations in wind power generation (the so-called ramps) helps achieve large scale wind power integration. One of the main issues to be confronted when addressing wind power ramp forecasting is the way in which relevant information is identified from large datasets to optimally feed forecasting models. To this end, an innovative methodology oriented to systematically relate multivariate datasets to ramp events is presented. The methodology comprises two stages: the identification of relevant features in the data and the assessment of the dependence between these features and ramp occurrence. As a test case, the proposed methodology was employed to explore the relationships between atmospheric dynamics at the global/synoptic scales and ramp events experienced in two wind farms located in Spain. The achieved results suggested different connection degrees between these atmospheric scales and ramp occurrence. For one of the wind farms, it was found that ramp events could be partly explained from regional circulations and zonal pressure gradients. To perform a comprehensive analysis of ramp underlying causes, the proposed methodology could be applied to datasets related to other stages of the wind-topower conversion chain.