• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.10
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• pp.947-952
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• 2006

It has been confirmed that the inner defect of insulator and the perfect diagnosis for aging are closely related to safe electric power transmission system and that the detection of accident and diagnosis technique turn out to be very important issues. But perfect diagnosis is difficult because discharge pattern is irregular. Thus, we investigated discharge pattern using the new distribution statistical models with cross-inked polyethylene(XLPE) specimens. Voltage was applied to power frequency by step method, and calibration of discharge was set to 50 pC. After the voltage was applied, it measured the discharge occurring during 10s. We investigated discharge pattern using the K-means analysis and Weibull function. We also investigated variation of centroid and shape parameter due to variation of voltage. As a result of analyzing K-means, it was confirmed that cluster including many object numbers was formed by the presence of void. And result of Weibull distribution, it was confirmed that shape parameter of discharge varied from 1.28 to 1.62 in no void specimens, and that shape parameter of discharge number varied from 1.28 to 1.62. In the void, shape parameter of discharge varied from 5.66 to 6.43, and shape parameter of discharge number varied from 5.05 to 5.08.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1149-1155
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• 2012

In this paper, we propose cause diagnosis method of semiconductor defects from semiconductor industrial images. Our method constructs feature database (DB) of defect images. Then, defect and input images are subdivided by uniform block. And the block similarity is measured using histogram kai-square distance after color histogram calculation. Then, searched blocks in each image are merged into connected objects using clustering. Finally, the most similar defect image from feature DB is searched with the defect cause by measuring cluster similarity based on features of each cluster. Our method was validated by calculating the search accuracy of n output images having high similarity. With n = 1, 2, 3, the search accuracy was measured to be 100% regardless of defect categories. Our method could be used for the industrial applications.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.11-19
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• 2014

In this paper, we design an intelligent agent system with the data mining module and information fusion module as the core components of the system and investigate the possibility for the medical expert system. In the data mining module, fuzzy neural network, OFUN-NET analyzes multi-view data and produces fuzzy cluster knowledge base. In the information fusion module and application module, they serve the diagnosis result with possibility degree and useful information for diagnosis, such as uncertainty decision status or detection of asymmetry. We also present the experiment results on the BI-RADS-based feature data set selected form DDSM benchmark database. They show higher classification accuracy than conventional methods and the feasibility of the system as a computer aided diagnosis system.

• Journal of Home Health Care Nursing
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• v.16 no.1
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• pp.12-21
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• 2009

Purpose: The survey-based study aimed to determine the distribution and clustering tendency of metabolic syndrome risk factors in urban residents, and cluster odds ratios. Methods: Cluster sampling involved 827 urban participants and analysis of the collected data. Results: Regarding the prevalence of metabolic syndrome risk factors used for diagnosis, abdominal obesity was higher in women(69.5%) than in men(34.3%), high blood pressure was higher in men(57%) than in women(46.5%), and blood sugar was higher in men(6.9%) than in women(5.7%). Clustering increased with increasing body mass index(BMI), weight:height ratio(W/Ht) and abdominal obesity Risk factors for females were 1.7 times higher than for males. Participants with a family history of metabolic syndrome displayed related risk factors 1.5 times more than participants without a family history. Participants having a BMI ranking them as obese were 9.5 times more likely to display metabolic syndrome risk factors than non-obese participants. Obese participants were 20 times more likely to display risk factors than non-obese participants. Conclusion: BMI, W/Ht and abdominal obesity correlate with clustering of metabolic syndrome risk factors. The risk is increased by smoking and family history. Exercise weight control and non-smoking are recommended for comprehensive management of clustering of metabolic syndrome risk factors.

• Korean Journal of Veterinary Research
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• v.44 no.4
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• pp.571-579
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• 2004

In order to obtain the genetic informations of the Korean isolates of porcine circovirus 2 (PCV2), nucleotide sequences of total genome of three isolates and open reading frame 2 (ORF2) of four isolates were determined and compared with those of other reference PCV2 isolates. Nucleotide sequences of 3 isolates showed over 99% homology with those of reference strain (GenBank accession no. AF027217). Point mutations were mainly determined on ORF2 regions but little on ORF1 regions. The patterns of pointmutated sites and nucleotide substitution on ORF2 regions were generally consistent between Korean isolates, and these mutated sites observed in Korean isolates were also relatively similar to those of foreign isolates. Phylogenetic analysis of nucleotide or amino acid sequences showed that there were minor branches consisting of three clusters; cluster of Korea, Canada and America, cluster of Spain and Taiwan, and the last cluster of French and China isolates. These results suggested that Korean PCV2s were probably originated from North America such as Canada or USA. The genetic informations obtained from this study could be useful for the research of diagnosis and pathogenecity of PCV2.

