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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 |
Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method
روش اندازه گیری جابجایی ساختاری همزمان با روشنایی مبتنی بر بینایی کامپیوتری-2022 Computer vision-based techniques for structural displacement measurement are rapidly becoming
popular in civil structural engineering. However, most existing computer vision-based displace-
ment measurement methods require man-made targets for object matching or tracking, besides
usually the measurement accuracies are seriously sensitive to the ambient illumination variations.
A computer vision-based illumination robust and multi-point simultaneous measuring method is
proposed for structural displacement measurements. The method consists of two part, one is for
segmenting the beam body from its background, the segmentation is perfectly carried out by fully
convolutional network (FCN) and conditional random field (CRF); another is digital image cor-
relation (DIC)-based displacement measurement. A simply supported beam is built in laboratory.
The accuracy and illumination robustness are verified through three groups of elaborately
designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation,
numbers of locations along with the segmented beam body can be chosen and measured simul-
taneously. It is verified that the method is illumination robust since the displacement measure-
ments are with the smallest fluctuations to the illumination variations. The proposed method does
not require any man-made targets attached on the structure, but because of the exploitation of
DIC in displacement measurement, the regions centered on the measuring points need to have
texture feature. keywords: پایش سلامت سازه | اندازه گیری جابجایی | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی شی | همبستگی تصویر دیجیتال | Structural health monitoring | Displacement measurement | Computer vision | Deep learning | Object segmentation | Digital image correlation |
مقاله انگلیسی |
3 |
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 |
4 |
Efficiency assessment in co-production systems based on modified emergy accounting approach
ارزیابی کارایی در سیستم های تولید مشترک بر اساس رویکرد حسابداری اضطراری اصلاح شده-2021 Emergy accounting in a system with co-production branch is of great scientific interest since each branch cor-
responds to a different transformity value. In previous studies, limitations associated with emergy accounting in
co-production systems have been highlighted where some “inputs” have to be added to obtain a “useful” product
from a “co-product” – giving rise to inaccuracies in the emergy accounting process. To address these method-
ological aspects of emergy assessment in co-production systems, a modified physical quantity method (MPQM) –
that goes in line with the standard emergy algebra – has been proposed in order to provide a different perspective
for accounting co-products efficiency. The robustness of MPQM has been verified by taking the case study of
Eucalyptus pulp production and a comparison is made against conventional and energy/exergy weighting
methods. As per the results, MPQM was able to provide accurate results for co-production systems as compared
with other emergy accounting methods. However, the case of Eucalyptus pulp production was found to be
“inefficient” following the MPQM approach. These findings are expected to strengthen the methodological as-
pects of emergy accounting based on the physical quantity criterion. keywords: ظهور | سیستم تولید همکاری | روش فیزیکی اصلاح شده | دگرگونی | ارزیابی کارایی | Emergy | Co-production system | Modified physical quantity method | Transformity | Efficiency assessment |
مقاله انگلیسی |
5 |
Microstructure, resistivity, and shear strength of electrically conductive adhesives made of silver-coated copper powder
ریزساختار، مقاومت و استحکام برشی چسب های رسانای الکتریکی ساخته شده از پودر مس با روکش نقره-2021 Electrically conductive adhesives were made using silver-coated copper powders (filler) and epoxy. Resistivity,
microstructure and shear strength of prepared adhesives were studied using two-point resistance measurements,
Scanning Electron Microscope (SEM) and universal tensile tests, respectively. Effect of filler concentration
(70–85 wt%), silver concentration (10–50 wt%), particles morphology (flake or spherical) and addition of
graphite (2–15 wt%) were investigated on prepared adhesives properties. Results showed that by increasing the
filler percentage from 70 to 85 wt%, the electrical resistivity decreases from 6.2 × 10− 3 to 3 × 10− 3 Ω⋅cm.
Furthermore, the electrical resistivity of adhesives is proportional to the silver content of the filler particles.
