An interactive multi-objective artificial neural network approach for machine setup optimization

In this paper, we develop an artificial neural network method for machine setup problems. We show that our new approach solves a very challenging problem in the area of machining i.e. machine setup. A review of machine setup concepts and methods, along with feedforward artificial neural network is presented. We define the problem of machine setup to assessing the values of machine speed, feed and depth of cut (process inputs) for a particular objective such as minimize cost, maximize productivity or maximize surface finish. We use cutting temperature, cutting force, tool life, and surface roughness (process outputs) rather than objective functions to communicate with the decision maker. We show the relationship between process inputs to process outputs. This relationship is used in determining machine setup parameters (speed, feed, and depth of cut). Back propagation neural network is used as a decision support tool. The network maps, the forward relationship, and backward relationship between process inputs and process outputs. This mapping facilitates an interactive session with the decision maker. The process input is appropriately selected. Our method has the advantage of forecasting machine setup parameters with very little resource requirement in terms of time, machine tool, and people. Forecast time is almost instantaneous. Accuracy of the forecast depends on training and a well determined training sample provides very high accuracy. Trained network replaces the knowledge of an experienced worker, hence labor cost can be potentially reduced.     

An innovative method for dynamic update of initial water table in XXT model based on neural network technique

The initial subsurface flow of whole basin plays a quite important role in daily rainfall–runoff simulation. However, general physically based rainfall–runoff model, such as the XXT model (a hybrid model of TOPographic MODEL and the Xinanjiang model), is difficult to catch the non-linear factors and take full advantages of previous information of rainfall and runoff that is essential to the initial watershed average saturation deficit of each time step. In order to address the issue, this study selected the initial subsurface flow for the whole time series of the XXT model as the breakthrough point, and used the observed runoff and rainfall data of two days before the present day as the inputs of artificial neural network (ANN) and initial subsurface flow of the present day as the output, then integrated ANN into runoff generation module of XXT model and finally tested the integrated model for daily runoff simulation in large-scale and semi-arid Linyi watershed, eastern China. In addition, this work employ particle swarm optimization (PSO) algorithm to seek the best combination of 6 physical parameters in XXT and a great number of weights in ANN to avoid the local optimization. The results show that the integrated model performs much better than XXT in terms of Nash–Sutcliffe efficiency coefficient (NE) and root mean square error (RMSE). Hence, the new integrating approach proposed here is promising for daily rainfall–runoff modeling and can be easily extended to other process-based models.     

Using the artificial neural network model for modeling the performance of the counter flow vortex tube

In this study, the effect of the nozzle number and the inlet pressure on the heating and cooling performance of the counter flow type vortex tube has been modeled with artificial neural networks (ANN) by using the experimentally obtained data. ANN has been designed by Pithiya software. In the developed system output parameter temperature gradient between the cold and hot outlets (ΔT) has been determined using inlet parameters such as the inlet pressure (P_inlet), nozzle number (N), and cold mass fraction (μ_c). The back-propagation learning algorithm with variant which is Levenberg–Marquardt (LM) and Fermi transfer function have been used in the network. In addition, the statistical validity of the developed model has been determined by using the coefficient of determination (R²), the root means square error (RMSE) and the mean absolute percentage error (MAPE). R², RMSE and MAPE have been determined for ΔT as 0.9947, 0.188224, and 0.0460, respectively.     

Constructing of the risk classification model of cervical cancer by artificial neural network

It is significant to build up the risk classification model of cervical cancer for the evaluation of high-risk population. Data were divided into two sub-data, one is model building sub-data, the other is model testing sub-data. By using of artificial neural network (ANN) analysis method (Back Propagation, BP), the risk classification model had been setup. The parameters were listed as following: the data had been treated as normalization, and the level of network was 3, and the number of neural in hidden level was 5, and the transmitting function between input level and hidden level was logsig, and the transmitting function between hidden level and output level was purelin, and the studying method was Levenberg–Marquardt optimizing, and the error parameter eg = 0.09, maximum epochs me = 8000. The model quality was good (sensitivity = 98%, specificity = 97%), and the back calculation fitting result was excellent. The predictive value of 10 unknown data was also good, during which the correct rate of control group was 100%, and that of case group was 80%. Because ANN is with the character of self-organizing, self-learning and self-adapting, the ANN risk classification model is fit for the screening of high-risk population of local cervical cancer, risk evaluation of cervical cancer and the effect evaluation of the prevention method after training the model by new data of some area.     

