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ردیف | عنوان | نوع |
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1 |
Classical Artificial Neural Network Training Using Quantum Walks as a Search Procedure
آموزش شبکه عصبی مصنوعی کلاسیک با استفاده از راه رفتن کوانتومی به عنوان یک روش جستجو-2022 This article proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks.
The goal of the procedure is to apply quantum walk as a search algorithm in a complete graph to find all synaptic weights of a classical
artificial neural network. Each vertex of this complete graph represents a possible synaptic weight set in the w-dimensional search space,
where w is the number of weights of the neural network. To know the number of iterations required a priori to obtain the solutions is one of
the main advantages of the procedure. Another advantage is that the proposed method does not stagnate in local minimums. Thus, it is
possible to use the quantum walk search procedure as an alternative to the backpropagation algorithm. The proposed method was
employed for a XOR problem to prove the proposed concept. To solve this problem, the proposed method trained a classical artificial
neural network with nine weights. However, the procedure can find solutions for any number of dimensions. The results achieved
demonstrate the viability of the proposal, contributing to machine learning and quantum computing researches.
Index Terms: Artificial neural networks training | quantum computing | quantum walk | search algorithm |
مقاله انگلیسی |
2 |
Information and Measurement System for Electric Power Losses Accounting in Railway Transport
اطلاعات و سیستم اندازه گیری برای حسابداری تلفات برق در حمل و نقل ریلی-2021 The purpose of the presented research is to minimize the loss of electricity during the operation of railway power systems. Losses
are defined as an unbalance between the released and consumed electricity, which is recorded by means of commercial electricity
ccounting. Given that electricity losses are divided into technical and non-technical (commercial) components, there are
currently no technical tools that can analyze the components of electricity losses in detail, and therefore prevent their occurrence.
To achieve this goal, the factors inherent in commercial electricity accounting systems in various areas of production activity that
affect the growth of electricity losses are identified. An algorithm is proposed that allows determining the presence of abnormal
power losses in real time for making organizational and technical decisions to reduce them. A block diagram of the information
and measurement system for accounting of power losses has been developed, which allows using the existing equipment without
replacement or modernization, which allows obtaining new technical capabilities. The method of intellectualization of the
process of classification of factors that cause the growth of abnormal power losses, based on artificial neural networks, is
posed. The intelligent module allows replacing the person who makes organizational and technical decisions, minimizing the
consequences of abnormal situations that lead to the growth of abnormal losses, applying the proposed solutions in departments
that do not have qualified specialists. The results of training an artificial neural network are considered, and the main parameters
of the efficiency of the information and measurement system for loss accounting on a real railway transport object are
determined.
Keywords: Power Loss | Artificial Neural Networks. |
مقاله انگلیسی |
3 |
Global assessment of marine phytoplankton primary production: Integrating machine learning and environmental accounting models
ارزیابی جهانی از تولید اولیه فیتوپلانکتون دریایی: یکپارچه سازی یادگیری ماشین و مدل های حسابداری محیطی-2021 The emergy accounting method has been widely applied to terrestrial and marine ecosystems although there is a
lack of emergy studies focusing on phytoplankton primary production. Phytoplankton production is a pivotal
process since it is intimately coupled with oceanic food webs, energy fluxes, carbon cycle, and Earth’s climate. In
this study, we proposed a new methodology to perform a biophysical assessment of the global phytoplankton
primary production combining Machine Learning (ML) techniques and an emergy-based accounting model.
Firstly, we produced global phytoplankton production estimates using an Artificial Neural Network (ANN)
model. Secondly, we assessed the main energy inputs supporting the global phytoplankton production. Finally,
we converted these inputs into emergy units and analysed the results from an ecological perspective. Among the
energy flows, tides showed the highest maximum emergy contribution to global phytoplankton production
highlighting the importance of thise flow in the complex dynamics of marine ecosystems. In addition, an emergy/
production ratio was calculated showing different global patterns in terms of emergy convergence into the
primary production process. We believe that the proposed emergy-based assessment of phytoplankton produc-
tion could be extremely valuable to improve our understanding of this key biological process at global scale
adopting a systems perspective. This model can also provide a useful benchmark for future assessments of marine
ecosystem services at global scale. keywords: تولید اولیه فیتوپلانکتون | اکولوژی سیستم ها | شبکه های عصبی مصنوعی | یادگیری ماشین | حسابداری امری | Phytoplankton primary production | Systems ecology | Artificial neural networks | Machine learning | Emergy accounting |
مقاله انگلیسی |
4 |
Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors
ارزیابی کیفی چای سیاه کیمون با ترکیب داده های بدست آمده از طیف سنجی بازتابنده مادون قرمز نزدیک و حسگرهای بینایی ماشین-2021 Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and
flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly
using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the
flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer
was used to record the spectrum of samples. A computer vision system was used to capture the image
of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance
of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric
feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six
geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of
different tea samples were used to describe their appearance. A feature-level fusion strategy was used
to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results
indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the
softmax classification model. The histograms of the combined shape features indicated that when the
softmax classification model was used, the classification accuracy was also higher than ANN. The fusion
of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation
and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a singlesensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used).
