• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.167-167
• /
• 2020

Annual sediment deposition in reservoirs behind weirs poses flood risk, while its accurate prediction remains a challenge. Sediment management by hydraulic flushing is an effective method to maintain reservoir storage. In this study, an integrated approach to predict sediment inflow and sediment flushing simulation in reservoirs is presented. The annual sediment inflow prediction was carried out with Artificial Neural Networks (ANN) modelling. RESCON model was applied for quantification of sediment flushing feasibility criteria. The integrated approach was applied on Sangju Weir and also on estuary of Nakdong River (NREB). The mean annual sediment inflow predicted at Sangju Weir and NREB was 400,000 ㎥ and 170,000 ㎥, respectively. The sediment characteristics gathered were used to setup RESCON model and sediment balance ratio (SBR) and long term capacity ratio (LTCR) were used as flushing efficiency indicators. For Sangju Weir, the flushing discharge, Q_f = 140 ㎥/s with a drawdown of 5 m, and flushing duration, T_f = 10 days was necessary for efficient flushing. At NREB site, the parameters for efficient flushing were Q_f = 80 ㎥/s, T_f = 5 days, N = 1, El_f = 2.24 m. The hydraulic flushing was concluded feasible for sediment management at both Sangju Weir and NREB.

• Journal of Sensor Science and Technology
• /
• v.24 no.5
• /
• pp.306-310
• /
• 2015

IoT(Internet of Things) based underground risk assessment system surrounding water pipeline enables an advanced monitoring and prediction for unexpected underground hazards such as abrupt road-side subsidence and urban sinkholes due to a leak in water pipeline. For the development of successful assessment technology, the PSU(Water Pipeline Safety Unit) which detects the leakage and movement of water pipes. Then, the IoT-based underground risk assessment system surrounding water pipeline will be proposed. The system consists of early detection tools for underground events and correspondence services, by analyzing leakage and movement data collected from PSU. These methods must be continuous and reliable, and cover certain block area ranging a few kilometers, for properly applying to regional water supply changes.

• Journal of Korean Institute of Industrial Engineers
• /
• v.16 no.2
• /
• pp.135-147
• /
• 1990

A nuclear power plant can be viewed as a large complex man-machine system where high system reliability is obtained by ensuring that sub-systems are designed to operate at a very high level of performance. The chance of severe accident involving at least partial core-melt is very low but once it happens the consequence is very catastrophic. The prediction of risk in low probability, high-risk incidents must be examined in the contest of general engineering knowledge and operational experience. Engineering knowledge forms part of the prior information that must be quantified and then updated by statistical evidence gathered from operational experience. Recently, Bayesian procedures have been used to estimate rate of accident and to predict future risks. The Bayesian procedure has advantages in that it efficiently incorporates experts opinions and, if properly applied, it adaptively updates the model parameters such as the rate or probability of accidents. But at the same time it has the disadvantages of computational complexity. The predictive distribution for the time to next incident can not always be expected to end up with a nice closed form even with conjugate priors. Thus we often encounter a numerical integration problem with high dimensions to obtain a predictive distribution, which is practically unsolvable for a model that involves many parameters. In order to circumvent this difficulty, we propose a method of approximation that essentially breaks down a problem involving many integrations into several repetitive steps so that each step involves only a small number of integrations.

• The Korean journal of helicobacter and upper gastrointestinal research
• /
• v.18 no.4
• /
• pp.225-230
• /
• 2018

Upper gastrointestinal (GI) bleeding (UGIB) is the most common GI emergency, and it is associated with significant morbidity and mortality. Early identification of low-risk patients suitable for outpatient management has the potential to reduce unnecessary costs, and prompt triage of high-risk patients could allow appropriate intervention and minimize morbidity and mortality. Several risk-scoring systems have been developed to predict the outcomes of UGIB. As each scoring system measures different primary outcome variables, appropriate risk scores must be implemented in clinical practice. The Glasgow-Blatchford score (GBS) should be used to predict the need for interventions such as blood transfusion or endoscopic or surgical treatment. Patients with GBS ${\leq}1$ have a low likelihood of adverse outcomes and can be considered for early discharge. The Rockall score was externally validated and is widely used for prediction of mortality. The recently developed AIMS65 score is easy to calculate and was proposed to predict in-hospital mortality. The Forrest classification is based on endoscopic findings and can be used to stratify patients into high- and low-risk categories in terms of rebleeding and thus is useful in predicting the need for endoscopic hemostasis. Early risk stratification is critical in the management of UGIB and may improve patient outcome and reduce unnecessary health care costs through standardization of care.

