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Journal articles on the topic 'ANN'

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Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Suhartina, Febby E. F. Kandou, and Marina F. O. Singkoh. "Isolasi dan Identifikasi Jamur Endofit Pada Tumbuhan Paku Asplenium nidus." Jurnal MIPA 7, no. 2 (July 1, 2018): 24. http://dx.doi.org/10.35799/jm.7.2.2018.20640.

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Penelitian ini bertujuan untuk mengisolasi dan mengidentifikasi jamur endofit pada tumbuhan paku Asplenium nidus. Metode yang digunakan dalam penelitian ini adalah isolasi jamur endofit, pemurnian jamur endofit, dan identifikasi jamur endofit secara makroskopik dan mikroskopik. Hasil isolasi dan pemurnian jamur endofit diperoleh lima isolat jamur endofit yaitu AN1, AN2, AN3, AN4 dan AN5. Teridentifikasi sebagai genus Gliocladium, Neoscytalidium, Humicola, Phialophoroides dan Aspergillus.This study aims to isolate and identify endophytic fungi in Asplenium nidus. The method used in this research is endophytic fungal isolation, purification of endophytic fungi, and identification of macroscopic and microscopic endophytic fungi. Results of isolation and purification of endophytic fungi obtained five endofit fungal isolates are AN1, AN2, AN3, AN4 and AN5. Identified as genus Gliocladium, Neoscytalidium, Humicola, Phialophoroides and Aspergillus.

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2

Mahapatra, Jayanta. "Ann." Hudson Review 39, no. 1 (1986): 95. http://dx.doi.org/10.2307/3851619.

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3

Stricklin-Parker, Ericka, and Barry A. Schneider. "Ann." Clinical Case Studies 4, no. 4 (October 2005): 315–28. http://dx.doi.org/10.1177/1534650105277713.

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4

Chen, Wei Lun, and Gong Cai Xin. "Research on ANN Dynamic Inversion Control of UAV." Advanced Materials Research 466-467 (February 2012): 1353–57. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1353.

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The paper proposes a method to design AANN dynamic inversion controller through online ANN compensating inversion error. It mainly aims at evident shortage of dynamic inversion controller of UAV. A single hidden layer ANN structure is constructed and the stability of the whole closed loop system is proved. Also the stable adjustment arithmetic of online ANN weight is proposed. The robustness, the adaptability to fault and the response capability to actuator delay time of the scheme are verified by simulation. It is also proved that the online ANN has improved the performance of dynamic inversion controller well. It has important reference value for designing advanced flight control systems of UAV.

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5

Lu, P. J., M. C. Zhang, T. C. Hsu, and J. Zhang. "An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks." Journal of Engineering for Gas Turbines and Power 123, no. 2 (January 1, 2001): 340–46. http://dx.doi.org/10.1115/1.1362667.

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Application of artificial neural network (ANN)-based method to perform engine condition monitoring and fault diagnosis is evaluated. Back-propagation, feedforward neural nets are employed for constructing engine diagnostic networks. Noise-contained training and testing data are generated using an influence coefficient matrix and the data scatters. The results indicate that under high-level noise conditions ANN fault diagnosis can only achieve a 50–60 percent success rate. For situations where sensor scatters are comparable to those of the normal engine operation, the success rates for both four-input and eight-input ANN diagnoses achieve high scores which satisfy the minimum 90 percent requirement. It is surprising to find that the success rate of the four-input diagnosis is almost as good as that of the eight-input. Although the ANN-based method possesses certain capability in resisting the influence of input noise, it is found that a preprocessor that can perform sensor data validation is of paramount importance. Autoassociative neural network (AANN) is introduced to reduce the noise level contained. It is shown that the noise can be greatly filtered to result in a higher success rate of diagnosis. This AANN data validation preprocessor can also serve as an instant trend detector which greatly improves the current smoothing methods in trend detection. It is concluded that ANN-based fault diagnostic method is of great potential for future use. However, further investigations using actual engine data have to be done to validate the present findings.

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6

Rice, Robin, and Joan Simon. "Ann Hamilton." Woman's Art Journal 24, no. 2 (2003): 37. http://dx.doi.org/10.2307/1358786.

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7

Green, Andrew. "Ann McGuiness." Lancet 400, no. 10359 (October 2022): 1190. http://dx.doi.org/10.1016/s0140-6736(22)01895-5.

