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).
ردیف | عنوان | نوع |
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81 |
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 |
82 |
How to Build a Scalable Quantum Controller
چگونه یک کنترلر کوانتومی مقیاس پذیر بسازیم-2022 We discuss quantum computers from the
perspective of one of their major building
blocks, the hardware controller, explaining how
it fits into the computer and the requirements
and challenges it poses for engineers and
scientists.
|
مقاله انگلیسی |
83 |
Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a Computer Vision System
تخمین غیر مخرب و بدون تماس محتویات کلروفیل و آمونیاک در برگ های موشک تازه برش خورده بسته بندی شده توسط یک سیستم کامپیوتر ویژن-2022 Computer Vision Systems (CVS) offer a non-destructive and contactless tool to assign visual quality level to fruit
and vegetables and to estimate some of their internal characteristics. The innovative CVS described in this paper
exploits the combination of image processing techniques and machine learning models (Random Forests) to
assess the visual quality and predict the internal traits on unpackaged and packaged rocket leaves. Its perfor-
mance did not depend on the cultivation system (traditional soil or soilless). The same CVS, exploiting its ma-
chine learning components, was able to build effective models for either the classification problem (visual quality
level assignment) and the regression problems (estimation of senescence indicators such as chlorophyll and
ammonia contents) just by changing the training data. The experiments showed a negligible performance loss on
packaged products (Pearson’s linear correlation coefficient of 0.84 for chlorophyll and 0.91 for ammonia) with
respect to unpackaged ones (0.86 for chlorophyll and 0.92 for ammonia). Thus, the non-destructive and con-
tactless CVS represents a valid alternative to destructive, expensive and time-consuming analyses in the lab and
can be effectively and extensively used along the whole supply chain, even on packaged products that cannot be
analyzed using traditional tools. keywords: Contactless quality level assessment | Diplotaxis tenuifolia L | Image analysis | Packaged vegetables | Senescence indicators prediction |
مقاله انگلیسی |
84 |
Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling
تکنیکهای بهینهسازی ترکیبی کلاسیک-کوانتومی برای حل مسائل برنامهنویسی عدد صحیح مختلط در زمانبندی تولید-2022 Quantum computing (QC) holds great promise to open up a new era of computing and has been
receiving significant attention recently. To overcome the performance limitations of near-term QC, utilizing
the current quantum computers to complement classical techniques for solving real-world problems is of
utmost importance. In this article, we develop QC-based solution strategies that exploit quantum annealing
and classical optimization techniques for solving large-scale scheduling problems in manufacturing systems.
The applications of the proposed algorithms are illustrated through two case studies in production scheduling.
First, we present a hybrid QC-based solution approach for the job-shop scheduling problem. Second, we propose a hybrid QC-based parametric method for the multipurpose batch scheduling problem with a fractional
objective. The proposed hybrid algorithms can tackle optimization problems formulated as mixed-integer
linear and mixed-integer fractional programs, respectively, and provide feasibility guarantees. Performance
comparison between state-of-the-art exact and heuristic solvers and the proposed QC-based hybrid solution
techniques is presented for both job-shop and batch scheduling problems. Unlike conventional classical
solution techniques, the proposed hybrid frameworks harness quantum annealing to supplement established
deterministic optimization algorithms and demonstrate performance efficiency over standard off-the-shelf
optimization solvers.
