فصلنامه مدل سازی در مهندسی
پیاپی 70 (پاییز 1401)

Predicting data, in the form of complex and chaotic time series, is one of the fundamental issues in various scientific and industrial fields. Data-driven models such as artificial neural networks and fuzzy neural networks compared to other models have been received more attention due to their special features. To develop and improve these models, the concepts of the mammalian brain limbic system are used. Therefore, the brain emotional learning machine is introduced. In this paper, the online sequential extreme learning machine is used as the main component in the processing centers of the brain emotional learning machine. To interact between processing centers, the online sequential extreme learning machine is designed in the form of a recurrent memory network with transfer learning ability. The proposed model is named the brain emotional learning based on recurrent memory online extreme learning machine (BEL-ORMS-ELM). To evaluate and compare the efficiency of the proposed model, the initial parameters of the models are adjusted according to the Mackey-Glass and Lorenz time series data under the same conditions. Different models are evaluated and compared based on the valid measurable criteria in regression problems prediction. The simulation results show that the proposed model with sigmoid and hyperbolic tangent activation function for Mackey-Glass and Lorenz time series test data has the highest performance criteria compared to similar online models. It also has acceptable performance for training data compared to similar models.

In this paper, selective energy contacts are used to design a multi-wavelength optical photodetector. Here, 3D spherical shape quantum dots with quantum well-based energy selective contacts are simulated. The introduced technique helps us to design a novel photodetector that can simultaneously detect multi-wavelengths. These wavelengths can include several ultraviolet wavelengths or several infrared wavelengths or a combination of them. This detection can be achieved by intera-band and inter-band transitions in quantum dot-based Super-lattices. To detect higher wavelengths, intra-band transmissions and for lower wavelengths, inter-band transmissions can be used. In this work, suitable energy selective contacts are designed using quantum wells. Also, quantum dots with different radii are used to detect multi-wavelengths simultaneously. Finally, as an application of this structure, the results have been presented for two quantum dots capable of simultaneous detection of two UV radiations, simultaneously. The detector can be further improved by applying several quantum dots with different sizes, shapes and materials and using more ESCs.

In this paper, the directional overcurrent relays are adjusted according to the input and output of resources in the microgrid. Changes in the network structure will change the performance of the relays, so they will cause interruptions and thus reduce the reliability of the network. In the method used, by considering the injection of a virtual current according to the source isolated from the system, an attempt has been made not to change the initial settings of the directional overcurrent relays. Accordingly, using a telecommunication platform, all the required information is sent to the relays, and the relays, due to the presence or absence of sources, have a virtual value equal to the amount of short-circuit current of the separated or added sources in a soft way. They receive hardware, and this prevents the relay settings from changing to the original state where all resources are in service. The method used in an IEC microgrid with different generation types and in different operating modes and with respect to the presence or absence of short circuit power supplies, has been investigated.

Nanoresonators can be used in some engineering fields such as high quality factor filters for electronic signals and sensors. So, the main purpose of this paper is to analyze the nonlinear dynamic behavior of electrostatically actuated nanoresonators. In the framework of the Von Karman's theory and the Euler-Bernoulli beam model, the nonlinear governing equations of motion are developed using the Hamilton's principle. Also, the coupling of lateral and longitudinal vibrations is considered. The governing partial differential equations are discretized by means of the Galerkin’s method. The analytical and numerical solution are obtained using the multiple-scales method and Runge–Kutta method, respectively. The variation of natural frequencies and the response of the nanoresonator with the levels of the electrostatic load are investigated. The results show that the nonlinear frequency response curves are greatly affected by the gap distance and detuning frequency. A hardening behavior is observed in the nonlinear frequency responses of the nanoresonator. Also, through the comparison between the effect of gap distance and dc voltage, it is found that the natural frequencies of the nanoresonator are more sensitive to the variation of the dc voltage. So, this suggests a tunable resonator over a wide range of frequencies.

