• Proceedings of the KSME Conference
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• 2001.06b
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• pp.670-675
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• 2001

This paper is to propose image segmentation method based on chamfer algorithm. First, we get original image from CCD camera and transform it into gray image. Second, we extract maximum gray value of background and reconstruct and eliminate the background using surface fitting method and bilinear interpolation. Third, we subtract the reconstructed background from gray image to remove noises in gray image. Fourth, we transform the subtracted image into binary image using Otsu's optimal thresholding method. Fifth, we use morphological filters such as areaopen, opening, filling filter etc. to remove noises and isolated points. Sixth, we use chamfer distance or Euclidean distance to this filtered image. Finally, we use watershed algorithm and count microorganisms in image by labeling. To prove the effectiveness, we apply the proposed algorithm to one of Ammonia-oxidizing bacteria, Acinetobacter sp. It is shown that both Euclidean algorithm and chamfer algorithm show over-segmentation. But Chamfer algorithm shows less over-segmentation than Euclidean algorithm.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.25-38
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• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.

• Development and Reproduction
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• v.21 no.3
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• pp.269-274
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• 2017

The PCR analysis was performed on DNA samples extracted from a total of 20 individuals using six oligonucleotides primers. The author accomplished clustering analyses to reveal the Euclidean genetic distances among four clam populations from Gochang, Seocheon, Taean and Anmyeon of the Korean peninsula. The oligonucleotides primer OPA-08 generated 5 unique loci to each population, approximately 550 bp and 600 bp, respectively, in the MCS population. Especially, the primer OPA-20 generated 15 unique loci to each population, which were identifying each population, approximately 400 bp, 750 bp and 800 bp, in the MCT population. Individuals from MCG clam population ($0.637{\pm}0.227$) exhibited higher band-sharing values than did individuals from MCG clam population ($0.402{\pm}0.115$) (P<0.05). The dendrogram obtained by the six oligonucleotides primers indicates four genetic clusters: cluster 1 (MCG 01, 02, 04 and 05), cluster 2 (MCS 06, 07, 08, 09 and 10), cluster 3 (MCT 11, 12, 13, 14 and 15) and cluster 4 (MCA 16, 17, 18, 19, 20 and MCG 03). Among the twenty clam individuals, the shortest genetic distance that displayed significant molecular differences was between individuals 14 and 15 from the MCT population (genetic distance = 0.094), while the longest genetic distance among the twenty individuals that displayed significant molecular differences was between individuals MCG no. 01 and MCG no. 02 (genetic distance = 0.687). Comparatively, individuals of MCS clam population were fairly closely related to that of MCT clam population, as shown in the hierarchical dendrogram of Euclidean genetic distances.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1293-1299
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• 2012

In this paper, a real-time vehicle detection and tracking algorithms is proposed. The vehicle detection could be processed using GMM (Gaussian Mixture Model) algorithm and mathematical morphological processing with HD CCTV camera images. The vehicle tracking based on separated vehicle object was performed using Euclidean distance between detected object. In more detail, background could be estimated using GMM from CCTV input image signal and then object could be separated from difference image of the input image and background image. At the next stage, candidated objects were reformed by using mathematical morphological processing. Finally, vehicle object could be detected using vehicle size informations dependent on distance and vehicle type in tunnel. The vehicle tracking performed using Euclidean distance between the objects in the video frames. Through computer simulation using recoded real video signal in tunnel, it is shown that the proposed system works well.

• ETRI Journal
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• v.38 no.4
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• pp.606-611
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• 2016

This paper presents antenna selection schemes for recently proposed quadrature spatial modulation (QSM) systems. The antenna selection strategy is based on Euclidean distance optimized antenna selection (EDAS). The symbol error rate (SER) performance of these schemes is compared with that of the corresponding algorithm associated with spatial modulation (SM) systems. It is shown through simulations that QSM systems using EDAS offer significant improvement in terms of SER performance over SM systems with EDAS. Their SER performance gains are seen to be about 2 dB-4 dB in $E_s/N_0$ values.

• Communications of the Korean Mathematical Society
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• v.21 no.3
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• pp.559-567
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• 2006

We are interested in the distance problem between two objects in three dimensional Euclidean space. There are many distance problems for various types of objects including line segments, boxes, polygons, circles, disks, etc. In this paper we present an iterative algorithm for finding the distance between two given ellipses. Numerical examples are given.

• Korean Management Science Review
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• v.33 no.4
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• pp.51-64
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• 2016

The maximum weight of single stock in mutual fund is limited by regulations to enforce diversification. Under incomplete information with added constraints on portfolio weights, enhanced performance had been reported in previous researches. We analyze a weight vector to examine the effects of additional constraints on the portfolio's performance by computing the Euclidean distance from the in-sample tangency portfolio, as opposed to previous researches which analyzed ex-post return only. Empirical experiment was performed on Mean-variance and Minimum-variance model with Fama French's 30 industry portfolio and 10 industry portfolio for the last 1,000 months from August 1932 to November 2015. We find that diversification-constrained portfolios have 7% to 26% smaller Euclidean distances with the benchmark portfolio compared to those of unconstrained portfolios and 3% to 11% greater Sharpe Ratio.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.5-12
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• 1995

In the case where the set of training vectors constitute clusters, the codevectors of the codebook which is used to compression for speech and images in the vector quantization are regarded as the central vectors of the clusters constituted by given training vectors. In this work, we consider the distribution of Euclidean distance obtaining in the process of searching for the minimum distance between vectors, and propose the method searching for the proper number of and the central vectors of clusters. And then, the proposed method shows more than the about 4[dB] SNR than the LBG algorithm and the competitive learning algorithm

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.4
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• pp.53-63
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• 2014

Moving context determination is an important issue to be resolved in a mobile computing environment. This paper presents a method for recognizing and classifying a mobile user's moving context by Euclidean distance similarity. In the proposed method, basic data are gathered using Global Positioning System (GPS) and accelerometer sensors, and by using the data, the system decides which moving situation the user is in. The decided situation is one of the four categories: stop, walking, run, and moved by a car. In order to evaluate the effectiveness and feasibility of the proposed scheme, we have implemented applications using several variations of Euclidean distance similarity on the Android system, and measured the accuracies. Experimental results show that the proposed system achieves more than 90% accuracy.

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.383-388
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• 2008

The safe and efficient operation of the chemical processes has become one of the primary concerns of chemical companies, and a variety of fault diagnosis methods have been developed to diagnose faults when abnormal situations arise. Recently, many research efforts have focused on fault diagnosis methods based on quantitative history data-based methods such as statistical models. However, when the history data-based models trained with the data obtained on an operation mode are applied to another operating condition, the models can make continuous wrong diagnosis, and have limits to be applied to real chemical processes with various operation modes. In order to classify operation modes of chemical processes, this study considers three multivariate models of Euclidean distance, FDA (Fisher's Discriminant Analysis), and PCA (principal component analysis), and integrates them with process dynamics to lead dynamic Euclidean distance, dynamic FDA, and dynamic PCA. A case study of the TE (Tennessee Eastman) process having six operation modes illustrates the conclusion that dynamic PCA model shows the best classification performance.