• 대한교통학회:학술대회논문집
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• 대한교통학회 1995년도 제27회 학술발표회
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• pp.84-98
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• 1995

Network-based model were developed to predict short term future traffic volume based on current traffic, historical average, and upstream traffic. It is presumed that upstream traffic volume can be used to predict the downstream traffic in a specific time period. Three models were developed for traffic flow prediction; a combination of historical average and upstream traffic, a combination of current traffic and upstream traffic, and a combination of all three variables. The three models were evaluated using regression analysis. The third model is found to provide the best prediction for the analyzed data. In order to balance the variables appropriately according to the present traffic condition, a heuristic adaptive weighting system is devised based on the relationships between the beginning period of prediction and the previous periods. The developed models were applied to 15-minute freeway data obtained by regular induction loop detectors. The prediction models were shown to be capable of producing reliable and accurate forecasts under congested traffic condition. The prediction systems perform better in the 15-minute range than in the ranges of 30-to 45-minute. It is also found that the combined models usually produce more consistent forecasts than the historical average.

• 한국군사과학기술학회지
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• 제10권2호
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• pp.12-20
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• 2007

The aiming ability is a key to improve the accuracy performance of the gun pointing system in the ground military vehicle. This paper describes the new detection method of chatter vibration using disturbance acceleration in the pointing structure. In order to analysis the vibration trends of the pointing system occurred while the vehicle driving, acceleration data obtained from vehicle was processed by using data processing algorithm with moving average and Hilbert transform. The specific mode constants of acceleration were obtained from various disturbances. Vehicle velocity, road condition and property of pointing structure were considered as factors which make the change of vibration trend in vehicle dynamics. Finally, back propagation neural networks have been applied to the pattern recognition of the classification of vibration signal in various driving conditions. Results of signal processing were compared with other condition result and analysed.

• Smart Structures and Systems
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• 제24권5호
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Nuclear Engineering and Technology
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• 제56권4호
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• pp.1284-1295
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• 2024

Due to increasing operational security demands, digital and intelligent condition monitoring of nuclear power plants is becoming more significant. However, establishing an accurate and effective anomaly detection model is still challenging. This is mainly because of data characteristics of nuclear power data, including the lack of clear class labels combined with frequent interference from outliers and anomalies. In this paper, we introduce a Transformer-based unsupervised model for anomaly detection of nuclear power data, a modified loss function based on the maximum correntropy criterion (MCC) is applied in the model training to improve the robustness. Experimental results on simulation datasets demonstrate that the proposed Trans-MCC model achieves equivalent or superior detection performance to the baseline models, and the use of the MCC loss function is proven can obviously alleviate the negative effect of outliers and anomalies in the training procedure, the F1 score is improved by up to 0.31 compared to Trans-MSE on a specific dataset. Further studies on genuine nuclear power data have verified the model's capability to detect anomalies at an earlier stage, which is significant to condition monitoring.

• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2008년도 추계학술논문발표회 논문집
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• pp.438-443
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• 2008

Fire model shall be verified and validated to reliably show the predictive capabilities for a specific use. In the process of model verification and validation, both the acceptable uses and limitation of fire model are established. In this study, the results of FDS simulation are compared with the data of PRISME experiment such as temperature, heat release rate, heat flux, product concentrations in the under-ventilated two-room condition. Furthermore, the sensitivity of FDS under ventilation condition changes are evaluated. FDS provide the reliable prediction for under-ventilated two-room fire scenario with slightly deviation.

• Journal of Advanced Marine Engineering and Technology
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• 제32권1호
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• pp.137-146
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• 2008

Nd:YAG pulse laser welding of stainless steel plate was simulated to find welding condition by using commercial finite element code MARC. Due to geometric symmetry, a half model of AISI 304 stainless steel plate was considered and user subroutines were applied to boundary condition for the heat transfer. Material properties such as conductivity, specific heat, mass density and latent heat were given as a function of temperature. As results, Three dimensional heat source model for pulse laser beam conditions of butt welding has been designed by the comparison between the finite element analysis results and experimental data on AISI 304 stainless steel plate. Nd:YAG laser welding for AISI 304 stainless steel was successfully simulated and it should be useful to determine optimal welding condition.

