• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1531-1539
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• 2017

The existing SSD storage tester processed logs in a 1-N structure between server and client using TCP and network file system. This method causes some problems for example, an increase in CPU usage and difficulty in exception handling, etc. In this paper, we implement a log processing message layer that can deal with asynchronous distributed processing using open source messaging system such as kafka, RabbitMQ and compare this layer with existing log transmission method. A log simulator was implemented to compare the transmission bandwidth and CPU usage. Test results show that the transmission using the message layer has higher performance than the transmission using the message layer, and the CPU usage does not show any significant difference The message layer can be implemented more easily than the conventional method and the efficiency is higher than that of the conventional method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.467-468
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• 2021

본 논문에서는 자료수집(데이터 크롤링)을 이용해 많은 채용정보를 쉽게 접근할 수 있도록 하는 시스템이다. 현재는 StackOverflow의 자료를 수집하고 데이터베이스에 자동으로 저장하도록 하였다. 수집해야 할 자료가 많아 Celery와 RabbitMQ를 사용하여 비동기 작업을 요청하여 즉시 응답을 받지 않아도 다른 일을 수행할 수 있다. 이렇게 수집한 자료들을 해당 사이트에 나열해줌으로 사용자들이 시간과 비용을 절감하여 효율적인 취업 준비를 할 수 있도록 하는 시스템을 설계 구현하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.645-648
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• 2017

The existing embedded tester used TCP and shared file system for log processing. In addition, the existing processing method was treated as 1-N structure. This method wastes resources of the tester for exception handling. We implemented a log processing message layer that can be distributed by messaging system. And we compare the transmission method using the message layer and the transmission method using TCP and the shared file system. As a result of comparison, transmission using the message layer showed higher transmission bandwidth than TCP. In the CPU usage, the message layer showed lower efficiency than TCP, but showed no significant difference. It can be seen that the log processing using the message layer shows higher efficiency.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.221-232
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational reliability of the ICT/Cold-Chain Unmanned Storage, a predictive maintenance system was implemented based on the LSTM model. In this paper, a server for data management, such as collection and monitoring, and an analysis server that notifies the monitoring server through data-based failure and defect analysis are separately distinguished. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on RabbitMQ, loading data in an InMemory method using Redis, and managing snapshot data DB in real time. The predictive maintenance platform can contribute to securing reliability by identifying potential failures and defects that may occur in the operation of the ICT/Cold-Chain Unmanned Storage in the future.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.39.1-39.1
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• 2021

The Local Volume Mapper(LVM) project in the fifth iteration of the Sloan Digital Sky Survey (SDSS-V) will produce large integral-field spectroscopic survey data to understand the physical conditions of the interstellar medium in the Milky Way, the Magellanic Clouds, and other local-volume galaxies. We are developing the LVM Instrument control software. The architecture design of the software follows a hierarchical structure in which the high-level software packages interact with the low-level and mid-level software and hardware components. We adopt the spiral software development model in which the software evolves by iteration of sequential processes, i.e., software requirement analysis, design, code generation, and testing. This spiral model ensures that even after being commissioned, the software can be revised according to new operational requirements. We designed the software by using the Unified Modeling Language, which can visualize functional interactions in structure diagrams. We plan to use the SDSS software framework CLU for the interaction between components, based on the RabbitMQ that implemented the Advanced Message Queuing Protocol (AMQP).

• Journal of the Korea Safety Management & Science
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• v.25 no.4
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• pp.141-151
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• 2023

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.