• Economic and Environmental Geology
• /
• v.42 no.2
• /
• pp.143-152
• /
• 2009

Recent raw material prices fluctuation has been unexpectedly high and that made Korean economic activities to be depressed. Because most raw material supply in Korea depends upon oversea imports, unexpected raw material price fluctuation affects Korean industrial economies through macroeconomic variables. So Korean government enforces some political measures such as demand management and the supply-security assurance as long-range policies, and reservation and general early warning system as short-range policies. In short-range policies, it is necessary to be expected short term fluctuation. Up to recently, there have been many researches and most of those researches use parametric methods or time series analyses. Because those methods and analyses often generate inadequate relations among variables, it is possible that some consistent variables are left out or the results are misunderstood. This study, therefore, is aim to mitigate those methodological problems and find the relatively appropriate model for economic explanation. So that, in this paper, by using non-parametric signal approach method mitigating some shortages of previous researches and forecasting properly short-range prices fluctuation of non-ferrous materials are presented empirically.

• Journal of Korean Society on Water Environment
• /
• v.27 no.4
• /
• pp.533-541
• /
• 2011

Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.146-146
• /
• 2022

A flash flood is one of the most hazardous natural events caused by heavy rainfall in a short period of time in mountainous areas with steep slopes. Early warning of flash flood is vital to minimize damage, but challenges remain in the enhancing accuracy and reliability of flash flood forecasts. The forecasters can easily determine whether flash flood is occurred using the flash flood guidance (FFG) comparing to rainfall volume of the same duration. In terms of this, the hydrological model that can consider the basin characteristics in real time can increase the accuracy of flash flood forecasting. Also, the predicted radar rainfall has a strength for short-lead time can be useful for flash flood forecasting. Therefore, using both hydrological models and radar rainfall forecasts can improve the accuracy of flash flood forecasts. In this study, FFG was applied to simulate some flash flood events in the Taehwa river basin by using of SURR model to consider soil moisture, and applied to the flash flood forecasting using predicted radar rainfall. The hydrometeorological data are gathered from 2011 to 2021. Furthermore, radar rainfall is forecasted up to 6-hours has been used to forecast flash flood during heavy rain in August 2021, Wulsan area. The accuracy of the predicted rainfall is evaluated and the correlation between observed and predicted rainfall is analyzed for quantitative evaluation. The results show that with a short lead time (1-3hr) the result of forecast flash flood events was very close to collected information, but with a larger lead time big difference was observed. The results obtained from this study are expected to use for set up the emergency planning to prevent the damage of flash flood.

• Journal of the Institute of Convergence Signal Processing
• /
• v.25 no.2
• /
• pp.119-126
• /
• 2024

This study aims to evaluate the feasibility of an early warning system for college dropout by machine learning the main patterns that affect college student dropout and to suggest ways to implement a system that can actively prevent it. For this purpose, a performance comparison experiment was conducted using five types of machine learning-based algorithms using data from the Korean Educational Longitudinal Study, 2005, conducted by the Korea Educational Development Institute. As a result of the experiment, the identification accuracy rate of students with the intention to drop out was up to 94.0% when using Random Forest, and the recall rate of students with the intention of dropping out was up to 77.0% when using Logistic Regression. It was measured. Lastly, based on the highest prediction model, we will provide counseling and management to students who are likely to drop out, and in particular, we will apply factors showing high importance by characteristic to the counseling method model. This study seeks to implement a model using IT technology to solve the career problems faced by college students, as dropout causes great costs to universities and individuals.

• Journal of international Conference on Electrical Machines and Systems
• /
• v.3 no.4
• /
• pp.428-432
• /
• 2014

Rotating machines are an integral part of large electrical power machinery in most of the industries. Any degradation or outages in the rotating electric machinery can result in significant losses in productivity. It is critical to monitor the equipment for any degradation's so that it can serve as an early warning for adequate maintenance activities and repair. Prior research and field studies have indicated that the rotating machines have a particular type of signal structure during the initial start-up transient. A machine performance can be studied based on the effect of degradation in signal parameters. In this paper a data-acquisition system and the FFT algorithm has been design and model using the MATLAB and Simulink. The implementation had been carried out on the TMS320 DSP Processor and various testing and verification of the machine performance had been carried out. The results show good agreement with expected results for both simulated and real-time data. The real-time data from AC water pumps which have rotating motors built-in were collected and analysed. The FFT algorithm provides frequency response and based on this frequency response performance of the machine had been measured.The FFT algorithm provides only approximation about the machine performances.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.7
• /
• pp.3446-3464
• /
• 2017

