• Journal of the military operations research society of Korea
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• v.37 no.1
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• pp.39-48
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• 2011

To predict the shelf-life of ammunition stockpiled in intermediate have practical meaning as a core value of combat support. This research is to Predict the shelf-life of ammunition by applying time series analysis based on report from ASRP of the 155mm, KD541 performed for 6 years. This study applied time series analysis using 'Mini-tab program' to measure the amount of stabilizer as time passes by is different from the other one that uses regression analysis. The average shelf-life of KD541 drawn by time series analysis was 43 years and the lowest shelf-life assessed on the 95% confidence level was 35 years.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• Journal of the Korean Society for Railway
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• v.9 no.6 s.37
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• pp.732-738
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• 2006

This research provides a scheme for Highly Accelerated Stress Test that is necessary to demonstrate reliability prediction of Korean Rapid Transit Railway Train Control System sub-equipment, which is calculated by a relevant standard for failure rate prediction of electronic products. Although determining failure information generated in the process of trial running by statistic analysis is widely accepted as a measure of confirmation for reliability prediction, this research suggests the modeling for System Life Test determined by accelerating stress factors as a measure of confirmation for reliability prediction of sub-equipment unit that is generated ahead of a trial running in System Life Cycle. Consequently, the research demonstrates sub-equipment unit reliability test, which is based on the model derived from Accelerated Stress Test, according to accuracy level and the number of samples, and conducts an official experiment by making out a reliability test procedure sheet based on test time as well.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.131-140
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• 2021

It is becoming more important to utilize reclaimed lands in South Korea, due to the increasing competition for its usage among different sectors. However, the high groundwater level and poor permeability are exposing them to deterioration by salinization. Salinization is difficult to predict because the pattern changes according to various characteristics of soil and groundwater. In this study, the capillary rising time was studied by the water content profile in the soil. The prediction equation of soil salinity was developed based on simulation result of the CHEMFLO model. to enable prediction considering various soil water content and groundwater level. The two terms constituting the equation showed the coefficients of determination of 0.9816 and 0.9824, respectively. Using the prediction equation of the study, the surface salinity can be easily predicted from the initial surface salinity and the salinity of the groundwater. In the future, more precise predictions will be possible with the results of studies on the hydraulic characteristics of various reclaimed soils, changes in water content profile by seasonal and climate events.

• Elastomers and Composites
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• v.49 no.2
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• pp.144-148
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• 2014

Compression set (CS) and weight loss by thermal degradation of the ACM rubber composite sample prepared for an automotive part were measured simultaneously at several given temperatures of $150^{\circ}C$, $160^{\circ}C$, $170^{\circ}C$, and $180^{\circ}C$. Using the relationship between them, thermal life of the sample could be predicted at a given operating temperature by applying Toops method which is based on the analysis of non-isothermal TGA thermograms. Conversion by weight loss showed a linear relationship with CS changes, exhibiting 4.2% at CS 40%. Activation energy of thermal degradation was calculated as 120.2 kJ/mol at 4.2% of weight loss from Flynn-Wall-Ozawa analysis. When the expected life was set as time to reach CS 40% at $120^{\circ}C$, the life time of the sample was calculated as 9,700 hrs when Toops method was applied.

• Journal of the Korean Society of Safety
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• v.31 no.1
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• pp.19-24
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• 2016

Rubber parts are widely used in many applications such as dampers, shock absorbers, and seals used in railway and automotive industries. Much research has thus far been conducted on property estimation and life prediction of rubber parts. To predict the service life of rubber parts at room temperature, most prior work adopts the well-known Arrhenius model that needs the accelerated life test in high-temperature conditions. However, they may not reflect the actual conditions of use that rubber parts are usually used under a specific strain condition during long period of time. In this context, we propose a method for the life prediction of rubber parts in actual conditions of use. The proposed method is based on the accelerated life test using stress relaxation during which three relatively high elongation percentages (100%, 200%, and 300%) are applied to the rubber specimens. Rubber specimens were prepared in accordance with KS M 6518 standard and three stress relaxation testers were fabricated for actual experiments. Finally, a inverse power model for life prediction was derived from experimental results. The predicted life was compared with the actual test life for validation.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.943-944
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• 2004

• Journal of Welding and Joining
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• v.23 no.4
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• pp.1-6
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.63-64
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• 2008