• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.2
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• pp.227-242
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• 2019

This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.

• 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 the Korea Society of Computer and Information
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• v.25 no.12
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• pp.261-270
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• 2020

In this study, we identify promising technologies for Piggyback transportation system by analyzing the relevant patent information. In order for this, we first develop the patent database by extracting relevant technology keywords from the pioneering research papers for the Piggyback flactcar system. We then employed textmining to identify the frequently referred words from the patent database, and using these words, we applied the LDA (Latent Dirichlet Allocation) algorithm in order to identify "topics" that are corresponding to "key" technologies for the Piggyback system. Finally, we employ the ARIMA model to forecast the trends of these "key" technologies for technology forecasting, and identify the promising technologies for the Piggyback system. with keyword search method the patent analysis. The results show that data-driven integrated management system, operation planning system and special cargo (especially fluid and gas) handling/storage technologies are identified to be the "key" promising technolgies for the future of the Piggyback system, and data reception/analysis techniques must be developed in order to improve the system performance. The proposed procedure and analysis method provides useful insights to develop the R&D strategy and the technology roadmap for the Piggyback system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.58-63
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• 2022

The daily number of confirmed cases of Coronavirus disease 2019(COVID-19) ranges between 1,000 and 2,000. Despite higher vaccination rates, the number of confirmed cases continues to increase. The Mu variant of COVID-19 reported in some countries by WHO has been identified in Korea. In this study, we predicted the number of confirmed COVID-19 cases in Korea using the SARIMA for the Covid-19 prevention strategy. Trends and seasonality were observed in the data, and the ADF Test and KPSS Test was used accordingly. Order determination of the SARIMA(p,d,q)(P, D, Q, S) model helped in extracting the values of p, d, q, P, D, and Q parameters. After deducing the p and q parameters using ACF and PACF, the data were transformed and schematized into stationary forms through difference, log transformation, and seasonality removal. If seasonality appears, first determine S, then SARIMA P, D, Q, and finally determine ARIMA p, d, q using ACF and PACF for the order excluding seasonality.

• Smart Media Journal
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• v.13 no.6
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• pp.54-61
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• 2024

Recently, due to the increase in domestic and international online e-commerce platforms and the increase in container traffic at domestic ports, the operating ratio of large trucks has increased, and the number of truck fires is continuously increasing. In particular, spontaneous combustion is the most common cause of truck fires. Various academic approaches have been attempted to prevent truck fires, but due to the lack of research on the spontaneous tire ignition phenomenon that occurs during braking, this research directly designed and manufactured an experimental device to establish an environment similar to the braking system of a truck. A non-contact temperature sensor was installed on the brake device of the experimental device to collect temperature data generated from the brake device. Based on the data collected from the temperature sensor of the brake device and the temperature sensor on the tire surface, the ARIMA model among the time series prediction models was used to Appropriate parameters were selected to suit the temperature change trend, and as a result of comparing and analyzing the measured and predicted data, an accuracy of over 90% was obtained. Based on this, a plan was proposed to reduce the rate of fires in trucks by providing real-time warnings and support for truck drivers to respond to overheating phenomena occurring in the braking system.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.936-949
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• 2023

On August 3, 2023, a brutal incident of unprovoked violence, termed as "Abnormal motivated crime," occurred in a multi-use facility, where retail and transportation facilities converge, near Seohyeon Station. The assailant drove onto the sidewalk, hitting pedestrians, and then entered a department store where a knife rampage ensued, resulting in a total of 14 victims. In the aftermath of this incident, numerous murder threats were posted on social media, causing widespread anxiety among the public. This fear was further exacerbated by the emergence of a "Terrorless.01ab.net" service. Purpose: This research aims to explore necessary institutional improvements for private security personnel who protect customers and employees in multi-use facilities, to enable them to perform their duties more effectively. Method: To assess the risk of Abnormal motivated crime, a time series analysis using the ARIMA model was conducted to analyze the domestic trends of such crimes. Additionally, Result: the study presents suggestions for improvements in the domestic security service law and emergency manuals for multi-use facilities. Conclusion: This is informed by a legal analysis of the indemnity rights for weapon use by private security guards abroad and their operational authority beyond weapon usage.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.326-326
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• 2021

