582 research outputs found

    Evaluation of Parametric and Nonparametric Statistical Models in Wrong-way Driving Crash Severity Prediction

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    Wrong-way driving (WWD) crashes result in more fatalities per crash, involve more vehicles, and cause extended road closures compared to other types of crashes. Although crashes involving wrong-way drivers are relatively few, they often lead to fatalities and serious injuries. Researchers have been using parametric statistical models to identify factors that affect WWD crash severity. However, these parametric models are generally based on several assumptions, and the results could generate numerous errors and become questionable when these assumptions are violated. On the other hand, nonparametric methods such as data mining or machine learning techniques do not use a predetermined functional form, can address the correlation problem among independent variables, display results graphically, and simplify the potential complex relationship between the variables. The main objective of this research was to demonstrate the applicability of nonparametric statistical models in successfully identifying factors affecting traffic crash severity. To achieve this goal, the performance of parametric and nonparametric statistical models in WWD crash severity prediction was evaluated. The following parametric methods were evaluated: Logistic Regression (LR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Linear Discriminant Analysis (LDA), and Gaussian Naïve Bayes (GNB). The following nonparametric methods were evaluated: Random Forests (RF), Decision Trees (DT), and Support Vector Machine (SVM). The evaluation was based on sensitivity, specificity, and prediction accuracy. The research also demonstrated the applicability of nonparametric supervised learning algorithms on crash severity analysis by combining tree-based data mining techniques and marginal effect analysis to show the correlation between the response and the predictor variables. The analysis was based on 1,475 WWD crashes that occurred on arterial road networks from 2012-2016 in Florida. The results showed that nonparametric models provided better prediction accuracy on predicting serious injury compared to parametric models. By conducting prediction accuracy comparison, contributor variables’ marginal effect analysis, variable importance evaluation, and crash severity pattern recognition analysis, the nonparametric models have been demonstrated to be valid and proved to serve as an alternative tool in transportation safety studies. The results showed that head-on collisions, weekends, high-speed facilities, crashes involving vehicles entering from a driveway, dark-not lighted roadways, older drivers, and driver impairment are important factors that play a crucial role in WWD crash severity on non-limited access facilities. This information may assist researchers and safety engineers in identifying specific strategies to reduce the severity of WWD crashes on arterial streets. Besides unveiling the factors contributing to WWD crash severity and their relationship with each other, this research has demonstrated the potential of using data mining techniques in yielding results that are easily understandable and interpretable

    Wind Farm Management Decision Support Systems For Short Term Horizon

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    Wind energy is one of the fastest growing energy sources and its technology maturity level is already higher than the majority of other renewables. Therefore, many countries started to change their financial support policies in an unfavourable way for the wind energy. This unsubsidised new era forces the wind industry to re-visit its expenditure components and to make improvements in operating strategies in order to minimise operational and maintenance (O&M) costs. The classical maintenance strategies focus on a year advanced programming of calendar based maintenance visits and corrective interventions. In this classical approach the maintenance programming flexibility is quite limited, since this kind of programming ignores dynamic environment of the wind farm and real time data-driven indicators. Then, downtimes, and corresponding revenue losses, due to wind turbine inaccessibility occur because wind turbines are exposed to challenging dynamic environmental conditions and located in remote areas. Low accessibility is one of the predominant problems, and remote control not always solves the problems. The cost optimal O&M strategies for the wind energy must consider condition based maintenance and a timely programming of wind turbine visit.Thus, an elaborate and flexible approach, which is capable of considering condition and accessibility of wind turbines using meteorological measurements and operational records is highly needed for the wind farm O&M management. The core objective of this thesis is the investigation of decision-making processes in wind farm management, and the generation of Decision Support Systems (DSSs) for O&M of wind farms. In order to develop practical and feasible DSSs, the research is conducted prioritising data-driven approaches. There still exist various inefficiently used data sources in an operational wind farm, therefore there is a room for an improvement to use efficiently available data. Generally, in a wind farm, two types of condition monitoring data can be collected as online inspection and offline inspection data. Online inspection data can be obtained from both condition monitoring system (CMS) and Supervisory Control and Data Acquisition (SCADA). CMS data require an additional investment in the turbines while, on the contrary, SCADA data are already available in the turbines. As a third source, offline inspection data consist of the records of all O&M visits to the wind farm, which are available but poorly recorded. In this study, the answer for the question of how to change a classical O&M strategy to an enhanced one using only the existing data sources without the need for an additional investment is searched.Firstly, analysis of key factors influencing in wind farm maintenance decisions is performed. In this regard, exploratory data analysis was considered to understand the monthly seasonality and the dependencies of day ahead hourly electricity market price, which is one of the decisive parameters for the wind farm revenue. Then, the connection between wind turbine failures, atmospheric variables and downtime is studied in order to provide additional information to a maintenance team and a maintenance planner for the intervention day. For the first part, well-structured and analysed electricity market price, electricity generation and demand data are needed. Therefore, the existing databases are reviewed for the case countries and a relevant analysis period is chosen. The electricity market data can be easily interpreted as time series data. To exhibit the characteristics of different electricity markets, various time series comparison tools are combined as an analysis guideline. By using this guideline, the drivers of the electricity market price are summarised for each case country. For the second part, available atmospheric and failure data for the relevant wind turbine components are gathered and combined. Then, convenient approaches among unsupervised learning models are selected. By combining the available tools and considering the needed information level for different purposes, the failure rules of prior to failure occurrence per month, in hours and in ten minutes increments are mined.Then, what-if analysis for revenue tracking of maintenance decisions is performed in order to generate a DSS for the evaluation of the major maintenance decisions taken in wind farms. To this purpose, the impact of country dynamics and subsidy frameworks considering the electricity market conditions are modelled. The impact of the intervention timing is analysed and the sensitivity of financial losses to environmental causes of underperformance are estimated.Finally, generation of decision support tool for planning of a maintenance day is studied to provide a useful maintenance DSS for in situ applications. The safe working rules considering the wind speed constraints for the accessibility to the wind turbine are reviewed taking into account the turbine manufacturer's O&M guidelines. The characteristics of the maintenance visits are summarised. Wind turbine accessibility trials using numerical weather prediction forecasting techniques for wind speed variable and synthetic forecasts for wind speed and wind gust variables are presented. An intervention decision pool considering safe working rules is generated, containing a list of plans capable of providing the optimal sequence of various tasks and ranked for revenue prioritised timing.This work has been part of the “Advanced Wind Energy Systems Operation and Maintenance Expertise" project, a European consortium with companies, universities and research centres from the wind energy sector. Parts of this work were developed in collaboration with other fellows in the project.<br /