In this research, an optimal energy management model of a residential microgrid with the aim of resiliency and flexibility improvement is presented. Accordingly, a residential microgrid consisting of a solar power plant with energy storage system is considered. Each of houses in the microgrid has an electric vehicle. The residential microgrid is connected to the main grid and has been equipped with smart grid infrastructures. The outage of the main grid causes load shedding that consequently reduces the resiliency. Keeping the security of supply during these distortions is necessary. Therefore, in this paper, a new model of microgrid operation based on solar power plant and energy storage system is proposed. The efficiency of the proposed model is evaluated through numerical calculation of resiliency using a new index proposed in this paper. In addition, the proposed model improves the system flexibility. The amount of this improvement is evaluated using practical and accurate indicators including self-consumption and storage efficiency. The results show that the proposed model improves the resiliency in off-peak and peak hours by 4.7% and 9.75% respectively. Moreover, two indexes of flexibility, including self-consumption and storage efficiency, improve by 9.1% and 2.2% respectively.

Droplet based microfluidic systems have found versatile industrial and scientific applications, like biological engineering, lab-on-chip, drug delivery, and encapsulation. In the current study, the breakup of mother droplets in a symmetric Y-junction microchannel under an electric field is numerically investigated. To this approach, a 2D numerical model base on the level set method is performed by the COMSOL Multi-physics software. Two methods for applying the electric field are considered: a symmetric and an asymmetric electric field. In the symmetric electric field, it is observed that the mother droplet splits faster in the presence of an electric field when compared with the case without the electric field. Also, the mother droplet is divided into two daughter droplets with equal sizes. For the asymmetric electric field, the results reveal that the mother droplet splits into two daughter droplets with unequal sizes. Furthermore, a novel flow pattern called hybrid pattern is determined in the asymmetric splitting of droplets. In addition, there is a critical electric capillary number above which a non-splitting pattern.

In this manuscript, the noise jammer is modeled on the common AM, FM, common digital, and spread spectrum telecommunication systems. Then, the performance of the noise jammer is evaluated on the telecommunication systems. The evaluations are done based on the line of sight (LoS) and two-ray channel models. In the tow-ray channel model, the received Signal-to-noise ratio depends on the second power of the height of the antenna and the fourth power of the distance. The simulation results show the significant difference in the signal-to-noise ratio in receivers between LoS and two-ray channel models. Also, the processing gain effectiveness of the different telecommunication systems on the jammer signals is investigated. In the proposed modeling, the parameters have been loaded according to the real conditions, and the results can be used in the design of telecommunication structures and noise components. The results demonstrate the suitable performance of the FM and spread spectrum techniques against noise and jammer.

Mosquitoes are the main cause of the spread of dangerous diseases such as malaria, yellow fever, dengue fever, and Zika. The most effective way to control these diseases is to correctly identify the types of mosquito species. In the traditional method of identifying mosquitoes, identification is based on morphological diagnoses by specialized human beings with special skills. The most important classification challenge is to reduce the number of experts and the great diversity of different species of mosquitoes. In order to overcome this challenge, developing an automated method based on deep learning architectures to identify and classify mosquitoes will be a valuable resource for non-specialists.This study proposes a convolutional network model that integrates the ResNet101 architecture and the Mask_RCNN technique to segment and classifies mosquito images. 2354 mosquito images of three species of Anopheles, Aedes, and Culex are compared with each other. In the proposed model, instead of entering the network as a complete image of a mosquito, first, the images are segmented, and then different parts of the abdomen, legs, wings, and head are given to the network as input. The corresponding binary mask of the described parts of the mosquito body is produced by the convolution network to extract the feature for each separate part and then calculate the loss value between the classified values and the image label. The evaluation results showed that the extraction of mosquito anatomy images affects the faster classification of images and the network performed better with 97.84% accuracy than normal.

