• Journal of the Korean Society of Hazard Mitigation
• /
• v.10 no.3
• /
• pp.119-131
• /
• 2010

Drought is one of disaster causing factors to produce severe damage in the World because drought is destroyed to the ecosystem as well as to make difficult the economy of the drought area. This study, using Palmer Drought Severity Index carries out correlation analysis with sea surface temperatures. Comparative analysis carries out by calculated Palmer Drought Severity Index and past drought occurrence year. Result of comparative analysis, PDSI indexes were in accord with the past drought. Cluster analysis for correlation analysis carries out using precipitation and temperature that is input datas palmer drought severity index, and the result of cluster analysis was classified as 6. Also, principal component carries out using result of cluster analysis. 14 principal component analyze out through principal component analysis. Using analyzed 14 principal component carries out correlation analysis with sea surface temperature that is delay time from 0month until 11month. Correlation analysis carries out sea surface temperatures and calculated cycle component of the low frequency through Wavelet Transform analysis form principal component. Result of correlation analysis, yang(+) correlation is bigger than yin(-) correlation. It is possible to check similar correlation statistically the area of sea surface temperature with sea surface temperature in the Pacific. Forecasting possibility of the future drought make propose using sea surface temperature.

• Journal of the Korean Society for information Management
• /
• v.35 no.1
• /
• pp.33-59
• /
• 2018

This study draws on the current momentum to diversify open government data research through multidimensional scaling and model development. It formulates a quality assessment model applicable to library open data, taking into consideration the paucity of such research in the field. The model was developed using the Delphi method and verified for validity and reliability on the basis of a survey administered to library open data users. The results of the fourth round exhibited an average of 4.00 for all measured elements and a minimum validity of .75, rendering the model appropriate for use in quality assessments of library open data. The convergence and stability results provided by the expert panel fell below .50, confirming that there was no need to conduct further surveys in order to establish the validity of the Delphi method. The model's reliability likewise garnered results of .60 and above in all three dimensions. This Model completed with the input of the Delphi panel was put through a verification process in which library open data users such as domestic and international librarians, developers, and open data activists reviewed the model for validity and reliability. The model scored low on validity on account of its failure to load all measure factors and elements pertaining to the three dimensions. Reliability results, on the other hand, were at 0.6 and above for all dimensions and measured elements.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.8 no.3
• /
• pp.148-157
• /
• 2005

In order to understand distribution characteristics and pollution of heavy metals in the artificial Lake Shihwa in the vicinity of Kyunggi Bay in relation with huge environmental changes, various environmental samples including seawaters, surface sediments and settling particulate matters were collected from Lake Shihwa in 2004. Due to extreme pollutant discharge from various anthropogenic sources such as the Banweol and Shihwa Industrial Complexes and cities, the highest metal concentrations in the samples such as waters, sediments and settling particulate matter were found in inner part of the lake. High metal contents (Cu, Zn and Hg) in sediments were observed at Sts. 2-4 and 9. The contents of Cr, Co, Ni, Cu, Zn and Pb in SPMs were high at St. 5 and low in the outer part of the lake. Spatial distribution of heavy metals were mainly controlled various biogeochemical factors and physical mixing as well as input of industrial and municipal wastewaters. Although tile environmental qualities of heavy metals in the lake have been improved partially due to inflow of outer seawater, it is not clear to reach a good environmental quality. Therefore, further environmental programs should be conducted continuously for environmental improvement.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.8
• /
• pp.3748-3759
• /
• 2013

