• Journal of Information Processing Systems
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• 제18권3호
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• pp.443-456
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• 2022

An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

• 대한임베디드공학회논문지
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• 제18권4호
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• pp.165-172
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• 2023

Heterogeneous computing is a technology that utilizes different types of processors to perform parallel processing. It maximizes task processing and energy efficiency by leveraging various computing resources such as CPUs, GPUs, and FPGAs. On the other hand, edge computing has developed with IoT and 5G technologies. It is a distributed computing that utilizes computing resources close to clients, thereby offloading the central server. It has evolved to intelligent edge computing combined with artificial intelligence. Intelligent edge computing enables total data processing, such as context awareness, prediction, control, and simple processing for the data collected on the edge. If heterogeneous computing can be successfully applied in the edge, it is expected to maximize job processing efficiency while minimizing dependence on the central server. In this paper, experiments were conducted to verify the feasibility of various parallel programming models on high-end and low-end edge devices by using benchmark applications. We analyzed the performance of five parallel programming models on the Raspberry Pi 4 and Jetson Orin Nano as low-end and high-end devices, respectively. In the experiment, OpenACC showed the best performance on the low-end edge device and OpenSYCL on the high-end device due to the stability and optimization of system libraries.

• Ocean Systems Engineering
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• 제6권2호
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• pp.143-159
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• 2016

Cargo, such as a Tension Leg Platform (TLP), Semi-submersible platform (Semi), Spar or a circular Floating Production Storage and Offloading (FPSO), are frequently dry-transported on a Heavy Lift Vessel (HLV) from the point of construction to the point of installation. The voyage can span months and the overhanging portions of the hull can be subject to frequent wave slamming events in rough weather. Tie-downs or sea-fastening are usually provided to ensure the safety of the cargo during the voyage and to keep the extreme responses of the cargo, primarily for the installed equipment and facilities, within the design limits. The proper design of the tie-down is dependent on the accurate prediction of the wave slamming loads the cargo will experience during the voyage. This is a difficult task and model testing is a widely accepted and adopted method to obtain reliable sea-fastening loads and extreme accelerations. However, it is crucial to realize the difference in the inherent stiffness of the instrument that is used to measure the tri-axial sea fastening loads and the prototype design of the tie-downs. It is practically not possible to scale the tri-axial load measuring instrument stiffness to reflect the real tie-down stiffness during tests. A correlation method is required to systematically and consistently account for the stiffness differences and correct the measured results. Direct application of the measured load tends to be conservative and lead to over-design that can reflect on the overall cost and schedule of the project. The objective here is to employ the established correlation method to provide proper high-frequency responses to topsides and hull design teams. In addition, guidance for optimizing tie-down design to avoid damage to the installed equipment, facilities and structural members can be provided.