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
A different sleep apnea classification system with neural network based on the acceleration signals
یک سیستم طبقه بندی sleep apnea متفاوت با شبکه عصبی مبتنی بر سیگنال های شتاب-2020
Background and objective: The apnea syndrome is characterized by an abnormal breath pause or reduction in the airflow during sleep. It is reported in the literature that it affects 2% of middle-aged women and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly, the disabled, and pediatric sleep apnea patients. Methods: In this study, a new diagnostic method is developed to detect the apnea event by using a microelectromechanical system (MEMS) based acceleration sensor. It records the value of acceleration by measuring the movements of the diaphragm in three axes during the respiratory. The measurements are carried out simultaneously, a medical spirometer (Fukuda Sangyo), to test the validity of measurement results. An artificial neural network model was designed to determine the apnea event. For the number of neurons in the hidden layer, 1-3-5-10-18-20-25 values were tried, and the network with three hidden neurons giving the most suitable result was selected. In the designed ANN, three layers were formed that three neurons in the hidden layer, the two neurons at the input, and two neurons at the output layer. Results: A study group was formed of 5 patients (having different characteristics (age, height, and body weight)). The patients in the study group have sleep apnea (SA) in different grades. Several 12.723 acceleration data (ACC) in the XYZ-axis from 5 different patients are recorded for apnea event training and detection. The measured accelerometer (ACC) data from one of the patients (called H1) are used to train an ANN. During the training phase, MSE is used to calculate the fitness value of the apnea event. Then Apnea event is detected successfully for the other patients by using ANN trained only with H1’s ACC data. Conclusions: The sleep apnea event detection system is presented by using ANN from directly acceleration values. Measurements are performed by the MEMS-based accelerometer and Industrial Spirometer simultaneously. A total of 12723 acceleration data is measured from 5 different patients. The best result in 7000 iterations was reached (the number of iterations was tried up to 10.000 with 1000 steps). 605 data of only H1 measurements are used to train ANN, and then all data used to check the performance of the ANN as well as H2, H3, H4, and H5 measurement results. MSE performance benchmark shows us that trained ANN successfully detects apnea events. One of the contributions of this study to literature is that only ACC data are used in the ANN training step. After training for one patient, the ANN system can monitor the apnea event situation on-line for others.
Keywords: Sleep apnea | Acceleration sensor | Acceleration data | Artificial neural network | Medical decision making
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
Development of a chemometric-assisted spectrophotometric method for quantitative simultaneous determination of Amlodipine and Valsartan in commercial tablet
توسعه یک روش اسپکتروفتومتری با کمک شیمیایی برای تعیین کمی همزمان آملودیپین و والرسارتان در قرص تجاری-2020
In this study, two drugs named Amlodipine (AML) and Valsartan (VAL) related to the high blood pressure were simultaneously determined in synthetic mixtures and Valzomix tablet. For this purpose, the chemometric-assisted spectrophotometric method was developed without any prepreparation. Artificial intelligence techniques, including artificial neural network (ANN) and least squares support vector machine (LS-SVM) as chemometrics procedures were proposed. Feed-forward back-propagation neural network (FFBP-NN) with two different algorithms, containing Levenberg–Marquardt (LM) and gradient descent with momentum and adaptive learning rate backpropagation (GDX) was applied. To select the best model, several layers and neurons were investigated. The results revealed that layer = 5 with 6 neurons and layer = 2 with 10 neurons had lower mean square error (MSE) (1.41 × 10−24, 1.16 × 10-23) for AML and VAL, respectively. In the LS-SVM method, gamma (γ) and sigma (σ) parameters were optimized. γ and σ were obtained 50, 30 and 40, 40 with the root mean square error (RMSE) of 0.4290 and 0.5598 for AML and VAL, respectively. Analysis of the pharmaceutical formulation was evaluated through the chemometrics methods and high-performance liquid chromatography (HPLC) as a reference technique. The obtained results were statistically compared with each other using the one-way analysis of variance (ANOVA) test. There were no significant differences between them and the proposed method was satisfactory for estimating the components of the Valzomix tablet.
Keywords: Spectrophotometry | Amlodipine | Valsartan | Artificial neural network | Least squares support vector machine
Wake modeling of wind turbines using machine learning
مدل سازی توربین های بادی با استفاده از یادگیری ماشین-2020
In the paper, a novel framework that employs the machine learning and CFD (computational fluid dynamics) simulation to develop new wake velocity and turbulence models with high accuracy and good efficiency is proposed to improve the turbine wake predictions. An ANN (artificial neural network) model based on the backpropagation (BP) algorithm is designed to build the underlying spatial relationship between the inflow conditions and the three-dimensional wake flows. To save the computational cost, a reduced-order turbine model ADM-R (actuator disk model with rotation), is incorporated into RANS (Reynolds-averaged Navier-Stokes equations) simulations coupled with a modified k − ε turbulence model to provide big datasets of wake flow for training, testing, and validation of the ANN model. The numerical framework of RANS/ADM-R simulations is validated by a standalone Vestas V80 2MW wind turbine and NTNU wind tunnel test of double aligned turbines. In the ANN-based wake model, the inflow wind speed and turbulence intensity at hub height are selected as input variables, while the spatial velocity deficit and added turbulence kinetic energy (TKE) in wake field are taken as output variables. The ANN-based wake model is first deployed to a standalone turbine, and then the spatial wake characteristics and power generation of an aligned 8-turbine row as representation of Horns Rev wind farm are also validated against Large Eddy Simulations (LES) and field measurement. The results of ANNbased wake model show good agreement with the numerical simulations and measurement data, indicating that the ANN is capable of establishing the complex spatial relationship between inflow conditions and the wake flows. The machine learning techniques can remarkably improve the accuracy and efficiency of wake predictions.
