基于反射波法的桩身完整性判别的神经网络模型

提出了一种基于应力波反射波法判别基桩桩身完整性的BP型神经网络模型。该模型以桩身应力波波形曲线、桩的几何尺寸和桩身混凝土波速等为网络的输入信息,预测作为网络输出信息的桩身完整性特征,如正常、缩径、扩径、离析和开裂等。通过采用多种运算改进技术,提高了网络的可行性和计算速度。对天津市软土地基中的数十根灌注桩的实测波形等资料学习和预测,取得了令人满意的精度。

Characterization of grain growth behaviors by BP-ANN and Sellars models for nickle-base superalloy and their comparisons

In order to deeply understand the grain growth behaviors of Ni80A superalloy,a series of grain growth experiments were conducted at holding temperatures ranging from 1223 to 1423 K and holding time ranging from 0 to 3600 s.A back-propagation artificial neural network(BP-ANN)model and a Sellars model were solved based on the experimental data.The prediction and generalization capabilities of these two models were evaluated and compared on the basis of four statistical indicators.The results show that the solved BP-ANN model has better performance as it has higher correlation coefficient(r),lower average absolute relative error(AARE),lower absolute values of mean value(μ)and standard deviation(ω).Eventually,a response surface of average grain size to holding temperature and holding time is constructed based on the data expanded by the solved BP-ANN model,and the grain growth behaviors are described.

电力系统中输变电设备智能化网络运维管理方法

针对电力系统中输变电设备运维过程中数据管理滞后、监控能力差的问题,设计一个新型的输变电设备智能化网络运维管理平台。该平台充分应用云计算、人工智能技术、大数据管理技术等多种技术手段,实现设备运维过程中数据采集、计算、传输以及远程应用。应用改进型人鱼算法模型(Artificial Fish Swarm Algorithm,AFSA),提高了运维设备数据信息的接收跟踪,设计出改进型BP神经网络模型,实现输变电设备运维过程中的故障诊断。试验表明,本研究数据跟踪量在90%以上,故障诊断精确率达90%以上。

Energy-absorption forecast of thin-walled structure by GA-BP hybrid algorithm

In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.

Coal mine safety production forewarning based on improved BP neural network

Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.

新能源发电系统基础数据监控及核查方法

新能源发电系统在电力系统中具有重要的作用,针对现有技术监控及核查能力落后等问题,设计了一种新型新能源发电系统基础数据监控及核查方法。通过提出改进型BP神经网络模型和增加经验模态分解(EMD)算法,提高了新能源发电系统的数据分析能力。试验表明,所提出的方法耗时短、精度高、误差低。

Study on Maize-water Model for Supplemental Irrigation in Loess Plateau

The Loess Plateau has a typical semi-arid climate, and the area suffers from very harsh ecological environment, severe soil erosion and water runoff, and uneven distributed precipitation. Due to the relatively low holding capacity, current rainwater-collecting and conservation facilities can only supplement a maximum of18 mm of water for crop production in each irrigation. In this study, mathematical models were constructed to identify the water requirement critical period of maize crop by evaluating response of each individual developmental stage to supplemental irrigation with harvested rainwater. In the transformed Jensen model, ETmin/Eta was used as the index of relative evapotranspiration. The use of relative yield and relative crop evapotranspiration was able to eliminate influences from unintended environmental factors. A BP neural network crop-water model for extreme water deficit condition was constructed using the index of relative evapotranspiration as the input and the index of relative yield as the output after iterative training and adjustment of weight values. Comparison of measured maize yields to those predicted by the two models confirmed that the BP neural network crop-water model is more accurate than the transformed Jensen model in predicting the sensitivity index to waterdeficit at various growth stages and maize yield when provided with supplemental irrigation with harvested rainwater.