Regarding the morphological effect of filler particles, it was found that by replacing 40 wt% of the flake with
spherical particles in the adhesive with a filler content of 75 wt%, the electrical resistivity of the adhesive reduces
from 5.6 × 10− 3 to 4.5 × 10− 3 Ω⋅cm, while the electrical resistivity of the adhesive containing 50 wt% of the
spherical particles reached to 1.1 × 10− 2 Ω⋅cm. Graphite addition to adhesive formula in concentrations less than
6 wt%, slightly lowered the resistivity of the adhesives. Increasing the graphite addition from 6 to 15 wt%
enhanced the electrical resistivity from 2.8 × 10− 3 to 1.7 × 10− 2 Ω⋅cm. The shear strength of the adhesives is
inversely proportional to the filler percentage in the adhesives.
Keywords: Silver | Copper | Core-shell | Electrical resistivity | Conductive | Adhesive |
مقاله انگلیسی |
6 |
How to foster scientific knowledge integration in coastal management
چگونه یکپارچه سازی دانش علمی را در مدیریت ساحلی پرورش دهیم-2021 Development of science-based coastal policies and strategies that effectively cope with coastal change and risk
requires transfer of scientific knowledge beyond the scientific community, and its integration in management
processes. However, scientists frequently convey their message to non-specialized audiences resourcing to their
own empirical experience, often leading to a high effort - low efficiency process.
This paper aims to propose a simple conceptual model to guide scientists in the process of knowledge transfer,
focusing on whom and how, and promoting the efficiency of both the science dissemination process and inte-
gration of scientific knowledge in management of coastal land and risk. The model proposed herein aims to guide
scientists to actively pursue the goal of transferring their knowledge to policymakers and managers besides
layman society, and is essentially based upon a review and integration of previous work.
We argue that selection of the most efficient scientific knowledge transfer mechanism (outreach, crowdsourcing
tools, managers-oriented tools or co-production) should be based following careful consideration of level of
engagement with the audience, and take into consideration political and social contexts. The level of engagement
also controls the amount of effort involved in message framing, and the nature and robustness of the feedback
from the target audience. The model acknowledges that communication strategy must be thought on a case-by-
case basis and ranks the proportion of effort distributed between message deliverer (framing) and receiver
(engagement) implicit in each transfer mechanism. This helps to select the most adequate mechanism and op-
timizes knowledge transfer efforts. In addition, it highlights the importance of encouraging scientists to develop
message framing skills and to acknowledge the benefits of engaging with others. keywords: انتقال دانش | نامزدی | تماس با ما | crowdsourcing | ابزار مدیریت گرا | تولید مشترک | Knowledge transfer | Engagement | Outreach | Crowdsourcing | Management-oriented tools | Co-production |
مقاله انگلیسی |
7 |
Power network robustness analysis based on electrical engineering and complex network theory
تجزیه و تحلیل استحکام شبکه قدرت بر اساس مهندسی برق و نظریه شبکه پیچیده-2021 The growing importance of power systems in the development of modern society
has increasingly focused the attention on the various dangers to which these systems
are exposed. This paper proposes a robust analysis framework based on complex
network theory with the aim of exploring the robustness of the power system from a
methodological perspective. The analysis framework establishes three models: a purely
topological model, an artificial flow model, and a direct current power flow model to
analyze the power system structure and functional robustness. We present different
analysis metrics under different models, simulate three fault scenarios, and conduct
an evaluation and analysis. The validity of the evaluation analysis was further verified
by adopting IEEE300 and two randomly generated 1000-node network models that
meet the characteristics of small world and scale, respectively, for detailed robustness
analysis. The results show that the proposed method can effectively analyze a power
system from the perspectives of pure topology, artificial flow, and direct current power
flow. The case analysis based on the IEEE300 network and systems with different
network characteristics proves that the framework is effective for the evaluation of
power systems with different characteristics.
