• The KIPS Transactions:PartB
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• v.11B no.5
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• pp.603-610
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• 2004

The distributed Information Retrieval System centrally controlled by mediator or meta search engine result in congestion of heavy traffic and int he problem of increment of cost for the reason of the design of complicated algorithm for central control and installation of hardware. So to figure out this problem, the way is needed that has independent retrieval functionality and can cooperate each other without dependency. In this paper, we overview a few works involved in distributed information retrieval system, then, implement algorithm and design the frame-work of distributed information retrieval system using sequential transferring method(STM) including multiple information retrieval system separated from central control. For this first of all, we present a web partition policy which devide and manage web logically and we present the sequential query processing way by means of illustration through changing numbered information retrieval system. Then, we also present 3-layered structure of framework and function and module of each layer suitable for information retrieval system. Last of ail, for effective implementation of STM algorithm we analysis module structure and present description of pseudocode of this, and show that the proposed STM algorithm works smoothly by demonstration of sequential query transfer process between servers.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.