• Journal of Pharmacopuncture
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• v.6 no.3
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• pp.15-21
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• 2003

Background : This study focuses on the new acupuncture method of embedding method which inserts a substance on the acupuncture points for continuous stimulation. Clinical applications and cautions were examined through literary investigations. Results : Based on the literary consideration of embedding method, the following results were obtained : 1. Embedding method is a combination of traditional and embedding technique to provide longer duration of stimulation on the acupuncture points. 2. To administer the embedding method, one needs to utilize a embedding thread besides acupuncture apparatus. Sheep gut is commonly used in China and the surgical thread is the choice in Korea. 3. Embedding method may vary from the patient to patient, depending on the nature and location of the illness. Piercing, embedding, and tying are some of the possibilities. 4. Embedding method may have different arrangement of threads based on the choice of usage. 5. Embedding method is effective for various chronic illnesses such as aches, functional diseases, and the diseases of internal organs. 6. When using the embedding methods, cautions against infection and side effects due to strong stimulation are mandatory.

• Journal of Korean Medical classics
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• v.32 no.1
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• pp.61-74
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• 2019

Objectives : The purpose of this study is to help select an appropriate word embedding method when analyzing East Asian traditional medicine texts as data. Methods : Based on prescription data that imply traditional methods in traditional East Asian medicine, we have examined 4 count-based word embedding and 2 prediction-based word embedding methods. In order to intuitively compare these word embedding methods, we proposed a "prescription generating game" and compared its results with those from the application of the 6 methods. Results : When the adjacent vectors are extracted, the count-based word embedding method derives the main herbs that are frequently used in conjunction with each other. On the other hand, in the prediction-based word embedding method, the synonyms of the herbs were derived. Conclusions : Counting based word embedding methods seems to be more effective than prediction-based word embedding methods in analyzing the use of domesticated herbs. Among count-based word embedding methods, the TF-vector method tends to exaggerate the frequency effect, and hence the TF-IDF vector or co-word vector may be a more reasonable choice. Also, the t-score vector may be recommended in search for unusual information that could not be found in frequency. On the other hand, prediction-based embedding seems to be effective when deriving the bases of similar meanings in context.

• Knowledge Management Research
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• v.21 no.1
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• pp.99-116
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• 2020

Text is the most widely used means of exchanging or expressing knowledge and information in the real world. Recently, researches on structuring unstructured text data for text analysis have been actively performed. One of the most representative document embedding method (i.e. doc2Vec) generates a single vector for each document using the whole corpus included in the document. This causes a limitation that the document vector is affected by not only core words but also other miscellaneous words. Additionally, the traditional document embedding algorithms map each document into only one vector. Therefore, it is not easy to represent a complex document with interdisciplinary subjects into a single vector properly by the traditional approach. In this paper, we introduce a multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. After introducing the previous study on multi-vector document embedding, we visually analyze the effects of the multi-vector document embedding method. Firstly, the new method vectorizes the document using only predefined keywords instead of the entire words. Secondly, the new method decomposes various subjects included in the document and generates multiple vectors for each document. The experiments for about three thousands of academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the multi-vector based method, we ascertained that the information and knowledge in complex documents can be represented more accurately by eliminating the interference among subjects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2723-2740
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• 2017

Steganographic schemes which are based on minimizing an additive distortion function defined the overall impacts after embedding as the sum of embedding costs for individual image element. However, mutual impacts during embedding are often ignored. In this paper, an adaptive JPEG steganographic method based on weight distribution for embedding costs is proposed. The method takes mutual impacts during embedding in consideration. Firstly, an analysis is made about the factors that affect embedding fluctuations among JPEG coefficients. Then the Distortion Update Strategy (DUS) of updating the distortion costs is proposed, enabling to dynamically update the embedding costs group by group. At last, a kind of adaptive JPEG steganographic algorithm is designed combining with the update strategy and well-known additive distortion function. The experimental result illustrates that the proposed algorithm gains a superior performance in the fight against the current state-of-the-art steganalyzers with high-dimensional features.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.310-315
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• 2003

