• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.9
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• pp.317-325
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• 2017

In this paper, we try to classify Korean hedge sentences, which are regarded as not important since they express uncertainties or personal assumptions. Through previous researches to English language, we found dependency information of words has been one of important features in hedge classification, but not used in Korean researches. Additionally, we found that word embedding vectors include the word usage information. We assume that the word usage information could somehow represent the dependency information. Therefore, we utilized word embedding and neural networks in hedge sentence classification. We used more than one and half million sentences as word embedding dataset and also manually constructed 12,517-sentence hedge classification dataset obtained from online news. We used SVM and CRF as our baseline systems and the proposed system outperformed SVM by 7.2%p and also CRF by 1.2%p. This indicates that word usage information has positive impacts on Korean hedge classification.

• The Journal of Korean Medicine
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• v.44 no.3
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• pp.74-86
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• 2023

Objectives: The purpose of this study was to examine search trends and their meanings for major uninsuired Korean medicine (KM) treatments through analysis of an online portal search results. Methods: Keywords searches were performed using Naver Datalab on 4 July 2023. From January 2016 to June 2023, monthly relative search volume (RSV) for keywords 'pharmacopuncture', 'Chuna', and 'needle-embedding therapy', and 'herbal decoction' were extracted with a score between 0 and 100. For the obtained RSVs, longitudinal changes over time, characteristics according to sex and age group, and correlations between them were investigated. Results: The ranking of RSV for each keyword has changed from 'Chuna', 'herbal decoction', 'needle-embedding therapy', and 'pharmacopuncture' to 'Chuna', 'herbal decoction', 'pharmacopuncture', and 'needle-embedding therapy' after 2019. Overall, the RSV of needle-embedding therapy continuously decreased, while that of pharmacopuncture continuously increased. In 2019, a rapid increase in the RSV of Chuna was observed, and in 2020, a rapid increase in the RSV of herbal decoction was observed. There was a difference in the longitudinal change pattern of RSV for the keywords by age group. Importantly, in the elderly, changes in RSV were observed in a favorable pattern to KM treatment. Conclusion: Our findings enable estimation of the public's interest and its changes for the four uninsuired KM treatment, and can be used as basic data to strengthen health insurance coverage in Korea. Specifically, changes in interest in KM treatments according to sex and age can be referred to.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.265-267
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• 1999

XML 언어로 설계된 SMIL(Synchronized Multimedia Integration Language)은 멀티미디어 객체들의 순차적 혹은 병렬적 동기화를 효율적으로 할 수 있는 마크업 언어로써, web을 이용한 원격 강의나 홍보 등을 더욱 생성하고 dynamic하게 보여 줄 수 있어, 그 사용이 확대될 전망이다. 본 논문에서는 각종 웹 단말기에 손쉽게 embedding 될 수 있는 SMIL 프로세서에 대한 설계가 제안된다. 웹 응용을 위해, 속도의 개선과 시스템 독립적인 function들로 구성되는 parser와 응용에 적합한 API의 설계에 주안점을 두었으며, 추후 XML parser function들과 API 설계를 위해 가능한 적은 수정을 통하여 재사용이 가능하도록 하는데 또한 주안점을 두고 있다.

• ETRI Journal
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• v.32 no.6
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• pp.969-971
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• 2010

Based on a de-embedding technique, a new method is proposed which is capable of evaluating chip impedance behavior over absorbed power in flip-chip bonded UHF radio frequency identification transponder ICs. For the de-embedding, four compact co-planar test fixtures, an equivalent circuit for the fixtures, and a parameter extraction procedure for the circuit are developed. The fixtures are designed such that the chip can absorb as much power as possible from a power source without radiating appreciable power. Experimental results show that the proposed modeling method is accurate and produces reliable chip impedance values related with absorbed power.