The purpose of this study was to examine the classification accuracies of ML, MAP, and EAP methods under DINA model. For this purpose, this study examined the classification accuracies of the classification methods under the various conditions: the number of attributes, the ability distribution of examinees, and test length. To accomplish this purpose, this study used a simulation method. For the simulation study, data was simulated under the various simulation conditions including the number of attributes (K= 5, 7), the ability distribution of examinees (high, middle, low), and test length (J= 15, 30, 45). Additionally, the percent of agreements between true skill patterns(true ${\alpha}$) and skill patterns estimated by the ML, MAP, and EAP methods were calculated. The summary of the main results of this study is as follows: First, When the number of attributes was 5 and 7, the EAP method showed relatively higher average in the percent of exact agreement than the ML and MAP methods. Second, under the same conditions, as the number of attributes increased, the average percent of exact agreement decreased in ML, MAP, and EAP methods. Third, when the prior distribution of examinees ability was different from low to high under the conditions of the same test length, the EAP method showed relatively higher average in the percent of exact agreement than those of the ML and MAP methods. Fourth, the average percent of exact agreement increased in all methods, ML, MAP, and EAP when the test length increased from 15 to 30 and 45 under the conditions of the same the ability distribution of examinees.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.8
• /
• pp.215-221
• /
• 2020

Navigation lights used in supersonic aircraft are used to identify the direction and location of the aircraft. The color of the navigation lights and location of installation are defined by aviation law as red for the left wing, green for the right wing, and white for the tail. Navigation lights operate in BRT and DIM modes. BRT is the brightest mode, and DIM is an output with dimmed brightness. Navigation lights serve to prevent aircraft collisions and are very important for stability and location identification. One phenomenon is that the inlet and tail navigation lights flicker abnormally. In this study, fault tree analysis was performed in two stages. The first step was derived from three causal factors, the second step developed five improvements, and the optimal improvement plan was drawn. The navigation lights confirmed that the initial input power was unstable as the main cause of abnormal flickering. As an improved method, the circuit was adjusted to stabilize the initial power, and it was confirmed that flickering did not occur as a result of the tests under the same conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.468-469
• /
• 2018

We design CNN(convolutional neural network) which is applicable to steering control system of autonomous vehicle. CNN has been widely used in many fields, especially in image classifications. But CNN has not been applied much to the regression problem such as function approximation. This is because the input of CNN has a multidimensional data structure such as image data, which makes it is not applicable to general control systems. Recently, autonomous vehicles have been actively studied, and many techniques are required to implement autonomous vehicles. For this purpose, many researches have been studied to detect the lane by using the image through the black box mounted on the vehicle, and to get the vanishing point according to the detected lane for control the autonomous vehicle. However, in detecting the vanishing point, it is difficult to detect the vanishing point with stability due to various factors such as the external environment of the image, disappearance of the instant lane and detection of the opposite lane. In this study, we apply CNN for steering control of an autonomous vehicle using a black box image of a car.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.12
• /
• pp.491-498
• /
• 2019

Digital twin is a technology that virtualizes physical objects of the real world on a computer. It is used by collecting sensor data through IoT, and using the collected data to connect physical objects and virtual objects in both directions. It has an advantage of minimizing risk by tuning an operation of virtual model through simulation and responding to varying environment by exploiting experiments in advance. Recently, artificial intelligence and machine learning technologies have been attracting attention, so that tendency to virtualize a behavior of physical objects, observe virtual models, and apply various scenarios is increasing. In particular, recognition of each robot's motion is needed to build digital twin for co-robot which is a heart of industry 4.0 factory automation. Compared with modeling based research for recognizing motion of co-robot, there are few attempts to predict motion based on sensor data. Therefore, in this paper, an experimental environment for collecting current and inertia data in co-robot to detect the motion of the robot is built, and a motion prediction model based on the collected sensor data is proposed. The proposed method classifies the co-robot's motion commands into 9 types based on joint position and uses current and inertial sensor values to predict them by accumulated learning. The data used for accumulating learning is the sensor values that are collected when the co-robot operates with margin in input parameters of the motion commands. Through this, the model is constructed to predict not only the nine movements along the same path but also the movements along the similar path. As a result of learning using SVM, the accuracy, precision, and recall factors of the model were evaluated as 97% on average.

