فصلنامه مدل سازی در مهندسی
پیاپی 75 (زمستان 1402)

Speaker recognition is a process of recognizing persons based on their voice which is widely used in many applications. Although many researches have been performed in this domain, there are some challenges that have not been addressed yet. In this research, Neutrosophic (NS) theory and convolutional neural networks (CNN) are used to improve the accuracy of speaker recognition systems. To do this, at first, the spectrogram of the signal is created from the speech signal and then transferred to the NS domain. In the next step, the alpha correction operator is applied repeatedly until reaching constant entropy in subsequent iterations. Finally, a convolutional neural networks architecture is proposed to classify spectrograms in the NS domain. Two datasets TIMIT and Aurora2 are used to evaluate the effectiveness of the proposed method. The precision of the proposed method on two datasets TIMIT and Aurora2 are 93.79% and 95.24%, respectively, demonstrating that the proposed model outperforms competitive models.

Deepfake refers to a category of fake and artificial data in which fake content is produced based on existing content. This content can include image, video and audio signals. Deepfake production is based on deep generative networks that manipulate data or produce fake images and videos. In recent years, many studies have been conducted to understand how deepfakes work, and many methods based on deep learning have been introduced to identify videos or images produced by deepfakes and distinguish them from real images. In order to improve the accuracy of deep-fake detection and simultaneously use the capabilities of different types of convolutional neural networks, in this article, a hybrid model is presented using four convolutional neural networks: DenseNet201, EfficientNetB2, Inception-ResNet-V2, and ResNet152. turns Relying on the high capabilities of these networks in extracting effective features from the input image, the proposed model is able to simultaneously recognize whether the input image is deep or not by these four models. The results presented on the three databases of 140k real and fake faces, DFDC faces and Deepfake and real images indicate the improvement of the results compared to the existing models.

Nowadays renewable energy sources, such as wind and solar, whether independently or integrated with other resources, are considered in power system, specifically self-scheduling, bidding and offering strategy problems. However, the uncertain nature of these sources has turned out the greatest challenge for their owners, which makes the bidding and offering in the restructured electricity market more complicated because wind energy generation may cause penalty fees for its generation mismatches. Hence, the primary objective of this paper is to suggest a novel bidding strategy framework based on fuzzy random variable for a wind-thermal system in the electricity market for the first time. The uncertainties associated with day ahead energy, spinning reserve market prices and imbalance prices, are characterized by random fuzzy variables and the uncertainties associated with wind power outputs are modeled as a LR fuzzy numbers. The proposed self-scheduling model maximizes the expected profits while it controls the risk by providing different possibility and probability levels for decision makers.A mathematical modeling approach is applied in this research by using a mixed-integer non-linear programming model which is implemented in Lingo software in a case study of thermal generation unit to investigate the efficiency of the proposed model. A sensitivity analysis is applied to validate the performance of the proposed model. Numerical results reveal that taking advantage of wind power generation alongside with thermal generation will substantially increase the profitability of the integrated generation company.

Liquid marbles are emerging discrete droplet microfluidic systems that are a simple alternative to conventional droplet microfluids encapsulated by hydrophobic nanoparticles or micro-particles. One of the most important applications of liquid marbles is their use in biochemistry, biomedicine, nanotechnology and so on. In this research, liquid marbles are studied in two parts of practical and simulation. In the practical part, for the production of liquid marbles, magnetite nanoparticles are first synthesized and hydrophobised with paraffin wax. In the next step, using a micropipette, a droplet of water is placed on the nanoparticles and by tilting the surface, it is rolled on the nanoparticles, which finally forms a "liquid marble". Finally, the opening and closing of the powdery shell studied using a magnetic field induced by a fixed magnet. In the simulation section, by modelling the marble with an elastic shell around the droplet, the effect of the gravitational force on it, was examined and it was observed that the simulation results show well agreement with the practical part.

