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ردیف | عنوان | نوع |
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31 |
Generation of Accessible Sets in the Dynamical Modeling of Quantum Network Systems
تولید مجموعههای قابل دسترس در مدلسازی دینامیکی سیستمهای شبکه کوانتومی-2022 In this article, we consider the dynamical modeling of a class of quantum network systems consisting of
qubits, where information extraction is allowed by performing measurement on several selected qubits of the system.
For a variety of applications, a state space model is a useful
approach to modeling the system dynamics. To construct
a state space model for a quantum network system, the
major task is to find an accessible set containing all of
the operators coupled to the measurement operators. This
article focuses on the generation of a proper accessible set
for a given system and measurement scheme. We provide
analytic results on simplifying the process of generating
accessible sets for systems with a time-independent Hamiltonian. Since the order of elements in the accessible set
determines the form of state space matrices, guidance is
provided to effectively arrange the ordering of elements in
the state vector. Defining a system state according to the
accessible set, one can develop a state space model with
a special pattern inherited from the system structure. As a
demonstration, we specifically consider a typical 1-D-chain
system with several common measurements and employ
the proposed method to determine its accessible set.
Index Terms: Accessible set | dynamical modeling | quantum network system | quantum system. |
مقاله انگلیسی |
32 |
Human perception of color differences using computer vision system measurements of raw pork loin
درک انسان از تفاوتهای رنگی با استفاده از اندازهگیریهای سیستم بینایی کامپیوتری گوشت خوک خام-2022 In the food industry, product color plays an important role in influencing consumer choices. Yet, there remains
little research on the human ability to perceive differences in product color; therefore, preference testing is
subjective rather than based on quantitative colors. Using a de-centralized computer-aided systematic discrim-
ination testing method, we ascertain consumers’ ability to discern between systematically varied colors. As a case
study, the colors represent the color variability of fresh pork as measured by a computer vision system. Our
results indicate that a total color difference (ΔE) of approximately 1 is discriminable by consumers. Furthermore,
we ascertain that a change in color along the b*-axis (yellowness) in CIELAB color space is most discernable,
followed by the a*-axis (redness) and then the L*-axis (lightness). As developed, our web-based discrimination
testing approach allows for large scale evaluation of human color perception, while these quantitative findings
on meat color discrimination are of value for future research on consumer preferences of meat color and beyond. keywords: تست تبعیض | تست مثلث | ترجیح رنگ | ظاهر غذا | رنگ گوشت | Discrimination testing | Triange test | Color preference | Food appearance | Meat color |
مقاله انگلیسی |
33 |
The application of computer vision systems in meat science and industry – A review
کاربرد سیستم های بینایی کامپیوتری در علم و صنعت گوشت – مروری-2022 Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital
cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisi-
tion devices make it technically feasible to obtain information about both the external features and internal
structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used
in research related to assessing the composition of animal carcasses, which might help determine the impact of
cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also
contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have
many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are
under constant development an improvement. The present review covers computer vision system techniques,
stages of measurements, and possibilities for using these to assess carcass and meat quality. keywords: سیستم بینایی کامپیوتری | گوشت | محصولات گوشتی | لاشه | Computer vision system | Meat | Meat products | Carcass |
مقاله انگلیسی |
34 |
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 |
35 |
Digital Twin-driven approach to improving energy efficiency of indoor lighting based on computer vision and dynamic BIM
رویکرد دیجیتال دوقلو برای بهبود بهره وری انرژی در روشنایی داخلی بر اساس بینایی کامپیوتر و BIM پویا-2022 Intelligent lighting systems and surveillance systems have become an important part of intelligent buildings. However, the current intelligent lighting system generally adopts independent sensor control and
does not perform multi-source heterogeneous data fusion with other digital systems. This paper fully
considers the linkage between the lighting system and the surveillance system and proposes a digital
twin lighting (DTL) system that mainly consists of three parts. Firstly, a visualized operation and maintenance (VO&M) platform for a DTL system was established based on dynamic BIM. Secondly, the environment perception, key-frame similarity judgment, and multi-channel key-frame cut and merge
mechanism were utilized to preprocess the video stream of the surveillance system in real-time.
Lastly, pedestrians detected using YOLOv4 and the ambient brightness perceived by the environment
perception mechanism were transmitted to the cloud database and were continuously read by the
VO&M platform. The intent here was to aid timely adaptive adjustment of the digital twin and realistic
lighting through the internet. The effectiveness of the proposed method was verified by experimenting
with a surveillance video stream for 14 days. The key results of the experiments are as follows: (1) the
accuracy rate of intelligent decision control reached 95.15%; (2) energy consumption and electricity costs
were reduced by approximately 79%; and (3) the hardware cost and energy consumption of detection
equipment and the time and cost of operation and maintenance (O&M) were greatly reduced.
keywords: Computer vision | Digital Twin | Dynamic BIM | Energy-efficient buildings | Intelligent lighting control |
مقاله انگلیسی |
36 |
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 |
37 |
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 |
38 |
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 |
39 |
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 |
40 |
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 |