• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5203-5217
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• 2018

In order to increase the capacity and improve the spectrum efficiency of wireless communication systems, this paper proposes a rate-based two-sided many-to-one matching game algorithm for energy-harvesting small cells with non-orthogonal multiple access (NOMA) in heterogeneous cellular networks (HCN). First, we use a heuristic clustering based channel allocation algorithm to assign channels to small cells and manage the interference. Then, aiming at addressing the user access problem, this issue is modeled as a many-to-one matching game with the rate as its utility. Finally, considering externality in the matching game, we propose an algorithm that involves swap-matchings to find the optimal matching and to prove its stability. Simulation results show that this algorithm outperforms the comparing algorithm in efficiency and rate, in addition to improving the spectrum efficiency.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.1 no.1
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• pp.35-53
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• 1997

This paper analyzes the time complexity of Genetic Algorithm based Task Scheduling (GATS) which is designed for the scheduling of parallel programs with diverse embedded parallelism types in a heterogeneous network systems. The analysis of time complexity is performed based on two representation methods (REIA, REIS) which are proposed in this paper to encode the scheduling information. And the heterogeneous network systems consist of a set of loosely coupled parallel and vector machines connected via a high-speed network. The objective of heterogeneous network computing is to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. Therefore, when scheduling in heterogeneous network systems, the matching of the parallelism characteristics between tasks and parallel machines should be carefully handled in order to obtain more speedup. This paper shows how the parallelism type matching affects the time complexity of GATS.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.311-321
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• 2007

For fast and robust face detection, this paper proposes an approach for face detection using fusion of heterogeneous template matching. First, we detect skin regions using a model of skin color which covers various illumination and races. After reducing a search space by region labelling and filtering, we apply template matching with skin color and edge to the detected regions. Finally, we detect a face by finding the best choice of template fusion. Experimental results show the proposed approach is more robust in skin color-like environments than with a single template matching and is fast by reducing a search space to face candidate regions. Also, using a global accumulator can reduce excessive space requirements of template matching.

• The Journal of the Korea Contents Association
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• v.6 no.10
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• pp.25-33
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• 2006

For semantic retrieval in semantic web environment, it is an important factor to manage and manipulate distributed resources. Ontology is essential for efficient search in distributed resources, but it is almost impossible to construct an unified ontology for all distributed resources in the web. In this paper, we assumed that most information in the web environment exist in the form of RDBMS, and propose a matching method between domain ontology and the existing RDBMS tables for semantic retrieval. Most previous studies about matching between RDBMS tables and domain ontology have extracted a local ontology from RDBMS tables at first, and conducted the matching between the local ontology and domain ontology. However in the processing of extracting a local ontology, some problems such as losing domain information can be occurred since its correlation with domain ontology has not been considered at all. In this paper, we propose a methods to prevent the loss of domain information through the similarity measure between instances of RDBMS tables and instances of ontology. And using the relational information between RDBMS tables and the relational information between classes in domain ontology, more efficient instance-based matching becomes possible.

• East Asian Economic Review
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• v.24 no.3
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• pp.275-301
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• 2020

We analyze state-level matching efficiencies in the Indian labor market using stochastic frontier analysis. The key contribution of this research is the estimation of matching efficiencies at the state level because these can be used for a state-level measure of labor market conditions. Next, we explore the relationship between the estimated matching efficiencies and population density, labor market flexibility, and the Ease of Doing Business index, respectively. The results show that matching efficiency is heterogeneous across states with considerable variation in accordance with the regional diversity in India. However, we find that there is little relationship between the estimated matching efficiencies and the labor market conditions of interest, suggesting that other regional diversity affects matching efficiencies across states in India.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.373-382
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• 2016

In this study, an EKF (Extended Kalman Filter) based database reference navigation using both gravity gradient and terrain data was performed to complement the weakness of using only one type of geophysical DB (Database). Furthermore, a new algorithm which combines the EKF and profile matching was developed to improve the stability and accuracy of the positioning. On the basis of simulations, it was found that the overall navigation performance was improved by the combination of geophysical DBs except the two trajectories in which the divergence of TRN (Terrain Referenced Navigation) occurred. To solve the divergence problem, the profile matching algorithm using the terrain data is combined with the EKF. The results show that all trajectories generate the stable performance with positioning error ranges between 14m to 23m although not all trajectories positioning accuracy is improved. The average positioning error from the combined algorithm for all nine trajectories is about 18 m. For further study, a development of a switching geophysical DB or algorithm between the EKF and the profile matching to improve the navigation performance is suggested.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.848-855
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• 2016

Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2009.04a
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• pp.23-25
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• 2009

Nowadays, owing to the development of techniques for collecting geographic information, an increasing need is thus appearing: integrating heterogeneous databases. This paper proposes an algorithm for finding matching relationship between two node sets in road network data. We found the corresponding node pair using link shape linked with them as well as their location. The accuracy of matching was grown by this process. Result then can be used to reflect the topological information in performing link matching.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.86-95
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• 2016

We propose a new approach to the stochastic version of Lanchester model. Commonly used approach to stochastic Lanchester model is through the Markov-chain method. The Markov-chain approach, however, is not appropriate to high dimensional heterogeneous force case because of large computational cost. In this paper, we propose an approximation method of stochastic Lanchester model. By matching the first and the second moments, the distribution of each unit strength can be approximated with multivariate normal distribution. We evaluate an approximation of discrete Markov-chain model by measuring Kullback-Leibler divergence. We confirmed high accuracy of approximation method, and also the accuracy and low computational cost are maintained under high dimensional heterogeneous force case.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.1-9
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• 2013

This paper proposes a matching algorithm to find corresponding polygon feature sets between heterogeneous digital maps. The algorithm finds corresponding sets in terms of optimizing their shape similarities based on the assumption that the feature sets describing the same entities in the real world are represented in similar shapes. Then, by using a binary code, it is represented that a polygon feature is chosen for constituting a corresponding set or not. These codes are combined into a binary string as a candidate solution of the matching problem. Starting from initial candidate solutions, a genetic algorithm iteratively optimizes the candidate solutions until it meets a termination condition. Finally, it presents the solution with the highest similarity. The proposed method is applied for the topographical and cadastral maps of an urban region in Suwon, Korea to find corresponding polygon feature sets for block areas, and the results show its feasibility. The results were assessed with manual detection results, and showed overall accuracy of 0.946.