با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
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
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
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
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)
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
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
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
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
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
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