Sistema supervisor inteligente para procesos de producción de petróleo

Maximizar la producción de pozos de crudo pesado y extra pesado es el principal beneficio que se desea obtener de los sistemas de control que están corrientemente operativos en empresas de petróleo. Dada la naturaleza compleja y cambiante con el tiempo de los métodos existentes de levantamiento artificial para extracción de crudo, se dificulta el cumplimiento de las especificaciones pre establecidas para el procesamiento del crudo por parte de los lazos de control regulatorios. Tomando esto en cuenta, en éste trabajo se propone un sistema de supervisión inteligente que permite detectar cambios en las condiciones de operación del proceso productivo y realizar ajustes automáticos de sus consignas. Además, el sistema supervisor propuesto tiene la capacidad de detectar fallas en los sensores involucrados en los lazos de control, garantizando de esta manera una operación confiable del proceso. La propuesta fue probada en un pozo de petróleo real obteniéndose resultados que superaron las expectativas iniciales.

Estudo de lacunas de rapidez e caracterização de processos difrativos em eventos de minimum bias a 7TeV no CMS/LHC

Neste trabalho estudamos as características das distribuições da lacuna de rapidez em amostras de eventos de minimum bias de colisões pp a ps=7 TeV no CMS/LHC. Tais eventos são constituídos por processos difrativos, além de processos de QCD mole. São investigados o tamanho e a localização das lacunas, assim como as correlações entre as distribuições obtidas a partir dos objetos reconstruídos no detector e as distribuições obtidas a partir das partículas geradas via simulação Monte Carlo. Uma boa compreensão dessas distribuições pode, eventualmente, possibilitar a caracterização de eventos difrativos nos dados.

Estudo da produção de multijatos em colisões próton-próton com √s= 7 TeV no detector CMS/LHC

O Compact Muon Solenoid (CMS) é um dos principais detectores instalados no LHC que possibilita o estudo de diferentes aspectos da Física, indo do Modelo Padrão à matéria escura. Esse detector de propósito geral, foi construído para ser capaz de medir múons com uma grande precisão e todos os seus subdetectores foram construídos com uma alta granularidade, tornando possível identificar e caracterizar as propriedades cinemáticas das partículas finais da colisão. O algoritmo de reconstrução de eventos inclui a identificação de jatos, ou seja, é possível identificar a assinatura da produção de pártons na colisão e a medida de seções de choque da produção de muitos jatos é um dos métodos para se explorar as contribuições da Cromodinâmica Quântica (Quantum Chromodynamics - QCD) perturbativa, permitindo avaliar as previsões implementadas nas simulações de eventos. Tendo em vista a caracterização de processos relacionados com a QCD em colisões de próton-próton a uma energia do centro de massa de 7 TeV, é apresentada a medida da seção de choque da produção inclusiva de multijatos no CMS. Para realizar essa medida foram utilizados dados reais coletados em 2010, onde não se apresentava muitas colisões por cruzamento de pacote, com uma luminosidade integrada de L = 2,869 pb-1 e utilizando jatos que estão em quase todo o espaço de fase acessível em pseudorapidez |n|≤ 4,8 e momentum transverso pT ≥ 30 GeV/ c2. Desse resultado foram removidos os efeitos de detecção comparado com predições simuladas.

Busca por dimensões extras no detector CMS do large hadron collider

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Urban network travel time estimation from stop-line loop detector data and signal controller data

This paper presents a model to estimate travel time using cumulative plots. Three different cases considered are i) case-Det, for only detector data; ii) case-DetSig, for detector data and signal controller data and iii) case-DetSigSFR: for detector data, signal controller data and saturation flow rate. The performance of the model for different detection intervals is evaluated. It is observed that detection interval is not critical if signal timings are available. Comparable accuracy can be obtained from larger detection interval with signal timings or from shorter detection interval without signal timings. The performance for case-DetSig and for case-DetSigSFR is consistent with accuracy generally more than 95% whereas, case-Det is highly sensitive to the signal phases in the detection interval and its performance is uncertain if detection interval is integral multiple of signal cycles.

CTCombine : rotating and combining CT data with an accurate detector model, to simulate radiotherapy portal imaging at non-zero beam angles

Established Monte Carlo user codes BEAMnrc and DOSXYZnrc permit the accurate and straightforward simulation of radiotherapy experiments and treatments delivered from multiple beam angles. However, when an electronic portal imaging detector (EPID) is included in these simulations, treatment delivery from non-zero beam angles becomes problematic. This study introduces CTCombine, a purpose-built code for rotating selected CT data volumes, converting CT numbers to mass densities, combining the results with model EPIDs and writing output in a form which can easily be read and used by the dose calculation code DOSXYZnrc. The geometric and dosimetric accuracy of CTCombine’s output has been assessed by simulating simple and complex treatments applied to a rotated planar phantom and a rotated humanoid phantom and comparing the resulting virtual EPID images with the images acquired using experimental measurements and independent simulations of equivalent phantoms. It is expected that CTCombine will be useful for Monte Carlo studies of EPID dosimetry as well as other EPID imaging applications.

A quasi four dimensional radiation detector

Techniques for the accurate measurement of ionising radiation have been evolving since Roentgen first discovered x-rays in 1895; until now experimental measurements of radiation fields in the three spatial dimensions plus time have not been successfully demonstrated. In this work, we embed an organic plastic scintillator in a polymer gel dosimeter to obtain the first quasi-4D experimental measurement of a radiation field.

