• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.1-11
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• 2009

Recently there has arisen concern in both the database community and the graphics society about data retrieval from large motion databases because the high dimensionality of motion data implies high costs. In this circumstance, finding an effective distance measure and an efficient query processing method for such data is a challenging problem. This paper presents an elaborate motion query processing system, SMoFinder (Similar Motion Finder), which incorporates a novel kinematic distance measure and an efficient indexing strategy via adaptive frame segmentation. To this end, we regard human motions as multi-linkage kinematics and propose the weighted Minkowski distance metric. For efficient indexing, we devise a new adaptive segmentation method that chooses representative frames among similar frames and stores chosen frames instead of all frames. For efficient search, we propose a new search method that processes k-nearest neighbors queries over only representative frames. Our experimental results show that the size of motion databases is reduced greatly (${\times}1/25$) but the search capability of SMoFinder is equal to or superior to that of other systems.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.113-122
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• 1999

When we want to retrieve the most similar image from the image database, the color histogram intersection, shape feature and texture feature comparing method are used as a metric to measure the similarity. In order to increase the accuracy of retrievals, we need to integrate two different features. In this paper, the histogram intersection and shape based block histogram intersection method are used. This method results in a high efficient algorithm that meets a similar accuracy and a relatively fast retrieval speed compared to the method of integration of two different features. The Proposed algorithm is tested on retrievals of image database consisting of various 600 images and we implemented that the proposed algorithm gives fast, high efficiency and reliability compared to others.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1243-1258
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• 2017

A new medical materials scheduling system and its modeling method for the complex rescue are presented. Different from other similar system, first both the BeiDou Satellite Communication System (BSCS) and the Special Fiber-optic Communication Network (SFCN) are used to collect the rescue requirements and the location information of disaster areas. Then all these messages will be displayed in a special medical software terminal. After that the bipartite graph models are utilized to compute the optimal scheduling of medical materials. Finally, all these results will be transmitted back by the BSCS and the SFCN again to implement a fast guidance of medical rescue. The sole drug scheduling issue, the multiple drugs scheduling issue, and the backup-scheme selection issue are all utilized: the Kuhn-Munkres algorithm is used to realize the optimal matching of sole drug scheduling issue, the spectral clustering-based method is employed to calculate the optimal distribution of multiple drugs scheduling issue, and the similarity metric of neighboring matrix is utilized to realize the estimation of backup-scheme selection issue of medical materials. Many simulation analysis experiments and applications have proved the correctness of proposed technique and system.

• Smart Media Journal
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• v.10 no.3
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• pp.23-30
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• 2021

Although the manufacturing time series data clustering technique is an important grouping solution in the field of detecting and improving manufacturing large data-based equipment and process defects, it has a disadvantage of low accuracy when applying the existing static data target clustering technique to time series data. In this paper, an evolutionary computation-based time series cluster analysis approach is presented to improve the coherence of existing clustering techniques. To this end, first, the image shape resulting from the manufacturing process is converted into one-dimensional time series data using linear scanning, and the optimal sub-clusters for hierarchical cluster analysis and split cluster analysis are derived based on the Pearson distance metric as the target of the transformation data. Finally, by using a genetic algorithm, an optimal cluster combination with minimal similarity is derived for the two cluster analysis results. And the performance superiority of the proposed clustering is verified by comparing the performance with the existing clustering technique for the actual manufacturing process image.

• Korean Journal of Environmental Biology
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• v.38 no.4
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• pp.594-602
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• 2020

This study investigated the effect of geography and altitude on epigeic spider communities in rice field levees in Jeollabuk-do. Spider communities in the mountainous and plain areas were compared to determine the effect of geography on the cultivation periods. The effect of altitude was compared between the Jeongeup and Jangsu areas during non-cultivation periods. Analysis using nMDS (non-metric multidimensional scaling), MRPP (multiple response permutation procedure), and ANOSIM (analysis of similarity) revealed differences in spider community structures between the two types of study areas. Lycosidae predominated at the family level, and its abundance was greater in the mountainous area than in the plains area. The total abundance did not differ between the two areas with different altitudes, but the abundance of three Pardosa species was greater at lower altitudes than at higher altitudes. Geography and altitude had a minimal effect on species richness and diversity indices at the community level. However, several Lycosidae species showed species-specific responses to both geography and altitude in the rice fields.