This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying
black tea samples of different grades. Keywords: Keemun black tea | Near-infrared reflectance spectroscopy | Computer vision system | Feature fusion | Convolutional neural network | Quality identification |
مقاله انگلیسی |
5 |
ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse
مدل سازی ANN سیستم خنک کننده کولر CO2 COP در یک انبار هوشمند-2020 Industrial cooling systems consume large quantities of energy with highly variable power demand. To
reduce environmental impact and overall energy consumption, and to stabilize the power requirements,
it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To
control these operations continuously in a complex energy system, an intelligent energy management
system can be employed using operational data and machine learning. In this work, we have developed
an artificial neural network based technique for modelling operational CO2 refrigerant based industrial
cooling systems for embedding in an overall energy management system. The operating temperature and
pressure measurements, as well as the operating frequency of compressors, are used in developing
operational model of the cooling system, which outputs electrical consumption and refrigerant mass
flow without the need for additional physical measurements. The presented model is superior to a
generalized theoretical model, as it learns from data that includes individual compressor type characteristics.
The results show that the presented approach is relatively precise with a Mean Average Percentage
Error (MAPE) as low as 5%, using low resolution and asynchronous data from a case study
system. The developed model is also tested in a laboratory setting, where MAPE is shown to be as low as
1.8%. Keywords: Industrial cooling systems | Carbon dioxide refrigerant | Artificial neural networks | Coefficient of performance | Energy storage | Smart warehouse |
مقاله انگلیسی |
6 |
AI-based Framework for Deep Learning Applications in Grinding
چارچوبی مبتنی بر هوش مصنوعی برای کاربردهای یادگیری عمیق در شبکه سازی-2020 Rejection costs for a finish-machined
gearwheel with grinding burn can rise to the order of 10,000 euros
each. A reduction in costs by reducing rejection rate by only 5-10
pieces per year already amortizes costs for data-acquisition
hardware for online process monitoring. The grinding wheel wear,
one of the major influencing factors responsible for the grinding
burn, depends on a large number of influencing variables like
cooling lubricant, feed rate, circumferential wheel speed and wheel
topography. In the past, machine learning algorithms such as
Support Vector Machines (SVM), Hidden Markov Models (HMM)
and Artificial Neural Networks (ANN) have proven effective for the
predictive analysis of process quality. In addition to predictive
analysis, AI-based applications for process control may raise the
resilience of machining processes. Using machine learning methods
may also lead to a heavy reduction of cost amassed due to a physical
inspection of each workpiece. With this contribution, information
from previous works is leveraged and an AI-based framework for
adaptive process control of a cylindrical grinding process is
introduced. For the development of such a framework, three
research objectives have been derived: First, the dynamic wheel
wear needs to be modelled and measured, because of its strong
impact on the resulting workpiece quality. Second, models to predict
the quality features of the produced workpieces depending on
process setup parameters and materials used have to be established.
Here, special focus is set on deriving models that are independent
of a specific wheel-workpiece-pair. The opportunity to use such a
model in a variety of grinding configurations gives the production
line consistent process support. Third, the resilience of analytical
models regarding graceful degradation of sensors needs to be
tackled, since the stability of such systems has to be guaranteed to
be used in productive environments. Process resilience against
human errors and sensor failures leads to a minimization of
rejection costs in production. To do so, a framework is presented,
where virtual sensors, upon the failure or detection of an erroneous
signal from physical sensors, will be activated and provide signals
to the downstream smart systems until the process is completed or
the physical sensor is changed. Keywords: Cylindrical Grinding | Wheel Wear | Virtual Sensors | Process Resilience | Artificial Intelligence |
مقاله انگلیسی |
7 |
Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux
مدل سازی فرآیند اسمزوز رو به جلو با استفاده از شبکه های عصبی مصنوعی (ANN) برای پیش بینی شار نفوذ-2020 Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based
models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN
models to evaluate the forward osmosis (FO) process performance. Therefore, in this study, a multi-layered
neural network model is developed to predict the permeate flux in forward osmosis. The developed model is
tested for its generalization capability by including lab-scale experimental data from several published studies.