Sewage flow path is the main component of urban sewerage network infrastructure. Damage to sewers is less noticeable due to invisibility, and this failure to handle the damage leads to emergencies and unreasonable costs. These vital arteries need to be maintained and rebuilt during service for optimal performance in all dimensions. Nowadays, the methods of processing and classifying photos and videos taken by mobile videometer robots are widely used to inspect the sewer network. One of the successful algorithms in the field of image processing is the convolutional neural network algorithm, which is a subset of deep learning algorithm. In this paper, a convolutional neural network algorithm is used to classify images of sewer network damage and cases affecting the improvement, accuracy and performance of this algorithm. The images were obtained by a videometric robot from the sewer network. Results of using the proposed algorithm in the sewerage network, achieving 98% accuracy in classifying network faults and compared to other methods and also reducing the relatively low execution time of the proposed architecture (91 minutes) compared to other architectures valid ones are the same in deep learning on the same hardware platform. Also, in the future, the proposed algorithm will be used to analyze networks without the need for specialized personnel and also to control an automatic network videometry robot.

Network interdiction problems are a group of problems in which two actors face each other with conflicting goals. In these matters, the benefit of one actor damages the other actor. In the case of maximum capacity path interdiction, a defender in the role of leader applies his interdicting actions according to the available budget. At the next level several attackers, as followers observing the defender's interdiction action, optimize the maximum capacity path problem from the origin to the destination. Interdiction is attacking the network arcs to destroy them and reduce the capacity of the arc. In this research, first, a two-level binary mathematical programming model for the problem is described. Then, due to the complexity of solving two-level problems, a hybrid algorithm including a revised Dijkstra algorithm and a simulated annealing algorithm is proposed to solve the problem. The revised Dijkstra algorithm always finds an optimal solution to the maximum capacity problem. Therefore, the hybrid algorithm can find good solutions in the search space. Then, the efficiency of the proposed algorithm was evaluated up to the size of 100 nodes and 150 arcs, which shows the ability of the algorithm to solve problems in different sizes. Based on the conclusions, increasing the defender budget results in the objective function being improved to a certain extent. The coefficient of attackers in the objective function is inversely related to the quality of the attackers' path and increases or decreases the maximum capacity of the attackers' path.

All-optical logic gates are the most important unit for achieving all-optical processing systems. Developing a fast and efficient method for studying the behavior of all-optical logic gates is very important and has been considered by researchers. In this paper, general regression neural networks and linear method are used to predict a three-input all-optical XOR logic gate output. The simulation results show that both methods can precisely model the behavior of the device. The training time of the neural network in the linear method with the optimal structure is about 93 seconds, which is much longer than the GRNN method with a training time of 8 seconds. Both models predict the output in less than 1 second which show a great improvement over the conventional method with 12 seconds. In the GRNN method with the smoothing factor of 0.001, the best results were obtained with MSE, RSE and MAE error values of 1.97×10-7, 5.95×10-6, and 1.6×10-4, respectively. In the linear method with 200 initial training data, the minimum values of MSE, RSE, and MAE are 1.11×10-22, 2.14×10-16 and 2.11×10-11, respectively, and the best modeled output is achieved. The value of correlation coefficient (R2) between the modeled output and the desired output of the logic gate is one for both neural network methods, which indicates a very good prediction for this method.

The methods used for maximum exploitation of wind farms’ potentials can be classified into two categories: turbines layout optimization and utilization of turbines with different hub heights. The layout optimizations are generally performed by optimization algorithms. A drawback of these algorithms is their inability to render a single result as the optimized result. In the present study, by using an analytical model for 3D wind velocity distribution in the wake and including wake interactions, a model has been presented for estimation of the generated power of turbines implemented in wind farms. By coupling this model with the cost model of turbines' implementation, a function for the optimized layout has been presented. By calculating this function for different layouts of turbines in farms, the optimized layout can be obtained. In the present study, the calculations of turbines' layout optimization were carried out using a MATLAB-implemented code. By dividing the solution domain into grids and taking into account the minimum optimized distance of turbines from one another from the economical and power output aspects, the value of the layout optimization function has been calculated for different possible layouts. Finally, the layout of kilowatt-scaled turbines located among megawatt-scale ones for Horns Rev offshore farm has been modeled to obtain the maximum power output. From the results obtained for the optimized and maximum number of kilowatt-scaled turbines, it is inferred that the power of the farm has been increased by 7% (3.226 MW) by 35.7% efficiency and 9.35% (4.712 MW) by 34.06% efficiency,respectively.

