• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.28 no.5
• /
• pp.505-513
• /
• 2010

This study investigate the characteristics of tidal constituents, and long-term mean sea level oscillations at Incheon bay. For this, the conditions of three tide stations around Incheon bay have examined, and carried out harmonic analysis on water level data for periods of about 40 years(1960~2007). Four major tidal constituents($M_2$, $S_2$, $K_1$, $O_1$) of each tide station showed tendency that change over the 18.61year lunar node cycle, and the type of tide at three stations is mainly semi-diurnal tides. And also, the past monthly tidal modulations are especially sensitive to the cumulative year of water level data in accuracy of tidal prediction. In case that regard the detached data at three tide stations as a single time series data of 40 years, the results of analysis on a single time series, long-term mean sea level oscillations and modulations of tidal datum at tide stations appears with a range of about 10cm, respectively. In addition, the predicted tides at the Inchcon harbor by global and regional tide models of OSU(Oregon State University) based on various satellite altimetric(Topex Poseidon, Topex Tandem, ERS, GFO) data are compared with the observed tides by KHOA(the Korea Hydrographic and Oceanographic Administration). The results show that the high resolution regional model is a quite good agreement at coastal shallow water region.

• Journal of Digital Contents Society
• /
• v.19 no.4
• /
• pp.789-799
• /
• 2018

This article presents machine learning based approach on Big data to analyzing time series data for anomaly detection in such industrial complex system. Long Short-Term Memory (LSTM) network have been demonstrated to be improved version of RNN and have become a useful aid for many tasks. This LSTM based model learn the higher level temporal features as well as temporal pattern, then such predictor is used to prediction stage to estimate future data. The prediction error is the difference between predicted output made by predictor and actual in-coming values. An error-distribution estimation model is built using a Gaussian distribution to calculate the anomaly in the score of the observation. In this manner, we move from the concept of a single anomaly to the idea of the collective anomaly. This work can assist the monitoring and management of Smart Factory in minimizing failure and improving manufacturing quality.

• Journal of Korean Society of Forest Science
• /
• v.81 no.2
• /
• pp.124-138
• /
• 1992

This study was carried out to estimate long-term demand functions, and to project consumption of roundwood to the year 2030, using time series data for the period 1970-1990. Especially, the unique features of this study are in the estimation of demand functions for roundwood by species group and by end-use with help of dummy variables. It also, attempts to show how dummy variables can be utilized for improving the estimation result. The result of this study reveals that hardwood roundwood consumption is being substituted by softwood roundwood due to the rapid increase in the relative price of softwood, and this trend is expected to continue in the near future. The consumption of roundwood by mining industry is projected to fall as the coal :mining is expected to decline. The parametric estimates of timber demand function by species group and by end-use indicate that the demand for timber in Korea is more responsive to the performance of domestic economy as a whole, represented by GDP in this study, than to other variables such as own and substitute prices. The effects of population growth and substitute prices could not be determined.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.9 no.3
• /
• pp.299-307
• /
• 2007

In this study, a new concept for simulating a physical damage of tunnel shotcrete lining due to a long-term chemical deterioration has been proposed. It is known that the damage takes place mainly by internal cracks, reduction of stiffness and strength, which results mainly from volume expansion of the lining and corrosion of cement materials, respectively. This damage mechanism of shotcrete lining appears similar in most kinds of chemical reactions in tunnels. Therefore, the mechanical deterioration mechanism induced by a series of chemical reactions was generalized in this study and mathematically formulated in the framework of thermodynamics. The numerical model was implemented to a 3D finite element code, which can be used to simulate behaviour of tunnel structures undergoing external loads as well as chemical deterioration in time. A number of illustrative examples were given to show a feasibility of the model in tunnel designs.

• Radiation Oncology Journal
• /
• v.32 no.3
• /
• pp.179-186
• /
• 2014

Purpose: To study the long-term outcomes and tolerance in our patients who received dose escalated radiotherapy in the early salvage post-prostatectomy setting. Materials and Methods: The medical records of 54 consecutive patients who underwent radical prostatectomy subsequently followed by salvage radiation therapy (SRT) to the prostate bed between 2003-2010 were analyzed. Patients included were required to have a pre-radiation prostate specific antigen level (PSA) of 2 ng/mL or less. The median SRT dose was 70.2 Gy. Biochemical failure after salvage radiation was defined as a PSA level >0.2 ng/mL. Biochemical control and survival endpoints were analyzed using the Kaplan-Meier method. Univariate and multivariate Cox regression analysis were used to identify the potential impact of confounding factors on outcomes. Results: The median pre-SRT PSA was 0.45 ng/mL and the median follow-up time was 71 months. The 4- and 7-year actuarial biochemical control rates were 75.7% and 63.2%, respectively. The actuarial 4- and 7-year distant metastasis-free survival was 93.7% and 87.0%, respectively, and the actuarial 7-year prostate cancer specific survival was 94.9%. Grade 3 late genitourinary toxicity developed in 14 patients (25.9%), while grade 4 late genitourinary toxicity developed in 2 patients (3.7%). Grade 3 late gastrointestinal toxicity developed in 1 patient (1.9%), and grade 4 late gastrointestinal toxicity developed in 1 patient (1.9%). Conclusion: In this series with long-term follow-up, early SRT provided outcomes and toxicity profiles similar to those reported from the three major randomized trials studying adjuvant radiation therapy.

