This book constitutes the proceedings of the 20th Chinese Intelligent Systems Conference, CISC 2024, which was held on October 2627, 2024, in Guilin, Guangxi, China. The book focuses on new theoretical results and techniques in the field of intelligent systems and control. This is achieved by providing in-depth studies of a number of important topics such as multi-agent systems, complex networks, intelligent robots, complex systems theory and swarm behaviour, event-driven and data-driven control, robust and adaptive control, big data and brain science, process control, intelligent sensors and detection technology, deep learning and learning control, navigation and control of flying vehicles, and so on. The book is particularly suitable for readers interested in learning intelligent systems and control and artificial intelligence. The book can benefit researchers, engineers and graduate students.

Focuses on intelligent systems both in theory and practice Comprehensively studies Linear and nonlinear systems design, implementation, testing and performance evaluation Presents the latest research results in the field of artificial intelligence and intelligent control

Inhalt

.- 1 Bipartite Consensus of Privacy-Preserving Multi-agent Systems under Adaptive Policy Control.

.- 2 Time-varying Constrained Consensus of Asynchronous Discrete-time Multi-agent System.

.- 3 Fusion of channel spatial attention enhances span-boundary awareness for nested named entity recognition.

.- 4 Denoising of cable signpartial dischargeals based on IACEEMDN and improved wavelet thresholding.

.- 5 Efficiency Evaluation of Deep Sea Operations Based ADC Model and Analytic Hierarchy Process.

.- 6 Design and Implementation of a Ceramic Pressure Sensor for Marine Applications.

.- 7 A Knowledge Graph Construction Method for Legal Documents.

.- 8 A Metal Grain Boundary Extraction Algorithm Based on Improved U-Net and Grain Boundary Repair.

.- 9 A Method of Feed-forward Control Command Calculation Based on Unscented Kalman Filter.

.- 10 Improved SLTV-MPC for Epidemic Prevention Robots based on Artificial Potential Field, etc.