• Journal of Korean Tunnelling and Underground Space Association
• /
• v.22 no.1
• /
• pp.1-21
• /
• 2020

The domestic abandoned mines are generating subsidence and it is difficult to predict this subsidence and evaluate the risk. The study of the subsidence risk evaluation using the existing numerical analysis only applies the integrative property to the geological structure and ground condition, and analyzes the goaf peripheral plastic domain. Also, there is a realistic limit that only restricted materials can be apprehended in securing the input information, which leads to the low reliability of the numerical analysis result. In this study, 2-dimensional modeling was performed by applying the field geological structure and ground information targeting abandoned mine where the subsidence occurred. Also, the analysis model was revised by repeating the numerical analysis for the difference between the real subsidence ground information and the analysis result to be minimized by modifying the ground property. This revision was automated by applying the optimization technique and the gradational optimal design method dividing multiple ground properties was developed.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.5
• /
• pp.59-67
• /
• 2017

This study analyzes the efficiency of oriental hospitals using DEA(Data Envelopment Analysis). The input variables include the numbers of doctors, nurses, medical technicians, and beds. The output variable iscomprised of the sales account. The analysis tools used are EnPas and IBM SPSS Statistics 19. As a result of efficiency analysis, the private hospitals(establishment), less than 10 years in operation(operating period), containing less than 50 beds (number of the beds), located in the metropolitan area(location) showed high efficiency in the BCC(Banker, Charnes & Cooper) model, but indicated relatively low efficiency in CCR(Charnes, Cooper & Rhodes) model. This contradictory result is caused by inefficiencies in hospital size. The logistic regression analysis conducted to analyze the variables that affect the efficiency of oriental hospitals found that the efficiency decreased by 0.955 with each increase of 1 bed in the hospital.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.194-198
• /
• 2008

Significant soil erosion and water quality degradation issues are occurring at highland agricultural areas of Kangwon province because of agronomic and topographical specialities of the region. Thus spatial and temporal modeling techniques are often utilized to analyze soil erosion and sediment behaviors at watershed scale. The Soil and Water Assessment Tool (SWAT) model is one of the watershed scale models that have been widely used for these ends in Korea. In most cases, the SWAT users tend to use the readily available input dataset, such as the Ministry of Environment (MOE) land cover data ignoring temporal and spatial changes in land cover. Spatial and temporal resolutions of the MOE land cover data are not good enough to reflect field condition for accurate assesment of soil erosion and sediment behaviors. Especially accelerated soil erosion is occurring from agricultural fields, which is sometimes not possible to identify with low-resolution MOD land cover data. Thus new land cover data is prepared with cadastral map and high spatial resolution images of the Doam-dam watershed. The SWAT model was calibrated and validated with this land cover data. The EI values were 0.79 and 0.85 for streamflow calibration and validation, respectively. The EI were 0.79 and 0.86 for sediment calibration and validation, respectively. These EI values were greater than those with MOE land cover data. With newly prepared land cover dataset for the Doam-dam watershed, the SWAT model better predicts hydrologic and sediment behaviors. The number of HRUs with new land cover data increased by 70.2% compared with that with the MOE land cover, indicating better representation of small-sized agricultural field boundaries. The SWAT estimated annual average sediment yield with the MOE land cover data was 61.8 ton/ha/year for the Doam-dam watershed, while 36.2 ton/ha/year (70.7% difference) of annual sediment yield with new land cover data. Especially the most significant difference in estimated sediment yield was 548.0% for the subwatershed #2 (165.9 ton/ha/year with the MOE land cover data and 25.6 ton/ha/year with new land cover data developed in this study). The results obtained in this study implies that the use of MOE land cover data in SWAT sediment simulation for the Doam-dam watershed could results in 70.7% differences in overall sediment estimation and incorrect identification of sediment hot spot areas (such as subwatershed #2) for effective sediment management. Therefore it is recommended that one needs to carefully validate land cover for the study watershed for accurate hydrologic and sediment simulation with the SWAT model.

