Relevant bibliographies by topics / Multi-attribute analysis

Academic literature on the topic 'Multi-attribute analysis'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Multi-attribute analysis"

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Braglia, Marcello. "MAFMA: multi‐attribute failure mode analysis." International Journal of Quality & Reliability Management 17, no. 9 (December 2000): 1017–33. http://dx.doi.org/10.1108/02656710010353885.

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Chang, Kuei-Hu. "Generalized multi-attribute failure mode analysis." Neurocomputing 175 (January 2016): 90–100. http://dx.doi.org/10.1016/j.neucom.2015.10.039.

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Jiménez, Antonio, Sixto Ríos-Insua, and Alfonso Mateos. "A generic multi-attribute analysis system." Computers & Operations Research 33, no. 4 (April 2006): 1081–101. http://dx.doi.org/10.1016/j.cor.2004.09.003.

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Gratzl, Samuel, Alexander Lex, Nils Gehlenborg, Hanspeter Pfister, and Marc Streit. "LineUp: Visual Analysis of Multi-Attribute Rankings." IEEE Transactions on Visualization and Computer Graphics 19, no. 12 (December 2013): 2277–86. http://dx.doi.org/10.1109/tvcg.2013.173.

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Bichler, Martin. "An experimental analysis of multi-attribute auctions." Decision Support Systems 29, no. 3 (October 2000): 249–68. http://dx.doi.org/10.1016/s0167-9236(00)00075-0.

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FOLLECO, ANDRES, and TAGHI KHOSHGOFTAAR. "ATTRIBUTE NOISE DETECTION USING MULTI-RESOLUTION ANALYSIS." International Journal of Reliability, Quality and Safety Engineering 13, no. 03 (June 2006): 267–88. http://dx.doi.org/10.1142/s0218539306002252.

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The value of knowledge inferred from information databases is critically dependent on the quality of data. The identification of noisy attributes which can easily corrupt and curtail valuable knowledge and information from a dataset can be very helpful to analysts. We present a novel detection method to identify noisy attributes in datasets of software metrics using multi-resolution transformations based on Discrete Wavelet Transforms. The proposed method has been applied to supervised datasets of scientific full-scale data from NASA's Software Metric Data Program (MDP) and to a military command, control, and communications system (CCCS). Empirical results have been favorably compared to those obtained from the robust Pairwise Attribute Noise Detection Algorithm (PANDA) using the same MDP datasets and with mixed results for the CCCS data. All results were verified with several case studies that included injecting known simulated noise into specific attributes with no class noise.
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Roth, Richard, Frank Field, and Joel Clark. "Materials selection and multi-attribute utility analysis." Journal of Computer-Aided Materials Design 1, no. 3 (October 1994): 325–42. http://dx.doi.org/10.1007/bf00712855.

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Ding, Li Li, Song Wang, and Wang Lin Kang. "Competitive Analysis of Multi-Attribute On-Line Auction." Advanced Materials Research 204-210 (February 2011): 205–8. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.205.

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The emergence of auction that supports bids characterized by several attributes is one of the most recent evolutions within auction theory. The purpose of this paper is to introduce multi-attribute into the on-line auction mechanism, proposed by Lavi and Nisan. Our mechanism is designed for auctioning multiple units of a good when the bidders arrive and depart dynamically. To incorporate the buyer’s preferences across attributes in an auction setting, we adopt a novel application of a scoring function. Furthermore, we describe a competitive reservation utility strategy (RUS), which helps the buyer decide when and how many goods he should buy. Finally, we also perform numerical examples showing our results.
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Amarchinta, Hemanth K., and Ramana V. Grandhi. "Multi-Attribute Structural Optimization Based on Conjoint Analysis." AIAA Journal 46, no. 4 (April 2008): 884–93. http://dx.doi.org/10.2514/1.30530.

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Guikema, Seth D., and Mark W. Milke. "Sensitivity analysis for multi-attribute project selection problems." Civil Engineering and Environmental Systems 20, no. 3 (September 2003): 143–62. http://dx.doi.org/10.1080/1028660021000018519.

