The Experts below are selected from a list of 120813 Experts worldwide ranked by ideXlab platform
Tommy S W Wong - One of the best experts on this subject based on the ideXlab platform.
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runoff forecasting for an asphalt plane by artificial neural networks and comparisons with kinematic wave and autoregressive moving average models
Journal of Hydrology, 2011Co-Authors: Lloyd H C Chua, Tommy S W WongAbstract:Summary Event-based runoff forecasting for 1, 2, 4 and 8 time steps ahead, based on rainfall and flow data of ten storm events for an asphalt plane, have been investigated by the Artificial Neural Network (ANN) technique. The investigation includes ANN models with three different types of inputs: (i) rainfall only, (ii) discharge only and (iii) a combination of rainfall and discharge. The results show that inclusion of discharge as an input in general, improved the performance of the ANN. However, model improvements were less significant for longer forecast lead times. Significant time shift errors in the predicted hydrographs were observed for ANN models that used discharge only as input. Although ANN models with the smallest time shift errors were models that included rainfall as inputs, these models produced hydrographs that were noisier. ANN model results were also evaluated by comparisons with results from the kinematic wave (KW) and autoregressive moving average (ARMA) models. It was found that ANN model forecasts compared favorably with runoff predictions by the KW and ARMA models. Specifically, ANN models that included discharge as input were superior to the KW model for all forecast ranges. However, the inclusion of discharge as an input to the ANN models implies that discharge measurements must be available during the model Simulation Stage; the KW model does not have this requirement. ANN models that did not include discharge as an input were better at long-term forecasts but poorer at short-term forecasts, when compared to the KW model. The poorer performance of the KW model at longer lead times is probably due to errors in the forecast rainfall used.
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runoff forecasting for an asphalt plane by artificial neural networks and comparisons with kinematic wave and autoregressive moving average models
Journal of Hydrology, 2011Co-Authors: Lloyd H C Chua, Tommy S W WongAbstract:Summary Event-based runoff forecasting for 1, 2, 4 and 8 time steps ahead, based on rainfall and flow data of ten storm events for an asphalt plane, have been investigated by the Artificial Neural Network (ANN) technique. The investigation includes ANN models with three different types of inputs: (i) rainfall only, (ii) discharge only and (iii) a combination of rainfall and discharge. The results show that inclusion of discharge as an input in general, improved the performance of the ANN. However, model improvements were less significant for longer forecast lead times. Significant time shift errors in the predicted hydrographs were observed for ANN models that used discharge only as input. Although ANN models with the smallest time shift errors were models that included rainfall as inputs, these models produced hydrographs that were noisier. ANN model results were also evaluated by comparisons with results from the kinematic wave (KW) and autoregressive moving average (ARMA) models. It was found that ANN model forecasts compared favorably with runoff predictions by the KW and ARMA models. Specifically, ANN models that included discharge as input were superior to the KW model for all forecast ranges. However, the inclusion of discharge as an input to the ANN models implies that discharge measurements must be available during the model Simulation Stage; the KW model does not have this requirement. ANN models that did not include discharge as an input were better at long-term forecasts but poorer at short-term forecasts, when compared to the KW model. The poorer performance of the KW model at longer lead times is probably due to errors in the forecast rainfall used.
Cano Ardila, Fabian Esneider - One of the best experts on this subject based on the ideXlab platform.