One of the most important challenges facing mechanical parts that are exposed to dynamic loads in various industries, such as automotive and aerospace, is their failure due to fatigue. Al-2024 series alloys are widely used due to their high strength-to-weight ratio and high fatigue resistance. Therefore, increasing the fatigue life of structures made of these types of alloys is always one of the most important design issues. In this study, the impact of changes in laser pulse pressure parameters as well as the percentage of overlap of laser effect points in the laser shock process, its effect on the fracture and fatigue behavior of aluminum alloy with the help of numerical modeling methods and also experimental tests were carried out and then evaluated. they got. After examining the results of the fracture tests, it was observed that the fracture toughness of the shocked samples increases by 38%. Also, the fatigue life of the samples treated with this method has been improved by 10-32% in different conditions, and in general, the shock process has been effective in improving the fracture and fatigue behavior of aluminum alloy.

One of the important factors that are always discussed in the study of power systems is the issue of power system stability. In this paper, the load angle control of a three-phase synchronous steam power plant generator is presented by overcoming the load angle fluctuations in both transient and permanent system conditions and expanding the range of stable generator performance. First, the equations of the linear state space of the steam power plant around the operating point are extracted. Then, based on Hankel single values, the model is approximated to the 6th order and based on that, a proportional, derivative, and integral controller for the load angle is presented. Then, using the arithmetic optimization algorithm, the controller coefficients are adjusted with two approaches reducing the settling time and reducing the overshoot of the step response. The stability of the proposed controller output power was also used using the stability criteria Bode and Nyquist was examined. The simulation results show that the arithmetic optimization algorithm has a better performance in controlling the load angle of the three-phase synchronous generator than the fuzzy controller and genetic and harris hawks algorithms. For example, in comparison with the genetic algorithm, the output power has good stability, and the rise time, settling time and the amount of overshoot in the step response of the proposed algorithm is reduced by 82.97, 82, and 40.18%, respectively.

In this paper, a robust control method for a double fed induction generator(DFIG) is proposed in which noise measurement is also considered. DFIG controllers are divided into two groups of the rotor side converter and the grid side converter controllers. The main purpose of an RSC controller is to control active and reactive power of the stator. The parameters of the double fed induction generator may deviate from the nominal values due to the operating conditions. For this parametric uncertainty, a robust H∞ vector control is employed using the complex sensitivity approach. The design of the rotor side controller is done using a vector control strategy and instead of PI controllers, a designed robust controller is used. One of the steps of vector control is to measure the rotor currents and use them in control equations. If the measured currents contain noise, the system control is disrupted. Therefore, to solve this problem, it is suggested to use Kalman filter. The effectiveness of the proposed method has been investigated using simulations under different conditions and compared with classical vector control and direct power control. The simulation results show the efficient performance and robustness of the proposed controller with model and measurement uncertainties.

In this article, we presented a method to detect trust in social networks according to individual and personal characteristics with the help of the adaptive neuro-fuzzy inference system method. The current research required a dataset, for this purpose we designed an online questionnaire and collected 1000 records with the variables of age, gender, occupation, hours of activity in the virtual space, the type of use of the virtual space and the type of relationships in the virtual space, this dataset is as reference data can be used for similar analyzes and has a high level of data security. First, we evaluated and descriptively analyzed the data set, for this purpose we used Excel and SPSS software, we modeled and analyzed using MATLAB simulation. To introduce change limits and fuzzy behavior for variables, dataset parameters were introduced to the algorithm using Bayesian membership functions. Due to the uncertainty of the type of membership functions, coverage of the space under control, less computational volume, reduction of analysis time and increase of accuracy, we used the deductive clustering method and trained the network using feedforward neural network and trained the network with data. We continued the training until we reached full convergence. We entered the test and check data and using the squared error performance function, we came to the conclusion that with the method used in this research, it is possible to predict people's trust in each other in virtual space with an error of less than 1.5%.

