• Journal of Distribution Science
• /
• v.12 no.2
• /
• pp.59-71
• /
• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Asia pacific journal of information systems
• /
• v.28 no.4
• /
• pp.308-319
• /
• 2018

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

• Applied Chemistry for Engineering
• /
• v.29 no.6
• /
• pp.789-798
• /
• 2018

The role of empirical equation to predict the performance of polymer electrolyte membrane fuel cell is important. The activation, ohmic and mass transfer losses were separated in a polarization curve, and the curve fitting according to each region was performed using Kim's model and Hao's model. Changes of each loss were compared according to operation variables of the temperature, pressure, oxygen concentration and membrane thickness. The existing model showed a good fitting convergence, but less fitting accuracy in the separated loss region. A new model using the convergence coefficient was suggested to improve the accuracy of performance prediction of fuel cells of which results were demonstrated.

• Journal of Korean Society of Transportation
• /
• v.26 no.3
• /
• pp.169-178
• /
• 2008

Level of Service (LOS) is one of ways to evaluate operational conditions. It is very important factor in evaluation especially for the facility of highways. However, some studies proved that ${\upsilon}/c$ ratio and accident rate is appeared like a second function which has a U-form. It means there is a gap between LOS and safety of highway facilities. Therefore, this study presents a method for evaluation of a signalized intersection which is considered both smooth traffic operation (delay) and traffic safety (accident). Firstly, as a result of our research, accident rates and EPDO are decreased when it has a big delay. In that reason, it is necessary to make a new Level of Service included traffic safety. Secondly, this study has developed a negative binominal regression model which is based on the relation between accident patterns and stream. Thirdly, standards of LOS are presented which is originated from calculation between annual delay costs and annual accident cost at each intersection. Lastly, worksheet form is presented as an expression to an estimation step of a signalized intersection with traffic accident prediction model and new LOS.

• Journal of Korean Society on Water Environment
• /
• v.23 no.1
• /
• pp.38-45
• /
• 2007

A one-dimensional kinematic wave model was used to calculate temporal and spatial changes of the highway runoff. Infiltration into pavement was considered using Darcy's law, as a function of flow depth and pavement hydraulic conductivity ($K_p$). The model equation was calculated using the method of characteristics (MOC), which provided stable solutions for the model equation. 22 storm events monitored in a highway runoff monitoring site in west Los Angeles in the U.S. were used for the model calculation and evaluation. Using three different values of $K_p$ ($5{\times}10^{-6}$, $10^{-5}$, and $2{\times}10^{-5}cm/sec$), total runoff volume and peak flow rate were calculated and then compared with the measured data for each storm event. According to the calculation results, $10^{-5}cm/sec$ was considered a site representative value of $K_p$. The study suggested a one-dimensional method to predict hydrodynamic behavior of highway runoff, which is required for the water quality prediction.

• Nuclear Engineering and Technology
• /
• v.55 no.3
• /
• pp.958-967
• /
• 2023

This work studies the defect features in a dilute FeMnNi alloy by an Object Kinetic Monte Carlo (OKMC) model based on the "grey-alloy" method. The dose rate effect is studied at 573 K in a wide range of dose rates from 10^-8 to 10^-4 displacement per atom (dpa)/s and demonstrates that the density of defect clusters rises while the average size of defect clusters decreases with increasing dose rate. However, the dose-rate effect decreases with increasing irradiation dose. The model considered two realistic mechanisms for producing <100>-type self-interstitial atom (SIA) loops and gave reasonable production ratios compared with experimental results. Our simulation shows that the proportion of <100>-type SIA loops could change obviously with the dose rate, influencing hardening prediction for various dose rates irradiation. We also investigated ways to compensate for the dose rate effect. The simulation results verified that about a 100 K temperature shift at a high dose rate of 1×10^-4 dpa/s could produce similar irradiation microstructures to a lower dose rate of 1×10^-7 dpa/s irradiation, including matrix defects and deduced solute migration events. The work brings new insight into the OKMC modeling and the dose rate effect of the Fe-based alloys.

• 한국정보통신설비학회:학술대회논문집
• /
• 2008.08a
• /
• pp.233-236
• /
• 2008

There are several kinds of service standards for 3G($3^{rd}$-Generation) wireless communication as WCDMA, CDMA2000 and WiBro(Wireless Broadband Internet). Especially WiBro Wave2 system is a marked currnt issue. In this paper, we describe on the cell plan tool to desgin WiBro Wave2 network. For this, we treat from basic theory to practical substance to produce new(or modified) path loss prediction model for 2.3GHz. And we explain the method how to implement new technology MIMO(Multiple Input Multiple Output) deployed in Wave2 system. Also we emphasize on the importance of LOS(Line Of Sight) analysis in WiBro network design.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.509-512
• /
• 2022

수술 중 저혈압 예측은 환자의 안전과 직결되는 중요한 과제이다. 그러나 인간이 저혈압을 예측하는 것은 많은 경험과 노하우를 필요로 하며, 현재 연구되고 있는 예측 기술은 단일 정보를 활용하여 복합적인 원인을 반영하지 못하거나, 침습적으로 데이터를 획득하여 환자에게 불편함을 준다. 비침습적으로 수집한 데이터를 통한 저혈압 발생 예측에 대한 연구는 꾸준히 진행되어 왔으나, 기존 딥러닝을 이용한 접근방법으로는 정확도가 낮다. 본 논문에서는 그 원인을 1)데이터 전처리 2)데이터 불균형 3)기존 모델의 한계로 구분하고, 이를 해결 가능한 방안을 제시한다. 실험 결과 CNN*CNN에서 Focal Loss를 사용할 때, 가장 높은 성능을 내는 것을 확인했다.

