• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1836-1842
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• 2015

On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don’t consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.153-154
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• 2008

This paper presents a window-based software package for the education and training for the reduction of power system by using locational marginal price (LMP), clustering, and similarity indices of each bus. The developed package consists of three modules: 1) the LMP module, 2) the Clustering module and 3) the Reduction module. Each module has a separated and interactive interface window. First of all, LMPs are created in the LMP module, and then the Clustering module carries out clustering based on the results of the LMP module. Finally, groups created in this Clustering module are reduced by using the similarity indices of each bus. The developed package displays a variety of tables for results of the LMPs of base network, voltages, phases and power flow of reduced network so that the user can easily understand the reduction of network. To demonstrate the performance of the developed package, it is tested for the IEEE 39-bus power system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4933-4956
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• 2016

Use-After-Free (UAF) is a common lethal form of software vulnerability. By using tools such as Web Browser Fuzzing, a large amount of samples containing UAF vulnerabilities can be generated. To evaluate the threat level of vulnerability or to patch the vulnerabilities, automatic deduplication and exploitability determination should be carried out for these samples. There are some problems existing in current methods, including inadequate pertinence, lack of depth and precision of analysis, high time cost, and low accuracy. In this paper, in terms of key dangling pointer and crash context, we analyze four properties of similar samples of UAF vulnerability, explore the method of extracting and calculate clustering eigenvalues from these samples, perform clustering by fast search and find of density peaks on a large number of vulnerability samples. Samples were divided into different UAF vulnerability categories according to the clustering results, and the exploitability of these UAF vulnerabilities was determined by observing the shape of class cluster. Experimental results showed that the approach was applicable to the deduplication and exploitability determination of a large amount of UAF vulnerability samples, with high accuracy and low performance cost.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.2
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• pp.175-191
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• 2008

In the early stage of industrial development, most of software companies were agglomerated at the CBD(Central Business Districts) in Seoul. However, the spatial distribution pattern of Korean Software industry has been changed according to the propagation of broadband, the change in rents, the governmental policy for industrial districts. In this research, using the software year book at 1997 and 2007, the emerging new pattern was analyzed using spatial clustering analysis. As a results of research, the spatial distribution was expanded in morphological changes. However, it was found that there was not a significant difference in a degree of accumulation. In the aspect of behavioral movement of companies, they tend to be relocated from the CBD to urban fringes and their movement is related to the product life cycle in selecting the clustered place.

• Journal of Platform Technology
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• v.6 no.4
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• pp.26-33
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• 2018

In this paper, we propose an image retrieval algorithm for real-time processing and design it as hardware. The proposed method is based on the classification of BoWs(Bag of Words) algorithm and proposes an image search algorithm using bit stream. K-fold cross validation is used for the verification of the algorithm. Data is classified into seven classes, each class has seven images and a total of 49 images are tested. The test has two kinds of accuracy measurement and speed measurement. The accuracy of the image classification was 86.2% for the BoWs algorithm and 83.7% the proposed hardware-accelerated software implementation algorithm, and the BoWs algorithm was 2.5% higher. The image retrieval processing speed of BoWs is 7.89s and our algorithm is 1.55s. Our algorithm is 5.09 times faster than BoWs algorithm. The algorithm is largely divided into software and hardware parts. In the software structure, C-language is used. The Scale Invariant Feature Transform algorithm is used to extract feature points that are invariant to size and rotation from the image. Bit streams are generated from the extracted feature point. In the hardware architecture, the proposed image retrieval algorithm is written in Verilog HDL and designed and verified by FPGA and Design Compiler. The generated bit streams are stored, the clustering step is performed, and a searcher image databases or an input image databases are generated and matched. Using the proposed algorithm, we can improve convenience and satisfaction of the user in terms of speed if we search using database matching method which represents each object.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3108-3120
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• 2019

Point cloud is widely used in 3D applications due to the recent advancement of 3D data acquisition technology. Polygonal mesh-based compression has been dominant since it can replace many points sharing a surface with a set of vertices with mesh structure. Recent point cloud-based applications demand more point-based interactivity, which makes point cloud compression (PCC) becomes more attractive than 3D mesh compression. Interestingly, an exploration activity has been started to explore the feasibility of PCC standard in MPEG. In this paper, a new color attribute compression method is presented for point cloud data. The proposed method utilizes the spatial redundancy among color attribute data to construct a color palette. The color palette is constructed by using K-means clustering method and each color data in point cloud is represented by the index of its similar color in palette. To further improve the compression efficiency, the spatial redundancy between the indices of neighboring colors is also removed by marking them using a flag bit. Experimental results show that the proposed method achieves a better improvement of RD performance compared with that of the MPEG PCC reference software.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.186-188
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• 2021

