• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.483-491
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• 2013

By a development of car navigation systems and mobile or positioning technology, it increases interest in location based services, especially pedestrian navigation systems. Updating of digital maps is important because digital maps are mass data and required to short updating cycle. In this paper, we proposed change detection for different network data-sets based on areal feature matching. Prior to change detection, we defined type of updating between different network data-sets. Next, we transformed road lines into areal features(block) that are surrounded by them and calculated a shape similarity between blocks in different data-sets. Blocks that a shape similarity is more than 0.6 are selected candidate block pairs. Secondly, we detected changed-block pairs by bipartite graph clustering or properties of a concave polygon according to types of updating, and calculated Fr$\acute{e}$chet distance between segments within the block or forming it. At this time, road segments of KAIS map that Fr$\acute{e}$chet distance is more than 50 are extracted as updating road features. As a result of accuracy evaluation, a value of detection rate appears high at 0.965. We could thus identify that a proposed method is able to apply to change detection between different network data-sets.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Journal of the Economic Geographical Society of Korea
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• v.14 no.2
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• pp.116-128
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• 2011

Some subway passengers may want to have their fresh vegetables purchased through internet at a service facility within the subway station of the Metropolitan Seoul subway system on the way to home, which raises further questions about which stations are chosen to locate service facilities and how many passengers can use the facilities. This problem is well known as the pickup problem, and it can be solved on a traffic network with traffic flows which should be identified from origin stations to destination stations. Since flows of the subway passengers can be found from the smart card transaction database of the Metropolitan Seoul smart card system, the pickup problem in the Metropolitan Seoul subway system is to select subway stations for the service facilities such that captured passenger flows are maximized. In this paper, we have formulated a model of the pickup problem on the Metropolitan Seoul subway system with subway passenger flows, and have proposed a fast heuristic algorithm to select pickup stations which can capture the most passenger flows in each step from an origin-destination matrix which represents the passenger flows. We have applied the heuristic algorithm to select the pickup stations from a large volume of traffic network, the Metropolitan Seoul subway system, with about 400 subway stations and five millions passenger transactions daily. We have obtained not only the experimental results in fast response time, but also displayed the top 10 pickup stations in a subway guide map. In addition, we have shown that the resulting solution is nearly optimal by a few more supplementary experiments.

• Korean Journal of Environment and Ecology
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• v.24 no.5
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• pp.575-581
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• 2010

This study was accomplished for verifying the biotope field survey accuracy in the forests. Biotope data is used as the standard for the preservation and restoration of the urban ecosystem. The study area is the forests of Gwanak-gu, Seoul. For verifying accuracy, first we compared biotope field survey results between 2000 and 2005, second we compared between field survey results and satellite imagery. For comparing with satellite imagery, we delineated the evergreen-coniferous forests from imagery taken during winter season. As a result of comparison, the ratio of most actual vegetation types by delineated detail field surveys were matched around 92% between 2000 and 2005. But, between 2 field surveys, around 60% of total vegetation type was regionally matched. Evergreen-coniferous forests extracted by satellite imagery were regionally matched 69.4% of field survey result in 2000, and matched 80% of the result in 2005. If we consider the delineating errors from deciphering the picture, the results have high accuracy, especially 2005. The processes of verifying accuracy have not been proceeding in the part of delineating actual vegetation works. The verification of accuracy is important for the renewal process. Thus, the various verification methods will be studied and criteria should be developed for enhancing objectivity.

• Journal of Conservation Science
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• v.22
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• pp.31-42
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• 2008

A stone cultural heritage often lacks design drawing and detailed geometric informations, thus it becomes more difficult to conservation and restoration. Even though there is active database of detail shape information and numerical measurement for stone monuments, most of the data is in hard-to-utilize two-dimensional images. The new technology developed to overcome this problem is three-dimensional image scanning system. The multi-storied stone pagoda of the Daewonsa temple was analysed with 3D scanning image data then survey map with orientation displacement was evaluated. The difference of each side became apparent with the members of the stone properties was measured, also horizontal and vertical displacement occurred. Horizontal displacement occurred in increasing severity from left to right and from body section to upper part. The 8th roof stones are leaning toward northwest direction due to lateral displacement. The evaluation and measurement of displacement could cause a little errors due to the characteristics uneven surface of stone monuments, computer program and mistakes from the researcher. In future, more precise measurement and stability studies should be done to suggest that accurate data for conservation and understanding of damage condition can be provided.

