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Journal articles on the topic 'Improvement of accuracy'

Author: Grafiati

Published: 30 May 2022

Last updated: 19 July 2025

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1

Lebedeva, Olena, Alla Kobozeva, and Viktoriya Zorilo. "Accuracy improvement of cloning area detection." Odes’kyi Politechnichnyi Universytet. Pratsi, no. 3 (December 23, 2016): 36–40. http://dx.doi.org/10.15276/opu.3.50.2016.09.

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2

G., Renith. "Accuracy Improvement in Diabetic Retinopathy Detection Using DLIA." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (2020): 133–49. http://dx.doi.org/10.5373/jardcs/v12i4/20201426.

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3

Hashim, N. M., A. H. Omar, S. N. M. Ramli, K. M. Omar, and N. Din. "CADASTRAL DATABASE POSITIONAL ACCURACY IMPROVEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W5 (October 5, 2017): 91–96. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w5-91-2017.

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Positional Accuracy Improvement (PAI) is the refining process of the geometry feature in a geospatial dataset to improve its actual position. This actual position relates to the absolute position in specific coordinate system and the relation to the neighborhood features. With the growth of spatial based technology especially Geographical Information System (GIS) and Global Navigation Satellite System (GNSS), the PAI campaign is inevitable especially to the legacy cadastral database. Integration of legacy dataset and higher accuracy dataset like GNSS observation is a potential solution for improving the legacy dataset. However, by merely integrating both datasets will lead to a distortion of the relative geometry. The improved dataset should be further treated to minimize inherent errors and fitting to the new accurate dataset. The main focus of this study is to describe a method of angular based Least Square Adjustment (LSA) for PAI process of legacy dataset. The existing high accuracy dataset known as National Digital Cadastral Database (NDCDB) is then used as bench mark to validate the results. It was found that the propose technique is highly possible for positional accuracy improvement of legacy spatial datasets.

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4

Vighnesam, N. V., Anatta Sonney, and B. Subramanian. "IRS Orbit Determination Accuracy Improvement." Journal of the Astronautical Sciences 50, no. 3 (2002): 355–66. http://dx.doi.org/10.1007/bf03546258.

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5

OGAWA, Hideo. "Accuracy Improvement for Sheet Metal Working." Journal of the Japan Society for Technology of Plasticity 48, no. 563 (2007): 1082–86. http://dx.doi.org/10.9773/sosei.48.1082.

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6

Drumond Silva, J. "An accuracy improvement in Egorov’s Theorem." Publicacions Matemàtiques 51 (January 1, 2007): 77–120. http://dx.doi.org/10.5565/publmat_51107_05.

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7

Malik, Jyoti, Dhiraj Girdhar, Ratna Dahiya, and G. Sainarayanan. "Accuracy Improvement in Palmprint Authentication System." International Journal of Image, Graphics and Signal Processing 7, no. 4 (2015): 51–59. http://dx.doi.org/10.5815/ijigsp.2015.04.06.

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8

Shafran, S. V., and I. A. Kudryavtsev. "Accuracy improvement for GNSS -based compass." IOP Conference Series: Materials Science and Engineering 984 (November 28, 2020): 012018. http://dx.doi.org/10.1088/1757-899x/984/1/012018.

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9

Atta, Khalid Tourkey, Andreas Johansson, and Thomas Gustafsson. "Accuracy Improvement of Extremum Seeking Control." IEEE Transactions on Automatic Control 62, no. 4 (2017): 1952–58. http://dx.doi.org/10.1109/tac.2016.2584184.

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10

Yao, Junjie. "Accuracy improvement: Modeling of elastic deflections." Robotica 9, no. 3 (1991): 327–33. http://dx.doi.org/10.1017/s0263574700006494.

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SUMMARYActual positions of industrial robot end-effectors differ from those commanded off-line. Consequently, it is then difficult for robots to fulfill certain tasks, such as automated assembly sequences or tasks where high performance accuracy is required. This paper shows that the accuracy of robot performance can be improved by introducing deviation matrices which are functions of many possible error sources. As a first approach, an experiment was carried out where structural elastic deflections, one of the many error sources, of a robot ASEA Irb 6/2 were taken into account. The experiment showed that using the improved model, the positioning accuracy of an ASEA Irb 6/2 robot carrying a weight of 5.6kg was improved from 2.5mm to 0.25mm and the orientation accuracy was improved from 0·45° to 0·3°.

