• Title/Summary/Keyword: PMD vector

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Theory of optimal second-order PMD compensation (최적의 2차 편광모드분산 보상에 관한 이론적 고찰)

  • 김상인
    • Korean Journal of Optics and Photonics
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    • v.14 no.6
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    • pp.583-587
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    • 2003
  • In this paper, the optimal performance of optical second-order polarization mode dispersion (PMD) compensation has been investigated theoretically in terms of minimization of the root-mean-square (RMS) pulse broadening. The optimal compensation vector in feedforward-type second-order PMD compensation has been derived, and the RMS pulse broadening factor after the optimal second-order PMD compensation has been analytically calculated. The calculated result has been compared with the previously reported simulation result where numerically optimized feedback scheme was adopted. They are in good agreement, which verifies the validity of the derivation. The investigation in this work will form the basis for the implementation of the feed-forward-type second-order PMD compensation.

Comparison of canine vector-borne diseases in rural dogs based on the prevention status

  • Yi, Seung-Won;Kim, Eunju;Oh, Sang-Ik;Oh, Seok Il;Kim, Jong Seok;Ha, Ji-Hong;Lee, Bugeun;Yoo, Jae Gyu;Do, Yoon Jung
    • Korean Journal of Veterinary Service
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    • v.42 no.3
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    • pp.145-152
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    • 2019
  • Canine vector-borne diseases (CVBDs) are transmitted by different groups of hematophagous arthropod vectors that are distributed worldwide and can cause significant health problems for dogs. The aim of this study was to investigate and compare the prevalence of selected CVBD pathogens in rural outdoor dogs based on prevention status. Between June 2017 and February 2019, blood samples were collected from 343 clinically healthy rural dogs composing two different groups: systematically managed dogs (SMD; n=92) and personally managed dogs (PMD; n=251). Vaccination and preventive medications were applied strictly following the programmed schedule for the SMD group; in contrast, in the PMD group, they were applied only when requested by the dog owners. Serological and molecular assessments showed that significantly more dogs in the PMD group were infected with B. gibsoni (P<0.001) and D. immitis (P=0.001) than those in the SMD group. These findings suggest that the regular use of preventive medications and environmental controlling efforts contribute to reducing the prevalence of CVBD pathogen infections. In addition, dogs infected with certain kinds of CVBD pathogens could remain asymptomatic, suggesting that continuous monitoring and periodic preventive treatment should be conducted even for clinically healthy dogs.

Calibration of Portable Particulate Mattere-Monitoring Device using Web Query and Machine Learning

  • Loh, Byoung Gook;Choi, Gi Heung
    • Safety and Health at Work
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    • v.10 no.4
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    • pp.452-460
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    • 2019
  • Background: Monitoring and control of PM2.5 are being recognized as key to address health issues attributed to PM2.5. Availability of low-cost PM2.5 sensors made it possible to introduce a number of portable PM2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R2) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R2 and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.