본 논문에서는 초음파 영상에서 DBSCAN(Density-based spatial clustering of applications with noise)과 FCM 클러스터링 기반 양자화 기법을 적용하여 결절종을 추출하는 방법을 제안한다. 본 논문에서는 초음파 영상 촬영 시 좌우 상단의 지방층 영역과 하단 영역의 명암도가 어두운 영역을 잡음 영역으로 설정한다. 그리고 초음파 영상에 퍼지스트레칭 기법을 적용하여 잡음 영역을 최대한 제거 한 후에 ROI 영역을 추출한다. 추출된 ROI 영역에서 밀도 분포를 분석하기 위하여 히스토그램을 분석한 후에 DBSCAN을 적용하여 초음파 영상에서 결절종 후보에 해당되는 명암도를 추출한다. 추출한 후보 명암도를 대상으로 FCM 클러스터링 기법을 적용한다. FCM을 적용하는 단계에서 결절종의 저에코 혹은 무에코의 특징을 이용하여 클러스터 중심 값이 가장 낮은 클러스터를 양자화 한 후에 라벨링 기법을 적용시켜 결절종의 후보 객체를 추출한다. 제안된 결절종 추출 방법의 성능을 분석하기 위해 전문의가 결절종 영역을 표기한 초음파 영상과 표기되지 않은 초음파 영상 120쌍을 대상으로 DBSCAN, FCM, 그리고 제안된 방법 간의 성능을 비교 분석하였다. 제안된 방법에서는 120개의 초음파 영상에서 106개 결절종 영역이 추출되었고 FCM 기법에서는 80개가 추출되었고 DBSCAN에서는 36개가 추출되었다. 따라서 제안된 방법이 결절종 추출에 효율적인 것을 확인하였다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.469-477
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• 2009

In mobile systems, energy efficiency is critical to extend battery life. Therefore, power consumption should be taken into account to develop software in addition to performance, Efficient software design in power and performance is possible if accurate power prediction is accomplished during the execution of software, In this paper, power estimation model is developed using statistical analysis, The proposed model analyzes processor behavior Quantitatively using the data of performance monitoring events and power consumption collected by executing various benchmark programs, And then representative hardware events on power consumption are selected using hierarchical clustering, The power prediction model is established by regression analysis in which the selected events are independent variables and power is a response variable, The proposed model is applied to a PXA320 mobile processor based on Intel XScale architecture and shows average estimation error within 4% of the actual measured power consumption of the processor.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1193-1201
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• 2016

In epidemiology or etiology, we are often interested in identifying areas of elevated risk, so called, hot spot or cluster. Many existing clustering methods only tend to a result if there exists any clustering pattern in study area. Recently, however, lots of newly introduced clustering methods can identify the location, size, and shape of clusters and test if the clusters are statistically significant as well. In this paper, one of most commonly used clustering methods, scan statistic, and its implementation SaTScan software, which is freely available, will be introduced. To exemplify the usage of SaTScan software, we used cancer data from the SEER program of National Cancer Institute of U.S.A.We aimed to help researchers and practitioners, who are interested in spatial cluster detection, using female lung cancer mortality data of the SEER program.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.1-18
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• 2023

Similar software is often developed with the Clone-And-Own (CAO) approach that copies and modifies existing artifacts. The CAO approach is considered as a bad practice because it makes maintenance difficult as the number of cloned products increases. Software product line engineering is a methodology that can solve the issue of the CAO approach by developing a product family through systematic reuse. Migrating product families that have been developed with the CAO approach to the product line engineering begins with finding, integrating, and building them as reusable assets. However, cloning occurs at various levels from directories to code lines, and their structures can be changed. This makes it difficult to build product line code base simply by finding clones. Successful migration thus requires unifying the source code's file path, class name, and method signature. This paper proposes a clustering method that identifies a set of similar codes scattered across product variants and some of their method full paths are different, so path unification is necessary. In order to show the effectiveness of the proposed method, we conducted an experiment using the Apo Games product line, which has evolved with the CAO approach. As a result, the average precision of clustering performed without preprocessing was 0.91 and the number of identified common clusters was 0, whereas our method showed 0.98 and 15 respectively.