Using internal fibre geometry to improve the performance of pin-loaded holes in composite materials

The anisotropic nature of fibre reinforced composites leads to large stress concentrations around pin-loaded holes through standard weave cloths. Proper understanding of how this anisotropic nature affects the load distribution around holes can be utilised to reduce these concentrations if sufficient thought is given to the internal fibre geometry near to the hole. Such local reinforcements need not be highly complex and can be readily produced without excessive effort, producing significant improvements in performance.     

粘钢技术在输水管竖井加固中的应用

１９７６年汛前发现响水潭水库输水管竖井圆筒底部与闸室连接处的下游出现环形水平裂缝，对此当年曾加固处理。１９８６年复查时，发现竖井原处又出现了类似的环形裂缝，并有新的发展，为确保工程安全，于１９９４年３月再次加固。此次加固采用先止水，后粘钢板的加固方法，收到很好的效果。     

MKR──一种有效的增量式概念获取系统

本文对示例式学习的ＩＤ５Ｒ算法进行了深入的研究并作了进一步的改进，提高了其计算效率．并提出一种适合于增量式学习的抗干扰机制，拓广了算法的适用范围．     

用奇异模型描述的受限机器人的学习控制

本文了一类受限机器人的迭代学习控制方案，控制器的设计是基于机器人模型的降阶形式。在存在有界未知干扰的情况下，对末端操纵器受线性、无摩擦约束面的受限机器人。本文给出的控制方案保证了机器人系统的完全运动跟踪，同时保证了力跟踪误差有界的，且界的大小是可调节的。     

Real-World Robotics: Learning to Plan for Robust Execution

In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, proves that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.     

Successive pattern classification based on test feature classifier and its application to defect image classification

A novel successive learning algorithm based on a Test Feature Classifier is proposed for efficient handling of sequentially provided training data. The fundamental characteristics of the successive learning are considered. In the learning, after recognition of a set of unknown data by a classifier, they are fed into the classifier in order to obtain a modified performance. An efficient algorithm is proposed for the incremental definition of prime tests which are irreducible combinations of features and capable of classifying training patterns into correct classes. Four strategies for addition of training patterns are investigated with respect to their precision and performance using real pattern data. A real-world problem of classification of defects on wafer images has been dealt with by the proposed classifier, obtaining excellent performance even through efficient addition strategies.     

Post-Supervised Template Induction for Information Extraction from Lists and Tables in Dynamic Web Sources

Dynamic web sites commonly return information in the form of lists and tables. Although hand crafting an extraction program for a specific template is time-consuming but straightforward, it is desirable to automatically generate template extraction programs from examples of lists and tables in html documents. Supervised approaches have been shown to achieve high accuracy, but they require manual labelling of training examples, which is also time consuming. Fully unsupervised approaches, which extract rows and columns by detecting regularities in the data, cannot provide sufficient accuracy for practical domains. We describe a novel technique, Post-supervised Learning, which exploits unsupervised learning to avoid the need for training examples, while minimally involving the user to achieve high accuracy. We have developed unsupervised algorithms to extract the number of rows and adopted a dynamic programming algorithm for extracting columns. Our method achieves high performance with minimal user input compared to fully supervised techniques.     

Value management and common accounting performance measures for corporations

Management is aware that the value of firms is the ultimate measure of company performance. However, management has been using common accounting measures as an operating guide because the linkage between operational planning and value is vague and complex and, therefore, difficult to apply. Managers need to have clear targets and performance measures to track progress. This paper examines if a set of common accounting performance measures are linked tightly to the overall value of the firm. An abductive learning network (ALN) approach, an artificial intelligence technique, is used in this research because an ALN approach is non-parametric and can capture a subtle dynamic relationship between input and output variables, which is not obvious in linear statistical analysis. Empirical results show that an ALN model is very effective in synthesizing the value of the firm using six common accounting variables. The results also provide four strategic variables that can be used for devising strategic and operating plans to maximize the value of firms.