Euclidean distance estimation in incomplete datasets

Mesquita, Diego; Gomes, João P. P.; Souza, Amauri H.; Nobre, Juvêncio S.

doi:10.1016/j.neucom.2016.12.081

Cited by 89 publications

(58 citation statements)

References 16 publications

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“…The measured frequency‐domain signals at the characteristic frequencies for the unknown bent angle of the coil sensor form a row matrix, S , which can be expressed as:

bold-italicS = ([], S_{1} S_{2} S_{2} S_{3} S_{4} S_{5} S_{6} S_{7})

where S 1 is the measured frequency‐domain magnitude at the first characteristic frequency, 40 MHz in this study, and so on. Based upon the k nearest neighbor (kNN) classifier of the machine learning, the component of S are used to calculate the n th Euclidean distance, D Eu,n , as follows

D_{Eu, normaln} = \sqrt[2]{\sum_{m = 1}^{7} {({normalS}_{normalm} - {normalV}_{italicnm})}^{2}}

where n = 1‐17.…”

Section: Resultsmentioning

confidence: 99%

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A radio frequency‐based antenna sensor as a protractor

2019

Micro & Optical Tech Letters

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An antenna sensor is proposed to play dual role of a functional antenna and a sensor in a wireless sensor system, in an effort to reduce data loss and to increase transmission rate by eliminating a certain interface. A function of protractor was implemented on the coil antenna, working at radio frequency, to demonstrate the antenna sensor. The coil antenna was made of a copper wire with a wire diameter of 1 mm and had a coil diameter of 60 mm. With the impedance matching for the antenna designed at a resonant frequency of 43 MHz, a signal composed of multiple waves at shifted frequencies was transmitted from a mixer to the bent coil sensor, which acts as the emitting antenna. Upon received at the receiver coil, the signal was analyzed using the fast Fourier transform (FFT) algorithm. The received frequency‐domain signals were used to establish the characteristic matrix for the antenna sensor during a calibration process. The characteristic matrix becomes the fingerprint pattern to identify test angle of the bent coil sensor through the calculation of the Euclidean distance based upon the k nearest neighbor (kNN) classifier of the machine learning. The results showed that all the 16 test angles were identified accurately in the correct angle range.

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“…The measured frequency‐domain signals at the characteristic frequencies for the unknown bent angle of the coil sensor form a row matrix, S , which can be expressed as:

bold-italicS = ([], S_{1} S_{2} S_{2} S_{3} S_{4} S_{5} S_{6} S_{7})

D_{Eu, normaln} = \sqrt[2]{\sum_{m = 1}^{7} {({normalS}_{normalm} - {normalV}_{italicnm})}^{2}}

where n = 1‐17.…”

Section: Resultsmentioning

confidence: 99%

“…Based upon the k nearest neighbor (kNN) classifier of the machine learning, the component of S are used to calculate the n th Euclidean distance, D Eu,n , as follows. 26 D Eu,n = ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi X 7…”

Section: Signal Analysis Of the Antenna Sensormentioning

confidence: 99%

A radio frequency‐based antenna sensor as a protractor

2019

Micro & Optical Tech Letters

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“…In general, the similarity is given as 1 when the spatial topology relationship is equal or containing; the similarity is set to 0 when the spatial topology relationship is disjoint or adjacent. The similarity is greater than 0 and less than 1 if the spatial topology relationship is inside or intersection, and its exact value is determined by the spatial distance (Euclidean distance) or area proportion (Mesquitaa et al 2017).…”

Section: Data Similarity Calculation Methodsmentioning

confidence: 99%

Automatic data matching for geospatial models: a new paradigm for geospatial data and models sharing

Zhu

Jie

2019

Annals of GIS

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With the development of global climate and environmental research, geospatial models are becoming more comprehensive and complex and, therefore require more and more input data. In order to improve the efficiency and to save time, labour and money during the preparation of input data, after a discussion of other related studies, this paper describes a new framework that matches existing open web data with geospatial models. The basic idea and general framework are introduced first; four key issues are then studied in detail. The advantages of the new framework are that it can automatically judge whether the open data is the consistent with the input data of the geospatial model according to data similarity values based on unified description factors; if they are not fully consistent, the differences can be intelligently handled using corresponding web processing services and finally, fully matched data can be obtained for use in the geospatial model. Thus, this framework may lead to the development of a new paradigm that links and promotes geospatial data and models sharing. This will not only lead to the greater application of geospatial models, but also greatly amplify the value and usability of open data.

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“…Evaluation of the distance between existing waste incineration plants and the site selection of the new ones is used as an alternative. Euclidean distance based models can make suitable decisions in layers with regular paths [16]. In this study, the relations between the siting of new MWIs and the built ones is measured by Euclidean distance, and reclassified into 5 categories ( fig.2(C)).…”

Section: Advances In Engineering Research Volume 125mentioning

confidence: 99%

Evaluation of municipal waste incinerators (MWI) site selection based on full cost accounting using GIS tools: a case study in Beijing, China

Zhao¹

2017

Proceedings of the 2017 International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2017)

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Abstract.A Geographic Information System (GIS) is used as a decision-making tool for site selection in this research. Many factors are related to the site selection of Municipal waste incinerators (MWI) due to the external costs. Consequently, a site selection model is built to help regulators to choose suitable sites for MWIs. To simplify the process and reduce human errors, an automatic model built by Model Builder of ArcGIS is used in this study. A choice has been made among the MWIs planned to select a proper one based on health, transportation and operation costs. This paper presents a model that could increase the success and eliminate possible wrong justifications in the management of MWIs in large cities.

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Euclidean distance estimation in incomplete datasets

Cited by 89 publications

References 16 publications

A radio frequency‐based antenna sensor as a protractor

A radio frequency‐based antenna sensor as a protractor

Automatic data matching for geospatial models: a new paradigm for geospatial data and models sharing

Evaluation of municipal waste incinerators (MWI) site selection based on full cost accounting using GIS tools: a case study in Beijing, China

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