• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.2
• /
• pp.126-134
• /
• 1990

In this study, eight function discrimination and recognition of the EMG signal from the biceps and triceps of 4 subjects were executed, using the Euclidean and weighted cepstral distance measure with LPC cepstrum coefficients. In case of Euclidean cepstral distance measure, as the number of LPC cepstrum coefficients was increased in 8, 10, 12, 14 the recognition rates of functions are 94.69, 95.63, 96.56, and 96.88[%], respectively, but increasing rates of recognition were inclined to decrease. In case of weighted cepstral distance measure, when the number of LPC cepstrum coefficients was 8, 10, 12 and 14, the recognition rates of functions were 91.88, 95, 99.69, and 96.63[%], respectively.

• Journal of the Korean Society for Library and Information Science
• /
• v.41 no.2
• /
• pp.335-357
• /
• 2007

There has been many studies representing intellectual structures with multidimensional scaling(MDS) However MDS configuration is limited in representing local details and explicit structures. In this paper, we identified two components of MDS mapping approach; one is MDS algorithm and the other is preparation of data matrix. Various combinations of the two components of MDS mapping are compared through some measures of fit. It is revealed that the conventional approach composed of ALSCAL algorithm and Euclidean distance matrix calculated from Pearson's correlation matrix is the worst of the compared MDS mapping approaches. Otherwise the best approach to make MDS map is composed of PROXSCAL algorithm and z-scored Euclidean distance matrix calculated from Pearson's correlation matrix. These results suggest that we could obtain more detailed and explicit map of a knowledge domain through careful considerations on the process of MDS mapping.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.18 no.4
• /
• pp.337-344
• /
• 1998

This study was conducted to find out the vegetational succession in abandoned hilly pasture, in Yeoju, Kyonggi Province from April, 1993 to October, 1996. The experiment was arranged as vegetation survey (Pflanzenaufuahme) with two different pastures((1) with forkcrane planed pasture and (2) forest pasture). After the abandoned management of pasture, the botanical composition of planed pasture and forest pasture was greatly changed into the type of natural vegetation in the first year and in the second years, respectively. The biomass of life forms of hemicryptophytes, geophytes and chamaephytes was greatly decreased, on the other hand, that of therophytes and nanophanerophytes after abandoned management of planed and forest pasture in three years was slightly increased than those of the vegetation with pasture management. The similarity coefficients among vegetation groups during the survey were greatly affected by botanical composition. The clustering analysis was showed that the communities of relatively similar botanical composition were grouped closely, and the other communities were clustered farther to the same group although the degree of similarity between communities was low. The communities of hilly pasture after abandoned management were gradually successive into the type of natural grassland or forest community in three years.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.22 no.3
• /
• pp.187-194
• /
• 2002

This study was carried out to investigate the ecological characteristics, forage value and similarity among the plant communities of the gazing pasture at Witzenhausen, Germany. Ten plant communities of the different grazing pasture were the Molinio-Arrhenatheretea that was named the class of plant sociological nomenclature. The forage value of the plant communities were ranged from 4.35 to 6.60 grade for roughage qualify. Hemicryptophyte of lift form and mesomorphic of anatomical structure were greately dominated in all the plant communities. The correlation coeffcient between class No. 3 and 4 of plant communities was highest by botanical composition. The clustering analysis by Euclidean distance showed that class No. 9 and 10 of plant communities were closely grouped as affected by the similar botanical composition.

• Land and Housing Review
• /
• v.10 no.1
• /
• pp.9-18
• /
• 2019

This study examined the dynamic change in the co-movement between the house price rates with the network methods of Mantegna (1999). First, Capital area and non-capital area form independent clusters which have the heterogeneous co-movement pattern. In other words, Capital and non-capital areas have low connectivity in the housing market. Also, if the co-movement between capital areas have been strengthened, the co-movement between non-capital areas have been weakened. The results of the dynamic analysis show that the degree of the co-movement in the housing market is continuously increased. The members of the co-movement group in the capital area are strongly steadied by all periods. However, the members in the non-capital area have been changed according to the period. Accordingly, it is necessary to establish policies based on various information for the housing market of the non-capital area rather than policies targeting the capital area. In addition, Apartments in Korea are more likely to be used as investment or speculative assets than other types of houses. It has been confirmed that this is Gangbuk, which is locatied in the northern part of Seoul, appears to be a region where the Spillover Effects of price fluctuation can be triggered in the housing and apartment market. However, the housing market in Gangnam, which is locatied in the southern part of Seoul, was divided into low systematic risk.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.26-26
• /
• 2022

