• Annual Conference of KIPS
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• 2002.11b
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• pp.1237-1240
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• 2002

본 논문에서는 ATM과 같은 연결 지향적 고속네트워크에서, 가중치를 가진 Flow들의 대역폭 할당과 라우팅문제에 있어 공정성과 처리량에 대하여 고려해 보았다. 가중치클 고려치 않은 Flow들에 대한 최적경로설정문제에 대하여, 기존의 QoS 서비스와 Best-Effort 서비스에서 연구된 라우팅알고리즘에서 벗어나, 본 논문은 가중치를 가진 Flow들에 대하여 Soft-QoS서비스를 지원함에 있어서 공정성과 최대 처리량을 정의하고, 또한 이를 바탕으로 가중치 최소극대 대역폭 할당과 가중치 최소극대 공정라우팅 알고리즘을 제안한다. 종단간 최적경로를 설정하는데, 최소비용으로 Bottleneck-Link를 구하고 대역폭을 할당하기 위하여 그래프 상의 노드에 두 가지 색을 사용하는 그래프문제(Graph Coloring)와 최악의 경우를 감안하면서 경로를 선택하는 최소극대화 문제(Maxmin)를 결부시켜 살펴본다. 나아가 Soft-QoS 서비스의 최대값과 최소값을 고려한 가중치를 가진 Weighted-Flow들의 대역폭 할당과 경로설정에 있어, 동적인 네트워크 환경에 보다 효율적으로 접근 가능한 근사 알고리즘을 제안한다.

• Annual Conference of KIPS
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• 2020.11a
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• pp.12-15
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• 2020

Data access characteristics can directly affect the efficiency of the system execution. This research is to design an accurate predictor by using historical memory access information, where highly accessible data can be migrated from low-speed storage (SSD/HHD) to high-speed memory (Memory/CPU Cache) in advance, thereby reducing data access latency and further improving overall performance. For this goal, we design a locally weighted linear regression prefetch scheme to cope with irregular access patterns in large graph processing applications for a DARM-PCM hybrid memory structure. By analyzing the testing result, the appropriate structural parameters can be selected, which greatly improves the cache prefetching performance, resulting in overall performance improvement.

• Genomics & Informatics
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• v.6 no.4
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• pp.223-226
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• 2008

We introduce a computational approach for analysis of glycosylation in Post-PKS tailoring steps. It is a computational method to predict the deoxysugar biosynthesis unit pathway and the substrate specificity of glycosyltransferases involved in the glycosylation of polyketides. In this work, a directed and weighted graph is introduced to represent and predict the deoxysugar biosynthesis unit pathway. In addition, a homology based gene clustering method is used to predict the substrate specificity of glycosyltransferases. It is useful for the rational design of polyketide natural products, which leads to in silico drug discovery.

• The Pure and Applied Mathematics
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• v.13 no.4 s.34
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• pp.269-280
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• 2006

Recently $L_{\delta}$-groups were introduced in the study of geometric group theory. Three levels of $L_{\delta}$-groups are difined and discussed. It is shown that each of these levels of $L_{\delta}$-groups is closed under taking a direct product.

• Bulletin of the Korean Mathematical Society
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• v.59 no.3
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• pp.745-756
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• 2022

We study a nonlinear wave equation on finite connected weighted graphs. Using Rothe's and energy methods, we prove the existence and uniqueness of solution under certain assumption. For linear wave equation on graphs, Lin and Xie [10] obtained the existence and uniqueness of solution. The main novelty of this paper is that the wave equation we considered has the nonlinear damping term |u_t|^p-1·u_t (p > 1).

• Spatial Information Research
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• v.20 no.4
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• pp.83-92
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• 2012

Location-based services (LBS) are increasingly popular due to the improvement of geo-positioning capabilities and wireless communication technology. However, in order to enjoy LBS services, a user requesting a query must send his/her exact location to the LBS provider. Therefore, it is a key challenge to preserve user's privacy while providing LBS. To solve this problem, the existing method employs a 2PASS cloaking framework that not only hides the actual user location but also reduces bandwidth consumption. However, 2PASS does not fully guarantee the actual user privacy because it does not take the real user distribution into account. Hence, in this paper, we propose a nearest neighbor query processing algorithm that supports K-anonymity property based on the weighted adjacency graph(WAG). Our algorithm not only preserves the location of a user by guaranteeing k-anonymity in a query region, but also improves a bandwidth usage by reducing unnecessary search for a query result. We demonstrate from experimental results that our algorithm outperforms the existing one in terms of query processing time and bandwidth usage.

• The KIPS Transactions:PartA
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• v.12A no.1 s.91
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• pp.13-22
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• 2005

Similarly to the edges defined in a 2D image, we can define the geometric features representing the boundary of the distinctive parts appearing on 3D meshes. The geometric features have been used as basic primitives in several applications such as mesh simplification, mesh deformation, and mesh editing. In this paper, we propose geometric livewire and geometric livelane for extracting geometric features in a 3D mesh, which are the extentions of livewire and livelane methods in images. In these methods, approximate curvatures are adopted to represent the geometric features in a 3D mesh and the 3D mesh itself is represented as a weighted directed graph in which cost functions are defined for the weights of edges. Using a well-known shortest path finding algorithm in the weighted directed graph, we extracted geometric features in the 3D mesh among points selected by a user. In this paper, we also visualize the results obtained from applying the techniques to extracting geometric features in the general meshes modeled after human faces, cows, shoes, and single teeth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.14-23
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• 2016

The problem of fitting B-spline curves to planar point clouds is studied in this paper. A novel method is proposed to deal with the most challenging case where multiple intersecting curves or curves with self-intersection are necessary for shape representation. A method based on Delauney Triangulation of data points is developed to identify connected components which is also capable of removing outliers. A skeleton representation is utilized to represent the topological structure which is further used to create a weighted graph for deciding the merging of curve segments. Different to existing approaches which utilize local shape information near intersections, our method considers shape characteristics of curve segments in a larger scope and is thus capable of giving more satisfactory results. By fitting each group of data points with a B-spline curve, we solve the problems of curve structure reconstruction from point clouds, as well as the vectorization of simple line drawing images by drawing lines reconstruction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.421-433
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• 2015

Recently, there have been fast-growing social network services based on the Internet environment and web technology development, the prevalence of smartphones, etc. Social networks also allow the users to convey the information and news so that they have a great influence on the public opinion formed by social interaction among users as well as the spread of information. On the other hand, these social networks also serve as perfect environments for rampant malware. Malware is rapidly being spread because relationships are formed on trust among the users. In this paper, an effective patch strategy is proposed to deal with malicious worms based on social networks. A graph is formed to analyze the structure of a social network, and subgroups are formed in the graph for the distributed patch strategy. The weighted directions and activities between the nodes are taken into account to select reliable key nodes from the generated subgroups, and the Incremental PageRanking algorithm reflecting dynamic social network features (addition/deletion of users and links) is used for deriving the high influential key nodes. With the patch based on the derived key nodes, the proposed method can prevent worms from spreading over social networks.