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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91 |
A turnaround control system to automatically detect and monitor the time stamps of ground service actions in airports: A deep learning and computer vision based approach
یک سیستم کنترل چرخش برای شناسایی و نظارت خودکار بر مهرهای زمانی اقدامات خدمات زمینی در فرودگاهها: یک رویکرد مبتنی بر یادگیری عمیق و بینایی کامپیوتری-2022 As it is widely known, several ground services are provided by the airports for the domestic and international
flights of the commercial passenger aircraft. Some of these services are conducted during the period called
as the turnaround which starts with the parking of the aircraft in the aprons before the flight and ends with
their leave from the aprons for the flight. Turnaround processes achieved in short time periods allow using the
limited airport resources including the service vehicles and staff effectively. In addition, commercial reputation
losses and financial losses that may arise from delays can be reduced as well as the delay-associated turnaround
penalties. In this article, a deep learning and computer vision based system that detects and allows monitoring
the airport service actions is proposed. The proposed system is capable of analyzing all the primary ground
services for an aircraft parking on its apron by employing the RGB video frame sequences obtained from a
single fixed camera focusing on the apron. In the service detection and analysis modules of the proposed airport
ground service analysis system, some deep learning-based subsystems and in-house-developed algorithms were
included and utilized. For the training of the machine learning models, a study-specific dataset was used and
the constructed learning models were evaluated on real-life cases. Experimental results obtained as a result of
the performance evaluations show that the proposed system is quite successful with precision rates over 90%
in the detection and analysis of the airport ground services. This study is one of the limited research studies
in which deep learning and computer vision techniques have been applied to detect and analyze the ground
service actions. The proposed system is also capable of real-time data processing/analysis and concurrent
service action monitoring. Furthermore, it allows monitoring when the service is received by stamping the
times of service start/end. In a consideration of industrial relevance or operational perspective, such a system
may facilitate the airport ground service management noticeably and reduce the delay-associated costs caused
by the timing of the ground services.
keywords: سیستم کنترل گردش فرودگاه | نظارت بر حرکت چرخشی | شناسایی وسایل نقلیه فرودگاهی | تشخیص چرخش | خدمات فرودگاهی | Airportturnaroundcontrolsystem | Turnaroundactionmonitoring | Airportvehicledetection | Turnaroundactiondetection | Airportgroundservices |
مقاله انگلیسی |
92 |
Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm
پیاده سازی انتخاب ویژگی گراف-نظری توسط الگوریتم بهینه سازی تقریبی کوانتومی-2022 Feature selection plays a significant role in computer science; nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k -subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(pn2) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.
Index Terms: Feature selection | graph theory | parameterized quantum circuit | quantum approximation optimization algorithm | quantum computing. |
مقاله انگلیسی |
93 |
Survey on deep learning based computer vision for sonar imagery
مروری بر بینایی کامپیوتری مبتنی بر یادگیری عمیق برای تصاویر سونار-2022 Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based,
approaches for a long time. Over the past 15 years, however, the application of deep learning in this research
field has constantly grown. This paper gives a broad overview of past and current research involving deep
learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture
sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural
networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up
to four layers outperform conventional methods.
The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods. keywords: یادگیری عمیق | تصویربرداری سوناری | کامپیوتری | تشخیص خودکار هدف | Statusquoreview | Deeplearning | Sonarimagery | Computervision | Automatictargetrecognition | Statusquoreview |
مقاله انگلیسی |
94 |
Incompressibility of Classical Distributions
تراکم ناپذیری توزیع های کلاسیک-2022 In blind compression of quantum states, a sender
Alice is given a specimen of a quantum state ρ drawn from
a known ensemble (but without knowing what ρ is), and she
transmits sufficient quantum data to a receiver Bob so that
he can decode a near perfect specimen of ρ. For many such
states drawn iid from the ensemble, the asymptotically achievable
rate is the number of qubits required to be transmitted per
state. The Holevo information is a lower bound for the achievable rate, and is attained for pure state ensembles, or in the
related scenario of entanglement-assisted visible compression of
mixed states wherein Alice knows what state is drawn. In this
paper, we prove a general and robust lower bound on the
achievable rate for ensembles of classical states, which holds
even in the least demanding setting when Alice and Bob share
free entanglement and a constant per-copy error is allowed.
