• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.427-435
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• 2005

With the accumulation of protein and its related data on the Internet, many domain based computational techniques to predict protein interactions have been developed. However, most of the techniques still have many limitations to be used in real fields. They usually suffer from a low accuracy problem in prediction and do not provide any interaction possibility ranking method for multiple protein pairs. In this paper, we reevaluate a domain combination based protein interaction prediction method and develop an interaction possibility ranking method for multiple protein pairs. Probability equations are devised and proposed in the framework of domain combination based protein interaction prediction method. Using the ranking method, one can discern which protein pair is more probable to interact with each other than other protein pairs in multiple protein pairs. In the validation of the ranking method, we revealed that there exist some correlations between the interacting probability and the precision of the prediction in case of the protein pair group having the matching PIP(Primary Interaction Probability) values in the interacting or non interacting PIP distributions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.772-780
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• 2008

K-means is one of the simplest unsupervised learning algorithms that solve the clustering problem. However K-means suffers the basic shortcoming: the number of clusters k has to be known in advance. In this paper, we propose extensions of X-means, which can estimate the number of clusters using Bayesian information criterion(BIC). We introduce two different versions of algorithm: modified X-means(MX-means) and generalized X-means(GX-means), which employ one full covariance matrix for one cluster and so can estimate the number of clusters efficiently without severe over-fitting which X-means suffers due to its spherical cluster assumption. The algorithms start with one cluster and try to split a cluster iteratively to maximize the BIC score. The former uses K-means algorithm to find a set of optimal clusters with current k, which makes it simple and fast. However it generates wrongly estimated centers when the clusters are overlapped. The latter uses EM algorithm to estimate the parameters and generates more stable clusters even when the clusters are overlapped. Experiments with synthetic data show that the purposed methods can provide a robust estimate of the number of clusters and cluster parameters compared to other existing top-down algorithms.

• Korean Journal of Food Science and Technology
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• v.53 no.4
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• pp.365-374
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• 2021

In this study, the effect of enzyme-modified starch used in the preparation of filled hydrogel powder loaded with curcumin (FHP) on redispersibility, thermal and UV stability, and curcumin retention during in vitro digestion was investigated. FHP maintained stability without layer separation when redispersed and showed more stability against UVB than the emulsion powder (EMP). There was no significant difference in the chemical stability of curcumin between rice starch-based filled hydrogel powder (RS-FHP) and enzyme-modified starch-based filled hydrogel powder (GS-FHP). However, the gel matrix of GS-FHP maintained greater stability of lipid droplets in the stomach compared to RS-FHP, thereby improving the retention rate of curcumin after in vitro digestion. GS-FHP could be used as a novel material for developing premixes that require stable formulation and maintenance of functional substances, as it can increase the dispersion stability and retention rate of functional substances after digestion.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.04a
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• pp.62-62
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• 2019

Bacterial leaf blight(BLB), caused by X. oryzae pv. oryzae(Xoo), is one of the most destructive diseases of rice due to its high epidemic potential. Understanding BLB resistance at a genetic level is important to further improve the rice breeding that provides one of the best approaches to control BLB disease. In the present investigation, a collection of 192 accessions was used in the genome-wide association study (GWAS) for BLB resistance loci against four Korean races of Xoo that were represented by the prevailing BLB isolates under Xoo differential system. A total of 192 accessions of rice germplasm were selected on the basis of the bioassay using four isolated races of Xoo such as K1 and K2. The selected accessions was used to prepare 384-plex genotyping by sequencing (GBS) libraries and Illumina HiSeq 2000 pairedend read was used for GBS sequencing. GWAS was conducted using TASSEL 5.0. The TASSEL program uses a mixed linear model (MLM). The results of the bioassay using a selected set of 192 accessions showed that a large number of accessions (93.75%) were resistant to K1 race and K2 resistant germplasm proportion remained between 66.67. The genotypic data produced SNP matrix for a total of 293,379 SNPs. After imputation the missing data was removed, which exhibited 34,724 SNPs for association analysis. GWAS results showed strong signals of association at a threshold of [-log10(P-value)] more than 5 (K1 and K2) for nine of the 39 SNPs, which are plausible candidate loci of resistance genes. These SNP loci were positioned on rice chromosome 2, 9, and 11 for K1 and K2 races. The significant loci detected have also been illustrated and make the CPAS markers for NBS-LRR type disease resistance protein, SNARE domain containing protein, Histone deacetylase 19, NADP-dependent oxidoreductase, and other expressed and unknown proteins. Our results provide a better understanding of the distribution of genetic variation of BLB resistance to Korean pathogen races and breeding of resistant rice.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.5
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• pp.1-14
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• 2021

