• Journal of the Korean Society for Railway
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• v.15 no.5
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• pp.423-428
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• 2012

As the speed of high speed train increases, the prediction of ride comfort becomes important. The exciting frequencies due to rail irregularity in high-speed train closes to the second and third natural frequencies of the carbody. The dynamic characteristics of railway vehicles should be checked by modal analysis numerically and experimentally. In this study the bending test for railway vehicle is reviewed and the impact test is suggested to find the natural frequencies and the mode shapes of the carbody. The validity of the impact test is checked with the test for a sample plate which reflects the aspect ratio of the original carbody. The bending test by the impact and the displacement methods of JIS E7105 for a prototype carbody were done in the field and compared. The results show that the impact test can find more accurate natural frequencies and the mode shapes of the carbody than those of the displacement method.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.23-29
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• 2015

Energy constraints of wireless sensor networks are an important challenge. Data Transmission requires energy. Distance between origin and destination has an important role in energy consumption. In addition, the location of base station has a large impact on energy consumption and a specific method not proposed for it. In addition, a obtain model for location of base station proposed. Also a model for distributed clustering is presented by cluster heads. Eventually, a combination of discussed ideas is proposed to improve the energy consumption. The proposed ideas have been implemented over the LEACH-C protocol. Evaluation results show that the proposed methods have a better performance in energy consumption and lifetime of the network in comparison with similar methods.

• Computational Structural Engineering
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• v.7 no.4
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• pp.137-144
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• 1994

As for the safety evaluation of existing large-scale structures, methods for the estimation of structural and dynamic properties are studied. Sequential prediction error method in time domain and frequency response function estimators in frequency domain are examined. For this purpose, impact tests are performed on a steel frame structure with 2 bays and 3 floors. Results from both methods are found to be consistent to each others. However those from the finite-element analysis are slightly different from the experimental results. The discrepancies may be caused by the improper modeling of the complex behavior at the connection joints of the model structure.

• Journal of Distribution Science
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• v.12 no.6
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• pp.49-57
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• 2014

Purpose - The world now recognizes environmental disruption as a serious issue when regarding growth-oriented strategies; therefore, environmental preservation issues become pertinent. Consequently, green distribution is continuously emphasized. However, studying the prediction and association of distribution and the environment is insufficient. Most existing studies about green distribution are about its necessity, detailed operation methods, and political suggestions; it is necessary to study the distribution service industry and environmental service industry together, for green distribution. Research design, data, and methodology - ARIMA (auto-regressive moving average model) was used to predict the environmental service and distribution service industries, and the Granger Causality Test based on VAR (vector auto regressive) was used to analyze the causal relationship. This study used 48 quarters of time-series data, from the 4th quarter in 2001 to the 3rd quarter in 2013, about each business type's production index, and used an unchangeable index. The production index about the business type is classified into the current index and the unchangeable index. The unchangeable index divides the current index into deflators to remove fluctuation. Therefore, it is easy to analyze the actual production index. This study used the unchangeable index. Results - The production index of the distribution service industry and the production index of the environmental service industry consider the autocorrelation coefficient and partial autocorrelation coefficient; therefore, ARIMA(0,0,2)(0,1,1)4 and ARIMA(3,1,0)(0,1,1)4 were established as final prediction models, resulting in the gradual improvement in every production index of both types of business. Regarding the distribution service industry's production index, it is predicted that the 4th quarter in 2014 is 114.35, and the 4th quarter in 2015 is 123.48. Moreover, regarding the environmental service industry's production index, it is predicted that the 4th quarter in 2014 is 110.95, and the 4th quarter in 2015 is 111.67. In a causal relationship analysis, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. Conclusions - This study predicted the distribution service industry and environmental service industry with the ARIMA model, and examined the causal relationship between them through the Granger causality test based on the VAR Model. Prediction reveals the seasonality and gradual increase in the two industries. Moreover, the environmental service industry impacts the distribution service industry, but the distribution service industry does not impact the environmental service industry. This study contributed academically by offering base line data needed in the establishment of a future style of management and policy directions for the two industries through the prediction of the distribution service industry and the environmental service industry, and tested a causal relationship between them, which is insufficient in existing studies. The limitations of this study are that deeper considerations of advanced studies are deficient, and the effect of causality between the two types of industries on the actual industry was not established.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1053-1065
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• 2018

Objective: To evaluate the clinical impact of using registration software for ablative margin assessment on pre-radiofrequency ablation (RFA) magnetic resonance imaging (MRI) and post-RFA computed tomography (CT) compared with the conventional side-by-side MR-CT visual comparison. Materials and Methods: In this Institutional Review Board-approved prospective study, 68 patients with 88 hepatocellulcar carcinomas (HCCs) who had undergone pre-RFA MRI were enrolled. Informed consent was obtained from all patients. Pre-RFA MRI and post-RFA CT images were analyzed to evaluate the presence of a sufficient safety margin (${\geq}3mm$) in two separate sessions using either side-by-side visual comparison or non-rigid registration software. Patients with an insufficient ablative margin on either one or both methods underwent additional treatment depending on the technical feasibility and patient's condition. Then, ablative margins were re-assessed using both methods. Local tumor progression (LTP) rates were compared between the sufficient and insufficient margin groups in each method. Results: The two methods showed 14.8% (13/88) discordance in estimating sufficient ablative margins. On registration software-assisted inspection, patients with insufficient ablative margins showed a significantly higher 5-year LTP rate than those with sufficient ablative margins (66.7% vs. 27.0%, p = 0.004). However, classification by visual inspection alone did not reveal a significant difference in 5-year LTP between the two groups (28.6% vs. 30.5%, p = 0.79). Conclusion: Registration software provided better ablative margin assessment than did visual inspection in patients with HCCs who had undergone pre-RFA MRI and post-RFA CT for prediction of LTP after RFA and may provide more precise risk stratification of those who are treated with RFA.

