Two‐handed human reach prediction models for ergonomic evaluation

As an essential function of computerized ergonomic evaluation models based on digital human models, realistic simulation or prediction of human reach profiles is of great importance. Although several human‐modeling efforts have been made to provide the capability of reach simulation, most studies have been limited to the reach of a single extremity. A variety of activities of human operators, however, frequently involve simultaneous positioning of two or more extremities to different target positions. Such a multiple reach problem cannot be satisfactorily resolved by means of conventional single‐extremity reach models because formulation of the problem as a series of single reaches rarely yields accurate trajectory of human‐reach profiles due to interactions of multiple extremities. In this research, a two‐handed reach prediction model was developed. The human upper body was modeled as a seven‐link system with 13 degrees of freedom, being regarded as a redundant open kinematic chain with two end‐effectors. As a way of solving the two‐handed reach problem, the resolved motion method was adopted among several inverse kinematics methods as the technique is fit for real‐time redundancy control. The method is also capable of incorporating the joint range availability criterion as a cost function to minimize excessive deviations of body joints from their neutral positions. Real human‐reach profiles were compared to those obtained from the prediction model and were found to be statistically similar. The methodology is expected to be applicable to the reach simulation of both upper and lower extremities without algorithmic difficulties. © 2010 Wiley Periodicals, Inc.     

Software defect prediction using cost-sensitive neural network

The software development life cycle generally includes analysis, design, implementation, test and release phases. The testing phase should be operated effectively in order to release bug-free software to end users. In the last two decades, academicians have taken an increasing interest in the software defect prediction problem, several machine learning techniques have been applied for more robust prediction. A different classification approach for this problem is proposed in this paper. A combination of traditional Artificial Neural Network (ANN) and the novel Artificial Bee Colony (ABC) algorithm are used in this study. Training the neural network is performed by ABC algorithm in order to find optimal weights. The False Positive Rate (FPR) and False Negative Rate (FNR) multiplied by parametric cost coefficients are the optimization task of the ABC algorithm. Software defect data in nature have a class imbalance because of the skewed distribution of defective and non-defective modules, so that conventional error functions of the neural network produce unbalanced FPR and FNR results. The proposed approach was applied to five publicly available datasets from the NASA Metrics Data Program repository. Accuracy, probability of detection, probability of false alarm, balance, Area Under Curve (AUC), and Normalized Expected Cost of Misclassification (NECM) are the main performance indicators of our classification approach. In order to prevent random results, the dataset was shuffled and the algorithm was executed 10 times with the use of n-fold cross-validation in each iteration. Our experimental results showed that a cost-sensitive neural network can be created successfully by using the ABC optimization algorithm for the purpose of software defect prediction.     

一种网络流量预测的小波神经网络模型

结合小波变换和人工神经网络的优势,建立一种网络流量预测的小波神经网络模型。首先对流量时间序列进行小波分解,得到小波变换尺度系数序列和小波系数序列,以系数序列和原来的流量时间序列分别作为模型的输入和输出,构造人工神经网络并且加以训练。用实际网络流量对该模型进行验证,结果表明,该模型具有较高的预测效果。     

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.     

Identification of faces obscured by noise: comparison of an artificial neural network with human observers

Face identification is easily accomplished by humans but is an exceptionally difficult task for machine vision algorithms. This report is the first to directly compare the face identification performance of humans with that of an artificialneural network,Dystal, using digitized images of the faces of eight individuals in an eight-alternative forced-response paradigm. The test images differed from the training images in facial expression, head tilt and rotation, amount and correlation of added noise, and the presence of a stocking mask in some of the images. The images were deliberately not preprocessed by a feature extraction algorithm to avoid confoundingthe performance of Dystal with the performance of the feature extraction algorithm.While human observers outperform Dystal at low noise levels, at high levels of correlated noise Dystal outperforms human observers, who score just above the chance level. The greater sensitivity to noise exhibited by human observers is attributed to the local feature extraction performed by human observers, but not by Dystal.     

Prediction of a Lorenz chaotic attractor using two-layer perceptron neural network

This paper investigates the prediction of a Lorenz chaotic attractor having relatively high values of Lypunov's exponents. The characteristic of this time series is its rich chaotic behavior. For such dynamic reconstruction problem, regularized radial basis function (RBF) neural network (NN) models have been widely employed in the literature. However, author recommends using a two-layer multi-layer perceptron (MLP) NN-based recurrent model. When none of the available linear models have been able to learn the dynamics of this attractor, it is shown that the proposed NN-based auto regressive (AR) and auto regressive moving average (ARMA) models with regularization have not only learned the true trajectory of this attractor, but also performed much better in multi-step-ahead predictions. However, equivalent linear models seem to fail miserably in learning the dynamics of the time series, despite the low values of Akaike's final prediction error (FPE) estimate. Author proposes to employ the recurrent NN-based ARMA model with regularization which clearly outperforms all other models and thus, it is possible to obtain good results for prediction and reconstruction of the dynamics of the chaotic time series with NN-based models.     

