• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.257-264
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• 2020

Vectorization is currently the main method in feature collection (extraction) during digital mapping using UAV-Photogrammetry. However, this method is time consuming and prone to gross elevation errors when extracted from a DSM (Digital Surface Model), because three-dimensional feature coordinates are vectorized separately: plane information from an orthophoto and height from a DSM. Consequently, the demand for stereo plotting method capable of acquiring three- dimensional spatial information simultaneously is increasing. However, this method requires an expensive equipment, a Digital Photogrammetry Workstation (DPW), and the technology itself is still incomplete. In this paper, we evaluated the accuracy of low-cost stereo plotting system, Menci's StereoCAD, by analyzing its three-dimensional spatial information acquisition. Images were taken with a FC 6310 camera mounted on a Phantom4 pro at a 90 m altitude with a Ground Sample Distance (GSD) of 3 cm. The accuracy analysis was performed by comparing differences in coordinates between the results from the ground survey and the stereo plotting at check points, and also at the corner points by layers. The results showed that the Root Mean Square Error (RMSE) at check points was 0.048 m for horizontal and 0.078 m for vertical coordinates, respectively, and for different layers, it ranged from 0.104 m to 0.127 m for horizontal and 0.086 m to 0.092 m for vertical coordinates, respectively. In conclusion, the results showed 1: 1,000 digital topographic map can be generated using a stereo plotting system with UAV images.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.5
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• pp.47-62
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• 2020

Rice paddy accounts for approximately 52.5% of all farmlands in South Korea, and it is closely related to the water environment. Climate change is expected to affect not only agricultural productivity also the water and the nutrient circulation. Therefore this study was aimed to evaluate changes of nitrogen load from rice paddy considering climate change scenario uncertainty. APEX-Paddy model which reflect rice paddy environment by modifying APEX (Agricultural Policy and Environmental eXtender) model was used. Using the AIMS (APCC Integrated Modeling Solution) offered by the APEC Climate Center, bias correction was conducted for 9 GCMs using non-parametric quantile mapping. Bias corrected climate change scenarios were applied to the APEX-Paddy model. The changes and uncertainty in runoff and nitrogen load were evaluated using multi-model ensemble. Paddy runoff showed a change of 23.1% for RCP4.5 scenario and 45.5% for RCP8.5 scenario compared the 2085s (2071 to 2100) against the base period (1976 to 2005). The nitrogen load was found to be increased as 43.9% for RCP4.5 scenario and 76.0% for RCP8.5 scenario. The uncertainty analysis showed that the annual standard deviation of nitrogen loads increased in the future, and the maximum entropy indicated an increasing tendency. And Duncan's analysis showed significant differences among GCMs as the future progressed. The result of this study seems to be used as a basis for mid- and long-term policies for water resources and water system environment considering climate change.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.31-37
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• 2015

The recent long-term economic recession and business depression are constantly increasing the occurence of the five major crimes(murder, robbery, rape, theft, violence). When looking into the previously-analyzed characteristics of how the five major crimes are committed, this study understands that the crimes mostly occur in these crime-ridden areas of poor public order and security and, in order to decrease the crime rates of the crime-prone areas, any relevant fields have been emphasizing the application of CPTED. In the light of that, referring to CPTED surveillance factors and the current crime rate data, the study presented ways to help the relevant fields draw up a crime-prone area grade map. In particular, the security center among monitoring elements was visualized by dividing it into point patrol and directed patrol and by dividing it into 3 steps monitoring levels with CCTV and street lights. In addition, we checked the crime rate by zoning through crime statistics occurred in the research areas and established a crime status map. We estimated the weight through AHP analysis on the built monitoring elements and the zoning of the occurred areas, as a result of making a map vulnerable to crime by monitoring steps by overlapping each element, we were able to confirm that 60% of theft, 52% of violence and 33% of rape in the 1st grade area were reduced compared to the 1st step in monitoring Step 3.

• Journal of Internet Computing and Services
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• v.6 no.2
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• pp.69-84
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• 2005

As the Internet users are increasing rapidly, a cluster web server system is attracted by many researchers and Internet service providers. The cluster web server has been developed to efficiently support a larger number of users as well as to provide high scalable and available system. In order to provide the high performance in the cluster web server, efficient load distribution is important, and recently many content-aware request distribution techniques have been proposed. In this paper, we propose a new content-aware load balancing technique that can evenly distribute the workload to each node in the cluster web server. The proposed technique is based on the hash histogram transformation, in which each URL entry of the web log file is hashed, and the access frequency and file size are accumulated as a histogram. Each user request is assigned into a node by mapping of (hashed value-server node) in the histogram transformation. In the proposed technique, the histogram is updated periodically and then the even distribution of user requests can be maintained continuously. In addition to the load balancing, our technique can exploit the cache effect to improve the performance. The simulation results show that the performance of our technique is quite better than that of the traditional round-robin method and we can improve the performance more than $10\%$ compared with the existing workload-aware load balancing(WARD) method.

