• IE interfaces
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• v.8 no.3
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• pp.269-278
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• 1995

본 논문은 분산환경하에서 생산정보, 데이터 분석의 일 방안으로서 데이터의 관리 주체와 데이터들간의 유사성에 기초한 분산 기준을 제시한다. 제안된 분산기준은 데이터베이스 설계의 초기과정에서 네트워크 환경의 각 노드에서의 트랜젝션의 발생 수와 데이터 이용량의 예측이 용이하지 않은 경우에도 적용 가능하다는 점에서 가치를 지닌다. 제시된 설계 기준을 이용하여 생산정보 데이터베이스의 분산 모형을 설계, 제시한다.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.573-582
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• 2015

Edit distance metrics are widely used for many applications such as string comparison and spelling error corrections. Hamming distance is a metric for two equal length strings and Damerau-Levenshtein distance is a well-known metrics for making spelling corrections through string-to-string comparison. Previous distance metrics seems to be appropriate for alphabetic languages like English and European languages. However, the conventional edit distance criterion is not the best method for agglutinative languages like Korean. The reason is that two or more letter units make a Korean character, which is called as a syllable. This mechanism of syllable-based word construction in the Korean language causes an edit distance calculation to be inefficient. As such, we have explored a new edit distance method by using consonant normalization and the normalization factor.

• Korean Journal of Ichthyology
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• v.30 no.1
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• pp.36-45
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• 2018

The temporal and depth-related variations in the species composition and abundance of demersal fish assemblage were studied in the coastal waters off Hupo, East Sea. Fish samples were collected seasonally between 2011 and 2017 at two stations of study area using trammel net and bottom gill net. In total, 46 fish species belonging to 17 families were collected during study period, with 36 and 22 species occurring in depths of ~80 m (site A) and ~140 m (site B), respectively. Glyptocephalus stelleri, Cleisthenes pinetorum and Gymnocanthus herzensteini were abundant at shallower site, and Dasycottus setiger at deeper site. The number of species, abundance, biomass and diversity fluctuated with water depth, but not temporally (both seasonally and annually). Analysis of similarity (ANOSIM) revealed that the fish assemblage structures were significantly different with water depth, but not by year or season. Non-metric multidimensional scaling (MDS) ordination plot emphasized visually in spatial difference of fish assemblages, and it was due to differential contributions of dominant species in relation to water depth and temperature.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.980-990
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• 2004

The component-based development methodology aims at the high state of abstraction and the reusability with components larger than classes. It is indispensible to measure the component so as to improve the quality of the component-based system and the individual component. And, the quality of the component should be improved through putting the results into the process of the development. So, it is necessary to study the component metric which can be applied in the stage of the component analysis and design. Hence, in this paper, we propose component cohesion, coupling, independence metrics reflecting the information extracted in the step of component analysis and design. The proposed component metric bases on the similarity information about behavior patterns of operations to offer the component's service. Also, we propose the redesigning process for the improvement of component design. That process uses the techniques of clustering and is for the thing that makes the component as the independent functional unit having the low complexity and easy maintenance. And, we examine that the component design model can be improved by the component metrics and the component redesigning process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3502-3509
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• 2010

Streaming service in the wireless mobile network environment has been a very challenging issue due to the dynamic uncertain nature of the channels. Overhead such as congestion, latency, and jitter lead to the problem of performance degradation of an adaptive multi-streaming service. This paper proposes a AMSS (Adaptive Multi-level Streaming Service) mechanism to reduce the performance degradation due to overhead such as variable network bandwidth, mobility and limited resources of the wireless mobile network. The proposed AMSS optimizes streaming services by: 1) use of fuzzy similarity metric, 2) minimization of packet loss due to buffer overflow and resource waste, and 3) minimization of packet loss due to congestion and delay. The simulation result shows that the proposed method has better performance in congestion control and packet loss ratio than the other existing methods of TCP-based method, UDP-based method and VBM-based method. The proposed method showed improvement of 10% in congestion control ratio and 8% in packet loss ratio compared with VBM-based method which is one of the best method.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.547-558
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• 2003

