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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 |
Power to the people: Applying citizen science and computer vision to home mapping for rural energy access
قدرت به مردم: به کارگیری علم شهروندی و بینش رایانه در نقشهبرداری خانه برای دسترسی به انرژی روستایی-2022 To implement effective rural electricity access systems, it is fundamental to identify where potential consumers
live. Here, we test the suitability of citizen science paired with satellite imagery and computer vision to map
remote off-grid homes for electrical system design. A citizen science project called “Power to the People” was
completed on the Zooniverse platform to collect home annotations in Uganda, Kenya, and Sierra Leone. Thou-
sands of citizen scientists created a novel dataset of 578,010 home annotations with an average mapping speed of
7 km2/day. These data were post-processed with clustering to determine high-consensus home annotations. The
raw annotations achieved a recall of 93% and precision of 49%; clustering the annotations increased precision to
69%. These were used to train a Faster R-CNN object detection model, producing detections useful as a first pass
for home-level mapping with a feasible mapping rate of 42,938 km2/day. Detections achieved a precision of 67%
and recall of 36%. This research shows citizen science and computer vision to be a promising pipeline for
accelerated rural home-level mapping to enable energy system design. keywords: دانش شهروندی | بینایی کامپیوتر | دسترسی به برق | نقشه برداری روستایی | تصویربرداری ماهواره ای | سنجش از دور | Citizen science | Computer vision | Electricity access | Rural mapping | Satellite imagery | Remote sensing |
مقاله انگلیسی |
3 |
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 |
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 |
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 |
6 |
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 |
7 |
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 |
8 |
Quantum Annealing Methods and Experimental Evaluation to the Phase-Unwrapping Problem in Synthetic Aperture Radar Imaging
روشهای آنیل کوانتومی و ارزیابی تجربی مسئله بازکردن فاز در تصویربرداری رادار دیافراگم مصنوعی-2022 The focus of this work is to explore the use of quantum annealing solvers for the problem of
phase unwrapping of synthetic aperture radar (SAR) images. Although solutions to this problem exist based
on network programming, these techniques do not scale well to larger sized images. Our approach involves
formulating the problem as a quadratic unconstrained binary optimization (QUBO) problem, which can
be solved on a quantum annealer. Given that present embodiments of quantum annealers remain limited
in the number of qubits they possess, we decompose the problem into a set of subproblems that can be
solved individually. These individual solutions are close to optimal up to an integer constant, with one
constant per subimage. In a second phase, these integer constants are determined as a solution to yet another
QUBO problem. This basic idea is extended to several passes, where each pass results in an image which is
subsequently decomposed to yet another set of subproblems until the resulting image can be accommodated
by the annealer at hand. Additionally, we explore improvements to the method by decomposing the original
image into overlapping subimages and ignoring the results on the overlapped (marginal) pixels. We test our
approach with a variety of software-based QUBO solvers and on a variety of images, both synthetic and real.
Additionally, we experiment using D-wave systems’ quantum annealer, the D-wave 2000Q_6 and developed
an embedding method which, for our problem, yielded improved results. Our method resulted in high quality
solutions, comparable to state-of-the-art phase-unwrapping solvers.
INDEX TERMS: Interferometric synthetic aperture radar (SAR) | phase unwrapping, quadratic unconstrained binary optimization (QUBO) | quantum annealing. |
مقاله انگلیسی |
9 |
Detection of moving objects using thermal imaging sensors for occupancy estimation
تشخیص اجسام متحرک با استفاده از سنسورهای تصویربرداری حرارتی برای تخمین اشغال-2022 Thermal imaging sensors have been increasingly integrated in a wide range of smart building
and Internet of Things systems. Low-resolution thermal imaging sensors are especially suitable
for applications that require non-intrusive monitoring with proper privacy protection. In this
paper, we present an in-depth investigation of a low-resolution thermal imaging sensor (i.e.,
Melexis MLX90640) focusing on algorithm design issues and solutions when detecting moving
objects. This type of sensors are designed to operate with a two-subpage chessboard reading
pattern, which gives rise to blob displacements across two subpages when target objects are
in motion. We have conducted systematic characterization of the sensor and demonstrated
issues through experimental measurements and analysis. We have also proposed a subpage
bilinear interpolation method and an enhanced sensor data preprocessing method for occupancy
estimation with moving objects. The performance of the proposed method is analyzed by
training and testing classification algorithms using two datasets collected with objects of
different moving speeds. Our performance results indicate that the proposed method could be
used for occupancy estimation in various smart building and Internet of Things applications.
keywords: طبقه بندی | حسگر مادون قرمز | اینترنت اشیا | یادگیری ماشین | برآورد اشغال | ساختمان های هوشمند | Classification | Infrared array sensor | Internet of Things | Machine learning | Occupancy estimation | Smart buildings |
مقاله انگلیسی |
10 |
A Remote Security Computational Ghost Imaging Method Based on Quantum Key Distribution Technology
یک روش تصویربرداری شبح محاسباتی امنیت از راه دور بر اساس فناوری توزیع کلید کوانتومی-2022 Computational ghost imaging (CGI) is a method of acquiring object information by measuring
light field intensity, which would be used to achieve imaging in a complicated environment. The main issue
to be addressed in CGI technology is how to achieve rapid and high-quality imaging while assuring the secure
transmission of detection data in practical distant imaging applications. In order to address the mentioned
issues, this paper proposes a remote secure CGI method based on quantum communication technology.
Using the quantum key distribution (QKD) network, the CGI system can be reconstructed while solving the
problem of information security transmission between the detector and the reconstructed computing device.
By exploring the influence of different random measurement matrices on the quality of image reconstruction,
it is found that the randomness of the numerical sequence constituting the matrix is positively correlated
with the imaging quality. Based on this discovery, a new type of quantum cryptography measurement
matrix is constructed using quantum cryptography with good randomness. In addition, through further
orthogonalization and normalization of the matrix, the matrix has both good randomness and orthogonality,
and high-quality imaging results can be obtained at a low sampling rate. The feasibility and effectiveness of
the method are verified by simulation imaging experiments. Compared with the traditional GI system, the
method proposed in this paper has higher transmission security and high-quality imaging under this premise,
which provides a new idea for the practical development of CGI technology.
INDEX TERMS: Computational ghost imaging | quantum key distribution | quantum cryptography | measurement matrix | randomness | schimidt orthogonalization. |
مقاله انگلیسی |