Excessive development and urbanization have destroyed animal, plant, habitats and reduced biodiversity. In order to preserve species diversity, habitat prediction studies are have been conducted at home and overseas using various modeling techniques. This study was conducted to suggest optimal habitat modeling research by comparing HSI and MaxEnt, which are widely used among habitat modeling techniques. The study was targeted on the endangered species of Prionailurus bengalensis in nearby areas (5460.35km2) including Cheonan City, and the same data were used for analysis to compare those models. According to the HSI analysis, Prionailurus bengalensis's habitat probability was 74.65% for less than 0.5 and 25.34% for more than 0.5 and the top 30% were forest (99.07%). MaxEnt's analysis showed that 56.22% of those below 0.5 and 43.79% of those above 0.5 were found to have a high explanatory power of 78.3% of AUC. The Paired Wilcoxn test, which evaluated the significance of thoes models, confirmed that the mean difference between the two models was statistically significant (p<0.05). Analysis of the differences in the results of those models using the matrix table shows that score 24.43% HSI and MaxEnt was accordance,12.44% of the 0.0 to 0.2 section, 7.22% of the 0.2 to 0.4 section, 2.73% of the 0.4 to 0.6 section, 1.96% of the 0.6 to 0.8, and 0.08% of the 0.9 to 1.0. To verify where the score difference appears, the result values of those models were reset to values from 1 to 5 and overlaid. Overlapping analysis resulted in 30.26% of the Strongly agree values, 56.77% of the agree values, and 11.92% of the Disagree values. The places where the difference in scores occurs were analyzed in the order of forest (45.23%), agricultural land (34.57%), and urbanization area (7.65%). This confirmed that the analysis of the same target species within the same target site also has differences in forecasts depending on the modelling method. Therefore, a novel analysis method combining the advantages of each modeling in habitat prediction studies should be developed, and future study may be used to select Prionailurus bengalensis and species-protected areas and species protection areas in the future. Further research is judged to require higher accuracy studies through the use of various modeling techniques and on-site verification.

• Elastomers and Composites
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• v.55 no.4
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• pp.306-313
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• 2020

To investigate the reinforcing effects of functional fillers in nitrile rubber (NBR) materials, high-structure carbon black (HS45), coated calcium carbonate (C-CaCO3), silica (200MP), and multi-walled carbon nanotubes (MWCNTs) were used as functional filler, and carbon black (SRF) as a common filler were used for oil-resistant rubber. The curing and mechanical properties of HS45-, 200MP-, and MWCNT-filled NBR compounds were improved compared to those of the SRF-filled NBR compound. The reinforcing effect also increased with a decrease in the particle size of the fillers. The C-CaCO3-filled NBR compound exhibited no reinforcing effect with increasing filler concentration because of their large primary particle size (2 ㎛). The reinforcing behavior based on 100% modulus of the functional filler based NBR compounds was compared by using several predictive equation models. The reinforcing behavior of the C-CaCO3-filled NBR compound was in accordance with the Smallwood-Einstein equation whereas the 200MP- and MWCNT-filled NBR compounds fitted well with the modified Guth-Gold (m-Guth-Gold) equation. The SRF- and HS45-filled NBR compounds exhibited reinforcing behavior in accordance with the Guth-Gold and m-Guth-Gold equations, respectively, at a low filler content. However, the values of reinforcement parameter (100Mf/100Mu) of the SRF- and HS45-filled NBR compounds were higher than those determined by the predictive equation model at a high filler content. Because the chains of SRF composed of spherical filler particles are similarly changed to rod-like filler particles embedded in a rubber matrix and the reinforcement parameter rapidly increased with a high content of HS45, the higher-structured filler. The reinforcing effectiveness of the functional fillers was numerically evaluated on the basis of the effectiveness index (��SRF/��f) determined by the ratio of the volume fraction of the functional filler (��f) to that of the SRF filler (��SRF) at three unit of reinforcing parameter (100Mf/100Mu). On the basis of their effectiveness index, MWCNT-, 200MP-, and HS45-filled compounds showed higher reinforcing effectiveness of 420%, 70%, and 20% than that of SRF-filled compound, respectively whereas C-CaCO3-filled compound exhibited lower reinforcing effectiveness of -50% than that of SRF-filled compound.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.183-191
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• 2021

Non-intrusive load monitoring is a technology that can be used for predicting and classifying the type of appliances through real-time monitoring of user power consumption, and it has recently got interested as a means of energy-saving. In this paper, we propose a system for classifying appliances from user consumption data by combining GAF(Gramian angular field) technique that can be used for converting one-dimensional data to the two-dimensional matrix with convolutional neural networks. We use REDD(residential energy disaggregation dataset) that is the public appliances power data and confirm the classification accuracy of the GASF(Gramian angular summation field) and GADF(Gramian angular difference field). Simulation results show that both models showed 94% accuracy on appliances with binary-state(on/off) and that GASF showed 93.5% accuracy that is 3% higher than GADF on appliances with multi-state. In later studies, we plan to increase the dataset and optimize the model to improve accuracy and speed.

