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AI-based computer vision using deep learning in 6G wireless networks
بینایی کامپیوتر مبتنی بر هوش مصنوعی با استفاده از یادگیری عمیق در شبکه های بی سیم 6G-2022 Modern businesses benefit significantly from advances in computer vision technology, one of the
important sectors of artificially intelligent and computer science research. Advanced computer
vision issues like image processing, object recognition, and biometric authentication can benefit
from using deep learning methods. As smart devices and facilities advance rapidly, current net-
works such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing
demand. Classification of images, object classification, and facial recognition software are some
of the most difficult computer vision problems that can be solved using deep learning methods. As
a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently
been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to
solve the above challenges by introducing a new methodology named Optimizing Computer
Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency al-
gorithms (DL-EA) for computer vision to address the issues mentioned and improve the system’s
outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper
to recognize pattern recognition and intelligent management systems and provide driven meth-
odology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can
summarize the significant areas for future research and potential solutions, including image
enhancement, machine vision, and access control. keywords: SHG | ارتباطات بی سیم | هوش مصنوعی | فراگیری ماشین | یادگیری عمیق | ارتباطات سیار | 6G | Wireless communication | AI | Machine learning | Deep learning | Mobile communication |
مقاله انگلیسی |
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A novel method of fish tail fin removal for mass estimation using computer vision
یک روش جدید حذف باله دم ماهی برای تخمین جرم با استفاده از بینایی کامپیوتر-2022 Fish mass estimation is extremely important for farmers to get fish biomass information, which could be useful to
optimize daily feeding and control stocking densities and ultimately determine optimal harvest time. However,
fish tail fin mass does not contribute much to total body mass. Additionally, the tail fin of free-swimming fish is
deformed or bent for most of the time, resulting in feature measurement errors and further affecting mass
prediction accuracy by computer vision. To solve this problem, a novel non-supervised method for fish tail fin
removal was proposed to further develop mass prediction models based on ventral geometrical features without
tail fin. Firstly, fish tail fin was fully automatically removed using the Cartesian coordinate system and image
processing. Secondly, the different features were respectively extracted from fish image with and without tail fin.
Finally, the correlational relationship between fish mass and features was estimated by the Partial Least Square
(PLS). In this paper, tail fins were completely automatically removed and mass estimation model based on area
and area square has been the best tested on the test dataset with a high coefficient of determination (R2) of 0.991,
the root mean square error (RMSE) of 7.10 g, the mean absolute error (MAE) of 5.36 g and the maximum relative
error (MaxRE) of 8.46%. These findings indicated that mass prediction model without fish tail fin can more
accurately estimate fish mass than the model with tail fin, which might be extended to estimate biomass of free-
swimming fish underwater in aquaculture. keywords: برداشتن باله دم | اتوماسیون | ماهی | تخمین انبوه | بینایی کامپیوتر | Tail fin removal | Automation | Fish | Mass estimation | Computer vision |
مقاله انگلیسی |
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Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data
ارزیابی شرایط زهکشی سطحی در مقیاس خیابان و محله: یک روش دید کامپیوتری و جهت جریان اعمال شده به داده های لیدار-2022 Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and
mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-
scale topographical information. This paper addresses this issue by providing a novel method for evaluating
surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging)
measurements. The developed method derives topographical properties and runoff accumulation by applying a
semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology
technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the
SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the
proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales
and identify problematic low points that could be susceptible to water ponding. Municipalities and property
owners can use this information to take targeted corrective maintenance actions. keywords: تقسیم بندی معنایی | جهت جریان | لیدار موبایل | زهکشی سطحی | زیرساخت های زهکشی | Semantic segmentation | Flow direction | Mobile lidar | Surface drainage | Drainage infrastructure |
مقاله انگلیسی |
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Detection of loosening angle for mark bolted joints with computer vision and geometric imaging
تشخیص زاویه شل شدن اتصالات پیچ شده با بینایی ماشین و تصویربرداری هندسی-2022 Mark bars drawn on the surfaces of bolted joints are widely used to indicate the severity of loosening. The
automatic and accurate determination of the loosening angle of mark bolted joints is a challenging issue that has
not been investigated previously. This determination will release workers from heavy workloads. This study
proposes an automated method for detecting the loosening angle of mark bolted joints by integrating computer
vision and geometric imaging theory. This novel method contained three integrated modules. The first module
used a Keypoint Regional Convolutional Neural Network (Keypoint-RCNN)-based deep learning algorithm to
detect five keypoints and locate the region of interest (RoI). The second module recognised the mark ellipse and
mark points using the transformation of the five detected keypoints and several image processing technologies
such as dilation and expansion algorithms, a skeleton algorithm, and the least square method. In the last module,
according to the geometric imaging theory, we derived a precise expression to calculate the loosening angle using
the information for the mark points and mark ellipse. In lab-scale and real-scale environments, the average
relative detection error was only 3.5%. This indicated that our method could accurately calculate the loosening
angles of marked bolted joints even when the images were captured from an arbitrary view. In the future, some
segmentation algorithms based on deep learning, distortion correction, accurate angle and length measuring
