• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.234-242
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• 2010

In this paper, we deal with the visualization of graphs, which are one of the most important data structures. As the size of a graph increases, it becomes more difficult to check the graph visually because of the increase of edge intersections. We propose a new method of overcoming such problem. Most of previous studies considered only the minimization of edge intersections, but we additionally pursue to preserve distances between vertices. We present a novel genetic algorithm using an evaluation function based on a weighted sum of two objectives. Our experiments could show effective visualization results.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.118-121
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• 1996

This paper describes ECG signal labeling based on Fuzzy clustering, which is necessary at automated ECG diagnosis. The NPPA(Non parametric partitioning algorithm) compares the correlations of wave forms, which tends to recognize the same wave forms as different when the wave forms have a little morphological variation. We propose to apply Fuzzy clustering to ECG QRS Complex labeling, which prevents the errors to mistake by using If-then comparision. The process is divided into two parts. The first part is a parameters extraction process from ECG signal, which is composed of filtering, QRS detection by mapping to a phase space by time delay coordinates and generation of characteristic vectors. The second is fuzzy clustering by FCM(Fuzzy c-means), which is composed of a clustering, an assessment of cluster validity and labeling.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1899-1904
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• 2017

This paper presents a new one-time password approach generated based on $4{\times}4$ pattern schedule. It demonstrates generation of passkey from initial seed of random codes and mapping out in table pattern schedule which will produce a new form of OTP scheme in protecting information or data. The OTP-2FA has been recognized by many organizations as a landmark to authentication techniques. OTP is the solution to the shortcomings of the traditional user name/password authentication. With the application of OTP, some have benefited already while others have had second thoughts because of some considerations like cryptographic issue. This paper presents a new method of algorithmic approach based on table schedule (grid authentication). The generation of OTP will be based on the random parameters that will be mapped out in rows and columns allowing the user to form the XY values to get the appropriate values. The algorithm will capture the values and extract the predefined characters that produce the OTP codes. This scheme can work in any information verification system to enhance the security, trust and confidence of the user.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.632-641
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• 2011

This paper presents a position error recognition system when the wafer is mounted in cleaning equipment among the wafer manufacturing processes. The proposed system is to enhance the performance in cost and reliability by preventing the wafer cleaning system from damaging by alerting it when it is put in correct position. The proposed algorithm is in obtaining a mapping function from camera and physical wafer by designing and manufacturing the radial shape calibrator to reduce the error by using the conventional chess board one. The system is to install in-line process using high reliable and high accurate position recognition. The experimental results show that the performance of the proposed system is better than that of the existing method for detecting errors within tolerance.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.103-103
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• 2019

본 연구에서는 Terra MODIS 위성영상과 Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) 모형을 이용하여 2012년부터 2017년까지 한반도 전국의 증발산량을 산정하고 플럭스 타워 실측 증발산량과 비교하였다. METRIC은 전 세계에 널리 적용된 바 있는 에너지 수지 기반의 Surface Energy Balance Algorithm for Land (SEBAL) 모형의 개념과 기술을 기반으로 현열(Sensible Heat Flux) 추정 모듈을 개선한 모형이다. 본 연구에서 METRIC 모형은 기존 C#으로 개발되어 있던 SEBAL 코드에서 현열 추정 모듈을 수정하였고 연산 속도 개선을 위해 Python으로 재작성하였다. METRIC 모형의 위성 자료로 Terra MODIS 위성의 MOD13A2(16day, 1km) NDVI, MOD11A1(Daily, 1km) Land Surface Temperature (LST) 및 MCD43A3(Daily, 500m) Albedo를 구축하였으며 500m 공간해상도의 Albedo는 1000m 해상도로 resample하여 활용하였다. 기상자료는 기상청 기상관측소의 풍속, 풍속측정높이, 습도, 10분 간격 이슬점 온도, 일사량 자료를 위성 자료와 같은 공간해상도로 내삽(Interpolation)하여 구축하였다. 모형결과 검증을 위해 국내 플럭스 타워 (설마천, 청미천, 덕유산) 증발산량 관측 자료와의 결정계수(Coefficient of determination, $R^2$), RMSE(Root mean square error) relative RMSE (RMSE%), Nash-Sutcliffe efficiency (NSE) 및 IOA(Index of Agreement)를 산정하고, 기존 SEBAL 모형 결과와의 비교를 통해 본 모형의 개선점을 보이고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2805-2826
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• 2021

In most cases, the routing policy of networks shows a preference for a static one-to-one mapping of communication pairs to routing paths, which offers adversaries a great advantage to conduct thorough reconnaissance and organize an effective attack in a stress-free manner. With the evolution of network intelligence, some flexible and adaptive routing policies have already proposed to intensify the network defender to turn the situation. Routing mutation is an effective strategy that can invalidate the unvarying nature of routing information that attackers have collected from exploiting the static configuration of the network. However, three constraints execute press on routing mutation deployment in practical: insufficient route mutation space, expensive control costs, and incompatibility. To enhance the availability of route mutation, we propose an OpenFlow-based route mutation technique called Polymorphic Path Transferring (PPT), which adopts a physical and virtual path segment mixed construction technique to enlarge the routing path space for elevating the security of communication. Based on the Markov Decision Process, with considering flows distribution in the network, the PPT adopts an evolution routing path scheduling algorithm with a segment path update strategy, which relieves the press on the overhead of control and incompatibility. Our analysis demonstrates that PPT can secure data delivery in the worst network environment while countering sophisticated attacks in an evasion-free manner (e.g., advanced persistent threat). Case study and experiment results show its effectiveness in proactively defending against targeted attacks and its advantage compared with previous route mutation methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.127-133
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• 2019

Since frequent item extraction so far requires searching for patterns and traversal for the FP-Tree, it is more likely to store the mining data in a tree and thus CPU time is required for its searching. In order to overcome these drawbacks, in this paper, we provide each item with its location identification of transaction data without relying on conditional FP-Tree and convert transaction data into 2-dimensional position information look-up table, resulting in the facilitation of time and spatial accessibility. We propose an algorithm that considers the mapping scheme between the location of items and items that guarantees the linear time complexity. Experimental results show that the proposed method can reduce many execution time and memory usage based on the data set obtained from the FIMI repository website.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.750-759
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• 2020

The wide application of various integrated location-based services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergency management of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposed to adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic two-dimensional dataset, four-dimensional Iris real dataset and scenic track retention point. The experiment results show that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and k-means. Finally, based on the spatial clustering results of the trajectory stay points, the Getis-Ord Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots of different tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.