• Journal of Nutrition and Health
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• v.54 no.4
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• pp.412-421
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• 2021

Purpose: Thioacetamide (TAA) produces reactive oxygen species (ROS) in the liver, and the generated ROS induces liver injury through inflammatory reactions. The current study was undertaken to investigate the hepatoprotective effect of Artemisiae Capillaris Herba water extract (AC), imparted via its antioxidant activity, in an animal model of TAA-induced liver injury. Methods: Animal experiments were conducted in 5 groups: normal, control (TAA 200 mg/kg), SM (TAA 200 mg/kg + silymarin 100 mg/kg), ACL (TAA 200 mg/kg + AC 100 mg/kg), ACH (TAA 200 mg/kg + AC 200mg/kg). TAA (intraperitoneal) and treatment compounds (per oral) were administered for 3 days. Serum levels of ammonia concentration and myeloperoxidase (MPO) activity were subsequently measured. Liver tissues were subjected to western blot analysis for measuring the oxidative stress (NADPH oxidase), anti-oxidative activity (Nrf2, heme oxygenase-1 [HO-1], superoxide dismutase [SOD], catalase, and GPx-1/2), tissue inhibitors of metalloproteinase (TIMP)-1, and matrix metalloproteinases (MMPs) protein expressions. Results: Serum ammonia levels and MPO activity were significantly increased in the TAA-induced control group, whereas groups administered AC treatment showed markedly reduced levels. Western blot analysis revealed significantly increased NOX2 and p22^phox expressions, (oxidative stress-related factors) in the TAA-induced control group. These levels were determined to be significantly decreased after AC exposure. Moreover, antioxidant-related factors including Nrf2, HO-1, SOD, catalase, and GPx-1/2 were significantly decreased in the control group and increased in the AC treated groups. In addition, MMP expressions were significantly suppressed in the AC treatment group due to increased levels of TIMP-1. Conclusion: Taken together, these data indicate that exposure to AC reduces the oxidative stress by inhibiting the expression of NADPH oxidase (NOX2 and p22^phox) through the Nrf2 signaling pathway. We therefore propose the potential of AC for the prevention and treatment of TAA-induced liver injury.

• The Korea Journal of Herbology
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• v.35 no.1
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• pp.35-44
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• 2020

Objectives : The objective of this study was to investigate the therapeutic effect of Corni Fructus 30% ethanol extract (CFE) on the monosodium iodoacetate (MIA)-induced osteoarthritis rats. Methods : The subjects were divided into 4 groups ; Normal group (N, n=10), MIA-induced osteoarthritis control group (Con, n=10), indomethacin 5 mg/kg treated group (INDO, n=10), CFE 200 mg/kg treated group (CFE, n=10). Blood and articulation tissues were collected after two weeks of drug administration. Oxidative stress was analyzed with reactive oxygen species (ROS), peroxynitrite (ONOO-). And the Nuclear factor erythroid-2 (Nrf2), heme oxygenase-1 (HO-1), superoxide dismutase (SOD), catalase, glutathione peroxidase-1/2 (GPx-1/2), Nuclear Factor Kappa B p65 (NF-κBp65), cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), tumor necrosis factor-alpha (TNFα), interleukin-6 (IL-6), Interleukin 1β (IL-1β), matrix metalloproteinase-1 (MMP-1), and tissue inhibitor of metalloproteinases-1 (TIMP-1) were investigated by western blot. Results : The administration of CFE showed a significant reduction of changes in relative hind paw weight distribution. Reactive oxygen species (ROS) and peroxy nitrite (ONOO-) levels of articulation tissues were significantly decreased in CFE compared to the control group. Western blot measurements of Nrf2, HO-1, SOD, catalase, GPx-1/2 showed that the CFE group was increased compared to the Con group. And western blot measurements of NF-κBp65, COX-2, iNOS, TNFα, IL-6, IL-1β showed that the CFE group was reduced compared to the Con group. Also CFE group decreased MMP-1 and increased TIMP-1. Conclusion : Based on the above results, it can be seen that osteoarthritis is improved when Corni Fructus 30% ethanol extract treated.