Cubical Mass-Spring Model design based on a tensile deformation test and nonlinear material model

Mass-Spring Models (MSMs) are used to simulate the mechanical behavior of deformable bodies such as soft tissues in medical applications. Although they are fast to compute, they lack accuracy and their design remains still a great challenge. The major difficulties in building realistic MSMs lie on the spring stiffness estimation and the topology identification. In this work, the mechanical behavior of MSMs under tensile loads is analyzed before studying the spring stiffness estimation. In particular, the performed qualitative and quantitative analysis of the behavior of cubical MSMs shows that they have a nonlinear response similar to hyperelastic material models. According to this behavior, a new method for spring stiffness estimation valid for linear and nonlinear material models is proposed. This method adjusts the stress-strain and compressibility curves to a given reference behavior. The accuracy of the MSMs designed with this method is tested taking as reference some soft-tissue simulations based on nonlinear Finite Element Method (FEM). The obtained results show that MSMs can be designed to realistically model the behavior of hyperelastic materials such as soft tissues and can become an interesting alternative to other approaches such as nonlinear FEM.     

阳极氧化铝的表面参数对硅酮结构的装锒密封胶长期黏结性的影响(连载一)

硅酮密封胶对阳极氧化铝的黏结质量千差万别.影响黏结性的关键因素是铝材表面的封孔程度、氧化层的着色情况、清洁剂的特性以及基材表面清洁与打胶之间允许的时间间隔.由于清洁剂将有机污染物从阳极氧化铝表面去除的效果不同,所以此效果并不和黏结质量有必然联系.据猜测,吸附在阳极氧化铝表面的清洁溶剂会改善基材表面从而提高硅酮胶的黏结性,但这种改善效果会随着清洁溶剂随时间的的挥发而降低.对于给定的溶剂,最佳的黏结效果取决于材料表面的封孔程度.依照ISO 2143酸刻蚀方法测量材料表面的封孔程度,可以预测未着色的阳极氧化铝基材的黏结性.对于着色的阳极氧化铝表面,依照ISO 2931的测试标准,用电相位漂移方法可以用来预测黏结质量.一种控制阳极氧化铝表面的方法被提了出来,此方法是测量不同频率下基材的电阻抗并将它成功地和硅酮密封胶与该材料表面的长期黏接性联系起来.     

Automatic categorisation of comments in social news websites

The use of the social web has brought a series of changes in the way how content is created. In particular, social news sites link stories and the different users can comment them. In this paper, we propose a new method based on different features extracted from the text able to categorise the comments. To this end, we use a combination of statistical, syntactic and opinion features and machine-learning classifiers to classify a comment within three different categorisation types: the focus of the comment, the type of information contained in the comment and the controversy level of the comment. We validate our approach with data from ‘Menéame’, a popular Spanish social news site.     

Improving image retrieval performance by inter-query learning with one-class support vector machines

Relevance feedback (RF) is an iterative process which improves the performance of content-based image retrieval by modifying the query and similarity metric based on the users feedback on the retrieval results. This short-term learning within a single query session is called intra-query learning. However, the interaction history of previous users over all past queries may also be potentially exploited to help improve the retrieval performance for the current query. The long-term learning accumulated over the course of many query sessions is called inter-query learning. We present a novel RF framework that learns one-class support vector machines (1SVM) from retrieval experience to represent the set memberships of users high-level concepts and stores them in a concept database. The concept database provides a mechanism for accumulating inter-query learning obtained from previous queries. By doing a fuzzy classification of a query into the regions of support represented by the 1SVMs, past experience is merged with current intra-query learning. The geometric view of 1SVM allows a straightforward interpretation of the density of past interaction in a local area of the feature space and thus allows the decision of exploiting past information only if enough past exploration of the local area has occurred. The proposed approach is evaluated on real data sets and compared against both traditional intra-query-learning-only RF approaches and other methods that also exploit inter-query learning.     

Image region re-weighting via multiple instance learning

In region-based approaches to content-based image retrieval each image is segmented into a set of regions and similarity between images is assessed by computing similarity between pairs of regions. A key factor in similarity measures that consider all pairs of regions to obtain an overall image-to-image similarity is the weighting of regions. The weight that is assigned to each region for determining similarity is usually based on heuristics that are often inconsistent with human perception of similarity. In this paper, we propose an approach that uses relevance feedback in conjunction with multiple instance learning to obtain more informed estimates of region weights. A comparative study is then carried out with alternative region re-weighting methods.     

SELECT: A computer program to identify associationally rich words for content analysis

Conclusions An overview of these results indicates that SELECT is, in general, producing results in which elicited themes parallel those produced by frequency-based word selection. More importantly —and consonant with the impetus for development of the method — SELECT is delivering factors which are larger, more easily interpretable, and more discriminable than these available from frequency data and is doing so within the constraint of basic thematic identities.     

An engineering approach for the estimation of slewing bearing stiffness in wind turbine generators

The stiffness of yaw and pitch slewing bearings has a critical influence on the structural behaviour of wind turbine generators. Thus, it is commonly required by designers for their simulations to estimate deformations and select the most suitable bearing for their working conditions in preliminary design stages. In this work, a design of experiments was carried out via finite element analysis to obtain the stiffness curves of all of the standard four‐point contact slewing bearings from the catalogues of manufacturers under radial, axial, and tilting loads. From these results, a set of simple formulas to calculate the ring deformations were adjusted. Combining them with contact deformation results obtained in previous work by the authors, a complete and efficient tool for slewing bearing stiffness estimation has been developed.