• ETRI Journal
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• v.34 no.3
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• pp.410-420
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• 2012

A series of reversible watermarking technologies have been proposed to increase embedding capacity and the quality of the watermarked image simultaneously. The major skills include difference expansion, histogram shifting, and optimizing embedding order. In this paper, an accurate predictor is proposed to enhance the difference expansion. An efficient sorter is also suggested to find a more desirable embedding order. The payload is differently distributed into two sub-images, split like a chessboard pattern, for better watermarked image quality. Simulation results of the accurate prediction and sorter based on the payload balancing method yield generally better performance over previous methods. The gap is wide, in particular, in low payload for natural images. The peak signal-to-noise ratio improvement is around 2 dB in low payload ranges.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.857-860
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• 1993

In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.4
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• pp.151-154
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• 2013

In this paper, we will analyze embedding between Folded Hypercube and HFH. We will show Folded Hypercube $FQ_{2n}$ can be embedded into HFH($C_n,C_n$) with dilation 4, expansion $\frac{(C_n)^2}{2^{2n}}$ and HFH($C_d,C_d$) can be embedded into $FQ_{4d-2}$ with dilation O(d).

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.3
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• pp.193-200
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• 2011

As larger ships and floating offshore structures are, and rougher the marine environment becomes nowadays, a drag embedment type anchor of more stable performance and higher holding power is requested. This paper describes an experimental study of the drag embedding motion and the resultant holding force of three types of drag embedment type anchor model (HALL, AC-14, SEC POOL-N, scale 1/10).

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.87-97
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• 2019

Reaction of people is importantly considered about specific case as a social network service grows. In the previous research on analysis of social network service, they predicted tendency of interesting topic by giving scores to sentences written by user. Based on previous study we proceeded research of sentiment analysis for social network service's sentences, which predict the result as positive or negative for movie reviews. In this study, we used movie review to get high accuracy. We classify the movie review into positive or negative based on the score for learning. Also, we performed embedding and morpheme analysis on movie review. We could predict learning result as positive or negative with a number 0 and 1 by applying the model based on learning result to social network service. Experimental result show accuracy of about 80% in predicting sentence as positive or negative.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1291-1306
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• 2022

The dual image-based reversible data hiding scheme embeds secret data into two images to increase the embedding capacity of secret data. The dual image-based reversible data hiding scheme can transmit a lot of secret data. Therefore, various schemes have been proposed until recently. In 2021, Chen and Hong proposed a dual image-based reversible data hiding scheme that embeds a large amount of secret data using a reference matrix, secret data, and bit values. However, in this paper, more secret data can be embedded than Chen and Hong's scheme. To achieve this goal, the proposed scheme generates polynomials and shared values using secret sharing scheme, and embeds secret data using reference matrix and septenary number, and random value. Experimental results show that the proposed scheme can transmit more secret data to the receiver while maintaining the image quality similar to other dual image-based reversible data hiding schemes.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.399-403
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• 2022

Natural texts are analyzed to obtain their intended meaning to be classified depending on the problem under study. One way to represent words is by generating vectors of real values to encode the meaning; this is called word embedding. Similarities between word representations are measured to identify text class. Word embeddings can be created using word2vec technique. However, recently fastText was implemented to provide better results when it is used with classifiers. In this paper, we will study the performance of well-known classifiers when using both techniques for word embedding with Arabic dataset. We applied them to real data collected from Wikipedia, and we found that both word2vec and fastText had similar accuracy with all used classifiers.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.75-88
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• 2023

In a specific domain, experts have different understanding of domain knowledge or different purpose of constructing ontology. These will lead to multiple different ontologies in the domain. This phenomenon is called the ontology heterogeneity. For research fields that require cross-ontology operations such as knowledge fusion and knowledge reasoning, the ontology heterogeneity has caused certain difficulties for research. In this paper, we propose a novel ontology matching model that combines word embedding and a concatenated continuous bag-of-words model. Our goal is to improve word vectors and distinguish the semantic similarity and descriptive associations. Moreover, we make the most of textual and structural information from the ontology and external resources. We represent the ontology as a graph and use the SimRank algorithm to calculate the structural similarity. Our approach employs a similarity queue to achieve one-to-many matching results which provide a wider range of insights for subsequent mining and analysis. This enhances and refines the methodology used in ontology matching.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.3
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• pp.37-43
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• 2009

Steganography conceals the fact that hidden message is being sent on the internet. Steganalysis can be detected the abrupt changes in the statistics of a stego-data. After message embedding, I have analyzed for the statistical significance of the fact the occurrence of differences among the four-neighboring pixels. In this case, when a embedding messages within a images is small, use EC value and chi-square test to determine whether a distribution in an images matches a distribution that shows distortion from stego-data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.11-12
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• 2023

본 논문에서는 상점 내 캡티브 포털을 활용하여 수집된 주문 정보 데이터를 바탕으로 사용자가 선호하는 메뉴를 추천하는 시스템을 제안한다. 이 시스템은 식품 관련 공공 데이터셋으로 학습된 단어 임베딩 모델(Word Embedding Model)로 메뉴명을 벡터화하여 그와 유사한 벡터를 가지는 메뉴를 추천한다. 이 기법은 캡티브 포털에서 수집되는 데이터 특성상 사용자의 개인정보가 비식별화 되고 선택 항목에 대한 정보도 제한되므로 기존의 단어 임베딩 모델을 추천 시스템에 적용하는 경우에 비해 유리하다. 본 논문에서는 실제 동일한 시스템을 사용하는 상점들의 구매 기록 데이터를 활용한 검증 데이터를 확보하여 제안된 추천 시스템이 Precision@k(k=3) 구매 예측에 유의미함을 보인다.