• Journal of Korea Multimedia Society
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• v.7 no.8
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• pp.1131-1144
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• 2004

In this paper, we investigate a problem with an e-learning system for e-business environments and introduce the solving method of the problem. To be more accurate, existing Web-hosted and ASP (Application Service Provider)-oriented service model is difficult to cooperate and integrate among the different kinds of systems. So we have produced sharable and reusable learning object, they have extracted a principle from pedagogical designs for units of reuse. We call LIO (Learning Item Object). This modeling makes use of a constructing for XML Web Services. So to speak, units of reuse from pedagogical designs are test tutorial, resource, case example, simulation, problem, test, discovery and discussion and then map introduction, fact, try, quiz, test, link-more, tell-more LIO learning object. These typed LIOs are stored in metadata along with the information for a content location. Each one of LIOs is designed with components and exposed in an interface for XML Web services. These services are module applications, which are used a standard SOAP (Simple Object Access Protocol) and locate any computer over Internet and publish, find and bind to services. This guarantees the interoperation and integration of the different kinds of systems. As a result, the problem of e-learning systems for e-business environments was resolved and then the power of understanding about learning objects based on pedagogical design was increased for learner and instruction designers. And organizations of education hope for particular decreased costs in constructing e-learning systems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.905-910
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• 2013

Road facility has the high possibility of being damaged or destroyed due to continuous pass of the vehicles, overloaded vehicles, traffic accidents and so on and stormwater manhole, sewerage manhole that are installed on the road may cause the functional degradation in case of periodic pavement of the road. So management through establishing DB of road facility and efficient updating plan are required. Thus, this study used DGPS camera for efficient establishment and improvement of road facility DB. Applicability of DGPS camera could be suggested by satisfying the allowable accuracy required for establishing DB of road facility through the comparative analysis with the result of establishment of existing road facility DB and the process of DB establishment by existing total solution could be improved through process analysis. And the existing DB of road facility was improved so that the present conditions of surrounding topography and road facility can be grasped by developing the module that can add the images of road facility to digital map and Google Earth-based KML Builder. It is expected that road facility service that provides various information can be available if the spatial data of each local self-governing body and study of automation that utilizes DGPS camera images are accomplished hereafter.

• Journal of KIISE
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• v.41 no.10
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• pp.762-773
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• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.

• The KIPS Transactions:PartA
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• v.15A no.3
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• pp.141-149
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• 2008

In this research we present a novel method which combines and visualizes the design model and the FDM-based simulation result of solidification. Moreover we employ VR displays and visualize stereoscopic images to provide an effective analysis environment. First we reconstruct the solidification simulation result to a rectangular mesh model using a conventional simulation software. Then each point color of the reconstructed model represents a temperature value of its position. Next we map the two models by finding the nearest point of the reconstructed model for each point of the design model and then assign the point color of the design model as that of the reconstructed model. Before this mapping we apply mesh subdivision because the design model is composed of minimum number of points and that makes the point distribution of the design model not uniform compared with the reconstructed model. In this process the original shape is preserved in the manner that points are added to the mesh edge which length is longer than a predefined threshold value. The implemented system visualizes the solidification simulation data on the design model, which allows the user to understand the object geometry precisely. The immersive and realistic working environment constructed with use of VR display can support the user to discover the defect occurrence faster and more effectively.

• Journal of KIISE
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• v.42 no.3
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• pp.307-319
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• 2015

Current ontology studies use the Hadoop distributed storage framework to perform map-reduce algorithm-based reasoning for scalable ontologies. In this paper, however, we propose a novel approach for scalable Web Ontology Language (OWL) Horst Lite ontology reasoning, based on distributed cluster memories. Rule-based reasoning, which is frequently used for scalable ontologies, iteratively executes triple-format ontology rules, until the inferred data no longer exists. Therefore, when the scalable ontology reasoning is performed on computer hard drives, the ontology reasoner suffers from performance limitations. In order to overcome this drawback, we propose an approach that loads the ontologies into distributed cluster memories, using Spark (a memory-based distributed computing framework), which executes the ontology reasoning. In order to implement an appropriate OWL Horst Lite ontology reasoning system on Spark, our method divides the scalable ontologies into blocks, loads each block into the cluster nodes, and subsequently handles the data in the distributed memories. We used the Lehigh University Benchmark, which is used to evaluate ontology inference and search speed, to experimentally evaluate the methods suggested in this paper, which we applied to LUBM8000 (1.1 billion triples, 155 gigabytes). When compared with WebPIE, a representative mapreduce algorithm-based scalable ontology reasoner, the proposed approach showed a throughput improvement of 320% (62k/s) over WebPIE (19k/s).