• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.260-260
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• 2017

This study aims to compare the performance of TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability model) and TiVaSS (Time-variant Slope Stability model) in the prediction of rainfall-induced shallow landslides. TRIGRS employs one-dimensional (1-D) subsurface flow to simulate the infiltration rate, whereas a three-dimensional (3-D) model is utilized in TiVaSS. The former has been widely used in landslide modeling, while the latter was developed only recently. Both programs are used for the spatiotemporal prediction of shallow landslides caused by rainfall. The present study uses the July 2011 landslide event that occurred in Mt. Umyeon, Seoul, Korea, for validation. The performance of the two programs is evaluated by comparison with data of the actual landslides in both location and timing by using a landslide ratio for each factor of safety class ( index), which was developed for addressing point-like landslide locations. In addition, the influence of surface flow on landslide initiation is assessed. The results show that the shallow landslides predicted by the two models have characteristics that are highly consistent with those of the observed sliding sites, although the performance of TiVaSS is slightly better. Overland flow affects the buildup of the pressure head and reduces the slope stability, although this influence was not significant in this case. A slight increase in the predicted unstable area from 19.30% to 19.93% was recorded when the overland flow was considered. It is concluded that both models are suitable for application in the study area. However, although it is a well-established model requiring less input data and shorter run times, TRIGRS produces less accurate results.

• Nuclear Engineering and Technology
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• v.26 no.4
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• pp.502-506
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• 1994

The design of safety computer system is described along with the case of software design and testing in the Core Protection Calculator System (CPCS). The application of computer system in safety system requires not only hardware qualification but thorough testing on software to verify its correctness and completeness. The testing on software for CPCS is performed by comparing the outputs of two versions of code. One is implemented in assembly language and the other is in Fortran. The testing is performed in sequencial and overlapping manner. Phase I test verifies that each software module is implemented correctly by executing every branch. Phase II test verifies that the integrated software is complete, meeting its requirements specification and also the integrated system meet its requirement and timing constraints. Through these testing, the Yonggwang Nuclear Power Plant Units (YGN) 3 and 4 CPCS software is verified to be correct and complete, and the integrated system is designed as in its requirements specification.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.267-276
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• 2021

The automotive industry and academic research have been continuously conducting research on standardization such as AUTOSAR (AUTomotive Open System ARchitecture) and ISO26262 to solve problems such as safety and efficiency caused by the complexity of electric/electronic architecture of automotive. AUTOSAR is an automotive standard software platform that has a layered structure independent of MCU (Micro Controller Unit) hardware, and improves product reliability through software modularity and reusability. And, ISO26262, an international standard for automotive functional safety and suggests a method to minimize errors in automotive ECU (Electronic Control Unit)s by defining the development process and results for the entire life cycle of automotive electrical/electronic systems. These design methods are variously applied in representative automotive safety-critical systems. However, since the functional and safety requirements are different according to the characteristics of the safety-critical system, it is essential to research the AUTOSAR functional safety design method specialized for each application domain. In this paper, a software functional safety mechanism design method using AUTOSAR is proposed, and a new failure management framework is proposed to ensure the high reliability of the product. The AUTOSAR functional safety mechanism consists of memory partitioning protection, timing monitoring protection, and end-to-end protection. The fault management framework is composed of several safety SWCs to maintain the minimum function and performance even if a fault occurs during the operation of a safety-critical system. Finally, the proposed method is applied to the Shift-by-Wire system design to prove the validity of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1025-1031
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• 2019

This study proposes a PCS that enables efficient operation of seawater pumps for ships by implementing ML-based algorithms. Seawater temperature, RPM and power consumption data are acquired from two ships with PCS, analyzed with regression analysis method, and new algorithms are presented. Using the algorithms presented, Ship A saved about 36% compared to the PCS application, and ML-based algorithms in certain sea temperatures of 19 to 27 degrees Celsius and above 32 degrees Celsius were about 1% lower than Ship A's PCS. Ship B saved about 50% compared to PCS not applied, and about 2% more than Ship B's PCS in waters above $19^{\circ}C$, a specified sea temperature. The derived data can be used to suggest the optimum pump speed and sea route. In addition, the trend of acquired data can be used to infer the performance of the pump or the timing of elimination of the MGPS when efficiency becomes poor.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.366-376
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• 2019

