• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.744-750
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• 2010

To segment instances of vehicle classes in a sparse stereo-matching data set, this paper presents an algorithm for clustering based on DP (Dynamic Programming). The algorithm is agglomerative: it begins with each element in the set as a separate cluster and merges them into successively larger clusters according to similarity of two clusters. Here, similarity is formulated as a cost function of DP. The proposed algorithm is proven to be effective by experiments performed on various images acquired by a moving vehicle.

• 한국전산유체공학회:학술대회논문집
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• 2006.10a
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• pp.207-207
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• 2006

Hardware technologies for high-performance computing has been developing continuously. However, actual performance of software cannot keep up with the speed of development in hardware technologies, because hardware architectures become more and more complicated and hardware scales become larger. So, software technique to utilize high-performance computing systems more efficiently plays more important role in realizing high-performance computing for computational science. In this paper, the effort to enhance software performance on large and complex high-performance computing systems such as performance optimization and parallelization will be presented. Our effort to serve high-performance computational kernels such as high-performance sparse solvers and the achievements through this effort also will be introduced.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.131-149
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• 2004

Collaborative filtering is one of popular techniques for personalized recommendation in e-commerce sites. An advantage of collaborative filtering is that the technique can work with sparse evaluation data to predict preference scores of new alternative contents or advertisements. There is, however, no in-depth study about the sparsity effect of customer's evaluation data to the performance of recommendation. In this study, we investigate the sparsity effect and hybrid usages of customers' evaluation data and purchase data using an experiment result. The result of the analysis shows that the performance of recommendation decreases monotonically as the sparsity increases, and also the hybrid usage of two different types of data; customers' evaluation data and purchase data helps to increase the performance of recommendation in sparsity situation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.227-232
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• 2003

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.

• ETRI Journal
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• v.33 no.3
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• pp.355-365
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• 2011

IPTV broadcast channels and video content distribution are increasingly saturating network paths. New solutions based on inter-domain multicast protocols could contribute to the enhancement of multimedia content distribution over the Internet. The aim of this paper is to propose new capabilities for an existing inter-domain multicast protocol, the Protocol Independent Multicast-Sparse Mode. We describe the modified protocol and analyze its behavior using newly developed tools based on an open-source software simulator. The resulting protocol does not require topology information, which is advantageous for easier deployment. In addition, the adopted solution avoids inherent problems with inter-domain multicast routing, such as multiple paths and path asymmetries.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.96-112
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• 2020

Solving the disclosure problem of sensitive information with the k-nearest neighbor query, location dummy technique, or interfering data in location-based services (LBSs) is a new research topic. Although they reduced security threats, previous studies will be ineffective in the case of sparse users or K-successive privacy, and additional calculations will deteriorate the performance of LBS application systems. Therefore, a model is proposed herein, which is based on geohash-encoding technology instead of latitude and longitude, memcached server cluster, encryption and decryption, and authentication. Simulation results based on PHP and MySQL show that the model offers approximately 10× speedup over the conventional approach. Two problems are solved using the model: sensitive information in LBS application is not disclosed, and the relationship between an individual and a track is not leaked.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.700-702
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• 2005

본 논문에서는 대규모 베이지안 망을 빠른 시간 안에 학습하기 위한 방법으로, 클러스터링을 이용한 방법을 제안한다. 제안하는 방법은 베이지안 구조 학습에 있어서 DAG(Directed Acyclic Graph)를 탐색하는 영역을 제한하기 위해 클러스터링을 사용한다. 기존의 베이지안 구조 학습 방법들이 고려하는 후보 DAG의 수가 전체 노드 수에 의해 제한되는 데 반해, 제안되는 방법에서는 미리 정해진 클러스터의 최대 크기에 의해 제한된다. 실험 결과를 통해, 제안하는 방법이 기존의 대규모 베이지안 망 학습에 활용되었던 SC(Sparse Candidate) 방법 보다 훨씬 적은 수의 후보 DAG만을 고려하였음에도 불구하고, 비슷한 정도의 정확도를 나타냄을 보인다.

• Water for future
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• v.29 no.1
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• pp.141-150
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• 1996

Steady state analysis of pressure and flow in water supply piping systems is a problem of great importance in hydraulic engineering. The basic equations consist of continuity equation and energy equation. The network equations are solved iteratively by using linear solution method. The resulting linear simultaneous equations are solved by frontal method. Frontal method, which is suitable to sparse matrix, gathers only non-zero entries in coefficient matrix. The suggested methodology can analyze faster than the existing routines by using smaller computer memory. The model presented in this study shows accurate and efficient results for various piping systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.231-237
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• 2008

This research proposes a modified version of single pass algorithm specialized for text clustering. Encoding documents into numerical vectors for using the traditional version of single pass algorithm causes the two main problems: huge dimensionality and sparse distribution. Therefore, in order to address the two problems, this research modifies the single pass algorithm into its version where documents are encoded into not numerical vectors but other forms. In the proposed version, documents are mapped into tables and the operation on two tables is defined for using the single pass algorithm. The goal of this research is to improve the performance of single pass algorithm for text clustering by modifying it into the specialized version.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.352-368
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• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.