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11

Kurtoglu, A., and G. Sohlenius. "The Accuracy Improvement of Machine Tools." CIRP Annals 39, no. 1 (1990): 417–19. http://dx.doi.org/10.1016/s0007-8506(07)61086-5.

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12

Schmid, Andreas. "Positioning Accuracy Improvement With Differential Correlation." IEEE Journal of Selected Topics in Signal Processing 3, no. 4 (2009): 587–98. http://dx.doi.org/10.1109/jstsp.2009.2023342.

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13

Bansevičius, Ramutis, and Vytautas Giniotis. "Mechatronic means for machine accuracy improvement." Mechatronics 12, no. 9-10 (2002): 1133–43. http://dx.doi.org/10.1016/s0957-4158(02)00018-1.

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14

Cheng, Yuanzhi, Shuguo Wang, Takaharu Yamazaki, Jie Zhao, Yoshikazu Nakajima, and Shinichi Tamura. "Hip cartilage thickness measurement accuracy improvement." Computerized Medical Imaging and Graphics 31, no. 8 (2007): 643–55. http://dx.doi.org/10.1016/j.compmedimag.2007.08.001.

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15

Saneie, Hamid, Zahra Nasiri-Gheidari, and Farid Tootoonchian. "Accuracy Improvement in Variable Reluctance Resolvers." IEEE Transactions on Energy Conversion 34, no. 3 (2019): 1563–71. http://dx.doi.org/10.1109/tec.2019.2902630.

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16

Kang, Yuan, Ming-Hsuan Tseng, Shih-Ming Wang, Chih-Pin Chiang, and Chun-Chieh Wang. "An accuracy improvement for balancing crankshafts." Mechanism and Machine Theory 38, no. 12 (2003): 1449–67. http://dx.doi.org/10.1016/s0094-114x(03)00097-1.

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17

Zhou, Dai, Si Chen, Lei Li, Huafeng Li, and Yaojun Zhao. "Accuracy Improvement of Smoothed Particle Hydrodynamics." Engineering Applications of Computational Fluid Mechanics 2, no. 2 (2008): 244–51. http://dx.doi.org/10.1080/19942060.2008.11015225.

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18

Vighnesam, N. V., Anatta Sonney, and B. Subramanian. "Erratum: IRS Orbit Determination Accuracy Improvement." Journal of the Astronautical Sciences 51, no. 2 (2003): 247. http://dx.doi.org/10.1007/bf03546311.

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19

Tae-In, Hyon, and Kim Gyu-Sik. "Accuracy Improvement of PM Measuring Instruments." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1396–98. https://doi.org/10.5281/zenodo.3590849.

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The PM10 concentration in the underground areas should be monitored to protect the health of the commuters in the underground subway system. The purpose of this work is to study the reliability of the instruments using light scattering method to measure the PM10 concentrations continuously. A linear regression analysis method is used to improve the performance of the instruments using light scattering method. Some experimental results show that a linear regression technique would be very helpful for the performance improvement of light scattering instruments such as Air test PM2500 and HCT 4103. Tae-In Hyon | Gyu-Sik Kim "Accuracy Improvement of PM Measuring Instruments" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26722.pdf

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20

FUKASAWA, Hiroaki, Hiroyuki KODAMA, Toshiki HIROGAKI, Eiichi AOYAMA, and Keiji OGAWA. "3317 Improvement Accuracy of Cutting Condition Decision Formula Using Catalog Mining." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2011.6 (2011): _3317–1_—_3317–6_. http://dx.doi.org/10.1299/jsmelem.2011.6._3317-1_.

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21

Torres-Niño, Javier, Alejandro Rodríguez-González, Ricardo Colomo-Palacios, Enrique Jiménez-Domingo, and Giner Alor-Hernández. "Improving Accuracy of Decision Trees Using Clustering Techniques." JUCS - Journal of Universal Computer Science 19, no. (4) (2013): 484–501. https://doi.org/10.3217/jucs-019-04-0484.

