Automatically mapping a requirement specification to design model in Software Engineering is an open complex problem. Existing methods use a complex manual process that use the knowledge from the requirement specifica...Automatically mapping a requirement specification to design model in Software Engineering is an open complex problem. Existing methods use a complex manual process that use the knowledge from the requirement specification/modeling and the design, and try to find a good match between them. The key task done by designers is to convert a natural language based requirement specification (or corresponding UML based representation) into a predominantly computer language based design model—thus the process is very complex as there is a very large gap between our natural language and computer language. Moreover, this is not just a simple language conversion, but rather a complex knowledge conversion that can lead to meaningful design implementation. In this paper, we describe an automated method to map Requirement Model to Design Model and thus automate/partially automate the Structured Design (SD) process. We believe, this is the first logical step in mapping a more complex requirement specification to design model. We call it IRTDM (Intelligent Agent based requirement model to design model mapping). The main theme of IRTDM is to use some AI (Artificial Intelligence) based algorithms, semantic representation using Ontology or Predicate Logic, design structures using some well known design framework and Machine Learning algorithms for learning over time. Semantics help convert natural language based requirement specification (and associated UML representation) into high level design model followed by mapping to design structures. AI method can also be used to convert high level design structures into lower level design which then can be refined further by some manual and/or semi automated process. We emphasize that automation is one of the key ways to minimize the software cost, and is very important for all, especially, for the “Design for the Bottom 90% People” or BOP (Base of the Pyramid People).展开更多
逆向合成规划是现代有机合成化学中合成路线设计的重要基础.合成化学发展至今,化学家们积累了大量的反应数据.自有机合成大师E.J.Corey将逆合成分析法与计算机结合提出LHASA(logic and heuristics applied to synthetic analysis)起,计...逆向合成规划是现代有机合成化学中合成路线设计的重要基础.合成化学发展至今,化学家们积累了大量的反应数据.自有机合成大师E.J.Corey将逆合成分析法与计算机结合提出LHASA(logic and heuristics applied to synthetic analysis)起,计算机根据反应数据自主学习并给出逆向合成路线成了化学家的愿景之一.近年来,基于数据驱动的研究范式不断发展,大量深度学习模型被提出并在逆向合成规划中取得了初步的成功,然而该类模型仍然存在高质量数据集稀缺、软硬件结合不佳、领域知识嵌入与发现困难等问题.通过深度学习实现逆向合成路线规划有待深入研究.展开更多
Artificial intelligence technology, mainly refers to strengthening the artificial way, so as to combinecomputer technology with product design. Firstly, the auxiliary innovation of bathroomproducts based on artificial...Artificial intelligence technology, mainly refers to strengthening the artificial way, so as to combinecomputer technology with product design. Firstly, the auxiliary innovation of bathroomproducts based on artificial intelligence technology is proposed, then the user characteristicsare analysed, and the auxiliary design framework of bathroom products is designed. Finally, theinnovation model is established to realise the auxiliary innovation design of bathroom products.Experimental results show that the proposed method can effectively improve the interactionefficiency and response time, and reduce the false response.展开更多
文摘Automatically mapping a requirement specification to design model in Software Engineering is an open complex problem. Existing methods use a complex manual process that use the knowledge from the requirement specification/modeling and the design, and try to find a good match between them. The key task done by designers is to convert a natural language based requirement specification (or corresponding UML based representation) into a predominantly computer language based design model—thus the process is very complex as there is a very large gap between our natural language and computer language. Moreover, this is not just a simple language conversion, but rather a complex knowledge conversion that can lead to meaningful design implementation. In this paper, we describe an automated method to map Requirement Model to Design Model and thus automate/partially automate the Structured Design (SD) process. We believe, this is the first logical step in mapping a more complex requirement specification to design model. We call it IRTDM (Intelligent Agent based requirement model to design model mapping). The main theme of IRTDM is to use some AI (Artificial Intelligence) based algorithms, semantic representation using Ontology or Predicate Logic, design structures using some well known design framework and Machine Learning algorithms for learning over time. Semantics help convert natural language based requirement specification (and associated UML representation) into high level design model followed by mapping to design structures. AI method can also be used to convert high level design structures into lower level design which then can be refined further by some manual and/or semi automated process. We emphasize that automation is one of the key ways to minimize the software cost, and is very important for all, especially, for the “Design for the Bottom 90% People” or BOP (Base of the Pyramid People).
文摘逆向合成规划是现代有机合成化学中合成路线设计的重要基础.合成化学发展至今,化学家们积累了大量的反应数据.自有机合成大师E.J.Corey将逆合成分析法与计算机结合提出LHASA(logic and heuristics applied to synthetic analysis)起,计算机根据反应数据自主学习并给出逆向合成路线成了化学家的愿景之一.近年来,基于数据驱动的研究范式不断发展,大量深度学习模型被提出并在逆向合成规划中取得了初步的成功,然而该类模型仍然存在高质量数据集稀缺、软硬件结合不佳、领域知识嵌入与发现困难等问题.通过深度学习实现逆向合成路线规划有待深入研究.
基金the 2022 first phase of the supply and demand docking employment education project of the Ministry of Education College Students Division,project number:20220104052,project name:Research and Practice on Talent Training Model for New Engineering Design Professionals Based on Interdisciplinary and Integration of Industry and EducationThe second batch of industryuniversity cooperative education projects in 2021 of the Higher Education Department of the Ministry of Education,project number:202102321010,project name:Exploration and practice of new engineering product design specialty construction based on multidisciplinary intersection andindustry-education integration.
文摘Artificial intelligence technology, mainly refers to strengthening the artificial way, so as to combinecomputer technology with product design. Firstly, the auxiliary innovation of bathroomproducts based on artificial intelligence technology is proposed, then the user characteristicsare analysed, and the auxiliary design framework of bathroom products is designed. Finally, theinnovation model is established to realise the auxiliary innovation design of bathroom products.Experimental results show that the proposed method can effectively improve the interactionefficiency and response time, and reduce the false response.
