• Title/Summary/Keyword: Correlation identification

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Validation of Five Organ Pattern Identification Questionnaire (오장변증설문지 예측 타당도 연구)

  • Jang, Eun Su;Kim, Yun Young;Yoo, Ho Ryong;Lee, Eun Jung;Choi, Jeong Jun;Kim, Eun Seok;Jung, In Chul
    • Journal of Physiology & Pathology in Korean Medicine
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    • v.32 no.3
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    • pp.165-170
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    • 2018
  • The aim of this study was to investigate the predictive validity of the five organ pattern identification questionnaire(FOPIQ). Data collection was conducted from 190 people who were randomly selected from the general population living in D city from October 2016 to June 2017, and the collected data were analyzed by SPSS 23.0 Statistics Program. Pearson correlation coefficient was used to know the relation between the expert's score and FOPIQ's one. The cut-off value, sensitivity and specificity were analyzed through ROC-curve. Significant p was <.05. The pearson correlation coefficient was .735, .756, .762, .736, and .513 between individual score of FOPIQ and that of the experts in liver, heart, spleen, lung, and kidney, respectively. The cut-off value of the FOPIQ was 46.209, 47.276, 45.336, 48.823, and 42.508 in liver, heart, spleen, lung, and kidney respectively. The AUC derived from the cut-off value of the FOPIQ was .907, .854, .888, .902, and .781 respectively. This study suggests that the FOPIQ could be valid to apply for general population in clinics as well as health checkups.

A study of methodology for identification models of cardiovascular diseases based on data mining (데이터마이닝을 이용한 심혈관질환 판별 모델 방법론 연구)

  • Lee, Bum Ju
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.4
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    • pp.339-345
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    • 2022
  • Cardiovascular diseases is one of the leading causes of death in the world. The objectives of this study were to build various models using sociodemographic variables based on three variable selection methods and seven machine learning algorithms for the identification of hypertension and dyslipidemia and to evaluate predictive powers of the models. In experiments based on full variables and correlation-based feature subset selection methods, our results showed that performance of models using naive Bayes was better than those of models using other machine learning algorithms in both two diseases. In wrapper-based feature subset selection method, performance of models using logistic regression was higher than those of models using other algorithms. Our finding may provide basic data for public health and machine learning fields.

Comparative Study on Detecting Methods for Total Coliform in Sewage Effluent (하수 방류수에서 대장균군의 검출방법의 비교)

  • Lee, Mi-Ae;Sung, Il-Wha
    • Journal of Environmental Health Sciences
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    • v.33 no.5
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    • pp.422-427
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    • 2007
  • The purposes of this study were to investigate the concentration of total coliforms in sewage effluents during the period from August 2004 to October 2005. The removal efficiency range of multi-tube method and plate count method were $31.3{\sim}99.5%$ and $66.8{\sim}99.2%$, respectively. Though a correlation between the multi-tube method and the plate count method in the same sample is low, not only is an experimental procedure very simple, but the time required also is short. The seasonal correlation between methods showed more sensitive spring and summer than autumn and winter. So the study indicated plate count method can be used in rapid and reliance identification of total coliform more than the multi-tube method.

Development of High-resolution 3-D PIV Algorithm by Cross-correlation (고해상도 3차원 상호상관 PIV 알고리듬 개발)

  • Kim, Mi-Young;Choi, Jang-Woon;Lee, Hyun;Lee, Young-Ho
    • Proceedings of the KSME Conference
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    • 2001.11b
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    • pp.410-416
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    • 2001
  • An algorithm of 3-D particle image velocimetry(3D-PIV) was developed for the measurement of 3-D velocity field of complex flows. The measurement system consists of two or three CCD camera and one RGB image grabber. In this study, stereo photogrammetty was applied for the 3-D matching of tracer particles. Epipolar line was used to decect the stereo pair. 3-D CFD data was used to estimate algorithm. 3-D position data of the first frame and the second frame was used to find velocity vector. Continuity equation was applied to extract error vector. The algorithm result involved error vecotor of about 0.13 %. In Pentium III 450MHz processor, the calculation time of cross-correlation for 1500 particles needed about 1 minute.

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Parameter Identification and Nonlinear Seismic Analysis of Soil-Structure Interaction System (지반-구조물 상호작용계의 계수추정 및 비선형 지진응답해석)

  • 윤정방
    • Proceedings of the Earthquake Engineering Society of Korea Conference
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    • 1997.04a
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    • pp.265-272
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    • 1997
  • This paper presents the result of an international cooperative research on the post-correlation analysis of forced vibration tests and the prediction of earthquake responses of a large-scale seismic test structure. Through the post-correlation analysis, the properties of the soil layers are revised so that the best correlation in the responses may be obtained compared with the measured force vibration test data. Utilizing the revised soil properties as the initial linear values, the seismic responses are predicted for an earthquake using the equivalent linearlization technique based on the specified strain dependent characteristics of the shear moduli and damping ratios. It has been found that the predicted responses by the equivalent nonlinear procedure are in excellent agreement with the observed responses, which those using the initial properties are fairly off from the measured results.

