با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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1 |
The physical and mechanical properties for flexible biomass particles using computer vision
خواص فیزیکی و مکانیکی ذرات زیست توده انعطاف پذیر با استفاده از بینایی کامپیوتری-2022 The combustion and fluidization behavior of biomass depend on the physical properties (size, morphology, and
density) and mechanical performances (elastic modulus, Poisson’s ratio, tensile strength and failure strain), but
their quantitative models have rarely been focused in previous researchers. Hence, a static image measurement
for particle physical properties is studied. Combining the uniaxial tension and digital image correlation tech-
nology, the dynamic image measurement method for the mechanical properties is proposed. The results indicate
that the average roundness, rectangularity, and sphericity of present biomass particles are 0.2, 0.4, and 0.16,
respectively. The equivalent diameter and density obey the skewed normal distribution. The tensile strength and
failure stress are sensitive to stretching rate, fiber size and orientation. The distribution intervals of elastic
modulus and Poisson’s ratio are 30–600 MPa and 0.25–0.307, respectively. The stress–strain curves obtained
from imaging experiments agree well with the result of finite element method. This study provides the operating
parameters for the numerical simulation of particles in the fluidized bed and combustor. Furthermore, the
computer vision measurement method can be extended to the investigations of fossil fuels. keywords: ذرات زیست توده | مشخصات فیزیکی | اجرای مکانیکی | تست کشش | آزمایش تصویربرداری | بینایی کامپیوتر | Biomass particle | Physical properties | Mechanical performances | Tensile testing | Imaging experiment | Computer vision |
مقاله انگلیسی |
2 |
Efficient Quantum Blockchain With a Consensus Mechanism QDPoS
بلاک چین کوانتومی کارآمد با مکانیزم اجماع QDPoS-2022 Quantum blockchain is expected to offer an alternative to classical blockchain to resist malicious attacks laughed by future quantum computers. Although a few quantum blockchain schemes have been constructed, their efficiency is low and unable to meet application requirements due to the fact that they lack of a suitable consensus mechanism. To tackle this issue, a consensus mechanism called quantum delegated proof of stake (QDPoS) is constructed by using quantum voting to provide fast decentralization for the quantum blockchain scheme at first. Then an efficient scheme is proposed for quantum blockchain based on QDPoS, where the classical information is initialized as a part of each single quantum state and these quantum states are entangled to form the chain. Compared with previous methods, the designed quantum blockchain scheme is more complete and carried out with higher efficiency, which greatly contributes to better adapting to the challenges of the quantum era.
Index Terms: Quantum blockchain | consensus mechanism | QDPoS | quantum voting | quantum entanglement. |
مقاله انگلیسی |
3 |
Spatiotemporal flow features in gravity currents using computer vision methods
ویژگی های جریان مکانی-زمانی در جریان های گرانشی با استفاده از روش های بینایی کامپیوتری-2022 Relationships between the features visually identified at the front of the flow’s current and parameters
regarding its velocity and turbulence were observed in early experimental works on the characterization of
gravity currents. Researches have associated front features, like lobes and clefts, with the flow’s turbulence, and
have used these associations ever since. In more recent works using numerical simulations, these connections
were still being validated for various flow parameters at higher front velocities. The majority of works regarding
measurements at the front of a gravity current rely on the front’s images for making its analysis and establish
relationships. Besides that, there is an interdisciplinary field related to computer science called computer vision,
devoted to study how digital images can be analyzed and how these results can be automated. This paper
describes the use of computer vision algorithms, particularly corner detection and optical flow, to automatically
track features at the front of gravity currents, either from physical or numerical experiments. To determine the
proposed approach’s accuracy, we establish a ground-truth method and apply it to numerical simulation results
data sets. The technique used to trace the front features along the flow showed promising results, especially
with higher Reynolds numbers flows.
keywords: جریان های گرانشی | ساختارهای لوب و شکاف | روش های کامپیوتری | ویژگی ردیابی | Gravitycurrents | Lobesandcleftsstructures | Computervisionmethods | Featurepointtracking |
مقاله انگلیسی |
4 |
Disintegration testing augmented by computer Vision technology
آزمایش تجزیه با فناوری Vision کامپیوتری تقویت شده است-2022 Oral solid dosage forms, specifically immediate release tablets, are prevalent in the pharmaceutical industry.
