基于全局搜索算法的太阳影子定位研究

目的为更好地解决已知某物体影长、测量的时间和日期的情况下确定物体的测量地点,或已知物体影长、测量日期及地点的情况下确定测量时间的问题,进而解决野外探险时地理位置的确定和根据某物体的视频或照片等影像资料而确定其拍摄地等实际问题。方法针对太阳影子定位,使用基于穷举法的全局搜索式算法、最小二乘法的参数估计法、比例消参法等方法,分别构建全局搜索网络模型、参数方程模型等模型,结合MATLAB软件进行算法的编写、图形的绘制、数据的拟合与预测等处理。结果结合第一组数据,所推算的测量地位于广西,结合第二组数据,所推算的测量地在蒙古国。结论利用全局搜索式算法及参数方程模型,结合MATLAB软件,可以解决太阳影长、直杆长度、测量地点、测量日期、测量时间等5个变量中知四求一、知三求二等问题。

Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm

Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was presented,which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion.In the proposed method,Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum.An implementation of proposed DE-BPNN was given,the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets.Two abnormity models were used to verify the feasibility and effectiveness of the proposed method,the results show that the proposed DE-BP algorithm has better performance than BP,conventional DE-BP and other chaotic DE-BP methods in stability and accuracy,and higher imaging quality than least square inversion.

Optimization of air quantity regulation in mine ventilation networks using the improved differential evolution algorithm and critical path method

In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were reviewed in the paper. Aiming at the high difficulty semi-controlled splitting problem, the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks. A new algorithm, which combined the improved differential evaluation and the critical path method （CPM） based on the multivariable separate solution strategy, was put forward to search for the global optimal solution more efficiently. In each step of evolution, the feasible solutions of air quantity distribution are firstly produced by the improved differential evolu- tion algorithm, and then the optimal solutions of regulator pressure drop are obtained by the CPM. Through finite steps iterations, the optimal solution can be given. In this new algorithm, the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity. Meanwhile, the individual neighbor- hood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching, thus improving the convergence rate. The computer program was developed based on this method. Finally, the two ventilation networks with single-fan and multi-fans were solved. The results show that this algorithm has advantages of high effectiveness, fast convergence, good robustness and flexibility. This computer program could be used to solve lar^e-scale ~eneralized ventilation networks o^timization problem in the future.

Fault Location in Transmission Lines Using BP Neural Network Trained with PSO Algorithm

In modem protection relays, the accurate and fast fault location is an essential task for transmission line protection from the point of service restoration and reliability. The applications of neural networks based fault location techniques to transmission line are available in many papers. However, almost all the studies have so far employed the FNN （feed-forward neural network） trained with back-propagation algorithm （BPNN） which has a better structure and been widely used. But there are still many drawbacks if we simply use feed-forward neural network, such as slow training rate, easy to trap into local minimum point, and bad ability on global search. In this paper, feed-forward neural network trained by PSO （particle swarm optimization） algorithm is proposed for fault location scheme in 500 kV transmission system with distributed parameters presentation, The purpose is to simulate distance protection relay. The algorithm acts as classifier which requires phasor measurements data from one end of the transmission line and DFT （discrete Fourier transform）. Extensive simulation studies carried out using MATLAB show that the proposed scheme has the ability to give a good estimation of fault location under various fault conditions.

Generalized Mechanism for the Solid Phase Transition of M_(2)O_(3)(M=Al,Ga)Featuring Single Cation Migration and Martensitic Lattice Transformation

Al_(2)O_(3)and Ga_(2)O_(3)exhibit numerous crystal phases with distinct stabilities and materialproperties.However,the phase transitions among thosematerialsare typicallyundesirable in industrial applications,making it imperative to elucidate the transition mechanisms between these phases.The configurational similarities between Al_(2)O_(3)and Ga_(2)O_(3)allow for the replication of phase transition pathways between these materials.In this study,we investigate the potential phase transition pathway of alumina from the 0-phase to the α-phase using stochastic surface walking global optimization based on global neural network potentials,while extending an existing Ga_(2)O_(3)phase transition path.Through this exploration,we identify a novel single-atom migration pseudomartensitic mechanism,which combines martensitic transformation with single-atom diffusion.This discovery offers valuable insights for experimental endeavors aimed at stabilizing alumina in transitional phases.