Hai WANG & DONG, Jin Song

本文介绍了董劲松博士的研究领域,包括模型分析与验证、情境感知与泛在计算、实时并发系统规范等,并列举了其部分发表的论文,涉及验证系统、时序自动机模式、语义网服务等内容。

Dr. DONG, Jin Song

National University of Singapore

http://www.comp.nus.edu.sg/~dongjs/

Research areas

  • model analysis and verification (PAT)
  • context awareness and pervasive computing (Semantic Space)
  • real-time concurrent system specification (TCOZ)
  • web semantics, services, agent and reasoning
  • formal methods and safety critical systems
  • object, component, and language semantics

Refereed Papers in Journals/Monographs/Conferences (partial list)

  • C. Chen, J. S. Dong, J. Sun and A. Martin. A Verification System for Interval-based Specification Languages, ACM Transactions on Software Engineering and Methodology. (accepted)
  • C. Chen, J. S. Dong and J. Sun. A Formal Framework for Modeling and Validating Simulink Diagrams. Formal Aspects of Computing. (accepted)
  • J. S. Dong, P. Hao, S. C. Qin, J. Sun and Y. Wang, Timed Automata Patterns. IEEE Transactions on Software Engineering, vol. 34(6), pp 844-859, Nov./Dec. 2008.
  • S. Ferndriger, A. Bernstein, J. S. Dong, Y. Z. Feng, Y. F. Li and J. Hunter. Enhancing Semantic Web Services with Inheritance, 7th International Semantic Web conference (ISWC'08), Karlsruhe, Germany, pp 162-177, 2008.
  • N. N. Tun and J. S. Dong. Ontology Generation through a Fusion of Partial Reuse and Relation Extraction, 11th International Conference on Principles of Knowledge Representation and Reasoning (KR'08), pp 318-328, 2008.
  • C. Chen, J. S. Dong and J. Sun. A Verification System for Timed Interval Calculus, The 30th International Conference on Software Engineering (ICSE'08), pp 271-280, 2008.

 Hai WANG

http://users.ecs.soton.ac.uk/hw/

Research

InterestsSemantic Web

Real-time concurrent system specification (TCOZ)

Web environment for software design (ZML)

Formal methods and safety critical systems Unified Modeling Language (UML) ,

XML/XSLUnifying Theories of Programming


 

 







转载于:https://www.cnblogs.com/lqcdkj/archive/2009/06/10/1500240.html

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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