جستجوی مقالات مرتبط با کلیدواژه « w » در نشریات گروه « فنی و مهندسی »

Wireless Sensor Networks (WSNs) serve as vital infrastructure across various domains; yet face escalating cyber threats that challenge their security and integrity. Current security approaches exhibit limitations, such as inadequate granularity in addressing node and cluster head vulnerabilities, and a lack of cohesive prevention-detection strategies. In response, this research proposes a novel two-phase security method tailored for WSNs. The first phase employs Convolutional Neural Networks (CNNs) optimized with the Emperor Penguin algorithm for precise intrusion detection at the node level. Enhanced data security is ensured through integration of the SHA256 hashing algorithm, bolstering both prevention and detection strategies. Subsequently, the second phase extends this approach to cluster heads, forming a cohesive security framework informed by the first phase's output. This comprehensive methodology not only addresses current challenges but also represents a significant leap forward in WSN security, promising robust protection against evolving threats. The obtained results indicate that the proposed method not only enhance the security of both nodes and clusterheads, but also integrating SHA256 can improve preventation without harming the detection phase.

Using externally bonded (EB) fibre-reinforced composites for the enhancement of reinforced concrete (RC) structures had been addressed in extensive research throughout recent years. Despite the numerous works on the flexural, shear, and axial strengthening of reinforced concrete members, there are not many works on the torsional strengthening. As a result, information about how reinforced RC members under torsion behave in EB composites tends to be rather limited. In this research, the torsional strengthening of RC beams using EB composites will be thoroughly analyzed and reviewed. After a thoroughly literature review, a database of experimental tests is established, containing beams made of EB Fiber Reinforced Polymer (FRP). To ascertain the effectiveness of the strengthening system, the geometric and mechanical features of RC beams, composite kinds, and casing configurations were assessed, along with the investigation of several failure modes for failure beams. It was concluded that, according to the experimental data, the torsional strength of RC beams can be improved by using FRCM composites and EB FRP. A torsional strength increase of the database beams ranged from 0% to 178%, with an average of 51%. Fully wrapped beams showed the greatest increase in torsional strength.

Several studies have been reported about the importance of excavator positioning on open pit. It is essential to study and evaluate the slope stability of non-metallic deposits to understand how various factors affect it. In this work, the relationship between slope stability and the positioning of excavators in non-metallic deposits (as sand and peat) was investigated in order to increase the safety and efficiency of excavators during excavation processes. A two-stage method is proposed. A model of excavator on the pit slope was designed and imported to ''Rocscience Slide 2D'' software with taking all slope parameters constant, except for the slope angle, excavator positioning on the slope and pressure of excavator on slope (NGP). After that the factor of safety value was collected using the Spencer Method. Moving forward with the Two-Level, Three-Factor Full Factorial Design at the Design Expert software to study the effect of each factor on the response variable (FoS), as well as the effects of interactions between factors.  The control of NGP value can reduce slope stability in average of 5 to 30% and slope angle can increase FoS by up to 5 to 15%. The study found that rational excavator positioning can significantly reduce the risk of slope failure and improve the overall productivity of the excavation process in non-metallic material deposits. Furthermore, the information obtained could be useful for the development of excavator artificial intelligence systems to control the excavator positioning on open-pit slope and minimize the effect of factor different factor on slope stability.

The prediction of a Sudden Cardiac Death (SCD) long enough before its occurrence is vital for cases outside the hospital. This study investigate the effect of the simultaneous application of Electrocardiogram (ECG) and Heart Rate Variability (HRV) signals in the SCD prediction 60 minutes before its incidence. To do so, first, the SCD prediction was performed in each of the one-minute intervals by different groups of linear and nonlinear ECG and HRV features using the Support Vector Machine (SVM) classifier. The results showed that the best accuracy for SCD prediction was 91.23%. Next, all features were ranked locally in each of the one-minute intervals before the incidence of the death using the Minimum Redundancy and Maximum Relevancy (MRMR) method. Then, the SCD was predicted by applying four top local features from the ECG and HRV signals in each one-minute interval an hour before the death, showing a mean accuracy and sensitivity of 99% and 98.76%, respectively. Finally, by selecting the four most effective features according to the number of times they have been chosen in all one-minute intervals, the mean accuracy and sensitivity of SCD prediction were calculated at 96.15% and 95.07%, respectively. Additionally, since there is a similarity between the ECG signal of the pre-SCD and the Congestive Heart Failure (CHF), the classification of the Normal, CHF, and pre-SCD was performed, indicating a mean accuracy of 79.7%; it was also discovered that the Normal data could be separated from the SCD and CHF data with higher accuracy.

