• The KIPS Transactions:PartA
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• v.16A no.4
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• pp.223-226
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• 2009

The twisted cube has received great attention as an interconnection network of parallel systems because it has several superior properties, especially in diameter, to the hypercube. It was recently known that, for even m, a mesh of size $2{\times}2^m$ can be embedded into a twisted cube with dilation 1 and expansion 1 and a mesh of size $4{\times}2^m$ with dilation 1 and expansion 2 [Lai and Tsai, 2008]. However, as we know, it has been a conjecture that a mesh with more than eight rows and columns can be embedded into a twisted cube with dilation 1. In this paper, we show that a mesh of size $2^n{\times}2^m$ can be embedded into a twisted cube with dilation 1 and expansion $2^{n-1}$ for even m and with dilation 1 and expansion $2^n$ for odd m where $1{\leq}n{\leq}m$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1300-1306
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• 2018

The purpose of this study is to see possibility of Char2Vec as alternative of Word2Vec that most famous word embedding model in Sentence Similarity Measure Problem by Deep-Learning. In experiment, we used the Siamese Ma-LSTM recurrent neural network architecture for measure similarity two random sentences. Siamese Ma-LSTM model was implemented with tensorflow. We train each model with 200 epoch on gpu environment and it took about 20 hours. Then we compared Word2Vec based model training result with Char2Vec based model training result. as a result, model of based with Char2Vec that initialized random weight record 75.1% validation dataset accuracy and model of based with Word2Vec that pretrained with 3 million words and phrase record 71.6% validation dataset accuracy. so Char2Vec is suitable alternate of Word2Vec to optimize high system memory requirements problem.

• The Journal of the Acoustical Society of Korea
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• v.40 no.2
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• pp.148-154
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• 2021

Text-Independent speaker verification needs to extract text-independent speaker embedding to improve generalization performance. However, deep neural networks that depend on training data have the potential to overfit text information instead of learning the speaker information when repeatedly learning from the identical time series. In this paper, to prevent the overfitting, we propose a segment unit shuffling layer that divides and rearranges the input layer or a hidden layer along the time axis, thus mixes the time series information. Since the segment unit shuffling layer can be applied not only to the input layer but also to the hidden layers, it can be used as generalization technique in the hidden layer, which is known to be effective compared to the generalization technique in the input layer, and can be applied simultaneously with data augmentation. In addition, the degree of distortion can be adjusted by adjusting the unit size of the segment. We observe that the performance of text-independent speaker verification is improved compared to the baseline when the proposed segment unit shuffling layer is applied.

• The Transactions of the Korea Information Processing Society
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• v.13 no.8
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• pp.367-373
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• 2024

The growth of mobile devices and network infrastructure has brought significant changes to the music industry. Online streaming services has allowed music consumption without constraints of time and space, leading to increased consumer engagement in music creation and sharing activities, resulting in a vast accumulation of music data. In this study, we define metadata as "song sentences" by using a user's playlist. To calculate similarity, we embedded them into a high-dimensional vector space using skip-gram with negative sampling algorithm. Performance eva luation results indicated that the recommended music algorithm, utilizing singers, genres, composers, lyricists, arrangers, eras, seasons, emotions, and tag lists, exhibited the highest performance. Unlike conventional recommendation methods based on users' behavioral data, our approach relies on the inherent information of the tracks themselves, potentially addressing the cold start problem and minimizing filter bubble phenomena, thus providing a more convenient music listening experience.

• The KIPS Transactions:PartC
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• v.11C no.5
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• pp.683-688
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• 2004

NAT is a very useful IP address translation technique that allows two connected networks using different and incompatible IP address schemes. But it is impractical to use NAT for an application which uses the encrypted IP packet, embedding IP addresses inside of data payloads, to guarantee End-to-End Security such as IPSec. In addition to rewriting the source/destination IP addresses in the packet, NAT must modify IP checksum every time, which could lead to considerable performance decrease of the overall system in the process of the address translation. RSIP is an alternative to solve these disadvantages of NAT and the address shortage problems. Both NAT and RSIP divide networks into inside and outside addressing realms. NAT translates addresses between internal network and external network, but RSIP uses a borrowed external address for outside communications. RSIP server assigns a routable public address to a RSIP client temporarily to communicate with public net-work outside of the private network. In this paper, RSIP gateway for intranet environment is designed and its performance is evaluated. From the results of performance evaluation, we knew that RSIP is operated less sensitive to the data traffic. Also, the experiment shows that RSIP performs better than NAT when the transmission data grows larger.

