Application of novel physical picture based on artificial neural networks to predict microstructure evolution of Al-Zn-Mg-Cu alloy during solid solution process

The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.

三地电网企业2018—2020年职业危害突发事件调查

目的分析我国电网企业各类职业危害突发事件的发生情况与影响因素。方法采用方便抽样法,选择吉林省、山东省和重庆市共8家电网企业的4191名电网工人为研究对象,调查其2018—2020年工作环境暴露情况与各类职业危害突发事件发生情况。结果研究对象从事户外作业者占71.7%;电光性眼炎、急性高原病、中暑、电光性皮炎、晒伤、冻伤、日光性眼炎、有限空间作业中毒等职业危害突发事件发生率由高到低排列依次为42.3%、42.3%、38.1%、24.3%、17.4%、16.5%、10.0%和1.3%(P<0.01)。多因素logistic回归分析结果显示,吉林省电网工人发生冻伤风险分别高于山东省和重庆市(P值均<0.01),重庆市电网工人发生日光性眼炎的风险高于吉林省(P<0.01);运检岗位电网工人发生中暑和晒伤的风险均高于变电岗位工人(P值均<0.05);单位有防护制度的电网工人晒伤和日光性眼炎事件的发生风险均低于单位无防护制度者(P值均<0.01);电网工人晒伤、日光性眼炎事件的发生风险均随年龄和每日户外时间的增加而升高(P值均<0.05);采取对应防护措施均是电网工人中暑和冻伤的保护因素(P值均<0.01)。结论电网工人面临发生多种职业危害突发事件的风险;有关单位应根据地域和工人特点开展针对性预防工作,并督促落实各种工作环境下的各项防护制度。

Neural network approach to predicting mercury emission from utility boiler

The feasibility of using an ANN method to predict the mercury emission and speciation in the flue gas of a power station under un-tested combustion/operational conditions is evaluated. Based on existing field testing datasets for the emissions of three utility boilers, a 3-layer back-propagation network is applied to predict the mercury speciation at the stack. The whole prediction procedure includes： collection of data, structuring an artificial neural network （ANN） model, training process and error evaluation. A total of 59 parameters of coal and ash analyses and power plant operating conditions are treated as input variables, and the actual mercury emissions and their speciation data are used to supervise the training process and verify the performance of prediction modeling. The precision of model prediction （ root- mean-square error is 0. 8 μg/Nm3 for elemental mercury and 0. 9 μg/Nm3 for total mercury） is acceptable since the spikes of semi- mercury continuous emission monitor （SCEM） with wet conversion modules are taken into consideration.

Design and Realization of CPW Circuits Using EC-ANN Models for CPW Discontinuities

Novel accurate and efficient equivalent circuit trained artificial neural-network （EC-ANN） models,which inherit and improve upon EC model and EM-ANN models＇ advantages,are developed for coplanar waveguide （CPW） discontinuities. Modeled discontinuities include ： CPW step, interdigital capacitor, symmetric cross junction, and spiral inductor, for which validation tests are performed. These models allow for circuit design, simulation, and optimization within a CAD simulator. Design and realization of a coplanar lumped element band pass filter on GaAs using the developed CPW EC-ANN models are demonstrated.

Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.

A Method of Identifying Electromagnetic Radiation Sources by Using Support Vector Machines

Electromagnetic Radiation Source Identification(ERSI) is a key technology that is widely used in military and radiation management and in electromagnetic interference diagnostics.The discriminative capability of machine learning methods has recently been used for facilitating ERSI.This paper presents a new approach to improve ERSI by adopting support vector machines,which are proven to be effective tools in pattern classification and regression,on the basis of the spatial distribution of electromagnetic radiation sources.Spatial information is converted from 3D cubes to 1D vectors with subscripts as inputs in order to simplify the model.The model is trained with 187 500 data sets in order to enable it to identify the types of radiation source types with an accuracy of up to 99.9%.The influence of parameters(e.g.,penalty parameter,reflection and noise from the ambient environment,and the scaling method for the input data) are discussed.The proposed method has good performance in noisy and reverberant environment.It has an identification accuracy of 82.15% when the signal-to-noise ratio is 20 dB.The proposed method has better accuracy in a noisy environment than artificial neural networks.Given that each Electromagnetic(EM) source has unique spatial characteristics,this method can be used for EM source identification and EM interference diagnostics.

High-Performance of Power System Based upon ANFIS （Adaptive Neuro-Fuzzy Inference System） Controller

The proposed controller incorporates FL （fuzzy logic） algorithm with ANN （artificial neural network）. ANFIS replaces the conventional PI controller, tuning the fuzzy inference system with a hybrid learning algorithm. A tuning method is proposed for training of the neuro-fuzzy controller. The best rule base and the best training algorithm chosen produced high performance in the ANFIS controller. Simulation was done on Matlab Ver. 2010a. A case study was chopper-fed DC motor drive, in continuous and discrete modes. Satisfactory results show the ANFIS controller is able to control dynamic highly-nonlinear systems. Tuning it further improved the results.