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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 |
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 |
5 |
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 |
6 |
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 |
7 |
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 |
8 |
Adaptive indirect neural network model for roughness in honing processes
مدل شبکه عصبی غیرمستقیم سازگار برای زبری در فرآیندهای آب بندی-2020 Honing processes provide a crosshatch pattern that allows oil flow, for example in combustion engine cylinders.
This paper provides an adaptive neural network model for predicting roughness as a function of process parameters.
Input variables are three parameters from the Abbott-Firestone curve, Rk, Rpk and Rvk. Output parameters
are grain size, density of abrasive, pressure, linear speed and tangential speed. The model consists of
applying a direct and an indirect model consecutively, with one convergence parameter and one error parameter.
The indirect model has one network with 48 neurons and the direct model has three networks having 25,
9 and 5 neurons respectively. The adaptive one allows selecting discrete values for some variables like grain size
or density. Keywords: Honing | Surface roughness | Artificial neural networks | Adaptive control |
مقاله انگلیسی |
9 |
Coupling an artificial neuron network daylighting model and building energy simulation for vacuum photovoltaic glazing
اتصال مدل نورپردازی شبکه عصبی مصنوعی و شبیه سازی انرژی ساختمان برای لعاب خلاء فتوولتائیک-2020 Window plays an essential role in the indoor environment and building energy consumption. As an innovative
building integrated photovoltaic (BIPV) window, the vacuum PV glazing was proposed to provide excellent
thermal performance and utilize renewable energy. However, the daylighting performance of the vacuum PV
glazing and the effect on energy consumption have not been thoroughly investigated. Most whole building
energy simulation used the daylighting calculation based on Daylight Factor (DF) method, which fails to address
realistic calculation for direct sunlight through complex glazing materials. In this study, a RADIANCE model was
developed and validated to adequately represent the daylight behaviour of a vacuum cadmium telluride photovoltaic
glazing with a three-layer structure. However, RADIANCE will consume too many computational resources
for a whole year simulation. Therefore, an artificial neuron network (ANN) model was trained based on
the weather conditions and the RADIANCE simulation results to predict the interior illuminance. Subsequently, a
preprocessing coupling method is proposed to determine the lighting consumption of a typical office with the
vacuum PV glazing. The performance evaluation of the ANN model indicates that it can predict the illuminance
level with higher accuracy than the daylighting calculation methods in EnergyPlus. Therefore, the ANN model
can adequately address the complex daylighting response of the vacuum PV glazing. The proposed coupling
method showed a more reliable outcome than the simulations sole with EnergyPlus. Furthermore, the computational
cost can be reduced dramatically by the ANN daylighting prediction model in comparison with the
RADIANCE model. Compared with the lighting consumption determined by the ANN-based coupling method,
the two approaches in EnergyPlus, the split-flux method and the DElight method, tend to underestimate the
lighting consumption by 5.3% and 9.7%, respectively. Keywords: Building integrated photovoltaic (BIPV) | Vacuum glazing | Semi-transparent photovoltaic | Daylighting model | Building energy model | Artificial neuron networks (ANNs) |
مقاله انگلیسی |
10 |
Using of Artificial Neural Networks (ANNs) to predict the thermal conductivity of Zinc Oxide–Silver (50%–50%)/Water hybrid Newtonian nanofluid
استفاده از شبکه های عصبی مصنوعی (ANN) برای پیش بینی هدایت حرارتی روی اکسید-نقره روی (50٪ -50٪) / نانوسیال نیوتنی ترکیبی آب-2020 In this study, after generating experimental data points of Zinc Oxide (ZnO)–Silver (Ag) (50%–50%)/Water
nanofluid, an algorithm is proposed to calculate the best neuron number in the Artificial Neural Network (ANN),
and the performance and correlation coefficient for ANN has been calculated. Then, using the fitting method, a
surface is fitted on the experimental data, and the correlation coefficient and performance of this method have
been calculated. Finally, the absolute values of errors in both methods have been compared. It can be seen that
the best neuron number in the hidden layer is 7 neurons. We concluded that both methods could predict the
behavior of nanofluid, but the fitting method had smaller errors. Also, the ANN method had better ability in
predicting the thermal conductivity of nanofluid based on the volume fraction of nanoparticles and temperature.
Finally, we found that, in ANN, all outputs, the maximum absolute value of error is 0.0095, and the train
performance is 1.6684e-05. Keywords: Artificial Neural Networks (ANNs) | Thermal conductivity | Hybrid Newtonian nanofluid |
مقاله انگلیسی |