New advances in aircraft MRO services: data mining enhancement

Aircraft Maintenance, Repair and Overhaul (MRO) agencies rely largely on row-data based quotation systems to select the best suppliers for the customers (airlines). The data quantity and quality becomes a key issue to determining the success of an MRO job, since we need to ensure we achieve cost and quality benchmarks. This paper introduces a data mining approach to create an MRO quotation system that enhances the data quantity and data quality, and enables significantly more precise MRO job quotations. Regular Expression was utilized to analyse descriptive textual feedback (i.e. engineer���s reports) in order to extract more referable highly normalised data for job quotation. A text mining based key influencer analysis function enables the user to proactively select sub-parts, defects and possible solutions to make queries more accurate. Implementation results show that system data would improve cost quotation in 40% of MRO jobs, would reduce service cost without causing a drop in service quality.

Required returns for real estate in UK pension fund portfolios

The position of Real Estate within a multi-asset portfolio has received considerable attention recently. Previous research has concentrated on the percentage holding property would achieve given its risk/return characteristics. Such studies have invariably used Modern Portfolio Theory and these approaches have been criticised for both the quality of the real estate data and problems with the methodology itself. The first problem is now well understood, and the second can be addressed by the use of realistic constraints on asset holdings. This paper takes a different approach. We determine the level of return that Real Estate needs to achieve to justify an allocation within the multi asset portfolio. In order to test the importance of the quality of the data we use historic appraisal based and desmoothed returns to examine the sensitivity of the results. Consideration is also given to the Holding period and the imposition of realistic constraints on the asset holdings in order to model portfolios held by pension fund investors. We conclude, using several benchmark levels of portfolio risk and return, that using appraisal based data the required level of return for Real Estate was less than that achieved over the period 1972-1993. The use of desmoothed series can reverse this result at the highest levels of desmoothing although within a restricted holding period Real Estate offered returns in excess of those required to enter the portfolio and might have a role to play in the multi-asset portfolio.

Cost effectiveness of mitigation measures for water security and water quality in the River Thames catchment: impacts of changing climate, land use and water scarcity

The catchment of the River Thames, the principal river system in southern England, provides the main water supply for London but is highly vulnerable to changes in climate, land use and population. The river is eutrophic with significant algal blooms with phosphorus assumed to be the primary chemical indicator of ecosystem health. In the Thames Basin, phosphorus is available from point sources such as wastewater treatment plants and from diffuse sources such as agriculture. In order to predict vulnerability to future change, the integrated catchments model for phosphorus (INCA-P) has been applied to the river basin and used to assess the cost-effectiveness of a range of mitigation and adaptation strategies. It is shown that scenarios of future climate and land-use change will exacerbate the water quality problems, but a range of mitigation measures can improve the situation. A cost-effectiveness study has been undertaken to compare the economic benefits of each mitigation measure and to assess the phosphorus reductions achieved. The most effective strategy is to reduce fertilizer use by 20% together with the treatment of effluent to a high standard. Such measures will reduce the instream phosphorus concentrations to close to the EU Water Framework Directive target for the Thames.

Construction of neurofuzzy models for imbalanced data classification

We propose a new class of neurofuzzy construction algorithms with the aim of maximizing generalization capability specifically for imbalanced data classification problems based on leave-one-out (LOO) cross validation. The algorithms are in two stages, first an initial rule base is constructed based on estimating the Gaussian mixture model with analysis of variance decomposition from input data; the second stage carries out the joint weighted least squares parameter estimation and rule selection using orthogonal forward subspace selection (OFSS)procedure. We show how different LOO based rule selection criteria can be incorporated with OFSS, and advocate either maximizing the leave-one-out area under curve of the receiver operating characteristics, or maximizing the leave-one-out Fmeasure if the data sets exhibit imbalanced class distribution. Extensive comparative simulations illustrate the effectiveness of the proposed algorithms.

Capturing and sharing our collective expertise on climate data: the CHARMe project

For users of climate services, the ability to quickly determine the datasets that best fit one's needs would be invaluable. The volume, variety and complexity of climate data makes this judgment difficult. The ambition of CHARMe ("Characterization of metadata to enable high-quality climate services") is to give a wider interdisciplinary community access to a range of supporting information, such as journal articles, technical reports or feedback on previous applications of the data. The capture and discovery of this "commentary" information, often created by data users rather than data providers, and currently not linked to the data themselves, has not been significantly addressed previously. CHARMe applies the principles of Linked Data and open web standards to associate, record, search and publish user-derived annotations in a way that can be read both by users and automated systems. Tools have been developed within the CHARMe project that enable annotation capability for data delivery systems already in wide use for discovering climate data. In addition, the project has developed advanced tools for exploring data and commentary in innovative ways, including an interactive data explorer and comparator ("CHARMe Maps") and a tool for correlating climate time series with external "significant events" (e.g. instrument failures or large volcanic eruptions) that affect the data quality. Although the project focuses on climate science, the concepts are general and could be applied to other fields. All CHARMe system software is open-source, released under a liberal licence, permitting future projects to re-use the source code as they wish.