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8

Lequenne, Michel. "Ann Radcliffe." Cahiers du féminisme 47, no. 1 (1988): 36–38. http://dx.doi.org/10.3406/cafem.1988.3817.

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9

Zhao, Kang, Liuyihan Song, Yingya Zhang, Pan Pan, Yinghui Xu, and Rong Jin. "ANN softmax." Proceedings of the VLDB Endowment 15, no. 1 (September 2021): 1–10. http://dx.doi.org/10.14778/3485450.3485451.

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Thanks to the popularity of GPU and the growth of its computational power, more and more deep learning tasks, such as face recognition, image retrieval and word embedding, can take advantage of extreme classification to improve accuracy. However, it remains a big challenge to train a deep model with millions of classes efficiently due to the huge memory and computation consumption in the last layer. By sampling a small set of classes to avoid the total classes calculation, sampling-based approaches have been proved to be an effective solution. But most of them suffer from the following two issues: i) the important classes are ignored or only partly sampled, such as the methods using random sampling scheme or retrieval techniques of low recall (e.g., locality-sensitive hashing), resulting in the degradation of accuracy; ii) inefficient implementation owing to incompatibility with GPU, like selective softmax. It uses hashing forest to help select classes, but the search process is implemented in CPU. To address the above issues, we propose a new sampling-based softmax called ANN Softmax in this paper. Specifically, we employ binary quantization with inverted file system to improve the recall of important classes. With the help of dedicated kernel design, it can be totally parallelized in mainstream training framework. Then, we find the size of important classes that are recalled by each training sample has a great impact on the final accuracy, so we introduce sample grouping optimization to well approximate the full classes training. Experimental evaluations on two tasks (Embedding Learning and Classification) and ten datasets (e.g., MegaFace, ImageNet, SKU datasets) demonstrate our proposed method maintains the same precision as Full Softmax for different loss objectives, including cross entropy loss, ArcFace, CosFace and D-Softmax loss, with only 1/10 sampled classes, which outperforms the state-of-the-art techniques. Moreover, we implement ANN Soft-max in a complete GPU pipeline that can accelerate the training more than 4.3X. Equipped our method with a 256 GPUs cluster, the time of training a classifier of 300 million classes on our SKU-300M dataset can be reduced to ten days.

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10

Wright, Jay. "Ann Street." Callaloo, no. 27 (1986): 309. http://dx.doi.org/10.2307/2930650.

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11

Connors, Ann. "Ann Connors." Canadian Theatre Review 180 (October 1, 2019): 96. http://dx.doi.org/10.3138/ctr.180.018.

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12

Andrew, Luong, and K. Tak Mak. "Ann. Statist." Communications in Statistics - Theory and Methods 20, no. 2 (January 1991): 721–33. http://dx.doi.org/10.1080/03610929108830525.

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13

Pearson, Linda J. "Ann Compian." Nurse Practitioner 11, no. 7 (July 1986): 50???61. http://dx.doi.org/10.1097/00006205-198607000-00005.

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14

Szulecki, Diane. "Ann Burgess." AJN, American Journal of Nursing 117, no. 2 (February 2017): 70–71. http://dx.doi.org/10.1097/01.naj.0000512306.08339.28.

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15

Kettl, Paul. "Raggedy Ann." American Journal of Psychiatry 165, no. 1 (January 2008): 21–22. http://dx.doi.org/10.1176/appi.ajp.2007.07060975.

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16

France, Ann. "Ann France." Self & Society 18, no. 5 (September 1990): 22–23. http://dx.doi.org/10.1080/03060497.1990.11085111.

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17

Schwemm, Kerri (Schwarze). "Ann Densmore." ASHA Leader 15, no. 5 (April 2010): 39. http://dx.doi.org/10.1044/leader.ga2.15052010.39.

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18

Light, Ann. "Ann Light." Interactions 24, no. 4 (June 23, 2017): 14–15. http://dx.doi.org/10.1145/3095759.

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19

Hogg, Peter. "Ann Poulos." Radiography 11, no. 2 (May 2005): 77. http://dx.doi.org/10.1016/j.radi.2005.02.006.

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20

Roth, Simon, and John Wyatt. "Ann Stewart." Early Human Development 81, no. 2 (February 2005): 181–82. http://dx.doi.org/10.1016/j.earlhumdev.2004.08.001.

