Artificial Neural Network Approach to Data Matrix Laser Direct Part Marking

Certain applications have recently appeared in industry where a traditional bar code printed on a label will not survive because the item to be tracked has to be exposed to harsh environments. Laser direct-part marking is a manufacturing process used to create permanent marks on a substrate that could help to alleviate this problem. In this research, artificial neural networks were employed to model the laser direct-part marking process of Data Matrix symbols on carbon steel substrates. Several experiments were conducted to study the laser direct-part marking process and to generate data to serve as training, validation and testing data sets in the artificial neural networks modeling process. Two performance measures, mean squared error and correlation coefficient, were utilized to assess the performance of the artificial neural network models. Single-output artificial neural network models corresponding to four performance measures specific to the Data Matrix bar code symbology were found to have good learning and predicting capabilities. The single-output artificial neural network models were compared to equivalent multiple linear regression models for validation purposes. The prediction capability of the single-output artificial neural network models with respect to laser direct-part marking of Data Matrix symbols on carbon steel substrates was superior to that of the multiple linear regression models.     

Automobile seat comfort prediction: statistical model vs. artificial neural network

The current automobile seat comfort development process, which is executed in a trial and error fashion, is expensive and outdated. The prevailing thought is that process improvements are contingent upon the implementation of empirical/prediction models. In this context, seat-interface pressure measures, anthropometric characteristics, demographic information, and perceptions of seat appearance were related to an overall comfort index (which was a single score derived from a previously published 10-item survey with demonstrated levels of reliability and validity) using two distinct modeling approaches-stepwise, linear regression and artificial neural network. The purpose of this paper was to compare and contrast the resulting models. While both models could be used to adequately predict subjective perceptions of comfort, the neural network was deemed superior because it produced higher r2 values (0.832 vs. 0.713) and lower average error values (1.192 vs. 1.779).     

基于B-P神经网络的环境空气质量预测模型

B-P神经网络是一种刻画非线性现象的强有力工具,可以将它应用到环境空气质量预测中。B-P神经网络针对不同的监测项目,根据不同的气象特征因子,将污染源排放数据为输入因子,监测点位监测数据作为输出因子,形成多组训练样本,进行学习训练,建立起不同的预测网络。然后用空气污染源排放监测数据输入相同气象条件的、已调整好权值的B-P神经网络系统,即可输出该项污染物的监测点位预测监测值。实验证明B-P神经网络预测模型取得了较好的结果,比现有预测模型具有更大的优势。     

Applying a general regression neural network for predicting development effort of short-scale programs

Software development effort prediction is considered in several international software processes as the Capability Maturity Model-Integrated (CMMi), by ISO-15504 as well as by ISO/IEC 12207. In this paper, data of two kinds of lines of code gathered from programs developed with practices based on the Personal Software Process (PSP) were used as independent variables in three models for estimating and predicting the development effort. Samples of 163 and 80 programs were used for verifying and validating, respectively, the models. The prediction accuracy comparison among a multiple linear regression, a general regression neural network, and a fuzzy logic model was made using as criteria the magnitude of error relative to the estimate (MER) and mean square error (MSE). Results accepted the following hypothesis: effort prediction accuracy of a general regression neural network is statistically equal than those obtained by a fuzzy logic model as well as by a multiple linear regression, when new and change code and reused code obtained from short-scale programs developed with personal practices are used as independent variables.     