• Journal of People, Plants, and Environment
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• v.24 no.5
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• pp.451-460
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• 2021

Background and objective: The prolonged COVID-19 pandemic has had significant impacts on mental health, which has emerged as a major public health issue around the world. This study aimed to analyze trends and network structure of 'grow-your-own (GYO)' through Instagram, one of the most influential social media platforms, to encourage and sustain home gardening activities for promotion of emotional support and physical health. Methods: A total of 6,388 posts including keyword hashtags '#gyo' and '#growyourown' on Instagram from June 13, 2020 to April 13, 2021 were collected. Word embedding was performed using Word2Vec library, and 7 clusters were identified with K-means clustering: GYO, garden and gardening, allotment, kitchen garden, sustainability, urban gardening, etc. Moreover, we conducted social network analysis to determine the centrality of related words and visualized the results using Gephi 0.9.2. Results: The analysis showed that various combinations of words, such as #growourrownfood, #growourrownveggies, and #growwhatyoueat revealed preference and interest of users in GYO, and appeared to encourage their activities on Instagram. In particular, #gardeningtips, #greenfingers, #goodlife, #gardeninglife, #gardensofinstagram were found to express positive emotions and pride as a gardener by sharing their daily gardening lives. Users were participating in urban gardening through #allotment, #raisedbeds, #kitchengarden and we could identify trends toward self-sufficiency and sustainable living. Conclusion: Based on these findings, it is expected that the trend data of GYO, which is a form of urban gardening, can be used as the basic data to establish urban gardening plans considering each characteristic, such as the emotions and identity of participants as well as their dispositions.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.116-121
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• 2017

cQA(Community-based Question Answering) 시스템은 온라인 커뮤니티를 통해 사용자들이 질문을 남기고 답변을 작성할 수 있도록 만들어진 시스템이다. 신규 질문이 인입되면, 기존에 축적된 cQA 저장소에서 해당 질문과 가장 유사한 질문을 검색하고, 그 질문에 대한 답변을 신규 질문에 대한 답변으로 대체할 수 있다. 하지만, 키워드 매칭을 사용하는 전통적인 검색 방식으로는 문장에 내재된 의미들을 이용할 수 없다는 한계가 있다. 이를 극복하기 위해서는 의미적으로 동일한 문장들로 학습이 되어야 하지만, 이러한 데이터를 대량으로 확보하기에는 어려움이 있다. 본 논문에서는 질문이 제목과 내용으로 분리되어 있는 대량의 cQA 셋에서, 질문 제목과 내용을 의미 벡터 공간으로 사상하고 두 벡터의 상대적 거리가 가깝게 되도록 학습함으로써 의사(pseudo) 유사 의미의 성질을 내재화 하였다. 또한, 질문 제목과 내용의 의미 벡터 표현(representation)을 위하여, semi-training word embedding과 CNN(Convolutional Neural Network)을 이용한 딥러닝 기법을 제안하였다. 유사 질문 검색 실험 결과, 제안 모델을 이용한 검색이 키워드 매칭 기반 검색보다 좋은 성능을 보였다.

• Transactions on Electrical and Electronic Materials
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• v.16 no.5
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• pp.250-253
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• 2015

A novel implantable CPW fed Apollian shaped antenna embedded into human tissue is proposed for ISM band biomedical applications. The proposed antenna is made compatible for implantation by embedding it in an alumina ceramic substrate(ε_r=9.8 and thickness= 0.65 mm). The proposed antenna covers the ISM band of 2.45 GHz. The radiation parameters such as return loss, xy-plane, xz-plane, and yz-plane etc., are measured and analyzed using the agilent vector network analyzer. The proposed antenna has substantial advantages, including low profile, miniaturization ability, lower return loss, better impedance matching, and high gain over conventional implanted antennas.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.633-636
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• 2000

본 논문에서는 하이퍼큐브보다 망비용이 개선된 HCN(n,n)과 2n-hypercube 사이의 임베딩을 분석한다. 2n-hypercube를 HCN(n.n)에 연장율 3에 임베딩 가능함을 보이고, HCN(n,n)을 2n-hypercube에 임베딩하는 비용이 O(n)임을 보인다.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.437-450
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• 2009

In this paper, we propose seven edge labeling methods. The methods produce three case of edge labels-sets of Fibonacci numbers {$F_k|k\;{\geq}\;2$}, {$F_{2k}|k\;{\geq}\;1$} and {$F_{3k+2}|k\;{\geq}\;0$}. When a sort of interconnection network, the circulant graph is designed, these edge labels are used for its jump sequence. As a result, the degree is due to the edge labels.