作者：
Zhou, NingHebei Univ
Key Lab Digital Med Engn Hebei Prov Coll Elect & Informat Engn Baoding 071002 Peoples R China
To solve the problem of damage due to large dynamic stress of the lateral plates during working process of the vibration screen, it is necessary to calculate and analyze natural modes and distribution of dynamic stres...
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To solve the problem of damage due to large dynamic stress of the lateral plates during working process of the vibration screen, it is necessary to calculate and analyze natural modes and distribution of dynamic stress of lateral plates, which is shown in results that the lateral plate structure shall be optimized. In this paper, with the total weight of the lateral plates for the banana-shaped vibration screen as the optimization objective, frequency constraints as the status variables, optimization for multi-frequency constraints is conducted based on the improvedgenetic algorithm. Next, a mathematical model of structure parameter optimization for the lateral plates of the vibration screen under frequency constraints is established to carry out optimization design in order to obtain a structure with smaller dynamic stress and lower weight. Sensitivity analysis is added into the improvedgenetic algorithm, and the optimization efficiency is increased simultaneously. The structure frequency is optimized by means of the improvedgenetic algorithm. Then, a modal experiment is carried out to the entire vibration screen so as to verify reliability of the finite element model, and the natural characteristics of the vibration screen before and after optimization are analyzed, and the top 6 orders natural frequency and vibration modes of the entire vibration screen are calculated, so as to indicate that optimized vibration screen is improved in terms of material saving, stiffness and stability. In addition, noise is directly related to vibration. As a result, the change of the vibration screen should be also analyzed. Noise of the vibration screen is also tested by sound array technology. Results showed that the radiated noise is reduced after optimization, and optimization in this paper is feasible.
The optimal placement method of sensorsis carried out based on improved adaptive genetic algorithm for solving the optimal placementproblem of sensorsin the health monitoring systemof long-span railway bridge. Dual st...
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ISBN:
(纸本)9787030323736
The optimal placement method of sensorsis carried out based on improved adaptive genetic algorithm for solving the optimal placementproblem of sensorsin the health monitoring systemof long-span railway bridge. Dual structure coding method is introduced to improve the individual encoding method in the genetic algorithm. Adaptive partial matching crossover andinversus mutation methodsareadopted in the optimal preservation strategy, andthe crossover probability and mutation probability are changed automatically according to the fitness value for obtaining the global optimal solution of the sensor placement. Therefore, some defects in other genetic algorithm applied in the optimal placement of sensors for major bridge structure, such as slow convergence speed and easily falling into local optimum etc., are overcome, and the convergence is speed up. Then the optimal sensors placement of the health monitoring system for one certain long-span railway steel truss cable-stayed bridge is taken as the example to verify the proposed improvedgenetic algorithm. The result shows that the proposed method has better global optimization, computational efficiency and reliability in compare with the Simple genetic Algorithm and General genetic Algorithm, and can be applied to the actual railway cable-stayed bridge health monitoring system for the sensors optimal placement.
The bioadhesive drug delivery systems using satrchbased colon-targeted drug carriers have drawn great attention in the field of pharmaceutical science in resent years.A Neural Network(NN) prediction model was develope...
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The bioadhesive drug delivery systems using satrchbased colon-targeted drug carriers have drawn great attention in the field of pharmaceutical science in resent years.A Neural Network(NN) prediction model was developed based on hibrid method of improved genetic algorithms(GA) and conjugate gradient algorithm for backpropagation(GDBP) NN according to key factors that affect releasing behaviors of satrch-based colontargeted drug *** particular,function approximation capability and high efflcciency of GDBP NN is used to simulate nonlinear relation between key factors and drug carrier releasing ***,the simulation results indicate that compared with traditional GA-BP NN,training efficiency of GAGDBP NN has been greatly ***,the model finds a new way to predict drug carrier releasing behaviors and instructs factors seting in real experiments.
作者：
Lai, LLMa, JTEnergy Systems Group
Dept of Electrical Electronic and Information Engineering City University London EC1V 0HB UKDept of Electrical Engineering Tokyo Metropolitan University 1-1 Minami Osawa Hachioji-shi Tokyo 192-03 Japan
This paper presents an improvedgenetic algorithm (IGA) to solve the problem of optimal power flow. The GA, with the dynamical hierarchy of the coding system developed in this paper, has the ability to code a large nu...
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This paper presents an improvedgenetic algorithm (IGA) to solve the problem of optimal power flow. The GA, with the dynamical hierarchy of the coding system developed in this paper, has the ability to code a large number of control variables in a practical system within a reasonable length string. It is, therefore, able to regulate the active power outputs of generators, bus voltages, shunt capacitors/ reactors and transformer tap-settings to minimize the fuel costs. Two cases in the IEEE 30-bus system for both normal and contingent operation states have been studied. In the contingent state, the circuit outage is simulated in one branch which causes a power flow violation in the other branch. The IGA always finds the best results and eliminates operational and insecure violations. (C) 1997 Elsevier Science Ltd.
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