• 대한기계학회논문집A
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• 제31권8호
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• pp.839-846
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• 2007

Nd:YAG pulse laser fillet welding of stainless steel plate was simulated to find welding condition by using commercial finite element code MARC. Full model of AISI 304 stainless steel plate was considered and user subroutines were applied to boundary condition for the heat transfer. Material properties such as conductivity, specific heat, mass density and latent heat were given as a function of temperature. As results, Three dimensional heat source model for pulse laser beam conditions of fillet welding has been designed by the comparison between the finite element analysis results and experimental data on AISI 304 stainless steel plate. Nd:YAG laser welding for AISI 304 stainless steel was successfully simulated and it should be useful to determine optimal welding condition.

• 한국수자원학회논문집
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• 제53권3호
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• pp.215-223
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• 2020

본 연구의 목적은 국내 하천을 대상으로 데이터 마이닝의 분류 및 예측 기법을 활용하여 비유사량 추정 모델을 개발하는 것이다. 이를 위해 유사이송에 영향을 미치는 요소들을 전반적으로 고려하여 유역인자를 추출하였으며, 유역의 지형학적 요소, 강우, 토지 피복, 토지 이용, 하상 재료 등이 고려되었다. 추출된 인자를 활용하여 모델을 도출한 결과 유역 형태학적 특성인자 중 평균 면적비에서 유역고도 및 토지피복인자 중 도시화 비율과 전체 유역 중 습지와 수역의 비율이 조건인자로 활용되었다. 도출된 모델은 실측값과의 비교를 통해 실측 비유사량의 발생 패턴이 유사하게 재현됨을 확인하였다. 또한 기존의 사용되던 산정 공식과 비교하였으며, 국외의 데이터를 기반으로 도출된 모델은 개발 배경 및 국내 하천 환경과의 차이로 인해 국내 하천 데이터 적용에 한계가 있는 것으로 나타났다. 이에 본 연구에서는 개발 및 적용 환경, 데이터 범위의 차이 등으로 인해 발생하던 기존 공식의 한계를 개선하고자 하였다.

• 지역사회간호학회지
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• 제20권1호
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• pp.12-22
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• 2009

Purpose: This study was done to investigate factors associated with depression and quality of life (QoL) among the community-dwelling elderly. Methods: This study used a descriptive correlational research design. The subjects were 730 elders aged over 65 living in D district of Daegu. Data were collected using questionnaires for 30 days in April, 2007. The research instruments utilized in this study were a physical function scale of long-term care insurance system, Geriatric Depression Scale Short Form Korea Version (GDSSF-K), and Korean Quality of Life Scale (KoQoLs). The collected data were analyzed by descriptive statistics, t-test, ANOVA, Duncan, stepwise multiple regression, and Spearman correlation. Results: The mean age of the subjects was 72.6, and 68.8% and 57.9% of subjects were, respectively, female and living alone. 12.3% of variance in depression was explained by age, education, economic status, subjective health, alcohol consumption, condition of teeth, and fall experience. 18.2% of variance in QoL was explained by economic status, number of diseases, condition of teeth, incontinence, paralysis, and IADL. Economic status and condition of teeth were contributing factors to depression and QoL of the elderly. Conclusion: Findings of this study may be useful in understanding the health status of the community-dwelling elderly and developing more regionally specific health promotion strategies.

• Tribology and Lubricants
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• 제39권3호
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• pp.118-122
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• 2023

In our study, we develop a finite element model based on Archard's wear law to predict the cumulative wear and the evolution of the tool profile in friction stir welding (FSW) applications. Our model considers the rotational and translational behaviors of the tool, providing a comprehensive description of the wear process. We validate the accuracy of our model by comparing it against experimental results, examining both the predicted cumulative wear and the resulting changes to the tool profile caused by wear. We perform a detailed comparison between the predictions of the model and experimental data by manipulating non-dimensional coefficients comprising model parameters, such as element sizes and time increments. This comparison facilitates the identification of a specific non-dimensional coefficient condition that best replicates the experimentally observed cumulative wear. We also directly compare the worn tool profiles predicted by the model using this specific non-dimensional coefficient condition with the profiles obtained from wear experiments. Through this process, we identify the model settings that yield a tool wear profile closely aligning with the experimental results. Our research demonstrates that carefully selecting non-dimensional coefficients can significantly enhance the predictive accuracy of finite element models for tool wear in FSW processes. The results from our study hold potential implications for enhancing tool longevity and welding quality in industrial applications.