Peanut shell (PS) waste and plastic waste are abundant in Indonesia and have the potential to pollute the environment. The limited availability of fossil fuels also makes all parties look for other energy sources. This research aims to find a solution to this problem. The effect of adding low density polyethylene (LDPE), polypropylene (PP), and biodegradable plastic to briquettes made from peanut shell waste was investigated in this research. Briquettes were made using starch adhesive and a compaction pressure of 70 N/m2 using a laboratory scale piston. The compression test is essential because, during the stacking process, the briquettes must withstand loads from external pressure, which significantly influences the quality of the briquettes during storage and transportation. This research found that adding biodegradable plastic increased the modulus of elasticity and compressive strength of briquettes, namely 20 MPa and 4.45 MPa, compared to briquettes without plastic. The highest calorific value of briquettes in this study was obtained from the addition of PP plastic, amounting to 6185 cal/g. Based on the quality of the briquettes, the lowest moisture content was obtained from the addition of LDPE plastic at 7.71%, and the lowest ash content was obtained from the addition of biodegradable plastic at 4.01%, the lowest volatile content was obtained from the addition of PP plastic amounting to 15.71%. The most considerable fixed carbon content was obtained from adding PP plastic, amounting to 80.25%.

The demand for various types of zeolites in the oil and gas industry is associated with their high ion exchange and adsorption properties. This study is aimed to identify the regularities of the process of obtaining zeolite precursors from waste silica gel and industrial aluminium hydroxide since water glass and aluminate solution are the initial hydrogel components for the hydrothermal synthesis of zeolites. In addition, the process of hydrothermal synthesis of zeolites was investigated, namely, the effect of the molar ratio of SiO2:Al2O3 between hydrogel components on the structure and type of zeolite produced was determined. Identification of phases and study of the morphology of initial substances and obtained samples were carried out using X-ray diffraction and scanning electron microscopy methods. For the sample representing the monophase of LTA zeolite, the value of ion exchange capacity for Ca2+ ion and the particle size of the main fraction were determined to be 550 mEq/100g and less than 10 μm, respectively.

The field of autonomous vehicles (AV) has been the subject of extensive research in recent years. It is possible that AVs could contribute greatly to the quality of daily lives if they were implemented. A safe driver model that controls autonomous vehicles is required before this can be accomplished. Reinforcement Learning (RL) is one of the methods suitable for creating these models. In these circumstances, RL agents typically perform random actions during training, which poses a safety risk when driving an AV. To address this issue, shielding has been proposed. By predicting the future state after an action has been taken and determining whether the future state is safe, this shield determines whether the action is safe. For this purpose, reachable zonotopes must be provided, so that at each planning stage, the reachable set of vehicles does not intersect with any obstacles. To this end, we propose a Safe Reinforcement Learning by Shielding-based Reachable Zonotopes (SRLSRZ) approach. It is built around Twin Delayed DDPG (TD3) and compared with it. During training and execution, shielded systems have zero collision. their efficiency is similar to or even better than TD3. A shield-based learning approach is demonstrated to be effective in enabling the agent to learn not to propose unsafe actions. Simulated results indicate that a car vehicle with an unsafe set adjacent to the area that provides the greatest reward performs better when SRLSRZ is used as compared with other methods that are currently considered to be state-of-the-art for achieving safe RL.

Microgrid, as a new structure of power systems, has challenges that should be addressed; regulating the frequency and voltage and power sharing are among the most critical challenges of AC microgrids (MGs). This paper studied a novel two-level control strategy composed of a modified droop controller at the primary level and a distributed finite-time (DFT) average controller in the secondary layer to achieve more precise active and reactive power sharing along with frequency and voltage alignment by implementing this strategy in an islanded AC MG. Thus, a typical AC MG, including three distributed generation (DG) units, is simulated in MATLAB/SIMULINK software, and the proposed method is first formulated, modeled, and then simulated. For validation and better comparison, the results obtained by the proposed method were compared to the conventional droop and other distributed methods in case studies of load changes and plugging in/out of DG units. The results proved that the proposed method appropriately shared active and reactive powers among DG units. It also efficiently restored the frequency and voltages of DG units to their nominal values. Moreover, a mathematical stability analysis is obtained that proved the stability of the proposed DFT-based secondary controller.

