• Title/Summary/Keyword: Accuracy Assessment

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Assessment of Position Degradation Due to Intermittent Broadcast of RTK MSM Correction Under Various Conditions

  • Yoon, Hyo Jung;Lim, Cheol soon;Park, Byungwoon
    • Journal of Positioning, Navigation, and Timing
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    • v.9 no.3
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    • pp.237-248
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    • 2020
  • GNSS has been evolving dramatically in recent years. There are currently 6 GNSS (4 GNSS, AND 2 RNSS) constellations, which are GPS (USA), GLONASS (Russia), BeiDou (China), Galileo (EU), QZSS (Japan), and IRNSS (India). The Number of navigation satellites is expected to be over 150 by 2020. As the number of both constellations and satellites used for the improvement of positioning performance, high accuracy, and robustness of precise positioning is more promising. However, a large amount of the correction messages is required to support the augmentation system for the available satellites of all the constellations. Since bandwidth for the correction messages is generally limited, sending or scheduling the correction messages might be a critical issue in the near future. In this study, we analyze the relationship between the size of the bandwidth and Real-Time Kinematics (RTK) performance. Multiple Signal Messages (MSM), the only Radio Technical Commission for Maritimes (RTCM) message that supports multi-constellation GNSS, has been used for this assessment. Instead of the conventional method that broadcasts all the messages at the same time, we assign the MSM broadcasting interval for each constellation in 5 seconds. An open sky static and dynamic test for this study was conducted on the roof of Sejong University. Our results show that the RTK fixed position accuracy is not affected by the 5-second interval corrections, but the ambiguity fixing rate is degraded for poor DOP cases when RTK correction are transmitted intermittently.

An Error Assessment of the Kriging Based Approximation Model Using a Mean Square Error (평균제곱오차를 이용한 크리깅 근사모델의 오차 평가)

  • Ju Byeong-Hyeon;Cho Tae-Min;Jung Do-Hyun;Lee Byung-Chai
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.8 s.251
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    • pp.923-930
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    • 2006
  • A Kriging model is a sort of approximation model and used as a deterministic model of a computationally expensive analysis or simulation. Although it has various advantages, it is difficult to assess the accuracy of the approximated model. It is generally known that a mean square error (MSE) obtained from the kriging model can't calculate statistically exact error bounds contrary to a response surface method, and a cross validation is mainly used. But the cross validation also has many uncertainties. Moreover, the cross validation can't be used when a maximum error is required in the given region. For solving this problem, we first proposed a modified mean square error which can consider relative errors. Using the modified mean square error, we developed the strategy of adding a new sample to the place that the MSE has the maximum when the MSE is used for the assessment of the kriging model. Finally, we offer guidelines for the use of the MSE which is obtained from the kriging model. Four test problems show that the proposed strategy is a proper method which can assess the accuracy of the kriging model. Based on the results of four test problems, a convergence coefficient of 0.01 is recommended for an exact function approximation.

Determining Quality Criteria for Online Health Information: A Qualitative Study

  • Cha, Myeong-Hwa;Park, Jyung-Rewng
    • Preventive Nutrition and Food Science
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    • v.11 no.4
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    • pp.305-310
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    • 2006
  • The Web is an important source of information for health care consumers, and the resources they find on the Web have a direct affect on their health outcomes. Despite the enormous benefits of online health care, the quality of health information on the Internet is an area of increasing concern. Therefore, there's a need to develop quality assessment tools that can filter out poor quality online health information. The purpose of this study is to explore the critical attributes for assessing website quality and for developing quality assessment measurements. We completed three focus group discussions with 24 participants that were administered by a moderator and based on specifically focused group questions. The results suggest that the most important quality criteria, as identified by the respondents, were related to issues of credibility and accuracy. To determine the credibility of Internet health information, the respondents stated one must consider the following: the information source, disclosure of the author's or organization's credentials/qualifications, disclosure of ownership and the updating of the content. For the accuracy of content, elements such as a statement of purpose, evidence-based information, relevance and completeness should be considered. Interactivity, accessibility, and design were additional quality criteria.

Assessment of RANS Models for 3-D Flow Analysis of SMART

  • Chun Kun Ho;Hwang Young Dong;Yoon Han Young;Kim Hee Chul;Zee Sung Quun
    • Nuclear Engineering and Technology
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    • v.36 no.3
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    • pp.248-262
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    • 2004
  • Turbulence models are separately assessed for a three dimensional thermal-hydraulic analysis of the integral reactor SMART. Seven models (mixing length, k-l, standard $k-{\epsilon},\;k-{\epsilon}-f{\mu},\;k-{\epsilon}-v2$, RRSM, and ERRSM) are investigated for flat plate channel flow, rotating channel flow, and square sectioned U-bend duct flow. The results of these models are compared to the DNS data and experiment data. The results are assessed in terms of many aspects such as economical efficiency, accuracy, theorization, and applicability. The standard $k-{\epsilon}$ model (high Reynolds model), the $k-{\epsilon}-v2$ model, and the ERRSM (low Reynolds models) are selected from the assessment results. The standard $k-{\epsilon}$ model using small grid numbers predicts the channel flow with higher accuracy in comparison with the other eddy viscosity models in the logarithmic layer. The elliptic-relaxation type models, $k-{\epsilon}-v2$, and ERRSM have the advantage of application to complex geometries and show good prediction for near wall flows.

