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Soo Min Kim,Yunsu Choi,Bo Youl Choi,Minjeong Kim,Sang Il Kim,Jun Young Choi,Shin-Woo Kim,Joon Young Song,Youn Jeong Kim,Mee-Kyung Kee,Myeongsu Yoo,Jeong Gyu Lee,Bo Young Park 한국역학회 2020 Epidemiology and Health Vol.42 No.-
OBJECTIVES: The aim of effective data quality control and management is to minimize the impact of errors on study results by identifying and correcting them. This study presents the results of a data quality control system for the Korea HIV/AIDS Cohort Study that took into account the characteristics of the data. METHODS: The HIV/AIDS Cohort Study in Korea conducts repeated measurements every 6 months using an electronic survey administered to voluntarily consenting participants and collects data from 21 hospitals. In total, 5,795 sets of data from 1,442 participants were collected from the first investigation in 2006 to 2016. The data refining results of 2015 and 2019 were converted into the data refining rate and compared. RESULTS: The quality control system involved 3 steps at different points in the process, and each step contributed to data quality management and results. By improving data quality control in the pre-phase and the data collection phase, the estimated error value in 2019 was 1,803, reflecting a 53.9% reduction from 2015. Due to improvements in the stage after data collection, the data refining rate was 92.7% in 2019, a 24.21%p increase from 2015. CONCLUSIONS: Despite this quality management strategy, errors may still exist at each stage. Logically possible errors for the post-review refining of downloaded data should be actively identified with appropriate consideration of the purpose and epidemiological characteristics of the study data. To improve data quality and reliability, data management strategies should be systematically implemented.
Epidemiological characteristics of HIV infected Korean: Korea HIV/AIDS Cohort Study
최윤수,최보율,Soo Min Kim,Sang-Il Kim,June Kim,Jun Young Choi,Shin Woo Kim,Joon Young Song,Youn Jeong Kim,Dae Won Park,Hyo Youl Kim,최희정,Mee-Kyung Kee,YoungHyun Shin,Myeongsu Yoo 한국역학회 2019 Epidemiology and Health Vol.41 No.-
OBJECTIVES: To manage evidence-based diseases, it is important to identify the characteristics of patients in each country. METHODS: The Korea HIV/AIDS Cohort Study seeks to identify the epidemiological characteristics of 1,442 Korean individuals with human immunodeficiency virus (HIV) infection (12% of Korean individuals with HIV infection in 2017) who visited 21 university hospitals nationwide. The descriptive statistics were presented using the Korea HIV/AIDS cohort data (2006-2016). RESULTS: Men accounted for 93.3% of the total number of respondents, and approximately 55.8% of respondents reported having an acute infection symptom. According to the transmission route, infection caused by sexual contact accounted for 94.4%, of which 60.4% were caused by sexual contact with the same sex or both males and females. Participants repeatedly answered the survey to decrease depression and anxiety scores. Of the total participants, 89.1% received antiretroviral therapy (ART). In the initial ART, 95.3% of patients were treated based on the recommendation. The median CD4 T-cell count at the time of diagnosis was 229.5 and improved to 331 after the initial ART. Of the patients, 16.6% and 9.4% had tuberculosis and syphilis, respectively, and 26.7% had pneumocystis pneumonia. In the medical history, sexually transmitted infectious diseases showed the highest prevalence, followed by endocrine diseases. The main reasons for termination were loss to follow-up (29.9%) and withdrawal of consent (18.7%). CONCLUSIONS: Early diagnosis and ART should be performed at an appropriate time to prevent the development of new infection.
GA-Based Adaptive Window Length Estimation for Highly Accurate Audio Segmentation
Myeongsu Kang,Jong-Myon Kim 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.1
Accurate audio segmentation has recently received increasing attention for its applications in automatic indexing, content analysis and information retrieval. Hence, this paper proposes a highly accurate audio segmentation methodology using a genetic algorithm-based approach to adapting and optimizing segmentation window lengths. Specifically, this paper analyzes the parameter sequence of the root-mean-square values of an input audio stream with optimal sliding window (or segmentation window) lengths found and adapted by a genetic algorithm. In addition, this paper determines whether an audio-cut occurs or not by utilizing the parameter sequences as inputs of a support vector machine. Experimental results indicate that the proposed approach achieves 100.00% and 98.69% in the average precision and recall rates of segmentation performance, respectively.
