铝电解故障诊断的研究现状及发展趋势     

周昊  李界家  李世涛  王奔 《科技广场》2010,(9)

铝电解是一个非线性、多耦合、时变和大时滞过程,受强电场、强磁场、强热场交互干扰,故障发生频繁。本文综述了国内外铝电解故障诊断研究状况,阐述了目前铝电解故障诊断的主要方法和发展趋势,为铝电解故障诊断技术的实用化提供理论依据。  相似文献   

适应人工智能快速发展的知识产权制度研究     

余翔  张润哲张奔 王华 《科研管理》2021,42(8):176-183

基于深度学习算法的进步,人工智能逐渐有能力独立进行发明创造和文艺作品创作。本文主要探讨现行专利及著作权制度中规定的保护对象、权利人资格、专利及著作权的权属、侵权判定、侵权责任主体等对人工智能技术快速发展的适应及协调程度,研究指出：现有的专利和版权制度应当对人工智能的发明和作品持鼓励的态度,在排除不适宜作为专利或著作权保护对象的同时,人工智能的发明或作品的权利授予标准应当与人类的有所区分;相关权利人仍须对应自然人或法人,而非人工智能本身;相关专利侵权行为应包括间接侵权,同时应对人工智能作品安排“登记-授权”的著作权制度、参考临摹作品为人工智能绘画作品提供相应的授权使用制度等。本文还探讨了当前的专利法及著作权法在人工智能时代符合公平原则的程度,并提出解决方案：在“强人工智能时代”将人工智能的发明创造或作品作为公共财产,授予相应的开发者“数据处理权”作为一种新的邻接权,赋予人工智能创造物新的特别权利（Sui Generis）,修改专利法与著作权法中关于主要权利的相关规定等。  相似文献   

Performance‐centered design for developing countries: Emphasizing context     

Sonia Arias 《Performance Improvement》2002,41(10):7-11

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Using causal models to bridge the divide between big data and educational theory     

Kirsty Kitto  Ben Hicks  Simon Buckingham Shum 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(5):1095-1124

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.

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Implementation Factors and Their Influence on Student Mathematics Outcomes     

Tasia Brafford  Beth Harn  Ben Clarke  Christian T. Doabler  Derek Kosty  Kathleen Scalise 《Learning disabilities research & practice》2023,38(1):5-14

Assessing implementation allows for a better understanding of an intervention's effects and the mechanisms that influence its impact. Two main areas of implementation are (a) the quality with which an intervention is delivered and (b) instructors’ adherence to the programmed intervention. The current study used data from a kindergarten mathematics intervention program to (a) examine if and how treatment adherence was associated with implementation quality and (b) explore implementation measures’ relation to student mathematics outcomes. Results indicated high implementation scores across time for both adherence and quality. Neither treatment adherence nor implementation quality was found to relate to a general outcome measure of student mathematics achievement; however, both were similarly related to the curricular-aligned measure.  相似文献   

电子对抗专业应用型人才实践教学研究 ——以"电子对抗技术与指挥"人才培养方案为例     

任明秋  王冰切  冷毅  商犇 《高等教育研究学报》2022,45(1):116-120

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