Energetic efficiency programs and power quality

This paper presents the results of the investigations that were done to identify and to quantify the power quality problems resultant from the actions done to improve the efficiency on electric energy consumption. The efficiencies of several electric devices were evaluated, among them: fluorescent bulb, electronic ballast, soft-starter, temperature controller for showers, dimmer and others. This evaluation allowed to establish a cause/effect analysis of the power quality.

A software to process daily solar radiation data

This work presents one software developed to process solar radiation data. This software can be used in meteorological and climatic stations, and also as a support for solar radiation measurements in researches of solar energy availability allowing data quality control, statistical calculations and validation of models, as well as ease interchanging of data. (C) 1999 Elsevier B.V. Ltd. All rights reserved.

Generating VRS data using atmospheric models: How far can we go?

Nowadays, with the expansion of the reference stations networks, several positioning techniques have been developed and/or improved. Among them, the VRS (Virtual Reference Station) concept has been very used. In this paper the goal is to generate VRS data in a modified technique. In the proposed methodology the DD (double difference) ambiguities are not computed. The network correction terms are obtained using only atmospheric (ionospheric and tropospheric) models. In order to carry out the experiments it was used data of five reference stations from the GPS Active Network of West of S��o Paulo State and an extra station. To evaluate the VRS data quality it was used three different strategies: PPP (Precise Point Positioning) and Relative Positioning in static and kinematic modes, and DGPS (Differential GPS). Furthermore, the VRS data were generated in the position of a real reference station. The results provided by the VRS data agree quite well with those of the real file data.

The Brazilian Seismographic Integrated Systems (BRASIS): Infrastructure and data management

In geophysics and seismology, raw data need to be processed to generate useful information that can be turned into knowledge by researchers. The number of sensors that are acquiring raw data is increasing rapidly. Without good data management systems, more time can be spent in querying and preparing datasets for analyses than in acquiring raw data. Also, a lot of good quality data acquired at great effort can be lost forever if they are not correctly stored. Local and international cooperation will probably be reduced, and a lot of data will never become scientific knowledge. For this reason, the Seismological Laboratory of the Institute of Astronomy, Geophysics and Atmospheric Sciences at the University of S��o Paulo (IAG-USP) has concentrated fully on its data management system. This report describes the efforts of the IAG-USP to set up a seismology data management system to facilitate local and international cooperation. �� 2011 by the Istituto Nazionale di Geofisica e Vulcanologia. All rights reserved.

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. �� 2013 Elsevier B.V. All rights reserved.

Estima����o do di��metro de furos em processo de fura����o utilizando sistemas de intelig��ncia artificial: uma an��lise comparativa entre diferentes t��cnicas

Coordena����o de Aperfei��oamento de Pessoal de N��vel Superior (CAPES)

Evaluation report of the national training workshop on capacity-building for the production of reliable disaggregated data

.--A. Introduction.--B. Summary of evaluation.

Detec����o, classifica����o e quantifica����o autom��tica de varia����es de tens��o de curta dura����o para aplica����o em an��lise de p��s-opera����o em sistemas de energia el��trica

A an��lise de ocorr��ncias no sistema de energia el��trica �� de fundamento mport��ncia para uma opera����o segura, e para manter a qualidade da energia el��trica lornecida aos consumidores. As concession��rias do setor de energia el��trica usam equipamentos, chamados registradores de perturba����o (RP's), para monitora diagnosticar problemas nos sistemas el��trico e de prote����o. As formas de onda normalmente analisadas nos centros de opera����o das concession��rias, s��o aquelas geradas por eventos que quase sempre causam a aocrtul je linhas devido a opera����o dos rel��s comandados pelos dispositivos de prote����o .Contudo, uma grande quantidade de registros armazenados que podem conte informa����es importantes sobre o comportamento e desempenho do sistema el��tricl jeixa de ser analisada. O objetivo desse trabalho �� usar os dados dispon��veis nos centros de ontrole, opera����o das concession��rias de energia el��trica obtidos pelos RP's, para classificar e quantificar de forma autom��tica sinais que caracterizem problemas de qualidade da energia, quanto a varia����es de tens��o de curta dura����o: afundamentos, eleva����es e interrup����es. O m��todo proposto usa a transformada wavelet para obter um vetor caracter��stico para as tens��es das fases A, B e C, e uma rede neural probabil��stica para classifica����o. Os sinais classificados como apresentando varia����es de curta dura����o s��o quantilicados quanto a dura����o e amplitude, usando-se as propriedades da an��lise nultiresolu����o da decomposi����o do sinal. Esses par��metros, ent��o, ir��o formar uma Jase de dados onde procedimentos de an��lise estat��stica podem ser usados para gerar relat��rios com as caracter��sticas da qualidade da energia. Os resultados obtidos com a metodologia proposta para um sistema real s��o tamb��m apresentados.