Classifying meteorological drought severity using a hidden Markov Bayesian classifier

  • Sattar, Muhammad Nouman;Park, Dong-Hyeok;Kwon, Hyun-Han;Kim, Tae-Woong
    • Proceedings of the Korea Water Resources Association Conference
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    • 2019.05a
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    • pp.150-150
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    • 2019
  • The development of prolong and severe drought can directly impact on the environment, agriculture, economics and society of country. A lot of efforts have been made across worldwide in the planning, monitoring and mitigation of drought. Currently, different drought indices such as the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) are developed and most commonly used to monitor drought characteristics quantitatively. However, it will be very meaningful and essential to develop a more effective technique for assessment and monitoring of onset and end of drought. Therefore, in this study, the hidden Markov Bayesian classifier (MBC) was employed for the assessment of onset and end of meteorological drought classes. The results showed that the probabilities of different classes based on the MBC were quite suitable and can be employed to estimate onset and end of each class for meteorological droughts. The classification results of MBC were compared with SPI and with past studies which proved that the MBC was able to account accuracy in determining the accurate drought classes. For more performance evaluation of classification results confusion matrix was used to find accuracy and precision in predicting the classes and their results are also appropriate. The overall results indicate that the MBC was effective in predicating the onset and end of drought events and can utilized for monitoring and management of short-term drought risk.

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Validation of Geostationary Earth Orbit Satellite Ephemeris Generated from Satellite Laser Ranging

  • Oh, Hyungjik;Park, Eunseo;Lim, Hyung-Chul;Lee, Sang-Ryool;Choi, Jae-Dong;Park, Chandeok
    • Journal of Astronomy and Space Sciences
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    • v.35 no.4
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    • pp.227-233
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    • 2018
  • This study presents the generation and accuracy assessment of predicted orbital ephemeris based on satellite laser ranging (SLR) for geostationary Earth orbit (GEO) satellites. Two GEO satellites are considered: GEO-Korea Multi-Purpose Satellite (KOMPSAT)-2B (GK-2B) for simulational validation and Compass-G1 for real-world quality assessment. SLR-based orbit determination (OD) is proactively performed to generate orbital ephemeris. The length and the gap of the predicted orbital ephemeris were set by considering the consolidated prediction format (CPF). The resultant predicted ephemeris of GK-2B is directly compared with a pre-specified true orbit to show 17.461 m and 23.978 m, in 3D root-mean-square (RMS) position error and maximum position error for one day, respectively. The predicted ephemeris of Compass-G1 is overlapped with the Global Navigation Satellite System (GNSS) final orbit from the GeoForschungsZentrum (GFZ) analysis center (AC) to yield 36.760 m in 3D RMS position differences. It is also compared with the CPF orbit from the International Laser Ranging Service (ILRS) to present 109.888 m in 3D RMS position differences. These results imply that SLR-based orbital ephemeris can be an alternative candidate for improving the accuracy of commonly used radar-based orbital ephemeris for GEO satellites.

The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

  • Song, Da-Yea;Kim, So Yoon;Bong, Guiyoung;Kim, Jong Myeong;Yoo, Hee Jeong
    • Journal of the Korean Academy of Child and Adolescent Psychiatry
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    • v.30 no.4
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    • pp.145-152
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    • 2019
  • Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

A systematic review of the accuracy and efficiency of dental movements with Invisalign®

  • Galan-Lopez, Lidia;Barcia-Gonzalez, Jorge;Plasencia, Eliseo
    • The korean journal of orthodontics
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    • v.49 no.3
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    • pp.140-149
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    • 2019
  • We are currently living in an era where the use of computer-aided design/computer-aided manufacturing has allowed individualized orthodontic treatments, but has also incorporated enhanced digitalized technology that does not permit improvisation. The purpose of this systematic review was to analyze publications that assessed the accuracy and efficiency of the $Invisalign^{(R)}$ system. A systematic review was performed using a search strategy to identify articles that referenced $Invisalign^{(R)}$, which were published between August 2007 and August 2017, and listed in the following databases: MEDLINE, Embase, Cochrane Library, Web of Knowledge, Google Scholar, and LILACS. Additionally, a manual search of clinical trials was performed in scientific journals and other databases. To rate the methodological quality of the articles, a grading system described by the Swedish Council on Technology Assessment in Health Care was used, in combination with the Cochrane tool for risk of bias assessment. We selected 20 articles that met the inclusion criteria and excluded 5 due to excess biases. The level of evidence was high. Although it is possible to treat malocclusions with plastic systems, the results are not as accurate as those achieved by treatment with fixed appliances.

Study of Personal Credit Risk Assessment Based on SVM

  • LI, Xin;XIA, Han
    • The Journal of Industrial Distribution & Business
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    • v.13 no.10
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    • pp.1-8
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    • 2022
  • Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

Automated Print Quality Assessment Method for 3D Printing AI Data Construction

  • Yoo, Hyun-Ju;Moon, Nammee
    • Journal of Information Processing Systems
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    • v.18 no.2
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    • pp.223-234
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    • 2022
  • The evaluation of the print quality of 3D printing has traditionally relied on manual work using dimensional measurements. However, the dimensional measurement method has an error value that depends on the person who measures it. Therefore, we propose the design of a new print quality measurement method that can be automatically measured using the field-of-view (FOV) model and the intersection over union (IoU) technique. First, the height information of the modeling is acquired from a camera; the output is measured by a sensor; and the images of the top and isometric views are acquired from the FOV model. The height information calculates the height ratio by calculating the percentage of modeling and output, and compares the 2D contour of the object on the image using the FOV model. The contour of the object is obtained from the image for 2D contour comparison and the IoU is calculated by comparing the areas of the contour regions. The accuracy of the automated measurement technique for determining, which derives the print quality value was calculated by averaging the IoU value corrected by the measurement error and the height ratio value.