نتیجه جستجو - Artificial neural networks

ردیف | عنوان | نوع |
---|---|---|

1 |
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 |

2 |
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 |

3 |
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 |

4 |
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 |

5 |
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 |

6 |
Predicting and explaining corruption across countries: A machine learning approach
پیش بینی و توضیح فساد در سراسر کشور: رویکرد یادگیری ماشینی-2020 In the era of Big Data, Analytics, and Data Science, corruption is still ubiquitous and is perceived as one of the
major challenges of modern societies. A large body of academic studies has attempted to identify and explain the
potential causes and consequences of corruption, at varying levels of granularity, mostly through theoretical
lenses by using correlations and regression-based statistical analyses. The present study approaches the phenomenon
from the predictive analytics perspective by employing contemporary machine learning techniques to
discover the most important corruption perception predictors based on enriched/enhanced nonlinear models
with a high level of predictive accuracy. Specifically, within the multiclass classification modeling setting that is
employed herein, the Random Forest (an ensemble-type machine learning algorithm) is found to be the most
accurate prediction/classification model, followed by Support Vector Machines and Artificial Neural Networks.
From the practical standpoint, the enhanced predictive power of machine learning algorithms coupled with a
multi-source database revealed the most relevant corruption-related information, contributing to the related
body of knowledge, generating actionable insights for administrator, scholars, citizens, and politicians. The
variable importance results indicated that government integrity, property rights, judicial effectiveness, and
education index are the most influential factors in defining the corruption level of significance Keywords: Corruption perception | Machine learning | Predictive modeling | Random forest | Society policies and regulations |Government integrity | Social development |
مقاله انگلیسی |

7 |
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 |

8 |
A review of learning in biologically plausible spiking neural networks
مروری بر یادگیری در شبکه های عصبی اسپایک بیولوژیکی قابل قبول-2020 Artificial neural networks have been used as a powerful processing tool in various areas such as
pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged
researchers to improve artificial neural networks by investigating the biological brain. Neurological
research has significantly progressed in recent years and continues to reveal new characteristics of
biological neurons. New technologies can now capture temporal changes in the internal activity of the
brain in more detail and help clarify the relationship between brain activity and the perception of a
given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking
Neural Network (SNN), that draws more faithfully on biological properties to provide higher processing
abilities. A review of recent developments in learning of spiking neurons is presented in this paper.
First the biological background of SNN learning algorithms is reviewed. The important elements of
a learning algorithm such as the neuron model, synaptic plasticity, information encoding and SNN
topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for
SNNs using single and multiple spikes is presented. Additionally, deep spiking neural networks are
reviewed, and challenges and opportunities in the SNN field are discussed. Keywords: Spiking neural network (SNN) | Learning | Synaptic plasticity |
مقاله انگلیسی |

9 |
Extreme learning machine for a new hybrid morphological/linear perceptron
دستگاه یادگیری شدید برای مورفولوژی جدید ترکیبی / پرسپترون خطی-2020 Morphological neural networks (MNNs) can be characterized as a class of artificial neural networks that
perform an operation of mathematical morphology at every node, possibly followed by the application
of an activation function. Morphological perceptrons (MPs) and (gray-scale) morphological associative
memories are among the most widely known MNN models. Since their neuronal aggregation functions
are not differentiable, classical methods of non-linear optimization can in principle not be directly
applied in order to train these networks. The same observation holds true for hybrid morphological/
linear perceptrons and other related models. Circumventing these problems of non-differentiability,
this paper introduces an extreme learning machine approach for training a hybrid morphological/linear
perceptron, whose morphological components were drawn from previous MP models. We apply the
resulting model to a number of well-known classification problems from the literature and compare
the performance of our model with the ones of several related models, including some recent MNNs
and hybrid morphological/linear neural networks. Keywords: Mathematical morphology | Lattice computing | Morphological neural networks | Hybrid morphological/linear perceptron | Extreme learning machine | Classification |
مقاله انگلیسی |

10 |
Neural network aided development of a semi-empirical interatomic potential for titanium
شبکه عصبی به توسعه پتانسیل متقابل نیمه تجربی تیتانیوم کمک کرده است-2020 Artificial neural networks, utilizing machine learning techniques to uncover subtle and complex patterns in big
data problems, are able to condense large amounts of computationally expensive density functional theory and
ab initio results into classical force field potentials. However, in order to produce a computationally efficient
network, with minimal network architecture, a structural fingerprint whose components are highly correlated to
the per atom energy is necessary. In this paper, we demonstrate the effectiveness a structural fingerprint motivated
by the highly successful MEAM formalism by creating an artificial neural network containing a single
hidden layer of 20 nodes which provides a semi-empirical force field potential for elemental titanium. This
potential is suitable for dynamic calculations of α-, β-, and ω-titanium at a variety of temperatures. This potential
is able to achieve a number of results in agreement with DFT calculations which surpass classical potential
formalisms with comparable computational performance. Keywords: Machine learning | Neural networks | Titanium |
مقاله انگلیسی |