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        Distributed control system architecture for deep submergence rescue vehicles

        Sun, Yushan,Ran, Xiangrui,Zhang, Guocheng,Wu, Fanyu,Du, Chengrong The Society of Naval Architects of Korea 2019 International Journal of Naval Architecture and Oc Vol.11 No.1

        The control architectures of Chuan Suo (CS) deep submergence rescue vehicle are introduced. The hardware and software architectures are also discussed. The hardware part adopts a distributed control system composed of surface and underwater nodes. A computer is used as a surface control machine. Underwater equipment is based on a multi-board-embedded industrial computer with PC104 BUS, which contains IO, A/D, D/A, eight-channel serial, and power boards. The hardware and software parts complete data transmission through optical fibers. The software part involves an IPC of embedded Vxworks real-time operating system, upon which the operation of I/O, A/D, and D/A boards and serial ports is based on; this setup improves the real-time manipulation. The information flow is controlled by the software part, and the thrust distribution is introduced. A submergence vehicle heeling control method based on ballast water tank regulation is introduced to meet the special heeling requirements of the submergence rescue vehicle during docking. Finally, the feasibility and reliability of the entire system are verified by a pool test.

      • KCI등재

        Distributed control system architecture for deep submergence rescue vehicles

        Yushan Sun,Xiangrui Ran,Guocheng Zhang,Fanyu Wu,Chengrong Du 대한조선학회 2019 International Journal of Naval Architecture and Oc Vol.11 No.1

        The control architectures of Chuan Suo (CS) deep submergence rescue vehicle are introduced. The hardware and software architectures are also discussed. The hardware part adopts a distributed control system composed of surface and underwater nodes. A computer is used as a surface control machine. Underwater equipment is based on a multi-board-embedded industrial computer with PC104 BUS, which contains IO, A/D, D/A, eight-channel serial, and power boards. The hardware and software parts complete data transmission through optical fibers. The software part involves an IPC of embedded Vxworks real-time operating system, upon which the operation of I/O, A/D, and D/A boards and serial ports is based on; this setup improves the real-time manipulation. The information flow is controlled by the software part, and the thrust distribution is introduced. A submergence vehicle heeling control method based on ballast water tank regulation is introduced to meet the special heeling requirements of the submergence rescue vehicle during docking. Finally, the feasibility and reliability of the entire system are verified by a pool test.

      • KCI등재

        Mechanical properties and neural network prediction of cement fly ash-enhanced roadbed soil in seasonal frozen zones under short-term curing

        Gao Hang Cui,Zhuo Cheng,Fanyue Sun,Shuxian Ma,Zhiqiang Liu 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.1

        The freezing and thawing of roadbed soils in seasonal frozen zones can cause uneven settlement and other road problems, which puts road operation at risk. This paper focuses on the rapid construction of expressways and analyzes the effects of using fly ash and cement as modifiers on the physical properties and chemical composition of subgrade soil. The study found that cement admixtures can improve soil mechanical attributes and frost resistance, while also increasing the degree of fly ash hydration.And the freeze-thaw cycle process can enhance the mechanical characteristics of soils with higher cement admixture content. This is due to the development of hydrates in cement fly ash-enhanced soils during freeze?thaw cycles, resulting in a denser interior structure. The use of neural network prediction analysis showed that using ANN4-10-3 to forecast soil mechanical property parameters can produce superior results. Therefore, it is recommended to use cement fly ash-enhanced soil for roadbed construction in seasonal freezing areas, and neural network can be used to predict soil mechanical parameters.

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