The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key fac...
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The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particleswarmoptimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimizationalgorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.
The handling stability of tracked vehicles not only affects the handling convenience of drivers but is also an important part of vehicle active safety. A zero-differential steering controller for tracked vehicles with...
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The handling stability of tracked vehicles not only affects the handling convenience of drivers but is also an important part of vehicle active safety. A zero-differential steering controller for tracked vehicles with hydraulic-mechanical transmissions was designed in this paper. First, the working principle of the steer-by-wire systems of tracked vehicles was analyzed, and the vehicle speed calculation model was established. Then, the steering dynamics model of the tracked vehicle was established based on the shear stress model. Finally, based on the particle swarm optimization algorithm, the established tracked vehicle steering dynamics model was iteratively solved, and the optimal yaw rate gain K-r was calculated in real time and used for vehicle steering control. The steering control simulation model of tracked vehicles was established in MATLAB/Simulink, and the control effect of the designed steering controller was verified by the simulation. The control effect was evaluated by the comprehensive evaluation index of the handling stability. The simulation results showed that the steering controller based on the particle swarm optimization algorithm effectively improved the handling stability of the tracked vehicle and reduced the burden on the driver.
Complex power plays a key role in maintaining and sustaining the fields i.e. magnetic and electric fields. The efficiency of the power system depends entirely on the loss and voltage profile level of the system. The l...
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Complex power plays a key role in maintaining and sustaining the fields i.e. magnetic and electric fields. The efficiency of the power system depends entirely on the loss and voltage profile level of the system. The lower the level of loss and the higher the voltage profile, the higher the system efficiency. Installation of the capacitors on a system greatly helps in controlling and reducing levels of the reactive energy of the system. The location and size of the capacitors that need to be installed are of great importance in the installation of the bank of capacitors. The idea of a Genetic algorithm and also a particleswarmalgorithm are deployed in this paper for calculating the optimal location and size of the capacitor. In this paper, the system of the 132 KV Manipur Transmission System is designed in ETAP considering a radial distribution part and the optimization method of a genetic method, and also the particleswarmalgorithm is utilized for finding the optimal location and size of the capacitor. The system is further optimized by reducing the loss and improving the voltage profile of the system. And the comparison of both the optimization techniques is presented. The results show that PSO provides optimal solutions while GA is simpler in the optimization process. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
Tunnel fires have received significant attention because they can cause large economic losses every year. To estimate the fire development and realize a prompt response to the fire disaster are thus crucial to tackle ...
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Tunnel fires have received significant attention because they can cause large economic losses every year. To estimate the fire development and realize a prompt response to the fire disaster are thus crucial to tackle the above issue. Here, a smart prediction method for fire state evolution is developed based on an improved fire simulation curve through the particle swarm optimization algorithm. The improved fire simulation curve is established by supplementing the stable stage during the tunnel fire based on a fire curve proposed in 2002. Experimental results from a prototypical tunnel fire test indicate that the proposed method can effectively portray the behavior of a typical tunnel fire development. Moreover, it is accurate over a wide range of conditions, including different heat release rates, different measurement noises, different sampling intervals, and different prior durations. Particularly, the fluctuation range of estimation results is becoming larger with the increasing noise level. The influence of the sampling interval is mainly reflected in the calculation time. Prior duration is the most important factor of the prediction accuracy, the longer prior duration corresponds to the more precise results. 200s and 300s can be regarded as the critical duration by considering the estimation error of the whole-time domain and the preliminary stage, respectively. The proposed method has a broad application prospect, it can avoid complicated temperature spreading mechanisms, the additional workload for prior information, and the limitation of the specific fire scene.
To improve the safety of steel cofferdams and reduce the cost to the extent possible, a multi-objective optimi-zation method based on response surface methodology (RSM) and particleswarmoptimization (PSO) algorithm ...
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To improve the safety of steel cofferdams and reduce the cost to the extent possible, a multi-objective optimi-zation method based on response surface methodology (RSM) and particleswarmoptimization (PSO) algorithm was proposed to optimize the structure of double-walled steel cofferdams in the deep-water bridge foundation construction. The thickness of the inner and outer wall plate, the height and thickness of the inner-wall brace and the vertical spacing between annular plates were selected as design variables, taking the stress of the inner-wall brace and inner wall plate as the optimization objectives and the cost as the constraint condition, an optimization model of double-wall steel cofferdam was proposed. On the basis of the finite element calculation results, the experimental design and RSM were used to obtain the explicit relationship between the design variables and the structural response of the cofferdam. The optimal values of the design parameters of the cofferdam were obtained using PSO algorithm. The optimal design parameters and the structural response of the cofferdam variation with the cost were investigated. It is shown that the combination of RSM and PSO is able to optimize the cofferdam structure, with a stress optimization rate of 49.8% for the inner-wall brace while keeping the original cost constant. In addition, by adjusting the cost threshold, the reasonable range of cost coefficient K for this cofferdam is 0.119 to 0.162, and the section size of inner-wall brace is the key design parameter for the cofferdam, which provides a design idea for the optimization of deep-water cofferdam structure.
A supply chain that is effective and of the highest caliber boosts customer happiness as well as sales and earnings, increasing the company's competitiveness in the market. It has been discovered that the standard...
