The systematic solutions to planning and scheduling for container terminal logistics systems (CTLS) have been scarce owing to their high complexity and strong randomness. This paper extended the past research on modeling CTLS with computer composition principles and architecture. The comparability and the further mapping relationships between CTLS and multi-processor system-on-chip (MPSoC) were discussed at great length within the theoretical viewpoints of computational thinking. Whereafter a container terminal logistics computational architecture was put forward for scheduling and execution using the perspective of MPSoC, which was established on the conceptual framework and fundamental principles of distributed, parallel, and cooperative computing in essence. Finally, the approach was demonstrated by investigating the stress testing, load balancing and process behavior of a typical container terminal logistics service case in contrast with the previous work based on the comprehensive computational experiments.
As a heating equipment, gas boiler is widely used in daily production and life. Considering the security and stability of the control system of gas boiler, this article design a combustion controller based on ARM9. The μCOSII as a real-time operating system is transplanted into nuclear of MCU. For gas boiler is a non-linear, high inertia multivariate and multidisturbance etc, this paper adopts the active disturbance rejection control (ADRC) method. For μCOSII can divide the system into multiple tasks, it can improve security and stability of the system effectively. Besides, ADRC, as a advance control method, can improve the efficiency of system.
Particle swarm optimization (PSO) is a daughter of artificial society and social learning. Hence, this paper excavates the ultimate source of PSO further, and then introduces the thinking of small world network and group decision information into it to obtain a new conceptual framework and algorithm variation for PSO, which is named PSO-WG. At the same time, the PSO-WG is discussed from the perspective of evolutionary computing to clarify the optimizing mechanism and improvement principles, which mainly includes the biological metaphor, implicit parallelism, operator mapping and feedback control analysis. Next, the computational model is proposed for achieve a self-contained optimization solution. Subsequently, a series of benchmark functions are tested and contrasted with the former representative algorithms to validate the feasibility and creditability of the new algorithm whose comprehensive performance is analyzed detailedly. Finally, the deficiency of PSO-WG and the working direction are pointed out clearly.