This paper presents a resilience-based approach for critical load restoration in distribution networks using microgrids during extreme events when the main supply is disrupted. Reconfiguration of the distribution network using graph theory is investigated, for which Dijkstra's algorithm is first used to determine the shortest paths between microgrids and critical loads, and then the feasible restoration trees are established by combining the restorable paths. A mixed-integer linear programming (MILP) model is then used to find the optimal selection of feasible restoration trees to make a restoration scheme. The service restoration is implemented with the objectives of maximizing the energy delivered to the critical loads and minimizing the number of switching operations. The limited fuel storage of the generation sources in microgrids, the operational constraints of the network and microgrids, as well as the radiality constraint of the restored sub-networks, are considered the constraints of the optimization problem. The presented method can be used for optimal restoration of critical loads including the number of switching operations which is essential for the ease of implementation of a restoration plan. The results of simulations on a 118-bus distribution network demonstrate the efficiency of the procedure.

Energy management (EM) in smart distribution networks (SDN) is to schedule the power transaction between the SDN and the existing distributed energy resources (DERs) e.g., distributed generations, especially renewable resources and electrical vehicles, from an eco-technical viewpoint. Due to the dual role of electric vehicles (EVs) acting as a power source and load, they presented both challenges and opportunities in EM. The complexity of EM increases as DERs become more prevalent in SDN. Moreover, the uncertainties of renewable resources, price, and load besides the uncertainties related to the place, amount, and time of EV’s charging makes EM a more intricate field. This supports the necessity of extensive tools and approaches to manage EM in SDNs. In this respect, this paper proposes an optimum scenario-based stochastic energy management scheme for intelligent distribution networks. The proposed approach is modeled as a MINLP problem and solved in GAMS software under the DICOPT solver. The test is conducted on a 33-bus SDN with and without factoring in uncertainties.

Nowadays micro-grids (MG) as one of the most important methods used for electric power generation from renewable energy to reduce dependence on fossil fuels and reducing environmental pollution have been considered. Due to the increasing number of distributed generation (DG) sources and MGs in the power grids, it is of particular importance to design and implement a suitable controller in order to use all the available capacities in these systems. The uncertainty in prediction of power generation can be considered as disturbances into the electrical system, making it difficult to control, and eventually resulting in an unstable system. With the use of power electronic converters the power and voltage of MG can be controlled. In this paper, a 13-bus MG is proposed. This MG includes 3 wind farms and 2 PV farms. A robust sliding mode controller (SMC) is used to control voltage source converters of PV farms. A load shedding program is proposed to avoid complete blackout of MG in case of islanding that recover MG voltage to normal range after a voltage collapse. Simulations were performed using MATLAB/SIMULINK software on a 13-bus IEEE micro grid, and the effectiveness of the proposed control and operational method was investigated and confirmed.

This paper presents two intelligent classifier schemes for classifying the faults in a series capacitor compensated transmission line (SCCTL). The first proposed intelligent classifier scheme is a particle swarm optimization-assisted artificial neural network (PSO-ANN). The second, proposed one is a teaching-learning optimization-assisted artificial neural network (TLBO-ANN). For each type of fault, the 3-phase current signals are acquired at the sending end and processed through empirical mode decomposition (EMD), to decompose into six intrinsic mode functions. The neighborhood component analysis is used to extract the best feature intrinsic mode functions. From the identified best feature intrinsic mode functions, the energy of each phase of the line is computed. The energy of each phase is fed as inputs for both PSO-ANN and TLBO-ANN classifiers. The practicability of the proposed intelligent classifier schemes has been tested on a 500$\,kV$, 50$\,Hz$, and 300$\,km$ long line with a midpoint series capacitor using MATLAB/Simulink Software. The results demonstrate that the classifier schemes are able to accurately classify faults in less than a half-cycle. Furthermore, the efficacy of the proposed intelligent classifier schemes has been evaluated using Performance Indices including Kappa Statistics, Mean Absolute Error, Root Mean Square Error, Precision, Recall, F-measure, and Receiver Operating Characteristics. From the results of Performance Indices, it is concluded that the proposed TLBO-based artificial neural network classifier outperforms the PSO-based artificial neural network classifier. Finally, the efficacies of proposed intelligent classifier schemes are compared to existing approaches.