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A supply chain that is effective and of the highest caliber boosts customer happiness as well as sales and earnings, increasing the company's competitiveness in the market. It has been discovered that the standard supply chain management technique leaves the supply chain with weak supply chain stability because it has a low ability to withstand the manufacturer's production behaviour. An enterprise supply chain resistance management model is built using the study's proposed particleswarm optimisation technique, which is based on a genetic algorithm with stochastic neighbourhood structure, to solve this issue. The suggested technique outperformed the other two algorithms utilised for comparison in a performance comparison test, with a stable particleswarm fitness value of 0.016 after 800 iterative iterations and the fastest convergence. The proposed model was then empirically examined, and the results revealed that the production team using the model completed the same volume of orders in 32 days while making $460,000 more in profit. With scores of 4.5, 4.5, 4.3, 4.3, 4.2, and 4.2, respectively, the team also had the lowest values of the six forms of employee anti-production conduct, outperforming the comparative management style. In summary, the study proposes an anti-disturbance management model for enterprise supply chains that can rationalise the scheduling of manufacturers' production behaviour and thus improve the stability of the supply chain.
The open winding permanent magnet synchronous motor is driven by two sets of inverters and has the characteristics of high power and torque, which is applied to electric vehicles. This article aims to reduce the coppe...
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The open winding permanent magnet synchronous motor is driven by two sets of inverters and has the characteristics of high power and torque, which is applied to electric vehicles. This article aims to reduce the copper loss of open winding permanent magnet synchronous motors and proposes a variable decoupling angle modulation strategy based on the system. This strategy takes the angle (decoupling angle) between the output voltage vectors of two sets of inverters as the control variable, and uses particle swarm optimization algorithm to select the optimal decoupling angle based on different load states of the motor, driving an open-winding permanent magnet synchronous motor to reduce copper consumption. Through simulation verification, this strategy effectively reduces motor copper consumption and provides theoretical support for controlling electric vehicle operating losses.
As the demand for alternative energy sources grows, wind energy systems are developing at a rapid pace. In this research paper, optimally tuned PI controllers are used for controlling a wind turbine based on of a Doub...
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ISBN:
(纸本)9798350349740;9798350349757
As the demand for alternative energy sources grows, wind energy systems are developing at a rapid pace. In this research paper, optimally tuned PI controllers are used for controlling a wind turbine based on of a Doubly Fed Induction Generator (DFIG). The particleswarmoptimization (PSO) algorithm for PI controller parameters tuning, with the objective of ensuring better energy quality and system performance. The speed Integral Square Error is used as a fitness function of the PSO. The performance evaluation of controllers with PSO implementation is carried out by the simulation through MATLAB /Simulink under linear and nonlinear load conditions. The PI and PI-PSO implementation results comparison proves the superiority of the PI-PSO controller.
Tomato is the main cultivated crop in China, and greenhouse tomato can improve the quality and yield of tomato by improving the planting environment. Predicting greenhouse tomato yield is an important basis for making...
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ISBN:
(纸本)9798350387780;9798350387797
Tomato is the main cultivated crop in China, and greenhouse tomato can improve the quality and yield of tomato by improving the planting environment. Predicting greenhouse tomato yield is an important basis for making production plan, and the accuracy of yield prediction directly affects economic benefits. In order to improve the accuracy of the prediction intervals model of greenhouse tomato yield, a new objective function was proposed to improve the Lower Upper Bound Estimation (LUBE) prediction intervals model. Then, based on the improved LUBE prediction intervals model, the particle swarm optimization algorithm (PSO) was further improved by adaptive inertia weight and escape strategy. Finally, a LUBE prediction intervals model based on the improved PSO algorithm was proposed in this paper. The prediction intervals of greenhouse tomato yield was simulated by using LUBE prediction intervals model, the improved LUBE prediction intervals model and the LUBE prediction intervals model based on the improved PSO algorithm. The experimental results showed that the prediction intervals coverage probability (PICP) of the LUBE prediction intervals model, the improved LUBE prediction intervals model and the LUBE prediction intervals model based on the improved PSO algorithm were 75%, 80% and 80%, respectively, and the prediction intervals normalized average width (PINAW) were 0.3938, 0.3725 and 0.3629, respectively. It can be concluded that the LUBE prediction intervals model based on the improved PSO algorithm has higher prediction accuracy and better fitting ability.
In order to cope with the increase in carbon emissions caused by the excessive use of fossil energy, the world's energy structure needs to be adjusted. Distributed photovoltaic (PV) hydrogen production system, as ...
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ISBN:
(纸本)9798350377477;9798350377460
In order to cope with the increase in carbon emissions caused by the excessive use of fossil energy, the world's energy structure needs to be adjusted. Distributed photovoltaic (PV) hydrogen production system, as an integrated energy system, is an important pathway to the zero-carbon goal. But distributed PV is prone to face the shading situation, while the PV module power output curve will show the phenomenon of multiple peaks. The traditional maximum power tracking methods are prone to fall into the local optimal situation, and the intelligent tracking methods are inherently more complex. The hydrogen production electrolyzer puts forward the requirement of stability on the output power of the PV power supply. Existing research focuses on the maximum power tracking of PV system, or the control of electrolyzer, lacking the coupled modeling control research of distributed PV hydrogen production system. In order to fill the gap of the existing research, this paper proposes a composite algorithm which combines the Improved particleswarmalgorithm (IPSO) with the Improved Perturbation Observation (IP&O) method. The initial population and termination conditions of the particleswarm search method (PSO) are improved to enhance the convergence speed of the algorithm and reduce the oscillations during the convergence process of the algorithm, and the IP&O method is utilized to provide stable inputs for the electrolytic tanks. The performance of the algorithm is verified through the comparative experiments with other algorithms.
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