Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

感应电机内部物理过程的框图描述

给出了感应电机内部物理过程的框图描述。这种描述准确地反映了电机学中主、漏磁路分别处理的一般方法，感应电机内部各物理量之间的数量关系和电机学所基于的若干基本电磁定律，感应电机内部物理量之间的关系。     

An efficient parallel strategy for the perfect domination problem on distance-hereditary graphs

A graph is distance-hereditary if the distance stays the same between any of two vertices in every connected induced subgraph containing both. Two well-known classes of graphs, trees and cographs, both belong to distance-hereditary graphs. In this paper, we first show that the perfect domination problem can be solved in sequential linear-time on distance-hereditary graphs. By sketching some regular property of the problem, we also show that it can be easily parallelized on distance-hereditary graphs.     

数控母排冲孔机液压系统

介绍了数控母排冲孔机液压系统工作原理及其组成，设计了双泵供油回路、压力控制的差动快进转慢进回路及过载、过热、污染堵塞报警系统。     

Java语言及其虚拟机技术探讨

随着Internet的迅猛发展，Internet编程语言Java愈来愈成为计算机行业的焦点。本文在简述了Java的主要特点后，重点对Java最关键的技术──虚拟机进行了深入的探讨。     

龙潭拱坝砼上坝运输方案选择

龙潭拱坝坝高９０ｍ，砼上坝运输是砼施工过程中的中间环节，控制进度的关键，必须因时因地制宜地选择适当的运输机械和运输方案，使砼运输能做到保质、保量、及时和经济，通过论证比较，结合本工程现实情况，砼上坝运输不采用缆机而采用门机方案．     

手写体汉字识别问题综论

本文在讨论了人对汉字的认知心理试验研究结果之后,认为人类的汉字识别是经由整字属性及分层结构的多种途径复合而成的。这些途径中的大多数都可以在手写体汉字的机器识别中模仿运用。由此可把汉字的机器识别方法归纳为整模式法和子模式法两个大类,对它们的有利方面和困难方面进行了讨论;对影响其性能的因素进行了分析;对识别率以及它与识别字集大小和后处理等的关系提出了评估的问题。     

Robotics in the factory of the future: AI in manufacturing: The NBS AMRF as an intelligent machine

Within its Automated Manufacturing Research Facility (AMRF), the U.S. National Bureau of Standards (NBS) is addressing research issues in interface standards for the fully automated factory of the future for the production of small batches of discrete parts. Consisting of robots, machine tools and computers, the AMRF is being integrated using a real-time, sensory-feedback, data-driven hierarchical control architecture. As such, the AMRF is a research tool for investigating the manufacturing enterprise as a system of intelligent machines. This paper: (1) describes the AMRF in terms of its real-time control system architecture; (2) notes the role of symbolic languages, knowledge-representation, sensory-processing and other aspects of artificial intelligence in its development; and (3) speculates on further application of AI in future intelligent manufacturing systems similar in form to the AMRF.     

Ranking with uncertain labels and its applications

The techniques for image analysis and classification generally consider the image sample labels fixed and without uncertainties. The rank regression problem studied in this paper is based on the training samples with uncertain labels, which often is the case for the manual estimated image labels. A core ranking model is designed first as the bilinear fusing of multiple candidate kernels. Then, the parameters for feature selection and kernel selection are learned simultaneously by maximum a posteriori for given samples and uncertain labels. The provable convergency Expectation Maximization (EM) method is used for inferring these parameters in an iterative manner. The effectiveness of the proposed algorithm is finally validated by the extensive experiments on age ranking task and human tracking task. The popular FG-NET and the large scale Yamaha aging database are used for the age estimation experiments, and our algorithm outperforms those state-of-the-art algorithms ever reported by other interrelated literatures significantly. The experiment result of human tracking task also validates its advantage over conventional linear regression algorithm. A short version of this paper appeared in ICME07.