We apply the bound to a specific ensemble of only two states
and prove a near-maximal separation (saturating the dimension
bound in leading order) between the best achievable rate and
the Holevo information for constant error. This also implies
that the ensemble is incompressible – compression does not
reduce the communication cost by much. Since the states are
classical, the observed incompressibility is not fundamentally
quantum mechanical. We lower bound the difference between
the achievable rate and the Holevo information in terms of
quantitative limitations to clone the specimen or to distinguish
the two classical states.
Index Terms— Blind compression | classical distributions | quantum states | free entanglement | Holevo information | constant error | incompressibility |
مقاله انگلیسی |
95 |
Automated bridge surface crack detection and segmentation using computer vision-based deep learning model
تشخیص و تقسیم خودکار ترک سطح پل با استفاده از مدل یادگیری عمیق مبتنی بر بینایی کامپیوتری-2022 Bridge maintenance will become a widespread trend in the engineering industry as the number of bridges
grows and time passes. Cracking is a common problem in bridges with concrete structures. Allowing it to
expand will result in significant economic losses and accident risks This paper proposed an automatic detection
and segmentation method of bridge surface cracks based on computer vision deep learning models. First, a
bridge surface crack detection and segmentation dataset was established. Then, according to the characteristics
of the bridge, we improved the You Only Look Once (YOLO) algorithm for bridge surface crack detection.
The improved algorithm was defined as CR-YOLO, which can identify cracks and their approximate locations
from multi-object images. Subsequently, the PSPNet algorithm was improved to segment the bridge cracks
from the non-crack regions to avoid the visual interference of the detection algorithm. Finally, we deployed
the proposed bridge crack detection and segmentation algorithm in an edge device. The experimental results
show that our method outperforms other baseline methods in generic evaluation metrics and has advantages
in Model Size(MS) and Frame Per Second (FPS).
keywords: Bridge crack Crack detection | Crack segmentation | Deep learning | Computer vision |
مقاله انگلیسی |
96 |
Intelligent Reflecting Surface (IRS) Allocation Scheduling Method Using Combinatorial Optimization by Quantum Computing
روش زمانبندی تخصیص سطح بازتابنده هوشمند (IRS) با استفاده از بهینهسازی ترکیبی توسط محاسبات کوانتومی-2022 Intelligent Reflecting Surface (IRS) significantly improves the energy utilization efficiency in
6th generation cellular communication systems. Here, we consider a system with multiple IRS and users, with
one user communicating via several IRSs. In such a system, the user to which an IRS is assigned for each unit
time must be determined to realize efficient communication. The previous studies on the optimization of various parameters for IRS based wireless systems did not consider the optimization of such IRS allocation scheduling. Therefore, we propose an IRS allocation scheduling method that limits the number of users who allocate
each IRS to one unit time and sets the reflection coefficients of the IRS specifically to the assigned user resulting
in the maximum IRS array gain. Additionally, as the proposed method is a combinatorial optimization problem,
we develop a quadratic unconstrained binary optimization formulation to solve this using quantum computing.
This will lead to the optimization of the entire system at a high speed and low power consumption in the future.
Using computer simulation, we clarified that the proposed method realizes a more efficient communication
compared to the method where one IRS is simultaneously used by multiple users.
