Mode I fatigue crack-propagation mechanism based on the renewal stochastic damage-accumulation model

A mechanism of mode I fatigue-crack propagation, which involves the initiation and opening of the cleavage-mode crack, is proposed. This approach uses a renewal stochastic damage-accumulation model for crack propagation, in which the parameters are defined based on the dislocation density and the elastic energy of dislocation. The calculated results of ΔK_eff for da/dn agree with the experimental data. ΔK_eff th is calculated under the condition that the energy of cleavage-crack initiation is equal to that given from the outside of material. The plastic-zone size is calculated based on the number of dislocations on each slip line contained within it.     

A new interactive model for improving the learning performance of back propagation neural network

The Back Propagation Neural Network (BPNN) has been used widely in construction management, but in fact, the BPNN is limited by a non-optimum weight adjustment manner and negatively influenced the convergence results. For this reason, this paper proposes the Individual Inference Adjusting Learning Rate technique (IIALR) to enhance the learning performance of the BPNN. The mechanism of the weight adjustment in the IIALR is an individual learning rate for each weight. Furthermore, this paper also establishes the Batch-Online Weight Updating Frequency mode (BOWUF) for the IIALR model, so as to adjust the connected weight of the BPNN properly and effectively. Finally, three cases are used to verify that the IIALR model can be more effective than other modifications of the BPNN. The IIALR model is conducive for assisting with the decision making process of construction management.     

Optimization of the Dynamic Measure of Spillover Effect Based on Knowledge Graph

This paper improves the dynamic Feder model based on the characteristics of knowledge production and separates the direct effect and spillover effect of R&D in order to determine the relationship between spillover effect of R&D and economic growth, and accurately measure it by examining Chinese provincial panel data from 2008–2016. The theoretical analysis shows that the spillover effect of R&D promotes economic growth. Empirical analysis using a combination of OLS, sysGMM, 2SLS and GLS shows that basic research and application research have significant spillover effects; the marginal revenue of the basic research is lower than that of the production sector, while the marginal revenue of the application research is higher than that of the production sector; and knowledge stock does not significantly promote innovation in China. However, any study on the influence of knowledge stock on innovation entails more than basic research.     

Training algorithms for Radial Basis Function Networks to tackle learning processes with imbalanced data-sets

Nowadays, many real applications comprise data-sets where the distribution of the classes is significantly different. These data-sets are commonly known as imbalanced data-sets. Traditional classifiers are not able to deal with these kinds of data-sets because they tend to classify only majority classes, obtaining poor results for minority classes. The approaches that have been proposed to address this problem can be categorized into three types: resampling methods, algorithmic adaptations and cost sensitive techniques.Radial Basis Function Networks (RBFNs), artificial neural networks composed of local models or RBFs, have demonstrated their efficiency in different machine learning areas. Centers, widths and output weights for the RBFs must be determined when designing RBFNs.Taking into account the locally tuned response of RBFs, the objective of this paper is to study the influence of global and local paradigms on the weights training phase, within the RBFNs design methodology, for imbalanced data-sets. Least Mean Square and the Singular Value Decomposition have been chosen as representatives of local and global weights training paradigms respectively. These learning algorithms are inserted into classical RBFN design methods that are run on imbalanced data-sets and also on these data-sets preprocessed with re-balance techniques. After applying statistical tests to the results obtained, some guidelines about the RBFN design methodology for imbalanced data-sets are provided.     

The effect of feedback training on lumbosacral compression during simulated occupational lifting

This study measured the effect of a feedback training program on lumbar compression during simulated occupational lifting. Two distinct types of feedback were compared: real-time electromyographic feedback, vs. an acceleration index delivered verbally post-lift. Kinematic data were collected from 28 subjects during symmetrical sagittal plane lifts. Following a baseline session, two feedback training sessions were provided, with a 1-week interval between each session. A control group followed the same protocols, but without receiving feedback training. A post-training session, using protocols identical to the baseline session, was used to assess pre-to-post changes in the dependent variable: peak lumbosacral compression computed using a dynamic linked-segment model. All three groups showed reductions in peak compression from pre-to-post: on average the control group improved by 11.2%, the EMG group by 16.7%, and the acceleration group by 25.3%. The results revealed an interaction between the improvement and the group (p?=?0.023), and a difference between the improvement in the control group and that in the verbal acceleration feedback group (p?<?0.01). These reductions in lumbosacral compression persisted after a 7-day interval without feedback training, suggesting that this approach could provide sustained risk-reduction during manual materials handling.     