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21

McPherson, Klim. "Ann McPherson." Lancet 377, no. 9782 (June 2011): 2000. http://dx.doi.org/10.1016/s0140-6736(11)60855-6.

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22

Rowland, Tom. "Ann Young." Nursing Standard 25, no. 47 (July 27, 2011): 33. http://dx.doi.org/10.7748/ns.25.47.33.s48.

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23

Ogden, Kirsten. "Raggedy Ann." English Journal 95, no. 5 (May 1, 2006): 114. http://dx.doi.org/10.2307/30046608.

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24

Warlow, C. "Ann McPherson." BMJ 342, jun02 1 (June 2, 2011): d3424. http://dx.doi.org/10.1136/bmj.d3424.

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25

Nikos and Maria Georgiadis. "Dear Ann." Journal of Curriculum and Pedagogy 5, no. 1 (June 2008): 84–85. http://dx.doi.org/10.1080/15505170.2008.10411686.

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26

Schell, Cathrina L., and Richard Rathe. "Geri Ann:." Gerontology & Geriatrics Education 16, no. 4 (July 29, 1996): 15–25. http://dx.doi.org/10.1300/j021v16n04_03.

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27

Elliot Bernstein, Lauren Hoffman, Laura Messenheimer, Jose Ortiz, Kate Pilkington, James Rodkey, Alan Vollman, and Judith P. Hallett. "Ann Raia." Classical World 103, no. 4 (2010): 533–34. http://dx.doi.org/10.1353/clw.2010.0032.

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28

James, Ann. "Ann James." British Journal of Healthcare Management 13, no. 11 (November 2007): 432–33. http://dx.doi.org/10.12968/bjhc.2007.13.11.27708.

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29

Esplin, Marti Dickey. "Mary Ann." Dialogue: A Journal of Mormon Thought 19, no. 3 (October 1, 1986): 103–8. http://dx.doi.org/10.2307/45225481.

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30

van Maanen, B., G. Coco, K. R. Bryan, and B. G. Ruessink. "The use of artificial neural networks to analyze and predict alongshore sediment transport." Nonlinear Processes in Geophysics 17, no. 5 (September 2, 2010): 395–404. http://dx.doi.org/10.5194/npg-17-395-2010.

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Abstract. An artificial neural network (ANN) was developed to predict the depth-integrated alongshore suspended sediment transport rate using 4 input variables (water depth, wave height and period, and alongshore velocity). The ANN was trained and validated using a dataset obtained on the intertidal beach of Egmond aan Zee, the Netherlands. Root-mean-square deviation between observations and predictions was calculated to show that, for this specific dataset, the ANN (εrms=0.43) outperforms the commonly used Bailard (1981) formula (εrms=1.63), even when this formula is calibrated (εrms=0.66). Because of correlations between input variables, the predictive quality of the ANN can be improved further by considering only 3 out of the 4 available input variables (εrms=0.39). Finally, we use the partial derivatives method to "open and lighten" the generated ANNs with the purpose of showing that, although specific to the dataset in question, they are not "black-box" type models and can be used to analyze the physical processes associated with alongshore sediment transport. In this case, the alongshore component of the velocity, by itself or in combination with other input variables, has the largest explanatory power. Moreover, the behaviour of the ANN indicates that predictions can be unphysical and therefore unreliable when the input lies outside the parameter space over which the ANN has been developed. Our approach of combining the strong predictive power of ANNs with "lightening" the black box and testing its sensitivity, demonstrates that the use of an ANN approach can result in the development of generalized models of suspended sediment transport.

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31

Puturuhu, Ferad, Projo Danoedoro, Junun Sartohadi, and Danang Srihadmoko. "Artificial neural network for landslide vulnerability mapping in Leitimur Peninsula Ambon Island." IOP Conference Series: Earth and Environmental Science 989, no. 1 (February 1, 2022): 012013. http://dx.doi.org/10.1088/1755-1315/989/1/012013.

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Abstract Artificial neural network (ANN) has been widely used in remote sensing to classify the various types of data. Moreover, it provides better accuracy results than statistical methods. In this study, AAN was applied to map landslide vulnerability zone in Leitimur Peninsula Ambon where landslide has frequently occurred in the period 2012 until now resulting in losses to community. The objective of this study was to determine the landslide vulnerability level zones of the study area. The method used was the logistic regression and ANN. The results showed that the largest zone (9957.33ha) or 65.47% of the study area is in low landslide vulnerability level, while the best result of the accuracy test is the ANN analysis with the value of 72.55%.