    Intelligent Radio Spectrum Monitoring

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    [EN] Spectrum monitoring is an important part of the radio spectrum management process, providing feedback on the workflow that allows for our current wirelessly interconnected lifestyle. The constantly increasing number of users and uses of wireless technologies is pushing the limits and capabilities of the existing infrastructure, demanding new alternatives to manage and analyse the extremely large volume of data produced by existing spectrum monitoring networks. This study addresses this problem by proposing an information management system architecture able to increase the analytical level of a spectrum monitoring measurement network. This proposal includes an alternative to manage the data produced by such network, methods to analyse the spectrum data and to automate the data gathering process. The study was conducted employing system requirements from the Brazilian National Telecommunications Agency and related functional concepts were aggregated from the reviewed scientific literature and publications from the International Telecommunication Union. The proposed solution employs microservice architecture to manage the data, including tasks such as format conversion, analysis, optimization and automation. To enable efficient data exchange between services, we proposed the use of a hierarchical structure created using the HDF5 format. The suggested architecture was partially implemented as a pilot project, which allowed to demonstrate the viability of presented ideas and perform an initial refinement of the proposed data format and analytical algorithms. The results pointed to the potential of the solution to solve some of the limitations of the existing spectrum monitoring workflow. The proposed system may play a crucial role in the integration of the spectrum monitoring activities into open data initiatives, promoting transparency and data reusability for this important public service.[ES] El control y análisis de uso del espectro electromagnético, un servicio conocido como comprobación técnica del espectro, es una parte importante del proceso de gestión del espectro de radiofrecuencias, ya que proporciona la información necesaria al flujo de trabajo que permite nuestro estilo de vida actual, interconectado e inalámbrico. El número cada vez más grande de usuarios y el creciente uso de las tecnologías inalámbricas amplían las demandas sobre la infraestructura existente, exigiendo nuevas alternativas para administrar y analizar el gran volumen de datos producidos por las estaciones de medición del espectro. Este estudio aborda este problema al proponer una arquitectura de sistema para la gestión de información capaz de aumentar la capacidad de análisis de una red de equipos de medición dedicados a la comprobación técnica del espectro. Esta propuesta incluye una alternativa para administrar los datos producidos por dicha red, métodos para analizar los datos recolectados, así como una propuesta para automatizar el proceso de recopilación. El estudio se realizó teniendo como referencia los requisitos de la Agencia Nacional de Telecomunicaciones de Brasil, siendo considerados adicionalmente requisitos funcionales relacionados descritos en la literatura científica y en las publicaciones de la Unión Internacional de Telecomunicaciones. La solución propuesta emplea una arquitectura de microservicios para la administración de datos, incluyendo tareas como la conversión de formatos, análisis, optimización y automatización. Para permitir el intercambio eficiente de datos entre servicios, sugerimos el uso de una estructura jerárquica creada usando el formato HDF5. Esta arquitectura se implementó parcialmente dentro de un proyecto piloto, que permitió demostrar la viabilidad de las ideas presentadas, realizar mejoras en el formato de datos propuesto y en los algoritmos analíticos. Los resultados señalaron el potencial de la solución para resolver algunas de las limitaciones del tradicional flujo de trabajo de comprobación técnica del espectro. La utilización del sistema propuesto puede mejorar la integración de las actividades e impulsar iniciativas de datos abiertos, promoviendo la transparencia y la reutilización de datos generados por este importante servicio público[CA] El control i anàlisi d'ús de l'espectre electromagnètic, un servei conegut com a comprovació tècnica de l'espectre, és una part important del procés de gestió de l'espectre de radiofreqüències, ja que proporciona la informació necessària al flux de treball que permet el nostre estil de vida actual, interconnectat i sense fils. El número cada vegada més gran d'usuaris i el creixent ús de les tecnologies sense fils amplien la demanda sobre la infraestructura existent, exigint noves alternatives per a administrar i analitzar el gran volum de dades produïdes per les xarxes d'estacions de mesurament. Aquest estudi aborda aquest problema en proposar una arquitectura de sistema per a la gestió d'informació capaç d’augmentar la capacitat d’anàlisi d'una xarxa d'equips de mesurament dedicats a la comprovació tècnica de l'espectre. Aquesta proposta inclou una alternativa per a administrar les dades produïdes per aquesta xarxa, mètodes per a analitzar les dades recol·lectades, així com una proposta per a automatitzar el procés de recopilació. L'estudi es va realitzar tenint com a referència els requisits de l'Agència Nacional de Telecomunicacions del Brasil, sent considerats addicionalment requisits funcionals relacionats descrits en la literatura científica i en les publicacions de la Unió Internacional de Telecomunicacions. La solució proposada empra una arquitectura de microserveis per a l'administració de dades, incloent tasques com la conversió de formats, anàlisi, optimització i automatització. Per a permetre l'intercanvi eficient de dades entre serveis, suggerim l'ús d'una estructura jeràrquica creada usant el format HDF5. Aquesta arquitectura es va implementar parcialment dins d'un projecte pilot, que va permetre demostrar la viabilitat de les idees presentades, realitzar millores en el format de dades proposat i en els algorismes analítics. Els resultats van assenyalar el potencial de la solució per a resoldre algunes de les limitacions del tradicional flux de treball de comprovació tècnica de l'espectre. La utilització del sistema proposat pot millorar la integració de les activitats i impulsar iniciatives de dades obertes, promovent la transparència i la reutilització de dades generades per aquest important servei públicSantos Lobão, F. (2019). Intelligent Radio Spectrum Monitoring. http://hdl.handle.net/10251/128850TFG

