Detecting and locating damage is a crucial endeavor within the field of structural integrity. While Artificial Neural Networks (ANNs) have shown promise in this regard, they have certain limitations that can be overco...
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Detecting and locating damage is a crucial endeavor within the field of structural integrity. While Artificial Neural Networks (ANNs) have shown promise in this regard, they have certain limitations that can be overcome through modifications in terms of their structural design and training methodologies. In this study, we propose a new optimization approach, specifically leveraging the Grasshopper optimization Algorithm (GOA), to enhance the performance of ANNs for predicting multiple damages represented by holes in the aluminum plate. Input parameters are derived from natural frequencies, while hole locations serve as outputs. We utilize a Finite Element Model (FEM) to generate data through simulation, varying hole locations for comprehensive analysis. To authenticate our method, we gather experimental data from vibration analyses of damaged plates spanning various hole locations. A comparative analysis is conducted of proposed algorithm by evaluating its performance against two established metaheuristic algorithms: the Genetic Algorithm (GA) and Ant Colony optimization (ACO). This comparison was performed to assess the relative effectiveness of our approach. Our novel approach demonstrates superior performance in damage forecasting, offering promising prospects for structural integrity applications.
An electromagnetic controller is applied to a flexible rotor supported by two oil-film bearings. The synchronous vibration is controlled by using a combined estimation and optimization algorithm. This requires no a pr...
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In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-d...
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In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet deliver
The regulators based on PI control law continue to be the key elements in many of the industrial systems for their control. Likewise, the wind power generation systems (WPGSs) also make extensive use of PI regulators ...
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The regulators based on PI control law continue to be the key elements in many of the industrial systems for their control. Likewise, the wind power generation systems (WPGSs) also make extensive use of PI regulators in their control schemes. The enhanced performance of these WPGS depends on proper selection of the PI regulator parameters. This paper deals with the control of a grid-tied permanent magnet synchronous generator (PMSG)-based WPGS wherein, a new attempt has been depicted to apply the most optimum design of the involved PI regulator parameters for the proposed WPGS based on standard performance indices making use of four popular optimization algorithms namely genetic algorithm (GA), cultural algorithm (CA), particle swarm optimization (PSO) and artificial bee colony (ABC). An informative discussion has also been presented which would be useful for practicing engineers/researchers to select flexibly and reasonably the PI regulators parameters meant for the control of the proposed WPGS. A detailed simulation model developed in MATLAB/Simulink has been used to analyze the performance of the proposed PMSG-based WPGS employed with the most optimum values of PI regulator parameters. The performances of WPGS have been compared while the most optimum PI regulator parameters have been included in the control system, and also when incorporating the PI regulator parameters in WPGS control designed via classical D-partition technique. The results obtained under gradually changing wind speed profile show the improvement in the performance of WPGS in terms of peak overshoot, time response and waveform oscillations. The experimental validation of the control performances have been carried out by way of real-time hardware-in-the-loop (HIL) testing making use of Typhoon HIL402 emulator and TMS320F28335 digital signal controller. The obtained real time HIL results are in close agreement to the results obtained in simulations using MATLAB/Simulink. A deviation of less than
An approach for generating test problems by a computer using the Monte Carlo method based upon user-given characterizations is described.A single point X~* is prespocified by the user to be a solution of the test *** ...
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An approach for generating test problems by a computer using the Monte Carlo method based upon user-given characterizations is described.A single point X~* is prespocified by the user to be a solution of the test *** approach is flex- ible enough to specify function values,gradients,Hesse,degeneracy degree and ill- conditioned degree at the point X~*.Other numerical features such as indefiniteness, convexity are also under user's control.
This paper presents optimization problem formulations to design meander-line antennas for passive radio frequency identification tags based on given specifications of input impedance, frequency range and geometric con...
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ISBN:
(纸本)9781424470594
This paper presents optimization problem formulations to design meander-line antennas for passive radio frequency identification tags based on given specifications of input impedance, frequency range and geometric constraints. In this application, there is a need for directive transponders to select properly the target tag, which must be ideally isotropic. The design of an effective meander-line antenna for RFID purposes requires balancing geometrical characteristics with the microchip impedance. Therefore, there is an issue of optimization in determining the antenna parameters for best performance. The antenna is analysed by a method of moments. Some results using a deterministic optimization algorithm are shown.
We address the question of optimal proactive service and demand shaping for content distribution in data networks through smart pricing. We develop a proactive download scheme that utilizes the probabilistic predictab...
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ISBN:
(纸本)9781467359443
We address the question of optimal proactive service and demand shaping for content distribution in data networks through smart pricing. We develop a proactive download scheme that utilizes the probabilistic predictability of the human demand by proactively serving potential users' future requests during the off-peak times. Thus, it smooths-out the network traffic and minimizes the time average cost of service. Moreover, we incorporate the varying economic responsiveness and demand flexibilities of users into our model to develop a demand shaping mechanism that further improves the gains of proactive downloads. To that end, we propose a model that captures the uncertainty about the users' demand as well as their responsiveness to the pricing employed by the service providers. We propose a joint proactive resource allocation and demand shaping scheme based on non-convex optimization algorithms, and show that it always leads to strictly better performance over its proactive counterpart without demand shaping.
The graph coloring problem (GCP) is a classic combinatorial optimization problem that has been widely applied in various fields such as mathematics, computer science, and biological science. Due to the NP hard nature ...
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This paper aims to present a control scheme based upon Particle Swarm optimization (PSO) algorithm in order to compensate the key power-quality disturbances, particularly voltage sags and harmonic voltages, using a Dy...
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ISBN:
(纸本)9781629931333
This paper aims to present a control scheme based upon Particle Swarm optimization (PSO) algorithm in order to compensate the key power-quality disturbances, particularly voltage sags and harmonic voltages, using a Dynamic Voltage Restorer (DVR). According to the aforesaid method, DVR's PI controller structure is regulated via multi-objective PSO algorithm. We introduce the method for distribution systems to modify both SAG and THD as major power quality indices in sensitive loads at fault conditions. Therefore, we apply the multi-objective optimization algorithm in order to attain a better performance in solving the related problems.
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