• 한국운동역학회지
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• 제31권3호
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• pp.199-204
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• 2021

Objective: The purpose of this study was to present a new idea of methodology to evaluate Poomsae performance using inertial measurement unit (IMU) sensors in terms of signal processing techniques. Method: Ten collegian Taekwondo athletes, consisting of five Poomsae elite athletes (age: 21.4 ± 0.9 years, height: 168.4 ± 11.3 cm, weight: 65.0 ± 10.6 kg, experience: 12 ± 0.7 years) and five breaking demonstration athletes (age: 21.0 ± 0.0 years, height: 168.4 ± 4.7 cm, weight: 63.8 ± 8.2 kg, experience: 13.0 ± 2.1 years), voluntarily participated in this study. They performed three different black belt Poomsae such as Goryeo, Geumgang, and Taebaek Poomsae repeatedly twice. Repeated measured motion data on the wrist and ankle were calculated by the methods of cosine similarity and Euclidean distance. Results: The Poomsse athletes showed superior performance in terms of temporal consistency at Goryeo and Taebaek Poomsae, cosine similarity at Geumgang and Taebaek Poomsae, and Euclidian distance at Geumgang Poomsae. Conclusion: IMU sensor would be a useful tool for monitoring and evaluating within-subject temporal variability of Taekwondo Poomsae motions. As well it distinguished spatiotemporal characteristics among three different Poomsae.

• Animal Bioscience
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• 제36권1호
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• pp.10-18
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• 2023

Objective: In this study, we aimed to position the Hungarian Merino among other Merinoderived sheep breeds, explore the characteristics of our sampled animals' genetic similarity network within the breed, and highlight single nucleotide polymorphisms (SNPs) associated with daily weight-gain. Methods: Hungarian Merino (n = 138) was genotyped on Ovine SNP50 Bead Chip (Illumina, San Diego, CA, USA) and positioned among 30 Merino and Merino-derived breeds (n = 555). Population characteristics were obtained via PLINK, SVS, Admixture, and Treemix software, within-breed network was analysed with python networkx 2.3 library. Daily weight gain of Hungarian Merino was standardised to 60 days and was collected from the database of the Association of Hungarian Sheep and Goat Breeders. For the identification of loci associated with daily weight gain, a multi-locus mixed-model was used. Results: Supporting the breed's written history, the closest breeds to Hungarian Merino were Estremadura and Rambouillet (pairwise F_ST values are 0.035 and 0.036, respectively). Among Hungarian Merino, a highly centralised connectedness has been revealed by network analysis of pairwise values of identity-by-state, where the animal in the central node had a betweenness centrality value equal to 0.936. Probing of daily weight gain against the SNP data of Hungarian Merinos revealed five associated loci. Two of them, OAR8_17854216.1 and s42441.1 on chromosome 8 and 9 (-log₁₀P>22, false discovery rate<5.5e-20) and one locus on chromosome 20, s28948.1 (-log₁₀P = 13.46, false discovery rate = 4.1e-11), were close to the markers reported in other breeds concerning daily weight gain, six-month weight, and post-weaning gain. Conclusion: The position of Hungarian Merino among other Merino breeds has been determined. We have described the similarity network of the individuals to be applied in breeding practices and highlighted several markers useful for elevating the daily weight gain of Hungarian Merino.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.775-784
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• 2006

This study is to investigate the effect of the number of co-rated users to the MAE. User based collaborative algorithm generally uses similarity weight to compute the relation of active user and other users. The original estimation algorithm of the GroupLens used the Pearson's correlation coefficient, soon after other researchers used various weighting. The Pearson’s correlation coefficient and Vector similarity, which is used in the field of information retrieval, are commonly used to the estimation algorithm. In prediction, we analyze the effect of the number of co-rated users on the user based recommender system.

• Korean Journal of Acupuncture
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• 제28권4호
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• pp.41-48
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• 2011

Objectives : This study aimed to explore the passive multi-channel time series analysis method by measuring bio-potentials of acupuncture point and the peripheral region Methods : Bio-potential was measured at ST36 and the peripherical region of ST36 of 5 healthy volunteers at three times. The diagram of the potential changes over time were smoothed by moving average method and similarities of ST36 and the other points were calculated. Results : In the normal weight group, bio-potential similarity tended to decrease in proportion to the distance from the acupuncture point. In the obesity group, bio-potential similarity appeared in a very wide area. Bio-potential similarity had positive correlation with BMI value. Conclusions : The passive multi-channel time series analysis method showed the possibility be appropriate for the electrical characteristics study of meridians.