In recent years, community detection in social networks has become one of the most important research areas. One of the ways to community detection is to use interactions between users. There are different types of interactions in social networks, which, if used together with network topology, improve the precision of community identification. In this paper, a new method based on the combination of user interactions and network topology is proposed to community detection. In the community formation stage, the effective nodes are identified based on eigenvector centrality, and the primary communities around these nodes are formed based on frequent pattern mining. In the community expansion phase, small communities expand using modularity and the degree of interactions among users. To calculate the degree of interaction between users, a new measure based on the local clustering coefficient and interactions between common neighbors is proposed, which improves the accuracy of the degree of user interactions. Analysis of Higgs Twitter and Flickr datasets utilizing internal density metric, NMI and Omega demonstrates that the proposed method outperforms the other five community detection methods.

In this research, the thermal performance of the earth -to-air exchanger with three parallel pipes is simulated in 3D. The thermal saturation of the soil in the considered exchanger with a length of 30 m and a burial depth of 3 m in Tehran city has been investigated. The aim of the research is to investigate the effect of the distance between the pipes on the thermal saturation of the around soil consequently, its effect on the exit temperature and the overall heat transfer rate. The results showed that in the first days of operation, the temperature of the air exiting from the middle and side pipes does not change much; But with the passage of time, the temperature of the air in the middle pipe decreases to a smaller amount than the side pipes. The distance between the pipes has a significant effect on the outlet temperature of the middle and side pipes, especially after a few days of system operation. By increasing the distance between the pipes from 25 to 50, 75 and 100 cm, the heat transfer amount from the middle pipe at a flow rate of 0.1 kg/s increases by 14.5%, 26.5 and 35.6%, respectively.

This paper presents a novel method based on the well-known Taguchi optimization method to design and optimize a high-speed permanent magnet synchronous machine (HS-PMSM). An HS-PMSG is analytically designed at the first step, and next, it has been optimized by the Taguchi optimization method and verified through FEM analysis. Results obtained from the electromagnetic and mechanical simulations of HS-PMSG show that in the optimized design: Owing to the reduction in the thicknesses of the retention sleeve (48.05%) and PM (16.66%), as a consequence, the total size and dimensions of the HS-PMSG are reduced. The weight of PM and the retention sleeve are reduced by about 16.31% and 29.28% responsively, and as a result, the total weight of HS-PMSG is reduced by approximately 1.94%. The Joule loss is reduced by about 9.80%. The HS-PMSG efficiency is improved by 0.02%, and finally, the cogging torque is reduced by 22.44%, compared with the initially designed machine.

The extensive and detailed network of oil and gas transmission lines in the National Oil Company passes through overpasses and underpasses in various places. The 33 km oil pipeline from Mansouri oil field to Booster Ahvaz, will bring many personal and environmental risks from residential areas. In order to assess the risk of constructing this pipeline, the amount of oil release in three different types of small, medium and large leaks was evaluated hydraulically and dynamically by OLGA software. Among the 6 analyzed scenarios, in the case of complete pipeline rupture about 220 tons of oil will be released in the environment within 5 minutes, which will cause many individual, and environmental risks. At the next step, the consequences of leaking this amount of oil were evaluated with PHAST software. Based on the results, the maximum safe space related to individual risk has been created in the vicinity of Masharahat village, where the individual risk area of the ALARP is 760 meters away and near the village of Abu Bakriyeh 730 meters from the center of the oil pipeline, and practically a part of the mentioned villages is in Within the ALARP zone, individual risk is placed. By installing line break valves in the middle and end of the pipeline, trying to reduce the corrosion rate of the pipeline, trying to reduce human errors in incorrect operation of the line will reduce the risk of using the pipeline, especially in places where the line is located near the mentioned villages.

