• Smart Media Journal
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• v.5 no.2
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• pp.84-89
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• 2016

For a place-recommendation model based on user's behavior and multi-attribute attitude in this thesis. We focus groups that show similar patterns of visiting restaurants and then compare one and the other. We make use of The Fishbein Equation, Pearson's Correlation Coefficient to calculate multi-attribute attitude scores. Furthermore, We also make use of Preference Prediction Algorithm and Distance based method named "Euclidean Distance" to provide accurate results. We can demonstrate how excellent this system is through several experiments carried out with actual data.

• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.213-230
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• 2020

Until now, various educational budgets, facilities, and programs have been put into school education, but the results have not been clearly evaluated. This study presents a model to analyze the effectiveness of educational support for students in high schools across the country. In this model, we first use EM cluster analysis to make clusters with similar inputs for school operation, and then calculate the relative efficiency in each cluster by using Network DEA analysis. The Network DEA analysis has a two-stage structure where the first stage uses six inputs in terms of school infrastructure to generate outputs such as the number of academic persistence. In the Network DEA analysis, the second stage uses 10 inputs in terms of school programs to generate outputs such as the number of enrollees to higher learning and the number of employees and per capita usage of library as the connection variable. Based on the efficiency analysis results, Tier analysis is performed by applying the Euclidean distance to select targets for benchmarking. In this study, we applied the model to analyze the efficiency of educational support by collecting data regarding student education support in general and vocational high school nationwide. The stepwise benchmarking method proposed that the target be selected for efficiency improvement step by step, taking into account inefficient school elements to complement the problem of the choice of benchmarking targets. Based on this study, it is expected that schools with low efficiency of educational support for students will be used as basic data for stepwise benchmarking for efficient operation of educational support for students.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.354-358
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• 2001

This paper presents a fuzzy neural network model which solves the underutilization problem. This fuzzy neural network has both stability and flexibility because it uses the control structure similar to AHT(Adaptive Resonance Theory)-l neural network. And this fuzzy nenral network does not need to initialize weights and is less sensitive to noise than ART-l neural network is. The learning rule of this fuzzy neural network is the modified and fuzzified version of Kohonen learning rule and is based on the fuzzification of leaky competitive leaming and the fuzzification of conditional probability. The similarity measure of vigilance test, which is performed after selecting a winner among output neurons, is the relative distance. This relative distance considers Euclidean distance and the relative location between a datum and the prototypes of clusters. To compare the performance of the proposed fuzzy neural network with that of Kohonen Self-Organizing Feature Map the IRIS data and Gaussian-distributed data are used.

• Journal of Distribution Science
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• v.10 no.12
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• pp.5-11
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• 2012

Purpose - This paper proposes measurement models to evaluate the management performance of small and medium-sized retail businesses on the basis of a trading area analysis that compares their proposed revenue to actual revenue in the trading area. Research design, data, methodology - The study proposes measurement models consisting of five stages, namely: (1) district background survey, (2) customer survey, (3) competitor survey, (4) business district survey, and (5) business performance analysis. Results - To identify business districts easily, this study preferred a minor-adjusted method based on the Euclidean distance, as it is simple to employ for the small and medium-sized businesses. This model was applied to select coffee shops in Daejeon. Results indicated that although the targeted shop was not located in an appropriate location, actual sales were higher than expected. Conclusions - Small- or medium-sized retail businesses face difficulties regarding the economies of scale and brand recognition and must choose an appropriate location to ensure management stability. However, such businesses will find it difficult to evaluate their competitive edge accurately using a trading area analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.257-267
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• 2009

This paper presents a training method for neural networks and the employment of MSE (mean scare error) values as the basis of a decision regarding the identity claim of a speaker in a recurrent neural networks based speaker verification system. Recurrent neural networks (RNNs) are employed to capture temporally dynamic characteristics of speech signal. In the process of supervised learning for RNNs, target outputs are automatically generated and the generated target outputs are made to represent the temporal variation of input speech sounds. To increase the capability of discriminating between the true speaker and an impostor, a discriminative training method for RNNs is presented. This paper shows the use and the effectiveness of the MSE value, which is obtained from the Euclidean distance between the target outputs and the outputs of networks for test speech sounds of a speaker, as the basis of speaker verification. In terms of equal error rates, results of experiments, which have been performed using the Korean speech database, show that the proposed speaker verification system exhibits better performance than a conventional hidden Markov model based speaker verification system.

• Animal Systematics, Evolution and Diversity
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• v.11 no.4
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• pp.417-434
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• 1995

Three species (0. kinbergi, 0. sarsi and 0 . sarsi vadicola) of the genus Ophiura (Echinodermacta: Ophiuroidea) were sampled from the various localities of Korean waters (Kyonggi Bay in March and September, 1989 and February, 1994; Yellow Sea in September and October, 1992; Southern Sea and Korean Strait in May, 1992; and eastern coasts adjacent to Kangnung in April, July, October, 1993 and January, 1994). Results of the examination of 250 Operational Taxonomical Units (OTUs) are presented based on the 20 morphometric variables to evaluate their taxonomic characters and positions. In cluster analysis, 250 OTUs were divided into 3 phenons (0. kinbergi, 0 . sarsi and 0 . sarsi vadicola) at the Euclidean distance levels of 6.84 and 2 phenons (a phenon composed of 0 . sarsi and 0 . sarsi vadicola and the other of 0. kinbergi) at 7.50. Stepwise discriminant analysis was used in order to produce a good discrimination model and 13 morphological characters (the total number of comb papillae, the number of primary comb papillae and shape of comb papillae (2). etc.) were extracted. The results of canonical discriminant analysis illustrated clear distinction among 3 phenons by the distance of 8.26 between 0 . sarsi and 0. sarsi vadicola, 24.24 between 0 . kinbergi and 0. sarsi vadicola and 21.63 between 0 . kinbergi and 0 . sarsi.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.1-12
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• 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 the Korean Institute of Telematics and Electronics T
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• v.36T no.1
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• pp.93-102
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• 1999

In this paper, we propose a data model and a retrieval method for content-based video query After partitioning a video into frame sets of same length which is called video-window, each video-window can be mapped to a point in a multidimensional space. A video can be represented a trajectory by connection of neighboring video-window in a multidimensional space. The similarity between two video-windows is defined as the euclidean distance of two points in multidimensional space, and the similarity between two video segments of arbitrary length is obtained by comparing corresponding trajectory. A new retrieval method with filtering and refinement step if developed, which return correct results and makes retrieval speed increase by 4.7 times approximately in comparison to a method without filtering and refinement step.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.661-666
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• 2010

In this paper, we apply a likelihood ratio test (LRT) to a non-negative matrix factorization (NMF) based voice activity detection (VAD) to find optimal threshold. In our approach, the NMF based VAD is expressed as Euclidean distance between noise basis vector and input basis vector which are extracted through NMF. The optimal threshold each of noise environments depend on NMF results distribution in noise region which is estimated statistical model-based VAD. According to the experimental results, the proposed approach is found to be effective for statistical model-based VAD using LRT.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.1
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• pp.55-60
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• 2014

In this paper, we proposed a method for face region extraction by skin-color hue, saturation and facial feature extraction in natural images. The proposed algorithm is composed of lighting correction and face detection process. In the lighting correction step, performing correction function for a lighting change. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. Eye detection using C element in the CMY color model and mouth detection using Q element in the YIQ color model for extracted candidate areas. Face area detected based on human face knowledge for extracted candidate areas. When an experiment was conducted with 10 natural images of face as input images, the method showed a face detection rate of 100%.