• Journal of Korean Society of Transportation
• /
• v.23 no.7 s.85
• /
• pp.77-86
• /
• 2005

Level of service(LOS) is a quantify measure describing operational conditions within a traffic stream, generally, in terms of such service measures as speed, travel time, freedom to measures, traffic interruptions, comfort and convenience. The LOS is leveled by highway facilities according to measure of effectiveness(MOE) and then used to evaluate performance capacity. The current evaluation of a urban road is performed by only a aspect of traffic operation without any concepts of safety. Therefore, this paper presents a method for evaluation of risk order for urban road with new MOE, user cost analysis, considering both smooth traffic operation(congestion) and traffic safety(accident). The user coat is included traffic accident cast by traffic safety and traffic congestion cost by traffic operation. First of all, a number of traffic accident and accident rate by highway geometric is inferred from urban road traffic accident prediction model (Poul Greibe(2001)) Secondly, a user cost is inferred as traffic accident cast and traffic congestion cost is putting together. Thirdly, a method for evaluation of a urban road is inferred by user cost analysis. Fourthly a accident rate by segment predict with traffic accidents and data related to the accidents in $1996{\sim}1998$ on 11 urban road segments, Gwang-Ju, predicted accident rate. Traffic accident cost predict using predicted accident rate, and, traffic congestion cost predict using predicted average traffic speed(KHCM). Fifthly, a risk order are presented by predicted user cost at each segment in urban roads. Finally, it si compared and evaluated that LOS of 11 urban road segments, Gwang-Ju, by only a aspect of traffic operation without any concepts of safety and risk order by a method for evaluation of urban road in this paper.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.182-182
• /
• 2020

최근 지구 온난화나 기상이변으로 인해 세계각지에서 많은 자연재해가 발생하고 있고 우리나라도 최근 전국 각지에서 국지성호우에 의한 많은 피해가 발생하고 있다. 특히 국지성호우로 인해 발생하는 산간지역의 토석류는 많은 재산피해를 일으키고 있다. 최근 토석, 토사, 혹은 부유 잡목 등의 유출로 인한 피해를 막기 위해 많은 사방댐을 축조하고 있으나 표면침식에 의해서 유출되는 토석량 혹은 토사량을 정확히 예측하지 못한다면 축조된 사방댐은 금방 제구실을 못할 수 있거나 혹은 과대 설계 및 시공되어 건설비를 낭비할 수 있다. 따라서 최적의 사방댐 건설을 위해 정확한 토석량의 산정은 매우 중요한 전제조건이라 할 수 있다. 본 연구에서는 강원도 인제군 산간지역 4곳의 사방댐유역에 대해 토석량 예측모형 MSDPM(Multi-Sequence Debris Prediction Model)과 LADMP(Los Angeles District Method for Prediction of sediments yield)를 이용하여 산정한 토석량과 실제 준설량을 비교하였다. 이를 위해 강원 산간지역에 맞도록 예측모형을 보정하였으며 토석류 유발 강우강도(Threshold Maximum 1-hr Rainfall Intensity)와 토석류 유발 최소강우량(Total Minimum Rainfall Amount)개념을 도입하여 예측모형식을 적용하였다. 위 식이 갖고 있는 대표적 특징 중 하나인 산불계수를 사용해야 하지만 본 연구지역은 산불 피해규모가 미미하여 산불의 영향은 고려하지 않고 토석량을 산정하였다. 두 예측모형의 계산결과와 실제 준설량을 비교해본 결과, MSDPM의 결과가 LADMP의 결과보다 준설량과 더 일치하는 것으로 나타났다. 실제 준설량과 MSDPM의 계산결과는 평균 17.37%의 차이를 나타냈고 LADMP의 계산결과는 평균 41.87%의 차이를 나타냈다. 본 연구에서 사용된 토석량 예측 모형은 앞으로 많은 산지유역의 토석량 예측에 사용이 가능 할 것으로 판단된다. 하지만 본 연구에서 사용된 자료의 제한성 때문에 앞으로 많은 실측 준설자료를 통하여 예측모형식을 보정하는 작업이 우선되어야 할 것으로 판단된다. 이를 위해서 많은 산지유역의 토석량을 장시간 실측하여 데이터를 축적하고 이를 사용하여 다양한 토석량 예측모형을 검보정하는 노력이 필요할 것으로 판단된다.