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1 |
Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105 The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database
systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM)
systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and
control systems are operated by different units at NYCDOT as an independent database or operation system. New York City
experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial
systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all
users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to
develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City.
This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS,
involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and
wireless communication features for data collection and reduction; 2) interface development among existing database and control
systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study
paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from
loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi
and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day.
GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept.
© 2014 The Authors. Published by Elsevier B.V.
Selection and peer-review under responsibility of Elhadi M. Shakshu
Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data |
مقاله انگلیسی |
2 |
Exploring Potential Applications of Quantum Computing in Transportation Modelling
بررسی کاربردهای بالقوه محاسبات کوانتومی در مدل سازی حمل و نقل-2022 The idea that quantum effects could be harnessed
to allow faster computation was first proposed by Feynman.
As of 2020 we appear to have achieved ‘quantum supremacy’,
that is, a quantum computer that performs a given task faster
than its classical counterpart. This paper examines some possibilities opened up by potential future application of quantum
computing to transportation simulation and planning. To date,
no such research was found to exist, therefore we begin with
an introduction to quantum computing for the programmers
of transport models. We discuss existing quantum computing
research relevant to transportation, finding developments in
network analysis, shortest path computation, multi-objective
routing, optimization and calibration – of which the latter three
appear to offer the greater promise in future research. Two
examples are developed in greater detail, (1) an application of
Grover’s quantum algorithm for extracting the mean, which has
general applicability towards summarizing distributions which
are expensive to compute classically, is applied to an assignment
or betweenness model - quantum speedup is elusive in the
general case but achievable when trading speed for accuracy
for limited outputs; (2) quantum optimization is applied to an
activity-based model, giving a theoretically quadratic speedup.
Recent developments notwithstanding, implementation of quantum transportation algorithms will for the foreseeable future
remain a challenge due to space overheads imposed by the
requirement for reversible computation.
Index Terms: Quantum computing | assignment | betweenness | flows, activity models | tour models. |
مقاله انگلیسی |
3 |
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 |
4 |
Learning Quantum Drift-Diffusion Phenomenon by Physics-Constraint Machine Learning
یادگیری پدیده رانش کوانتومی- انتشار با یادگیری ماشین محدودیت فیزیک-2022 Recently, deep learning (DL) is widely used to
detect physical phenomena and has obtained encouraging results.
Several works have shown that it can learn quantum phenomenon. Subsequently, quantum machine learning (QML) has
been paid more attention by academia and industry. Quantum
drift-diffusion (QDD) is a commonplace physical phenomenon,
which is a macroscopic description of electrons and holes in
a semiconductor. They are commonly used to attain an understanding of the property of semiconductor devices in physics
and engineering. We are motivated by the relaxation-time limit
from the quantum-Navier-Stokes-Poisson system (QNSP) to the
QDD equation and the existence of finite energy weak solutions
to the QDD equation has been proved. Therefore, in this work,
the quantum drift-diffusion learning neural network (QDDLNN)
is proposed to investigate the quantum drift phenomena from
limited observations. Furthermore, a piece of numerical evidence
is found that the NNs can describe quantum transport phenomena by simulating the quantum confinement transport equationquantum Navier-Stokes equation.
