• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.3
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• pp.73-82
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• 2003

This paper proposes a work procedure for generalizing large-scale digital maps ver. 2.0(1/5,000) into a small-scale digital map(1/25,000). Unlike a existent digital map, the digital map ver. 2.0 has a variety of attribute data as well as graphic data. To perform an efficient map generalization with these structural properties, we establish a work procedure as follow; firstly, delete layers which don't exist in small-scale digital map's feature code, and secondly, generalize features which have been classified into 8 layers, and finally merge 8 layers which have been generalized into 1 layer. Therefore, we expect that a work procedure which is proposed in this paper will play a fundamental role in automated generalization system and will contribute to small-scale digital mapping and thematic mapping.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.3
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• pp.109-121
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• 2020

Converting the agricultural land-use of rice field to upland has been increasingly conducted as farmers encourages themselves to grow higher value-added crops on rice fields under the policy support. Comparing to rice field, Upland shows different characteristic of discharge due to the slope, scale, and shape of field and characteristics of rainfall event. In this study, we designed the experiment fields reflecting flat-upland characteristics with different land scale, and tried to collect the discharge and load data. Soybeans and corn were selected as target crops considering the possibility of large-scale cultivation and crop demand. The cultivation was conducted during the growth period in 2019 with 3 different field scales. Hence, we have collected the discharge data from 17 rainfall events and the load data for 8 rainfall events. As a result, the magnitude of rainfall events and the discharge duration were found to have a strong positive correlation and field discharge occurred during the period by 55% to 83% of rainfall duration. Besides we found other relationships and characteristics of rainfall event, discharge, and pollutant load and also pointed out that continuous monitoring and more data are required to derive statistically significant results. Compared with slope-field monitoring data obtained from the precedent research, the runoff ratio of the flat-fields was significantly lower than slope-fields. Overall the discharge in the slop and flat-fields shows appreciably different characteristics so that the related researches need to be further conducted to reasonably assess environmental impact of agricultural activities at flat-field.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.411-426
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• 2012

The Canton Tower (formerly named Guangzhou New TV Tower) of 610 m high has been instrumented with a long-term structural health monitoring (SHM) system consisting of over 700 sensors of sixteen types. Under the auspices of the Asian-Pacific Network of Centers for Research in Smart Structures Technology (ANCRiSST), an SHM benchmark problem for high-rise structures has been developed by taking the instrumented Canton Tower as a host structure. This benchmark problem aims to provide an international platform for direct comparison of various SHM-related methodologies and algorithms with the use of real-world monitoring data from a large-scale structure, and to narrow the gap that currently exists between the research and the practice of SHM. This paper first briefs the SHM system deployed on the Canton Tower, and the development of an elaborate three-dimensional (3D) full-scale finite element model (FEM) and the validation of the model using the measured modal data of the structure. In succession comes the formulation of an equivalent reduced-order FEM which is developed specifically for the benchmark study. The reduced-order FEM, which comprises 37 beam elements and a total of 185 degrees-of-freedom (DOFs), has been elaborately tuned to coincide well with the full-scale FEM in terms of both modal frequencies and mode shapes. The field measurement data (including those obtained from 20 accelerometers, one anemometer and one temperature sensor) from the Canton Tower, which are available for the benchmark study, are subsequently presented together with a description of the sensor deployment locations and the sensor specifications.

• Geophysics and Geophysical Exploration
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• v.14 no.4
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• pp.316-323
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• 2011

To test the applicability of resistivity survey methods for the archaeological prospection of a large-scale tumulus, a three-dimensional resistivity survey was conducted at the $3^{rd}$ tumulus at Bokam-ri, in Naju city, South Korea. Since accurate topographic relief of the tumulus and electrode locations are required to obtain a high resolution image of the subsurface, electrodes were installed after making grids by threads, which is commonly used in the archaeological investigation. In the data acquisition, data were measured using a 2 m electrode spacing with the line spacing of 1 m and each survey line was shifted 1 m to form an effective grid of 1 m ${\times}$ 1 m. Though the 3-D inversion of data, we could obtain the 3-D image of the tumulus, where we could identify the brilliant signature of buried tombs made of stones. The results were compared with the previous excavation results and we could convince that a 3-D resistivity imaging method is very useful to investigate a large-scale tumulus.