Among four joints of human foot in the ankle area, the tibiotalar and subtalar joints play the most important role in making it possible for the foot to perform rotational movements such that kinematic behavior of foot is almost completely affected by orientation of their rotation axes. Deviation of the axis of rotation from the normal position can impair the function of the ankle and even the lower extremity. In this study, a new non-invasive method has been proposed, through which, using a gyroscopic mechanism, the orientation of the rotation axes of the tibiotalar and subtalar joints can be determined. This method is based on indirect data acquisition from the kinematic behavior of the foot. Using the calculated matrices and through the optimization method, the orientation and position of the rotation axes were respectively calculated at relatively high precision. These results were also assessed in practice by building an ankle mechanical model and a robotic gyroscopic mechanism which is used as a robotic rehabilitation device for ankle rehabilitation. Obtained results show that the maximum error in determining the orientation of the rotation axes is about 2 degrees.

P2P energy trading is a new technology for increasing the integration of DERs with the power system. This technology enables customers to locally trade energy with each other. The DERs can actively participate in the day ahead and real-time balancing markets. This paper proposes a new two-level framework for the integrated operation of the power and natural gas systems through P2P energy trading considering the demand response capability. In the first level, the optimal operation schedules of the customer are determined through the MINLP optimization problem considering the AC power flow and natural gas steady-state model. In the following (level two), the customers trade energy with each other through the P2P framework. In order to increase the customers' profits and simulate the human trader behaviors, we employed the ZIP trader assumption in the proposed framework. In order to evaluate the introduced framework, it is implemented on the standard IEEE 33 bus test system and 33-node modified gas network. The results of the numerical study revealed that the proposed method can dramatically reduce the total operation cost

The synchronization of neutral chaotic systems with disturbance, uncertainty, unknown parameters, and unknown delays is studied in this article. Initially, the neutral chaotic system is introduced using the Lyapunov exponent and then the adaptive-robust controller is designed using the Lyapunov stability theory and the Lipschitz condition. The update laws for the estimation of disturbance and uncertainty bounds, as well as unknown parameters and delays, are determined such that the chaotic master and slave systems are synchronized in a robust manner. In addition, Lyapunov stability guarantees that the proposed controller will synchronize neutral chaotic systems in the presence of disturbance and uncertainty. In order to assess the proposed adaptive-robust controller, the synchronization of two parallel chaotic systems, jerk and Genesio–Tesi, with bounded nonlinear uncertainty, external disturbances, and unknown constant time delays is simulated. The simulation results demonstrate that the proposed controller provides the best performance for robust synchronization of neutral chaotic systems.

This paper presents a model-based technique for building retrofit planning. The approach is based on the building energy model. An algorithm has been developed to calibrate building energy model based on hourly data. The calibrated model is used to select candidate retrofit actions and assess the energy saving potential associated with each candidate action. The proposed technique has been applied for retrofit planning in Department of Energy Engineering (DOEE) at the Sharif University of Technology in 2015. An smart reading system has been also developed to capture the hourly gas consumption for DOEE. Building energy model for DOEE has been developed in EnergyPlus and calibrated using the proposed algorithm. A set of retrofit action has been selected and implemented in DOEE based on the proposed technique. The results showed that the average cooling energy use in summers was reduced by 20% compared to the 6-year average summer energy use before retrofit. The results showed that only HVAC control has more than 8% impact on cooling energy use.

With the increasing use of composites in medicine, the use of biocompatible composites of short fibers as fixtures for broken bones has also found its place. This paper uses damage mechanics to optimize a composite fixture for a wrist fracture - a fracture of the distal radius - to delay damage in the fixture. To model the fixture behavior, a model with two stages of homogenization as well as continuous Lemaitre damage is used to predict the damage. In this modeling, Mori-Tanaka homogenization is used to homogenize the grain hardness modulus and the Voight model is used for homogenizing the composite. The introduced algorithm for the numerical implementation of the two-stage homogenization model could easily be used to predict damage in other short fiber composites. After verifying the model presented in a valid fixture, the geometric shape of this fixture has been optimized. The research results show the proposed model; It is an efficient model for predicting damage in short fiber composite plates and can be used to optimize fixtures without clinical and experimental tests.