• Journal of Soil and Groundwater Environment
• /
• v.18 no.6
• /
• pp.8-17
• /
• 2013

The contamination of chlorinated ethenes at an industrial complex, Wonju, Korea, was examined based on sixteen rounds of groundwater quality data collected from 2009 to 2013. Remediation technologies such as soil vapor extraction, soil flushing, biostimulation, and pumping-and-treatment have been applied to eliminate the contaminant sources of trichloroethylene (TCE) and to prevent the migration of TCE plume from remediation target zones. At each remediation target zone, temporal monitoring data before and after the application of remediation techniques showed that the aqueous concentrations of TCE plume present at and around the main source areas decreased significantly as a result of remediation technologies. However, the TCE concentration of the plumes at the downstream area remained unchanged in response to the remediation action, but it showed a great fluctuation according to seasonal recharge variation during the monitoring period. Therefore, variations in the contaminant flux across three transects were analyzed. Prior to the remediation action, the concentration and mass discharges of TCE at the transects were affected by seasonal recharge variation and residual DNAPLs sources. After the remediation, the effect of remediation took place clearly at the transects. By tracing a time-series of plume evolution, a greater variation in the TCE concentrations was detected at the plumes near the source zones compared to the relatively stable plumes in the downstream. The difference in the temporal profiles of TCE concentrations between the plumes in the source zone and those in the downstream could have resulted from remedial actions taken at the source zones. This study demonstrates that long term monitoring data are useful in assessing the effectiveness of remediation practices.

• Journal of Korean Society for Quality Management
• /
• v.49 no.4
• /
• pp.581-594
• /
• 2021

Purpose: The purpose of this study is to compare machine learning models for anomaly detection of mechanical facility equipment and suggest an anomaly detection system for mechanical facility equipment in subway stations. It helps to predict failures and plan the maintenance of facility. Ultimately it aims to improve the quality of facility equipment. Methods: The data collected from Daejeon Metropolitan Rapid Transit Corporation was used in this experiment. The experiment was performed using Python, Scikit-learn, tensorflow 2.0 for preprocessing and machine learning. Also it was conducted in two failure states of the equipment. We compared and analyzed five unsupervised machine learning models focused on model Long Short-Term Memory Variational Autoencoder(LSTM-VAE). Results: In both experiments, change in vibration and current data was observed when there is a defect. When the rotating body failure was happened, the magnitude of vibration has increased but current has decreased. In situation of axis alignment failure, both of vibration and current have increased. In addition, model LSTM-VAE showed superior accuracy than the other four base-line models. Conclusion: According to the results, model LSTM-VAE showed outstanding performance with more than 97% of accuracy in the experiments. Thus, the quality of mechanical facility equipment will be improved if the proposed anomaly detection system is established with this model used.

• Journal of International Area Studies (JIAS)
• /
• v.22 no.1
• /
• pp.91-112
• /
• 2018

This study examined the dynamic relationship between urbanization and energy consumption in China. As an alternative to the conventional method of having the same integration of time series and large samples, ARDL method and Toda-Yamamoto causality analysis were applied. As a result, urbanization income, income, and energy consumption have a long-term stable equilibrium. Urbanization and income have a positive effect on energy consumption in the long run, but short-term changes of urbanization and income have no significant effect on energy consumption changes. The adjusted coefficient was -0.2395, which was statistically significant. In the causality test, income and energy consumption are useful to predict each other, but urbanization is exogenous because there are no causality with other variables. Since the process of urbanization in China has been proceeding slowly and deliberately by the government, it can be seen that the long-term effects of urbanization are clear and exogenous.

• Asia-Pacific Journal of Business
• /
• v.14 no.3
• /
• pp.167-186
• /
• 2023

Purpose - The purpose of this study is to empirically investigate whether the auditor accreditation system for IPO firms improves the efficiency of the KOSDAQ IPO market. To verify the effectiveness of the auditor designation system, we time series compare four measures of IPO firms (earnings management, long-term stock performance, change in operating performance, and possibility of delisting). Design/methodology/approach - We test the hypothesis through event research method and regression analysis. Specifically, the dependent variables of the regression model are discretionary accruals in the year of IPO, 36-month holding period excess return after IPO, change in operating performance for 3 years after IPO, and dummy variable for delisting. And the explanatory variable is a dummy variable that separates the period before and after the implementation of the auditor designation system. Findings - We find that earnings management and delisting risks decreased more in the period after the implementation of the auditor accreditation system than in the previous period. In addition, we find that long-term stock performance and operating performance after IPO increase further after the implementation of the auditor accreditation system. Research implications or Originality - Overall, the results of this study suggest that the implementation of the auditor accreditation system for IPO firms contributes to improving market efficiency in the KOSDAQ market, where information asymmetry is high. Our study differs from previous studies in that it demonstrates the effectiveness of the auditor designation system using various measures.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1107-1118
• /
• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.