Disintegration testing is often the first step of commercialization and large-scale production of these dosage
forms. Current disintegration testing in the pharmaceutical industry, according to United States Pharmacopeia
(USP) chapter 〈701〉, only gives information about the duration of the tablet disintegration process. This infor-
mation is subjective, variable, and prone to human error due to manual or physical data collection methods via
the human eye or contact disks. To lessen the data integrity risk associated with this process, efforts have been
made to automate the analysis of the disintegration process using digital lens and other imaging technologies.
This would provide a non-invasive method to quantitatively determine disintegration time through computer
algorithms. The main challenges associated with developing such a system involve visualization of tablet pieces
through cloudy and turbid liquid. The Computer Vision for Disintegration (CVD) system has been developed to
be used along with traditional pharmaceutical disintegration testing devices to monitor tablet pieces and
distinguish them from the surrounding liquid. The software written for CVD utilizes data captured by cameras or
other lenses then uses mobile SSD and CNN, with an OpenCV and FRCNN machine learning model, to analyze
and interpret the data. This technology is capable of consistently identifying tablets with ≥ 99.6% accuracy. Not
only is the data produced by CVD more reliable, but it opens the possibility of a deeper understanding of
disintegration rates and mechanisms in addition to duration. keywords: از هم پاشیدگی | اشکال خوراکی جامد | تست تجزیه | یادگیری ماشین | شبکه های عصبی | Disintegration | Oral Solid Dosage Forms | Disintegration Test | Machine Learning | Neural Networks |
مقاله انگلیسی |
5 |
High-Performance Reservoir Computing With Fluctuations in Linear Networks
محاسبات مخزن با کارایی بالا با نوسانات در شبکه های خطی-2022 Reservoir computing has emerged as a powerful
machine learning paradigm for harvesting nontrivial information
processing out of disordered physical systems driven by sequential inputs. To this end, the system observables must become
nonlinear functions of the input history. We show that encoding
the input to quantum or classical fluctuations of a network of
interacting harmonic oscillators can lead to a high performance
comparable to that of a standard echo state network in several
nonlinear benchmark tasks. This equivalence in performance
holds even with a linear Hamiltonian and a readout linear in the
system observables. Furthermore, we find that the performance of
the network of harmonic oscillators in nonlinear tasks is robust to
errors both in input and reservoir observables caused by external
noise. For any reservoir computing system with a linear readout,
the magnitude of trained weights can either amplify or suppress
noise added to reservoir observables. We use this general result to
explain why the oscillators are robust to noise and why having
precise control over reservoir memory is important for noise
robustness in general. Our results pave the way toward reservoir
computing harnessing fluctuations in disordered linear systems.
Index Terms: Dynamical systems | machine learning | quantum mechanics | recurrent neural networks | reservoir computing | supervised learning. |
مقاله انگلیسی |
6 |
High-Stability Cryogenic System for Quantum Computing With Compact Packaged Ion Traps
سیستم برودتی با پایداری بالا برای محاسبات کوانتومی با تله های یونی بسته بندی شده فشرده-2022 Cryogenic environments benefit ion trapping experiments by offering lower motional heating
rates, collision energies, and an ultrahigh vacuum (UHV) environment for maintaining long ion chains
for extended periods of time. Mechanical vibrations caused by compressors in closed-cycle cryostats can
introduce relative motion between the ion and the wavefronts of lasers used to manipulate the ions. Here,
we present a novel ion trapping system where a commercial low-vibration closed-cycle cryostat is used
in a custom monolithic enclosure. We measure mechanical vibrations of the sample stage using an optical
interferometer, and observe a root-mean-square relative displacement of 2.4 nm and a peak-to-peak displacement of 17 nm between free-space beams and the trapping location. We packaged a surface ion trap
in a cryopackage assembly that enables easy handling while creating a UHV environment for the ions. The
trap cryopackage contains activated carbon getter material for enhanced sorption pumping near the trapping
location, and source material for ablation loading. Using 171Yb+ as our ion, we estimate the operating
pressure of the trap as a function of package temperature using phase transitions of zig-zag ion chains as a
probe. We measured the radial mode heating rate of a single ion to be 13 quanta/s on average. The Ramsey
coherence measurements yield 330-ms coherence time for counter-propagating Raman carrier transitions
using a 355-nm mode-locked pulse laser, demonstrating the high optical stability.