instruments, and advanced transformation methods can be applied to further improve detection accuracy. keywords: Mark bolted joint | Loosening detection | Keypoint-RCNN | Image processing | Geometric imaging |
مقاله انگلیسی |
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Smart sensors network for accurate indirect heat accounting in apartment buildings
سنسورهای هوشمند شبکه برای حسابداری دقیق غیر مستقیم در ساختمان های آپارتمان-2021 A new method for accurate indirect heat accounting in apartment buildings has been recently
developed by the Centre Suisse d’Electronique et de Microtechnique (CSEM). It is based on a data
driven approach aimed to the smart networking of any type of indirect heat allocation devices,
which can provide, for each heat delivery point of an apartment building, measurements or es-
timations of the temperature difference between the heat transfer fluid and the indoor environ-
ment. The analysis of the data gathered from the devices installed on the heating bodies, together
with the measurements of the overall building heat consumption provided by direct heat
metering, allows the evaluation of the characteristic thermal model parameters of heating bodies
at actual installation and working conditions. Thus overcoming the negative impact on accuracy
of conventional indirect heat accounting due to off-design operation, in which these measurement
systems normally operate. The method has been tested on conventional heat cost allocators
(HCA), and on innovative smart radiator thermostatic valves developed by CSEM. The evaluations
were carried out at the centralized heating system mock-up of the Istituto Nazionale di Ricerca
Metrologica (INRIM), and also in a real building in Neuchatel, Switzerland. The method has
proven to be an effective tool to improve the accuracy of indirect heat metering systems;
compared to conventional HCA systems, the error on the individual heating bill is reduced by
20%–50%. keywords: شبکه سنسورهای هوشمند | حسابداری غیر مستقیم | اندازه گیری حرارت | مخزن هزینه های حرارتی | سیستم های گرمایش متمرکز | Smart sensors network | Indirect heat accounting | Heat metering | Heat cost allocators | Centralized heating systems |
مقاله انگلیسی |
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New method for a SEM-based quantitative microstructural clay analysis - MiCA
روش جدید برای تجزیه و تحلیل کمی خاک رس ریزساختاری مبتنی بر SEM - MiCA-2021 The soil microstructure is recognised to strongly influence the mechanical behaviour of both coarse and fine geomaterials. Proper identification and tracking of the shape and position of the particles has become more and more critical to form a link between the micro and macro behaviour. Scanning Electron Microscopy (SEM) has been widely used in the last decades to study the clay fabric variation with its mechanical behaviour and physical properties. However, the particles orientation has so far been discussed only from a qualitative point of view due to the lack of updated, automatised quantification tools. Consequently, developing constitutive models that correlate the soil micro structure to its mechanical behaviour is not feasible. In order to make a step forward in this direction, an Image Analysis based code called MiCA (microstructural clay analyser) capable of quantifying the particle orientation and the porosity of clay samples through the analysis of SEM micrographs was developed in this study. The code reliability was first validated through the application to geometrical reference patterns, then to textbook micrographs illustrating typical clay fabrics (dispersed, honeycomb, flocculated and aggre- gated), and finally to high quality images. MiCA showed good accuracy in the results obtained, regardless of the number of lines in the image, the complexity of the geometrical shapes and the pixel size of the analysed graphs. Therefore, MiCA can be considered suitable for quantitative analysis of the particle orientation and/or pores shape in clay materials. Keywords: Image analysis | Computer vision | Quantitative analysis | Clay particles orientation | SEM | Clay porosity |
مقاله انگلیسی |
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A novel biometric recognition method based on multi kernelled bijection octal pattern using gait sound
یک روش جدید بیومتریک شناختی مبتنی بر الگوی هشت جداره چند هسته ای با استفاده از صدای راه رفتن-2021 Background: Many gait based methods have been presented about biometric identification in the literature. Gait recognition methods have generally used images and sensors signals. In this work, a novel gait based biometric recognition method is presented. A novel Multi Kernelled Bijection Octal Pattern (MK- BOP) is presented in this study. Object: The main aim of the proposed MK-BOP is to extract distinctive and comprehensive features from a signal (gait sound). By using the proposed MK-BOP, a novel biometric recognition method is proposed. Gait sounds are collected, and two novel datasets are collected. The first dataset is a noisy and heterogeneous dataset. The second dataset is a clear and homogenous dataset. A multileveled method is presented to authenticate subjects from these datasets. One dimensional discrete wavelet transform (1D-DWT) is applied to sound signal with Symlet 6 (sym6) filter, and levels are calculated. Conclusion: The proposed MK-BOP generates features from each level signals, and the generated features are concatenated. A hybrid feature selector (RFNCA) selects the most discriminative feature, and selected most discriminative features are forwarded to classifiers. 0.980 and 0.949 success rates were achieved for clear and noisy datasets, respectively.© 2020 Elsevier Ltd. All rights reserved. Keywords: Gait recognition | Biometrics | Multi kernelled bijection octal pattern | Information fusion | Sound recognition |
مقاله انگلیسی |
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Global assessment of marine phytoplankton primary production: Integrating machine learning and environmental accounting models
ارزیابی جهانی از تولید اولیه فیتوپلانکتون دریایی: یکپارچه سازی یادگیری ماشین و مدل های حسابداری محیطی-2021 The emergy accounting method has been widely applied to terrestrial and marine ecosystems although there is a
lack of emergy studies focusing on phytoplankton primary production. Phytoplankton production is a pivotal
process since it is intimately coupled with oceanic food webs, energy fluxes, carbon cycle, and Earth’s climate. In
this study, we proposed a new methodology to perform a biophysical assessment of the global phytoplankton
primary production combining Machine Learning (ML) techniques and an emergy-based accounting model.
