• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.35-42
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• 2009

This paper proposes a new coverage algorithm for intelligent robot. Many algorithms for improving the performance of coverage have been focused on minimizing the total coverage completion time. However, if one does not have enough time to finish the whole coverage, the optimal path could be different. To tackle this problem, we propose a new coverage algorithm, which we call MaxCoverage algorithm, for covering maximal area within the deadline. The MaxCoverage algorithm decides the navigation flow by greedy algorithm for Set Covering Problem. The experimental results show that the MaxCoverage algorithm performs better than other algorithms for random deadlines.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.375-384
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• 2021

Deep learning techniques have been proven to have high performance in image processing and are applied in various fields. The most widely used methods for validating a deep learning model include a holdout verification method, a k-fold cross verification method, and a bootstrap method. These legacy methods consider the balance of the ratio between classes in the process of dividing the data set, but do not consider the ratio of various features that exist within the same class. If these features are not considered, verification results may be biased toward some features. Therefore, we propose a deep learning model validation method based on data feature coverage for image classification by improving the legacy methods. The proposed technique proposes a data feature coverage that can be measured numerically how much the training data set for training and validation of the deep learning model and the evaluation data set reflects the features of the entire data set. In this method, the data set can be divided by ensuring coverage to include all features of the entire data set, and the evaluation result of the model can be analyzed in units of feature clusters. As a result, by providing feature cluster information for the evaluation result of the trained model, feature information of data that affects the trained model can be provided.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.200-206
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• 2024

Computing music similarity is an indispensable component in developing music search service. This paper proposes a relevance weight of each chromagram vector for cover song identification in computing a music similarity function in order to boost identification accuracy. We derive a music similarity function using the relevance weight based on the probabilistic relevance model, where higher relevance weights are assigned to less frequently-occurring discriminant chromagram vectors while lower weights to more frequently-occurring ones. Experimental results performed on two cover music datasets show that the proposed music similarity improves the cover song identification performance.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.518-524
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• 2018

The cover song refers to live recordings or reproduced albums. This paper studies two-dimensional Fourier transform as a feature-dimension reduction method to search cover song fast. The two-dimensional Fourier transform is conducive in feature-dimension reduction for cover song search due to musical-key invariance. This paper extends the previous work, which only utilize the magnitude of the Fourier transform, by introducing an invariant from phase based on the assumption that adjacent frames have the same musical-key change. We compare the cover song retrieval accuracy of the Fourier-transform based methods over two datasets. The experimental results show that the addition of the invariant from phase improves the cover song retrieval accuracy over the previous magnitude-only method.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.85-90
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• 2008

In this paper, we propose a new robot cleaning algorithm, which we call SetClean. The new algorithm cleans from the most less complex area. Sometimes, when the cleaning completion time can be longer or can not be estimated, cleaning larger area first is better than optimizing the whole time for cleaning. To do this, SetClean algorithm divides the whole area into cleanable sub-areas using Set Cover algorithm and cleans the area in the order of high efficiency that maximize the cleanable area per unit time. SetClean algorithm decides the navigation flow by considering not only the size of the area but also the distance from the current robot location to the area to be cleaned and the delay time caused by the number of turns within the area. The experimental results show the mechanism and performance of the SetClean algorithm.

• The Journal of the Acoustical Society of Korea
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• v.36 no.6
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• pp.436-441
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• 2017

Computing music similarity is indispensable in constructing music retrieval system. This paper focuses on the cover song search among various music-retrieval tasks. We investigate the cover song search method based on the chroma n-gram to reduce storage for feature DB and enhance search accuracy. Specifically we propose t-tab n-gram, n-gram selection method, and n-gram set comparison method. Experiments on the widely used music dataset confirmed that the proposed method improves cover song search accuracy as well as reduces feature storage.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.314-319
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• 2019

Computing chromagram similarity is indispensable in constructing cover song identification system. This paper proposes a code-based chromagram similarity to reduce the computational and the storage costs for cover song identification. By learning a song-specific codebook, a chromagram sequence is converted into a code sequence, which results in the reduction of the feature storage cost. We build a lookup table over the learned codebooks to compute chromagram similarity efficiently. Experiments on two music datasets were performed to compare the proposed code-based similarity with the conventional one in terms of cover song search accuracy, feature storage, and computational cost.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.662-667
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• 2022

Computing music similarity is an indispensable component in developing music search service. This paper focuses on learning a music similarity function in order to boost cover song identification performance. By using the probabilistic linear discriminant analysis, we construct a latent music space where the distances between cover song pairs reduces while the distances between the non-cover song pairs increases. We derive a music similarity function by testing hypothesis, whether two songs share the same latent variable or not, using the probabilistic models with the assumption that observed music features are generated from the learned latent music space. Experimental results performed on two cover music datasets show that the proposed music similarity improves the cover song identification performance.

• Journal of the Korean Geographical Society
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• v.48 no.2
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• pp.259-271
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• 2013

This paper addresses the efficient facility placements to adopt a super WiFi network, taking significant considerations as the next generation 'information highway'. Since the super WiFi has a wider geographic coverage by utilizing the white spaces of TV broadcasting which are empty and available frequencies for the wireless communications, it would play an important role in releasing digital divide of the internet access for low populated or mountainous areas. The purpose of this paper is to explore systematic and efficient spatial plans for the super WiFi. For doing this, we applied optimal location covering models to Gurye-gun, Jeonlanamdo. From the application, we presented optimal locations for super WiFi facilities and significant analytical results, such as the tradeoff between the number of facilities and coverage and marginal coverage for establishing super WiFi network. The results of this research would be usefully utilized for decision makers who wish to adopt a super WiFi, to extend wireless networks in a city or build a regional infrastructure of wireless facilities.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.775-784
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• 2011

In this paper we introduce the notion of multi-receiver certificateless encryption that avoids the inherent key escrow problem of multi-receiver identity-based encryption, and also present a highly efficient multi-receiver certificateless encryption scheme which eliminates pairing computation to encrypt a message for multiple receivers, Moreover, the proposed scheme only needs one pairing computation to decrypt the ciphertext. Finally, we discuss how to properly transform our scheme into a new public key broadcast encryption scheme for stateless receivers based on the subset-cover framework, which enjoys the advantages of certificateless cryptography.