Keywords: Power network | Robustness | Topological model | Artificial flow | Direct current power flow |
مقاله انگلیسی |
8 |
Multi-objective robust energy management for all-electric shipboard microgrid under uncertain wind and wave
مدیریت انرژی چند منظوره قدرتمند برای ریز شبکه برد حامل تمام برقی تحت باد و موج نامشخص-2020 An all-electric ship (AES) uses diesel generators and energy storage system (ESS) to meet both propulsion and
service loads. Thus, it can be viewed as a mobile microgrid. During the operation of an AES, significant uncertainties
such as water wave and wind introduce considerable speed loss, which may lead to severe voyage
delays. To fully address this issue, a new robust energy management model is proposed to coordinately schedule
an AES’s power generation and voyage considering the uncertain wave and wind. Two objectives are minimized
simultaneously: the fuel consumption (FC) and energy efficiency operational indicator (EEOI). The problem is
formulated as a bi-level robust optimization model after certain constraint decomposition. Normal boundary
intersection method is utilized to solve this multi-objective programming. Compared with existing joint scheduling
methods, the proposed method can fully guarantee the on-time rates of AES in various uncertain scenarios
and providing high-quality Pareto solutions. Keywords: All-electric ship | Mobile microgrid | Robustness | Energy management system | Joint generation and voyage scheduling | Uncertain wave and wind |
مقاله انگلیسی |
9 |
A robust online energy management strategy for fuel cell/battery hybrid electric vehicles
یک استراتژی مدیریت انرژی آنلاین قوی برای خودروهای برقی هیبریدی سلول / باتری-2020 Traditional optimization-based energy management strategies (EMSs) do not consider the
uncertainty of driving cycle induced by the change of traffic conditions, this paper proposes
a robust online EMS (ROEMS) for fuel cell hybrid electric vehicles (FCHEV) to handle the
uncertain driving cycles. The energy consumption model of the FCHEV is built by
considering the power loss of fuel cell, battery, electric motor, and brake. An offline linear
programming-based method is proposed to produce the benchmark solution. The ROEMS
instantaneously minimizes the equivalent power of fuel cell and battery, where an
equivalent efficiency of battery is defined as the efficiency of hydrogen energy transforming
to battery energy. To control the state of charge of battery, two control coefficients
are introduced to adjust the power of battery in objective function. Another penalty coefficient
is used to amend the power of fuel cell, which reduces the load change of fuel cell
so as to slow the degradation of fuel cell. The simulation results indicate that ROEMS has
good performance in both fuel economy and load change control of fuel cell. The most
important advantage of ROEMS is its robustness and adaptivity, because it almost produces
the optimal solution without changing the control parameters when driving cycles are
changed. Keywords: Fuel cell | Hybrid electric vehicles | Online energy management strategy | Robustness | Uncertaint |
مقاله انگلیسی |
10 |
Investigation and analysis of hyper and hypo neuron pruning to selectively update neurons during unsupervised adaptation
بررسی و تجزیه و تحلیل هرس هایپر و هیپو نورون برای بروزرسانی انتخابی نورون ها در طی سازگاری بدون نظارت-2020 Unseen or out-of-domain data can seriously degrade the performance of a neural network model, indicating the model’s failure to generalize to unseen data. Neural net pruning can not only help to reduce a model’s size but can improve the model’s generalization capacity as well. Pruning approaches look for low-salient neurons that are less contributive to a model’s decision and hence can be removed from the model. This work investigates if pruning approaches are successful in detecting neurons that are either high-salient (mostly active or hyper) or low-salient (barely active or hypo), and whether removal of such neurons can help to improve the model’s generalization capacity. Traditional blind adaptation techniques update either the whole or a subset of layers, but have never explored selectively updating individual neurons across one or more layers. Focusing on the fully connected layers of a convolutional neural network (CNN), this work shows that it may be possible to selectively adapt certain neurons (consisting of the hyper and the hypo neurons) first, followed by a full-network fine tuning. Using the task of automatic speech recognition, this work demonstrates how the removal of hyper and hypo neurons from a model can improve the model’s performance on out-of-domain speech data and how selective neuron adaptation can ensure improved performance when compared to traditional blind model adaptation. Keywords: Neural net pruning | Unsupervised adaptation | Convolutional neural network speech | recognition robustness machine learning |
مقاله انگلیسی |