Due to the increasing occurrence of natural disasters, importance of maintaining sustainable energy for cities and society is felt more than ever. On the other hand, power loss reduction is a challenging issue of active distribution networks (ADNs). Therefore, the distribution network operators (DNOs) should have a certain view on these two problems in today’s smart grids. In this paper, a new convex optimization model is proposed with two objective functions including energy loss reduction in normal operating mode and system load shedding minimization in critical conditions after the occurrence of natural disasters. This purpose is fulfilled through optimal allocation of distributed generation (DG) units from both conventional and renewable types as well as energy storage systems (ESSs). In addition, a new formulation has been derived to form optimal micro-grids (MGs) aiming at energy loss reduction in normal operating condition and resiliency index improvement under emergency situations. The developed model is implemented in GAMS software and the studies have been tested and analyzed on the IEEE 33-bus system. The results verify the effectiveness of the proposed method in terms of energy loss reduction as well as resilience enhancement in extreme operation condition following severe disruptions in the system.

For providing required load in n coastal and island regions, tidal barrage can be integrated in microgrids. To produce electricity from tides, in tidal barrage, water is moved between sea and reservoir through sluices containing turbines to generate electricity. In operation phase, produced power of tidal barrages depends on number of turbines, sluices and hydro-pumps. Thus, to maximize generated energy of tidal barrage, optimum number of turbines, sluices and hydro-pumps can be obtained through heuristic optimization techniques. Because of tidal level variation, generated power of tidal barrages changes over time. Thus, for load supplying, other renewable resources such as photovoltaic units, batteries, fuel-based generation units and grid-connected mode of microgrid are utilized. In this research, two-stage optimal operation of microgrids composed of tidal barrage, photovoltaic units, batteries and fuel-based generation units is done. In first stage, optimum number of turbines, sluices and hydro-pumps related to tidal barrage is determined for maximizing produced energy of tidal unit during time horizon of the study. In second stage, remaining load of microgrid is provided by photovoltaic units, batteries, fuel-based generation units and main network. To this end, generated power of fuel-based plants and power exchanged between microgrid and main grid are determined for minimizing operating cost of microgrid. The operating cost including operating cost of fuel-based generation units, cost of exchanged power between main grid and microgrid and penalties of load curtailment is optimized using particle swarm optimization method. Numerical results presents among different optimization algorithms, particle swarm method has performed best in operation studies of tidal barrage. For understudied microgrid, maximum generated energy of tidal barrage is 25.052 MWh, and minimum operating cost of the microgrid is 39868 $.

Switched capacitor multilevel inverters with low input DC voltage sources and voltage boost capability are very attractive to producing a high voltage levels in the output. The paper introduces a modified switched capacitor multilevel inverter with voltage boost capability. The suggested topology can be extended into symmetric and asymmetric configurations. Nearest-level modulation method is employed to generate high-quality output waveforms. The presented multilevel inverter is compared with the similar configurations by considering various criteria. Finally, to confirm the operation of the suggested topology, a laboratory scale of the suggested inverter is implemented and the results are given.

Magnetic Resonance (MR) images have many applications in medical science and play an essential role in the diagnosis and treatment of diseases. However, unavoidable artifacts and noise reduce the resolution of these images. In this paper,  we propose a hybrid noise reduction framework using the wavelet transform, the exponential function thresholding, and the Wiener filter. In particular, we first employ the Genetic algorithm to optimize the exponential function coefficient. Furthermore, we adopt the Winner filter to increase the robustness of the proposed scheme against different types of noise, such as Gaussion and Rician noise. Some common performance measures, such as Mean Square Error (MSE) and Peak Signal-to-Noise-Ratio (PSNR), have been used to evaluate the performance of the proposed method compared to existing counterparts. The results show that the performance of the proposed hybrid method is better than the existing methods, such as universal thresholding and plain exponential function thresholding. For example, for human brain images with Gaussian noise, the obtained PSNR using the proposed method is 53.3947, while the PSNR value is 51.7532 using the universal threshold. Moreover, the results indicate that by using the Winner filter, we can effectively control the robustness against noise and image blurring.