Backoff mechanism serves as one of the key technologies in the MAC-layer of wireless mobile networks. The traditional Binary Exponential Backoff (BEB) mechanism in IEEE 802.11 Distributed Coordination Function (DCF) and other existing backoff mechanisms poses several performance issues. For instance, the Contention Window (CW) oscillations occur frequently; a low delay QoS guarantee cannot be provided for real-time transmission, and services with different priorities are not differentiated. For these problems, we present a novel Multi-Priority service differentiated and Adaptive Backoff (MPAB) algorithm over IEEE 802.11 DCF for wireless mobile networks in this paper. In this algorithm, the backoff stage is chosen adaptively according to the channel status and traffic priority, and the forwarding and receding transition probability between the adjacent backoff stages for different priority traffic can be controlled and adjusted for demands at any time. We further employ the 2-dimensional Markov chain model to analyze the algorithm, and derive the analytical expressions of the saturation throughput and average medium access delay. Both the accuracy of the expressions and the algorithm performance are verified through simulations. The results show that the performance of the MPAB algorithm can offer a higher throughput and lower delay than the BEB algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.6
• /
• pp.851-858
• /
• 2012

In this paper, we design a seismic data acquisition system(SDAS) and implement it. This system is essential for development of a noble local earthquake disaster preventing system in population center. In the system, we choose a proper MEMS-type triaxial accelerometer as a sensor, and FPGA and ARM processor are used for implementing the system. In the SDAS, each module is realized by Verilog HDL and C Language. We carry out the ModelSim simulation to verify the performances of important modules. The simulation results show that the FPGA-based data acquisition module can guarantee an accurate time-synchronization for the measured data from each axis sensor. Moreover, the FPGA-ARM based embedded technology in system hardware design can reduce the system cost by the integration of data logger, communication sever, and facility control system. To evaluate the data acquisition performance of the SDAS, we perform experiments for real seismic signals with the exciter. Performances comparison between the acquired data of the SDAS and the reference sensor shows that the data acquisition performance of the SDAS is valid.

• International Journal of Computer Science & Network Security
• /
• v.21 no.5
• /
• pp.23-30
• /
• 2021

In recent years, hazardous flash flooding has caused deaths and damage to infrastructure in Saudi Arabia. In this paper, our aim is to assess patterns and trends in climate means and extremes affecting flash flood hazards and water resources in Saudi Arabia for the purpose to improve risk assessment for forecast capacity. We would like to examine temperature, precipitation climatology and trend magnitudes at surface stations in Saudi Arabia. Based on the assessment climate patterns maps and trends are accurately used to identify synoptic situations and tele-connections associated with flash flood risk. We also study local and regional changes in hydro-meteorological extremes over recent decades through new applications of statistical methods to weather station data and remote sensing based precipitation products; and develop remote sensing based high-resolution precipitation products that can aid to develop flash flood guidance system for the flood-prone areas. A dataset of extreme events has been developed using the multi-decadal station data, the statistical analysis has been performed to identify tele-connection indices, pressure and sea surface temperature patterns most predictive to heavy rainfall. It has been combined with time trends in extreme value occurrence to improve the potential for predicting and rapidly detecting storms. A methodology and algorithms has been developed for providing a well-calibrated precipitation product that can be used in the early warning systems for elevated risk of floods.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.54 no.6
• /
• pp.965-973
• /
• 2021

The behavior of angelfish Pterophyllum scalare exposed to low and high temperatures was monitored by video tracking, and information such as the initial speed, changes in speed, and locations of the fish in the tank were analyzed. The water temperature was raised from 26℃ to 36℃ or lowered from 26℃ to 16℃ for 4 h. The control group was maintained at 26℃ for 8 h. The experiment was repeated five times for each group. Machine learning analysis comprising a long short-term memory model was used to train and test the behavioral data (80 s) after pre-processing. Results showed that when the water temperature changed to 36℃ or 16℃, the average speed, changes in speed and fractal dimension value were significantly lower than those in the control group. Machine learning analysis revealed that the accuracy of 80-s video footage data was 87.4%. The machine learning used in this study could distinguish between the optimal temperature group and changing temperature groups with specificity and sensitivity percentages of 86.9% and 87.4%, respectively. Therefore, video tracking technology can be used to effectively analyze fish behavior. In addition, it can be used as an early warning system for fish health in aquariums and fish farms.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.1
• /
• pp.177-187
• /
• 2022

Theoretical literature agrees on the interaction between financial instability and economic activity but explains it's dynamic in two points of view: one is that the transmission mechanism occurs in one unique regime and the other reckons a shift of regime leads to the alteration of the transmission mechanism. This study aims to find evidence of the multi-regime transmission for ASEAN developing countries. The author employs the technique of Threshold vector auto regression using the financial stress index standing for financial instability. Monthly data is collected, covering a period long enough with many episodes of high stress in recent decades. There are two conclusions: (1) A financial shock has a negative and stronger impact on economic activity during a high-stress period than it does during a low-stress period; (2) the response of economic activity to a negative financial shock during high-stress periods is stronger than it is during normal times. The findings point to the importance of the financial stress index as an additional early warning indicator for the real economy sector, as well as the positive effect that a reduction in financial stress may have on economic activity, implying the importance of "unconventional" monetary policy in times of high financial stress.