LED路灯电源中EMI传导干扰解决方法的研究

本文是利用分离器的思想针对不同的对策分别来降低差模噪声和共模噪声，同时达到降低传导干扰的作用。     

Open source software: The effects of training on acceptance

Open Source Software (OSS) is an alternative to proprietary software. It is growing in popularity, which has brought about an increase in research interest. Most of the research studies have focused on identifying individual personal motives for participating in the development of an OSS project, analyzing specific solutions, or the OSS movement, itself. No studies have been found which have undertaken research on the impact of user experience and training on OSS. The study reported here sought to identify factors that predict acceptance of technologies based on OSS after training in these solutions. A research model based on the Technology Acceptance Model (Davis, 1989) was developed. Furthermore, the possible moderating effects of users’ gender, age and level of education were analyzed. It was found that external determinants such as user training, user fit, technological complexity and trainers’ support were important indicators in the success of adopting these solutions.     

Optimization-based methodology for training set selection to synthesize composite correlation filters for face recognition

Face recognition has been addressed with pattern recognition techniques such as composite correlation filters. These filters are synthesized from training sets which are representative of facial classes. For this reason, the filter performance depends greatly on the appropriate selection of the training set. This set can be selected either by a filter designer or by a conventional method. This paper presents an optimization-based methodology for the automatic selection of the training set. Given an optimization algorithm, the proposed methodology uses its main mechanics to iteratively examine a given set of available images in order to find the best subset for the training set. To this end, three objective functions are proposed as optimization criteria for training set selection. The proposed methodology was evaluated by undertaking face recognition under variable illumination and facial expressions. Four optimization algorithms and three composite correlation filters were used to test the proposed methodology. The Maximum Average Correlation Height filter designed by Grey Wolf Optimizer obtained the best performance under homogeneous illumination and facial expressions, while the Unconstrained Nonlinear Composite Filter designed by either Grey Wolf Optimizer or (1+1)-Evolution Strategy obtained the best performance under variable illumination. The proposed methodology selects training sets for the synthesis of composite filters with competitive results comparable to the results reported in the face recognition literature.     

基于CSP的中小制造企业虚拟互助培训系统平台研究

针对中小制造企业在培训中面临的难题，提出了基于云服务提供商（CSP）的中小制造企业虚拟互助培训模式。首先对模式的拓扑结构及功能进行了详细分析；然后对该模式下的培训流程以及模式特点进行了阐述；最后对基于CSP的中小制造企业培训系统平台的建设方案进行了研究，给出了平台的体系结构，并对平台经营模式及发展规划进行了阐述。该模式具有方便企业管理、促进企业交流与进步、节约培训成本、提高培训效果等优点，对促进中小制造企业培训工作的开展具有现实意义。     

圆阵模式空间的文化粒子群极大似然测向算法

在冲击噪声背景下给出了一种估计相干和非相干信源的均匀圆阵测向方法,即基于模式空间的低阶极大似然算法(MODE-FLOM-ML)。该算法对均匀圆形阵列的输出信号进行模式激励,使其成为模式空间内的虚拟阵列;在此基础上构造模式空间的分数低阶矩阵,成功地估计出信源的来波方向。为了快速求解所提的基于模式空间的测向算法,利用文化算法和粒子群算法的优点,设计了一种可快速进行多维搜索的文化粒子群算法。Monte-Carlo仿真试验证明了所提的CP-MODE-FLOM-ML方法在检测性能上优于基于模式空间的ROC-MUSIC和FLOM-MUSIC。