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Análisis teórico y experimental de la combustión de mezclas gas natural-hidrógeno bajo el régimen de combustión sin llama
Medellín Colombia, 2019Co-Authors: Cano Ardila, Fabian EsneiderAbstract:ABSTRACT : In 2015, Colombia undertook to reduce by 20% the polluting emissions projected for the year 2030, so it is necessary to appropriate the use of renewable fuels and implement high efficiency technologies in all sectors of the economy such as industry, transport, residential and electricity generation. In addition, it is necessary to deepen the study of energy conversion using low emission factor fuels and advanced combustion technologies where there are stable combustion, high energy efficiency and very low polluting emissions. Although GN is a fossil fuel with a low carbon dioxide (CO2) emission factor, it can improve its combustibility and further reduce emissions when mixed with a fuel of potential renewable origin and zero CO2 emissions such as H2. Therefore, this project presents the study of the combustion of natural gas-hydrogen (GN-H2) mixtures with proportions of H2 ranging from 0% to 45% in volume, which begins to contribute to overcome the scientific and technological backwardness of the country in this matter, as it is the first project carried out in Colombia, despite the importance of research and technological development around these mixtures that is observed in the international scientific literature and in the energy policies of many countries. Additionally, the combustion was studied under the flameless combustion regime, as a flexible combustion technology, using a furnace available in the combustion laboratory of the GASURE research group that was operated at constant load, 28 kW thermal power and different aeration factors. This technology is considered promising because it can obtain ultra low pollutant emissions, uniform temperature profiles, and low dynamic instabilities. The general objective of the project was to numerically and experimentally study the behavior of the flameless combustion regime with GN-H2 mixtures in sub-atmospheric conditions, in relation to the morphology of the reaction zone, the stability of the regime and the intensity of the dynamic instabilities. During the experimental Stage, chimney emissions tests and the spontaneous chemoluminescence of CH* were performed using ICCD camera to observe the reaction zone. Likewise, the temperature profile in the central axis of the oven was measured using a temperature probe and the efficiency of the oven was determined by means of energy diagnosis of the system. Finally, a methodology for measuring the dynamic instability of pressure inside the furnace was implemented. In the Simulation Stage the FLUENT software solver was used to evaluate the behavior of the furnace using the gas mixtures, the power and the aeration factor of the real tests. The models were validated with the experimental results and a good agreement was found between them, mainly the temperature field. For all the mixtures, a stable flameless combustion regime and ultra low NO and CO emissions were obtained (
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Análisis teórico y experimental de la combustión de mezclas gas natural-hidrógeno bajo el régimen de combustión sin llama
Medellín Colombia, 2019Co-Authors: Cano Ardila, Fabian EsneiderAbstract:RESUMEN : En el año 2015, Colombia se comprometió a reducir un 20% las emisiones contaminantes proyectadas para el año 2030, por lo que se hace necesario apropiar el uso de combustibles de origen renovable e implementar tecnologías de alta eficiencia en todos los sectores de la economía como la industria, transporte, residencial y de generación de electricidad. Además, se requiere de mayor profundización en el estudio de la conversión de energía usando combustibles de bajo factor de emisión y tecnologías de combustión avanzada donde se tenga una combustión estable, alta eficiencia energética y muy bajas emisiones contaminantes. Si bien el gas natural (GN) es un combustible fósil de bajo factor de emisión de dióxido de carbono (CO2), se puede mejorar su combustibilidad y reducir aún más las emisiones al mezclarse con un combustible de potencial origen renovable y nulas emisiones de CO2 como el hidrógeno (H2). Por lo tanto, en este proyecto se presenta el estudio de la combustión de mezclas gas natural-hidrógeno (GN-H2) con proporciones de H2 variando de 0% a 45% en volumen, con lo cual se comienza contribuir para superar el rezago científico y tecnológico del país en esta materia, en tanto es el primer proyecto que se realiza en Colombia, no obstante la importancia de la investigación y el desarrollo tecnológico en torno a estas mezclas que se observa en la literatura científica internacional y en las políticas energéticas de muchos países. Adicionalmente, la combustión se estudió bajo el régimen de combustión sin llama, como una tecnología de combustión