• Structural Monitoring and Maintenance
• /
• v.2 no.1
• /
• pp.77-93
• /
• 2015

The idea of using measured dynamic characteristics for damage detection is attractive because it allows for a global evaluation of the structural health and condition. However, vibration-based damage detection for complex structures such as long-span cable-supported bridges still remains a challenge. As a suspension or cable-stayed bridge involves in general thousands of structural components, the conventional damage detection methods based on model updating and/or parameter identification might result in ill-conditioning and non-uniqueness in the solution of inverse problems. Alternatively, methods that utilize, to the utmost extent, information from forward problems and avoid direct solution to inverse problems would be more suitable for vibration-based damage detection of long-span cable-supported bridges. The auto-associative neural network (ANN) technique and the probabilistic neural network (PNN) technique, that both eschew inverse problems, have been proposed for identifying and locating damage in suspension and cable-stayed bridges. Without the help of a structural model, ANNs with appropriate configuration can be trained using only the measured modal frequencies from healthy structure under varying environmental conditions, and a new set of modal frequency data acquired from an unknown state of the structure is then fed into the trained ANNs for damage presence identification. With the help of a structural model, PNNs can be configured using the relative changes of modal frequencies before and after damage by assuming damage at different locations, and then the measured modal frequencies from the structure can be presented to locate the damage. However, such formulated ANNs and PNNs may still be incompetent to identify damage occurring at the deck members of a cable-supported bridge because of very low modal sensitivity to the damage. The present study endeavors to enhance the damage identification capability of ANNs and PNNs when being applied for identification of damage incurred at deck members. Effort is first made to construct combined modal parameters which are synthesized from measured modal frequencies and modal shape components to train ANNs for damage alarming. With the purpose of improving identification accuracy, effort is then made to configure PNNs for damage localization by adapting the smoothing parameter in the Bayesian classifier to different values for different pattern classes. The performance of the ANNs with their input being modal frequencies and the combined modal parameters respectively and the PNNs with constant and adaptive smoothing parameters respectively is evaluated through simulation studies of identifying damage inflicted on different deck members of the double-deck suspension Tsing Ma Bridge.

• Korean Chemical Engineering Research
• /
• v.46 no.2
• /
• pp.316-329
• /
• 2008

Recently the increased attention pays on the processing of multiple, relatively low quantity, high value-added products resulted in adoption of batch process in the chemical process industry such as pharmaceuticals, polymers, bio-chemicals and foods. As there are more possibilities of the improvement of operations in batch process than continuous processes, a lot of effort has been made to enhance the productivity and operability of batch processes. But the chemical process industry faces a range of uncertainties factors such as demands for products, prices of product, lead time for the supply of raw materials and in the production, and the distribution of product. And global competition has made it imperative for the process industries to manage their supply chains optimally. Supply chain management aims to integrate plants with their supplier and customers so that they can be managed as a single entity and coordinate all input/output flows (of materials, information) so that products are produced and distributed in the right quantities, to the right locations, and at the right time.The objective of this study is to solve the purchase, distribution, production planning and scheduling problem, which minimizes the total costs of production, inventory, and transportation under uncertainty. And development of SCM model in chemical industry including batch mode operations. Through that, the enterprise can respond to uncertainty. Also integrated process optimal planning and scheduling model for manufacturing supply chain. The result shows that, the advantage of supply chain integration are quality matters seen by customers and suppliers, order schedules, flexibility, cost reduction, and increase in sales and profits. Also, an integration of supply chain (production and distribution system) generates significant savings by trading off the costs associated with the whole, rather than minimizing supply chain costs separately.