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Data mining is an important part of information management technology. Simply put, it is a method to extract and analyze meaningful patterns and correlations in a large relational database. In Data mining, Decision trees are one of the most worldwide used tools for decision support. In the emerging area of Data mining applications, users of data mining tools are faced with the problem of data sets that are comprised of large numbers of features and instances. Such kinds of data sets are not easy to handle for mining because decision trees generally depends on several parameters like dataset used and configuration of the tree itself among others in order to build an accurate model classification. In this work a novel hybrid classifier system is presented for improving accuracy of decision trees using clustering techniques. This system is formed by a clustering algorithm, a decision tree and an optional module for identifying appropriate parameters for the clustering algorithm. These three modules working together are capable to increase the accuracy of the solutions. The validation of the results of this work has been performed using several well-known datasets and applying two decision trees algorithms. The accuracy percentages are compared in order to show our proposal improvement, obtaining good results. Finally two clustering algorithms have been used to compare the accuracy between different proposals.

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22

López-Vázquez, Carlos. "Positional Accuracy Improvement Using Empirical Analytical Functions." Cartography and Geographic Information Science 39, no. 3 (2012): 133–39. http://dx.doi.org/10.1559/15230406393133.

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23

ONIKURA, Hiromichi, Akio KATSUKI, Toshikazu KANDA, and Seiichiro HORIIKE. "Improvement of Hole Accuracy by Piloting Drills." Journal of the Japan Society for Precision Engineering 58, no. 1 (1992): 111–16. http://dx.doi.org/10.2493/jjspe.58.111.

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24

Simionescu, Mihaela. "The Improvement of Unemployment Rate Predictions Accuracy." Prague Economic Papers 24, no. 3 (2015): 274–86. http://dx.doi.org/10.18267/j.pep.519.

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25

Chen, Hongyue, Wei Yang, Ying Ma, and Liyong Tian. "Accuracy improvement for linear array photocell sensor." Measurement 179 (July 2021): 109436. http://dx.doi.org/10.1016/j.measurement.2021.109436.

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26

Lee, Hyoseong. "Accuracy Improvement of the ICP DEM Matching." Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 33, no. 5 (2015): 443–51. http://dx.doi.org/10.7848/ksgpc.2015.33.5.443.

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27

Otay, Emre N., and Robert G. Dean. "Nearshore Surveying: Accuracy and Techniques for Improvement." Journal of Surveying Engineering 121, no. 2 (1995): 87–103. http://dx.doi.org/10.1061/(asce)0733-9453(1995)121:2(87).

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28

Saitoh, Ayumu, Kenta Miyashita, Taku Itoh, et al. "Accuracy Improvement of Extended Boundary-Node Method." IEEE Transactions on Magnetics 49, no. 5 (2013): 1601–4. http://dx.doi.org/10.1109/tmag.2013.2243121.

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29

Yozevitch, Roi, Boaz Ben-Moshe, and Amit Dvir. "GNSS Accuracy Improvement Using Rapid Shadow Transitions." IEEE Transactions on Intelligent Transportation Systems 15, no. 3 (2014): 1113–22. http://dx.doi.org/10.1109/tits.2013.2294537.

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30

Abbas, M. A., H. Setan, Z. Majid, A. K. Chong, and D. D. Lichti. "Improvement in measurement accuracy for hybrid scanner." IOP Conference Series: Earth and Environmental Science 18 (February 25, 2014): 012066. http://dx.doi.org/10.1088/1755-1315/18/1/012066.

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31

Dlugosz, Tomasz, and Hubert Trzaska. "Non-stationary electromagnetic field measurements accuracy improvement." Environmentalist 31, no. 2 (2011): 130–33. http://dx.doi.org/10.1007/s10669-011-9306-0.

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32

Kulik, J., S. Rosenblum, K. Weaver, S. Peterson, and S. Send. "Malnutrition Documentation: Accuracy and Areas for Improvement." Journal of the Academy of Nutrition and Dietetics 118, no. 9 (2018): A37. http://dx.doi.org/10.1016/j.jand.2018.06.151.