• Database Research
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• v.34 no.3
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• pp.58-68
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• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.199-206
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• 2004

Epileptic seizures result from a temporary electrical disturbance of the brain. In this paper, a method of discriminating EEG for diagnoses of temporal lobe epilepsy is proposed. The proposed method for classification of epilepsy and sleep EEG is based on the wavelet transform and the fuzzy c-means. The magnitude and mean of wavelet coefficients for each EEG band are applied to the cluster of the FCM classifier. The proposed system show a little more accurate diagnosis for EEG by analysis of frequency for Wavelet and the success rate of 95% classification using FCM. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and realize quantitative diagnosis of EEG.

• The Journal of the Korean dental association
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• v.50 no.5
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• pp.244-255
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• 2012

Temporomandibular disorder(TMD) is described as a cluster of disorders characterized by pain in the preauricular area and/or the muscles of mastication; limitations or deviations in mandibular range of motion; and noises in the TMJ during mandibular function. The most common symptom in TMD patients is pain that is aggravated by chewing or other jaw function. These symptoms are appeared when the stimuli loaded in TMJ are over the physiologic tolerance. The primary goal in treatment of TMD is to alleviate pain and lor mandibular dysfunction. TMD treatment can be divided into 2 categories: reversible and irreversible methods. Reversible methods include medication, thermal therapy, habit modification, physical therapy, appliance therpy and arthrocentesis and lavage and irreversible methods include arthroscopic lysis, surgery, occlusal adjustment et al. It is widely accepted that reversible methods are ther first choice of treatments. However if reversible ones are not effective, irreversible methods are considered.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2110-2116
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• 2012

This research focused on the strategy formulation for the development of the innovation cluster in Gyeonggi Province - 'Pankyo & Gwanggyo Technovalley'. The study was performed based on both theoretical study and quantitative & qualitative study approaches. Particularly, questionnaire survey was used for the diagnosis of the developmental stages of the innovation clusters. The major determinants for the development of the innovation cluster in Gyeonggi Province can be summarized as follows; the establishment of S&T based host institution, the gradual enlargement of the role of host institution and the enhancement of the network capability of innovation clusters. In terms of the needs of times, this study regarding the strategy for the development of the innovation clusters is anticipated to be a good reference for the R&D organizations and technology cluster participants in coming years.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.33 no.2
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• pp.35-40
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• 2022

Objectives: Restricted and repetitive behaviors (RRBs) are a core symptom in the diagnosis of autism spectrum disorder (ASD). The complexity of behavioral patterns has called for the creation of phenotypically homogeneous subgroups among individuals with ASD. The purpose of this study was 1) to investigate the different types of RRBs and 2) to explore whether subgroups created by RRBs would show unique levels of functioning in toddlers and young children with ASD. Methods: A total of 313 children with ASD, aged 12-42 months were included in the analysis. The Autism Diagnostic Interview-Revised was used to obtain information on the different types of RRBs by grouping 15 items into six categories. The Vineland Adaptive Behaviors Scale, a parent-reported questionnaire, was used to measure adaptive functioning. A portion of the children were analyzed separately for verbal-related RRBs based on their expressive language level. Two-step cluster analysis using RRB groups as features was used to create subgroups. Analysis of covariance while covarying for age and language was performed to explore the clinical characteristics of each cluster group. Results: Sensory-related RRBs were the most prevalent, followed by circumscribed interests, interest in objects, resistance to change, and repetitive body movements. A subset of the children was analyzed separately to explore verbal-related RRBs. Four cluster groups were created based on reported RRBs, with multiple RRBs demonstrating significant delays in adaptive functioning. Conclusion: Heterogeneity of RRBs emerges at a young age. The different patterns of RRBs can be used as valuable information to determine developmental trajectories with better implications for treatment approaches.