面向高铁站的热舒适度和能耗综合预测

针对高铁站这类半封闭建筑的热舒适度影响因素众多,影响机制复杂以及热舒适度与能耗存在背反等问题,提出了基于机器学习的高铁站热舒适度与能耗综合预测方法。首先采用传感器数据捕获及Energy Plus仿真两种方式对高铁站室内外状态、多联机及热交换机等控制单元及热能传导环境进行建模;其次提出影响高铁站热舒适度的八类因素——多联机开启台数、多联机设置温度、热交换机开启台数、客流密度、室外温度、室内温度、室内湿度和室内二氧化碳浓度,并设计424种模型运行工况以及3 714 240个实例;最后设计6种机器学习模型——深度神经网络、支持向量回归、决策树回归、线性回归、岭回归和贝叶斯岭回归,来对高铁站室内热舒适度和空调能耗进行有效预测。实验结果表明,6种机器学习模型中决策树回归预测模型能够在较短的时间内获得最优的预测性能,其平均均方误差低至0.002 2。所得研究成果可直接为下一阶段的温控策略提供主动预判的环境状态参数并实现实时决策。     

基于鸟群算法优化BP神经网络的热舒适度预测

热舒适度是室内环境舒适性的评价指标,由于热舒适度的计算是一个复杂的非线性迭代过程,不便应用于空调实时控制系统中,为解决这一问题,可利用BP神经网络算法对热舒适度进行预测.但为了改善传统BP神经网络收敛速度慢的问题,将采用鸟群算法（BSA）来优化BP神经网络初始的权值与阈值.最后,将BSA算法与相近的粒子群算法（PSO）进行对比分析,并利用MATLAB软件进行仿真,使BSA-BP预测模型的仿真结果与基本的BP神经网络预测模型、PSO-BP预测模型的仿真结果进行对比分析.结果表明,BSA-BP预测模型具有较快的收敛速度和较高的预测精度.     

BP网络和多元回归在葡萄酒质量模型中的应用

利用因子分析法筛选出对葡萄酒质量影响较大的12种理化指标,将其作为多元线性回归的自变量和BP网络输入层神经元,分别用多元线性回归和改进的BP神经网络两种方法建立葡萄酒和酿酒葡萄的主要理化指标与葡萄酒质量的关系模型。比较了两种模型的泛化能力,得出多元线性回归模型对新样本预测的平均相对误差是1.93%,而BP神经网络模型的平均相对误差是0.37%。仿真实验表明,BP神经网络的泛化能力和稳定性明显优于多元回归模型。     

Predicting the performance measures of a message-passing multiprocessor architecture using artificial neural networks

In this paper, we develop multi-layer feed-forward artificial neural network (MFANN) models for predicting the performance measures of a message-passing multiprocessor architecture interconnected by the simultaneous optical multiprocessor exchange bus (SOME-Bus), which is a fiber-optic interconnection network. OPNET Modeler is used to simulate the SOME-Bus multiprocessor architecture and to create the training and testing datasets. The performance of the MFANN prediction models is evaluated using standard error of estimate (SEE) and multiple correlation coefficient (R). Also, the results of the MFANN models are compared with the ones obtained by generalized regression neural network (GRNN), support vector regression (SVR), and multiple linear regression (MLR). It is shown that MFANN models perform better (i.e., lower SEE and higher R) than GRNN-based, SVR-based, and MLR-based models for predicting the performance measures of a message-passing multiprocessor architecture.     

多功能网络监控与辅助教学系统设计

计算机网络教学已经普及,现有的虚拟教室软件由于价格昂贵以及网络监控功能较弱等特点并不适合一般学校使用。针对这些问题,设计了一个基于UDP网络通信协议的多功能网络监控和辅助教学系统方案。此方案由控制台系统和工作站系统组成,在功能上兼顾网络监控和辅助教学,具备自动监控和控制计算机教学网络的功能;根据系统的特点,设计了基于UDP的控制台与工作站之间的数据通信协议。最后给出了此系统实现的主要软件流程图以及对系统的最终实现方式。     

Multi-position ergonomic computer workstation design to increase comfort of computer work