• International Journal of Highway Engineering
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• v.16 no.3
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• pp.35-41
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• 2014

PURPOSES : The dynamic modulus for a specimen can be determined by using either the non-destructed or destructed testing method. The Impact Resonance Testing (IRT) is the one of the non-destructed testing methods. The MTS has proved the source credibility and has the disadvantages which indicate the expensive equipment to operate and need a lot of manpower to manufacture the specimens because of the low repeatability with an experiment. To overcome these shortcomings from MTS, the objective of this paper is to compare the dynamic modulus obtained from IRT with MTS result and prove the source credibility. METHODS : The dynamic modulus obtained from IRT could be determined by using the Resonance Frequency (RF) from the Frequency Response Function (FRF) that derived from the Fourier Transform based on the Frequency Analysis of the Digital Signal Processing (DSP)(S. O. Oyadigi; 1985). The RF values are verified from the Coherence Function (CF). To estimate the error, the Root Mean Squared Error (RMSE) method could be used. RESULTS : The dynamic modulus data obtained from IRT have the maximum error of 8%, and RMSE of 2,000MPa compared to the dynamic modulus measured by the Dynamic Modulus Testing (DMT) of MTS testing machine. CONCLUSIONS : The IRT testing method needs the prediction model of the dynamic modulus for a Linear Visco-Elastic (LVE) specimen to improve the suitability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.110-122
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• 2020

Many companies make considerable efforts in implementing supply chain management systems to increase their competitiveness. In particular, there are significant investments in their ICT (Information and Communication Technology) and supply chain. However, considerable diversity exists in how well firms have been able to assimilate ICT and leverage the business value of it. In addition, studies on supply chains, particularly Real Time Enterprise (RTE) competence, which is cited for rapid information sharing and dynamic capabilities for each role in the supply chain, are insufficient. The purpose of this paper is to explore and evaluate effect of ICT utilization capabilities, including 'ICT internal cooperative capabilities' and 'ICT operational flexibility capabilities', on RTE capabilities (flexibility, visibility and prediction) and supply chain performance. To validate the research model proposed in the study, survey was conducted on companies using ICT along the supply chain of manufacturing industries. 216 data were used, SPSS and AMOS were used for the analysis methods. Study results showed that ICT internal collaboration capabilities affect agility, one of RTE capabilities, but not visibility and prediction, and ICT operational flexibility capabilities have affected all three RTE capacities. And, RTE capabilities had a huge impact on supply chain performance as expected. In this paper, it has been found that the ICT capabilities in manufacturing are an important factor in improving RTE capabilities that are important in the supply chain and improving the performance of the supply chain.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.3940-3955
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• 2023

A new method of numerical simulating prediction and decontamination effect evaluation for abrasive jet decontamination to radioactively contaminated metal is proposed. Based on the Computational Fluid Dynamics and Discrete Element Model (CFD-DEM) coupled simulation model, the motion patterns and distribution of abrasives can be predicted, and the decontamination effect can be evaluated by image processing and recognition technology. The impact of three key parameters (impact distance, inlet pressure, abrasive mass flow rate) on the decontamination effect is revealed. Moreover, here are experiments of reliability verification to decontamination effect and numerical simulation methods that has been conducted. The results show that: ⁶⁰Co and other homogeneous solid solution radioactive pollutants can be removed by abrasive jet, and the average removal rate of Co exceeds 80%. It is reliable for the proposed numerical simulation and evaluation method because of the well goodness of fit between predicted value and actual values: The predicted values and actual values of the abrasive distribution diameter are Ф57 and Ф55; the total coverage rate is 26.42% and 23.50%; the average impact velocity is 81.73 m/s and 78.00 m/s. Further analysis shows that the impact distance has a significant impact on the distribution of abrasive particles on the target surface, the coverage rate of the core area increases at first, and then decreases with the increase of the impact distance of the nozzle, which reach a maximum of 14.44% at 300 mm. It is recommended to set the impact distance around 300 mm, because at this time the core area coverage of the abrasive is the largest and the impact velocity is stable at the highest speed of 81.94 m/s. The impact of the nozzle inlet pressure on the decontamination effect mainly affects the impact kinetic energy of the abrasive and has little impact on the distribution. The greater the inlet pressure, the greater the impact kinetic energy, and the stronger the decontamination ability of the abrasive. But in return, the energy consumption is higher, too. For the decontamination of radioactively contaminated metals, it is recommended to set the inlet pressure of the nozzle at around 0.6 MPa. Because most of the Co elements can be removed under this pressure. Increasing the mass and flow of abrasives appropriately can enhance the decontamination effectiveness. The total mass of abrasives per unit decontamination area is suggested to be 50 g because the core area coverage rate of the abrasive is relatively large under this condition; and the nozzle wear extent is acceptable.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.