基于块方向图的指纹中心点定位

提出了一种基于块方向图的指纹中心点定位方法,该方法先利用灰度梯度法得到指纹块方向,再利用块搜索得到指纹的大致中心点位置,最后根据块的Poincare index算法得到指纹确定的中心点位置,该算法简单、快速、鲁棒性好。     

A Model Coupling Method for Shape Prediction

 The shape of strip is calculated by iterative method which combines strip plastic deformation model with rolls elastic deformation model through their calculation results, which can be called results coupling method. Because the shape and rolling force distribution are very sensitive to strip thickness transverse distribution′s variation, the iterative course is rather unstable and sometimes convergence cannot be achieved. In addition, the calculating speed of results coupling method is low, which restricts its usable range. To solve the problem, a new model coupling method is developed, which takes the force distribution between rolls, rolling force distribution and strip′s exit transverse displacement distribution as basic unknowns, and integrates strip plastic deformation model and rolls elastic deformation model as a unified linear equations through their internal relation, so the iterative calculation between the strip plastic deformation model and rolls elastic deformation model can be avoided. To prove the effectiveness of the model coupling method, two examples are calculated by results coupling method and model coupling method respectively. The results of front tension stress, back tension stress, strip′s exit gauge, the force between rolls and rolling force distribution calculated by model coupling method coincide very well with results coupling method. However the calculation course of model coupling method is more steady than results coupling method, and its calculating speed is about ten times as much as the maximal speed of results coupling method, which validates its practicability and reliability.     

Utilization and impacts of hydrogen in the ironmaking processes: A review from lab-scale basics to industrial practices

Hydrogen (H₂) energy is a promising candidate to replace carbon monoxide (CO) as a reductant for iron oxide reduction in revolutionary ironmaking industrial processes, and numerous studies have been conducted to intensively study the utilization and impact of H₂ in ironmaking processes. Therefore, this review first collects and compares the H₂-assisted reduction mechanism and kinetics. The impacts of H₂ on the reduction accompanying behaviors, such as the disintegration, swelling, sticking, softening, and melting of iron ores, are then summarized. Third, the performance of H₂ predicted by either mass and heat balance models or numerical simulation models in various ironmaking processes is highlighted. Finally, the different applications of hydrogen-rich materials in blast furnace and non-blast furnace ironmaking processes are further compared to illuminate H₂ utilization before our outlook on the use of H₂ in the ironmaking industry.     

Channel measurement-based access point selection in IEEE 802.11 WLANs

With the wide deployment of IEEE 802.11 Wireless Local Area Networks, it has become common for mobile nodes (MNs) to have multiple access points (APs) to associate with. With the Received Signal Strength Indicator (RSSI)-based AP selection algorithm, which is implemented in most commercial IEEE 802.11 clients, the AP with the best signal strength is selected regardless of the candidate AP’s available throughput, resulting in unbalanced distribution of clients among the APs in the network. Several studies have shown performance improvement in not just the new MN (nMN), but also the network as a whole when the selection process considers the current load status of candidate APs. However, the proposed algorithms in these studies assume that there are no hidden terminal problems that severely affect the performance of the network. Hidden terminal problems frequently occur in wireless networks with unlicensed frequencies, like IEEE 802.11 in the 2.4 GHz band. Moreover, none of the previous studies have considered frame aggregation, a major improvement in transmission efficiency introduced and widely deployed with the IEEE 802.11n standard. In this paper, we propose a new AP selection algorithm based on the estimation of available throughput calculated with a model based on the IEEE 802.11 distributed coordination function in consideration of hidden terminal problems and frame aggregation. The proposed algorithm is evaluated through extensive simulation, and the results show that the nMN with the proposed AP selection algorithm can achieve up to 55.84% and 22.31% higher throughput compared to the traditional RSSI-based approach and the selection algorithm solely based on the network load, respectively.     

Cohesive/adhesive failure interaction in ductile adhesive joints Part II: Quasi-static and fatigue analysis of double lap-joint specimens subjected to through-thickness compressive loading

This paper proposes a new methodology for the finite element (FE) modelling of failure in adhesively bonded joints. Cohesive and adhesive failure are treated separately which allows accurate failure predictions for adhesive joints of different thicknesses using a single set of material parameters. In a companion paper (part I), a new smeared-crack model for adhesive joint cohesive failure was proposed and validated. The present contribution gives an in depth investigation into the interaction among plasticity, cohesive failure and adhesive failure, with application to structural joints. Quasi-static FE analyses of double lap-joint specimens with different thicknesses and under different levels of hydrostatic pressure were performed and compared to experimental results. In all the cases studied, the numerical analysis correctly predicts the driving mechanisms and the specimens’ final failure. Accurate fatigue life predictions are made with the addition of a Paris based damage law to the interface elements used to model the adhesive failure.     

