Dynamic adhesive forces in rough contacting bodies including normal and sliding conditions

Using a high-resolution, high-dynamic bandwidth capacitive force transducer and two piezoelectric actuators, adhesive pull-off forces between nominally flat rough silicon surfaces were measured under various dynamic conditions in normal and tangential directions and environmental humidity levels. The upper specimen approached and retracted with a constant velocity in the vertical (normal) direction, while the lower specimen started moving in the horizontal (tangential) direction during the middle of the contact. The experiments were performed under 35 and 60% relative humidity conditions. It was found that sliding of the contacting surfaces led to a significant reduction in pull-off forces under low-humidity contact conditions, whereas it caused higher pull-off forces under partially wet contact conditions. Comparing the effects of sliding velocity and sliding distance on the measured pull-off force values, it was found that the sliding distance played an important role in the increase in pull-off forces.     

Stability-based preference selection in affinity propagation

Recently, as one of the most popular exemplar-based clustering algorithms, affinity propagation has attracted a great amount of attention in various fields. The advantages of affinity propagation include the efficiency, insensitivity to cluster initialization and capability of finding clusters with less error. However, one shortcoming of the affinity propagation algorithm is that, the clustering results generated by affinity propagation strongly depend on the selection of exemplar preferences, which is a challenging model selection task. To tackle this problem, this paper investigates the clustering stability of affinity propagation for automatically selecting appropriate exemplar preferences. The basic idea is to define a novel stability measure for affinity propagation, based on which we can select exemplar preferences that generate the most stable clustering results. Consequently, the proposed approach is termed stability-based affinity propagation (SAP). Experimental results conducted on extensive real-world datasets have validated the effectiveness of the proposed SAP algorithm.     

Radiation damage study of MeV ions-implanted Nd:YVO4 crystal

Damage formation mechanism of Nd:YVO₄ implanted with MeV ions is investigated. MeV Si⁺ ions were implanted into Nd:YVO₄ crystal, and the lattice damage was measured using Rutherford backscattering spectroscopy/channeling (RBS/C) method. The damage creation kinetic indicates a significant contribution from electronic energy loss to the surface damage. A detailed analysis allows us to deduce the different contributions from electronic and nuclear stopping powers to the lattice damage production. An obvious difference in extent of damage from 1 MeV and 3 MeV Si⁺ implantations also implies that there exists a threshold value of the electronic energy deposition for damage formation. The exact value of threshold is obtained by comparison with the experimental data obtained from 3 MeV O⁺, F⁺ and Si⁺ implantation results, which turns out to be (1.7 ± 0.1) keV/nm.     

Improved reliability of low-temperature polysilicon TFT by post-annealing gate oxide

We have investigated the electrical characteristics of gate oxide films deposited by plasma enhanced chemical vapor deposition (PECVD) with respect to gate oxide integrity (GOI) and its reliability. In the investigation, post-annealed gate oxide was compared with as-deposited oxide. It was shown that the characteristics of GOI strongly depended on the charge trapping characteristics and deep level interface states generation under FN stress, which was remarkably improved by post-annealing after gate oxide deposition. Improved FN stress and hot carrier stress reliability of CMOS devices implemented on the glass substrate are also discussed.     

Dynamic adhesive force measurements under vertical and horizontal motions of interacting rough surfaces

An instrument to measure dynamic adhesive forces between interacting rough surfaces has been developed. It consists of four parts, namely, main instrument body, vertical positioning system with both micrometer and nanometer positioning accuracies, horizontal positioning system with nanometer positioning accuracy, and custom-built high-resolution, and high dynamic bandwidth capacitive force transducer. The vertical piezoelectric actuator (PZT) controls the vertical (approaching and retracting) motion of the upper specimen, while the horizontal PZT controls the horizontal (reciprocal) motion of the lower specimen. The force transducer is placed in line with the upper specimen and vertical PZT, and directly measures the adhesive forces with a root-mean-square load resolution of 1.7 microN and a dynamic bandwidth of 1.7 kHz. The newly developed instrument enables reliable measurements of near-contact and contact adhesive forces for microscale devices under different dynamic conditions. Using the developed instrument, dynamic pull-in and pull-off force measurements were performed between an aluminum-titanium-carbide sphere and a 10 nm thick carbon film disk sample. Three different levels of contact force were investigated; where for each contact force level the vertical velocity of the upper sample was varied from 0.074 to 5.922 microms, while the lower sample was stationary. It was found that slower approaching and retracting velocities result in higher pull-in and pull-off forces. The noncontact attractive force was also measured during horizontal movement of the lower sample, and it was found that the periodic movements of the lower disk sample also affect the noncontact surface interactions.     