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33

Wang, Yongbo, Huapeng Wu, Heikki Handroos, et al. "Accuracy improvement studies for remote maintenance manipulators." Fusion Engineering and Design 124 (November 2017): 532–36. http://dx.doi.org/10.1016/j.fusengdes.2017.04.097.

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34

Jou, Hyeong-Tae, Sujeong Lee, and Han-Joon Kim. "Improvement of Alignment Accuracy in Electron Tomography." Applied Microscopy 43, no. 1 (2013): 1–8. http://dx.doi.org/10.9729/am.2013.43.1.1.

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35

Mahmoud, Yousef, and Ehab El-Saadany. "Accuracy Improvement of the Ideal PV Model." IEEE Transactions on Sustainable Energy 6, no. 3 (2015): 909–11. http://dx.doi.org/10.1109/tste.2015.2412694.

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36

Ababneh, Ahmad A. "Target localization accuracy improvement via sensor mobility." International Journal of Parallel, Emergent and Distributed Systems 34, no. 5 (2017): 594–614. http://dx.doi.org/10.1080/17445760.2017.1357720.

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37

Ackermann, Sven, and Matthias W. Beckmann. "Accuracy of cervical cancer staging needs improvement." American Journal of Obstetrics and Gynecology 192, no. 2 (2005): 659. http://dx.doi.org/10.1016/j.ajog.2004.07.090.

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38

Stephenson, C. A., J. K. Dutcher, L. McProud, and S. J. Vinson. "Continual quality improvement: A trayline accuracy model." Journal of the American Dietetic Association 93, no. 9 (1993): A45. http://dx.doi.org/10.1016/0002-8223(93)91140-l.

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39

Vítek, M., J. Pulkrábek, L. Vališ, L. David, and J. Wolf. "Improvement of accuracy in the estimation of lean meat content in pig carcasses." Czech Journal of Animal Science 53, No. 5 (2008): 204–11. http://dx.doi.org/10.17221/314-cjas.

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Fat thickness including skin and muscle depth measured on the left carcass side between the second and third from the last rib 70 mm off the dorsal midline were measured in a total of 168 pig hybrid carcasses. The lean meat content was then determined on the basis of simplified dissections of the carcasses. Multiple regressions of the measurements of the fat and muscle thickness on the lean meat content obtained by dissections were used to construct the following basic regression formulae for the ultrasound and probe apparatuses: &gamma;<sub>IS-D-05</sub> = 60.69798 – 0.89211S<sub>IS-D-05</sub> + 0.10560M<sub>IS-D-05</sub> and &gamma; <sub>IS-D-15</sub> = 60.92452 – 0.77248S<sub>IS-D-15</sub> + 0.11329M<sub>IS-D-15</sub>, respectively. To increase the accuracy of the prediction formulae, additional measures were included in the calculation which reduced s<sub>e</sub> by 0.48 to 0.54 percent points. The relationships between the lean meat content and other indicators of carcass value were also assessed. The highest correlation coefficient was determined in the ratio of the fat cover area above the <I>musculus longissimus lumborum et thoracis</I> (MLLT) to the MLLT area (<I>r</I> = –0.87). On the contrary, the lean meat content demonstrated the lowest correlation with the cold carcass weight (<I></I>r = –0.25). Major carcass cuts (ham, loin, shoulder, belly with bones) from the carcasses classified in different SEUROP classes were evaluated. Significant differences between the classes were found in the proportions of cuts without fat cover, fat thickness measured at point “P<sub>2</sub>”, and fat thickness measured on the midline plane separating the left and right sides of the carcass.

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40

Liu, Shuai, Zhonghao Li, Bin Li, et al. "Accuracy improvement of AC Stark shift microwave detection based on a “frequency ruler”." Chinese Optics Letters 23, no. 7 (2025): 071203. https://doi.org/10.3788/col202523.071203.

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41

Liu, Jiang Sheng, and Juan Qian. "Research on Measuring Accuracy Improvement for Tool Presetter." Key Engineering Materials 572 (September 2013): 261–64. http://dx.doi.org/10.4028/www.scientific.net/kem.572.261.

