A Temperature Measurement Method Based on Visible Light Chromaticity Index and k-nearest Neighbors Algorithm

A tropical ball is a ball defined by the tropical metric over the tropical projective torus. In this paper we show several properties of tropical balls over the tropical projective torus and also over the space of phylogenetic trees with a given set of leaf labels. Then we discuss its application to the K nearest neighbors (KNN) algorithm, a supervised learning method used to classify a high-dimensional vector into given categories by looking at a ball centered at the vector, which contains K vectors in the space.

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Turbine Blade Three-Wavelength Radiation Temperature Measurement Method Based on Reflection Error Correction

Applied Sciences ◽

10.3390/app11093913 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3913

Author(s):

Kaifeng Zheng ◽

Jinguang Lü ◽

Yingze Zhao ◽

Jin Tao ◽

Yuxin Qin ◽

...

Keyword(s):

Temperature Measurement ◽

Turbine Blade ◽

Measurement Method ◽

Turbine Blades ◽

Target Surface ◽

Maximum Error ◽

Radiation Temperature ◽

Average Error ◽

Real Surface ◽

Wavelength Radiation

The turbine blade is a key component in an aeroengine. Currently, measuring the turbine blade radiation temperature always requires obtaining the emissivity of the target surface in advance. However, changes in the emissivity and the reflected ambient radiation cause large errors in measurement results. In this paper, a three-wavelength radiation temperature measurement method was developed, without known emissivity, for reflection correction. Firstly, a three-dimensional dynamic reflection model of the turbine blade was established to describe the ambient radiation of the target blade based on the real surface of the engine turbine blade. Secondly, based on the reflection correction model, a three-wavelength radiation temperature measurement algorithm, independent of surface emissivity, was proposed to improve the measurement accuracy of the turbine blade radiation temperature in the engine. Finally, an experimental platform was built to verify the temperature measurement method. Compared with three conventional colorimetric methods, this method achieved an improved performance on blade temperature measurement, demonstrating a decline in the maximum error from 6.09% to 2.13% and in the average error from 2.82% to 1.20%. The proposed method would benefit the accuracy in the high-temperature measurement of turbine blades.

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Classification of soil quality using K-Nearest Neighbors methods

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/739/1/012011 ◽

2021 ◽

Vol 739 (1) ◽

pp. 012011

Author(s):

I D Ratih ◽

S M Retnaningsih ◽

V M Dewi

Keyword(s):

Soil Quality ◽

Nearest Neighbors ◽

K Nearest Neighbors

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Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks

Sensors ◽

10.3390/s21123994 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3994

Author(s):

Yuxi Li ◽

Fucai Zhou ◽

Yue Ge ◽

Zifeng Xu

Keyword(s):

Social Networks ◽

Access Control ◽

Location Privacy ◽

Nearest Neighbors ◽

Search Pattern ◽

Broadcast Encryption ◽

Mobile Social Networks ◽

K Nearest Neighbors ◽

Fine Grained ◽

Server Architecture

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.

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A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots

Sensors ◽

10.3390/s21092940 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2940

Author(s):

Luciano Ortenzi ◽

Simone Figorilli ◽

Corrado Costa ◽

Federico Pallottino ◽

Simona Violino ◽

...

Keyword(s):

Machine Vision ◽

Nearest Neighbors ◽

Calibration Method ◽

Mobile App ◽

Harvest Time ◽

Automatic Assessment ◽

K Nearest Neighbors ◽

Olive Ripeness ◽

Olive Ripening

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method—an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

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