Index Terms: Quantum machine learning | quantum drift diffusion | physical-information learning | quantum transport | quantum fluid model. |
مقاله انگلیسی |
5 |
Computer vision technique for freshness estimation from segmented eye of fish image
تکنیک بینایی کامپیوتری برای تخمین تازگی از چشم تقسیم شده تصویر ماهی-2022 Preserving the quality of fish is a challenging task. Several different cooling methods and materials are used
during their storage, transportation purpose. These are responsible factors that decide the freshness of a post
harvested fish. In this proposed algorithm, a computer vision-based technique is developed to predict the
freshness level of fish from its image. Eyes of the fish are considered as the region of interest, as a good corre-
lation has been observed between the colour of the eye and different duration of storage day. It is segmented
from the image of a fish sample and then a strategic framework is used for extraction of the discriminatory
features. These extracted features show a degradation pattern which acts as an indicative parameter to determine
the level of freshness of sample of fish. The proposed method provides a recognition accuracy of 96.67%. The
experimental results indicate that this is an efficient and non-destructive methodology for detecting the fish
freshness. The high accuracy of freshness detection and low computation time makes this non-destructive
methodology efficient for real-world usage in the fish industry and market. keywords: استخراج ویژگی | چشم ماهی | تکنیک های پردازش تصویر | سطح تازگی | تقسیم بندی | Feature extraction | Fish eye | Image processing techniques | Level of freshness | Segmentation |
مقاله انگلیسی |
6 |
An overview of Human Action Recognition in sports based on Computer Vision
مروری بر تشخیص کنش انسانی در ورزش بر اساس بینایی کامپیوتری-2022 Human Action Recognition (HAR) is a challenging task used in sports such as volleyball, basketball, soccer, and
tennis to detect players and recognize their actions and teams activities during training, matches, warm-ups, or
competitions. HAR aims to detect the person performing the action on an unknown video sequence, determine the
actions duration, and identify the action type. The main idea of HAR in sports is to monitor a players performance, that is, to detect the player, track their movements, recognize the performed action, compare various
actions, compare different kinds and skills of acting performances, or make automatic statistical analysis.
As an action that can occur in the sports field refers to a set of physical movements performed by a player in
order to complete a task using their body or interacting with objects or other persons, actions can be of different
complexity. Because of that, a novel systematization of actions based on complexity and level of performance and
interactions is proposed.
The overview of HAR research focuses on various methods performed on publicly available datasets, including actions of everyday activities. That is just a good starting point; however, HAR is increasingly represented in sports and is becoming more directed towards recognizing similar actions of a particular sports domain. Therefore, this paper presents an overview of HAR applications in sports primarily based on Computer Vision as the main contribution, along with popular publicly available datasets for this purpose. keywords: یادگیری ماشین | تشخیص عمل انسانی | سیستم سازی اقدام | مجموعه داده های ورزشی | شناخت کنش انسان در ورزش | ورزش | Machine learning | Human Action Recognition | Action systematization | Sports dataset | Human action recognition in sports | Sport |
مقاله انگلیسی |
7 |
A computer vision-based method to identify the international roughness index of highway pavements
یک روش مبتنی بر بینایی کامپیوتری برای شناسایی شاخص ناهمواری بینالمللی روسازی بزرگراه-2022 The International Roughness Index (IRI) is one of the most critical parameters in the field of pavement performance management. Traditional methods for the measurement of IRI rely on expensive instrumented vehicles and well-trained professionals. The equipment and labor costs of traditional measurement methods limit the timely updates of IRI on the pavements. In this article, a novel imaging-based Deep Neural Network (DNN) model, which can use pavement photos to directly identify the IRI values, is proposed. This model proved that it is possible to use 2-dimensional (2D) images to identify the IRI other than the typically used vertical accelerations or 3-dimensional (3D) images. Due to the fast growth in photography equipment, small and convenient sports action cameras such as the GoPro Hero series are able to capture smooth videos at a high framerate with built-in electronic image stabilization systems. These significant improvements make it not only more convenient to collect high-quality 2D images, but also easier to process them than vibrations or accelerations. In the proposed method, 15% of the imaging data were randomly selected for testing and had never been touched during the training steps. The testing results showed an averaged coefficient of determination (R square) of 0.6728 and an averaged root mean square error (RMSE) of 0.50.