• Journal of Korean Society on Water Environment
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• v.39 no.2
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• pp.181-193
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• 2023

Distribution changes in microalgae communities were studied in the Nakdonggang River at two sampling stations (St.1 Gyeongcheongyo Bridge (GB) and St.2 Daedong Wharf (DW)) at monthly intervals from January 2021 to November 2021. A total of 83 taxa included 82 species, 1 forma, belonging to 49 genera, 32 families, 21 orders, and 8 classes. The most important groups were Bacillariophyta and Chlorophyta. The number of species ranged from 5 to 24 in GB, and from 9 to 21 taxa in DW. The contribution of Bacillariophyta to the total species richness was the highest during all survey periods, and Chlorophyta yielded the next highest value in the study area. The dominant taxa were Aulacoseira ambigua, A. ambigua f. japonica, and Ulnaria acus in this study. Cluster analysis and non-metric multidimensional scaling (nMDS) analysis based on Bray- Curtis similarity identified 4 major groups, which corresponded to microalgae assemblages and their characteristic species. Correlation was analyzed through the CCA analysis. It was found that there was a correlation between the microalgae and environmental factors. It was revealed that the divided groups were distinguished because of the differences by the survey period. Therefore, seasonal change was judged as a major factor affecting the distribution of microalgae communities.

• Animal Bioscience
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• v.36 no.1
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• pp.63-74
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• 2023

Objective: The objective of this study was to investigate the effects of feeding active dry yeast (ADY) and yeast culture (YC) on fecal bacterial community in finishing bulls fed high-concentrate diets in the same experimental environment. Methods: Forty-five healthy finishing cattle (Simmental×Chinese Luxi yellow bulls; 24 months; 505±29 kg) were randomly divided into three groups: i) CON group (control group, only fed basal diet), ii) ADY group (fed basal diet + active dry yeast), and iii) YC group (fed basal diet + yeast culture). At the end of the trial, nine rectum fecal samples were randomly selected from each group for bacterial DNA sequencing. Results: There was no difference among groups about alpha diversity indices (all p>0.05), including ACE, Chao 1, Shannon, and Simpson indices. Principal component analysis and non-metric multidimensional scaling analysis showed a high similarity among three groups. Compared with CON group, ADY and YC groups had greater relative abundance of c_Clostridia, o_Oscillospirales, and f_Oscillospiraceae, but lesser relative abundance of g_Megasphaera, and s_Megasphaera_elsdenii (all p<0.01). And, the relative abundances of p_Firmicutes (p = 0.03), s_Prevotella_sp (p = 0.03), o_Clostridiales (p<0.01), g_Clostridium (p<0.01), f_Caloramatoraceae (p<0.01), and f_Ruminococcaceae (p = 0.04) were increased in the ADY group. The PICRUSt2 prediction results showed that the metabolic pathways had no significant differences among groups (p>0.05). Besides, the relative abundance of c_Clostridia (r = 0.42), and f_Oscillospiraceae (r = 0.40) were positively correlated to average daily gain of finishing bulls (p<0.05). Conclusion: Both of ADY and YC had no effect on diversity of fecal bacteria in finishing bulls, but the supplementation of ADY and YC can improve the large intestinal function in finishing bulls by increasing the abundance of cellulolytic bacteria and altering the abundance of lactic acid-utilizing bacteria.

• Journal of Korean Society for Quality Management
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• v.51 no.2
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• pp.315-327
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• 2023

Purpose: Recently, research has continued to predict the time of failure of the facility through measurement data obtained by attaching a sensor to the facility. However, depending on the facility, it may be difficult to attach a sensor. The purpose of this study is to propose a power generation maintenance plan system based on failure record data obtained from Continuous Ship Unloader, one of the facilities that is difficult to attach sensors. Methods: This study uses data collected from 2012 to 2022 from the 'CSU-1B' model among Continuous Ship Unloader operated by Korea Midland Power Co., LTD. By fitting fault record data to the Weibull distribution, appropriate maintenance cycles and ranges for each target facility subsystem are derived. In addition, maintenance group between subsystems is selected through Euclidean distance, a metric often used for time series data similarity. Through this, a system for establishing an maintenance plan for power generation facilities is proposed. Results: The results of this study are as follows. For the 17 subsystems of the Continuous Ship Unloader, proper maintenance cycles and ranges were determined, and a total of four maintenance groups were chosen. This resulted in the creation of an power generation maintenance plan system and the establishment of an maintenance plan. Conclusion: This study is a case study of power generation facilities. We proposed a maintenance plan system for Continuous Ship Unloader among power generation facilities.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.163-177
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• 2024

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used