Myeongsu Kang,Jaeyoung Kim,Jong-Myon Kim,Tan, Andy C. C.,Kim, Eric Y.,Byeong-Keun Choi Institute of Electrical and Electronics Engineers 2015 IEEE transactions on power electronics Vol. No.
<P>This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposed GA-based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.</P>
Myeongsu Kang,Islam, Md Rashedul,Jaeyoung Kim,Jong-Myon Kim,Pecht, Michael IEEE 2016 IEEE transactions on industrial electronics Vol.63 No.5
<P>In practice, outliers, defined as data points that are distant from the other agglomerated data points in the same class, can seriously degrade diagnostic performance. To reduce diagnostic performance deterioration caused by outliers in data-driven diagnostics, an outlier-insensitive hybrid feature selection (OIHFS) methodology is developed to assess feature subset quality. In addition, a new feature evaluation metric is created as the ratio of the intraclass compactness to the interclass separability estimated by understanding the relationship between data points and outliers. The efficacy of the developed methodology is verified with a fault diagnosis application by identifying defect-free and defective rolling element bearings under various conditions.</P>
Myeongsu Kang,Jaeyoung Kim,Jong-Myon Kim IEEE 2015 IEEE transactions on industrial electronics Vol.62 No.4
<P>The demand for online fault diagnosis has recently increased in order to prevent severe unexpected failures in machinery. To address this issue, this paper first proposes a comprehensive bearing fault diagnosis algorithm, which consists of fault signature extraction through time-frequency analysis and one-against-all multiclass support vector machines in order to make reliable decisions. In addition, acoustic emission (AE) signals sampled at 1 MHz are used for the early identification of bearing failures. Despite the fact that the proposed fault diagnosis methodology shows satisfactory classification accuracy, its computation complexity limits its use in real-time applications. Therefore, this paper also presents a high-performance multicore architecture, including 64 processing elements operating at 50 MHz in a Xilinx Virtex-7 field-programmable gate array device to support online fault diagnosis. The experimental results indicate that the multicore approach executes 1339.3x and 1293.1x faster than the high-performance Texas Instrument (TI) TMS320C6713 and TMS320C6748 digital signal processors (DSPs), respectively, by exploiting the massive parallelism inherent in the bearing fault diagnosis algorithm. In addition, the multicore approach outperforms the equivalent sequential approach that runs on the TI DSPs by substantially reducing the energy consumption.</P>
Myeongsu Kim,Haerin Rhim,Seulgi Gim,Chang-Eun Lee,Hakyoung Yoon,Jae-Ik Han 대한수의학회 2023 大韓獸醫學會誌 Vol.63 No.3
An adult raccoon dog with extensive, deep, and contaminated wounds on the right hip and multiple fractures was rescued. The open wound was managed daily by debridement and flushing for 3 weeks. Modified active drainage was then performed, and antibiotics administered according to the antibiotic susceptibility test. After 2 weeks, the exudate disappeared and the drain was removed. After monitoring for 1 month, the animal was released in to the wild. This case shows that even if infection remains, rapid wound repair is possible if appropriate antibiotic selection through regular examination and active drainage are combined.
Myeongsu Kang,Jaeyoung Kim,Wills, Linda M.,Jong-Myon Kim Institute of Electrical and Electronics Engineers 2015 IEEE transactions on industrial electronics Vol. No.
<P>This paper presents a reliable fault diagnosis methodology for various single and multiple combined defects of low-speed rolling element bearings. This method temporally partitions an acoustic emission (AE) signal and selects a portion of the signal, which contains intrinsic information about the bearing failures. This paper then performs frequency analysis for the selected time-domain AE signal by using multilevel finite-impulse response filter banks to obtain the most informative subband signals involving abnormal symptoms of the bearing defects. It does this by using a 2-D visualization tool that represents the percentage of the Gaussian-mixture-model-based residual component-to-defect component ratios via time-varying and multiresolution envelope analysis (TVMREA). Then, fault signatures in the time and frequency domains are extracted in the informative subband signals. Since all the extracted fault features may not be equally useful for diagnosis, the proposed genetic algorithm (GA)-based discriminative feature analysis (GADFA) selects the most discriminative subset of fault signatures. In experiments, single and multiple combined bearing defects under various conditions are used to validate the effectiveness of this fault diagnosis scheme using TVMREA and GADFA. Experimental results indicate that this reliable fault diagnosis methodology accurately identifies bearing failure type across a variety of conditions. In addition, GADFA outperforms other state-of-the-art feature analysis techniques, yielding 7.3%-46.6% performance improvements in average classification accuracy.</P>