• Journal of The Korean Society of Grassland and Forage Science
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• v.24 no.4
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• pp.293-300
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• 2004

This study was carried out to investigate the ecological behaviour forage value and similarity among the plant communities of the grazing pasture near Witzenhausen in middle part of Germany. Sixteen plant communities of the different grazing pasture were mostly the Molinio-Arrhenatheretea and Festuco-Brometea, and those were named the class of plant sociological nomenclature. The ecological behaviour and forage value of the communities except mesobromion(half dry grassland community) were relatively good for forage production. The correlation coefficient between class No. 14 and 12 of plant communities was highest, and the similarity among the communities were greatly affected by botanical composition. The resemblance measure of the cluster-analysis by complete-linkage-method for the similarity among plant communities was better the euclidean distance than those of others. The clustering analysis showed that the communities of relatively similar botanical composition were closely grouped.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.243-248
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• 2006

In this paper, we propose the unsupervised change detection algorithm that apply the similarity measure techniques to the hyperspectral image. The general similarity measures including euclidean distance and spectral angle were compared. The spectral similarity scale algorithm for reducing the problems of those techniques was studied and tested with Hyperion data. The thresholds for detecting the change area were estimated through EM(Expectation-Maximization) algorithm. The experimental result shows that the similarity measure techniques and EM algorithm can be applied effectively for the unsupervised change detection of the hyperspectral data.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.66-69
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• 1997

In this paper a fuzzy similarity measure is proposed. The proposed fuzzy similarity measure considers the relative distance between data and cluster centers in addition to the Euclidean distance to decide the degree of similarity. The boundary of a cluster center is constracted on the competitive region and expanded on the less competitive region. This result shows the possibility of using relative distance as a similarity measure.

• Korean Journal of Acupuncture
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• v.33 no.1
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• pp.18-32
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• 2016

Objectives : This study aims to analyze the co-occurrence of pathological symptoms and corresponding acupoints as documented by the comprehensive acupuncture and moxibustion records in the classical texts of Far East traditional medicine as an aid to a more efficient understanding of the tacit treatment principles of ancient physicians. Methods : The Classic of Nourishing Life with Acupuncture and Moxibustion(Zhenjiu Zisheng Jing; hereinafter ZZJ) was selected as the primary reference book for the analysis. The pathology-acupoint co-occurrence analysis was performed by applying 4 values of vector space measures(weighted Euclidean distance, Euclidean distance, $Cram\acute{e}r^{\prime}s$ V and Canberra distance), which measure the distance between the observed and expected co-occurrence counts, and 3 values of probabilistic measures(association strength, Fisher's exact test and Jaccard similarity), which measure the probability of observed co-occurrences. Results : The treatment records contained in ZZJ were preprocessed, which yielded 4162 pathology-acupoint sets. Co-occurrence was performed applying 7 different analysis variables, followed by a prediction simulation. The prediction simulation results revealed the Weighted Euclidean distance had the highest prediction rate with 24.32%, followed by Canberra distance(23.14%) and association strength(21.29%). Conclusions : The weighted Euclidean distance among the vector space measures and the association strength among the probabilistic measures were verified to be the most efficient analysis methods in analyzing the correlation between acupoints and pathologies found in the classical medical texts.

• Journal of Digital Contents Society
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• v.10 no.3
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• pp.423-431
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• 2009

There are various image retrieval methods using shape, color and texture features. One of the most active area is using shape and color information. A number of shape representations have been suggested to recognize shapes even under affine transformation. There are many kinds of method for shape recognition, the well-known method is Fourier descriptors and moment invariant. The other method is CSS(Curvature Scale Space). The maxima of curvature scale space image have already been used to represent 2-D shapes in different applications. Because preexistence CSS exists several problems, in this paper we use improved CSS method for retrieval image. There are two kinds of method, One is using RGB color information feature and the other is using HSI color information feature. In this paper we used HSI color model to represent color histogram before, then use it as comparison measure. The similarity is measured by using Euclidean distance and for reduce search time and accuracy, We use DTW for measure similarity. Compare with the result of using Euclidean distance, we can find efficiency elevated.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.667-673
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• 2009

This paper presents an efficient facial expression recognition method by measuring the histogram distance based on preprocessing. The preprocessing that uses both centroid shift and histogram equalization is applied to improve the recognition performance, The distance measurement is also applied to estimate the similarity between the facial expressions. The centroid shift based on the first moment balance technique is applied not only to obtain the robust recognition with respect to position or size variations but also to reduce the distance measurement load by excluding the background in the recognition. Histogram equalization is used for robustly recognizing the poor contrast of the images due to light intensity. The proposed method has been applied for recognizing 72 facial expression images(4 persons * 18 scenes) of 320*243 pixels. Three distances such as city-block, Euclidean, and ordinal are used as a similarity measure between histograms. The experimental results show that the proposed method has superior recognition performances compared with the method without preprocessing. The ordinal distance shows superior recognition performances over city-block and Euclidean distances, respectively.

• Smart Structures and Systems
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• v.23 no.3
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.338-343
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• 2015

We suggest a method to configure feature functions of continuous conditional random field (C-CRF). Regression tree and similarity analysis are introduced to construct the first and second feature functions of C-CRF, respectively. Rules from the regression tree are transformed to logic functions. If a logic in the set of rules is true for a data then it returns the corresponding value of leaf node and zero, otherwise. We build an Euclidean similarity matrix to define neighborhood, which constitute the second feature function. Using two feature functions, we make a C-CRF model and an illustrate example is provided.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.369-380
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• 2004

Clustering algorithms with different similarity measures are commonly used to find an optimal clustering or close to original clustering. The recovery level of using Euclidean distance and distances transformed from correlation coefficients is evaluated and compared using Rand's (1971) C statistic. The C values present how the resultant clustering is close to the original clustering. In simulation study, the recovery level is improved by applying the correlation coefficients between objects. Using the data set from Spellman et al. (1998), the recovery levels with different similarity measures are also presented. In general, the recovery level of true clusters was increased by using the correlation coefficients.

• Smart Media Journal
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• v.8 no.4
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• pp.80-84
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• 2019

As a result of AMI rapidly expanding and distributing its products, variety of services that utilize data on the use of electricity are increasing. In order to make these services more effective, missing metric data needs to be corrected, compensating for which Euclidean similarity is used to find customers with similar usage patterns. Throughout such a process, we propose a method for correcting missing data and provide comparison with the preceding methods.