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Dissertations / Theses on the topic "Multi-attribute analysis"

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Brownlow, Susan Ann. "Structuring problems for multi-attribute value analysis." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.257945.

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Amarchinta, Hemanth K. "Multi-Attribute Optimization Based on Conjoint Analysis." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1169057769.

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Karakaya, Gulsah. "Approaches For Multi-attribute Auctions." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610780/index.pdf.

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There is a growing interest in electronic auctions in the literature. Many researchers work on the single attribute version of the problem. Multi-attribute version of the problem is more realistic. However, this brings a substantial difficulty in solving the problem. In order to overcome the computational difficulties, we develop an Evolutionary Algorithm (EA) for the case of multi-attribute multi-item reverse auctions. We generate the whole Pareto front using the EA. We also develop heuristic procedures to find several good initial solutions and insert those in the initial population of the EA. We test the EA on a number of randomly generated problems and compare the results with the true Pareto optimal front obtained by solving a series of integer programs. We also develop an exact interactive approach that provides aid both to the buyer and the sellers for a multi-attribute single item multi round reverse auction. The buyer decides on the provisional winner at each round. Then the approach provides support in terms of all attributes to each seller to be competitive in the next round of the auction.
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Taiyabi, Asif A. "A multi-attribute analysis of nuclear waste disposal alternatives." Master's thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-02022010-020127/.

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Hammel, Evan Martin. "A multi-attribute framework for risk analysis of natural hazards." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1446078.

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Lieuw, Iris. "Time Frequency Analysis of Neural Oscillations in Multi-Attribute Decision-Making." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/scripps_theses/556.

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In our daily lives, we often make decisions that require the use of self-control, weighing trade-offs between various attributes: for example, selecting a food based on its health rather than its taste. Previous research suggests that re-weighting attributes may rely on selective attention, associated with decreased neural oscillations over posterior brain regions in the alpha (8-12 Hz) frequency range. Here, we utilized the high temporal resolution and whole-brain coverage of electroencephalography (EEG) to test this hypothesis in data collected from hungry human subjects exercising dietary self-control. Prior analysis of this data has found time-locked neural activity associated with each food’s perceived taste and health properties from approximately 400 to 650 ms after stimulus onset (Harris et al., 2013). We conducted time-frequency analyses to examine the role of alpha-band oscillations in this attribute weighting. Specifically, we predicted that there would be decreased alpha power in posterior electrodes beginning approximately 400 ms after stimulus onset for the presentation of healthy food relative to unhealthy food, reflecting shifts in selective attention. Consistent with this hypothesis, we found a significant decrease in alpha power for presentations of healthy relative to unhealthy foods. As predicted, this effect was most pronounced at posterior occipital and parietal electrodes and was significant from approximately 450 to 700 ms post-stimulus onset. Additionally, we found significant alpha-band decreases in right temporal electrodes during these times. These results extend previous attention research to multi-attribute choice, suggesting that the re-weighting of attributes can be measured neuro-computationally.
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Field, Frank Remsen. "Application of multi-attribute utility analysis to problems in materials selection." Thesis, Massachusetts Institute of Technology, 1985. http://hdl.handle.net/1721.1/15275.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Material Science and Engineering, 1985.
MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE.
Vita.
Bibliography: leaves 208-229.
by Frank Remsen Field III.
Ph.D.
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Wright, Robert L. "Multi-attribute utility analysis using the Mark Johnson model in defense contracting." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA300830.

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Ruderman, Alex Michael. "A framework for simulation-based multi-attribute optimum design with improved conjoint analysis." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31811.

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Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Choi, Seung-Kyum; Committee Member: Allen, Janet K.; Committee Member: Paredis, Chris. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Giustozzi, Filippo. "Life Cycle Assessment of Sustainable Road Pavements: Carbon Footprinting and Multi-attribute Analysis." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77108.