Youth unemployment reached a record high in 2017, and business revitalization is emerging as a means of overcoming this situation. The number of venture companies and amounts of new venture investments are increasing year on year, and the government has upgraded Small and Medium Business Administration to the Ministry of SMEs and Startups. However, the success rate of startups is relatively poor. Over the past three years only 39.1% of Korean startups survived and 90% of companies failed in the Valley of Death phase. Survival this phase is critical for early startups, and thus, the amount and timing of investments are critical. Current models for establishing business startups do not effectively attract investments because they lack practical, corporate evaluation variables. In this paper, we develop a mixed process that incorporates the factors and business models focused on by venture capital investors. In addition, we compared our Death Valley Venture (DVV) process with current methods and provide an example of its application.

• Journal of IKEEE
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• v.23 no.3
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• pp.800-804
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• 2019

This paper presents a dual-loop sub-sampling digital PLL for a 2.4 GHz IoT applications. The PLL initially performs a divider-based coarse lock and switches to a divider-less fine sub-sampling lock. It achieves a low in-band phase noise performance by enabling the use of a high resolution time-to-digital converter (TDC) and a digital-to-time converter (DTC) in a selected timing range. To remove the difference between the phase offsets of the coarse and fine loops, a phase offset calibration scheme is proposed. The phase offset of the fine loop is estimated during the coarse lock and reflected in the coarse lock process, resulting in a smooth transition to the fine lock with a stable fast settling. The proposed digital PLL is designed by SystemVerilog modeling and Verilog-HDL and fully verified with simulations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.57-64
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• 2020

Recently, due to the proliferation of IoT sensors, the development of big data and artificial intelligence, time series prediction research on fine dust pollution is actively conducted. However, because the data representing fine dust contamination changes rapidly, traditional time series prediction methods do not provide a level of accuracy that can be used in the field. In this paper, we propose a method that reflects the classification results of environmental conditions through CNN when predicting micro dust contamination using LSTM. Although LSTM and CNN are independent, they are integrated into one network through the interface, so this method is easier to understand than the application LSTM. In the verification experiments of the proposed method using Beijing PM2.5 data, the prediction accuracy and predictive power for the timing of change were consistently improved in various experimental cases.

• Molecules and Cells
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• v.44 no.8
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• pp.580-590
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• 2021

Patients with severe asthma have unmet clinical needs for effective and safe therapies. One possibility may be mesenchymal stem cell (MSC) therapy, which can improve asthma in murine models. However, it remains unclear how MSCs exert their beneficial effects in asthma. Here, we examined the effect of human umbilical cord blood-derived MSCs (hUC-MSC) on two mouse models of severe asthma, namely, Alternaria alternata-induced and house dust mite (HDM)/diesel exhaust particle (DEP)-induced asthma. hUC-MSC treatment attenuated lung type 2 (Th2 and type 2 innate lymphoid cell) inflammation in both models. However, these effects were only observed with particular treatment routes and timings. In vitro co-culture showed that hUC-MSC directly downregulated the interleukin (IL)-5 and IL-13 production of differentiated mouse Th2 cells and peripheral blood mononuclear cells from asthma patients. Thus, these results showed that hUC-MSC treatment can ameliorate asthma by suppressing the asthmogenic cytokine production of effector cells. However, the successful clinical application of MSCs in the future is likely to require careful optimization of the route, dosage, and timing.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1537-1544
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• 2022

When designing chip, it considers design specification, timing problem, and clock synchronization on place & route (P&R) process. P&R process is complicated because of considering various factors. Chip uses clock tree synthesis (CTS) to reduce clock path delay. The purpose of this study is to examine shallow-CTS algorithm for checking clock tree synthesizable. Using open source Parser-Verilog, register transfer level (RTL) synthesizable Verilog file is parsed and it uses Pre-CTS and Post-CTS process that is included shallow-CTS. Based on longest clock path in the Pre-CTS and Post-CTS stages, the standard deviation before and after buffer insertion is compared and analyzed for the accuracy of CTS. In this paper, It is expected that the cost and time problem could be reduced by providing a pre-clock tree synthesis verification method at the RTL level without confirming the CTS result using the time-consuming licensed EDA tool.