• Journal of the Korean Society of Systems Engineering
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• v.7 no.2
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• pp.7-11
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• 2011

A high-speed railway system represents a typical example of large-scale multi-disciplinary system, consisting of subsystems such as rolling-stock, electrical hardware, electronics, control, information, communication, civil technology etc. The system design and acquisition data of the large-scale system must be the subject under strict configuration control and management. Systems engineering technology development project for Korea next generation High-speed Electric Multiple Unit (HEMU) system in progress is a national large system development project that is not only a large-size and complex but also multi-disciplinary in nature. Therefore, all stakeholders must understand and share the functional and performance requirements of HEMU throughout its life-cycle phases. Also in the test and evaluation phase, all systems requirements must be verified. In 2011, the prototype train manufacturing will be completed. It will do test run on the commercial line and all systems requirements are verified until 2012. For the system verification, the test and evaluation process have to be established before the test trial run. Using a systems engineering tool, the system design database(SDD) with requirements traceability and development process management in the course of the development have to be established. This paper represents the test and evaluation process development based on the SEMP(Systems Engineering Management Plan) developed in the design stage. The test and evaluation process is refined and updated in comparison to the design stage one. The test and evaluation process consists of procedure, test and evaluation method and schedule. So through this process, it is defined that each systems requirements is verified on which test and about what time.

• Geophysics and Geophysical Exploration
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• v.23 no.4
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• pp.230-242
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• 2020

We performed a magnetotelluric (MT) survey to delineate the geological structures below the depth of 20 km in the Gyeongju area where an earthquake with a magnitude of 5.8 occurred in September 2016. The measured MT data were severely distorted by electrical noise caused by subways, power lines, factories, houses, and farmlands, and by vehicle noise from passing trains and large trucks. Using machine-learning methods, we classified the MT time series data obtained near the railway and highway into two groups according to the inclusion of traffic noise. We applied three schemes, stochastic gradient descent, support vector machine, and random forest, to the time series data for the highspeed train noise. We formulated three datasets, Hx, Hy, and Hx & Hy, for the time series data of the large truck noise and applied the random forest method to each dataset. To evaluate the effect of removing the traffic noise, we compared the time series data, amplitude spectra, and apparent resistivity curves before and after removing the traffic noise from the time series data. We also examined the frequency range affected by traffic noise and whether artifact noise occurred during the traffic noise removal process as a result of the residual difference.