본 연구는 하천중심선을 활용하여 Sentinel-1 위성영상기반 중소규모 하천 수체(水體) 추출 방법을 제안한다. 한강 유역의 한탄강 일부를 연구지역으로 선정하였으며, 이 지역을 촬영한 Sentinel-1 위성영상자료를 수집하였다. 여기에 개발한 방법의 검증을 위하여 유사시간대의 고해상도 광학위성 PlanetScope을 함께 수집하였다. 본 연구에서는 하천의 수체를 효과적으로 추출하기 위하여 국토지리정보원에서 제공하는 하천중심선 자료를 활용하였다. 하천중심선을 따라 유클리드 거리를 가중치로 산정한 자료(DST)와 Sentinel-1의 VH, VV 편광을 조합한 k-means 방법을 통해 위성영상의 픽셀을 군집화하였고, 최적의 매개변수 값을 산출하였다. 이 매개변수를 활용하여 Sentinel-1의 VV편광, VH편광 그리고 DST의 상관관계에 따른 타원방정식 형태의 계산식을 도출할 수 있었다. 수집한 자료의 검증결과 평균적으로 정확도는 0.65~0.75, kappa 계수는 0.8 내외를 보여 상당히 일치함을 확인할 수 있었다. 또한, 추가 확보한 30여 개의 Sentinel-1 위성영상을 제안 방법으로 추출한 수체의 면적과 유량 값을 비교해 본 결과, 유사한 변화 양상을 보였다. 본 연구는 하천 중심선자료를 활용하여 참값이 없더라도 수체 면적 추정이 가능함을 확인하였다. 제안한 방법은 현존하는 수체추출 방법보다 간단하고 신속하게 수체를 추출할 수 있을 것으로 보인다. 추후, 딥러닝을 통한 수체 식별을 추가 진행을 통해. 정확도를 높일 수 있을 것으로 기대한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.11a
• /
• pp.714-717
• /
• 2008

협업여과 추천기법에는 사용자 기반 협업여과와 아이템 기반 협업여과가 있으며, 절차는 유사도 측정, 이웃 선정, 예측값 생성 단계로 이루어진다. 유사도 측정 단계에는 유클리드 거리(Euclidean Distance), 코사인 유사도(Cosine Similarity), 피어슨 상관계수(Pearson Correlation Coefficient) 방법 등이 있고, 이웃 선정 단계에는 상관 한계치(Correlation-Threshold), 근접 N 이웃(Best-N-Neighbors) 방법 등이 있다. 마지막으로 예측값 생성 단계에는 단순평균(Simple Average), 가중합(Weighted Sum), 조정 가중합(Adjusted Weighted Sum) 등이 있다. 이처럼 협업여과 추천기법에는 다양한 기법들이 사용되고 있다. 따라서 본 논문에서는 사용자 기반 협업여과와 아이템 기반 협업여과 추천기법에 사용되는 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 알아보기 위해 성능 실험 및 비교 분석을 하였다. 실험은 GroupLens의 MovieLens 데이터 셋을 활용하였고 MAE(Mean Absolute Error)값을 이용하여 추천기법을 비교 하였다. 실험을 통해 유사도 측정 기법과 예측값 생성 기법의 최적화된 조합을 찾을 수 있었고, 사용자 기반 협업여과와 아이템 기반 협업여과의 성능비교를 통해 아이템 기반 협업여과의 성능이 보다 우수했음을 확인 하였다.

• The Korean Journal of Applied Statistics
• /
• v.37 no.1
• /
• pp.1-12
• /
• 2024

In many real-world data, multiple response variables are often dependent on the same set of explanatory variables. In particular, if several response variables are correlated with each other, simultaneous estimation considering the correlation between response variables might be more effective way than individual analysis by each response variable. In this multivariate regression analysis, least distance estimator (LDE) can estimate the regression coefficients simultaneously to minimize the distance between each training data and the estimates in a multidimensional Euclidean space. It provides a robustness for the outliers as well. In this paper, we examine the least distance estimation method in multivariate linear regression analysis, and furthermore, we present the penalized least distance estimator (PLDE) for efficient variable selection. The LDE technique applied with the adaptive group LASSO penalty term (AGLDE) is proposed in this study which can reflect the correlation between response variables in the model and can efficiently select variables according to the importance of explanatory variables. The validity of the proposed method was confirmed through simulations and real data analysis.

• Journal of Digital Convergence
• /
• v.13 no.10
• /
• pp.287-293
• /
• 2015

This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.3
• /
• pp.292-297
• /
• 2014

As a Social Network Service (SNS) has become an integral part of our everyday lives, millions of users can express their opinion and share information regardless of time and place. Hence sentiment analysis using micro-blogs has been studied in various field to know people's opinion on particular topics. Most of previous researches on movie reviews consider only positive and negative sentiment and use it to predict movie rating. As people feel not only positive and negative but also various emotion, the sentiment that people feel while watching a movie need to be classified in more detail to extract more information than personal preference. We measure sentiment distributions of each movie from tweets according to the Thayer's model. Then, we find similar movies by calculating similarity between each sentiment distributions. Through the experiments, we verify that our method using micro-blogs performs better than using only genre information of movies.