Browsing by Subject "Optimization"

Now showing 1 - 5 of 5
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    A multi-pollutant methodology to locate a single air quality monitoring station in small and medium-size urban areas
    (Elsevier, 2020-08-11) Baeza Caracena, Antonia; Doval Miñarro, Marta; Bañón Gómez, Daniel; Costa Gómez, Isabel; Egea, José A.; Ingeniería Química
    Air quality management is underpinned by continuous measurements of concentrations of target air pollutants in monitoring stations. Although many approaches for optimizing the number and location of air quality monitoring stations are described in the literature, these are usually focused on dense networks. However, there are small and medium-size urban areas that only require one monitoring station but also suffer from severe air pollution. Given that target pollutants are usually measured at the same sampling points; it is necessary to develop a methodology to determine the optimal location of the single station. In this paper, such a methodology is proposed based on maximizing an objective function, that balances between different pollutants measured in the network. The methodology is applied to a set of data available for the city of Cartagena, in southeast Spain. A sensitivity analysis reveals that 2 small areas of the studied city account for 80% of the optimal potential locations, which makes them ideal candidates for setting up the monitoring station. The methodology is easy to implement, robust and supports the decision-making process regarding the siting of fixed sampling sites.
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    Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions
    (Elsevier, 2021-10-15) Sabzi, Sajad; Pourdarbani, Razieh; Rohban, Mohammad H.; García Mateos, Ginés; Arribas, J. I.; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
    In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods.
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    Optimización de las dosis de radiación en la artrografía de hombro
    (Elsevier, 2009-05-01) Campos, P.A.; Redondo, M.V.; Reus, M.; Martínez Martínez, Francisco; Berná Serna, Juan De Dios; Cirugía, Pediatría y Obstetricia y Ginecología
    Objetivo: El objetivo de este estudio fue reducir la dosis de radiación recibida por los pacientes sometidos a una artrografía de hombro y en los que se utiliza como sistema de guiado una placa con coordenadas radiopacas situada sobre el área de interés. Material y métodos: La dosis a la entrada se obtuvo en 34 pacientes con edades comprendidas entre 15 y 75 años, media de 44 años. La dosis a órganos de riesgo y la dosis efectiva se estimaron mediante técnicas de Monte Carlo, donde los parámetros de entrada son: anatomía del paciente, geometría de la exploración y kerma en aire a la entrada del paciente sin retrodispersión. Las artrografías se realizaron en un equipo telemando y las imágenes se obtuvieron mediante adquisición digital sin fluoroscopia. Resultados: El espesor medio de los hombros estudiados fue 14,6±2,1 cm (9–20 cm). Las imágenes se obtuvieron con 80±10 kVp (60–85 kVp) y 6,5±3,5 mAs (1,4–17 mAs). El tiempo medio de irradiación para cada paciente fue 20±6 ms (6,9–47,9 ms). El kerma en aire calculado fue de 0,41±0,19 mGy y la dosis efectiva de 0,79±0,40 μ Sv. Conclusiones: La técnica descrita en este trabajo ha permitido reducir la dosis de radiación al paciente respecto a otros procedimientos descritos en la bibliografía y que el radiólogo que realiza la artrografía no se irradie durante el procedimiento.
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    Splash-4: Improving Scalability with Lock-Free Constructs
    (2021-04) Gómez-Hernández, Eduardo José; Shao, Ruixiang; Sakalis, Christos; Kaxiras, Stefanos; Ros, Alberto; Ingeniería y Tecnología de Computadores
    Over the past three decades, the parallel applications of the Splash-2 benchmark suite have been instrumental in advancing multiprocessor research. Recently, the Splash-3 benchmarks eliminated performance bugs, data races, and improper synchronization that plagued Splash-2 benchmarks after the definition of the C memory model. In this work, we revisit the Splash-3 benchmarks and adapt them for contemporary architectures with atomic operations and lock-free constructs. With our changes, we improve the scalability of most benchmarks for up to 32 and 64 cores, showing an improvement of up to 9x in actual machines, and up to 5x in simulation, over the unmodified Splash-3 benchmarks. To denote the substantive nature of the improvements in the Splash-3 benchmarks and to re-introduce them in contemporary research, we refer to the new collection as Splash-4.
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    Static Instruction Scheduling for High Performance on Limited Hardware
    (IEEE, 2018-04-01) Tran, Kim-Anh; Carlson, Trevor E.; Koukos, Konstantinos; Själander, Magnus; Spiliopoulos, Vasileios; Kaxiras, Stefanos; Jimborean, Alexandra; Ingeniería y Tecnología de Computadores
    Complex out-of-order (OoO) processors have been designed to overcome the restrictions of outstanding long-latency misses at the cost of increased energy consumption. Simple, limited OoO processors are a compromise in terms of energy consumption and performance, as they have fewer hardware resources to tolerate the penalties of long-latency loads. In worst case, these loads may stall the processor entirely. We present Clairvoyance, a compiler based technique that generates code able to hide memory latency and better utilize simple OoO processors. By clustering loads found across basic block boundaries, Clairvoyance overlaps the outstanding latencies to increases memory-level parallelism. We show that these simple OoO processors, equipped with the appropriate compiler support, can effectively hide long-latency loads and achieve performance improvements for memory-bound applications. To this end, Clairvoyance tackles (i) statically unknown dependencies, (ii) insufficient independent instructions, and (iii) register pressure. Clairvoyance achieves a geomean execution time improvement of 14 percent for memory-bound applications, on top of standard O3 optimizations, while maintaining compute-bound applications' high-performance.