• Journal of Bio-Environment Control
• /
• v.29 no.3
• /
• pp.277-284
• /
• 2020

To increase the utilization of the intelligent methodology of smart farm management, estimation modeling techniques are required to assess prior examination of crops and environment changes in realtime. A mandatory environmental factor such as CO₂ is challenging to establish a reliable estimation model in time domain accounted for indoor agricultural facilities where various correlated variables are highly coupled. Thus, this study was conducted to develop an artificial neural network for reducing time complexity by using environmental information distributed in adjacent areas from a time perspective as input and output variables as CO₂. The environmental factors in the smart farm were continuously measured using measuring devices that integrated sensors through experiments. Modeling 1 predicted by the mean data of the experiment period and modeling 2 predicted by the day-to-day data were constructed to predict the correlation of CO₂. Modeling 2 predicted by the previous day's data learning performed better than Modeling 1 predicted by the 60-day average value. Until 30 days, most of them showed a coefficient of determination between 0.70 and 0.88, and Model 2 was about 0.05 higher. However, after 30 days, the modeling coefficients of both models showed low values below 0.50. According to the modeling approach, comparing and analyzing the values of the determinants showed that data from adjacent time zones were relatively high performance at points requiring prediction rather than a fixed neural network model.

• Journal of Energy Engineering
• /
• v.25 no.2
• /
• pp.94-100
• /
• 2016

ESS(Energy Storage System) is an important source that keeps power supply stable and utilizes electricity efficiently. For example, ESS contributes to resolve power supply imbalance, stabilize new renewable energy output and regulate frequency. ESS is predicted to be expanded to 55.9GWh of installed capacity by 2023, which is 30 times more than that of 2014. To raise competitiveness of domestic ESS industry in this increasing world market, we have disseminated load-shift ESS for continuous power supply imbalance with FR ESS, and also necessity to secure domestic track record is required. However in case of FR ESS, utility of installing thermal power plant is generally generated within 5% range of rated capacity, so that scalability of domestic market is low without dramatic increase of thermal power plant. Necessity of load-shift ESS dissemination is also decreasing effected by surplus backup power securement policy, raising demand for new dissemination model. New dissemination model is promising for $CO_2$ reduction effect in spite of intermittent output. By stabilizing new renewable energy output in connection with new renewable energy, and regulating system input timing of new renewable energy generation rate, it is prospected model for 'post-2020' regime and energy industry. This research presents a policy alternatives of REC multiplier calculation method to induce investment after outlining PV-connected ESS charge/discharge mode to reduce GHG emission, This alternative is projected to utilize GHG emission reduction methodology for 'Post-2020' regime, big issue of new energy policy.

• Journal of Veterinary Clinics
• /
• v.33 no.2
• /
• pp.107-112
• /
• 2016

The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

• Journal of Digital Policy
• /
• v.1 no.2
• /
• pp.9-15
• /
• 2022

The World Economic Forum has included Water-Energy-Food among the three major risk groups in the world, and Water-Energy-Food is related to the development strategies of countries and the lives of their citizens. This study calculates the combined Water-Energy-Food use efficiency in China for 2011-2020 based on the SBM-Malmquist index. The results show that the overall combined Water-Energy-Food efficiency in China is low, but shows an upward trend. There is a clear variability in the combined Water-Energy-Food utilization efficiency in China, with an overall geographic distribution pattern of East > Middle > West. Only Beijing and Shanghai have reached the real above effective nationwide, and all other provinces have inefficiency between input and output. The Malmquist index of integrated Water-Energy-Food utilization efficiency is 1.136, with an up ward trend, and technical efficiency and technological progress lead the improvement of integrated Water-Energy-Food utilization efficiency in China at the sametime. The Water-Energy-Food issue should be raised to a strategic level as soon as possible, and policy support should be provided for its development. Each region should establish a cross-regional coordinating body to formulate targeted measures according to the province's food production and water distribution, so as to promote economic transformation from sloppy development to green development as soon as possible.

• Journal of Korea Water Resources Association
• /
• v.55 no.8
• /
• pp.565-575
• /
• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.