flexible, utilizando un horno disponible en el laboratorio de combustión del grupo de investigación GASURE que fue operado a carga constante, potencia térmica de 28 kW y a diferentes factores de aireación. Esta tecnología es considerada como promisoria debido a que se pueden obtener ultra bajas emisiones contaminantes, perfiles de temperatura uniformes y bajas inestabilidades dinámicas. El objetivo general del proyecto fue estudiar numérica y experimentalmente el comportamiento del régimen de combustión sin llama con mezclas GN-H2 en condiciones sub-atmosférica, en relación con la morfología de la zona de reacción, la estabilidad del régimen y la intensidad de las inestabilidades dinámicas. Durante la etapa experimental se realizaron mediciones de emisiones en chimenea y quimioluminiscencia espontánea de radical CH* usando cámara ICCD para observar la zona de reacción. Así mismo se midió el perfil de temperatura en el eje central del horno usando una sonda de temperatura y se determinó la eficiencia del horno mediante diagnóstico energético del sistema. Finalmente, se implementó una metodología de medición de la inestabilidad dinámica de presión al interior del horno. En la etapa de simulación se usó el solucionador del software FLUENT para evaluar el comportamiento del horno usando las mezclas de gases, la potencia y el factor de aireación de las pruebas reales. Los modelos fueron validados con los resultados experimentales y se encontró buena concordancia entre ellos, principalmente el campo de temperatura. Para todas las mezclas se obtuvo régimen estable de combustión sin llama y ultra bajas emisiones de NO y CO (< 12 ppm). Adicionalmente se determinó el límite de operación estable del horno basado en las emisiones de NO mediante uso de pruebas dinámicas donde se varió el factor de aireación en el tiempo. Los niveles de inestabilidad dinámica o fluctuación de presión se disminuyen con la adición de H2 y fueron menores a 35 Pa para todos los casos.ABSTRACT: In 2015, Colombia undertook to reduce by 20% the polluting emissions projected for the year 2030, so it is necessary to appropriate the use of renewable fuels and implement high efficiency technologies in all sectors of the economy such as industry, transport, residential and electricity generation. In addition, it is necessary to deepen the study of energy conversion using low emission factor fuels and advanced combustion technologies where there are stable combustion, high energy efficiency and very low polluting emissions. Although GN is a fossil fuel with a low carbon dioxide (CO2) emission factor, it can improve its combustibility and further reduce emissions when mixed with a fuel of potential renewable origin and zero CO2 emissions such as H2. Therefore, this project presents the study of the combustion of natural gas-hydrogen (GN-H2) mixtures with proportions of H2 ranging from 0% to 45% in volume, which begins to contribute to overcome the scientific and technological backwardness of the country in this matter, as it is the first project carried out in Colombia, despite the importance of research and technological development around these mixtures that is observed in the international scientific literature and in the energy policies of many countries. Additionally, the combustion was studied under the flameless combustion regime, as a flexible combustion technology, using a furnace available in the combustion laboratory of the GASURE research group that was operated at constant load, 28 kW thermal power and different aeration factors. This technology is considered promising because it can obtain ultra low pollutant emissions, uniform temperature profiles, and low dynamic instabilities. The general objective of the project was to numerically and experimentally study the behavior of the flameless combustion regime with GN-H2 mixtures in sub-atmospheric conditions, in relation to the morphology of the reaction zone, the stability of the regime and the intensity of the dynamic instabilities. During the experimental Stage, chimney emissions tests and the spontaneous chemoluminescence of CH* were performed using ICCD camera to observe the reaction zone. Likewise, the temperature profile in the central axis of the oven was measured using a temperature probe and the efficiency of the oven was determined by means of energy diagnosis of the system. Finally, a methodology for measuring the dynamic instability of pressure inside the furnace was implemented. In the Simulation Stage the FLUENT software solver was used to evaluate the behavior of the furnace using the gas mixtures, the power and the aeration factor of the real tests. The models were validated with the experimental results and a good agreement was found between them, mainly the temperature field. For all the mixtures, a stable flameless combustion regime and ultra low NO and CO emissions were obtained (
Lloyd H C Chua - One of the best experts on this subject based on the ideXlab platform.
