Adaptive full domain covering meshes for parallel finite element computations

Summary  In this work, we present a new parallelization concept for adaptive finite element methods. Compared to classical domain decomposition approaches, the concept of adaptive full domain covering meshes reduces the parallel communication overhead. Furthermore, it provides an easy way to transform sequential codes into parallel software by changing only a few lines of source code.      

Efficient domain decomposition methods for elliptic problems arising from flows in heterogeneous porous media

In this paper we study domain decomposition methods for solving some elliptic problem arising from flows in heterogeneous porous media. Due to the multiple scale nature of the elliptic coefficients arising from the heterogeneous formations, the construction of efficient domain decomposition methods for these problems requires a coarse solver which is adaptive to the fine scale features, [4]. We propose the use of a multiscale coarse solver based on a finite volume – finite element formulation. The resulting domain decomposition methods seem to induce a convergence rate nearly independent of the aspect ratio of the extreme permeability values within the substructures. A rigorous convergence analysis based on the Schwarz framework is carried out, and we demonstrate the efficiency and robustness of the preconditioner through numerical experiments which include problems with multiple scale coefficients, as well as problems with continuous scales. Communicated by: G. Wittum     

A novel boundary elemental analysis based frequency domain adaptive sampling technique for aerospace application

In aerospace applications, the spacecraft operation depends on data acquisition and operational parameters of both internal and external factors. To construct global approximation, an adaptive sampling is carried out. In general, Boundary Elemental Analysis is used in solving elasto-dynamic problems. In this paper, Boundary Elemental Analysis is used to minimize the computation and sampling time for aerospace applications. In this work, a novel model is introduced to enhance the efficiency of frequency domain adaptive sampling. The space time geostatistical approaches can offer a few advantages including more information to help boundary assessment and forecast and, if present, the abuse of fleeting just as spatial autocorrelation in watched esteems. This has prompted the turn of events and use of room time geostatistical models. The application of boundary elemental analysis reduces the number of sampling frequencies in adaptive frequency sampling. The proposed frequency domain adaptive sampling provides improved convergence iteration with minimal value of 90 iterations than the existing methods. Simulation results demonstrate that this method when combined with Frequency domain adaptive sampling produces12% reduction in delay for convergence iteration and 56% reduction in computation cost compared to the existing state-of-the-art techniques for aerospace applications.     

Adaptive multi-dimensional filters

In the present work, adaptive multi-dimensional filtering technique is proposed, which in addition to numerical stabilization, results in significant reduction of computational efforts. A criterion for adaptive filtering is proposed with algorithm provided. A new buffer domain technique based on adaptive filtering is developed and demonstrated for flow past a stationary cylinder for different Reynolds numbers. This technique also allows calculations in a reduced domain, showing another use of adaptive filtering. Rotary oscillation of a circular cylinder at different forcing parameters show excellent match with results in the literature, demonstrating potential benefits of adaptive filtering.     

Universal embedding strategy for batch adaptive steganography in both spatial and JPEG domain

Multimedia Tools and Applications - In this paper, we present a universal embedding strategy for batch adaptive steganography in both spatial and JPEG domain. This strategy can make up for the...     

Evaluating and improving adaptive educational systems with learning curves

Personalised environments such as adaptive educational systems can be evaluated and compared using performance curves. Such summative studies are useful for determining whether or not new modifications enhance or degrade performance. Performance curves also have the potential to be utilised in formative studies that can shape adaptive model design at a much finer level of granularity. We describe the use of learning curves for evaluating personalised educational systems and outline some of the potential pitfalls and how they may be overcome. We then describe three studies in which we demonstrate how learning curves can be used to drive changes in the user model. First, we show how using learning curves for subsets of the domain model can yield insight into the appropriateness of the model’s structure. In the second study we use this method to experiment with model granularity. Finally, we use learning curves to analyse a large volume of user data to explore the feasibility of using them as a reliable method for fine-tuning a system’s model. The results of these experiments demonstrate the successful use of performance curves in formative studies of adaptive educational systems.     

Adapting domain ontology for personalized knowledge search and recommendation

This paper presents research on the development of a domain ontology adaptation system for personalized knowledge search and recommendation that adapts a suitable domain ontology according to the previous browsing and reading behavior of users (i.e., usage history log). An adaptive domain ontology can satisfy the future requirements of users and promote use value. In developing the system, a domain ontology adaptation model is first designed. Based on the designed adaptation model, a methodology for domain ontology adaptation is developed. Subsequently, a domain ontology adaptation system is implemented with an illustrative example of securities trading. Finally, a system evaluation for user satisfaction and a methodology evaluation are conducted to demonstrate that the developed methodology and system worked efficiently.     

Unsupervised language model adaptation for handwritten Chinese text recognition

This paper presents an effective approach for unsupervised language model adaptation (LMA) using multiple models in offline recognition of unconstrained handwritten Chinese texts. The domain of the document to recognize is variable and usually unknown a priori, so we use a two-pass recognition strategy with a pre-defined multi-domain language model set. We propose three methods to dynamically generate an adaptive language model to match the text output by first-pass recognition: model selection, model combination and model reconstruction. In model selection, we use the language model with minimum perplexity on the first-pass recognized text. By model combination, we learn the combination weights via minimizing the sum of squared error with both L2-norm and L1-norm regularization. For model reconstruction, we use a group of orthogonal bases to reconstruct a language model with the coefficients learned to match the document to recognize. Moreover, we reduce the storage size of multiple language models using two compression methods of split vector quantization (SVQ) and principal component analysis (PCA). Comprehensive experiments on two public Chinese handwriting databases CASIA-HWDB and HIT-MW show that the proposed unsupervised LMA approach improves the recognition performance impressively, particularly for ancient domain documents with the recognition accuracy improved by 7 percent. Meanwhile, the combination of the two compression methods largely reduces the storage size of language models with little loss of recognition accuracy.     

基于熵增强的无监督域适应遥感图像语义分割

为了实现利用有标注源数据获得在无标注目标数据上可用的遥感图像语义分割模型,提出了一种基于熵增强的域适应端到端语义分割方法.首先,为了充分利用遥感图像多尺度信息并且减少域之间传感器分辨率带来的域偏移,采用空洞空间金字塔池化模块作为分类器;其次,为了使无标注的目标域类别正确对应,使用了两个分类器进行协同训练;将像素点预测值的信息熵当做分类置信度的度量,将其作为对抗损失的权重,从而使训练能专注于难分类的像素,降低域偏移.在ISPRS(WGII/4)2D数据集上进行实验,所提方法相对于直接使用分割模型和使用传统对抗方法,mIoU分别提高了18％和12％.实验结果表明,所提方法在遥感图像域适应语义分割表现上优于直接使用分割模型或使用传统对抗域适应分割方法.     

Supporting agile software development through active documentation

Agile development processes are adaptive rather than predictive. Therefore, agile processes emphasize operational system code rather than its documentation. To overcome the absence of comprehensive documentation artifacts, agile methods require constant interaction between the system stakeholders. Ironically, however, some traditional documentation artifacts come to support this kind of interaction. In this study, we examine the relationship between software and documentation. We develop an approach that enables incorporating domain documentation to agile development, while keeping the processes adaptive. We also provide a system design that actively uses domain knowledge documentation. These ideas have been applied through the implementation and use of agile documentation support components.