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32

Bardeji, Saeideh Farajzadeh, Seyed Hadi Mirghaderi, Alireza Pooya, and Amirmohammad Fakoor Saghih. "Perishable Inventory Management Using GA-ANN and ICA-ANN." International Journal of Procurement Management 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijpm.2020.10022121.

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33

Bardeji, Saeideh Farajzadeh, Amir Mohammad Fakoor Saghih, Alireza Pooya, and Seyed Hadi Mirghaderi. "Perishable inventory management using GA-ANN and ICA-ANN." International Journal of Procurement Management 13, no. 3 (2020): 347. http://dx.doi.org/10.1504/ijpm.2020.107466.

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34

Dopson, Laurence. "Gertrude Ann Ramsden." Nursing Standard 19, no. 11 (November 24, 2004): 31. http://dx.doi.org/10.7748/ns.19.11.31.s44.

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35

Landau, Misia. "Ann M. Gronowski." Clinical Chemistry 67, no. 4 (March 31, 2021): 579–82. http://dx.doi.org/10.1093/clinchem/hvab025.

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36

Gal-Or, Noemi. "Revisiting Ann Tickner." Journal of Human Security 5, no. 2 (2009): 83–93. http://dx.doi.org/10.3316/jhs0502083.

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37

Josephy, Kate. "Josephy, Greta Ann." Nursing Standard 27, no. 22 (January 30, 2013): 32. http://dx.doi.org/10.7748/ns2013.01.27.22.32.p10392.

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38

Jnah, Amy J., and Desi M. Newberry. "Dear ANN Colleagues." Neonatal Network 37, no. 5 (September 2018): 265–69. http://dx.doi.org/10.1891/0730-0832.37.5.263.

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39

Benavides, Gustavo, Thomas J. Coleman III, Ralph W. Hood Jr., Richard Sosis, and Ann Taves. "Portrait: Ann Taves." Religion and Society 6, no. 1 (January 1, 2015): 1–26. http://dx.doi.org/10.3167/arrs.2015.060102.

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40

Chiarappa, Michael J., and Gabrielle A. Berlinger. "Michael Ann Williams." Special Issue - Storied Spaces: Renewing Folkloristic Perspectives on Vernacular Architecture 90-91 (April 29, 2021): 151–55. http://dx.doi.org/10.7202/1076802ar.

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41

Edie, R. W. "Vivian Ann Templeton." Bulletin of Canadian Petroleum Geology 52, no. 2 (June 1, 2004): 199–200. http://dx.doi.org/10.2113/52.2.199.

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42

Nina Notman, special to C&EN. "F. Ann Walker." C&EN Global Enterprise 100, no. 27 (August 8, 2022): 36. http://dx.doi.org/10.1021/cen-10027-obits17.

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43

Watts, Geoff. "Deborah Ann Nickerson." Lancet 399, no. 10335 (April 2022): 1600. http://dx.doi.org/10.1016/s0140-6736(22)00700-0.

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44

Hasselkus, B. R. "Dear Ann Landers..." American Journal of Occupational Therapy 55, no. 3 (May 1, 2001): 247–48. http://dx.doi.org/10.5014/ajot.55.3.247.

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45

Lederer, Eppie. "Dear Ann Landers." Teachers College Record: The Voice of Scholarship in Education 110, no. 14 (November 2008): 44. http://dx.doi.org/10.1177/016146810811001413.

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46

Duszynski, Donald W., and Eric S. Loker. "LYNN ANN HERTEL." Journal of Parasitology 92, no. 1 (February 2006): 155. http://dx.doi.org/10.1645/ge-657.1.

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47

Newlyn, A. "Carol Ann Robinson." British Dental Journal 199, no. 7 (October 2005): 477. http://dx.doi.org/10.1038/sj.bdj.4812879.

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48

Dun, Helen. "Margaret Ann Green." British Journal of Occupational Therapy 61, no. 4 (April 1998): 181. http://dx.doi.org/10.1177/030802269806100414.

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49

Copland, JW. "Marjorie Ann Reid." Australian Veterinary Journal 79, no. 2 (February 2001): 119. http://dx.doi.org/10.1111/j.1751-0813.2001.tb10719.x.

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50

Shaw, Valerie. "Nílim in ann." Comhar 51, no. 2 (1992): 33. http://dx.doi.org/10.2307/25571715.

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