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runoff forecasting for an asphalt plane by artificial neural networks and comparisons with kinematic wave and autoregressive moving average models
Journal of Hydrology, 2011Co-Authors: Lloyd H C Chua, Tommy S W WongAbstract:Summary Event-based runoff forecasting for 1, 2, 4 and 8 time steps ahead, based on rainfall and flow data of ten storm events for an asphalt plane, have been investigated by the Artificial Neural Network (ANN) technique. The investigation includes ANN models with three different types of inputs: (i) rainfall only, (ii) discharge only and (iii) a combination of rainfall and discharge. The results show that inclusion of discharge as an input in general, improved the performance of the ANN. However, model improvements were less significant for longer forecast lead times. Significant time shift errors in the predicted hydrographs were observed for ANN models that used discharge only as input. Although ANN models with the smallest time shift errors were models that included rainfall as inputs, these models produced hydrographs that were noisier. ANN model results were also evaluated by comparisons with results from the kinematic wave (KW) and autoregressive moving average (ARMA) models. It was found that ANN model forecasts compared favorably with runoff predictions by the KW and ARMA models. Specifically, ANN models that included discharge as input were superior to the KW model for all forecast ranges. However, the inclusion of discharge as an input to the ANN models implies that discharge measurements must be available during the model Simulation Stage; the KW model does not have this requirement. ANN models that did not include discharge as an input were better at long-term forecasts but poorer at short-term forecasts, when compared to the KW model. The poorer performance of the KW model at longer lead times is probably due to errors in the forecast rainfall used.
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runoff forecasting for an asphalt plane by artificial neural networks and comparisons with kinematic wave and autoregressive moving average models
Journal of Hydrology, 2011Co-Authors: Lloyd H C Chua, Tommy S W WongAbstract:Summary Event-based runoff forecasting for 1, 2, 4 and 8 time steps ahead, based on rainfall and flow data of ten storm events for an asphalt plane, have been investigated by the Artificial Neural Network (ANN) technique. The investigation includes ANN models with three different types of inputs: (i) rainfall only, (ii) discharge only and (iii) a combination of rainfall and discharge. The results show that inclusion of discharge as an input in general, improved the performance of the ANN. However, model improvements were less significant for longer forecast lead times. Significant time shift errors in the predicted hydrographs were observed for ANN models that used discharge only as input. Although ANN models with the smallest time shift errors were models that included rainfall as inputs, these models produced hydrographs that were noisier. ANN model results were also evaluated by comparisons with results from the kinematic wave (KW) and autoregressive moving average (ARMA) models. It was found that ANN model forecasts compared favorably with runoff predictions by the KW and ARMA models. Specifically, ANN models that included discharge as input were superior to the KW model for all forecast ranges. However, the inclusion of discharge as an input to the ANN models implies that discharge measurements must be available during the model Simulation Stage; the KW model does not have this requirement. ANN models that did not include discharge as an input were better at long-term forecasts but poorer at short-term forecasts, when compared to the KW model. The poorer performance of the KW model at longer lead times is probably due to errors in the forecast rainfall used.
Simone Gambini - One of the best experts on this subject based on the ideXlab platform.
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feasibility of energy autonomous wireless microsensors for biomedical applications powering and communication
IEEE Reviews in Biomedical Engineering, 2015Co-Authors: Farhad Goodarzy, Efstratios Skafidas, Simone GambiniAbstract:In this review, biomedical-related wireless miniature devices such as implantable medical devices, neural prostheses, embedded neural systems, and body area network systems are investigated and categorized. The two main subsystems of such designs, the RF subsystem and the energy source subsystem, are studied in detail. Different application classes are considered separately, focusing on their specific data rate and size characteristics. Also, the energy consumption of state-of-the-art communication practices is compared to the energy that can be generated by current energy scavenging devices, highlighting gaps and opportunities. The RF subsystem is classified, and the suitable architecture for each category of applications is highlighted. Finally, a new figure of merit suitable for wireless biomedical applications is introduced to measure the performance of these devices and assist the designer in selecting the proper system for the required application. This figure of merit can effectively fill the gap of a much required method for comparing different techniques in Simulation Stage before a final design is chosen for implementation.
Celaya-echarri Mikel - One of the best experts on this subject based on the ideXlab platform.
