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Gotowa bibliografia na temat „AQUA SOFTWARE”

Autor: Grafiati

Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "AQUA SOFTWARE"

1

LI, JINGZHOU, and GUENTHER RUHE. "SOFTWARE EFFORT ESTIMATION BY ANALOGY USING ATTRIBUTE SELECTION BASED ON ROUGH SET ANALYSIS." International Journal of Software Engineering and Knowledge Engineering 18, no. 01 (2008): 1–23. http://dx.doi.org/10.1142/s0218194008003532.

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Estimation by analogy (EBA) predicts effort for a new project by learning from the performance of former projects. This is done by aggregating effort information of similar projects from a given historical data set that contains projects, or objects in general, and attributes describing the objects. While this has been successful in general, existing research results have shown that a carefully selected subset, as well as weighting, of the attributes may improve the performance of the estimation methods. In order to improve the estimation accuracy of our former proposed EBA method AQUA, which supports data sets that have non-quantitative and missing values, an attribute weighting method using rough set analysis is proposed in this paper. AQUA is thus extended to AQUA+ by incorporating the proposed attribute weighting and selection method. Better prediction accuracy was obtained by AQUA+ compared to AQUA for five data sets. The proposed method for attribute weighting and selection is effective in that (1) it supports data sets that have non-quantitative and missing values; (2) it supports attribute selection as well as weighting, which are not supported simultaneously by other attribute selection methods; and (3) it helps AQUA+ to produce better performance.

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2

Acharya, Swarup, Phillip B. Gibbons, Viswanath Poosala, and Sridhar Ramaswamy. "The Aqua approximate query answering system." ACM SIGMOD Record 28, no. 2 (1999): 574–76. http://dx.doi.org/10.1145/304181.304581.

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3

Muresan, D. D., and T. W. Parks. "Adaptively Quadratic (AQua) Image Interpolation." IEEE Transactions on Image Processing 13, no. 5 (2004): 690–98. http://dx.doi.org/10.1109/tip.2004.826097.

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4

Pohl-Mayerhöfer, Renate, Anneka Beck, Silke Grinke, et al. "Die Online-Netzwerkstatt AQUA e.V. für Promovierende: Weggefährt*innen zwischen Technik, E-Science und Gruppenprozessen." Qualitatives Methodenlernen im Kontext digitaler Medien 21, no. 2-2020 (2021): 233–44. http://dx.doi.org/10.3224/zqf.v21i2.06.

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Die Online-Forschungswerkstatt AQUA besteht seit ca. 17 Jahren, der Beitrag zeichnet nach, in welche Richtung sie sich entwickelte. Nach Darstellung der Genese von AQUA (1) erfolgt eine Anbindung an den Diskurs über die besonderen Herausforderungen der Promotionsphase (2). Es wird die Online-Netzwerkstatt AQUA e.V. in ihrer spezifischen Positionierung als Variante einer Struktur qualitativen Forschens vorgestellt (3). Der Artikel schließt mit der Beschreibung vom Ablauf der Sitzungen, der Lernumgebung und der genutzten Technologie, insbesondere der Nutzung bestimmter Funktionen der neuen Generation sozialer Software im Web 2.0 (4).

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5

Norvill, Craig, Felix Bockelmann, Patrick Blumenberg, and Simon Goodall. "Investigation of Elekta AQUA software for kilovoltage to megavoltage radiation isocenter coincidence." Physical and Engineering Sciences in Medicine 44, no. 3 (2021): 667–75. http://dx.doi.org/10.1007/s13246-021-01014-1.

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Li, Jingzhou, and Guenther Ruhe. "Analysis of attribute weighting heuristics for analogy-based software effort estimation method AQUA+." Empirical Software Engineering 13, no. 1 (2007): 63–96. http://dx.doi.org/10.1007/s10664-007-9054-4.

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van Leeuwen, J., M. Holmes, U. Kaeding, R. Daly, and D. Bursill. "Development and implementation of the software mEnCo© to predict coagulant doses for DOC removal at full-scale WTPs in South Australia." Journal of Water Supply: Research and Technology-Aqua 58, no. 4 (2009): 291–98. http://dx.doi.org/10.2166/aqua.2009.054.

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Hsiang-Ying Wang, Hsiang-Ying Wang, Rich C. Lee Hsiang-Ying Wang, Hsien-I. Lin Rich C. Lee, and Yung-Yao Chen Hsien-I Lin. "The Challenge of Aqua Creatures Specie Classification in the Aquarium Innovation Theme." 網際網路技術學刊 22, no. 7 (2021): 1473–81. http://dx.doi.org/10.53106/160792642021122207002.

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9

Sunarni, Esse, La Ode Muhammad Yasir Haya, and Nur Irawati. "STUDI SEBARAN SUHU PERMUKAAN LAUT DI PERAIRAN TELUK BONE MENGGGUNAKAN CITRA AQUA MODIS." Jurnal Sapa Laut (Jurnal Ilmu Kelautan) 5, no. 3 (2020): 263. http://dx.doi.org/10.33772/jsl.v5i3.13456.

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Suhu permukaan laut merupakan salah satu parameter oseanografi yang sangat penting bagi kehidupan organisme di lautan. Penelitian ini bertujuan untuk mengetahui sebaran suhu permukaan laut dengan menggunakan citra satelit Aqua MODIS pada perairan Teluk Bone secara spasial dan temporal. Data yang digunakan yaitu data suhu permukaan laut bulanan April 2018-Maret 2019 yang diperoleh dari sensor satelit Aqua MODIS level-3 yang diunduh dari NASA (OCEAN COLOR atau link http://modis.gfsc.nasa.gov/data kemudian pengolahan data citra dilakukan menggunakan software SeaDas. Hasil dari data citra sebaran suhu permukaan laut tertinggi pada Mei 2018 berkisar antara 29,13-32,00oC terletak di perairan Palopo dan suhu permukaan laut terendah berada pada bulan Maret 2019 berkisar antara 27,10-30,60oC. terletak di perairan Bulukumba.Kata kunci: Teluk Bone, satelit Aqua MODIS, suhu permukaan laut.

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10

Chen, L., C. Kow, N. A. Afira, E. Mok, S. Teng, and A. M. Gilmore. "Use of simultaneous absorbance-transmittance and excitation-emission matrix (A-TEEM) spectroscopy to monitor source water contamination for waterworks." Journal of Water Supply: Research and Technology-Aqua 71, no. 1 (2021): 62–71. http://dx.doi.org/10.2166/aqua.2021.120.

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Abstract Conventional oil-in-water analyzers used by waterworks have hydrocarbon detection limits at mg/L levels and do not identify the type of oil compounds. The objectives of this study were to evaluate a more sensitive optical instrument and the analysis method to (1) determine the signature excitation and emission matrixs of each type of oil (such as diesel, heavy oil, gasoline and kerosene) or their indicator organic compounds and enter them into the instrument's software library and (2) test out the effectiveness of the instrument in detecting the above-mentioned oil in local waterworks’ source and treated water. The patented simultaneous absorbance-transmittance excitation-emission matrix (A-TEEM) instrument method was used to identify and quantify low levels of organic contaminants present in a much higher background of other dissolved organic matter components in raw and treated water. Multivariate regression and machine learning techniques were applied and shown to have potential for alerting plant operators to organic contamination events.

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