From the viewpoint of that music melody has the similar features to time-series data, music melody is transformed to a time-series data with normalization and corrections and the similarity between melodies is defined as the Euclidean distance between the transformed time-series data. Then, based the similarity between melodies of a music object, melodies are clustered and the representative of each cluster is extracted as one of theme melodies for the music. To construct the theme melody index, a theme melody is represented as a point of the multidimensional metric space of M-tree. For retrieval of user's query melody, the query melody is also transformed into a time-series data by the same way of indexing phase. To retrieve the similar melodies to the query melody given by user from the theme melody index the range query search algorithm is used. By the implementation of the prototype system using the proposed theme melody index we show the effectiveness of the proposed methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.756-760
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• 2017

Meeting the growing demand deadlines, reducing the production cost and upgrading the quality control measurements are the reasons why the automotive part manufacturers are venturing into automation. Attaining these objectives is impossible with human inspection for many reasons. Accordingly, the introduction of inspection system purposely for door hinge bracket inspection is presented in this study as an alternative for human inspection. This proposal is designed to meet the demands, features and specifications of door hinge bracket manufacturing companies in striving for increased throughput of better quality. To improve demerits of this manual operation, inspection system is introduced. As the inspection algorithm, template matching algorithm is applied to distinguish the articles of good quality and the poorly made articles. Through the verification test of the inspection process algorithm and the similarity metric matching algorithm, the detection accuracy was 98%, and it was applied to the production site to contribute to the improvement of the productivity due to the decrease of the defective product.

• Journal of Computing Science and Engineering
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• v.6 no.1
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• pp.1-11
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• 2012

Computed tomography (CT) images are widely used for the analysis of the temporal evaluation or monitoring of the progression of a disease. The follow-up examinations of CT scan images of the same patient require a 3D registration technique. In this paper, an automatic and robust registration is proposed for the rigid registration of 3D CT images. The proposed method involves two steps. Firstly, the two CT volumes are aligned based on their principal axes, and then, the alignment from the previous step is refined by the optimization of the similarity score of the image's voxel. Normalized cross correlation (NCC) is used as a similarity metric and a downhill simplex method is employed to find out the optimal score. The performance of the algorithm is evaluated on phantom images and knee synthetic CT images. By the extraction of the initial transformation parameters with principal axis of the binary volumes, the searching space to find out the parameters is reduced in the optimization step. Thus, the overall registration time is algorithmically decreased without the deterioration of the accuracy. The preliminary experimental results of the study demonstrate that the proposed method can be applied to rigid registration problems of real patient images.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.119-135
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• 2012

Among various recommendation techniques, neighborhood-based Collaborative Filtering (CF) techniques have been one of the most widely used and best performing techniques in literature and industry. This paper proposes new approaches that can enhance the neighborhood-based CF techniques by identifying a few best neighbors (the most similar users to a target user) more accurately with more information about neighbors. The proposed approaches put more weights to the users who have more items co-rated by the target user in similarity computation, which can help to better understand the preferences of neighbors and eventually improve the recommendation quality. Experiments using movie rating data empirically demonstrate simultaneous improvements in both recommendation accuracy and diversity. In addition to the typical single rating setting, the proposed approaches can be applied to the multi-criteria rating setting where users can provide more information about their preferences, resulting in further improvements in recommendation quality. We finally introduce a single metric that measures the balance between accuracy and diversity and discuss potential avenues for future work.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.163-170
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• 2023

The user-based collaborative filtering technique, one of the implementation methods of the recommendation system, recommends the preferred items of neighboring users based on the calculations of neighboring users with similar rating histories. However, it fundamentally has a data scarcity problem in which the quality of recommendations is significantly reduced when there is little common rating history. To solve this problem, many existing studies have proposed various methods of combining Jaccard index with a similarity measure. In this study, we introduce a time-aware concept to Jaccard index and propose a method of weighting common items with different weights depending on the rating time. As a result of conducting experiments using various performance metrics and time intervals, it is confirmed that the proposed method showed the best performance compared to the original Jaccard index at most metrics, and that the optimal time interval differs depending on the type of performance metric.