• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.47-56
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• 2009

Effective detection and correction of outliers of raw traffic data collected from the field is of keen interest because reliable traffic information is highly dependent on the quality of raw data. Global positioning system (GPS) based traffic surveillance systems are capable of producing individual vehicle speeds that are invaluable for various traffic management and information strategies. This study proposed a locally weighted regression (LWR) based filtering method for individual vehicle speed data. An important feature of this study was to propose a technique to generate synthetic outliers for more systematic evaluation of the proposed method. It was identified by performance evaluations that the proposed LWR-based method outperformed an exponential smoothing. The proposed method is expected to be effectively utilized for filtering out raw individual vehicle speed data.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.297-304
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• 2017

In this study, we have investigated the relationship between festivals evaluation and the theme of festivals programs by using 'AHP survey of experts' on two festivals in each highest and lowest evaluated festivals. As a result, the musical performance of the opening ceremony of Hwajun Festival, the experimental village program of hansung bakjae festival, the street parade of Itaewon and the prehistoric street parade of Gandong cultural festival were found to have the highest relations with the themes. The result reveals the fact that the representative program of festival has been selected, program name was closely related to festival theme, matrix type program and experiential type program where leads the direct participation of visitors were prerequisite, and the festivals with highly related to the theme were also highly evaluated. Therefore, in order for the festival to receive a good evaluation, the high theme-related programs should be focused as main programs of festivals, and the low theme-related programs should pay more attention on their themes for promoting and branding diverse, thematic festivals.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.8-16
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• 2007

Finite element analysis (FEA) is an effective means for the analysis of bioelectromagnetism. It has been successfully applied to various problems over conventional methods such as boundary element analysis and finite difference analysis. However, its utilization has been limited due to the overwhelming computational load despite of its analytical power. We have previously developed a novel mesh generation scheme that produces FE meshes that are content-adaptive to given MR images. MRI content-adaptive FE meshes (cMeshes) represent the electrically conducting domain more effectively with far less number of nodes and elements, thus lessen the computational load. In general, the cMesh generation is affected by the quality of feature maps derived from MRI. In this study, we have tested various feature maps created based on the improved differential geometry measures for more effective cMesh head models. As performance indices, correlation coefficient (CC), root mean squared error (RMSE), relative error (RE), and the quality of cMesh triangle elements are used. The results show that there is a significant variation according to the characteristics of specific feature maps on cMesh generation, and offer additional choices of feature maps to yield more effective and efficient generation of cMeshes. We believe that cMeshes with specific and improved feature map generation schemes should be useful in the FEA of bioelectromagnetic problems.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.438-449
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• 2018

Recently, various graph data have been utilized in various fields such as social networks and citation networks. As the graph changes dynamically over time, it is necessary to manage the graph historical data for tracking changes and retrieving point-in-time graphs. Most historical data changes partially according to time, so unchanged data is stored redundantly when data is stored in units of time. In this paper, we propose a graph history storage management method to minimize the redundant storage of time graphs. The proposed method continuously detects the change of the graph and stores the overlapping subgraph in intersection snapshot. Intersection snapshots are connected by a number of delta snapshots to maintain change data over time. It improves space efficiency by collectively managing overlapping data stored in intersection snapshots. We also linked intersection snapshots and delta snapshots to retrieval the graph at that point in time. Various performance evaluations are performed to show the superiority of the proposed scheme.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.42-53
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• 2020

With the development of social network services, graph structures have been utilized to represent relationships among objects in various applications. Recently, a demand of subgraph matching in real-time graph streams has been increased. Therefore, an efficient approximate Top-k subgraph matching scheme for low latency in real-time graph streams is required. In this paper, we propose an approximate Top-k subgraph matching scheme considering data reuse in graph stream environments. The proposed scheme utilizes the distributed stream processing platform, called Storm to handle a large amount of stream data. We also utilize an existing data reuse scheme to decrease stream processing costs. We propose a distance based summary indexing technique to generate Top-k subgraph matching results. The proposed summary indexing technique costs very low since it only stores distances among vertices that are selected in advance. Finally, we provide k subgraph matching results to users by performing an approximate Top-k matching on the summary indexing. In order to show the superiority of the proposed scheme, we conduct various performance evaluations in diverse real world datasets.