Kernelized multi-view subspace clustering via auto-weighted graph learning

Multi-view subspace clustering has been an important and powerful tool for partitioning multi-view data, especially multi-view high-dimensional data. Despite great success, most of the existing multi-view subspace clustering methods still suffer from three limitations. First, they often recover the subspace structure in the original space, which can not guarantee the robustness when handling multi-view data with nonlinear structure. Second, these methods mostly regard subspace clustering and affinity matrix learning as two independent steps, which may not well discover the latent relationships among data samples. Third, many of them ignore the different importance of multiple views, whose performance may be badly affected by the low-quality views in multi-view data. To overcome these three limitations, this paper develops a novel subspace clustering method for multi-view data, termed Kernelized Multi-view Subspace Clustering via Auto-weighted Graph Learning (KMSC-AGL). Specifically, the proposed method implicitly maps the multi-view data from linear space into nonlinear space via kernel-induced functions, so as to exploit the nonlinear structure hidden in data. Furthermore, our method aims to enhance the clustering performance by learning a set of view-specific representations and their affinity matrix in a general framework. By integrating the view weighting strategy into this framework, our method can automatically assign the weights to different views, while learning an optimal affinity matrix that is well-adapted to the subsequent spectral clustering. Extensive experiments are conducted on a variety of multi-view data sets, which have demonstrated the superiority of the proposed method.

     

融合显式反馈与隐式反馈的协同过滤推荐算法

显式反馈与隐式反馈相结合,可以有效提升推荐性能.但是现有的融合显式反馈与隐式反馈的推荐系统存在未能发挥隐式反馈数据缺失值反映用户隐藏偏好的能力,或者未能保留显式反馈数据反映用户偏好程度的能力的局限性.为了解决这个问题,提出了一种融合显式反馈与隐式反馈的协同过滤推荐算法.该算法分为两个阶段:第1阶段利用加权低秩近似处理隐式反馈数据,训练出隐式用户/物品向量;第2阶段引入了基线评估,同时将隐式用户/物品向量作为补充,通过显隐式用户/物品向量结合,训练得出用户对物品的预测偏好程度.该算法与多个典型算法在标准数据集上进行了实验比较,其可行性和有效性得到验证.     

柔性有源OLED显示器制造几点技术方面的考虑

AMOLED在柔性显示领域前途似锦。LG显示展示了一款全彩4in柔性AMOLED样机,衬底为80μm厚的不锈钢薄片,曲率达到5cm弯曲半径。本文就此项柔性显示技术所面临的挑战进行了探讨．包括柔性衬底衬底的传送、如何获得特性稳定可靠的TFT以使OLED的亮度和一致性适用于此项技术的商业化推广。     

酒钢文丘里水膜除尘器节水技术改造

针对酒钢文丘里水膜除尘器用水量大、运行成本高等问题，在保证文丘里水封完整和除尘效果的前提下，对其进行节水技术改造，并在收缩口上方增设4个供水喷嘴，实现除尘器96%的除尘用水循环使用，有效降低了除尘器水耗。     

Energy based competitive learning

This paper addresses the three important issues associated with competitive learning clustering, which are auto-initialization, adaptation to clusters of different size and sparsity, and eliminating the disturbance caused by outliers. Although many competitive learning methods have been developed to deal with some of these problems, few of them can solve all the three problems simultaneously. In this paper, we propose a new competitive learning clustering method termed energy based competitive learning (EBCL) to simultaneously tackle these problems. Auto-initialization is achieved by extracting samples of high energy to form a core point set, whereby connected components are obtained as initial clusters. To adapt to clusters of different size and sparsity, a novel competition mechanism, namely, size-sparsity balance of clusters (SSB), is developed to select a winning prototype. For eliminating the disturbance caused by outliers, another new competition mechanism, namely, adaptive learning rate based on samples' energy (ALR), is proposed to update the winner. Data clustering experiments on 2000 simulated datasets comprising clusters of different size and sparsity, as well as with outliers, have been performed to verify the effectiveness of the proposed method. Then we apply EBCL to automatic color image segmentation. Comparison results show that the proposed EBCL outperforms existing competitive learning algorithms.