• Journal of the Korean earth science society
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• v.27 no.6
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• pp.643-652
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• 2006

Data of the carbon monoxide concentration observed in Mt. Waliguan in China (WLG), Ulaan Uul in Mongolia (UUM), Tae-ahn Peninsula in Korea (TAP), and Ryori in Japan (RYO) were analyzed for a long period between 1991 and 2004. The annual average concentration of carbon monoxide was the highest at TAP $(233{\pm}41ppb)$ followed by $RYO(171{\pm}36ppb),\;UUM(155{\pm}26ppb),\;and\;WLG(135{\pm}22ppb)$. The seasonal variations being high in spring and low in summer were observed in other areas of Eastern Asia except WLG. TAP was high in carbon monoxide concentration in all seasons compared to WLG, UUM and RYO and shows wide distribution of concentration in the histogram, which is caused by the influence of large-scale air pollution due to its downwind location close to the East Asian continent, China in particular. Also, our data was compared with data measured at Mauna Loa (MLO) in Hawaii. According to the origin of the isentropic backward trajectory and its transport passage, carbon monoxide concentration observed in TAP was analyzed as follows: continental background airflows (CBG) were $216{\pm}47ppb$; regionally polluted continental airflows (RPC) were $316{\pm}56ppb$; Oceanic background airflows (OBG) were $108{\pm}41ppb$; and Partly perturbed oceanic airflows (PPO) were $161{\pm}6ppb$. The high concentration of carbon monoxide in TAP is due to the airflow from East Asian continent origin rather than that from the North Pacific origin. Especially, RPC which passes through the eastern China appeared to be the highest in concentration in spring, fall, and winter. However, OBG was affected by the North Pacific air mass with a low carbon monoxide concentration in summer. The NOAA satellite images and GEOS-CHEM model simulation confirmed a large-scale air pollution event that was in the course of expansion from southeastern China bound to the Korean Peninsula and the Korea East Sea by way of the Yellow Sea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.841-854
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• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.

• Journal of the Korea Society of Computer and Information
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• v.22 no.4
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• pp.9-16
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• 2017

Machine learning has found widespread implementations and applications in many different domains in our life. Logistic regression is a type of classification in machine leaning, and is used widely in many fields, including medicine, economics, marketing and social sciences. In this paper, we present the MapReduce implementation of three existing algorithms, this is, Gradient Descent algorithm, Cost Minimization algorithm and Newton-Raphson algorithm, for logistic regression on RHadoop that integrates R and Hadoop environment applicable to large scale data. We compare the performance of these algorithms for estimation of logistic regression coefficients with real and simulated data sets. We also compare the performance of our RHadoop and RHIPE platforms. The performance experiments showed that our Newton-Raphson algorithm when compared to Gradient Descent and Cost Minimization algorithms appeared to be better to all data tested, also showed that our RHadoop was better than RHIPE in real data, and was opposite in simulated data.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1201-1211
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• 2015

WSN acts as an effective tool for tracking the large scale environments. In such environment, the battery life of the sensor networks is limited due to collection of the data, usage of sensing, computation and communication. To resolve this, a mobile robot is presented to identify the data present in the partitioned sensor networks and passed onto the sink. In novel data collection algorithm, the performance of the data collecting operation is reduced because mobile robot can be used only within the limited range. To enhance the data collection in a changing environment, Non Linear Error Identifier (NLEI) algorithm has been developed and presented in this paper to configure the robot by means of error models which are non-linear. Experimental evaluation has been conducted to estimate the performance of the proposed NLEI and it has been observed that the proposed NLEI algorithm increases the error correction rate upto 42% and efficiency upto 60%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.7 no.1
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• pp.49-57
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• 1989

On this study, we performed the landuse classification using the Landsat data acquired before and after reclamation, and extracted the ground temperature from infrared band(TM band6) data. Using the satellite data, it was possible to extract changes of landuses effectively according to the reclamation, and could obtain the thermal characteristics of the reclaimed area and the surroundings by converting infrared data value into temperatures of surfaces of ground and water. The result of this analysis will be used for the land management of large-scale reclaimed area applying the satellite data and related information.