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نتیجه جستجو - شبکه های عصبی مصنوعی

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 |
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 |

3 |
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 |

4 |
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 |

5 |
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 |

6 |
Predicting consumer gaze hits: A simulation model of visual attention to dynamic marketing stimuli
پیش بینی بازدیدها از نگاه مصرف کننده : یک مدل شبیه سازی توجه بصری به محرک های بازاریابی پویا-2020 The purpose of the present study is to build and test a simulation model for the prediction of gaze hits in the
context of dynamic marketing stimuli. Forecasting the attentional effect of dynamic stimuli is of particular
interest when it comes to indirect forms of marketing communication such as sponsorship, product placement, or
in-game-advertising. Based on large-scale eye tracking data an artificial neural network was trained, providing
high predictive accuracy. The models business applicability is demonstrated with the case of a soccer sponsorship,
using media data and color features as model input. The study highlights the value of eye tracking data
for the ex-ante valuation of visual communication stimuli which benefits marketing management at the initiation,
implementation, and evaluation stages. Keywords: Eye tracking | Visual attention | Indirect marketing | Simulation model | Artificial neural network |
مقاله انگلیسی |

7 |
Sparsity through evolutionary pruning prevents neuronal networks from overfitting
Sparsity از طریق هرس تکاملی شبکه های عصبی جلوگیری می از Over-fitting-2020 Modern Machine learning techniques take advantage of the exponentially rising calculation power in
new generation processor units. Thus, the number of parameters which are trained to solve complex
tasks was highly increased over the last decades. However, still the networks fail – in contrast to our
brain – to develop general intelligence in the sense of being able to solve several complex tasks with
only one network architecture. This could be the case because the brain is not a randomly initialized
neural network, which has to be trained from scratch by simply investing a lot of calculation power, but
has from birth some fixed hierarchical structure. To make progress in decoding the structural basis of
biological neural networks we here chose a bottom-up approach, where we evolutionarily trained small
neural networks in performing a maze task. This simple maze task requires dynamic decision making
with delayed rewards. We were able to show that during the evolutionary optimization random
severance of connections leads to better generalization performance of the networks compared to fully
connected networks. We conclude that sparsity is a cent Keywords: Evolution | Artificial neural networks | Maze task | Evolutionary algorithm | Overfitting | Biological plausibility |
مقاله انگلیسی |

8 |
Extract interpretability-accuracy balanced rules from artificial neural networks: A review
استخراج قوانین متعادل با دقت تفسیر از شبکه های عصبی مصنوعی: بررسی-2020 Artificial neural networks (ANN) have been widely used and have achieved remarkable achievements. However, neural networks with high accuracy and good performance often have extremely complex in- ternal structures such as deep neural networks (DNN). This shortcoming makes the neural networks as incomprehensible as a black box, which is unacceptable in some practical applications. But pursuing ex- cessive interpretation of the neural networks will make the performance of the model worse. Based on this contradictory issue, we first summarize the mainstream methods about quantitatively evaluating the accuracy and interpretability of rule set. And then review existing methods on extracting rules from Mul- tilayer Perceptron (MLP) and DNN in three categories: Decomposition Approach (Extract rules in neuron level such as visualizing the structure of network), Pedagogical Approach (By studying the correspon- dence between input and output such as by computing gradient) and Eclectics Approach (Combine the above two ideas). Some potential research directions about extracting rules from DNN are discussed in the last. Keywords: Rule extraction | Accuracy | Interpretability | Multilayer Perceptron | Deep neural network |
مقاله انگلیسی |

9 |
Prediction of BLEVE mechanical energy by implementation of artificial neural network
پیش بینی انرژی مکانیکی BLEVE با اجرای شبکه عصبی مصنوعی-2020 In the event of a BLEVE, the overpressure wave can cause important effects over a certain area. Several thermodynamic
assumptions have been proposed as the basis for developing methodologies to predict both the
mechanical energy associated to such a wave and the peak overpressure. According to a recent comparative
analysis, methods based on real gas behavior and adiabatic irreversible expansion assumptions can give a good
estimation of this energy. In this communication, the Artificial Neural Network (ANN) approach has been
implemented to predict the BLEVE mechanical energy for the case of propane and butane. Temperature and
vessel filling degree at failure have been considered as input parameters (plus vessel volume), and the BLEVE
blast energy has been estimated as output data by the ANN model. A Bayesian Regularization algorithm was
chosen as the three-layer backpropagation training algorithm. Based on the neurons optimization process, the
number of neurons at the hidden layer was five in the case of propane and four in the case of butane. The transfer
function applied in this layer was a sigmoid, because it had an easy and straightforward differentiation for using
in the backpropagation algorithm. For the output layer, the number of neurons had to be one in both cases, and
the transfer function was purelin (linear). The model performance has been compared with experimental values,
proving that the mechanical energy of a BLEVE explosion can be adequately predicted with the Artificial Neural
Network approach. Keywords: BLEVE | Vessel explosion | Explosion energy | Blast overpressure | Pressure wave | Artificial neural network |
مقاله انگلیسی |

10 |
Integration of data-driven modeling techniques for lean zone and shale barrier characterization in SAGD reservoirs
ادغام تکنیک های مدل سازی داده محور برای منطقه ناب و خصوصیات سد شیل در مخازن SAGD -2019 High water saturation zone, which is also known as lean zone, and shale barrier, are two common types of
heterogeneous features in steam-assisted gravity drainage (SAGD) reservoirs. Lean zone poses a detrimental
influence on conventional SAGD operations, as it causes steam utilization efficiency to decrease and increases
the steam-oil ratio. Shale barrier would also impede the vertical growth and lateral spread of a steam chamber
and potentially reduce oil production. An efficient characterization workflow is proposed to estimate the
quantity, location, and volume of these heterogeneous features by integration of data-driven modeling techniques.
Field data including geophysical, operational and production data corresponding to several existing SAGD
projects are extracted from the public domain and used to build a series synthetic homogeneous models. Lean
zones and shale barriers with varying distribution and volume are randomly added. From the corresponding
production time-series data, a set of input features are identified through a hybrid method comprised of discrete
wavelet transformation (DWT) and principal component analysis (PCA), while the output parameters are formulated
to describe the actual number and geological parameters of two types of reservoir heterogeneities. A
two-level data-driven model based on artificial neural networks (ANN) is employed to correlate the complex
relationship between input and output attributes. Finally, this calibrated model is integrated into a novel
characterization workflow to infer an ensemble of probable realizations of lean zone and shale barrier distribution
that are conditioned to a given production historical profile.
This work demonstrates the potential of practical application of data-driven models in correlating complex
reservoir heterogeneity properties and production time-series data. Results from the case study illustrate the
utility of the proposed workflow in facilitating the efficient identification of heterogeneous features from SAGD
profiles. The potential savings of computational efforts associated with the proposed methodology are also explained. Keywords: Artificial neural networks | Reservoir heterogeneities | Pattern recognition | Enhanced oil recovery | Time-series analysis |
مقاله انگلیسی |