• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.45-53
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• 2018

Recently, studies about bilingual word embedding have been gaining much attention. However, bilingual word embedding with Korean is not actively pursued due to the difficulty in obtaining a sizable, high quality corpus. Local embeddings that can be applied to specific domains are relatively rare. Additionally, multi-word vocabulary is problematic due to the lack of one-to-one word-level correspondence in translation pairs. In this paper, we crawl 868,163 paragraphs from a Korean-English law corpus and propose three mapping strategies for word embedding. These strategies address the aforementioned issues including multi-word translation and improve translation pair quality on paragraph-aligned data. We demonstrate a twofold increase in translation pair quality compared to the global bilingual word embedding baseline.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.179-180
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• 2019

Nowadays, clustering algorithm is considered as a promising solution for lacking human-labeled and massive data of social media sites in numerous machine learning tasks. Many researchers propose disaster event detection systems have ability to determine special local events, such as missing people, public transport damage by clustering similar tweets and hashtags together. In this paper, we try to extend tweet hashtag feature definition by applying word embedding. The experimental results are described that word embedding achieve better performance than the reference method.

• Journal of Science and Technology Studies
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• v.11 no.1
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• pp.1-29
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• 2011

The purpose of this study is to develop a theoretical framework to analyze the social processes of embedding new technologies, among others, green technologies, in society, and based on this, to identify problems and challenges in introducing and assimilating biogas technologies in local communities in Korea. Chapter Two strives to develop a framework to analyze the social processes of embedding new technologies in society. A couple of key concepts such as technology community, technology learning and technology politics are introduced and discussed. Chapter Three and Four examine the problems arising from the social processes of embedding biogas plant technologies in local communities in Korea and tries to suggest policy options to tackle these problems.

• Journal of Pharmacopuncture
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• v.24 no.4
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• pp.173-181
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• 2021

Objectives: The aim of this review was to appraise Korean studies published between 2010 and 2021 which examined the role of acupuncture in the treatment of obesity. Methods: We performed a search of the NDSL, KISS, RISS, OASIS, PubMed, EMBASE electronic databases for relevant animal researches, case reports, and clinical trials, using the following search terms: 'obesity', 'acupuncture', 'electroacupuncture', and 'pharmacopuncture'. We excluded previous reviews and meta-analyses, studies not related to obesity or acupuncture treatment, as well as studies conducted in countries other than Korea. We also excluded studies where relevant information on acupuncture treatment in obesity could not be obtained. Results: Most studies were conducted in animals, followed by case reports and clinical trials. In animal researches and case reports, pharmacopuncture was the most used intervention. In case studies, electroacupuncture, thread-embedding therapy, manual acupuncture, acupotomy, and auricular acupuncture were also used. In animal researches, pharmacopuncture treatment was associated with improvement in obesity indices. In the case of local obesity, specific acupuncture techniques such as thread-embedding therapy and pharmacopuncture were associated with significant improvements in local obesity, even when diet and exercise were not controlled for. Conclusion: Acupuncture treatment showed significant benefit in the treatment of obesity, with a local effect evident for certain approaches, such thread-embedding therapy and acupotomy.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.522-541
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• 2016

Network virtualization provides an effective way to overcome the Internet ossification problem. As one of the main challenges in network virtualization, virtual network embedding refers to mapping multiple virtual networks onto a shared substrate network. However, existing heuristic embedding algorithms evaluate the embedding potential of the nodes simply by the product of different resource attributes, which would result in an unbalanced embedding. Furthermore, ignoring the hops of substrate paths that the virtual links would be mapped onto may restrict the ability of the substrate network to accept additional virtual network requests, and lead to low utilization rate of resource. In this paper, we introduce and extend five node attributes that quantify the embedding potential of the nodes from both the local and global views, and adopt the technique for order preference by similarity ideal solution (TOPSIS) to rank the nodes, aiming at balancing different node attributes to increase the utilization rate of resource. Moreover, we propose a novel two-stage virtual network embedding algorithm, which maps the virtual nodes onto the substrate nodes according to the node ranks, and adopts a shortest path-based algorithm to map the virtual links. Simulation results show that the new algorithm significantly increases the long-term average revenue, the long-term revenue to cost ratio and the acceptance ratio.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.617-625
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• 2017

In this paper, an effective reversible data embedding algorithm was proposed to embed secrete data into image. In the proposed algorithm, prediction image is generated by accurately predicting pixel values using local similarity existing in image, difference sequence is generated using the generated prediction image and original cover image, and then histogram shift technique is applied to create a stego-image with secrete data hidden. Applying the proposed algorithm, secrete data can be extracted from the stego-image and the original cover image can be restored without loss. Experimental results show that it is possible to embed more secrete data into cover image than APD algorithm by applying the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.794-814
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• 2012

We propose a novel method for improving Wi-Fi positioning accuracy while reducing the energy consumption of mobile devices. Our method presents three contributions. First, we jointly and intelligently select the optimal subset of access points for positioning via maximum mutual information criterion. Second, we further propose local discriminant embedding algorithm for nonlinear discriminative feature extraction, a process that cannot be effectively handled by existing linear techniques. Third, to reduce complexity and make input signal space more compact, we incorporate clustering analysis to localize the positioning model. Experiments in realistic environments demonstrate that the proposed method can lower energy consumption while achieving higher accuracy compared with previous methods. The improvement can be attributed to the capability of our method to extract the most discriminative features for positioning as well as require smaller computation cost and shorter sensing time.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.606-608
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• 1998

Digital watermarking is the technique which embeds the invisible signal into multimedia data such as audio, video, images, for copyright protection, including owner identification and copy control information. In this paper, a new watermark detection algorithm by local masking cross covariance between watermarked signal and pseudo noise signal is proposed. The proposed algorithm enhances the detection probability for embedding information. Since reducing detection errors for the weak embedding signals, the algorithm improves the image quality and robusts against illegal attack to delete the embedding information and data compression applications such as JPEG and MPEGs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3594-3607
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• 2017

The traditional feature extraction methods such as principal component analysis (PCA) cannot obtain the local structure of the samples, and locally linear embedding (LLE) cannot obtain the global structure of the samples. However, a common drawback of existing PCA and LLE algorithm is that they cannot deal well with the sparse problem of the samples. Therefore, by integrating the globality of PCA and the locality of LLE with a sparse constraint, we developed an improved and unsupervised difference algorithm called Sparse Difference Embedding (SDE), for dimensionality reduction of high-dimensional data in small sample size problems. Significantly differing from the existing PCA and LLE algorithms, SDE seeks to find a set of perfect projections that can not only impact the locality of intraclass and maximize the globality of interclass, but can also simultaneously use the Lasso regression to obtain a sparse transformation matrix. This characteristic makes SDE more intuitive and more powerful than PCA and LLE. At last, the proposed algorithm was estimated through experiments using the Yale and AR face image databases and the USPS handwriting digital databases. The experimental results show that SDE outperforms PCA LLE and UDP attributed to its sparse discriminating characteristics, which also indicates that the SDE is an effective method for face recognition.