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Tool presetter is a type of precision measuring instrument that associates to CNC machine tools and machining centers, which integrates the technologies of optics, mechanics and electrics all together, and its measuring accuracy gives direct influence on machining accuracy of CNC machines. The main factors that influence measuring accuracy of tool presetter include: image edge detection, image positioning and accuracy of its mechanical system. This paper gives analysis on these main factors and puts forward three newly developed algorithms for improving measuring accuracy of tool presetter. First algorithm is image edge detection algorithm based on subpixel that increases the edge positioning accuracy by more than 10 times. Second one is uniformity compensation algorithm for whole view measurement that is able to capture accurate real pixel size so that image movement is more precise, which further increases the measuring accuracy. The third one is linear compensation algorithm in the measuring space that makes effective compensation to the mechanical system errors, which can compensate any position in measurement space so that system accuracy increases significantly. These algorithms are tested in CoVis software and the results show that the total measuring accuracy of tool presetter is improved dramtically to 2 μm.

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42

Jiang, Yizhou, Liandong Yu, Huakun Jia, Huining Zhao, and Haojie Xia. "Absolute Positioning Accuracy Improvement in an Industrial Robot." Sensors 20, no. 16 (2020): 4354. http://dx.doi.org/10.3390/s20164354.

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The absolute positioning accuracy of a robot is an important specification that determines its performance, but it is affected by several error sources. Typical calibration methods only consider kinematic errors and neglect complex non-kinematic errors, thus limiting the absolute positioning accuracy. To further improve the absolute positioning accuracy, we propose an artificial neural network optimized by the differential evolution algorithm. Specifically, the structure and parameters of the network are iteratively updated by differential evolution to improve both accuracy and efficiency. Then, the absolute positioning deviation caused by kinematic and non-kinematic errors is compensated using the trained network. To verify the performance of the proposed network, the simulations and experiments are conducted using a six-degree-of-freedom robot and a laser tracker. The robot average positioning accuracy improved from 0.8497 mm before calibration to 0.0490 mm. The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed network on an industrial robot.

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43

Choi, Jung Su, and In-Gul Kim. "A Study on Satellite Alignment Measurements Accuracy Improvement." Journal of the Korean Society for Aeronautical & Space Sciences 48, no. 12 (2020): 987–95. http://dx.doi.org/10.5139/jksas.2020.48.12.987.

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44

SAKAI, Takeyasu, and Kazunobu KOREMURA. "GPS Positioning Accuracy Improvement Using Multiple Reference Stations." Journal of Japan Institute of Navigation 101 (1999): 15–20. http://dx.doi.org/10.9749/jin.101.15.

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45

Liao, Nancy, Rena Kasick, Karen Allen, et al. "Pediatric Inpatient Problem List Review and Accuracy Improvement." Hospital Pediatrics 10, no. 11 (2020): 941–48. http://dx.doi.org/10.1542/hpeds.2020-0059.

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46

Lee, Gyesung. "Improvement of Accuracy of Decision Tree By Reprocessing." KIPS Transactions:PartB 10B, no. 6 (2003): 593–98. http://dx.doi.org/10.3745/kipstb.2003.10b.6.593.

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47

Buch, A. "Improvement in the Accuracy of Fatigue Life Prediction." Key Engineering Materials 16 (January 1987): 1–48. http://dx.doi.org/10.4028/www.scientific.net/kem.16.1.

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48

Choi, Jung Su, and In-Gul Kim. "A Study on Satellite Alignment Measurements Accuracy Improvement." Journal of the Korean Society for Aeronautical & Space Sciences 48, no. 12 (2020): 987–95. http://dx.doi.org/10.5139/jksas.2020.48.12.987.

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49

Park, Raegeun, and Jaechoon Jo. "Reference Class-Based Improvement of Object Detection Accuracy." International Journal on Advanced Science, Engineering and Information Technology 10, no. 4 (2020): 1526. http://dx.doi.org/10.18517/ijaseit.10.4.12792.

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50

Kim, Heeae, and Chang Woo Lee. "Detection Accuracy Improvement of Hang Region using Kinect." Journal of the Korea Institute of Information and Communication Engineering 18, no. 11 (2014): 2727–32. http://dx.doi.org/10.6109/jkiice.2014.18.11.2727.

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