• Journal of Internet Computing and Services
• /
• v.21 no.3
• /
• pp.83-92
• /
• 2020

Recently, many companies and organizations are interested in predictive process monitoring for the efficient operation of business process models. Traditional process monitoring focused on the elapsed execution state of a particular process instance. On the other hand, predictive process monitoring focuses on predicting the future execution status of a particular process instance. In this paper, we implement the function of the business process remaining time prediction, which is one of the predictive process monitoring functions. In order to effectively model the remaining time, normalization by activity is proposed and applied to the predictive model by taking into account the difference in the distribution of time feature values according to the properties of each activity. In order to demonstrate the superiority of the predictive performance of the proposed model in this paper, it is compared with previous studies through event log data of actual companies provided by 4TU.Centre for Research Data.

• 한국지역지리학회:학술대회
• /
• 2009.08a
• /
• pp.108-115
• /
• 2009

An increase in oil and gas plants caused by development of process industry have brought into the increase in use of flammable and toxic materials in the complex process under high temperature and pressure. There is always possibility of fire and explosion of dangerous chemicals, which exist as raw materials, intermediates, and finished goods whether used or stored in the industrial plants. Since there is the need of efforts on disaster damage reduction or mitigation process, we have been conducting a research to relate explosion model on the background of real 3D terrain model. By predicting the extent of damage caused by recent disasters, we will be able to improve efficiency of recovery and, sure, to take preventive measure and emergency counterplan in response to unprepared disaster. For disaster damage prediction, it is general to conduct quantitative risk assessment, using engineering model for environmentaldescription of the target area. There are different engineering models, according to type of disaster, to be used for industry disaster such as UVCE (Unconfined Vapor Cloud Explosion), BLEVE (Boiling Liquid Evaporation Vapor Explosion), Fireball and so on, among them.we estimate explosion damage through UVCE model which is used in the event of explosion of high frequency and severe damage. When flammable gas in a tank is released to the air, firing it brings about explosion, then we can assess the effect of explosion. As 3D terrain information data is utilized to predict and estimate the extent of damage for each human and material. 3D terrain data with synthetic environment (SEDRIS) gives us more accurate damage prediction for industrial disaster and this research will show appropriate prediction results.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.3-5
• /
• 2008

An increase in oil and gas plants caused by development of process industry have brought into the increase in use of flammable and toxic materials in the complex process under high temperature and pressure. There is always possibility of fire and explosion of dangerous chemicals, which exist as raw materials, intermediates, and finished goods whether used or stored in the industrial plants. Since there is the need of efforts on disaster damage reduction or mitigation process, we have been conducting a research to relate explosion model on the background of real 3D terrain model. By predicting the extent of damage caused by recent disasters, we will be able to improve efficiency of recovery and, sure, to take preventive measure and emergency counterplan in response to unprepared disaster. For disaster damage prediction, it is general to conduct quantitative risk assessment, using engineering model for environmental description of the target area. There are different engineering models, according to type of disaster, to be used for industry disaster such as UVCE (Unconfined Vapour Cloud Explosion), BLEVE (Boiling Liquid Evaporation Vapour Explosion), Fireball and so on, among them, we estimate explosion damage through UVCE model which is used in the event of explosion of high frequency and severe damage. When flammable gas in a tank is released to the air, firing it brings about explosion, then we can assess the effect of explosion. As 3D terrain information data is utilized to predict and estimate the extent of damage for each human and material. 3D terrain data with synthetic environment (SEDRIS) gives us more accurate damage prediction for industrial disaster and this research will show appropriate prediction results.

• Korean Journal of Ecology and Environment
• /
• v.46 no.1
• /
• pp.1-9
• /
• 2013

Algal blooms in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. In addition to precipitating taste and odor problems, blooms damage the environment, and some classes like cyanobacteria (blue-green algae) release toxins that can threaten human health, even causing death. There is a recognized need in the water industry for models that can accurately forecast in real-time algal bloom events for planning and mitigation purposes. In this study, using data for an interconnected system of rivers and reservoirs operated by a New Jersey water utility, various ANN models, including both discrete prediction and classification models, were developed and tested for forecasting counts of three different algal classes for one-week and two-weeks ahead periods. Predictor model inputs included physical, meteorological, chemical, and biological variables, and two different temporal schemes for processing inputs relative to the prediction event were used. Despite relatively limited historical data, the discrete prediction ANN models generally performed well during validation, achieving relatively high correlation coefficients, and often predicting the formation and dissipation of high algae count periods. The ANN classification models also performed well, with average classification percentages averaging 94 percent accuracy. Despite relatively limited data events, this study demonstrates that with adequate data collection, both in terms of the number of historical events and availability of important predictor variables, ANNs can provide accurate real-time forecasts of algal population counts, as well as foster increased understanding of important cause and effect relationships, which can be used to both improve monitoring programs and forecasting efforts.