深层优质碎屑岩储层全生命周期分析方法论

油气勘探领域的深层是指沉积盆地中埋深在3 500~4 500 m及以深的地层。在含油气盆地的深层碎屑岩储层中，优质储层是重要的勘探目标。优质储层具有大量孔隙，其形成与沙或砂岩的沉积条件、埋藏方式、区域温压场、膏盐岩效应等多种因素有关。通过系统研究全球不同区域不同时代不同类型深层碎屑岩储层，提出优质深层碎屑岩储层的全生命周期研究方法，即从储层形成演变的全部时间出发，将深部储层的形成过程划分为早期剥蚀搬运沉积阶段、中期埋藏阶段、中期或/和晚期改造阶段，从全生命周期的尺度分阶段综合分析深层碎屑岩储层的形成及演化过程，推演在地质历史时期中的优质储层演化时序及发育阶段、分布位置。     

Manifold label prediction for low dimensional palmprint recognition

Minimum Squared Error Classification (MSEC) is a learning method for predicting the class labels of samples in real time. However, as a regression algorithm, MSEC tries its best to map the training samples into their class labels using a linear projection without considering the manifold structure of the data. In this paper, we introduce a supervised label learning framework using an effective manifold learning strategy. This method which is referred to as Manifold Supervised Label Prediction (MSLP) generalizes MSEC objective function to incorporate intra-class relationships of data. Thus, in addition to relying on the relationship between a training sample and its label, we propose to also learn the relationship between the training samples while transforming them. As a testbed for MSLP, we apply it to an image identification venue in which image samples with a very low spatial resolution (16 × 16) are used. These images have been dramatically influenced by a down-sampling process in order to reduce their size and hence, improving over computation time. We also show that the blurring process for reducing the artifacts introduced by down-sampling serendipitously results in better identification accuracies. Finally, unlike MSEC that classifies a query sample based on the deviation between the predicted and the true class labels, we compare both the training and the query samples in the label prediction space. A set of comprehensive experiments on benchmark palmprint databases including Multispectral PolyU, PolyU 2D/3D, and PolyU Contact-free I shows meaningful improvements over existing state-of-the-art algorithms.     

An effective hybrid learning system for telecommunication churn prediction

Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Moreover, high predictive accuracy and good interpretability of the results are two key measures of a classification model. More studies have shown that single model-based classification methods may not be good enough to achieve a satisfactory result. To obtain more accurate predictive results, we present a novel hybrid model-based learning system, which integrates the supervised and unsupervised techniques for predicting customer behaviour. The system combines a modified k-means clustering algorithm and a classic rule inductive technique (FOIL).Three sets of experiments were carried out on telecom datasets. One set of the experiments is for verifying that the weighted k-means clustering can lead to a better data partitioning results; the second set of experiments is for evaluating the classification results, and comparing it to other well-known modelling techniques; the last set of experiment compares the proposed hybrid-model system with several other recently proposed hybrid classification approaches. We also performed a comparative study on a set of benchmarks obtained from the UCI repository. All the results show that the hybrid model-based learning system is very promising and outperform the existing models.     

Thermal-mechanical fatigue simulation of a P91 steel in a temperature range of 400–600°C

Abstract

This paper deals with the identification of material constants to simulate the effect of cyclic mechanical loading and temperatures. A Chaboche viscoplasticity model was used in this study to model the thermal-mechanical behaviour of a P91 martensitic steel. A fully-reversed cyclic mechanical testing programme was conducted isothermally between 400 and 600°C with a strain amplitude of 0.5%, to identify the model constants using a thermo-mechanical fatigue (TMF) test machine. Thermo-mechanical tests of P91 steel were conducted for two temperature ranges of 400 – 500°C and 400 – 600°C. From the test results, it can be seen that the P91 steel exhibits cyclic softening throughout the life of the specimens, for both isothermal and thermal-mechanical loading and this effect can be modelled by the set of viscoplasticity constants obtained. Finite element simulations of the test specimens show good comparison to isothermal and TMF experimental data.