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This study considered the role of coal as China’s basic energy source and examines the development of the coal industry. We focused on the intelligent development of coal mines, and introduced the “Chinese mode” of intelligent mining in underground coal mines, which uses complete sets of technical equipment to propose classification and grading standards. In view of the basic characteristics and technical requirements of intelligent coal mine systems, we established a digital logic model and propose an information entity and knowledge map construction method. This involves an active information push strategy based on a knowledge demand model and an intelligent portfolio modeling and distribution method for collaborative control of coal mines. The top-level architecture of 5G+ intelligent coal mine systems combines intelligent applications such as autonomous intelligent mining, human–machine collaborative rapid tunneling, unmanned auxiliary transportation, closed-loop safety control, lean collaborative operation, and intelligent ecology. Progress in intelligent mining technology was described in terms of a dynamic modified geological model, underground 5G network and positioning technology, intelligent control of the mining height and straightness of the longwall working face, and intelligent mining equipment. The development of intelligent coal mines was analyzed in terms of its imbalances, bottlenecks, and the compatibility of large-scale systems. Implementation ideas for promoting the development of intelligent coal mines were proposed, such as establishing construction standards and technical specifications, implementing classification and grading standards according to mining policy, accelerating key technology research, and building a new management and control model.

In the past few years, edge detection models based on convolutional neural networks have made remarkable progress. These models consist of the encoding network and the decoding network. The classification networks (e.g. VGG16) are generally used as the encoding network and the researchers mainly focus on the design of the decoding network. Because natural images contain many edges with different scales, how to make full use of rich and hierarchical convolutional features for edge detection is pivotal. We propose a decoding network that effectively integrates multi-level features from all convolutional layers, named multi-decoding network. Firstly, the side outputs from VGG16 are divided into shallow-, mid-, and deep-level edge features. These edge features are processed separately by multiple independent decoding networks composed of several refinement blocks. Subsequently, to improve the ability of edge fusion, we design a multi-level top-down architecture and a multi-scale attention module, which utilizes the attention mechanism to gradually refine edges. Ultimately, three types of predictions are averaged to produce the final prediction. We evaluate our method on the BSDS500, NYUDv2, and Multicue datasets. Experimental results demonstrate that our model is superior to multiple state-of-the-art edge detection models.

In this study, a flexible energy‐ and delay‐aware mapping approach is proposed for the co‐optimisation of energy consumption and communication latency for network‐on‐chips (NoCs). A novel genetic‐based hyper‐heuristic algorithm (GHA) is proposed as the core algorithm. This algorithm consists of bottom‐level optimisation which includes a variety of operators and top‐level optimisation which selects suitable operators through a ‘reward’ mechanism. As this algorithm can select suitable operators automatically during the mapping process, it noticeably improves convergence speed and demonstrates excellent stability. Compared to the random algorithm, GHA can achieve on average 23.28% delay reduction and 11.81% power reduction. Compared to state‐of‐the‐art mapping algorithms, GHA produces improved mapping results with less time, especially when the size of NoC is large.

Knowledge about human beings is an integral part of any intelligent agent of considerable significance. Delimiting, modeling and acquiring such knowledge are the central topics of this paper. Because of the tremendous complexity in knowledge of human beings we introduce a top-level ontology of human beings from the perspectives of psychology, sociology, physiology and pathology. This ontology is not only an explicit conceptualization of human beings, but also an efficient way of acquiring and organizing relevant knowledge.

This chapter contains sections titled: Introduction Definitions Systems Architectures Architecture Modelling and Trade‐off Example of a Developing Architecture Evolution of Avionics Architectures References Further Reading