INDEX TERMS: Optomechanical design | quantum computing | trapped ions. |
مقاله انگلیسی |
7 |
Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks
چشم انداز کامپیوتری برای تجزیه و تحلیل آناتومیکی تجهیزات در پروژه های زیرساختی عمرانی: نظریه پردازی توسعه شبکه های عصبی عمیق مبتنی بر رگرسیون-2022 There is high demand for heavy equipment in civil infrastructure projects and their performance is a determinant
of the successful delivery of site operations. Although manufacturers provide equipment performance hand-
books, additional monitoring mechanisms are required to depart from measuring performance on the sole basis
of unit cost for moved materials. Vision-based tracking and pose estimation can facilitate site performance
monitoring. This research develops several regression-based deep neural networks (DNNs) to monitor equipment
with the aim of ensuring safety, productivity, sustainability and quality of equipment operations. Annotated
image libraries are used to train and test several backbone architectures. Experimental results reveal the pre-
cision of DNNs with depthwise separable convolutions and computational efficiency of DNNs with channel
shuffle. This research provides scientific utility by developing a method for equipment pose estimation with the
ability to detect anatomical angles and critical keypoints. The practical utility of this study is the provision of
potentials to influence current practice of articulated machinery monitoring in projects. keywords: هوش مصنوعی (AI) | سیستم های فیزیکی سایبری | معیارهای ارزیابی خطا | طراحی و آزمایش تجربی | تخمین ژست کامل بدن | صنعت و ساخت 4.0 | الگوریتم های یادگیری ماشین | معماری های ستون فقرات شبکه | Artificial intelligence (AI) | Cyber physical systems | Error evaluation metrics | Experimental design and testing | Full body pose estimation | Industry and construction 4.0 | Machine learning algorithms | Network backbone architectures |
مقاله انگلیسی |
8 |
Using social media photos and computer vision to assess cultural ecosystem services and landscape features in urban parks
استفاده از عکس های رسانه های اجتماعی و بینایی کامپیوتری برای ارزیابی خدمات اکوسیستم فرهنگی و ویژگی های چشم انداز در پارک های شهری-2022 Urban parks are important public places that provide an opportunity for city dwellers to interact with nature. In
recent years, social media data have become a promising data source for the assessment of cultural ecosystem
services (CES) and landscape features in urban parks. However, it is a challenging task to identify and classify the
CES and landscape features from social media photos by manual content analysis. In addition, relatively few
studies focused on the differences in landscape preferences between tourists and locals in urban parks. In this
study, we used geotagged social media photos from Flickr and computer vision methods (scene recognition,
image clustering and image labeling) based on the convolutional neural networks (CNN) and the Google Cloud
Vision platform to assess the spatial preferences and landscape preferences (cultural ecosystem services and
landscape features) of tourists and locals in the urban parks of Brussels. The spatial analysis results showed that
the tourists’ photos were spatially concentrated on well-known parks located in the city center while the locals’
photos were rather spatially dispersed across all parks of the city. We identified 10 main landscape themes
(corresponding to 4 CES categories and 10 landscape feature categories) from 20 image clusters by automated
image analysis on social media photos. We also noticed that tourists paid more attention to the place identity
featured by symbolic sculptures and buildings, while locals showed more interest in local species of plants,
flowers, insects, birds, and animals. This research contributes to social media-based user preferences analysis and
CES assessment, which could provide insights for urban park planning and tourism management. keywords: داده های رسانه های اجتماعی | خدمات اکوسیستم فرهنگی | ویژگی های چشم انداز | پارک های شهری | بینایی کامپیوتر | Social media data | Cultural ecosystem services | Landscape features | Urban parks | Computer vision |
مقاله انگلیسی |
9 |
In-situ optimization of thermoset composite additive manufacturing via deep learning and computer vision
بهینه سازی درجای تولید افزودنی کامپوزیت ترموست از طریق یادگیری عمیق و بینایی کامپیوتری-2022 With the advent of extrusion additive manufacturing (AM), fabrication of high-performance thermoset com-
posites without the need of tooling has become a reality. However, finding an optimal set of printing parameters
for these thermoset composites during extrusion requires tedious experimentation as composite ink properties
can vary significantly with respect to environmental parameters such as temperature and relative humidity.