INDEX TERMS: Intelligent reflecting surface | IRS allocation scheduling | quantum computing | quantum annealing | combinatorial optimization |
مقاله انگلیسی |
97 |
Computer vision for package tracking on omnidirectional wheeled conveyor: Case study
بینایی کامپیوتری برای ردیابی بسته در نوار نقاله چرخدار همه جهته: مطالعه موردی-2022 In this paper, a real-time camera tracking system for package transportation on omnidirectional wheeled
conveyor is presented. The camera tracking system is integrated with a closed-loop controller for packages
path planning. No additional sensors are used for the controller implementation, only a 2-D Camera. The
package’s position and orientation are detected by the camera tracking system in real-time. Two proposed
systems are presented, System I is implemented using the conventional image processing technique threshold
method while System II is implemented using computer vision. In System II, three computer vision models
were evaluated: Detectron2, YOLOv5 and Faster R-CNN. Experimental results in real-time show that System
I have lower accuracy rate 85.7% compared to System II which reported 98% and 88.1% for YOLOv5 and
Detectron2, respectively. YOLOv5 reported the best results among the computer vision models with 1% missing
rate, 45.5 FPS and average precision of 99.8%.
keywords: Camera tracking | Computer vision | Deep learning | Hexagonal conveyor | Image processing | Omnidirectional wheels | YOlO |
مقاله انگلیسی |
98 |
Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
لایه VQE: یک رویکرد متغیر برای بهینه سازی ترکیبی در کامپیوترهای کوانتومی پر سر و صدا-2022 Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high noise or
error rates. In this article, inspired by the variational quantum eigensolver (VQE), we propose an iterative
layer VQE (L-VQE) approach. We present a large-scale numerical study, simulating circuits with up to
40 qubits and 352 parameters, that demonstrates the potential of the proposed approach. We evaluate
quantum optimization heuristics on the problem of detecting multiple communities in networks, for which we
introduce a novel qubit-frugal formulation. We numerically compare L-VQE with the quantum approximate
optimization algorithm (QAOA) and demonstrate that QAOA achieves lower approximation ratios while
requiring significantly deeper circuits. We show that L-VQE is more robust to finite sampling errors and has
a higher chance of finding the solution as compared with standard VQE approaches. Our simulation results
show that L-VQE performs well under realistic hardware noise.
INDEX TERMS: Combinatorial optimization | hybrid quantum-classical algorithm | quantum optimization. |
مقاله انگلیسی |
99 |
Computer vision for solid waste sorting: A critical review of academic research
بینایی کامپیوتری برای تفکیک زباله جامد: مروری انتقادی تحقیقات دانشگاهی-2022 Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer
vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-
enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little atten-
tion has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To
address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled
MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are
introduced and compared. The distribution of academic research outputs is also examined from the aspects of
waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of
shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is
increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were un-
evenly distributed in different sectors such as household, commerce and institution, and construction. Too often,
researchers reported some preliminary studies using simplified environments and artificially collected data.
Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in
industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested
researchers to train and evaluate their CV algorithms. keywords: زباله جامد شهری | تفکیک زباله | بینایی ماشین | تشخیص تصویر | یادگیری ماشین | یادگیری عمیق | Municipal solid waste | Waste sorting | Computer vision | Image recognition | Machine learning | Deep learning |
مقاله انگلیسی |
100 |
Learning Quantum Circuits of Some T Gates
آموزش مدارهای کوانتومی برخی از T Gates-2022 In this paper, we study the problem of learning
an unknown quantum circuit of a certain structure. If the
unknown target is an n-qubit Clifford circuit, we devise an
efficient algorithm to reconstruct its circuit representation by
using O(n2) queries to it. For decades, it has been unknown how
to handle circuits beyond the Clifford group since the stabilizer
formalism cannot be applied in this case. Herein, we study
quantum circuits of T -depth one on the computational basis.
We show that the output state of a T -depth one circuit can
be represented by a stabilizer pseudomixture with a specific
algebraic structure. Using Pauli and Bell measurements on copies
of the output states, we can generate a hypothesis circuit that is
equivalent to the unknown target circuit on computational basis
states as input. If the number of T gates of the target is of
the order O(log n), our algorithm requires O(n2) queries to it
and produces its equivalent circuit representation on the computational basis in time O(n3). Using further additional O(43n)
classical computations, we can derive an exact description of the
target for arbitrary input states. Our results greatly extend the
previously known facts that stabilizer states can be efficiently
identified based on the stabilizer formalism.
Index Terms: Stabilizer formalism | Clifford circuits | T -depth | stabilizer pseudomixture. |
مقاله انگلیسی |