新冠
尊敬的听众们，
我们非常荣幸能够在今天向大家介绍一个令人兴奋的主题：基于 CGAN（生成对抗网络）和城市形态要素结合的新冠病例分布预测方法。作为建筑学博士生，我们一直关注着城市规划和设计领域的创新技术和研究。在当前全球新冠疫情的背景下，我们面临着许多挑战，其中之一就是如何有效地预测和应对病例的传播。
这个主题引入了两个关键的技术：CGAN 和城市形态要素。生成对抗网络（CGAN）是一种强大的机器学习方法，它结合了生成模型和判别模型的能力。我们可以利用 CGAN 来模拟和生成城市病例分布的可能性，从而提供重要的决策支持。另一方面，城市形态要素包括了城市的空间布局、建筑密度、交通网络等因素，这些要素与病例传播密切相关。通过将城市形态要素与 CGAN 相结合，我们可以更准确地预测病例的分布情况，并为城市规划者和决策者提供有力的指导。
我们的研究旨在开发一种全新的方法，能够在考虑城市形态要素的同时，利用 CGAN 生成模型来预测新冠病例的分布。这项工作具有重要的实际意义，它可以帮助我们理解疫情传播的规律，指导我们采取合适的干预措施，以及规划和设计更具抗疫能力的城市。通过运用这一方法，我们有望提高疫情防控的效果，减少疫情对社会和经济造成的影响。
在本次演讲中，我们将详细介绍我们的研究方法和实验结果，探讨 CGAN 和城市形态要素结合在新冠病例分布预测中的应用潜力。我们将强调这一方法的创新性、可行性和实用性，并展望未来的研究方向。我们相信这项研究对于城市规划和公共卫生领域都具有重要意义，希望通过分享我们的工作，能够激发更多人对于这一领域的兴趣，并促进跨学科的合作和进步。
谢谢大家！
We are very honored to introduce an exciting topic to you today: a new crown case distribution prediction method based on the combination of CGAN (generated confrontation network) and urban form elements. As PhD students in Architecture, we kee
p an eye on innovative technologies and research in the field of urban planning and design. In the context of the current global COVID-19 pandemic, one of the many challenges we face is how to effectively predict and respond to the spread of cases.
This topic introduces two key techniques: CGAN and urban form elements. Generative Adversarial Networks (CGANs) are a powerful machine learning method that combines the capabilities of generative and discriminative models. We can leverage CGANs to model and generate likelihoods of urban case distributions, providing important decision support. On the other hand, the elements of urban form include factors such as the spatial layout of the city, building de
nsity, and transportation network, which are closely related to the spread of cases. By combining urban morphology elements with CGAN, we can more accurately predict the distribution of cases and provide powerful guidance for urban planners and policy makers.
Our research aims to develop a novel method
capable of predicting the distribution of COVID-19 cases using CGAN generative models while considering elements of urban form. This work has important practical implications, as it can help us understa
nd how the epidemic spreads, guide us to take appropriate interventions, and plan and design more resilient cities. By applying this approach, we are expected to improve the effectiveness of epidemic prevention and control and reduce the impact of the epidemic on society and the economy.
