• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.6
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• pp.422-426
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• 2018

The lithium-ion battery pack of an electric vehicle (EV) deserves to be considered for an alternative use within smart-grid infrastructure. Despite the long automotive service life, EV batteries retain over 70~80% of their initial capacity. These battery packs must be managed for their reliability and safety. Therefore, a battery management system (BMS) should use specific algorithms to measure and estimate the status of the battery. Most importantly, the BMS of a grid-connected energy storage system (ESS) must ensure that the lithium-ion battery does not catch fire or explode due to an internal short from uncontrolled dendrite growth. In other words, the BMS of a lithium-ion battery pack should be capable of detecting the battery's status based on the electrochemical reaction continuously until the end of the battery's lifespan. In this paper, we propose a new protection algorithm for a dendritic lithium battery. The proposed algorithm has applied a parameter from battery pack aging results and has control power managing.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.31-37
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• 2019

The inter-development of FMECA is very important to assess the effect of potential failures during system operation on mission, safety and performance. Among these, criticality analysis is a core task that identifies items with high risk and selects the analyzed objects as the key management targets and reflects their effects to the design optimization. In this paper, we analyze the theory related to criticality analysis following US military standard, and propose a method to quantify the failure effect probability for objective criticality analysis. The criticality analysis according to the US military standard depends on the subjective judgment of the failure probability. The methodology for quantifying the failure effect probability is presented by using the reliability theory and the Bayes theorem. The failure rate is calculated by applying the method to quantify failure effect probability.

• Proceedings of the KSR Conference
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• 2009.05b
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• pp.473-479
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• 2009

Dynamic analysis is necessary for the High-Speed Railway vehicle which aims to run on max 400km/h. Especially, dynamic simulation using CAE(Computer Aided Engineering) can help to reduce the time of development of the High-Speed Railway vehicles. Also, it helps to reduce prices and improve the quality such as safety, stability and ride. There are many dynamic software for a railway vehicle, such as Vampire and ADAMS-Rail. There are limitations for each software and difficulties to analyze overall dynamics for entire railway system. To overcome these limitations, in this study, a program which can simulate entire railway vehicles was developed. This program is easy to use because it was developed using C++, which is object-oriented programming language. In addition, the basic platform for the development of dynamic solver is prepared using the nodal, modal coordinate system with a wheel-rail contact module. Rigid, flexible and large deformable body systems can be modeled by a user according to the characteristic of a desired system. Its reliability is verified by comparison with a commercial analysis program.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.7
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• pp.21-27
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• 2022

This study was conducted to evaluate the structural stability of a CNC automatic lathe structure and avoid resonance. The analysis conditions were analyzed by applying the weight of the upper assembly. From the structural analysis, the stress and deformation were low, and the safety factor was high. From the dynamic characteristic analysis, it was determined that resonance does not occur because the natural frequency is outside the driving range. The error between the dynamic characteristic analysis and vibration test results is very low; thus, the reliability of the analysis results can be secured.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.577-589
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• 2023

Ministry of National Defense plans to harness AI as a key technology to bolster overall defense capability for cultivation of an advanced strong military based on science and technology based on Defense Innovation 4.0 Plan. However, security threats due to the characteristics of AI can be a real threat to AI-based defense information system. In order to solve them, systematic security activities must be carried out from the development stage. This paper proposes security activities and considerations that must be carried out at each stage of AI-based defense information system. Through this, It is expected to contribute to preventing security threats caused by the application of AI technology to the defense field and securing the safety and reliability of defense information system.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.141-146
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• 2024

Station blackout (SBO) risk is one of the most significant contributors to nuclear power plant risk. In this paper, the sequence probability formulas derived by the convolution approach are compared with those derived by the conventional event tree/fault tree (ET/FT) approach for the SBO situation in which emergency diesel generators fail to start. The comparison identifies what makes the ET/FT approach more conservative and raises the issue regarding the mission time of a turbine-driven auxiliary feedwater pump (TDP), which suggests a possible modeling improvement in the ET/FT approach. Monte Carlo simulations with up-to-date component reliability data validate the convolution approach. The sequence probability of an alternative alternating current diesel generator (AAC DG) failing to start and the TDP failing to operate owing to battery depletion contributes most to the SBO risk. The probability overestimation of the scenario in which the AAC DG fails to run and the TDP fails to operate owing to battery depletion contributes most to the SBO risk overestimation determined by the ET/FT approach. The modification of the TDP mission time renders the sequence probabilities determined by the ET/FT approach more consistent with those determined by the convolution approach.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2003.11a
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• pp.539-543
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• 2003

The objective of this to improve the reliability of the safety evaluation code for Wolsong Tritium Removal Facility(WTRF) which is on the development for environmental assessment. To achieve this, tritium concentrations calculated in the Wolsong Units of this study are compared with that of the existing reference. As the result, the tritium concentration in each Wolsong nuclear power plant unit just before operating WTRF is 60.9Ci/kg, 36.3Ci/kg, 30.0Ci/kg, 26.5Ci/kg under the assumption that the WTRF begins operation in 2005, respectively. This result is almost same with that of the existing reference. But the reducing rate of tritium concentration in the moderator is faster than that of the reference result Finally it is expected to drop below 10Ci/kg after WTRF operation. And this result is also similar with that of the existing reference.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.11-15
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• 2009

In this study, we have developed the SMPS(Switched-mode power supply) power module for the medical kit. It is used the medical kit for improved supplying power better than the existing power module in performance, safety and reliability. The developed SMPS(Switched-mode power supply) is composed of the three fundamental electronic circuits, first one is for converting AC power to DC power, second one is for converting to high frequency, and the other is for absorbing noise frequency and preventing malfunction. It is possible for developed SMPS module to enlarge applications for PC, home appliances, switchboard as well as medical instruments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.52-61
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• 2000

The role of quantitative nondestructive evaluation of defects is becoming more important to assure the reliability and the safety of structure, which can eventually be used for residual life evaluation of structure on the basis of fracture mechanics approach. Although ultrasonic technique is one of the most widely used techniques for application of practical field test among the various nondestructive evaluation technique, there are still some problems to be solved in effective extraction and classification of ultrasonic signal from their noisy ultrasonic waveforms. Therefore, crack size determination through a neural network based on the back-propagation algorithm using back-scattered ultrasonic signals is established in this study. For this purpose, aluminum plate containing vertical or inclined surface breaking crack with different crack length was used to receive the back-scattered ultrasonic signals by pulse echo method. Some features extracted from these signals and sizes of cracks were used to train neural network and the neural network's output of the crack size are compared with the true answer.