Imputa����o de dados baseado em otimiza����o por enxame de part��culas considerando os principais mecanismos de aus��ncia de dados

Durante o processo de extra����o do conhecimento em bases de dados, alguns problemas podem ser encontrados como por exemplo, a aus��ncia de determinada inst��ncia de um atributo. A ocorr��ncia de tal problem��tica pode causar efeitos danosos nos resultados finais do processo, pois afeta diretamente a qualidade dos dados a ser submetido a um algoritmo de aprendizado de m��quina. Na literatura, diversas propostas s��o apresentadas a fim de contornar tal dano, dentre eles est�� a de imputa����o de dados, a qual estima um valor plaus��vel para substituir o ausente. Seguindo essa ��rea de solu����o para o problema de valores ausentes, diversos trabalhos foram analisados e algumas observa����es foram realizadas como, a pouca utiliza����o de bases sint��ticas que simulem os principais mecanismos de aus��ncia de dados e uma recente tend��ncia a utiliza����o de algoritmos bio-inspirados como tratamento do problema. Com base nesse cen��rio, esta disserta����o apresenta um m��todo de imputa����o de dados baseado em otimiza����o por enxame de part��culas, pouco explorado na ��rea, e o aplica para o tratamento de bases sinteticamente geradas, as quais consideram os principais mecanismos de aus��ncia de dados, MAR, MCAR e NMAR. Os resultados obtidos ao comprar diferentes configura����es do m��todo �� outros dois conhecidos na ��rea (KNNImpute e SVMImpute) s��o promissores para sua utiliza����o na ��rea de tratamento de valores ausentes uma vez que alcan��ou os melhores valores na maioria dos experimentos realizados.

Tools and methods for the quality assurance of force platforms in human movement analysis

The human movement analysis (HMA) aims to measure the abilities of a subject to stand or to walk. In the field of HMA, tests are daily performed in research laboratories, hospitals and clinics, aiming to diagnose a disease, distinguish between disease entities, monitor the progress of a treatment and predict the outcome of an intervention [Brand and Crowninshield, 1981; Brand, 1987; Baker, 2006]. To achieve these purposes, clinicians and researchers use measurement devices, like force platforms, stereophotogrammetric systems, accelerometers, baropodometric insoles, etc. This thesis focus on the force platform (FP) and in particular on the quality assessment of the FP data. The principal objective of our work was the design and the experimental validation of a portable system for the in situ calibration of FPs. The thesis is structured as follows: Chapter 1. Description of the physical principles used for the functioning of a FP: how these principles are used to create force transducers, such as strain gauges and piezoelectrics transducers. Then, description of the two category of FPs, three- and six-component, the signals acquisition (hardware structure), and the signals calibration. Finally, a brief description of the use of FPs in HMA, for balance or gait analysis. Chapter 2. Description of the inverse dynamics, the most common method used in the field of HMA. This method uses the signals measured by a FP to estimate kinetic quantities, such as joint forces and moments. The measures of these variables can not be taken directly, unless very invasive techniques; consequently these variables can only be estimated using indirect techniques, as the inverse dynamics. Finally, a brief description of the sources of error, present in the gait analysis. Chapter 3. State of the art in the FP calibration. The selected literature is divided in sections, each section describes: systems for the periodic control of the FP accuracy; systems for the error reduction in the FP signals; systems and procedures for the construction of a FP. In particular is detailed described a calibration system designed by our group, based on the theoretical method proposed by ?. This system was the ���starting point��� for the new system presented in this thesis. Chapter 4. Description of the new system, divided in its parts: 1) the algorithm; 2) the device; and 3) the calibration procedure, for the correct performing of the calibration process. The algorithm characteristics were optimized by a simulation approach, the results are here presented. In addiction, the different versions of the device are described. Chapter 5. Experimental validation of the new system, achieved by testing it on 4 commercial FPs. The effectiveness of the calibration was verified by measuring, before and after calibration, the accuracy of the FPs in measuring the center of pressure of an applied force. The new system can estimate local and global calibration matrices; by local and global calibration matrices, the non���linearity of the FPs was quantified and locally compensated. Further, a non���linear calibration is proposed. This calibration compensates the non��� linear effect in the FP functioning, due to the bending of its upper plate. The experimental results are presented. Chapter 6. Influence of the FP calibration on the estimation of kinetic quantities, with the inverse dynamics approach. Chapter 7. The conclusions of this thesis are presented: need of a calibration of FPs and consequential enhancement in the kinetic data quality. Appendix: Calibration of the LC used in the presented system. Different calibration set���up of a 3D force transducer are presented, and is proposed the optimal set���up, with particular attention to the compensation of non���linearities. The optimal set���up is verified by experimental results.