장기간에 걸쳐 넓은 지역에 대해 발생하는 가뭄을 예측하기위해 많은 학자들의 기술적, 학술적 시도가 있어왔다. 본 연구에서는 복잡한 시계열을 가진 가뭄을 전망하는 방법 중 시나리오에 기반을 둔 가뭄전망 방법과 실시간으로 가뭄을 예측하는 비시나리오 기반의 방법 등을 이용하여 미래 가뭄전망을 실시했다. 시나리오에 기반을 둔 가뭄전망 방법으로는, 3개월 GCM(General Circulation Model) 예측 결과를 바탕으로 2009년도 PDSI(Palmer Drought Severity Index) 가뭄지수를 산정하여 가뭄심도에 대한 단기예측을 실시하였다. 또, 통계학적 방법과 물리적 모델(Physical model)에 기반을 둔 확정론적 수치해석 방법을 이용하여 비시나리오 기반 가뭄을 예측했다. 기존 가뭄을 통계학적 방법으로 예측하기 위해서 시도된 대표적인 방법으로 ARIMA(Autoregressive Integrated Moving Average) 모델의 예측에 대한 한계를 극복하기위해 서포트 벡터 회귀(support vector regression, SVR)와 웨이블릿(wavelet neural network) 신경망을 이용해 SPI를 측정하였다. 최적모델구조는 RMSE(root mean square error), MAE(mean absolute error) 및 R(correlation Coefficient)를 통해 선정하였고, 1-6개월의 선행예보 시간을 갖고 가뭄을 전망하였다. 그리고 SPI를 이용하여, 마코프 연쇄(Markov chain) 및 대수선형모델(log-linear model)을 적용하여 SPI기반 가뭄예측의 정확도를 검증하였으며, 터키의 아나톨리아(Anatolia) 지역을 대상으로 뉴로퍼지모델(Neuro-Fuzzy)을 적용하여 1964-2006년 기간의 월평균 강수량과 SPI를 바탕으로 가뭄을 예측하였다. 가뭄 빈도와 패턴이 불규칙적으로 변하며 지역별 강수량의 양극화가 심화됨에 따라 가뭄예측의 정확도를 높여야 하는 요구가 커지고 있다. 본 연구에서는 복잡하고 비선형성으로 이루어진 가뭄 패턴을 기상학적 가뭄의 정도를 나타내는 표준강수증발지수(SPEI, Standardized Precipitation Evapotranspiration Index)인 월SPEI와 일SPEI를 기계학습모델에 적용하여 예측개선 모형을 개발하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.1323-1326
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• 2001

서브네트워크에서 실시간으로 통신 트래픽을 감시하고, 트래픽 정보를 바탕으로 시계열 분석을 이용해 트래픽의 변화추이를 예측할 수 있는 시스템을 설계 및 구현한다. SNMP를 이용한 MIB-II 정보를 바탕으로 하는 분석 방법은 누적 데이터를 기본으로 하는 관리 방법으로 이상 징후의 판단이 실시간 감시에는 적합하지 않은 점이 있다. 따라서, 본 논문에서는 실시간 트래픽 감시를 위해 서브네트워크에 들어오거나 나가는 트래픽의 양을 측정하여 분석하고, 이 정보를 바탕으로 특정 시점 이후의 트래픽 추이를 시계열 분석 방법을 이용하여 미래의 트래픽 양을 예측하는 알고리즘을 시스템으로 구현한다. 예측 알고리즘으로는 AR, MA, ARMA, ARIMA 모델중에 평균 제곱 오차를 최소로 가지는 알고리즘을 선택하여 예측하도록 설계한다. 개발되는 시스템을 망 관리자가 전체 통신 네트워크의 부하 상태를 예상할 수 있게 하여 신속하고 예방적인 대응을 할 수 있다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.2
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• pp.77-83
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• 2002

본 연구에서는 가솔린 시계열 예측전문가들이 수요를 예측하고, 더 나아가 직감적으로 행하고 있는 보정과정을 자동화하기 위해 신경망을 사용한다. 가솔린 수요 예측분야에서 보정을 위해 사용되는 전형적인 판단요소는 정부 에너지 절약 정책, 에너지 산업의 파업, 공휴일 등이 있다. 주요 추세가 순환신경망에 의해 예측되고 이들 판단요소의 효과가 다층신경망에 의해 탐지되어 보정된다. 가솔린 수요에 대한 실험결과는 보정과정을 갖는 예측구조가 하나의 신경망을 사용하는 예측구조 보다 더 나은 예측력을 보였다. 그리고 본 연구에서 제시한 접근방법이 순환신경망이나 ARIMA 모델을 사용하는 것보다 더 나은 결과를 가졌다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.383-388
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• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.