Keywords: Wind turbine wake | Wake model | Artificial neural network (ANN) | Machine learning | ADM-R (actuator-disk model with rotation) | model | Computational fluid dynamics (CFD)
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
Prediction of the ground temperature with ANN, LS-SVM and fuzzy LS-SVM for GSHP application
پیش بینی دمای زمین با شبکه های عصبی، LS-SVM و LS-SVM فازی برای استفاده GSHP-2020
Ground source heat pump (GSHP) system has received more and more attentions for its energy-conserving and environmental-friendly properties. Acquisition of the undisturbed ground temperature is the prerequisite for designing of GSHP system. Measurement by burying temperature sensors underground is the conventional means for obtaining the ground temperature data. However, this way is usually time consuming and high investment, and also easily encounter with certain technical difficulties. The rapid development of intelligent computation algorithm provides solutions for many realistic difficult problems. Basing on a great number of the measured data of the ground temperature from two boreholes with 100m depth located in Chongqing, ground temperature prediction models basing on artificial neural network (ANN) and support vector machine based on least square (LS-SVM) are established, respectively. And then, two kinds of validation works, i.e., holdout validation and k-fold validation are conducted toward the two models, respectively. Furthermore, a new method that correlating fuzzy theory with LS-SVM is proposed to solve the big computation burden problem encountered by LS-SVM model. By comparing with the above two models, it is concluded that the newly proposed model can not only improve the calculation speed obviously but also be able to promote the prediction accuracy, especially superior to the single LS-SVM model.
Keywords: Ground temperature | Fuzzy | Support vector machine | Ground source heat pump
Micro-combined heat and power using dual fuel engine and biogas from discontinuous anaerobic digestion
گرما و قدرت میکرو ترکیبی با استفاده از موتور سوخت دوگانه و بیوگاز از هضم بی هوازی ناپیوسته-2020
The modeling of the Micro-CHP unit operating in dual-fuel mode is performed based on experimental results carried out at the laboratory scale. The engine tests were performed on an AVL engine, with a maximum power of 3.5 kW, using conventional diesel as pilot fuel and synthetic biogas as primary fuel. The biogas flow rate is evaluated using the experimental results from the literature, based on the anaerobic digestion in batch reactor of a mixture of 26% of Oat Straw and 74% of Cow Manure, diluted to contain only 4% of volatile solid. The engine operation was modeled using the Artificial Neuron Network (ANN) method. Experimental engine tests were used as a database for training and validation phases of ANN models. Three different ANN models are developed to model respectively the pilot fuel flow rate, the airflow rate and the exhaust gas temperature. Engine power output, biogas flow rate and biogas methane content were used as the same input layer. Given that the evolution of the biogas flow evolves along the entire digestion duration (50 days), the simulation work is performed by varying the number of digesters to be used in parallel mode. It is obtained that the optimal operation condition, minimizing the number of digesters and using less than 10% of the energy from diesel fuel, is to use 5 digesters and run the engine under load of 70%. It is concluded that a micro-CHP unit of 1 kWe, requires a dual fuel generator with a nominal power of 1 kWe, five digesters and a daily availability of effluents of 171 kg/day, consisting of 45 kg/day of oat straw and 126 kg/day of cow manure. It can also produce up to 2.45 kW of thermal power from the exhaust.
Keywords: Micro CHP | Anaerobic digestion | Dual fuel engine | Artificial Neural Network | Cogeneration
Application of optimized Artificial and Radial Basis neural networks by using modified Genetic Algorithm on discharge coefficient prediction of modified labyrinth side weir with two and four cycles
استفاده از شبکه های عصبی بهینه سازی شده مصنوعی و شعاعی با استفاده از الگوریتم ژنتیک اصلاح شده بر پیش بینی ضریب تخلیه ریزگرد سمت اصلاح شده با دو و چهار چرخه-2020
Determining the discharge coefficient is one of the most important processes in designing side weirs. In this study, the structure of Artificial Neural Network (ANN) and Radial Basis Neural Network (RBNN) methods are optimized by a modified Genetic Algorithm (GA). So two new hybrid methods of Genetic Algorithm Artificial neural network (GAA) and Genetic Algorithm Radial Basis neural network (GARB), were introduced and compared with each other. The modified GA was used to find the neuron number in the hidden layers of the ANN and to find the spread value and the neuron number of the RBNN method, as well. GAA and GARB were tested for predicting the discharge coefficient of a modified labyrinth side weir he GARB method could successfully predict the accurate discharge coefficient even in cases where there is a limited number of train datasets available.
Keywords: Artificial neural network | Discharge coefficient | Hybrid model | Labyrinth side weir | Modified | Genetic algorithm | Radial basis neural network
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