• Ocean and Polar Research
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• v.35 no.1
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• pp.15-26
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• 2013

The toxic, Harmful Algal Blooms (HABs) caused by the Cochlodinium polykrikoides have a serious impact on the coastal waters of Korea. In this study, the acoustic detection system was developed for rapid HABs detection, based on the acoustic backscattering properties of the C. polykrikoides. The developed system was mainly composed of a pulser-receiver board, a signal processor board, a control board, a network board, a power board, ultrasonic sensors (3.5 and 5.0 MHz), an environmental sensor, GPS, and a land-based control unit. To evaluate the performance of the system, a trail was done at a laboratory, and two in situ trials were conducted: (1) when there was no red tide, and (2) when there was red tide. In the laboratory evaluation, the system performed well in accordance with the number of C. polykrikoides in the received level. Second, under the condition when there was no red tide in the field, there was a good correlation between the acoustic data and sampling data. Finally, under the condition when there was red tide in the field, the system successfully worked at various densities in accordance with the number of C. polykrikoides, and the results corresponded with the sampling data and monitoring result of NFRDI (National Fisheries Research & Development Institute). From the laboratory and field evaluations, the developed acoustic detection system for early detecting HABs has demonstrated that it could be a significant system to monitor the occurrence of HABs in coastal regions.

• The Journal of the Korea Contents Association
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• v.17 no.8
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• pp.619-630
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• 2017

With the growing popularity of smart devices, various location based services have been providing to users. Recently, some location based social applications that combine social services and location based services have been emerged. The demands of a k-nearest neighbors(k-NN) query which finds k closest locations from a user location are increased in the location based social network services. In this paper, we propose an approximate k-NN query processing method for fast response time in a large number of users environments. The proposed method performs efficient stream processing using big data distributed processing technologies. In this paper, we also propose a modified grid index method for indexing a large amount of location data. The proposed query processing method first retrieves the related cells by considering a user movement. By doing so, it can make an approximate k results set. In order to show the superiority of the proposed method, we conduct various performance evaluations with the existing method.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.60-69
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• 2017

Recently, with the rapid development of internet and smart phones, social network services that can create and share various information through relationships among users have been actively used. Especially as the amount of information becomes enormous and unreliable information increases, expert recommendation that can offer necessary information to users have been studied. In this paper, we propose an expert recommendation scheme considering users' interests, human relations, and response quality. The users' interests are evaluated by analyzing their past activities in social network. The human relations are evaluated by extracting the users who have the same interesting fields. The response quality is evaluated by considering the user's response speed and response contents. The proposed scheme determines the user's expert score by combining the users' interests, the human relations, and the response quality. Finally, we recommend proper experts by matching queries and expert groups. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.715-725
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• 2020

In this paper, we propose an optimization scheme for a k-Nearest Neighbor(k-NN) query, which finds k objects closest to the query in the high dimensional feature vectors. The k-NN query is converted and processed into a range query based on the range that is likely to contain k data. In this paper, we propose an optimization scheme using DNN model to derive an optimal range that can reduce processing cost and accelerate search speed. The entire system of the proposed scheme is composed of online and offline modules. In the online module, a query is actually processed when it is issued from a client. In the offline module, an optimal range is derived for the query by using the DNN model and is delivered to the online module. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of KIISE:Information Networking
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• v.36 no.2
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• pp.143-152
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• 2009

IEEE 802.11 WLANs provide multiple transmission rates to improve the system throughput by adapting the transmission rate to the current wireless channel conditions. Many rate adaptation schemes have been proposed because IEEE 802.11 standard does not contain any specifications for the rate adaptation scheme. In this paper, in order to overcome limitations of the previous research, we propose a new rate adaptation scheme called SARA(Sender-oriented Automatic Rate Adaptation). The SARA scheme, a proposed rate adaptation scheme, appropriately adjusts the data transmission rate based on the estimated wireless channel conditions, specifically the measured RSSI at the sender-side. Moreover it continuously updates the thresholds for selecting the transmission rate and selectively enforces the RTS/CTS exchanges to adapt the changes in the wireless channel conditions. Through the performance evaluations, we prove that the SARA scheme overcomes the limitations of the previous research and improves the wireless link utilization.