Design and Simulation of intelligent dimmer based on Fuzzy PID

Xiaogang, Yue; Wang, Wenqing; Li, Fenna

doi:10.1109/icid52250.2020.00025

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…Una de las principales causas de la deficiencia visual es la miopía, la cual puede prevenirse teniendo los niveles de iluminación adecuados. Debido a esto, la OMS recomienda mantener niveles de iluminancia en 500 lx en las áreas de estudio o trabajo, de preferencia con fuentes de luz blanca, con el fin de prevenir este padecimiento [18].…”

Section: Caso De Estudio: Lámpara De Protección Ocularunclassified

Control por ganancias programadas aplicado a un convertidor DC-DC para la regulación de la intensidad luminosa de una lámpara LED

Olivar-Castellanos

Vela-Valdés

Aguayo

2023

ings

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En la actualidad existen diversas aplicaciones de la iluminación LED, sin embargo, algunas de estas requieren regular la iluminación con precisión y en un amplio rango de operación, principalmente en tareas de seguimiento. Es en estos casos en donde es necesario considerar la no linealidad del LED para el diseño de esquemas de control, además, se necesita de una fuente de alimentación eficiente ante los cambios de tensión y corriente requeridos por la lámpara. En este trabajo se presenta el diseño de un convertidor DC-DC capaz de realizar y soportar estas variaciones, así como el diseño, simulación y comparación de esquemas de control PI clásico, PI difuso y ganancias programadas para estas aplicaciones. Con el fin de validar los esquemas de control descritos, y con el propósito de cumplir las recomendaciones de la Organización Mundial de la Salud, se toma el control de la iluminancia producida por una lámpara de protección ocular como caso de estudio, en donde el controlador varía el ciclo de trabajo del convertidor, ajustando de esta manera la tensión de la lámpara, y en consecuencia regulando la intensidad luminosa. Comparando los esquemas de control, el desempeño del sistema con el control por ganancias programadas tiene mejores características para el caso de estudio descrito con anterioridad, presentándose un error en estado estable de 0 % y menor sobretiro en comparación con los otros esquemas de control desarrollados.

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Section: Caso De Estudio: Lámpara De Protección Ocularunclassified

Control por ganancias programadas aplicado a un convertidor DC-DC para la regulación de la intensidad luminosa de una lámpara LED

Olivar-Castellanos

Vela-Valdés

Aguayo

2023

ings

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“…Fuzzy systems have been used as a decision-making strategy in multiple fields [19]- [22], for example, decision-making in cleaning robots regarding the shape, size, and distribution of garbage [23], [24], with high effectiveness. Fuzzy controllers are another important application on developments like robotic navigation systems [25], or specific task like improve the contact force in systems of pantograph-catenary to deliver power to a train [26].…”

Section: Introductionmentioning

confidence: 99%

Paper biological risk detection through deep learning and fuzzy system

Sanabria

Moreno²,

Baquero³

2023

IJECE

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<span lang="EN-US">Given the recent events worldwide due to viral diseases that affect human health, automatic monitoring systems are one of the strong points of research that has gained strength, where the detection of biohazardous waste of a sanitary nature is highlighted related to viral diseases stands out. It is essential in this field to generate developments aimed at saving lives, where robotic systems can operate as assistants in various fields. In this work an artificial intelligence algorithm based on two stages is presented, one is the recognition of paper debris using a ResNet-50, chosen for its object localization capacity, and the other is a fuzzy inference system for the generation of alarm states due to biological risk by such debris, where fuzzy logic helps to establish a model for a non-predictive system as the one exposed. A biohazard detection algorithm for paper waste is described, oriented to operate on an assistive robot in a residential environment. The training parameters of the network, which achieve 100% accuracy with confidence levels between 82% for very small waste and 100% in direct view, are presented. Timing cycles are established for validation of the exposure time of the waste, where through the fuzzy system, risk alarms are generated, which allows establishing a system with an average reliability of 98%.</span>

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“…Fuzzy systems have been used as a lighting control strategy [11], so they fit very well in traffic light control schemes, for example, for a large number of intersections [12], seeking to maximize the duration time of the green light [13], among others [14]- [18]. Some of these cases have been strengthened with the integration of fuzzy systems to neural networks [19], where nowadays one of the main neural architectures in pattern recognition are convolutional networks [20].…”

Section: Introductionmentioning

confidence: 99%

Ambulance detection for smart traffic light applications with fuzzy controller

Moreno

Baquero²,

Umaña³

2022

IJECE

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<p>In the development of intelligent cities, the automation of vehicular mobility is one of the strong points of research, where intelligent traffic lights stand out. It is essential in this field to prioritize emergency vehicles that can help save lives, where every second counts in favor of the transfer of a patient or injured person. This paper presents an artificial intelligence algorithm based on two stages, one is the recognition of emergency vehicles through a ResNet-50 and the other is a fuzzy inference system for timing control of a traffic light, both lead to an intelligent traffic light. An application of traffic light vehicular flow control for automatic preemption when detecting emergency vehicles, specifically ambulances, is oriented. The training parameters of the network, which achieves 100% accuracy with confidence levels between 65% with vehicle occlusion and 99% in direct view, are presented. The traffic light cycles are able to extend the green time of the traffic light with almost 50% in favor of the road that must yield the priority, in relation to not using the fuzzy inference system.</p>

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Design and Simulation of intelligent dimmer based on Fuzzy PID

Cited by 4 publications

References 1 publication

Control por ganancias programadas aplicado a un convertidor DC-DC para la regulación de la intensidad luminosa de una lámpara LED

Control por ganancias programadas aplicado a un convertidor DC-DC para la regulación de la intensidad luminosa de una lámpara LED

Paper biological risk detection through deep learning and fuzzy system

Ambulance detection for smart traffic light applications with fuzzy controller

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