    Advances in Robotics, Automation and Control

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    The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

    Pertanika Journal of Science & Technology

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    Pertanika Journal of Science & Technology

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    Volume II: Mining Innovation

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    Contemporary exploitation of natural raw materials by borehole, opencast, underground, seabed, and anthropogenic deposits is closely related to, among others, geomechanics, automation, computer science, and numerical methods. More and more often, individual fields of science coexist and complement each other, contributing to lowering exploitation costs, increasing production, and reduction of the time needed to prepare and exploit the deposit. The continuous development of national economies is related to the increasing demand for energy, metal, rock, and chemical resources. Very often, exploitation is carried out in complex geological and mining conditions, which are accompanied by natural hazards such as rock bursts, methane, coal dust explosion, spontaneous combustion, water, gas, and temperature. In order to conduct a safe and economically justified operation, modern construction materials are being used more and more often in mining to support excavations, both under static and dynamic loads. The individual production stages are supported by specialized computer programs for cutting the deposit as well as for modeling the behavior of the rock mass after excavation in it. Currently, the automation and monitoring of the mining works play a very important role, which will significantly contribute to the improvement of safety conditions. In this Special Issue of Energies, we focus on innovative laboratory, numerical, and industrial research that has a positive impact on the development of safety and exploitation in mining

    Search Rank Fraud Prevention in Online Systems

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    The survival of products in online services such as Google Play, Yelp, Facebook and Amazon, is contingent on their search rank. This, along with the social impact of such services, has also turned them into a lucrative medium for fraudulently influencing public opinion. Motivated by the need to aggressively promote products, communities that specialize in social network fraud (e.g., fake opinions and reviews, likes, followers, app installs) have emerged, to create a black market for fraudulent search optimization. Fraudulent product developers exploit these communities to hire teams of workers willing and able to commit fraud collectively, emulating realistic, spontaneous activities from unrelated people. We call this behavior “search rank fraud”. In this dissertation, we argue that fraud needs to be proactively discouraged and prevented, instead of only reactively detected and filtered. We introduce two novel approaches to discourage search rank fraud in online systems. First, we detect fraud in real-time, when it is posted, and impose resource consuming penalties on the devices that post activities. We introduce and leverage several novel concepts that include (i) stateless, verifiable computational puzzles that impose minimal performance overhead, but enable the efficient verification of their authenticity, (ii) a real-time, graph based solution to assign fraud scores to user activities, and (iii) mechanisms to dynamically adjust puzzle difficulty levels based on fraud scores and the computational capabilities of devices. In a second approach, we introduce the problem of fraud de-anonymization: reveal the crowdsourcing site accounts of the people who post large amounts of fraud, thus their bank accounts, and provide compelling evidence of fraud to the users of products that they promote. We investigate the ability of our solutions to ensure that fraud does not pay off
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