• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.4
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• pp.240-246
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• 2013

In heterogeneous networks, the almost blank subframes (ABS) for inter-cell interference coordination (ICIC), which can be protected from the CCI due to unutilized subframes (i.e., ABS) is proposed. However, the analytical model for ABS-based systems has not been fully studied yet. In this paper, we derive a new analytical model to evaluate the performance of ABS-based systems. In an analytic model, we assume that each carrier in multicarrier systems, such as in OFDMA, is subject to large-scale fading, which is independent of other carriers. As a performance measure, we present the cumulative distribution function (CDF) for the effective SINR. We show the accuracy of the analytical model via simulation results.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1182-1199
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• 2023

Fractional repetition (FR) codes can achieve exact uncoded repair for multiple failed nodes, with lower computational complexity and bandwidth overhead, and effectively improve repair performance in distributed storage systems (DSS). The actual distributed storage system is dynamic, that is, the parameters such as node storage overhead and number of storage nodes will change randomly and dynamically. Considering that traditional FR codes cannot be flexibly applied to dynamic distributed storage systems, a new construction scheme of adaptive-and-resolvable FR codes based on hypergraph coloring is proposed in this paper. Specifically, the linear uniform regular hypergraph can be constructed based on the heuristic algorithm of hypergraph coloring proposed in this paper. Then edges and vertices in hypergraph correspond to nodes and coded packets of FR codes respectively, further, FR codes is constructed. According to hypergraph coloring, the FR codes can achieve rapid repair for multiple failed nodes. Further, FR codes based on hypergraph coloring can be generalized to heterogeneous distributed storage systems. Compared with Reed-Solomon (RS) codes, simple regenerating codes (SRC) and locally repairable codes (LRC), adaptive-and-resolvable FR codes have significant advantages over repair locality, repair bandwidth overhead, computational complexity and time overhead during repairing failed nodes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8B
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• pp.716-729
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• 2006

The information systems in the most enterprise environments are distributed locally and are comprised with various heterogeneous data sources, so that it is difficult to obtain necessary and integrated information for supporting user decision. For solving 'this problems efficiently, it provides uniform interface to users and constructed database systems between heterogeneous systems make a consistence each independence and need to provide transparency like one interface. This paper presents XMDR that consists of category, standard ontology, location ontology and knowledge base. Standard ontology solves heterogeneous problem about naming, attributes, relations in data expression. Location ontology is a mediator that connects each legacy systems. Knowledge base defines the relation for sharing glossary. Adaptive retrieve proposes integrated retrieve system through reflecting site weight by location ontology, information sharing of various forms of knowledge base and integration and propose conceptual domain model about how to share unstructured knowledge.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.83-91
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• 2011

The roadmap DB update for unknown roads is a very important factor for car navigation systems. In this paper, we propose a cloud computing based roadmap generation method for use between heterogeneous navigation system for unknown roads. While the drivers drive on unknown roads, the proposed method extracts the road attribute information, and then generates the metadata in an XML format that is available for the heterogeneous navigation systems in a cloud environment. The metadata is proposed to be used as a replacement for conventional proprietary roadmap formats which used by roadmap providers, which is efficient for heterogeneous navigation system providers in a cloud computing environment. Then, this metadata is provided to the roadmap DB providers through the cloud computing interfaces. With the proposed method, the roadmap DB providers update the own roadmap DB for navigation systems in real time. Therefore, the proposed method can reduce the costs of an actual traveling test and the maintenance for the roadmap DB provides. Thus, the cloud-oriented road map generation method can more efficiently update the unknown road information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1445-1450
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• 2020

Recently, a hologram-like system to provide a realistic experience has been serviced in performances, exhibitions, education. The constructing method for the hologram-like system can be configured in various forms such as a pyramid-typed, a semi-transparent large screen form. However, in various types of hologram-like systems, it is difficult to provide adjustment by changing and revising the content according to the configured hardware characteristics. In this paper, we propose a novel technique that can automatically visualize virtual contents running on heterogeneous hologram-like systems. To change the content to a given hardware configuration, we receive pre-built simple text-based configuration data, and correcting process was performed. According to the results of this paper, we found automatically and easily corrected visualization with the given configuration of the hologram-like system. Also, the problem of reducing the time by manual control in various types of heterogeneous hologram systems was solved.

• Journal of Communications and Networks
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• v.16 no.2
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• pp.202-208
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• 2014

Wireless network information services allow end systems to discover heterogeneous networks and spectrum available for secondary use at or near their current location, helping them to cope with increasing traffic and finite spectrum resources. We propose a unified architecture that allows end systems to find nearby base stations that are using either licensed, shared or unlicensed spectrum across multiple network operators. Our study evaluates the performance and scalability of spatial databases storing base station coverage area geometries. The measurement results indicate that the current spatial databases perform well even when the number of coverage areas is very large. A single logical spatial database would likely be able to satisfy the query load for a large national cellular network. We also observe that coarse geographic divisions can significantly improve query performance.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1802-1814
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• 2012

In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4534-4555
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• 2015

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.