• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.90-99
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• 2003

Object clustering, which makes classification for a set of objects into a number of groups such that objects included in a group have similar characteristic and objects in different groups have dissimilar characteristic each other, has been exploited in diverse area such as information retrieval, data mining, group technology, etc. In this study, an object-clustering problem with similarity coefficients between objects is considered. At first, an evaluation function for the optimization problem is defined. Then, a genetic algorithm and local optimization technique based on heuristic method are proposed and used in order to obtain near optimal solutions. Solutions from the genetic algorithm are improved by local optimization techniques based on object relocation and cluster merging. Throughout extensive experiments, the validity and effectiveness of the proposed algorithms are tested.

• ETRI Journal
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• v.46 no.3
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• pp.526-537
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• 2024

Monitoring fetal growth in utero is crucial to anomaly diagnosis. However, current computer-vision models struggle to accurately assess the key metrics (i.e., head circumference and occipitofrontal and biparietal diameters) from ultrasound images, largely owing to a lack of training data. Mitigation usually entails image augmentation (e.g., flipping, rotating, scaling, and translating). Nevertheless, the accuracy of our task remains insufficient. Hence, we offer a U-Net fetal head measurement tool that leverages a hybrid Dice and binary cross-entropy loss to compute the similarity between actual and predicted segmented regions. Ellipse-fitted two-dimensional ultrasound images acquired from the HC18 dataset are input, and their lower feature layers are reused for efficiency. During regression, a novel region of interest pooling layer extracts elliptical feature maps, and during segmentation, feature pyramids fuse field-layer data with a new scale attention method to reduce noise. Performance is measured by Dice similarity, mean pixel accuracy, and mean intersection-over-union, giving 97.90%, 99.18%, and 97.81% scores, respectively, which match or outperform the best U-Net models.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.375-378
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• 2000

In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10C
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• pp.942-947
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• 2006

In this paper, a new feature representation technique called Improved 2-directional 2-dimensional direct linear discriminant analysis (Improved $(2D)^2$ DLDA) is proposed. In the case of face recognition, thesmall sample size problem and need for many coefficients are often encountered. In order to solve these problems, the proposed method uses the direct LDA and 2-directional image scatter matrix. Moreover the selection method of feature vector and the method of similarity measure are proposed. The ORL face database is used to evaluate the performance of the proposed method. The experimental results show that the proposed method obtains better recognition rate and requires lesser memory than the direct LDA.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.686-691
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• 2011

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.19-26
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• 2010

In this paper, we suggest a efficient handwritten character verification method in on-line environments which automatically analyses two input character string and computes their similarity degrees. The proposed algorithm first applies the circular projection method to input handwritten strings and extracts their representative features including shape, directions, etc. It then calculates the similarity between two character strings by using an improved dynamic time warping (DTW) algorithm. We improved the conventional DTW algorithm efficiently through adopting the branch-and-bound policy to the existing DTW algorithm which is well-known to produce good results in the various optimization problems. The experimental results to verify the performance of the proposed system show that the suggested handwritten character verification method operates more efficiently than the existing DTW and DDTW algorithms in terms of the speed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1800-1817
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• 2022

With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure. Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords. In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.299-307
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• 2013

The aim of this paper is the improvement of identification performance and the reduction of computational quantities in the face recognition system based on SIFT-Grid. Firstly, we propose a composition method of integrated template by removing similar SIFT keypoints and blending different keypoints in variety training images of one face class. The integrated template is made up of computation of similarity matrix and threshold-based histogram from keypoints in a same sub-region which divided by applying SIFT-Grid of training images. Secondly, we propose a computation method of similarity for identify of test image from composed integrated templates efficiently. The computation of similarity is performed that a test image to compare one-on-one with the integrated template of each face class. Then, a similarity score and a threshold-voting score calculates according to each sub-region. In the experimental results of face recognition tasks, the proposed methods is founded to be more accurate than both two other methods based on SIFT-Grid, also the computational quantities are reduce.

• Korean Business Review
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• v.20 no.1
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• pp.145-168
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• 2007

Recommender system helps customers to find easily items and helps the e-biz companies to set easily their target customer by automated recommending process. Recommender systems are being adopted by several e-biz companies and from these systems, both of customers and companies take some benefits. This study sets several thresholds to the similarity weight, which indicates a degree of similarity of two customers' preference, to improve the performance of prediction accuracy. According to the threshold, the accuracy of prediction is being improved but some threshold setting shows the reduction of the prediction rate, which is the coverage. This coverage reduction has male effect on the prediction accuracy of customers, so more study on the prediction accuracy of recommender system and to maximize the coverage are needed.

• The Plant Pathology Journal
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• v.30 no.4
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• pp.384-396
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• 2014

Vegetative compatibility groups (VCGs) are determined for many fungi to test for the ability of fungal isolates to undergo heterokaryon formation. In several fungal plant pathogens, isolates belonging to a VCG have been shown to share significantly higher genetic similarity than those of different VCGs. In this study we sought to examine the relationship between VCG and genetic similarity of an important cool season turfgrass pathogen, Sclerotinia homoeocarpa. Twenty-two S. homoeocarpa isolates from the Midwest and Eastern US, which were previously characterized in several studies, were all evaluated for VCG using an improved nit mutant assay. These isolates were also genotyped using 19 microsatellites developed from partial genome sequence of S. homoeocarpa. Additionally, partial sequences of mitochondrial genes cytochrome oxidase II and mitochondrial small subunit (mtSSU) rRNA, and the atp6-rns intergenic spacer, were generated for isolates from each nit mutant VCG to determine if mitochondrial haplotypes differed among VCGs. Of the 22 isolates screened, 15 were amenable to the nit mutant VCG assay and were grouped into six VCGs. The 19 microsatellites gave 57 alleles for this set. Unweighted pair group methods with arithmetic mean (UPGMA) tree of binary microsatellite data were used to produce a dendrogram of the isolate genotypes based on microsatellite alleles, which showed high genetic similarity of nit mutant VCGs. Analysis of molecular variance of microsatellite data demonstrates that the current nit mutant VCGs explain the microsatellite genotypic variation among isolates better than the previous nit mutant VCGs or the conventionally determined VCGs. Mitochondrial sequences were identical among all isolates, suggesting that this marker type may not be informative for US populations of S. homoeocarpa.