Perception and visualization of human posture information for computer-aided ergonomic analysis

This article reports three experiments that examined the effects of photographic method, computerized visualization scheme, and posture complexity on posture perception and specification for computer-aided ergonomic analysis. The subjects were presented with photographs of working postures, and were required to manipulate human forms generated by an ergonomics software program to match the postures in the photographs. The first experiment showed a clear advantage of using a three-dimensional (3-D) human form graphic with two photographs when complex, asymmetric postures were analysed. However, the use of a 3-D human graphic display and two photographs jeopardized the subjects' posture specification performance when simple, symmetric postures were analysed. The results of the second and the third experiment demonstrated the importance of achieving congruency between photographic and computer display perspectives in improving posture specification performance. Implications for ergonomic job analysis and ergonomics software design are discussed.     

Optimization-based posture reconstruction for digital human models

Digital human modeling provides a valuable tool for designers when implemented early in the design process. Motion capture experiments offer a means of validation of the digital human simulation models. However, there is a gap between the motion capture experiments and the simulation models, as the motion capture results are marker positions in Cartesian space and the simulation model is based on joint space. Therefore, it is necessary to map the motion capture data to simulation models by employing a posture reconstruction algorithm. Posture reconstruction is an inherently redundant problem where the collective distance error between experimental joint centers and simulation joint centers is minimized. This paper presents an optimization-based method for determining an accurate and efficient solution to the posture reconstruction problem. The procedure is used to recreate 120 experimental postures. For each posture, the algorithm minimizes the distance between the simulation model joint centers and the corresponding experimental subject joint centers which is called the mean measurement error.     

计算机网络安全综合评价的神经网络模型

灰色评价法、模糊综合评价等需确定隶属函数、各指标权重,明显受人为因素的影响。尝试应用神经网络技术进行网络安全的综合评价,并通过在单指标评价标准范围内随机取值方法,生成建立神经网络模型所需的训练样本、检验样本和测试样本,在遵循BP网络建模基本原则和步骤的情况下,建立了可靠、有效的网络安全综合评价模型。16个实例研究表明：提出的样本生成方法、建模过程是可靠的,并能有效地避免出现“过训练”和“过拟合”现象,建立的BP模型具有较好的泛化能力,不受人为因素的影响,各评价指标与网络安全等级之间存在明显的非线性关系,网络安全策略对网络安全的影响最大。     

Constructing neural network sediment estimation models using a data-driven algorithm

Artificial neural network (ANN) models are designed for suspended sediment estimation using statistical pre-processing of the data. Statistical properties such as cross-, auto- and partial auto-correlation of the data series are used for identifying a unique input vector to the ANN that best represents the sediment estimation process for a basin. The methodology is evaluated using the flow and sediment data from the stations Quebrada Blanca and Rio Valenciano in USA. The result of the study indicates that the statistical pre-processing of the data could significantly reduce the effort and computational time required in developing an ANN model. Three ANN training algorithms are also compared with each other for the selected input vector.     

基于神经网络的装载机故障诊断模型

首先分析了故障诊断的常用方法及其优缺点,设计了装载机故障诊断的流程,并阐述了流程中一些重要环节的设计和功能。然后在分析装载机信号的基础上提取了装载机信号的故障特征,相继建立了用于装载机故障诊断的BP神经网络和组合神经网络模型,并比较两者的优缺,选择更适合装载机故障诊断的模型。     

Prediction of chenille yarn and fabric abrasion resistance using radial basis function neural network models

The abrasion resistance of chenille yarn is crucially important in particular because the effect sought is always that of the velvety feel of the pile. Thus, various methods have been developed to predict chenille yarn and fabric abrasion properties. Statistical models yielded reasonably good abrasion resistance predictions. However, there is a lack of study that encompasses the scope for predicting the chenille yarn abrasion resistance with artificial neural network (ANN) models. This paper presents an intelligent modeling methodology based on ANNs for predicting the abrasion resistance of chenille yarns and fabrics. Constituent chenille yarn parameters like yarn count, pile length, twist level and pile yarn material type are used as inputs to the model. The intelligent method is based on a special kind of ANN, which uses radial basis functions as activation functions. The predictive power of the ANN model is compared with different statistical models. It is shown that the intelligent model improves prediction performance with respect to statistical models.     

Prediction of indoor temperature and relative humidity using neural network models: model comparison

The use of neural networks grows great popularity in various building applications such as prediction of indoor temperature, heating load and ventilation rate. But few papers detail indoor relative humidity prediction which is an important indicator of indoor air quality, service life and energy efficiency of buildings. In this paper, the design of indoor temperature and relative humidity predictive neural networks in our test house was developed. The test house presented complicated physical features which are difficult to simulate with physical models. The work presented in this paper aimed to show the suitability of neural networks to perform predictions. Nonlinear AutoRegressive with eXternal input (NNARX) model and genetic algorithm were employed to construct networks and were detailed. The comparison between the two methods was also made. Applicability of some important mathematical validation criteria to practical reality was examined. Satisfactory results with correlation coefficients 0.998 and 0.997 for indoor temperature and relative humidity were obtained in the testing stage.     