• ETRI Journal
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• v.35 no.3
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• pp.512-522
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• 2013

In this paper, the radius weight mean (RWM) and the feature-embedding technique are used to present a novel watermarking scheme for color images. Simulations validate that the stego-images generated by the proposed scheme are robust against most common image-processing operations, such as compression, color quantization, bit truncation, noise addition, cropping, blurring, mosaicking, zigzagging, inversion, (edge) sharpening, and so on. The proposed method possesses outstanding performance in resisting high compression ratio attacks: JPEG2000 and JPEG. Further, to provide extra hiding storage, a steganographic method using the RWM with the least significant bit substitution technique is suggested. Experiment results indicate that the resulting perceived quality is desirable, whereas the peak signal-to-noise ratio is high. The payload generated using the proposed method is also superior to that generated by existing approaches.

• ETRI Journal
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• v.35 no.1
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• pp.142-145
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• 2013

Network virtualization opens the door to novel infrastructure services offering connectivity and node manageability. In this letter, we focus on the cost-efficient embedding of on-demand virtual optical network requests for interconnecting geographically distributed data centers. We present a mixed integer linear programming formulation that introduces flexibility in the virtual-physical node mapping to optimize the usage of the underlying physical resources. Illustrative results show that flexibility in the node mapping can reduce the number of add-drop ports required to serve the offered demands by 40%.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.631-635
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• 2004

The rapid development of digital media and communication network urgently brings about the need of data certification technology to protect IPR (Intellectual property right). This paper proposed a new watermarking method for embedding contents owner's audio signal in order to protect color image IPR. Since this method evolves the existing static model and embeds audio signal of big data, it has the advantage of restoring signal transformed due to attacks. Three basic stages of watermarking include: 1) Encode analogue ID owner's audio signal using PCM and create new 3D audio watermark; 2) Interleave 3D audio watermark by linear bit expansion and 3) Transform Y signal of color image into wavelet and embed interleaved audio watermark in the low frequency band on the transform domain. The results demonstrated that the audio signal embedding in color image proposed in this paper enhanced robustness against lossy JPEG compression, standard image compression and image cropping and rotation which remove a part of image.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.172-175
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• 2002

This paper presents perceptual model with a stochastic rnultiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the SSQ(successive subband quantization). The watermark embedding is based on the computation of a NVF(noise visibility function) that have local image properties. This method uses non-stationary Gaussian model stationary Generalized Gaussian model because watermark has noise properties. In order to determine the optimal NVF, we consider the watermark as noise. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model use the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark benchmark test.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.75-81
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• 2019

Though recent advancement of deep learning methods have provided satisfactory results from large data domain, somehow yield poor performance on few-shot classification tasks. In order to train a model with strong performance, i.e. deep convolutional neural network, it depends heavily on huge dataset and the labeled classes of the dataset can be extremely humongous. The cost of human annotation and scarcity of the data among the classes have drastically limited the capability of current image classification model. On the contrary, humans are excellent in terms of learning or recognizing new unseen classes with merely small set of labeled examples. Few-shot learning aims to train a classification model with limited labeled samples to recognize new classes that have neverseen during training process. In this paper, we increase the backbone depth of the embedding network in orderto learn the variation between the intra-class. By increasing the network depth of the embedding module, we are able to achieve competitive performance due to the minimized intra-class variation.

• Proceedings of The KACE
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• 2017.08a
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• pp.157-160
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• 2017

최근 자연 언어 처리 분야에서는 단어를 실수벡터로 임베딩하는 워드 임베딩(Word embedding) 기술이 많은 각광을 받고 있다. 최근에는 서로 다른 두 언어를 이용한 이중 언어 위드 임베딩(Bilingual word embedding) 방법을 사용하는 연구가 많이 이루어지고 있는데, 이중 언어 워드 임베딩에서 임베딩 절과의 질은 학습하는 코퍼스의 정렬방식에 따라 많은 영향을 받는다. 본 논문은 자막 병렬 코퍼스를 이용하여 밑바탕 어휘집(Seed lexicon)을 구축하여 번역 연결 강도를 향상시키고, 이중 언어 워드 임베딩의 사천(Vocabulary) 확장을 위한 언어별 연결 함수(Language-specific mapping function)을 학습하는 새로운 방식의 모델을 제안한다. 제안한 모델은 기존 모델과의 성능비교에서 비교할만한 수준의 결과를 얻었다.