INDEX TERMS: Hybrid techniques | optimization | quantum annealing | quantum computing (QC) | scheduling. |
مقاله انگلیسی |
85 |
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 |
86 |
Hybrid CV-DV Quantum Communications and Quantum Networks
ارتباطات کوانتومی ترکیبی CV-DV و شبکه های کوانتومی-2022 Quantum information processing (QIP) opens new opportunities for high-performance
computing, high-precision sensing, and secure communications. Among various QIP features, the entanglement is a unique one. To take full advantage of quantum resources, it will be necessary to interface quantum
systems based on different encodings of information both discrete and continuous. The goal of this paper
is to lay the groundwork for the development of a robust and efficient hybrid continuous variable-discrete
variable (CV-DV) quantum network, enabling the distribution of a large number of entangled states over
hybrid DV-CV multi-hop nodes in an arbitrary topology. The proposed hybrid quantum communication
network (QCN) can serve as the backbone for a future quantum Internet, thus providing extensive longterm impacts on the economy and national security through QIP, distributed quantum computing, quantum
networking, and distributed quantum sensing. By employing the photon addition and photon subtraction
modules we describe how to generate the hybrid DV-CV entangled states and how to implement their
teleportation and entanglement swapping through entangling measurements. We then describe how to
extend the transmission distance between nodes in hybrid QCN by employing macroscopic light states,
noiseless amplification, and reconfigurable quantum LDPC coding. We further describe how to enable
quantum networking and distributed quantum computing by employing the deterministic cluster state
concept introduced here. Finally, we describe how the proposed hybrid CV-DV states can be used in an
entanglement-based hybrid QKD.
INDEX TERMS: Entanglement | photon addition | photon subtraction | hybrid CV-DV entangled states | teleportation | entanglement swapping | entanglement distribution | hybrid quantum communication networks | entanglement-based hybrid QKD. |
مقاله انگلیسی |
87 |
Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network
طبقه بندی مبتنی بر بینایی کامپیوتری شدت پاشش بتن با استفاده از ماشین تقویت کننده گرادیان قویا بهینه شده فراابتکاری و شبکه عصبی پیچیده عمیق-2022 This paper presents alternative solutions for classifying concrete spall severity based on computer vision ap-
proaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are
employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the
properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center sym-
metric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern
(ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is
enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification
directly without the need for texture descriptors. Experimental results with a dataset containing real-world
concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the
Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a
classification accuracy rate of roughly 99%. keywords: شدت ریزش بتن | دستگاه افزایش گرادیان | الگوی باینری محلی | فراماسونری | یادگیری عمیق | Concrete spall severity | Gradient boosting machine | Local binary pattern | Metaheuristic | Deep learning |
مقاله انگلیسی |
88 |
Hybrid Quantum-Classical Neural Network for Cloud-Supported In-Vehicle Cyberattack Detection
شبکه عصبی ترکیبی کوانتومی کلاسیک برای تشخیص حمله سایبری در خودرو با پشتیبانی از ابر-2022 A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously
compared to classical computers. In a cloud-supported cyber−physical system environment, running a machine learning
application in quantum computers is often difficult, due to the existing limitations of the current quantum devices. However,
with the combination of quantum-classical neural networks (NN), complex and high-dimensional features can be extracted
by the classical NN to a reduced but more informative feature space to be processed by the existing quantum computers.
In this study, we developed a hybrid quantum-classical NN to detect an amplitude shift cyberattack on an in-vehicle
controller area network dataset. We showed that by using the hybrid quantum-classical NN, it is possible to achieve an
attack detection accuracy of 94%, which is higher than a long short-term memory NN (88%) or quantum NN alone (62%).
Index Terms: Sensor applications, clouds | cyberattack | sensor applications | quantum computing | quantum neural network (NN). |
مقاله انگلیسی |
89 |
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 |
90 |
Implementation of Quantum Annealing: A Systematic Review
پیاده سازی آنیل کوانتومی: مروری سیستماتیک-2022 Quantum annealing is a quantum computing approach widely used for optimization and
probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based
quantum computing models. The method is observed to have a significant impact on different fields such as
machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security,
portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of
research development trends in the field of quantum annealing and analyzes how it has been implemented
in different problem domains. The results are expected to serve as the basis to identify the opportunities
and challenges of research related to its implementation. The main contribution of this systematic review
is to summarize different implementations of quantum annealing. It is also to analyze the prospect and
opportunities in one of the problem domains with the greatest interest which is machine learning.
INDEX TERMS: Quantum annealing | implementation | review. |
مقاله انگلیسی |