• 대한수학회보
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• 제35권1호
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• pp.91-97
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• 1998

In this paper we study the Weyl spectrum of weight $\alpha, \omega_\alpha(T)$, of an operator T acting on an infinite dimensional Hilbert space. Main results are as follows. Firstly, we show that the Weyll spectrum of weight $\alpha$ of a polynomially $\alpha$-compact operator is finite, and that similarity preserves polynomial $\alpha$-compactness and the $\alpha$-Weyl's theorem both. Secondly, we give a sufficient condition for an operator to be the sum of an unitary and a $\alpha$-compact operators.

• 한국측량학회지
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• 제34권4호
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• pp.391-402
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• 2016

Among the various remote sensing sensors compared to the electro-optical sensors, SAR (Synthetic Aperture Radar) is very suitable for assessing damaged areas induced by disaster events owing to its all-weather day and night acquisition capability and sensitivity to geometric variables. The conventional CD (Change Detection) method that uses two-date data is typically used for mapping damage over extensive areas in a short time, but because data from only two dates are used, the information used in the conventional CD is limited. In this paper, we propose a novel CD method that is extended to use data consisting of two pre-disaster SAR data and one post-disaster SAR data. The proposed CD method detects changes by using a similarity weight image derived from the neighborhood information of a pixel in the data from the three dates. We conducted an experiment using three single polarization ALOS PALSAR (Advanced Land Observing Satellite/Phased Array Type L-Band) data collected over Miyagi, Japan which was seriously damaged by the 2011 east Japan tsunami. The results demonstrated that the mapping accuracy for damaged areas can be improved by about 26% with an increase of the g-mean compared to the conventional CD method. These improved results prove the performance of our proposed CD method and show that the proposed CD method is more suitable than the conventional CD method for detecting damaged areas induced by disaster.

• 한국어류학회지
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• 제23권3호
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• pp.206-216
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• 2011

To investigate seasonal and yearly variation of fishes composition in Yeoja Bay of Korea, fisheries survey were carried out using beam trawl from 2006 to 2009. A total of 44 fish species were collected. The major dominant species were Pennahia argentatus, Thryssa adelae, Thryssa kammalensis and Cynoglossus joyneri, which were occupied over 63% total individuals, and 50% of wet weight. The diversity index (H') was about 1.62 (1.46~1.77) by seasons, and seasons of similarity by fishes were divided into two groups, which were March with December and June with September. ANOVA test showed that there were not significant difference between individuals and catch weight (kg) per unit area (km$^2$) by year and season, except for catch weight per unit area by season.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1407-1423
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• 2020

A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (LSTM) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an LSTM autoencoder on the extracted node-weight sequences and encode each nodeweight sequence into a fixed-length vector using the trained LSTM autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권3_4호
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• pp.239-250
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• 2003

전자 상거래 분야에서 증가하고 있는 정보들 중에 사용자가 자신의 기호에 맞는 정보 만들 만을 선택하기 위해서 각 정보를 일일이 검토하기 어려운 일이다. 이를 보완하기 위해 정보 여과 기술이 사용되는데 최근 추천 시스템은 협력적 여과 시스템의 희박성과 초기 평가 문제를 해결하기 위해서 내용 기반 여과 시스템과 협력적 적과 시스템을 병합하늘 방법을 사용한다. 본 논문에서는 혼합형 추천시스템에서의 예측의 정확도를 향상시키기 위해서 조화 평균 가중치(CBCF_harmonic_mean)를 사용자 유사도 가중치를 구할 때 사용한다. 내용 기반의 성능을 고려하여 임계치 값을 45로 설정한 후, n/45의 Significance weight을 사용자 유사도 가중치에 적용한다. 제안된 방법의 성능을 평가하기 위해서 기존의 협력적 여과 시스템과 내용 기반 여과 시스템을 병합한 방법과 비교 평가하였다. 그 결과 기존의 협력적 여과 시스템의 문제점을 해결하여 예측의 정확도를 높이는데 효과적임을 확인하였다.

• 디지털융복합연구
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• 제6권4호
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• pp.177-183
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• 2008

This paper aims to offer a solution based on intelligent document classification to create a user-centric information retrieval system allowing user-centric linguistic expression. So, structures expressing user intention and fine document classifying process using EBL, similarity, knowledge base, user intention, are proposed. To overcome the problem requiring huge and exact semantic information, a hybrid process is designed integrating keyword, thesaurus, probability and user intention information. User intention tree hierarchy is build and a method of extracting group intention between key words and user intentions is proposed. These structures and processes are implemented in HDCI(Hybrid Document Classification with Intention) system. HDCI consists of analyzing user intention and classifying web documents stages. Classifying stage is composed of knowledge base process, similarity process and hybrid coordinating process. With the help of user intention related structures and hybrid coordinating process, HDCI can efficiently categorize web documents in according to user's complex linguistic expression with small priori information.