• Safety and Health at Work
• /
• v.3 no.2
• /
• pp.92-100
• /
• 2012

Objectives: The French National Occupational Diseases Surveillance and Prevention Network (RNV3P) is a French network of occupational disease specialists, which collects, in standardised coded reports, all cases where a physician of any specialty, referred a patient to a university occupational disease centre, to establish the relation between the disease observed and occupational exposures, independently of statutory considerations related to compensation. The objective is to compare the relevance of disproportionality measures, widely used in pharmacovigilance, for the detection of potentially new disease ${\times}$ exposure associations in RNV3P database (by analogy with the detection of potentially new health event ${\times}$ drug associations in the spontaneous reporting databases from pharmacovigilance). Methods: 2001-2009 data from RNV3P are used (81,132 observations leading to 11,627 disease ${\times}$ exposure associations). The structure of RNV3P database is compared with the ones of pharmacovigilance databases. Seven disproportionality metrics are tested and their results, notably in terms of ranking the disease ${\times}$ exposure associations, are compared. Results: RNV3P and pharmacovigilance databases showed similar structure. Frequentist methods (proportional reporting ratio [PRR], reporting odds ratio [ROR]) and a Bayesian one (known as BCPNN for "Bayesian Confidence Propagation Neural Network") show a rather similar behaviour on our data, conversely to other methods (as Poisson). Finally the PRR method was chosen, because more complex methods did not show a greater value with the RNV3P data. Accordingly, a procedure for detecting signals with PRR method, automatic triage for exclusion of associations already known, and then investigating these signals is suggested. Conclusion: This procedure may be seen as a first step of hypothesis generation before launching epidemiological and/or experimental studies.

• Journal of the Korean Geosynthetics Society
• /
• v.12 no.2
• /
• pp.67-74
• /
• 2013

In this study, the prediction of slope hazard probability was performed to the study area located in Hadae-ri, Woochun-myeon, Hoengsung-gun, Gangwon Province around Youngdong express way using the computer program SHAPP ver 1.0 developed by a decision tree model. The soil samples were collected at total 10 points, and soil tests were performed to measure soil properties. The thematic maps of soil properties such as coefficient of permeability and void ratio were made on the basis of soil test results. The slope angle analysis of topography was performed using a digital map. As the prediction result of slope hazard probability, 2,120 cells among total 27,776 cells were predicted to be in the event of slope hazards. Therefore, the predicted area of occurring slope hazards may be $53,000m^2$ because the analyzed cell size was $5m{\times}5m$.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.1 no.2
• /
• pp.81-89
• /
• 2008

Improved EKF suggests variable path prediction to reduce the event traffic caused by the information sharing among multi-users in networked virtual environment. The three dimensional virtual space is maintained consistently by endless status information exchange among dispersed users, and periodic status transmission brings traffic overhead in network. By using the error between the measured movement trace of dynamic information and the EKF predicted, we propose the method applied to predict the mobile packet of dynamic data which is simultaneously changing. And, the simulation results of DIS dead reckoning algorithms and EKF path prediction is compared here. It followed the specific path and while moving, the proposed method which it proposes predicting with DIS dead reckoning algorithm and to compare to the mobile path of the actual object and it got near it predicts the possibility of knowing it was.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.260-260
• /
• 2017

This study aims to compare the performance of TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability model) and TiVaSS (Time-variant Slope Stability model) in the prediction of rainfall-induced shallow landslides. TRIGRS employs one-dimensional (1-D) subsurface flow to simulate the infiltration rate, whereas a three-dimensional (3-D) model is utilized in TiVaSS. The former has been widely used in landslide modeling, while the latter was developed only recently. Both programs are used for the spatiotemporal prediction of shallow landslides caused by rainfall. The present study uses the July 2011 landslide event that occurred in Mt. Umyeon, Seoul, Korea, for validation. The performance of the two programs is evaluated by comparison with data of the actual landslides in both location and timing by using a landslide ratio for each factor of safety class ( index), which was developed for addressing point-like landslide locations. In addition, the influence of surface flow on landslide initiation is assessed. The results show that the shallow landslides predicted by the two models have characteristics that are highly consistent with those of the observed sliding sites, although the performance of TiVaSS is slightly better. Overland flow affects the buildup of the pressure head and reduces the slope stability, although this influence was not significant in this case. A slight increase in the predicted unstable area from 19.30% to 19.93% was recorded when the overland flow was considered. It is concluded that both models are suitable for application in the study area. However, although it is a well-established model requiring less input data and shorter run times, TRIGRS produces less accurate results.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.3
• /
• pp.1-12
• /
• 2022

In the event of a maritime distress accident, rapid search and rescue operations using rescue assets are very important to ensure the safety and life of drowning person's at sea. In this paper, we analyzed the surface layer current in the northwest sea area of Ulleungdo by applying machine learning such as multiple linear regression, decision tree, support vector machine, vector autoregression, and LSTM to the meteorological information collected from the maritime observation buoy. And we predicted the drowning person's route at sea based on the predicted current direction and speed information by constructing each prediction model. Comparing the various machine learning models applied in this paper through the performance evaluation measures of MAE and RMSE, the LSTM model is the best. In addition, LSTM model showed superior performance compared to the other models in the view of the difference distance between the actual and predicted movement point of drowning person.

• Journal of the Korean earth science society
• /
• v.45 no.1
• /
• pp.37-47
• /
• 2024

In the summer of 2018, the Korea-Japan (KJ) region experienced an extremely severe and prolonged heatwave. This study examines the GloSea6 model's prediction performance for the 2018 KJ heatwave event and investigates how its prediction skill is related to large-scale circulation patterns identified by the k-means clustering method. Cluster 1 pattern is characterized by a KJ high-pressure anomaly, Cluster 2 pattern is distinguished by an Eastern European high-pressure anomaly, and Cluster 3 pattern is associated with a Pacific-Japan pattern-like anomaly. By analyzing the spatial correlation coefficients between these three identified circulation patterns and GloSea6 predictions, we assessed the contribution of each circulation pattern to the heatwave lifecycle. Our results show that the Eastern European high-pressure pattern, in particular, plays a significant role in predicting the evolution of the development and peak phases of the 2018 KJ heatwave approximately two weeks in advance. Furthermore, this study suggests that an accurate representation of large-scale atmospheric circulations in upstream regions is a key factor in seasonal forecast models for improving the predictability of extreme weather events, such as the 2018 KJ heatwave.