scholarly journals Restoration of Dimensions for Ancient Drawing Recognition

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2269
Author(s):  
Kwang-cheol Rim ◽  
Pankoo Kim ◽  
Hoon Ko ◽  
Kitae Bae ◽  
Tae-gyun Kwon
Keyword(s):  
Artificial Intelligence ◽  
Reference Values ◽  
Southern Region ◽  
Weights And Measures ◽  
Actual Size

This study aims to investigate and determine the actual size of the “cheok” scale—the traditional weights and measures of Korea—to aid in data construction on the recognition of ancient drawings in the field of artificial intelligence. The cheok scale can be divided into Yeongjocheok, Jucheok, Pobaekcheok, and Joryegicheok. This study calculated the actual dimensions used in the drawings of Tonga and Eonjo contained in Jaseungcha Dohae by Gyunam Ha BaeckWon, which helped us analyze the scale used in the southern region of Korea in the 1800s. The scales of 1/15 cheok and 1/10 cheok were used in the Tonga and Eonjo sections in Jaseungcha Dohae, and the actual dimensions in the drawing were converted to the scale used at the time. Owing to the conversion, the dimensions in the drawings of Tonga were converted to 30.658 cm per cheok, and ~31.84 cm per cheok for Eonjo. In this manner, the actual dimensions used in the southern region of Korea around the year 1800 were restored. Through this study, the reference values for drawing recognition of machinery drawings in Korea around 1800 were derived.

scholarly journals Study on Quasi-Period and Chaos Synchronizations of a Laser Local Area Network by Shifting the Currents of One Chain Node Lasers

2022 ◽  
Vol 2160 (1) ◽  
pp. 012067
Author(s):  
Senlin Yan
Keyword(s):  
Complex System ◽  
Artificial Intelligence ◽  
Reference Values ◽  
Chaos Synchronization ◽  
Local Area Network ◽  
Local Area ◽  
Area Network ◽  
Double Period

Abstract Quasi-period and chaos synchronizations of a laser local area network (LAN) are discussed deep by shifting or controlling the current parameters of one chain node lasers of the LAN. The two coupling-lasers as network’s double-driver nodes and other two laser as network’s receiver node lasers perform two chains of laser LAN. Multi-dynamics states and their synchronizations, such as quasi-period, chaos and their synchronizations, are guided to show in the LAN by varying the current parameters of one chain node lasers. We find that multi-dynamics state synchronizations, such double-period, period-3, period-4, period-5, other quasi-period and chaos synchronizations, are guided to present at two chains of the LAN. This LAN and its obtained results have import reference values for complex system, network, artificial intelligence, chaos synchronization.

scholarly journals Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

2021 ◽  
Author(s):  
Omar Alfarisi ◽  
Aikifa Raza ◽  
Hongtao Zhang ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Image Recognition ◽  
Reference Values ◽  
Carbonate Rock ◽  
Image Resolution ◽  
Blind Test ◽  
Thin Sections ◽  
Clastic Rocks

<p>Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists and petroleum engineers face difficulties in setting the direction of the optimum method for determining petrophysical properties from core plug images of optical thin-sections, Micro-Computed Tomography (μCT), or Magnetic Resonance Imaging (MRI). Most of the successful work is from the homogeneous and clastic rocks focusing on 2D images with less focus on 3D and requiring numerical simulation. Currently, image analysis methods converge to three approaches: image processing, artificial intelligence, and combined image processing with artificial intelligence. In this work, we propose two methods to determine the porosity from 3D μCT and MRI images: an image processing method with Image Resolution Optimized Gaussian Algorithm (IROGA); advanced image recognition method enabled by Machine Learning Difference of Gaussian Random Forest (MLDGRF).</p><p>Meanwhile, we have built reference 3D micro models and collected images for calibration of the IROGA and MLDGRF methods. To evaluate the predictive capability of these calibrated approaches, we ran them on 3D μCT and MRI images of natural heterogeneous carbonate rock. We also measured the porosity and lithology of the carbonate rock using three and two industry-standard ways, respectively, as reference values. Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96.2% and 97.1% on the training set and 91.7% and 94.4% on blind test validation, respectively, in comparison with the three experimental measurements. We measured limestone and pyrite reference values using two methods, X-ray powder diffraction, and grain density measurements. MLDGRF has produced lithology (limestone and pyrite) volume fractions with an accuracy of 97.7% in comparison to reference measurements.</p>

scholarly journals Real Distance Measurement Using Object Detection of Artificial Intelligence

2021 ◽  
Vol 12 (6) ◽  
pp. 557-563
Author(s):  
Jae Moon Lee Et.al
Keyword(s):  
Artificial Intelligence ◽  
Object Detection ◽  
Mobile Phone ◽  
Empirical Data ◽  
Visually Impaired ◽  
Distance Measurement ◽  
Actual Measurement ◽  
Detection Technology ◽  
Artificial Intelligence Technology ◽  
Actual Size