用神经网络实现一般访问结构的多重秘密共享

利用人工神经网络,结合RSA密码体制,实现了一种基于一般访问结构的多重秘密共享方案.在该方案中,秘密份额是人工神经网络收敛结果,各参与者共享多个秘密只需要维护一个秘密份额.共享多个秘密只需要进行一次人工神经网络训练,从而提高了方案的效率;在秘密分发和恢复时,利用RSA密码体制保证方案的安全性和正确性.分析表明,该方案是一个安全的、实用的秘密共享方案.     

An artificial neural network model of LRU-cache misses on out-of-order embedded processors

For in-order processors, the stack distance theory is a well-known means to fast model LRU-cache behaviors . However, it cannot be applied directly on out-of-order processors due to the changing of stack distance histograms by mechanisms such as reordering executions, speculative loads, load-in-store operations and non-blocking issues.This paper proposes an Artificial Neural Network (ANN) model to fast forecast private LRU-cache behaviors on out-of-order processors. To verify our model in real commercial applications, the evaluation scenarios chosen in this paper, not only include traditional embedded benchmark suits, such as Mibench 1.0 and Mediabench II, but also embrace Android applications from Mobybench 2.0 benchmark suit as well.Compared with results from Gem5 simulations, the average root mean square error of our ANN model is less than 6% with the prediction speed increasing about 2.5× –3×.     

采用附加动量法的BP技术进行脱硫可行性数学模型的研究

控制SO2污染是当今世界关心的重大课题。煤是中国能源支柱，由燃煤造成的硫污染尤为突出。研究煤中硫分与产率之间的关系并建立适用的 模型是脱除硫工艺中至关重要的一步。本研究利用神经网络技术来研究硫分与产率之间的关系模型，此研究为寻求一种技术上可行、经济上合理的脱硫工艺，进而减少硫污染具有重要的理论价值和实际应用价值。     

An artificial neural network model for estimating crop yields using remotely sensed information

Crop yield forecasting is a very important task for researchers in remote sensing. Problems exist with traditional statistical modelling (especially regression models) of nonlinear functions with multiple factors in the cropland ecosystem. This paper describes the successful application of an artificial neural network in developing a model for crop yield forecasting using back-propagation algorithms. The model has been adapted and calibrated using on the ground survey and statistical data, and it has proven to be stable and highly accurate.     

互感器故障诊断人工神经网络法

基于神经网络的基本结构和算法,该文建立了一个用于高压电磁式互感器故障诊断的人工神经网络。其中采用了有效的网络学习算法,旨在全面、快速和准确地实现互感器故障诊断,以提高互感器及电力系统运行的可靠性。根据互感器的故障特征,该文建立一个3层前向神经网络,采用误差逆传播学习算法进行了讨论,并由仿真计算结果加以论证。     

Prediction approach of software fault-proneness based on hybrid artificial neural network and quantum particle swarm optimization

The identification of a module's fault-proneness is very important for minimizing cost and improving the effectiveness of the software development process. How to obtain the correlation between software metrics and module's fault-proneness has been the focus of much research. This paper presents the application of hybrid artificial neural network (ANN) and Quantum Particle Swarm Optimization (QPSO) in software fault-proneness prediction. ANN is used for classifying software modules into fault-proneness or non fault-proneness categories, and QPSO is applied for reducing dimensionality. The experiment results show that the proposed prediction approach can establish the correlation between software metrics and modules’ fault-proneness, and is very simple because its implementation requires neither extra cost nor expert's knowledge. Proposed prediction approach can provide the potential software modules with fault-proneness to software developers, so developers only need to focus on these software modules, which may minimize effort and cost of software maintenance.     

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

The ability of artificial neural networks (ANN) to model the unsteady aerodynamic force coefficients of flapping motion kinematics has been studied. A neural networks model was developed based on multi-layer perception (MLP) networks and the Levenberg–Marquardt optimization algorithm. The flapping kinematics data were divided into two groups for the training and the prediction test of the ANN model. The training phase led to a very satisfactory calibration of the ANN model. The attempt to predict aerodynamic forces both the lift coefficient and drag coefficient showed that the ANN model is able to simulate the unsteady flapping motion kinematics and its corresponding aerodynamic forces. The shape of the simulated force coefficients was found to be similar to that of the numerical results. These encouraging results make it possible to consider interesting and new prospects for the modelling of flapping motion systems, which are highly non-linear systems.     

Application of a radial basis function artificial neural network to seismic data inversion

We investigate here the performance and the application of a radial basis function artificial neural network (RBF-ANN) type, in the inversion of seismic data. The proposed structure has the advantage of being easily trained by means of a back-propagation algorithm without getting stuck in local minima. The effects of network architectures, i.e. the number of neurons in the hidden layer, the rate of convergence and prediction accuracy of ANN models are examined. The optimum network parameters and performance were decided as a function of testing error convergence with respect to the network training error. An adequate cross-validation test is run to ensure the performance of the network on new data sets. The application of such a network to synthetic and real data shows that the inverted acoustic impedance section was efficient.     

