The Optimal Placement of Sensors in Square Target Regions with Varying Boundary Length

Li, Sen; Cheng, Xudong; Chen, Yanwei; Zhang, Heping

doi:10.1016/j.proeng.2013.08.141

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The solution depends on the boundary shape and relative size of the circles and boundary. Square and hexagonal packings are each optimal in certain circumstances, however hexagonal packing tends to be best over larger areas 69 . Li et al provide a formula to calculate the minimum number of nodes required to cover a square area with hexagonal packing.…”

Section: Methodsmentioning

confidence: 99%

Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery

et al. 2018

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The use of automated, unmanned aerial vehicles (drones) to deliver commercial packages is poised to become a new industry, significantly shifting energy use in the freight sector. Here we find the current practical range of multi-copters to be about 4 km with current battery technology, requiring a new network of urban warehouses or waystations as support. We show that, although drones consume less energy per package-km than delivery trucks, the additional warehouse energy required and the longer distances traveled by drones per package greatly increase the life-cycle impacts. Still, in most cases examined, the impacts of package delivery by small drone are lower than ground-based delivery. Results suggest that, if carefully deployed, drone-based delivery could reduce greenhouse gas emissions and energy use in the freight sector. To realize the environmental benefits of drone delivery, regulators and firms should focus on minimizing extra warehousing and limiting the size of drones.

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Section: Methodsmentioning

confidence: 99%

Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery

et al. 2018

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“…The sensor placement problem has been recognized and studied in other fields. These include fire detection in a target region ( Li et al, 2013 ), air and water quality monitoring ( Du et al, 2014 ; Fontanini et al, 2016 ), and monitoring physical activity in humans with a three-dimensional accelerator ( Boerema et al, 2014 ). Others include structural health monitoring based on modal data ( Chang and Pakzad, 2014 ; Tong et al, 2014 ) and mid and low frequency range methods ( Rao et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems

et al. 2022

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Irregular changes in the internal climates of protected cultivation systems can prevent attainment of optimal yield when the environmental conditions are not adequately monitored and controlled. Key to indoor environment monitoring and control and potentially reducing operational costs are the strategic placement of an optimal number of sensors using a robust method. A multi-objective approach based on supervised machine learning was used to determine the optimal number of sensors and installation positions in a protected cultivation system. Specifically, a gradient boosting algorithm, a form of a tree-based model, was fitted to measured (temperature and humidity) and derived conditions (dew point temperature, humidity ratio, enthalpy, and specific volume). Feature variables were forecasted in a time-series manner. Training and validation data were categorized without randomizing the observations to ensure the features remained time-dependent. Evaluations of the variations in the number and location of sensors by day, week, and month were done to observe the impact of environmental fluctuations on the optimal number and location of placement of sensors. Results showed that less than 32% of the 56 sensors considered in this study were needed to optimally monitor the protected cultivation system’s internal environment with the highest occurring in May. In May, an average change of −0.041% in consecutive RMSE values ranged from the 1st sensor location (0.027°C) to the 17th sensor location (0.013°C). The derived properties better described the ambient condition of the indoor air than the directly measured, leading to a better performing machine learning model. A machine learning model was developed and proposed to determine the optimal sensors number and positions in a protected cultivation system.

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Crack Identification of Steel Bar Based on Sensor Optimal Placement

Wang

Liu

et al. 2019

J Fail. Anal. and Preven.

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The Optimal Placement of Sensors in Square Target Regions with Varying Boundary Length

Cited by 3 publications

References 8 publications

Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery

Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery

Grid Search for Lowest Root Mean Squared Error in Predicting Optimal Sensor Location in Protected Cultivation Systems

Crack Identification of Steel Bar Based on Sensor Optimal Placement

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