Artificial intelligence technology is developing rapidly in recent years. The purpose of this paper is to measure the distance to an object using this. In order to measure the distance, two separate pictures from same angles of the object will be taken. It extracts sizes for the same object in two pictures. In order to do this in real time, object detection technology of Artificial Intelligence on mobile phone was used.  In this paper, a method for measuring the distance from two pictures is presented. The proposed method was implemented as a prototype on iOS. In order to measure the performance of distance measurement, experiments were conducted in various environments. In the experiments, the empirical data yielded some discrepancies with the actual measurement. This was a result of errors occurring in the object detection process where the actual size of the object was calculated. Despite these discrepancies, this method of object detection may be widely used in instances where accurate measurements are not necessarily required such as guidance systems for the visually impaired.

scholarly journals Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

2021 ◽  
Author(s):  
Omar Alfarisi ◽  
Aikifa Raza ◽  
Hongtao Zhang ◽  
Mohamed Sassi ◽  
TieJun Zhang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Image Recognition ◽  
Reference Values ◽  
Carbonate Rock ◽  
Image Resolution ◽  
Blind Test ◽  
Thin Sections ◽  
Clastic Rocks

<p>Automated image processing algorithms can improve the quality, efficiency, and consistency of classifying the morphology of heterogeneous carbonate rock and can deal with a massive amount of data and images seamlessly. Geoscientists and petroleum engineers face difficulties in setting the direction of the optimum method for determining petrophysical properties from core plug images of optical thin-sections, Micro-Computed Tomography (μCT), or Magnetic Resonance Imaging (MRI). Most of the successful work is from the homogeneous and clastic rocks focusing on 2D images with less focus on 3D and requiring numerical simulation. Currently, image analysis methods converge to three approaches: image processing, artificial intelligence, and combined image processing with artificial intelligence. In this work, we propose two methods to determine the porosity from 3D μCT and MRI images: an image processing method with Image Resolution Optimized Gaussian Algorithm (IROGA); advanced image recognition method enabled by Machine Learning Difference of Gaussian Random Forest (MLDGRF).</p><p>Meanwhile, we have built reference 3D micro models and collected images for calibration of the IROGA and MLDGRF methods. To evaluate the predictive capability of these calibrated approaches, we ran them on 3D μCT and MRI images of natural heterogeneous carbonate rock. We also measured the porosity and lithology of the carbonate rock using three and two industry-standard ways, respectively, as reference values. Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96.2% and 97.1% on the training set and 91.7% and 94.4% on blind test validation, respectively, in comparison with the three experimental measurements. We measured limestone and pyrite reference values using two methods, X-ray powder diffraction, and grain density measurements. MLDGRF has produced lithology (limestone and pyrite) volume fractions with an accuracy of 97.7% in comparison to reference measurements.</p>

Artificial Intelligence

2009 ◽  
Author(s):  
David L. Poole ◽  
Alan K. Mackworth
Keyword(s):  
Artificial Intelligence

EURRECA’s Approach for Estimating Micronutrient Requirements

2011 ◽  
Vol 81 (4) ◽  
pp. 256-263 ◽  
Author(s):  
Christophe Matthys ◽  
Pieter van ‘t Veer ◽  
Lisette de Groot ◽  
Lee Hooper ◽  
Adriënne E.J.M. Cavelaars ◽  
...  
Keyword(s):  
Reference Values ◽  
Policy Decision ◽  
Individual Variability ◽  
The Body ◽  
Decision Makers ◽  
Nutrient Requirements ◽  
Factorial Approach ◽  
Dietary Reference Values ◽  
Health Policy Decision ◽  
Quantitative Integration

In Europe, micronutrient dietary reference values have been established by (inter)national committees of experts and are used by public health policy decision-makers to monitor and assess the adequacy of diets within population groups. The approaches used to derive dietary reference values (including average requirements) vary considerably across countries, and so far no evidence-based reason has been identified for this variation. Nutrient requirements are traditionally based on the minimum amount of a nutrient needed by an individual to avoid deficiency, and is defined by the body’s physiological needs. Alternatively the requirement can be defined as the intake at which health is optimal, including the prevention of chronic diet-related diseases. Both approaches are confronted with many challenges (e. g., bioavailability, inter and intra-individual variability). EURRECA has derived a transparent approach for the quantitative integration of evidence on Intake-Status-Health associations and/or Factorial approach (including bioavailability) estimates. To facilitate the derivation of dietary reference values, EURopean micronutrient RECommendations Aligned (EURRECA) is developing a process flow chart to guide nutrient requirement-setting bodies through the process of setting dietary reference values, which aims to facilitate the scientific alignment of deriving these values.

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