• The KIPS Transactions:PartD
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• v.16D no.6
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• pp.851-858
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• 2009

Mobbing is not restricted to problem of young people but the bigger recent problem occurs in workspaces. According to reports of ILO and domestic case mobbing in the workplace is increasing more and more numerically from 9.1%('03) to 30.7%('08). These mobbing brings personal and social losses. The proposed algorithm makes it possible to grasp not only current mobbing victims but also potential mobbing victims through user profile and contribute to efficient personnel management. This paper extracts user profile related to mobbing, in a way of selecting seven factors and fifty attributes that are related to this matter. Next, expressing extracting factors as '1' if they are related me or not '0'. And apply similarity function to attributes summation included in factors to calculate similarity between the users. Third, calculate optimizing weight choosing factors included attributes by applying neural network algorithm of SPSS Clementine and through this summation Mobbing-Value(MV) can be calculated . Finally by mapping MV of online social network users to G2 mobbing propensity classification model(4 Groups; Ideal Group of the online social network, Bullies, Aggressive victims, Victims) which is designed in this paper, can grasp mobbing propensity of users, which will contribute to efficient personnel management.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.10
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• pp.1422-1432
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• 1993

Nonlinear mapping function of the HCNN( Hidden Control Neural Network ) can change over time to model the temporal variability of a speech signal by combining the nonlinear prediction of conventional neural networks with the segmentation capability of HMM. We have two things in this paper. first, we showed that the performance of the HCNN is better than that of HMM. Second, the HCNN with its prediction error measure given by weighted distance is proposed to use suitable distance measure for the HCNN, and then we showed that the superiority of the proposed system for speaker-independent speech recognition tasks. Weighted distance considers the differences between the variances of each component of the feature vector extraced from the speech data. Speaker-independent Korean digit recognition experiment showed that the recognition rate of 95%was obtained for the HCNN with Euclidean distance. This result is 1.28% higher than HMM, and shows that the HCNN which models the dynamical system is superior to HMM which is based on the statistical restrictions. And we obtained 97.35% for the HCNN with weighted distance, which is 2.35% better than the HCNN with Euclidean distance. The reason why the HCNN with weighted distance shows better performance is as follows : it reduces the variations of the recognition error rate over different speakers by increasing the recognition rate for the speakers who have many misclassified utterances. So we can conclude that the HCNN with weighted distance is more suit-able for speaker-independent speech recognition tasks.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.651-664
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• 2014

Recently, the demands of accurate forest carbon stock estimation and mapping are increasing in Korea. This study investigates the feasibility of two methods, k-Nearest Neighbor (kNN) and Regression Tree Analysis (RTA), for carbon stock estimation of pilot areas, Gongju and Sejong cities. The 3rd and 5th ~ 6th NFI data were collected together with Landsat TM acquired in 1992, 2010 and Aster in 2009. Additionally, various vegetation indices and tasseled cap transformation were created for better estimation. Comparison between two methods was conducted by evaluating carbon statistics and visualizing carbon distributions on the map. The comparisons indicated clear strengths and weaknesses of two methods: kNN method has produced more consistent estimates regardless of types of satellite images, but its carbon maps were somewhat smooth to represent the dense carbon areas, particularly for Aster 2009 case. Meanwhile, RTA method has produced better performance on mean bias results and representation of dense carbon areas, but they were more subject to types of satellite images, representing high variability in spatial patterns of carbon maps. Finally, in order to identify the increases in carbon stock of study area, we created the difference maps by subtracting the 1992 carbon map from the 2009 and 2010 carbon maps. Consequently, it was found that the total carbon stock in Gongju and Sejong cities was drastically increased during that period.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.3
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• pp.227-246
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• 2021

The study seeks to analyze the procedures and methods of preparing the records management reference table of the electronic human resource management system dataset, the roles of participating organizations, and the contents of each management reference table area from the records manager's perspective to help the person in charge of establishing the management reference table. Improvement plans were suggested based on the problems that appeared during the process of preparing the reference table. As a major improvement plan, a separate selecting policy at the level of the national archives should be designed for the national important dataset records in the government information system, which should be operated such that it preserves the entire dataset rather than a part. It is necessary to set the unit function-data table-unstructured data mapping data as mandatory items, and the selection and management criteria for unstructured data that significantly influence system operation should be additionally prepared. Regarding the setting of the disposition delay period, because there is an aspect of increasing complexity, it is deemed desirable to operate it by integrating related unit functions or setting the retention period longer.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.177-188
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• 2019

Recently, citizens are feeling anxious as 'Motiveless Crime' increases. The quality of citizens life is degraded and the degree of crime fear is increasing. In this study, based on various variables related to crime other than actual crime occurrence status, crime occurrence points (point line polygon) felt by citizens are created by using mental map methodology. And the purpose of this study is to derive the area of concern for crime through spatial overlap analysis using kernel density estimation analysis. It also uses spatial overlay analysis using kernel density estimation to derive areas of concern for crime occurrence. As a result, the local residents' request point and the areas of concern for crime were overlapped. In addition, the mental map indicating the fear of crime was constructed by mapping mainly the areas between the facilities, the non-construction area such as the narrow area, the security CCTV, the streetlight. This study is meaningful in that it tried to derive a crime occurrence concern area by using mental map method unlike the previous study related to crime. The results of this study, such as mental map, could be used in various fields such as construction of fragile crime map, guideline of crime prevention through environment design.