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Sustainability is increasingly becoming a significant part of strategic asset management worldwide. Road agencies are providing guidelines to assess the relative sustainability of road projects. Unfortunately, environmental features of a road project are still considered as stand-alone evaluations, an added value. Very little has been done to integrate environmental impacts as a part of pavement management systems and other decision support tools to choose between different strategies. In this way, being awarded with a "green" certificate for a specific road project could result in the belief that recognition would correspond to the optimal strategy. Furthermore, a road project awarded with a "green" rating during the construction phase does not mean that the project results "green" if a life cycle approach is considered. Indeed, the most environmental friendly strategies may not be the ones with the highest performance. Using "greener" materials or performing recycle-related practices may lead to a lower performance over the life cycle and therefore produce an increase in maintenance needed, which could in turn result into more congestion due to work zones and higher total emissions. Therefore, construction and maintenance strategies should be analyzed according to three main parameters: cost, performance or effectiveness, and environmental impacts. The cost analysis part takes into account outflows over the service life of the pavement according to the well-known Life Cycle Cost Analysis methodology. The cheapest maintenance technique over the analysis period was expounded and sensitivity analyses to involved factors were conducted. Performance assessment was developed according to experimental on site data gathered and analyzed over several years to develop deterioration pavement models. Effectiveness of maintenance treatments is further provided and compared to the volume of traffic. In addition, environmental impacts related to maintenance and rehabilitation strategies were analyzed. Emissions were computed over the life cycle of the pavement from the manufacture of raw materials for the initial construction, placement, and maintenance phase. Finally, an optimization procedure was developed for including environmental impacts into a Pavement Management System. A methodology to set a multi-attribute approach system, computing costs, performance, and eco-efficiency over the life cycle of the pavement, is therefore proposed.
Ph. D.
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Books on the topic "Multi-attribute analysis"

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Andrew, Allan D. Multi-attribute decision making analysis with evolutionary programming applied to large scale vehicle II. Springfield, Va: Available from National Technical Information Service, 1998.

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APEC Committee on Trade and Investment. Sub-Committee on Standards and Conformance. Trade impact of life cycle analysis in multi-attribute certification programs for flooring materials and plumbing fixtures in five focus APEC member economies. Singapore: Produced for Asia Pacific Economic Cooperation Secretariat, 2012.

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Garcia-Martinez, Salvador. Demand and profitability for albacore products: A multi-attribute analysis. 1996.

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Robust Sensitivity Analysis for Multi-Attribute Deterministic Hierarchical Value Models. Storming Media, 2002.

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Book chapters on the topic "Multi-attribute analysis"

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Mateo, José Ramón San Cristóbal. "Multi-Attribute Utility Theory." In Multi Criteria Analysis in the Renewable Energy Industry, 63–72. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2346-0_10.

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Jansen, Sylvia J. T. "The Multi-attribute Utility Method." In The Measurement and Analysis of Housing Preference and Choice, 101–25. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-8894-9_5.

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Tofallis, Chris. "Multi-Attribute Selection Using Discrete Frontier Profiling." In Advances in Decision Analysis, 121–30. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-0647-6_8.

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Jumadinova, Janyl, and Prithviraj Dasgupta. "Multi-attribute Regret-Based Dynamic Pricing." In Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis, 73–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15237-5_6.

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Conversano, Claudio, and Francesco Mola. "Detecting Subset of Classifiers for Multi-attribute Response Prediction." In Data Analysis and Classification, 233–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_27.

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Słowiński, Roman. "Rough Set Analysis of Multi-Attribute Decision Problems." In Rough Sets, Fuzzy Sets and Knowledge Discovery, 136–42. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-3238-7_16.

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Roy, Santanu, Bibekananda Shit, and Soumya Sen. "Association Based Multi-attribute Analysis to Construct Materialized View." In Advances in Intelligent Systems and Computing, 115–31. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3409-1_8.

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Chen, Runxue, Peng Yan, and Lian Xu. "Multi-granularity Attribute Sentiment Analysis Based on Neural Network." In Advances in Intelligent Systems and Computing, 43–54. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5887-0_7.