• Environmental Engineering Research
• /
• v.24 no.1
• /
• pp.150-158
• /
• 2019

Soil contaminated with heavy metals from artisanal gold mining in Anka Local Government Area in Northwestern Nigeria was investigated to evaluate the human health risk as a result of heavy metals. Measured concentration of heavy metals and exposure parameters were used to estimate human carcinogenic and non-carcinogenic risk. GIS-based Kriging method was utilized to create a prediction maps of human health risks and probability maps of heavy metals concentrations exceeding their threshold limits. Hazard index calculation showed that 21 out of 23 locations are posing non-cancer risk for children. Adults and children are at high cancer risk in all locations as the total cancer risk exceeded $1{\times}10^{-6}$ (the lower limit CTR value). Kriging model showed that only a very small area in Anka has a hazard index of less than unity and cumulative target risk of less than $1{\times}10^{-4}$, indicating a significant carcinogenic and non-carcinogenic risks for children. The probability of heavy metals to exceed their threshold concentrations around the study area was also found to be high.

• Smart Structures and Systems
• /
• v.33 no.5
• /
• pp.365-374
• /
• 2024

This paper adopts a new approach in which nonlinear vibrations can be controlled using fuzzy controllers by optimal grey evolutionary algorithm. If the fuzzy controller cannot stabilize the systems, then the high frequency is injected into the system to assist the controller, and the system is asymptotically stabilized by adjusting the parameters. This paper uses the GM (grey model) and the neural network prediction model. The structure of the neural network is improved from a single factor, and multiple data inputs are extended to various factors and numerous data inputs. The improved model expands the applicable range of uncontrolled elements and improves the accuracy of controlled prediction, using the model that has been trained and stabilized by multiple learning. The simulation results show that the improved gray neural network model has higher prediction accuracy and reliability than the traditional GM model, improving controlled management and pre-control ability. In the combined prediction, the time series parameters and the predicted values obtained from the GM (1,1) (Grey Model of first order and one variable) are simultaneously used as the input terms of the neural network, considering the influence of the non-equal spacing of the data, which makes the results of the combined gray neural network model more rationalized. By adjusting the model structure and system parameters to simulate and analyze the controlled elements, the corresponding risk change trend graphs and prediction numerical calculation results are obtained, which also realize the effective prediction of controlled elements. According to the controlled warning principle and objective, the fuzzy evaluation method establishes the corresponding early warning response method. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.781-791
• /
• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Journal of the Korea Society for Simulation
• /
• v.19 no.3
• /
• pp.127-135
• /
• 2010

Modern marine navigation requires officers on the bridge to monitor a torrent of data on both the insides and outsides of the ship from numerous useful devices. But despite these tools, navigators can still find it difficult to make a safe decision for two reasons: one is that too much data if provided too quickly tends to cause fatigue and overwhelm the officer, and the other is that any inconsistency across data from several different types of devices can lead to confusion. Indeed, the fact remains that the many marine accidents can be attributed to human error, and hence there is a strong need for decision-support tools for marine navigation. One technique of providing decision support is through the use of simulation to evaluate or predict system dynamics over time using an accurate model. This paper, as a simulation method for risk prediction for a navigation safety information system of ship, suggests a navigation prediction simulation system using various knowledge bases and discrete event simulation methodology, and supports the validity of the system through the examples of components in a restricted navigation situation scenario.

• KEPCO Journal on Electric Power and Energy
• /
• v.7 no.1
• /
• pp.171-177
• /
• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• Asia pacific journal of information systems
• /
• v.30 no.1
• /
• pp.31-52
• /
• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.