Important parameters of strong ground motions in a site in terms of potential damage to structures can be represented by an intensity measure parameter. One of the intensity measures that is widely used in the quantitative representation of earthquake intensity in earthquake engineering is the spectral acceleration at the first-mode period of the structure. Recent studies have shown that the use of this parameter as an intensity measure may lead to high dispersion in predicting the nonlinear response of buildings. For this purpose, using intensity measures with higher sufficiency and efficiency has been received attention in recent years. One of these intensity measures that has been proposed in recent research is the optimal combination of spectral displacements. Approximately, the first-order Taylor series expansion can be used to derive an attenuation relationship for this intensity measure based on the existing relationships for spectral accelerations. However, the derivation of an independent attenuation relationship for this intensity measure in order to reduce the calculation error is inevitable. For this purpose, a ground motion prediction model was proposed for this intensity measure. In developing the attenuation relationship, more than five hundred and fifty pairs of accelerograms were used, which were recorded in 284 earthquakes with a magnitude of 4 to 7.6 and at distances of less than 100 km in different regions of Iran. The proposed model has been investigated by examining the residual values against magnitude and distance as well as its application in comparison with other attenuation relationships.

This paper proposed a new flexible approach to optimization of hybrid transmission Operating companies (HTOC) in which the maximization of the revenue of combined company as well as maximizing social welfare from the perspective of the independent system operator (ISO) is considered. The company's strategy is to maximize its revenue both through the transmission and through the sale and buying of energy in the day-ahead energy market using storage resources. The use of a bi-level model to optimize the objective functions of the problem along with the proposed strategy to maximize the company's revenue has been proposed, which has been transformed into a single-level model using the Karush–Kuhn–Tucker (KKT). In order to achieve a guaranteed income, the uncertainty of competitors' behavior is modeled by a modified scenario based method(MSBM). The proposed model is first simulated on a IEEE 9-Bus Standard System and then on a IEEE 57-Bus Standard System using GAMS software. The results show the effectiveness of the proposed strategy to maximize the revenue of the combined company.

The increasing use of wind resources has led to new challenges in transmission network expansion planning (TNEP). Wind curtailment due to improper expansion of transmission lines prevents the optimal use of these renewable resources. In addition, the uncertainty of these resources puts expansion decisions at risk. In this paper, risk-constrained TNEP in electricity markets in the presence of wind resources is presented with the aim of increasing the utilization of these resources by considering the wind curtailment cost and reducing expected energy not supplied (EENS). For this purpose, a risk-constrained bi-level stochastic model is used. At the upper level, transmission network expansion decisions are made by the independent system operator (ISO) with the aim of minimizing network investment cost and EENS cost. At the lower level, the market-clearing problems are solved maximizing social welfare. Using duality theory, the bi-level problem is converted into a mixed-integer linear programming (MILP) problem. The proposed framework is analyzed using Garver’s six-bus test system and the IEEE 24-bus reliability test system (RTS). The simulation results indicate that considering wind curtailment cost has increased the use of wind units and reduced the expected energy produced by fossil units, which is important from an environmental point of view. However, considering the risk, due to the uncertainty of wind resources, the energy produced by these resources has decreased.

Extracting the structure of space around a robot, is an important issue for robot navigation. In order to identify the structure of a corridor, it is necessary to analyze its composition. We use a camera to take some photos from the corridor in front of the robot, we apply the Sobel algorithm to detect edge lines in two directions, Sobel algorithm finds horizontal and vertical edge lines, the vertical and horizontal edges are respectively related to the wall and floor. then we use a threshold to reduce unnecessary edge linesc and refuse the useless edge lines and eliminate them by introduced threshold methods, so it reduces the computation time and prevent the impact of unnecessary data. We also use the intersection points of the edge lines to obtain the boundary when vertical and horizontal lines cannot give us the wall-floor boundary lines. for more robustness we use a segmentation algorithm based on clustering. These three together help to identify structural cues of corridor such as wall, floor, wall-floor borderline. These cues are necessary for robot navigation in indoor environment.