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Li, Jun, Xiang-yang Li, and Rui Yang. "Urban Power Network Vulnerability Assessment Based on Multi-attribute Analysis." In Computational Science and Its Applications -- ICCSA 2015, 126–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21404-7_10.

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Barron, F. Hutton, and Bruce E. Barrett. "Linear Inequalities and the Analysis of Multi-Attribute Value Matrices." In Decision Science and Technology, 211–25. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5089-1_12.

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Conference papers on the topic "Multi-attribute analysis"

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Valdés, Fabio, and Ralf Hartmut Güting. "Efficient Multi-Attribute Analysis for Trajectories." In SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3139958.3139977.

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Li, Xin‐Gong, and Tadeuss J. Ulrych. "Multi‐scale attribute analysis and trace decomposition." In SEG Technical Program Expanded Abstracts 1996. Society of Exploration Geophysicists, 1996. http://dx.doi.org/10.1190/1.1826439.

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Vijayan, Priyesh, Shivashankar Subramanian, and Balaraman Ravindran. "Multi-label collective classification in multi-attribute multi-relational network data." In 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2014. http://dx.doi.org/10.1109/asonam.2014.6921634.

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FENG, JIALI, and NENGZHI LU. "A MULTI-ATTRIBUTE EVALUATING APPROACH BASED ON ANALYSIS AND LEARNING OF ATTRIBUTE COORDINATE." In Proceedings of the 6th International FLINS Conference. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702661_0030.

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Ampilov, I. P., and R. J. Arts. "Seismic Multi-Attribute Analysis Techniques for Reservoir Characterisation." In 62nd EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2000. http://dx.doi.org/10.3997/2214-4609-pdb.28.p24.

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Wu, Jun, and YaoXin Chen. "Multi-Attribute Data Visualization Analysis Model for Multimedia." In 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC). IEEE, 2019. http://dx.doi.org/10.1109/besc48373.2019.8963246.

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Wang, Siyuan, Kai Shuang, and Sen Su. "Multi-attribute Stereo Model Based IMS Security Analysis." In 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing. IEEE, 2010. http://dx.doi.org/10.1109/nswctc.2010.60.

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Fa-Chao Li, Qing Dong, and Chen-Xia Jin. "Fuzzy programming models based on multi-attribute synthesizing effect." In 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635750.

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Chopra, Satinder, Vladimir Alexeev, and Doug Pruden. "Non‐linearity in multi‐attribute analysis — A comparative study." In SEG Technical Program Expanded Abstracts 2004. Society of Exploration Geophysicists, 2004. http://dx.doi.org/10.1190/1.1851156.

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Napoli, O., V. M. Do Rosario, J. P. P. N. De Oliveira, P. M. C. E. Silva, and E. Borin. "Accelerating Multi-attribute Unsupervised Seismic Facies Analysis With RAPIDS." In Digital Subsurface Conference in Latin America. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202181012.

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Reports on the topic "Multi-attribute analysis"

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Kuswa, Glenn W., Jeffrey Yeenien Tsao, Thomas E. Drennen, Jason V. Zuffranieri, Orman Henrie Paananen, Scott A. Jones, Juergen G. Ortner, Jeffrey D. Brewer, and Maximo M. Valdez. Multi-attribute criteria applied to electric generation energy system analysis LDRD. Office of Scientific and Technical Information (OSTI), October 2005. http://dx.doi.org/10.2172/923152.

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NAVAL POSTGRADUATE SCHOOL MONTEREY CA. Multi Attribute Decision Analysis in Public Health - Analyzing Effectiveness of Alternate Modes of Dispensing. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada474082.

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Davis, F., K. Kuzio, K. Sorenson, R. Weiner, and T. Wheeler. A Multi-Attribute Utility Decision Analysis for Treatment Alternatives for the DOE/SR Aluminum-Based Spent Nuclear Fuel. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/1577.

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