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Robust System Identification Algorithm Using Cross Correlation Function

  • Takeyasu, Kazuhiro;Amemiya, Takashi;Goto, Hiroyuki;Masuda, Shiro
    • Industrial Engineering and Management Systems
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    • v.1 no.1
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    • pp.79-86
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    • 2002
  • This paper proposes a new algorithm for estimating ARMA model parameters. In estimating ARMA model parameters, several methods such as generalized least square method, instrumental variable method have been developed. Among these methods, the utilization of a bootstrap type algorithm is known as one of the effective approach for the estimation, but there are cases that it does not converge. Hence, in this paper, making use of a cross correlation function and utilizing the relation of structural a priori knowledge, a new bootstrap algorithm is developed. By introducing theoretical relations, it became possible to remove terms, which is liable to include much noise. Therefore, this leads to robust parameter estimation. It is shown by numerical examples that using this algorithm, all simulation cases converge while only half cases succeeded with the previous one. As for the calculation time, judging from the fact that we got converged solutions, our proposed method is said to be superior as a whole.

Local damage detection of a fan blade under ambient excitation by three-dimensional digital image correlation

  • Hu, Yujia;Sun, Xi;Zhu, Weidong;Li, Haolin
    • Smart Structures and Systems
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    • v.24 no.5
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    • pp.597-606
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    • 2019
  • Damage detection based on dynamic characteristics of a structure is one of important roles in structural damage identification. It is difficult to detect local structural damage using traditional dynamic experimental methods due to a limited number of sensors used in an experiment. In this work, a non-contact test stand of fan blades is established, and a full-field noncontact test method, combined with three-dimensional digital image correlation, Bayesian operational modal analysis, and damage indices, is used to detect local damage of a fan blade under ambient excitation without use of baseline information before structural damage. The methodology is applied to detect invisible local damage on the fan blade. Such a method has a seemingly high potential as an alternative to detect local damage of blades with complex high-precision surfaces under extreme working conditions because it is a noncontact test method and can be used under ambient excitation without human participation.

A precise sensor fault detection technique using statistical techniques for wireless body area networks

  • Nair, Smrithy Girijakumari Sreekantan;Balakrishnan, Ramadoss
    • ETRI Journal
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    • v.43 no.1
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    • pp.31-39
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    • 2021
  • One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

A Cross-sectional Study of Deficiency-Excess Pattern Identification with Blood Cytokines and Characteristics of Patients with Asthma (천식환자 허실변증별 혈액 싸이토카인 및 임상적 특성에 관한 단면적 연구)

  • Yu, Chang-hwan;Kang, Sung-woo;Hong, Sung-eun;Kim, Kwan-il;Lee, Beom-joon;Jung, Hee-jae
    • The Journal of Internal Korean Medicine
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    • v.41 no.4
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    • pp.583-598
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    • 2020
  • Objective: The aims of this study were to analyze deficiency-excess pattern identification and to compare the blood cytokines in patients with asthma. Methods: A total of 112 patients with asthma who met the inclusion and exclusion criteria were divided into deficiency syndrome and excess syndrome groups. Blood was examined for eotaxin, interleukin (IL)-1β, IL-4, IL-5, IL-6, IL-13, and tumor necrosis factor (TNF)-α. The Quality of Life Questionnaire for Adult Korean Asthmatics (QLQAKA), a Visual Analogue Scale (VAS), and heart rate variability (HRV) tests were administered to both groups. Results: Pattern identification divided the 112 patients into two categories: a deficiency syndrome group (N=52) and an excess syndrome group (N=60). Analysis of blood cytokines showed higher levels of IL-4, IL-5, and IL-13 in the deficient pattern than in the excess pattern group, but the difference was not statistically significant. Analysis of the HRV revealed a significantly higher mean value for the very-low-frequency (VLF) and high-frequency (HF) bands in the deficiency than in the excess syndrome group. The morbidity duration was longer in the deficiency than in the excess syndrome group, but the difference was not statistically significant. Analysis of the QLQAKA and VAS scores showed a negative correlation, whereas BMI and VAS showed a positive correlation. Conclusions: Levels of blood cytokines, including eotaxin, IL-1β, IL-4, IL-5, IL-6, IL-13, and TNF-α, did not differ statistically between the deficiency and excess syndrome groups. The development of a more accurate asthma-specific pattern identification tool would be useful in asthma control.

Rank Correlation Coefficient of Energy Data for Identification of Abnormal Sensors in Buildings (에너지 데이터의 순위상관계수 기반 건물 내 오작동 기기 탐지)

  • Kim, Naeon;Jeong, Sihyun;Jang, Boyeon;Kim, Chong-Kwon
    • Journal of KIISE
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    • v.44 no.4
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    • pp.417-422
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    • 2017
  • Anomaly detection is the identification of data that do not conform to a normal pattern or behavior model in a dataset. It can be utilized for detecting errors among data generated by devices or user behavior change in a social network data set. In this study, we proposed a new approach using rank correlation coefficient to efficiently detect abnormal data in devices of a building. With the increased push for energy conservation, many energy efficiency solutions have been proposed over the years. HVAC (Heating, Ventilating and Air Conditioning) system monitors and manages thousands of sensors such as thermostats, air conditioners, and lighting in large buildings. Currently, operators use the building's HVAC system for controlling efficient energy consumption. By using the proposed approach, it is possible to observe changes of ranking relationship between the devices in HVAC system and identify abnormal behavior in social network.