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Building Decentralized Fog Computing-Based Smart Parking Systems: From Deterministic Propagation Modeling to Practical Deployment
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Celaya-echarri Mikel, Froiz-míguez Iván, Azpilicueta Leyre, Fraga-lamas Paula, López Iturri Peio, Falcone Francisco, Fernández-caramés, Tiago M.Abstract:[Abstract] The traditional process of finding a vacant parking slot is often inefficient: it increases driving time, traffic congestion, fuel consumption and exhaust emissions. To address such problems, smart parking systems have been proposed to help drivers to find available parking slots faster using latest sensing and communications technologies. However, the deployment of the communications infrastructure of a smart parking is not straightforward due to multiple factors that may affect wireless propagation. Moreover, a smart parking system needs to provide not only accurate information on available spots, but also fast responses while guaranteeing the system availability even in the case of lacking connectivity. This article describes the development of a decentralized low-latency smart parking system: from its conception, design and theoretical Simulation, to its empirical validation. Thus, this work first characterizes a real-world scenario and proposes a fog computing and Internet of Things (IoT) based communications architecture to provide smart parking services. Next, a thorough analysis on the wireless channel properties is carried out by means of an in-house developed deterministic 3D-Ray Launching (3D-RL) tool. The obtained results are validated through a real-world measurement campaign and then the communications architecture is implemented by using ZigBee sensor nodes. The implemented architecture also makes use of Bluetooth Low Energy beacons, an Android app, a decentralized database and fog computing gateways, whose performance is evaluated in terms of response latency and processing rate. Results show that the proposed system is able to deliver information to the drivers fast, with no need for relying on remote servers. As a consequence, the presented development methodology and communications evaluation tool can be useful for future smart parking developers, which can determine the optimal locations of the wireless transceivers during the Simulation Stage and then deploy a system that can provide fast responses and decentralized services.Xunta de Galicia; ED431G2019/01Agencia Estatal de Investigación of Spain; TEC2016-75067-C4-1-RAgencia Estatal de Investigación of Spain; RED2018-102668-TAgencia Estatal de Investigación of Spain; PID2019-104958RB-C42Ministerio de Ciencia, Innovación y Universidades; RTI2018-095499-B-C3
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Building decentralized fog computing-based smart parking systems: from deterministic propagation modeling to practical deployment
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Celaya-echarri Mikel, Froiz-míguez Iván, López Iturri Peio, Azpilicueta Fernández De Las Heras, Leyre, Falcone Lanas, Francisco JavierAbstract:The traditional process of finding a vacant parking slot is often inefficient: it increases driving time, traffic congestion, fuel consumption and exhaust emissions. To address such problems, smart parking systems have been proposed to help drivers to find available parking slots faster using latest sensing and communications technologies. However, the deployment of the communications infrastructure of a smart parking is not straightforward due to multiple factors that may affect wireless propagation. Moreover, a smart parking system needs to provide not only accurate information on available spots, but also fast responses while guaranteeing the system availability even in the case of lacking connectivity. This article describes the development of a decentralized low-latency smart parking system: from its conception, design and theoretical Simulation, to its empirical validation. Thus, this work first characterizes a real-world scenario and proposes a fog computing and Internet of Things (IoT) based communications architecture to provide smart parking services. Next, a thorough analysis on the wireless channel properties is carried out by means of an in-house developed deterministic 3D-Ray Launching (3D-RL) tool. The obtained results are validated through a real-world measurement campaign and then the communications architecture is implemented by using ZigBee sensor nodes. The implemented architecture also makes use of Bluetooth Low Energy beacons, an Android app, a decentralized database and fog computing gateways, whose performance is evaluated in terms of response latency and processing rate. Results show that the proposed system is able to deliver information to the drivers fast, with no need for relying on remote servers. As a consequence, the presented development methodology and communications evaluation tool can be useful for future smart parking developers, which can determine the optimal locations of the wireless transceivers during the Simulation Stage and then deploy a system that can provide fast responses and decentralized services.This work was supported in part by the School of Engineering and Sciences, Tecnológico de Monterrey, in part by the Xunta de Galicia under Grant ED431G2019/01, in part by the Agencia Estatal de Investigación of Spain under Grant TEC2016-75067-C4-1-R, Grant RED2018-102668-T, and Grant PID2019-104958RB-C42, in part by the European Regional Development Fund (ERDF) funds of the European Union (EU) (AEI/FEDER, UE), and in part by the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (MCI-U/AEI/FEDER,UE) under Grant RTI2018-095499-B-C31