• Tunnel and Underground Space
• /
• v.24 no.2
• /
• pp.155-165
• /
• 2014

Multiple thermal energy storage (TES) caverns can be used for storing thermal energy on a large scale and for a high-aspect-ratio heat storage design to provide good thermal performance. It may also be necessary to consider the use of multiple caverns with a reduced length when a single, long tunnel-shaped cavern is not suitable for connection to aboveground heat production and injection equipments. When using multiple TES caverns, the separation distance between the caverns is one of the significant factors that should be considered in the design of storage space, and the optimal separation distance should be determined based on a quantitative stability criterion. In this paper, we described a numerical approach for determining the optimal separation distance between multiple caverns for large-scale TES utilization. For reliable stability evaluation of multiple caverns, we employed a probabilistic method which can quantitatively take into account the uncertainty of input parameters by probability distributions, unlike conventional deterministic approaches. The present approach was applied to the design of a conceptual TES model to store hot water for district heating. The probabilistic stability results of this application demonstrated that the approach in our work can be effectively used as a decision-making tool to determine the optimal separation distance between multiple caverns. In addition, the probabilistic results were compared to those obtained through a deterministic analysis, and the comparison results suggested that care should taken in selecting the acceptable level of stability when using deterministic approaches.

• Journal of Korea Water Resources Association
• /
• v.43 no.10
• /
• pp.865-881
• /
• 2010

To evaluate influence of the future climate change on water environment, it is necessary to use a rainfall-runoff model, or a basin model allowing us to simultaneously simulate water quality factors such as sediment and nutrient material. Thus, SWAT is selected as a watershed-based model and Nakdong river basin is chosen as a target basin for this study. To apply climate change scenarios as input data to SWAT, Australian model (CSIRO: Mk3.0, CSMK) and Canadian models (CCCma: CGCM3-T47, CT47) of GCMs are used. Each GCMs which have A2, B1, and A1B scenarios effectively represent the climate characteristics of the Korean peninsula. For detecting climate change in Nakdong river basin, precipitation and temperature, increasing rate of these were analyzed in each scenarios. By simulation results, flow and increasing rate of these were analyzed at particular points which are important in the object basin. Flow and variation of flow in the scenarios for present and future climate changes were compared and analyzed by years, seasons, divided into mid terms. In most of the points temperature and flow rate are increased, because climate change is expected to have a significant effect on rising water temperature and flow rate of river and lake, further on the basis of this study result should set enhancing up water control project of hydraulic structures caused by increasing outer discharge of the Nakdong River Basin due to climate change.

• Korean Journal of Soil Science and Fertilizer
• /
• v.44 no.1
• /
• pp.118-126
• /
• 2011

Ester-linked fatty acid methyl ester (EL-FAME) profiles were used to describe differences in soil microbial communities influenced by conventional farming system (CFS), and organic farming system (OFS) in controlled horticultural land. Soil physicochemical properties and soil microbial communities were determined in the experimental fields. Higher organic matter content in OFS reduced soil bulk density which in turn increased the soil porosity. Generally, soil chemical properties in OFS were higher than those of CFS, but EC value in OFS was significantly lower than that of CFS. With the exception of Fe content, other macronutrient contents and pH in both farming system decreased with the soil depth. Soil microbial biomass of OFS was approximately 1.3 times in topsoil and 1.8 times in subsoil higher than those of CFS. Lower ratios of cy17:0 to $16:1{\omega}7c$ and cy19:0 to $18:1{\omega}7c$ were found in the CFS soils than the OFS soils, indicating that microbial stress decreased. The ratio of MUFA to SFA was higher in OFS due to organic input to the soil. In principal components analysis (PCA), the first variable accounted for 54.3%, while the second for 27.3%, respectively. The PC1 of the PCA separated the samples from CFS and OFS, while the PC2 of the PCA separated the samples from topsoil and subsoil. EL-FAMEs with the positive eigenvector coefficients for PC1 were cy17: 0 to $16:1{\omega}7c$ ratio, cy19:0 to $18:1{\omega}7c$ ratio, soil pH, soil organic matter, and soil $NO_3$-N content. Our findings suggest that the shifting cy19:0 to $18:1{\omega}7c$ ratio should be considered as potential factors responsible for the clear microbial community differentiation observed between different cultivation systems and soil depth in controlled horticultural land.