In this paper, We introduce an embedding driven synchronization method using SC-CNN(State-Controlled Cellular Neural Network) which has the purpose to secure communication method through the embedding driven synchronization method in the SC-CNN. we proposed new embedding driven synchronization that this method is only using one state variable compare to the general driven synchronization methods which is using all state variables. In this paper, We achieved the usage of embedding driven synchronization and we also applied it to secure communication.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.483-490
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• 2021

In order to learn an existing song and create a new song using an artificial neural network, it is necessary to convert the song into numerical data that the neural network can recognize as a preprocessing process, and one-hot encoding has been used until now. In this paper, we proposed a note embedding method using notes as a basic unit and a bar embedding method that uses the bar as the basic unit, and compared the performance with the existing one-hot encoding. The performance comparison was conducted based on quantitative evaluation to determine which method produced a song more similar to the song composed by the composer, and quantitative evaluation methods used in the field of natural language processing were used as the evaluation method. As a result of the evaluation, the song created with bar embedding was the best, followed by note embedding. This is significant in that the note embedding and bar embedding proposed in this paper create a song that is more similar to the song composed by the composer than the existing one-hot encoding.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.76-82
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• 2010

Reversible watermarking hides some information in a digital image in such a way that an authorized party could decode the hidden information and also restore the image to its original state. In this paper, a high capacity reversible watermarking method using histogram shifting is proposed. In order to increase embedding capacity, the proposed method divides the image into $2{\times}2$ blocks and uses a paring(horizontal, vertical, diagonal) inside each block, then finds a maximum embedding bin which has the most frequent difference values among the parings. Also, the proposed method removes the overflow and underflow by using location map which including the maximum embedding bin and increases the embedding capacity by embedding iteratively. The experimental results show that the proposed method provides a high embedding capacity and good visual quality compared with the conventional reversible watermarking methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.505-508
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• 2003

In this paper, we introduce embedding drive synchronigation method using SC-CNN. SC-CNN provide us a good method to separate interconnected state variables of a system respectively and to make it possible to change current component to voltage component in the state variables. Using this advantage, We suggest another synchronization method called Embedding Drive Synchronization. Proposed Embedding Drive Synchronization change general drive synchronization method a little bit. we put transmitted signal between into the input of the state variables of the received part, not entirely replacing it. and we showed it proved.

• Journal of Broadcast Engineering
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• v.12 no.2
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• pp.112-119
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• 2007

Reversible data embedding theory has marked a new epoch for data hiding and information security. Being reversible, the original data and the embedded data as well should be completely restored. Difference expansion transform is a remarkable breakthrough in reversible data hiding scheme. The difference expansion method achieves high embedding capacity and keeps the distortion low. This paper shows that the difference expansion method with simplified location map, and new expandability and changeability can achieve more embedding capacity while keeping the distortion almost the same as the original expansion method.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.7
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• pp.1030-1040
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• 1998

One of the main unresolved issues in large-eddy simulation(LES) of wall-bounded turbulent flows is the requirement of high spatial resolution in the near-wall region, especially in the spanwise direction. Such high resolution required in the near-wall region is generally used throughout the computational domain, making simulations of high Reynolds number, complex-geometry flows prohibitive. A grid-embedding strategy using a nonconforming spectral domain-decomposition method is proposed to address this limitation. This method provides an efficient way of clustering grid points in the near-wall region with spectral accuracy. LES of transitional and turbulent channel flow has been performed to evaluate the proposed grid-embedding technique. The computational domain is divided into three subdomains to resolve the near-wall regions in the spanwise direction. Spectral patching collocation methods are used for the grid-embedding and appropriate conditions are suggested for the interface matching. Results of LES using the grid-embedding strategy are promising compared to LES of global spectral method and direct numerical simulation. Overall, the results show that the spectral domain-decomposition grid-embedding technique provides an efficient method for resolving the near-wall region in LES of complex flows of engineering interest, allowing significant savings in the computational CPU and memory.