Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

Design of Robotic Visual Servo Control Based on Neural Network and Genetic Algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation （BP） neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP Mgorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.

Application of quantum neural networks in localization of acoustic emission

Due to defects of time-difference of arrival localization,which influences by speed differences of various model waveforms and waveform distortion in transmitting process,a neural network technique is introduced to calculate localization of the acoustic emission source.However,in back propagation（BP） neural network,the BP algorithm is a stochastic gradient algorithm virtually,the network may get into local minimum and the result of network training is dissatisfactory.It is a kind of genetic algorithms with the form of quantum chromosomes,the random observation which simulates the quantum collapse can bring diverse individuals,and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity.Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy,so it has a good application prospect and is worth researching further more.

Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.

Hybrid algorithm combining genetic algorithm with back propagation neural network for extracting the characteristics of multi-peak Brillouin scattering spectrum

In this study, a hybrid algorithm combining genetic algorithm （GA） with back propagation （BP） neural network （GA-BP） was proposed for extracting the characteristics of multi-peak Brillouin scattering spectrum. Simulations and experimental results show that the GA-BP hybrid algorithm can accurately identify the position and amount of peaks in multi-peak Brillouin scattering spectrum. Moreover, the proposed algorithm obtains a fitting degree of 0.9923 and a mean square error of 0.0094. Therefore, the GA-BP hybrid algorithm possesses a good fitting precision and is suitable for extracting the characteristics of multi-peak Brillouin scattering spectrum.

Auto-Generation Method of Child Basic Block Structure

In order to realize the auto-generation of clothing paper pattern making and reduce the reliance on the experience of clothing pattern makers,by simulating the experience of the clothing pattern maker through back propagation(BP)neural network,400 children’s body measurements are collected and drawn into the clothing paper pattern,and the children’s body measurements and the pattern sizes generated through the children’s clothing structure design rules are imported into MATLAB neural network toolbox and a neural network model is established to automatically become the predicted pattern size.Then the parametric mathematical model of children’s clothing paper pattern is established and the children’s body measurements is imported into Auto-CAD parametric function to generate children’s clothing paper pattern automatically.The experimental interface and the virtual try-on interface are demonstrated and their effects are evaluated.The results show that the production rate of clothing paper patterns is improved by the auto-generation method,which is of positive significance to the intelligent production of clothing enterprises.

Gesture Recognition Based on BP Neural Network Improved by Chaotic Genetic Algorithm

Aim at the defects of easy to fall into the local minimum point and the low convergence speed of back propagation（BP）neural network in the gesture recognition, a new method that combines the chaos algorithm with the genetic algorithm（CGA） is proposed. According to the ergodicity of chaos algorithm and global convergence of genetic algorithm, the basic idea of this paper is to encode the weights and thresholds of BP neural network and obtain a general optimal solution with genetic algorithm, and then the general optimal solution is optimized to the accurate optimal solution by adding chaotic disturbance. The optimal results of the chaotic genetic algorithm are used as the initial weights and thresholds of the BP neural network to recognize the gesture. Simulation and experimental results show that the real-time performance and accuracy of the gesture recognition are greatly improved with CGA.

A fast computational method for the landing footprints of space-to-ground vehicles

Fast computation of the landing footprint of a space-to-ground vehicle is a basic requirement for the deployment of parking orbits, as well as for enabling decision makers to develop real-time programs of transfer trajectories. In order to address the usually slow computational time for the determination of the landing footprint of a space-to-ground vehicle under finite thrust, this work proposes a method that uses polynomial equations to describe the boundaries of the landing footprint and uses back propagation(BP) neural networks to quickly determine the landing footprint of the space-to-ground vehicle. First, given orbital parameters and a manoeuvre moment, the solution model of the landing footprint of a space-to-ground vehicle under finite thrust is established. Second, given arbitrary orbital parameters and an arbitrary manoeuvre moment, a fast computational model for the landing footprint of a space-to-ground vehicle based on BP neural networks is provided.Finally, the simulation results demonstrate that under the premise of ensuring accuracy, the proposed method can quickly determine the landing footprint of a space-to-ground vehicle with arbitrary orbital parameters and arbitrary manoeuvre moments. The proposed fast computational method for determining a landing footprint lays a foundation for the parking-orbit configuration and supports the design of real-time transfer trajectories.

An Intelligent Otologic Drill

Objective To test a modified otologic drill under different drilling conditions for its ability to identify drilling faults and stop drilling.Methods Based on force analysis and previous works,an otologic drill was modified and equipped with three sensors.Under various conditions,the drill was used to simulate three drilling faults and normal drilling,and signals from the drill were analyzed to extract the characteristic signal.A multi-sensor information fusion system and a stop program were designed to recognize drilling faults and stop drilling.Results Signals from each sensor changed consistently in response to drilling condition changes,with high repeatability and regularity.The average identification rate was 72.625%,68.575%,70.5% and 81.3% respectively for the three simulated drilling faults and normal drilling.The stop program stopped drilling in 0.2～ 0.3 seconds when a drilling faults was detected.Conclusions This study shows that the forces acting on the drill bit change predictably in the three simulated drilling conditions;that using suitable BP neural networks,the drilling faults can be reliably identified,and that a stop program based upon characteristic signal recognition can stop drilling quickly upon detecting drilling faults.This lays a foundation for development of a system capable of predicting drilling faults and automatic drill control.Further studies are being undertaken for practical application of such a system.