A hybrid radiation detector for simultaneous spatial and temporal dosimetry

In this feasibility study an organic plastic scintillator is calibrated against ionisation chamber measurements and then embedded in a polymer gel dosimeter to obtain a quasi-4D experimental measurement of a radiation field. This hybrid dosimeter was irradiated with a linear accelerator, with temporal measurements of the dose rate being acquired by the scintillator and spatial measurements acquired with the gel dosimeter. The detectors employed in this work are radiologically equivalent; and we show that neither detector perturbs the intensity of the radiation field of the other. By employing these detectors in concert, spatial and temporal variations in the radiation intensity can now be detected and gel dosimeters can be calibrated for absolute dose from a single irradiation.

An exploration of feature detector performance in the thermal-infrared modality

Thermal-infrared images have superior statistical properties compared with visible-spectrum images in many low-light or no-light scenarios. However, a detailed understanding of feature detector performance in the thermal modality lags behind that of the visible modality. To address this, the first comprehensive study on feature detector performance on thermal-infrared images is conducted. A dataset is presented which explores a total of ten different environments with a range of statistical properties. An investigation is conducted into the effects of several digital and physical image transformations on detector repeatability in these environments. The effect of non-uniformity noise, unique to the thermal modality, is analyzed. The accumulation of sensor non-uniformities beyond the minimum possible level was found to have only a small negative effect. A limiting of feature counts was found to improve the repeatability performance of several detectors. Most other image transformations had predictable effects on feature stability. The best-performing detector varied considerably depending on the nature of the scene and the test.

Fusing loop detector and probe vehicle data to estimate travel time statistics on signalized urban networks

This article presents a methodology that integrates cumulative plots with probe vehicle data for estimation of travel time statistics (average, quartile) on urban networks. The integration reduces relative deviation among the cumulative plots so that the classical analytical procedure of defining the area between the plots as the total travel time can be applied. For quartile estimation, a slicing technique is proposed. The methodology is validated with real data from Lucerne, Switzerland and it is concluded that the travel time estimates from the proposed methodology are statistically equivalent to the observed values.

Decentralized detector generation in cooperative intrusion detection system

We consider Cooperative Intrusion Detection System (CIDS) which is a distributed AIS-based (Artificial Immune System) IDS where nodes collaborate over a peer-to-peer overlay network. The AIS uses the negative selection algorithm for the selection of detectors (e.g., vectors of features such as CPU utilization, memory usage and network activity). For better detection performance, selection of all possible detectors for a node is desirable but it may not be feasible due to storage and computational overheads. Limiting the number of detectors on the other hand comes with the danger of missing attacks. We present a scheme for the controlled and decentralized division of detector sets where each IDS is assigned to a region of the feature space. We investigate the trade-off between scalability and robustness of detector sets. We address the problem of self-organization in CIDS so that each node generates a distinct set of the detectors to maximize the coverage of the feature space while pairs of nodes exchange their detector sets to provide a controlled level of redundancy. Our contribution is twofold. First, we use Symmetric Balanced Incomplete Block Design, Generalized Quadrangles and Ramanujan Expander Graph based deterministic techniques from combinatorial design theory and graph theory to decide how many and which detectors are exchanged between which pair of IDS nodes. Second, we use a classical epidemic model (SIR model) to show how properties from deterministic techniques can help us to reduce the attack spread rate.

Algorithm for queue estimation with loop detector of time occupancy in off-ramps on signalized motorways

Long traffic queues on off-ramps significantly compromise the safety and throughput of motorways. Obtaining accurate queue information is crucial for countermeasure strategies. However, it is challenging to estimate traffic queues with locally installed inductive loop detectors. This paper deals with the problem of queue estimation with the interpretation of queuing dynamics and the corresponding time-occupancy distribution over motorway off-ramps. A novel algorithm for real-time queue estimation with two detectors is presented and discussed. Results derived from microscopic traffic simulation validated the effectiveness of the algorithm and revealed some of its useful features: (a) long and intermediate traffic queues could be accurately measured, (b) relatively simple detector input (i.e., time occupancy) was required, and (c) the estimation philosophy was independent with signal timing changes and provided the potential to cooperate with advanced strategies for signal control. Some issues concerning field implementation are also discussed.

Traffic queue estimation for metered motorway on-ramps through use of loop detector time occupancies

The primary objective of this study is to develop a robust queue estimation algorithm for motorway on-ramps. Real-time queue information is a vital input for dynamic queue management on metered on-ramps. Accurate and reliable queue information enables the management of on-ramp queue in an adaptive manner to the actual traffic queue size and thus minimises the adverse impacts of queue flush while increasing the benefit of ramp metering. The proposed algorithm is developed based on the Kalman filter framework. The fundamental conservation model is used to estimate the system state (queue size) with the flow-in and flow-out measurements. This projection results are updated with the measurement equation using the time occupancies from mid-link and link-entrance loop detectors. This study also proposes a novel single point correction method. This method resets the estimated system state to eliminate the counting errors that accumulate over time. In the performance evaluation, the proposed algorithm demonstrated accurate and reliable performances and consistently outperformed the benchmarked Single Occupancy Kalman filter (SOKF) method. The improvements over SOKF are 62% and 63% in average in terms of the estimation accuracy (MAE) and reliability (RMSE), respectively. The benefit of the innovative concepts of the algorithm is well justified by the improved estimation performance in congested ramp traffic conditions where long queues may significantly compromise the benchmark algorithm’s performance.