Nine input variables are considered including membrane type, the orientation of membrane, molarity of feed
solution and draw solution, type of feed solution and draw solution, crossflow velocity of the feed solution, and
the draw solution and temperature of the feed solution and the draw solution. The development of optimum
network architecture is supported by studying the impact of the number of neurons and hidden layers on the
neural network performance. The optimum trained network shows a high R2 value of 97.3% that is the efficiency
of the model to predict the targeted output. Furthermore, the validation and generalized prediction capability of
the model is tested against untrained published data. The performance of the ANN model is compared with a
transport-based model in the literature. A simple machine learning technique such as a multiple linear regression
(MLR) model is also applied in a similar manner to be compared with the ANN model. ANN demonstrates its
ability to form a complex relationship between inputs and output better than MLR. Keywords: Artificial neural network | Forward osmosis | Water treatment | Desalination | Machine learning |
مقاله انگلیسی |
8 |
Correlation minimizing replay memory in temporal-difference reinforcement learning
حداقل سازی همبستگی پاسخ در یادگیری تقویتی متفاوت موقت -2020 Online reinforcement learning agents are now able to process an increasing amount of data which makes their approximation and compression into value functions a more demanding task. To improve approx- imation, thus the learning process itself, it has been proposed to select randomly a mini-batch of the past experiences that are stored in the replay memory buffer to be replayed at each learning step. In this work, we present an algorithm that classifies and samples the experiences into separate contextual memory buffers using an unsupervised learning technique. This allows each new experience to be as- sociated to a mini-batch of the past experiences that are not from the same contextual buffer as the current one, thus further reducing the correlation between experiences. Experimental results show that the correlation minimizing sampling improves over Q-learning algorithms with uniform sampling, and that a significant improvement can be observed when coupled with the sampling methods that prioritize on the experience temporal difference error. Keywords: Reinforcement learning | Temporal-difference learning | Replay memory | Artificial neural networks |
مقاله انگلیسی |
9 |
Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network
پیش بینی طول عمر خستگی مواد فلزی با توجه به میانگین اثرات استرس با استفاده از شبکه عصبی مصنوعی-2020 The mean stress effect plays an important role in the fatigue life predictions, its influence significantly changes
high-cycle fatigue behaviour, directly decreasing the fatigue limit with the increase of the mean stress. Fatigue
design of structural details and mechanical components must account for mean stress effects in order to guarantee
the performance and safety criteria during their foreseen operational life. The purpose of this research
work is to develop a new methodology to generate a constant life diagram (CLD) for metallic materials, based on
assumptions of Haigh diagram and artificial neural networks, using the probabilistic Stüssi fatigue S-N fields.
This proposed methodology can estimate the safety region for high-cycle fatigue regimes as a function of the
mean stress and stress amplitude in regions where tensile loading is predominance, using fatigue S-N curves only
for two stress R-ratios. In this approach, the experimental fatigue data of the P355NL1 pressure vessel steel
available for three stress R-ratios (−1, −0.5, 0), are used. A multilayer perceptron network has been trained
with the back-propagation algorithm; its architecture consists of two input neurons (σm, N) and one output
neuron (σa). The suggested CLD based on trained artificial neural network algorithm and probabilistic Stüssi
fatigue fields applied to dog-bone shaped specimens made of P355NL1 steel showed a good agreement with the
high-cycle fatigue experimental data, only using the stress R-ratios equal to 0 and −0.5. Furthermore, a procedure
for estimating the fatigue resistance reduction factor, Kf , for the fatigue life prediction of structural
details (stress R-ratios equal to 0, 0.15 and 0.3) in extrapolation regions is suggested and used to generate the Kf
results for stress R-ratios from −1 to 0.3, based on machine learning artificial neural network algorithm. Keywords: Fatigue | Artificial neural network | Back-propagation algorithm | Stüssi model | Constant life diagram |
مقاله انگلیسی |
10 |
Ensemble Learning Against Adversarial AI-driven Fake Task Submission in Mobile Crowdsensing
آموزش گروهی در برابر ارسال ترفند کار جعلی هدایت شده توسط هوش مصنوعی در ازدحام جمعیت سیار-2020 Non-dedicated nature of mobile crowdsensing
(MCS) systems introduces vulnerabilities for MCS platforms
in terms of sensing, computing, storage, and battery resources.
The advent of adversarial artificial intelligence (AI) leads to
high impact malicious behavior when adversaries aim to clog
the resources of such a non-dedicated and ubiquitous system.
This paper proposes an ensemble learning-based methodology for
MCS platforms in order to mitigate the impacts of adversarial
AI-driven fake task submission attacks, which are intelligently
designed so to clog resources such as batteries, sensing, or
memory resources. We validate our proposal through realistic
simulations to generate crowdsensing data under two different
cities, and intelligent fake task submissions under adversarial
self-organizing maps. The experimental results show that when
the submitted tasks undergo a Gradient Boosting-based classifier
prior to being assigned to participants, the proposed solution
can introduce battery savings at the participant devices up to
23%, and the impacted recruit population can be reduced from
24% to 6% whereas the defense mechanism can achieve an
overall accuracy level above 98% concerning the legitimacy of
the submitted tasks. Index Terms: Mobile crowdsensing | IoT | self-organizing feature map | Machine learning | Artificial neural networks | Clogging attacks |
مقاله انگلیسی |