This paper presents a new design of computer workstation that is aimed at increasing the comfort of a user working for long periods at a computer. As we have become a society that spends a lot of time working on computers, the computer workstation needs to provide comfort to users. Discomfort and an improper position can negatively affect overall health and productivity. A new type of ergonomic computer workstation, which allows users to sit in multiple working positions, is proposed in order to provide better comfort to people who spend a long time sitting at their workstations. We have designed and developed a new multi-position ergonomic computer workstation which has 19 degrees of freedom and which can accommodate from 5th to 95th percentile human size. Four types of working position (upright, lean-back, zero-gravity and lean-forward) are preset by choosing different angular positions of the workstation parts. Positions of the workstation parts can be changed by controlling the actuators. These four positions were used to evaluate the comfort of the workstation. Subjective and objective evaluations, including comparison of the prototype and standard computer setup, were carried out using human subjects and ergonomic principles. Results showed that the new workstation is much more comfortable, supporting the body in a balanced way. Users have the freedom to stretch and relax in different working positions before they feel any noticeable discomfort; as a result, it lets users work for a longer period without strain, thus resulting in higher productivity.     

神经网络在大坝安全监控数据分析系统中的应用

研究神经网络在水电站大坝安全监控工程观测数据分析系统中的具体应用。基于水电站大坝安全监控项目的实际需求,以BP算法为基础的前馈神经网络用于东北水电站大坝安全监控数据分析软件系统中,通过与多元线性回归和逐步回归处理方法相比较,神经网络方法能够有效地提高观测数据的拟合精度和预测质量,使用数据分析软件系统对东北某水电站的历史数据处理,结果证明了神经网络用于水电站大坝安全监控项目数据分析具有独特优势和深远的研究前景。     

基于LSSVR和LSTM的多模型优化集成负荷预测

针对负荷需求受多源因素影响和现有单模型预测方法精度较低的问题,提出了一种基于最小二乘支持向量回归(LSSVR)和长短期记忆循环神经网络(LSTM)的多模型优化集成负荷预测方法。首先探究负荷相关特征的特性并由互信息进行特征选择,获取最优特征集。在此基础上采用随机抽样(bootstrap)生成多个训练集,然后使用具有良好预测能力的LSSVR和LSTM模型对多个训练集分别进行预测。利用混沌粒子群优化算法(CPSO)进一步提高模型预测精度。最后,在决策阶段中使用偏最小二乘回归(PLSR)组合各个子模型的最优预测输出并提供最终预测结果。对真实电网数据进行了仿真,并与其它预测方法进行了比较。本文所提方法的应用范围广泛且预测精度提高显著。     

An optimized instance based learning algorithm for estimation of compressive strength of concrete

This article proposes an optimized instance-based learning approach for prediction of the compressive strength of high performance concrete based on mix data, such as water to binder ratio, water content, super-plasticizer content, fly ash content, etc. The base algorithm used in this study is the k nearest neighbor algorithm, which is an instance-based machine leaning algorithm. Five different models were developed and analyzed to investigate the effects of the number of neighbors, the distance function and the attribute weights on the performance of the models. For each model a modified version of the differential evolution algorithm was used to find the optimal model parameters. Moreover, two different models based on generalized regression neural network and stepwise regressions were also developed. The performances of the models were evaluated using a set of high strength concrete mix data. The results of this study indicate that the optimized models outperform those derived from the standard k nearest neighbor algorithm, and that the proposed models have a better performance in comparison to generalized regression neural network, stepwise regression and modular neural networks models.     

无线传感器网络中基于RSSI的节点距离预测

无线传感器网络大量应用在环境监测、目标跟踪、安全监控等领域,因此网络的自身定位是大多数应用的基础。常用的定位方法必须测量节点间的距离。为了预测距离值,根据实验获取的RSSI值与对应的距离值,先对实验数据进行滤波处理,建立面向Matlab神经网络工具箱的神经网络预测模型,利用神经网络的特性和Matlab工具箱的强大功能,通过实测数据对网络进行训练。预测结果表明,距离精度达到1 m之内。     