An artificial neural network noise reduction method for chaotic attractors

A method is presented to reduce noise in chaotic attractors without knowing the underlying maps. The method is based on using Artificial Neural Network (ANN) for moderate levels of additive noise. For high levels of additive noise, a combination of a refinement procedure with ANN is used. In this case, only one refinement is needed for the successful use of ANN. The obtained ANN model is used for long-term predictions of the future behavior of a Henon attractor, using information based only on past values.     

用于构建人工大脑的神经网络模型的分析与评估

目前人工脑的研究还处于起步阶段,构造智能化人工脑的方法正在探索中.影响人工脑性能的关键部分在于所选用的人工神经网络,针对目前已提出的三个网络模型,即CoDi模型、TiPo模型和DePo模型,进行了评估研究.采用的评估方法是通过解决曲线跟踪问题对模型进行测试.测试结果显示DePo模型曲线跟踪取得的效果较另两个更好,TiPo模型跟CoDi模型的性能相似.人工脑的进一步研究工作将包括提出更接近生物机制的模型或工程角度更有进化能力的模型.     

An artificial neural network for nonlinear estimation of the turbine flow-meter coefficient

Turbine flow meters find various applications in the process industries, such as batch control, measuring fuel oil and gas consumption, controlling blending processes, etc. The turbine meter is a rotor driven by the fluid being metered, at a speed proportional to the flow rate.The actual behavior of a turbine flow meter is a complex function of many variables; among these are the temperature, pressure, and viscosity of the fluid; the lubricating qualities of the fluid; bearing wear; and environmental factors. The turbine meter coefficient is referred to as the ‘K factor’, and is defined as the number of pulses per unit volume. At present, there is no single mathematical equation to predict the actual K factor. More accurate estimations and trending of the K factor will not only facilitate preventive maintenance, replacement analysis, etc., but will also ensure that material flow accounting is accurate.This research explores the use of neural-network models to aid in the estimation of the actual K factor that reflects the effect of the actual operating conditions of the turbine meter. This research analyzed data from three different turbine flow meters measuring the rate of pumping oil from the North Sea, for a company that operates off-shore oil platforms. The use of neural networks presents a new approach to the capturing of the underlying nonlinear relationships among the various input variables and the K factor. The results from this study report significant percentage reductions in mean absolute errors for the neural-network predictions over the company’s present estimation practices for the turbine flow-meter coefficient.     

Remote health monitoring adoption model based on artificial neural networks

The purpose of this research is to utilize the adoption model of remote health monitoring established by artificial neural networks (ANNs). The adoption model by the naming is the healthcare information adoption model (HIAM) that it is created first time by myself. The HIAM focused on citizens in Taiwan as research subjects. The main research result showed that people’s perceived usefulness and benefits (PUB) must be raised in order to effectively increase the adoption of remote health monitoring. Moreover, this research has proved that the utilization of the adoption model of remote health monitoring established by ANN based on the HIAM is feasible. These findings may offer significant reference for subsequent studies.     

A genetic algorithm-based artificial neural network model for the optimization of machining processes

Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a time-saving model that satisfies all the accuracy requirements.     

Network information criterion-determining the number of hiddenunits for an artificial neural network model

The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set.     

An artificial neural network approach to the problem of wireless sensors network localization

One of the imperative problems in the realm of wireless sensor networks is the problem of wireless sensors localization. Despite the fact that much research has been conducted in this area, many of the proposed approaches produce unsatisfactory results when exposed to the harsh, uncertain, noisy conditions of a manufacturing environment. In this study, we develop an artificial neural network approach to moderate the effect of the miscellaneous noise sources and harsh factory conditions on the localization of the wireless sensors. Special attention is given to investigate the effect of blockage and ambient conditions on the accuracy of mobile node localization. A simulator, simulating the noisy and dynamic shop conditions of manufacturing environments, is employed to examine the neural network proposed. The neural network performance is also validated through some actual experiments in real-world environment prone to different sources of noise and signal attenuation. The simulation and experimental results demonstrate the effectiveness and accuracy of the proposed methodology.     

基于神经网络的软件水印实现方案

本文总结了现有的软件水印算法并给出了水印系统的形式化定义,继而提出一种新的基于神经网络的软件水印实现方案,并对其进行了简单的性能分析。