cement is used as the most widely used and common soil stabilizer. However, due to the adverse environmental effects of cement, the need to use geopolymers is important because of the favorable environmental aspects as a suitable alternative to cement. In this study, geopolymer based on lime and nanomaterials, which includes nanoclucosilica , was used to stabilize kaolinite . Various parameters such as type of nanomaterials, amount of nanomaterials, the ratio of alkaline activating solution and the curing time were considered as factors affecting the behavior of stabilized samples. To evaluate the effect of geopolymer and nanomaterials on the mechanical behavior of the samples, unrestricted compressive strength and tensile strength and melting cycle tests were performed, which were the main criteria for comparing the samples. On other hand, XRD and SEM analysis were performed to investigate the microstructure of the stabilized soil. The results show that fiber-based and nanomaterial based geopolymers are a suitable alternative to cementitious materials in soil stabilization, so that the addition of fibers and nanomaterials to the soil increases the strength and improves the mechanical behavior of the samples. other hand, with increasing the amount of nanomaterials to 5% (and alkali solution), the compressive and tensile strength of the samples increases. The obtained strengths have been compared with the strength of cement-stabilized samples. the results of microstructural analysis show Gives a strong reaction to chemical additives and the formation of aluminosilicate gels in geopolymer compounds, which in turn increases the soil bearing capacity and stabilizes it.

The increasing growth of vehicles and the decrease in road safety in developing countries have led to an increase in urban accident statistics. Urban management policymakers have aimed to implement effective solutions to improve the level of safety, reduce economic costs caused by accidents and environmental consequences of fossil fuel consumption. This is possible by implementing traffic calming measures, which change the behavior of drivers according to the environment by limiting the speed of vehicles. This study has tried to evaluate traffic calming measures by presenting 4 proposed scenarios, such as changing the speed reduction facilities, building a signalized intersection, and reducing the road width to calm the traffic flow on one of the high-traffic and accident-prone roads in Qom using Aimsun simulator software. The evaluation of accidents showed that the construction of new signalized intersections has reduced the average speed of vehicles by 35.5%, and the total number of accidents has increased from 474 in 2018 to 217. But this has led to an increase in the delay time and more time has been wasted. Further, the results of the investigation showed that by removing the speed reduction facilities and increasing the width of the road, the travel time was reduced by 19.9 and 9.3 percent, respectively, compared to the existing situation, and by constructing the signalized intersection, the speed of vehicles decreased by 12.9 percent.

Environmental issues due to the emission of pollutants produced by fossil fuel power plants have recently become an important issue. In this study, the coefficients of particle swarm optimization (PSO) algorithm to solve the problem of economic dispatching to reduce the emission of environmental pollutants were obtained. According to Clerk method, personal learning coefficient was equal to 1.4962, global learning coefficient was equal to 1.4962 and inertia coefficient was equal to 0.73. Also, the penalty coefficient according to the Co evolution particle swarm (CPSO) optimization algorithm was 15.8. As a result, optimization of coefficients by Taguchi method, it showed that the optimal value of personal learning coefficient is equal to 1.5, global learning coefficient is equal to 1.5, inertia coefficient is equal to 0.70 and penalty coefficient is equal to 15, in this case the amount emission of environmental pollutants were reduced by 6.5% compared to the coefficients determined by Clerk and 1.2% compared to the coefficients determined by the Co evolution particle swarm (CPSO) optimization algorithm.

Coronavirus disease 2019 or Covid-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future. Nine popular forecasting techniques are tested on the data of Covid-19 in Yazd city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. According to the selected evaluation criteria, the results of the comprehensive analysis emphasize that the most efficient models are the ARIMA model for predicting the cumulative cases of hospitalization of Covid-19 and the Theta model for the cumulative cases of death. Also, the autoregressive neural network model has the worst performance among other models for both hospitalization and death cases.