keywords: شاخص بین المللی زبری | شبکه عصبی عمیق | بینایی کامپیوتر | ارزیابی وضعیت روسازی | International roughness index | Deep neural network | Computer vision | Pavement condition assessment |
مقاله انگلیسی |
8 |
Random Telegraph Noise of a 28-nm Cryogenic MOSFET in the Coulomb Blockade Regime
نویز تصادفی تلگراف یک ماسفت برودتی 28 نانومتری در رژیم بلوک کولن-2022 We observe rich phenomena of two-level random telegraph noise (RTN) from a commercial bulk 28-nm
p-MOSFET (PMOS) near threshold at 14 K, where a Coulomb
blockade (CB) hump arises from a quantum dot (QD) formed
in the channel. Minimum RTN is observed at the CB hump
where the high-current RTN level dramatically switches to
the low-current level. The gate-voltage dependence of the
RTN amplitude and power spectral density match well with
the transconductance from the DC transfer curve in the CB
hump region. Our work unequivocally captures these QD
transport signatures in both current and noise, revealing
quantum confinement effects in commercial short-channel
PMOS even at 14 K, over 100 times higher than the typical dilution refrigerator temperatures of QD experiments
(<100 mK). We envision that our reported RTN characteristics rooted from the QD and a defect trap would be
more prominent for smaller technology nodes, where the
quantum effect should be carefully examined in cryogenic
CMOS circuit designs.
Index Terms: 28-nm CMOS | cryogenic CMOS | random telegraph noise | quantum dot | Coulomb blockade. |
مقاله انگلیسی |
9 |
Solving Vehicle Routing Problem Using Quantum Approximate Optimization Algorithm
حل مسئله مسیریابی خودرو با استفاده از الگوریتم بهینه سازی تقریبی کوانتومی-2022 Intelligent transportation systems (ITS) are a critical component of Industry 4.0 and 5.0, particularly having
applications in logistic management. One of their crucial utilization is in supply-chain management and scheduling for
optimally routing transportation of goods by vehicles at a given
set of locations. This paper discusses the broader problem of
vehicle traffic management, more popularly known as the Vehicle
Routing Problem (VRP), and investigates the possible use of
near-term quantum devices for solving it. For this purpose,
we give the Ising formulation for VRP and some of its constrained
variants. Then, we present a detailed procedure to solve VRP
by minimizing its corresponding Ising Hamiltonian using a
hybrid quantum-classical heuristic called Quantum Approximate
Optimization Algorithm (QAOA), implemented on the IBM
Qiskit platform. We compare the performance of QAOA with
classical solvers such as CPLEX on problem instances of up to
15 qubits. We find that performance of QAOA has a multifaceted
dependence on the classical optimization routine used, the depth
of the ansatz parameterized by p, initialization of variational
parameters, and problem instance itself.
Index Terms— Vehicle routing problem | ising model | combinatorial optimization | quantum approximate algorithms | variational quantum algorithms. |
مقاله انگلیسی |
10 |
The big picture on the internet of things and the smart city: a review of what we know and what we need to know
تصویر بزرگ در اینترنت اشیا و شهر هوشمند: مروری بر آنچه میدانیم و آنچه باید بدانیم-2022 This study examines how the application of the IoT in smart cities is discussed in the current
academic literature. Based on bibliometric techniques, 1,802 articles were retrieved from the
Scopus database and analyzed to identify the temporal nature of IoT research, the most relevant
journals, authors, countries, keywords, and studies. The software tool VOSviewer was used to
build the keyword co-occurrence network and to cluster the pertinent literature. Results show the
significant growth of IoT research in recent years. The most productive authors, journals, and
countries were also identified. The main findings from the keyword co-occurrence clustering and
an in-depth qualitative analysis indicate that the IoT is used alongside other technologies
including cloud computing, big data analytics, blockchain, artificial intelligence, and wireless
telecommunication networks. The major applications of the IoT for smart cities include smart
buildings, transportation, healthcare, smart parking, and smart grids. This review is one of the
first attempts to map global IoT research in a smart city context and uses a comprehensive set of
articles and bibliometric techniques to provide scholars and practitioners with an overview of
what has been studied so far and to identify research gaps at the intersection of the IoT and the
smart city. keywords: اینترنت اشیا | شهر هوشمند | مرور | کتاب سنجی | Internet of things | Smart city | Review | Bibliometrics |
مقاله انگلیسی |