基于虚拟样机的智能诊断系统知识组织与表达

基于虚拟样机的智能诊断系统由协同支撑平台、模型库、样机引擎和虚拟环境组成.按系统-分系统-部件的次序进行故障诊断.诊断知识分为结构、故障处理、故障机理等10类.故障知识分经验、因果和定律三种类型.其知识表示把抢修对象的原理、维护、故障机理和维修等知识,以集合框架形式分类封装于诊断辅助系统知识库.试验证明该系统能准确表达诊断知识.     

热镀锌渣真空蒸馏回收锌

在钢件热镀锌过程中，产出约占锌总量１０％的热镀锌渣，渣中含锌和少量铁，铅，铜，铝等杂质。用直空蒸馏方法从热镀锌渣中提取锌；具空度１．３３×１０＾２－１．３３×１０＾３Ｐａ，温度９００℃，作业时间１１ｈ／炉，电耗约２０００度／ｔ锌，试验表明，锌的直收离可达８４％，为热镀锌渣中锌的回收提供了一英经济而合理的方法。     

Development of an Expert System for Selection of Dam Type on Alluvium Foundations

. A knowledge-based expert system was developed to aid in the selection of the type of dam. The dam type selector expert system (DTSA ES) was designed to determine the type of dam on the alluvium foundations. Detailed expert knowledge is required to estimate the type of dam and to develop an expert system. The DTSA ES utilizes rules of thumb used by an expert for determining the selection of the type of dam. The DTSA ES was developed using a shell program. The expert system was tested on several dam sites in order to validate the decision obtained. The use of this expert system, containing knowledge about the selection of dam type, can be helpful to students, potential owners or contractors in selecting dam types. The current prototype always needs additional parameters for more detailed analyses of new developments. However, the current DTSA ES is designed to include existing information about dam types.     

Practice characteristics that lead to 21st century learning outcomes

Abstract The key research question for this study was to ask whether or not innovative teaching practices would lead to the development of learning outcomes essential for preparing the younger generation for the challenges of life in the knowledge society of the 21st century, and if so, how are the pedagogical features related to the different learning outcomes. Preliminary analyses of the case study data collected from the SITES M2 Study in Hong Kong reveal that where the development of more significant learning gains were observed, the cases possess characteristics additional to the criteria defined in the Study for selection of innovation. More importantly, it was found that the impact of the pedagogical practices was not determined simply by the aggregation of characteristics of the practices per se , nor on the technologies used, but on whether 'empowerment' permeates the curriculum goal and process. Further, this paper claims that these affective and socio-cognitive learning outcomes are more important as preparation for lifelong learning in the 21st century than 'knowledge management competencies'.     

甲醇与碳酸二甲酯共沸液的分离研究进展

综述了DMC-CH_3OH二元共沸液的各种分离方法。讨论了共沸精馏和萃取精馏的工艺流程。     

萃取精馏回收环己烷的双组分溶剂的研究

在研究萃取精馏分离回收环己烷的单溶剂的基础上，研究了双组分溶剂。当剂油比为7:1时，环己醇和四甘醇双组分溶剂可以将正庚烷对环己烷的相对挥发度由单溶剂时的小于1提高到1．11；将2，3—二甲基戊烷对环己烷的相对挥发度由单溶剂时的小于1．07提高到1．21。用该溶剂在小型萃取精馏实验装置上运行，结果表明，环己烷的纯度为98．5％，收率为80％。     

第二套常减压装置低温热回收技术的应用

常减压装置低温余热较多.利用较少,造成热量损失。利用初顶油气、稳定石脑油等低温余热加热除盐水建立热水站,在节汽、节电等方面取得了较好的效果。     

An illustration of verification and validation in the modelling phase of KBS development

Reliability has become a key factor in KBS development. For this reason, it has been suggested that verification and validation (V&V) should become an integrated part of activities throughout the whole KBS development cycle. In this paper, it will be illustrated how the PROLOGA workbench integrates V&V aspects into its modelling environment, such that these techniques can be of assistance in the process of knowledge acquisition and representation. To this end, verification has to be performed incrementally and can no longer be delayed until after the system has been completed. It will be shown how this objective can be realised through an approach that uses the decision table formalism as a modelling instrument.     

Active symbols and internal models: Towards a cognitive connectionism

In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this is appropriate for building models of human cognition. However, we do accept the point that there are cognitive capacities for which any purely associative model cannot provide a satisfactory account. The implication that we draw from is this is not that associationist models and mechanisms should be scrapped, but rather that they should be enhanced.In the next section of the article, we identify a set of connectionist approaches which are characterized by active symbols — recurrent circuits which are the basis of knowledge representation. We claim that such approaches avoid criticisms of behaviorism and are, in principle, capable of supporting full cognition. In the final section of the article, we speculate at some length about what we believe would be the characteristics of a fully realized active symbol system. This includes both potential problems and possible solutions (for example, mechanisms needed to control activity in a complex recurrent network) as well as the promise of such systems (in particular, the emergence of knowledge structures which would constitute genuine internal models).     

Transformation of semantic knowledge into simulation-based decision support

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.