arXiv subjects

Dacheng Zhou

Publications and source records attributed to Dacheng Zhou.

3 recordsLinked to original sources

arXiv2024

A New World Framework: The Three Realms and Six Layers Model

This paper introduces an innovative framework for understanding the world, termed the "Three Realms and Six Layers Model". Based on the concept of scale, the world is divided into three realms, each encompassing six layers, with a ten-thousand-fold difference in scale between adjacent layers. This unique division reveals the variations in laws at different levels and the fundamental changes in laws when transitioning between realms. The model offers a new perspective for addressing interdisciplinary issues, especially in understanding the behavior of large-scale systems and their connection to microscopic phenomena. In summary, the "Three Realms and Six Layers Model" provides a novel tool for comprehending the diversity and complexity of the universe.

physics.soc-ph↗

arXiv2024

UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite

In the nascent domain of urban digital twins (UDT), the prospects for leveraging cutting-edge deep learning techniques are vast and compelling. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datasets. To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI. Along with this article, we introduce the road pothole detection task, the first online competition published within this benchmark suite. This task provides a well-annotated dataset, comprising 1,000 RGB images and their pixel/instance-level ground-truth annotations, captured in diverse real-world scenarios under different illumination and weather conditions. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks, developed based on either convolutional neural networks or Transformers. We anticipate that our benchmark will serve as a catalyst for the integration of advanced UDT techniques into IRI. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential and foster innovation in this critical domain.

cs.CV↗

arXiv2021

Asymptotic and Finite-time Cluster Synchronization of Neural Networks via Two Different Controllers

In this paper, by using pinning impulse controller and hybrid controller respectively, the research difficulties of asymptotic synchronization and finite time cluster synchronization of time-varying delayed neural networks are studied. On the ground of Lyapunov stability theorem and Lyapunov-Razumikhin method, a novel sufficient criterion on asymptotic cluster synchronization of time-varying delayed neural networks is obtained. Utilizing Finite time stability theorem and hybrid control technology, a sufficient criterion on finite-time cluster synchronization is also obtained. In order to deal with time-varying delay and save control cost, pinning pulse control is introduced to promote the realization of asymptotic cluster synchronization. Following the idea of pinning control scheme, we design a progressive hybrid control to promote the realization of finite time cluster synchronization. Finally, an example is given to illustrate the theoretical results.

math.DS↗