基于深度前馈神经网络的多因子人体表面积计算模型

人体表面积(BSA)在临床医学上有着至关重要的作用,但现有BSA计算方法大多只使用身高和体重2个参数且采用匹配简单函数的方法来估计体表面积,临床上也认为现有的BSA计算方法误差较大。针对这些问题,提出一种BSA回归预测模型。该回归预测模型包含2个部分：首先,借助相关性和显著性分析选择相关性较高的体表面积影响因子;其次,利用人体数据训练深度前馈神经网络,构建回归模型。实验分别采取5-折交叉验证与测试集验证2种方法。首先,将深度前馈神经网络模型与传统人体表面积计算方法进行精度评估和结果对比分析;其次将深度前馈神经网络模型与3种模型进行精度评估和结果对比分析。在与传统方法对比中,深度前馈神经网络模型的决定系数高于2种传统方法的,且比传统方法提高了6%,误差与传统方法的相比降低了近一倍。在与3种模型的对比中,深度前馈神经网络的决定系数比其他模型的提高了至少2%,误差降低。一致性分析实验结果也显示,深度前馈神经网络95%一致性界限最小,一致性最好。总体来说,提出的回归预测模型可以得到更加精确的体表面积预测值。     

Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws

Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process.     

Neural network models for group behavior prediction: a case of soccer match attendance

Soccer match attendance is an example of group behavior with noisy context that can only be approximated by a limited set of quantifiable factors. However, match attendance is representative of a wider spectrum of context-based behaviors for which only the aggregate effect of otherwise individual decisions is observable. Modeling of such behaviors is desirable from the perspective of economics, psychology, and other social studies with prospective use in simulators, games, product planning, and advertising. In this paper, we evaluate the efficiency of different neural network architectures as models of context in attendance behavior by comparing the achieved prediction accuracy of a multilayer perceptron (MLP), an Elman recurrent neural network (RNN), a time-lagged feedforward neural network (TLFN), and a radial basis function network (RBFN) against a multiple linear regression model, an autoregressive moving average model with exogenous inputs, and a naive cumulative mean model. We show that the MLP, TLFN, and RNN are superior to the RBFN and achieve comparable prediction accuracy on datasets of three teams from the English Football League Championship, which indicates weak importance of context transition modeled by the TLFN and the RNN. The experiments demonstrate that all neural network models outperform linear predictors by a significant margin. We show that neural models built on individual datasets achieve better performance than a generalized neural model constructed from pooled data. We analyze the input parameter influences extracted from trained networks and show that there is an agreement between nonlinear and linear measures about the most significant attributes.

     

结合灰色扩充的GA-BP神经网络模型在渭河水质遥感反演中的应用

以渭河陕西段水域为研究对象,用遗传算法改进的BP神经网络,结合灰色理论,建立了一种结合灰色扩充的GA-BP神经网络模型,对渭河水质中的主要污染指标CODmn(高锰酸盐指数)、COD(化学需氧量)、NH₃-N(氨氮)、DO(溶解氧)进行了遥感反演建模。实验证明：改进后的人工神经网络模型在预测精度上高于普通的BP神经网络模型和传统的多元线性回归模型,可用于渭河水质遥感反演建模。
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Application of neural networks for predicting program faults

Accurately predicting the number of faults in program modules is a major problem in the quality control of large software development efforts. Some software complexity metrics are closely related to the distribution of faults across program modules. Using these relationships, software engineers develop models that provide early estimates of quality metrics that do not become available until late in the development cycle. By considering these early estimates, software engineers can take actions to avoid or prepare for emerging quality problems. Most often, the predictive models are based upon multiple regression analysis. However, measures of software quality and complexity exhibit systematic departures from the assumptions of these analyses. With extreme violations of these assumptions, multiple regression models become unstable and lose most of their predictive quality. Since neural network models carry no data assumptions, these models could be more appropriate than regression models for modeling software faults. In this paper, we explore a neural network methodology for developing models that predict the number of faults in program modules. We apply this methodology to develop neural network models based upon data collected during the development of two commercial software systems. After developing neural network models, we apply multiple linear regression methods to develop regression models on the same data. For the data sets considered, the neural network methodology produced better predictive models in terms of both quality of fit and predictive quality.