Firstly, we produced global phytoplankton production estimates using an Artificial Neural Network (ANN)
model. Secondly, we assessed the main energy inputs supporting the global phytoplankton production. Finally,
we converted these inputs into emergy units and analysed the results from an ecological perspective. Among the
energy flows, tides showed the highest maximum emergy contribution to global phytoplankton production
highlighting the importance of thise flow in the complex dynamics of marine ecosystems. In addition, an emergy/
production ratio was calculated showing different global patterns in terms of emergy convergence into the
primary production process. We believe that the proposed emergy-based assessment of phytoplankton produc-
tion could be extremely valuable to improve our understanding of this key biological process at global scale
adopting a systems perspective. This model can also provide a useful benchmark for future assessments of marine
ecosystem services at global scale. keywords: تولید اولیه فیتوپلانکتون | اکولوژی سیستم ها | شبکه های عصبی مصنوعی | یادگیری ماشین | حسابداری امری | Phytoplankton primary production | Systems ecology | Artificial neural networks | Machine learning | Emergy accounting |
مقاله انگلیسی |
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Optical solitons and other solutions to the dual-mode nonlinear Schrödinger equation with Kerr law and dual power law nonlinearities
Solitons نوری و راه حل های دیگر برای معادله Schrödinger دو حالت غیرخطی با قانون Kerr و غیرخطی های قانون قدرت دوگانه-2020 In this article, we apply four insightful integration algorithms, namely, the tanh-coth method, the
unified Riccati equation method, the modified simple equation method and the new extended
auxiliary equation method for constructing new optical soliton solutions and other solutions to
the dual-mode nonlinear Schrödinger equation with Kerr law and dual power law nonlinearities
related to the optics. Many families of Jacobi elliptic solutions, dark, bright, singular soliton
solutions and other solutions have been found. Keywords: The tanh-coth method | The unified Riccati equation method | The modified simple equation method | The new extended auxiliary equation method | Dual-mode nonlinear Schrödingers equation | Optical soliton solutions |
مقاله انگلیسی |
10 |
A new approach for identifying the Kemeny median ranking
یک روش جدید برای شناسایی رتبه بندی متوسط Kemeny-2020 Condorcet consistent rules were originally developed for preference aggregation in the theory of social choice. Nowadays these rules are applied in a variety of fields such as discrete multi-criteria analysis, defence and security decision support, composite indicators, machine learning, artificial intelligence, queries in databases or internet multiple search engines and theoretical computer science. The cycle issue, known also as Condorcets paradox, is the most serious problem inherent in this type of rules. Solutions for dealing with the cycle issue properly already exist in the literature; the most important one being the identification of the median ranking, often called the Kemeny ranking. Unfortunately its identification is a NP-hard problem. This article has three main objectives: (1) to clarify that the Kemeny median order has to be framed in the context of Condorcet consistent rules; this is important since in the current practice sometimes even the Borda count is used as a proxy for the Kemeny ranking. (2) To present a new exact algorithm, this identifies the Kemeny median ranking by providing a searching time guarantee. (3) To present a new heuristic algorithm identifying the Kemeny median ranking with an optimal trade-off between convergence and approximation . Keywords : Decision analysis | Combinatorial optimisation | Social choice| Multiple criteria | Artificial intelligence| Defence and security| Big data |
مقاله انگلیسی |