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.
Establishment and application of intelligent city building information model based on BP neural network model
ایجاد و کاربرد مدل اطلاعات هوشمند شهرسازی براساس مدل شبکه عصبی BP-2020
The construction of smart cities in our country has received extensive attention. Under the situation that smart cities are vigorously promoted nowadays, compared with traditional construction and operation and maintenance methods, building information model (BIM) technology is more suitable to serve as an important foundation for intelligent management in the whole process of construction projects. BIM is an abbreviation for building information model. BIM relies on a variety of digital technologies, which can be used to realize information modeling of urban buildings and infrastructure. The efficiency of information exchange in the process of intelligence construction ensures the integrity and accuracy of information data exchange and maintains the consistency of information data exchange. Data and information have objectivity, applicability, transferability, and sharing. Geographic data is a digital representation of various geographical features and phenomena and their relationships. BIM is a digital representation of physical and functional characteristics of a facility. It can It is used as a shared knowledge resource for facility information. It becomes a reliable basis for facility life-cycle decision-making. Input BP neural network, and then learn and train by BP neural network.
Keywords: BP neural network | Smart city | Building information model
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
A mesh-free method for interface problems using the deep learning approach
روشی بدون مش برای مشکلات رابط با استفاده از روش یادگیری عمیق-2020
In this paper, we propose a mesh-free method to solve interface problems using the deep learning approach. Two types of PDEs are considered. The first one is an elliptic PDE with a discontinuous and high-contrast coefficient. While the second one is a linear elasticity equation with discontinuous stress tensor. In both cases, we represent the solutions of the PDEs using the deep neural networks (DNNs) and formulate the PDEs into variational problems, which can be solved via the deep learning approach. To deal with inhomogeneous boundary conditions, we use a shallow neural network to approximate the boundary conditions. Instead of using an adaptive mesh refinement method or specially designed basis functions or numerical schemes to compute the PDE solutions, the proposed method has the advantages that it is easy to implement and is mesh-free. Finally, we present numerical results to demonstrate the accuracy and efficiency of the proposed method for interface problems.
Keywords: Deep learning | Variational problems | Mesh-free method | Linear elasticity | High-contrast | Interface problems
Random recurrent neural networks with delays
شبکه های عصبی مکرر تصادفی با تأخیر-2020
An infinite lattice model of a recurrent neural network with random connection strengths between neurons is developed and analyzed. To incorporate the presence of various type of delays in the neural networks, both discrete and distributed time varying delays are considered in the model. For the existence of random pullback attractors and periodic attractors, the nonlinear terms of the resulting system are not expected to be Lipschitz continuous, but only satisfy a weaker continuity assumption along with growth conditions, under which the uniqueness of the underlying Cauchy problem may not hold. Then after extending the concept and theory of monotone multi-valued semiflows to the random context, the structure of random pullback attractors with or without periodicity is investigated. In particular, the existence and stability properties of extremal random complete trajectories are studied.
Keywords:Random recurrent neural network | Random attractor | Multi-valued non compact random dynamical system | Extremal random complete trajectory | Variable delay | Monotone
A biomorphic neuroprocessor based on a composite memristor-diode crossbar
یک پردازشگر عصبی بیومورفیک بر اساس یک قطر کامپوزیت دیود ممیستور-2020
A concept of biomorphic neuroprocessor that implements hardware spiking neural network for traditional tasks of information processing and can simulate operation of brain cortical column or its fragment is proposed. The key units of hardware neural network are memory and logic matrices, previously developed on the basis of composite memristor-diode crossbar. These matrices provide high element integration and energy efficiency compared to known neuroprocessors and individual matrices. Such efficiency is achieved by application of mixed analogdigital computations, including those that use memristors integrated in composite memristor-diode crossbars. Neuron electrical model was constructed on the basis of these matrices and the Hodgkin-Huxley biomorphic model for neuron membrane. Unlike existing neural networks with synapses based on discrete memristors, the generation of new association was demonstrated in memristor-diode crossbar by SPICE modeling of associative self-learning processes. The adaptation procedure for biomorphic neural network program to neuroprocessor hardware is defined. In essence, presented neuroprocessor is a prototype of new generation computers with artificial intelligence.
Keywords: Biomorphic neuroprocessor | Biomorphic software and hardware electrical | models of neuron | Composite memristor-diode crossbar | Memory matrix | Logic matrix | Associative self-learning
Data mining and application of ship impact spectrum acceleration based on PNN neural network
داده کاوی و کاربرد شتاب طیف تأثیر کشتی بر اساس شبکه عصبی PNN-2020
The selection of the smoothing coefficient of the probabilistic neural network directly affects the performance of the network. Traditionally, all the mode layer neurons use a uniform smoothing coefficient, and then the optimal smoothing parameters suitable for this problem are searched by the optimization algorithm. In this study, the smoothing coefficients of the mode layer neurons connected by the same summation layer are set to the same value, which not only reflects the relationship between the training samples of the same pattern, but also highlights the difference between the training samples of different modes. Two probabilistic neural network models are applied to the ship impact environment prediction respectively. The results show that the classification effect of multiple smoothing factors is further improved than the single smoothing factor network.
Keywords: Ship impact environment prediction | Probabilistic neural network | Smoothing coefficient | Optimization algorithm
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
Neural network with multiple connection weights
شبکه های عصبی با وزن اتصال چندگانه-2020
Biological studies have shown that the interaction between neurons are based on neurotransmitters, which transmit signals between neurons, and that one neuron sends information to another neuron by releasing a number of different neurotransmitters, which play different roles. Motivated by this biolog- ical discovery, a novel neural networks model is proposed by extending the dimension of connection weights from one to multiple, i.e. there are multiple not only one connections between each two units. The number of dimensions of connection weight represents the number of categories of neurotransmit- ters and different com ponents of the weight correspond to different neurotransmitters. In order to make these neurotransmitters collaborate and compete appropriately, the input and output for each unit in our proposed model have been heuristically defined. From the biological perspective, the proposed neural network is much closer to biological neural network. From the viewpoint of new model structure, the characteristic that the activation of each hidden unit is based on several filters, can improve the inter- pretability of features learned by the proposed neural network. Experimental results on MNIST, NORB and several other data sets have demonstrated that the performances of traditional neural networks can be improved by extending the dimension of connection weight between units, and the idea of multiple connection weights provides a new paradigm for the design of neural networks.
Keywords: Neural network | Neurotransmitter | Interpretability | Extending dimension