A memory-based model for planning target reach postures in the presence of obstructions

Existing posture prediction and motion simulation models generally lack the capability of simulating human obstruction avoidance during target reach. This compromises the utility of digital human models for ergonomics, as many design problems involve interactions between humans and obstructions. To address this problem, this paper presents a novel memory-based posture planning (MBPP) model, which plans reach postures that avoid obstructions. In this model, the task space is partitioned into small regions called cells. For a given human figure, each cell is linked to a memory that stores various alternative postures for reaching the cell. When a posture planning problem is given in terms of a target and an obstruction configuration, the model examines postures belonging to the relevant cell, selects collision-free ones and modifies them to exactly meet the hand target acquisition constraint. Simulation results showed that the MBPP model is capable of rapidly and robustly planning reach postures for various scenarios.     

Edge detection using a neural network

Artificial neural networks have been shown to perform well in many image processing applications such as coding, pattern recognition and texture segmentation. In a typical multi-layer model of this class, neurons in each layer are linked by synaptic weights to a receptive field region in the layer below it. The input image itself is linked to the lowest layer. We propose here a two stage encoder-detector network for edge detection. The single layer encoder stage, trained in a competitive mode, compresses data from an input receptive field and drives a back-propagation-trained detector network whose two outputs represent components of an edge vector. Experimental results show that for the case of step edges in noisy images, the performance of the neural edge detector is comparable to that of the Canny detector.     

基于循环神经网络的电信行业容量数据预测方法

在电信运维的容量预测过程中,存在容量指标和部署业务种类繁多的问题。现有研究未考虑指标数据类型的差异,对所有类型的数据使用同种预测方法,使得预测效果参差不齐。为了提升指标预测效率,提出一种指标数据类型分类方法,利用该方法将数据类型分为趋势型、周期型和不规则型。针对其中的周期型数据预测,提出基于双向循环神经网络（BiRNN）的周期型容量指标预测模型,记作BiRNN-BiLSTM-BI。首先,为分析容量数据的周期特征,提出一种忙闲分布分析算法;其次,搭建循环神经网络（RNN）模型,该模型包含一层BiRNN和一层双向长短时记忆网络（BiLSTM）;最后,充分利用系统忙闲分布信息,对BiRNN输出的结果进行优化。与传统的三次指数平滑、差分自回归移动平均（ARIMA）模型和反向传播（BP）神经网络模型进行比较的实验结果表明,在统一日志数据集和分布式缓存数据集上,提出的BiRNN-BiLSTM-BI模型的均方误差（MSE）分别比对比模型中表现最优的模型降低了15.16%和45.67%,可见预测准确率得到了很大程度的提升。     

神经网络MRAS在异步电机转速估计中的仿真研究

根据模型参考自适应在异步电机转速估计中的基本原理,结合神经网络自学习的特点,构造了神经网络MRAS模型。并利用MATLAB对模型进行了仿真试验分析,结果表明:建立的转速估计系统能够较为精确的跟踪电动机实际转速。     

BP网络在设备故障诊断中的应用与实现

神经网络技术为电子设备故障诊断提供了一种新的推理诊断方法。本文分析和介绍了BP人工神经网络的结构和学习算法，并加以实现。利用该方法，对测得的样本数据进行实验分析，证明此系统具有推理效率及准确性较高的特点。     

Computer-related posture and discomfort in primary school children: The effects of a school-based ergonomic intervention

This study investigated the effect of a school-based ergonomic intervention on childrens’ posture and discomfort while using computers using a pre/post test study design. The sample comprised 23 children age 9 and 10 years. Posture was assessed with Rapid Upper Limb Assessment (RULA) and a workstation assessment was completed using a Visual Display Unit (VDU) checklist. Self reported discomfort was also recorded at the beginning and end of the computer class. Following an ergonomic intervention that included education of the children and workstation changes, the outcome measures were repeated. There was a positive response to the intervention with significant changes between the pre-intervention and post-intervention scores for posture (p = 0.00) and workstation (p = 0.00). The change in discomfort scores from beginning to end of the computer class between the pre-intervention class and the post-intervention class was also significant (p = 0.00). The study highlights the need for continuing concern about the physical effects of children’s computer use and the implications of school-based interventions.     

基于深度学习的超声心动图切面识别方法

提出了一种基于深度卷积神经网络自动识别超声心动图标准切面的方法,并可视化分析了深度模型的有效性。针对网络全连接层占有模型大部分参数的缺点,引入空间金字塔均值池层化替代全连接层,获得更多空间结构信息,并大大减少模型参数、降低过拟合风险,通过类别显著性区域将类似注意力机制引入模型可视化过程。通过超声心动图标准切面的识别问题案例,对深度卷积神经网络模型的鲁棒性和有效性进行解释。在超声心动图上的可视化分析实验表明,改进深度模型作出的识别决策依据,同医师辨别分类超声心动图标准切面的依据一致,表明所提方法的有效性和实用性。