AN INFORMATION FUSION METHOD FOR SENSOR DATA RECTIFICATION

In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of water regime monitoring information, this paper addresses this issue and proposes an information fusion method to implement data rectification. An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit, which takes Field-Programmable Gate Array (FPGA) as the core component. In order to verify the effectiveness, five measurements including water level, discharge and velocity are selected from three different points in a water regime monitoring station. The simulation results show that this method can recitify random errors as well as gross errors significantly.

Softw are Maintainability Prediction with UML Class Diagram

Software system can be classified into many function modules from the perspective of user. Unified modeling language( UML) class diagram of each function module was extracted,and design characteristic metrics which influenced software maintainability were selected based on UML class diagram.Choosing metrics of UML class diagram as predictors,and mean maintenance time of function module was regarded as software maintainability parameter. Software maintainability models were built by using back propagation( BP) neural network and radial basis function( RBF) neural network, respectively and were simulated by MATLAB. In order to evaluate the performance of models,the training results were analyzed and compared with leaveone-out cross-validation and model performance evaluation criterion. The result indicated that RBF arithmetic was superior to BP arithmetic in predicting software maintainability.

Development of the ignition delay prediction model of n-butane/hydrogen mixtures based on artificial neural network

Based on the experimental ignition delay results of n-butane/hydrogen mixtures in a rapid compression machine,a Genetic Algorithm(GA)optimized Back Propagation(BP)neural network model is originally developed for ignition delay prediction.In the BP model,the activation function,learning rate and the neurons number in the hidden layer are optimized,respectively.The prediction ability of the BP model is validated in wide operating ranges,i.e.,compression pressures from 20 to 25 bar,compression temperatures from 722 to 987 K,equivalence ratios from 0.5 to 1.5 and molar ratios of hydrogen(X_(H2))from 0 to 75%.Compared with the BP model,the GA optimized BP model could increase the average correlation coefficient from 0.9745 to 0.9890,in the opposite,the average Mean Square Error(MSE)decreased from 2.21 to 1.06.On the other hand,to assess the BP-GA model prediction ability in the never-seen-before cases,a limited BP-GA model is fostered in the𝑋X_(H2) range from 0 to 50%to predict the ignition delays at the cases of𝑋X_(H2)=75%.It is found that the predicted ignition delays are underestimated due to the training dataset lacking of“acceleration feature”that happened at𝑋X_(H2)=75%.However,three possible options are reported to improve the prediction accuracy in such never-seen-before cases.

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and intelligence.However,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough.Hence,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper.Firstly,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed qualitatively.The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in depth.Secondly,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic characteristics.Finally,the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics.The experimental results show that the DBN network,LeNet5 network,and BP network have synchronous characteristics.And the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural networks.The KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic characteristics.Compared with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

Energy flexibility characteristics of centralized hot water system in university dormitories

The large-scale application of renewable energy is an important strategy to achieve the goal of carbon neutrality in the building sector.Energy flexibility is essential for ensuring balance between energy demand and supply when targeting the maximum penetration rate of renewable energy during the operation of regional integrated energy systems.Revealing the energy flexibility characteristics of centralized hot water systems,which are an important source of such flexibility,is of great significance to the optimal operation of regional integrated energy systems.Hence,in this study,based on the annual real-time monitoring data,the energy flexibility of the centralized hot water system in university dormitories is evaluated from the perspective of available storage capacity(C_(ADR)),recovery time(t_(recovery)),and storage efficiency(η_(ADR)),by the data-driven simulation method.The factors influencing the energy flexibility of the centralized hot water system are also analyzed.Available storage capacity has a strong positive correlation with daily water consumption and a strong negative correlation with daily mean outdoor temperature.These associations indicate that increased water use on the energy flexibility of the centralized hot water system is conducive to optimal dispatching.In contrast,higher outdoor temperature is unfavorable.The hourly mean value of the available storage capacity in spring and winter is found to be around 80 kWh in the daytime,and about twice that in summer and autumn.Recovery time is evenly distributed throughout the year,while t_(recovery)/C_(ADR)in spring and winter is about half that in summer.The storage efficiency was significantly higher in spring,summer,and winter than in autumn.The hourly mean storage efficiency was found to be about 40%in the daytime.The benefits of activating energy flexibility in spring and winter are the best,because these two seasons have higher available storage capacity and storage efficiency,while the benefit of activating energy flexibility is the highest at 6:00 a.m.,and very low from midnight to 3:00 a.m.

Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction

Urban subway tunnel construction inevitably disturbs the surrounding rock and causes the deformation of existing subway structures. Dynamic predictions of the tunnel horizontal displacement, tunnel ballast settlement, and tunnel differential settlement are important for ensuring the safety of buildings and tunnels. First, based on the Hangzhou Metro project, we analyzed the influence of construction on the deformation of existing subway structures and the difficulties and key points in monitoring. Then, a deformation prediction model, based on a back propagation(BP) neural network, was established with massive monitoring data. In particular, we analyzed the influence of four structures of the BP neural network on prediction performance, i.e., single input–single hidden layer–single output, multiple inputs–single hidden layer–single output, single input–double hidden layers–single output, and multiple inputs–double hidden layers–single output, and verified them using measured data.