Photosynthetic microorganisms such as Chlorella vulgaris can be used for carbon dioxide (CO2) biofixation to reduce greenhouse gas emissions and combat global warming. In this study, the potential of using wastewater from a tuna processing factory as a cultivation medium for C. vulgaris was investigated with the aim of reducing cultivation costs and water consumption while treating the wastewater. Different CO2 concentrations (5%, 10% and 15% in N2) and wastewater dilutions (1:4, 2:4, 3:4 and 4:4) were tested at ambient temperature, controlled pH and cyclic illumination (12h light-12h dark) at 6000 lux, using both batch and semi-continuous cultures. In the batch system, 100% CO2 biofixation was achieved at an effluent dilution of 1:4 and 5% CO2, with biomass concentration doubling after one week. High removal rates of COD (94%), total phosphorus (99%), nitrate (98%) and ammonium (99%) were observed. The semi-continuous photobioreactor showed a CO2 stabilization of more than 50% (at 5% CO2) and a 40% biomass increase, with over 50% nitrate removal in a 1:4 dilution effluent. These promising results demonstrate the potential of the system for simultaneous CO2 biofixation and wastewater treatment and underline the effectiveness of this circular economy approach to reduce greenhouse gas emissions and treat industrial wastewater.

A surface-mounted permanent magnet synchronous motor (SPMSM) is an electric machine applied widely in the fields of electric vehicles (EVs) and electrical drives due to good characteristics such as  high power density, lower mass, high efficiency and lower torque of inertia. For the SPMSM, there are two types of  SPMSM, i.e., the inner rotor SPMSM and outer rotor SPMSM. In order to analyze, compute and compare advantages and disadvantages of these two motor types,  this research proposes an analytical model in detail to calculate and design the electromagnetic parameters and thermal characteristics of the SPMSM with both inner and outer rotor configurations. Subsequently, a finite element method is developed to validate output parameters obtained from the analytical model. Simulation results also indicate the performance of both types of motors. However, the inner rotor SPMSM has advantages in terms of high speed and low temperature, while the outer rotor SPMSM has higher torque and stability but it operates at a higher temperature.

Twisted and coiled polymer actuators (TCPAs), typically made from fishing lines or sewing threads, became increasingly popular across various applications due to their unique capacity to contract or twist when heated, mimicking the behavior of artificial muscles. Their performance can be highly variable due to some manufacturing factors like fishing lines diameter. The study investigates TCPAs' mechanical behavior, considering changes in the diameters of fishing lines, operating temperatures, and applied tensile forces. For this purpose, this study addresses these challenges by experimenting with TCPAs of different diameters (0.5, 0.7, and 0.8 mm), using a hot water circulation system to control actuator temperature, enabling rapid and consistent actuation. Testing TCPAs under various thermal conditions reveals that both displacement and tensile strokes increase with temperature and decrease with tensile force. Also, the TCPA with a 0.7 mm diameter which has the smallest coil spring index and the smallest coil bias angle achieved the best performance, with a maximum displacement of 17 mm and a tensile stroke of 7.65% at 80°C and 1.422 N. These findings provide a clear pathway for creating reliable, high-performing TCPAs, making them suitable for applications requiring precise and consistent actuation.

Dunaliela salina has advantages over other microalgae species, including rapid growth, high salt concentration, simple growth requirements, and fast production. However, the harvesting process of D. Salina requires a particular harvesting method due to its tiny size. This research aims to develop an effective D. Salina harvesting method using spiral electrocoagulation (SEC). Optimization of operating parameters including initial D. Salina concentration, voltage, reactor slope, and electrocoagulation time is carried out using response surface methodology (RSM) to maximize the D. Salina harvesting process analysis of wastewater quality produced shortly after the harvesting process. The results showed that the optimum operation for D. Salina harvest until a harvesting efficiency of 85.77% was achieved required 25 V; 4.17 min as a time of electrocoagulation; 68,39 degrees as the angle of the reactor; and 25% initial concentration of D salina. The variable voltage, time, and initial concentration of D. Salina significantly affect harvesting efficiency, while the reactor angle has an insignificant impact. Based on the Central Composite Design (CCD) design, the minimum COD concentration is 3.36 mg/L when SEC operations use a voltage of 25V, time of electrocoagulation 5 min, angle of reactor 75-degree, and concentration of D. Salina 70% of the initial concentration. The concentration of nutrients (nitrate, phosphate, ammonia) produced after the harvesting process varies depending on variations in voltage, time, angle of reactor, and initial concentration of D. salina.