Carnot comparison of multi-temperature level absorption heat cycles

Comparison of different absorption heat cycles is not always made on the correct manner. This also includes comparison of an ideal absorption cycle with a mechanical analogy. A new Carnot model operating with two heat engines and two mechanical heat pumps is defined to be the correct and logical way to describe the mechanical analogy for an absorption heat pump and an absorption heat transformer. General equations for the Carnot coefficient of performance, COP_r, are exemplified and simulated for an absorption heat pump and an absorption heat transformer, and an entropy flow fraction diagram is introduced. The important fact that the absorption heat cycles must operate under the same conditions when they are compared is discussed.     

台达变频器在纺织织带盘带机上的应用

盘带机是纺织织带成品分卷设备，需要使用到收放卷控制。本文基于台达变频器在盘带机上的应用案例，论述盘带机自动化系统设计调试过程以及遇到的问题分析。     

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.     

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.     

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.     

THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT

Most research on learning to identify sentiment ignores "neutral" examples, learning only from examples of significant (positive or negative) polarity. We show that it is crucial to use neutral examples in learning polarity for a variety of reasons. Learning from negative and positive examples alone will not permit accurate classification of neutral examples. Moreover, the use of neutral training examples in learning facilitates better distinction between positive and negative examples.