Addressing this challenge, this study presents a novel optimization framework that utilizes computer vision and
deep learning (DL) to optimize the calibration and printing processes of thermoset composite AM. Unlike
traditional DL models where printing parameters are determined prior to printing, our proposed framework
dynamically and autonomously adjusts the printing parameters during extrusion. A novel DL integrated extrusion
AM system is developed to determine the optimal printing parameters including print speed, road width, and
layer height for a given composite ink. This closed loop system is consisted of a computer communicating with an
extrusion AM system, a camera to perform in-situ imaging and several high accuracy convolution neural net-
works (CNNs) selecting the ideal process parameters for composite AM. The results show that our proposed
process optimization framework was able to autonomously determine these parameters for a carbon fiber-
composite ink. Consequently, specimens with complex geometries could be fabricated without visible defects
and with maximum fiber alignment and thus enhancing the mechanical performance of the specimen’s com-
posite material. Moreover, our proposed framework minimizes a labor-intensive procedure required to additively
manufacture thermoset composites by optimizing the extrusion process without any user intervention. keywords: یادگیری عمیق | بینایی کامپیوتر | اکستروژن | پرینت سه بعدی کامپوزیت | Deep learning | Computer vision | Extrusion | Composite 3D printing |
مقاله انگلیسی |
10 |
تکنیک ها و کاربردهای توالی یابی RNA تک سلولی در تحقیقات تکوین تخمدان و بیماری های مرتبط
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 23 تخمدان یک ارگان بسیار سازمان یافته متشکل از سلول های زایا و انواع مختلف سلول های سوماتیک است که ارتباطات آنها منجر به تکوین تخمدان و تولید تخمک های عملکردی می شود. تفاوت بین سلول های منفرد ممکن است اثرات عمیقی بر عملکرد تخمدان داشته باشد. تکنیکهای توالییابی RNA تک سلولی، رویکردهای امیدوارکنندهای برای کشف ترکیب انواع سلولی ارگانیسم ها، پویایی رونوشتها یا ترنسکریپتوم، شبکه تنظیمکننده بین ژنها و مسیرهای سیگنالدهی بین انواع سلولها در وضوح تک سلولی هستند. در این مطالعه، ما یک مرور کلی از تکنیکهای توالییابی RNA تک سلولی موجود از جمله Smart-seq2 و Drop-seq و همچنین کاربردهای آنها در تحقیقات بیولوژیکی و بالینی ارائه میکنیم تا درک بهتری از مکانیسمهای مولکولی زیربنای تکوین تخمدان و بیماری های مرتبط با آن ارائه کنیم.
کلیدواژگان: تکوین تخمدانن | توالی یابی RNA تک سلولی | شبکه تنظیمی | بیماری ها |
مقاله ترجمه شده |