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Clouds, p-boxes, Fuzzy Sets, and Other Uncertainty Representations in Higher Dimensions

Martin Fuchs
2009 Acta Cybernetica  
We introduce intuitively the concept of potential clouds, our latest approach which successfully copes with both higher dimensions and incomplete information.  ...  Uncertainty modeling in real-life applications comprises some serious problems such as the curse of dimensionality and a lack of sufficient amount of statistical data.  ...  Potential clouds This section gives an intuitive introduction of uncertainty representation by means of clouds [71] , inspired by and combining ideas from p-boxes, random and fuzzy sets, convex methods  ... 
doi:10.14232/actacyb.19.1.2009.5 fatcat:nkpvlt4t4bhdpguncuyvav6jn4

Drilling High Precision Holes in Ti6Al4V Using Rotary Ultrasonic Machining and Uncertainties Underlying Cutting Force, Tool Wear, and Production Inaccuracies

M. Chowdhury, A. Sharif Ullah, Saqib Anwar
2017 Materials  
A possibility distribution is a probability-distribution-neural representation of uncertainty, and is effective in quantifying the uncertainty underlying physical quantities when there is a limited number  ...  The effects of the machining conditions on each performance parameter have been determined by constructing a set of possibility distributions (i.e., trapezoidal fuzzy numbers) from the experimental data  ...  On the other hand, compared to P = 40%, P = 20% exhibits a somewhat better control over CE.  ... 
doi:10.3390/ma10091069 pmid:28895876 pmcid:PMC5615723 fatcat:mforkqraczbmvh5pzrvx6ly3am

Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering [article]

Charles Aguiar, Daniel Leite
2020 arXiv   pre-print
We develop the notion of double-boundary fuzzy granules and elaborate on its implications. Type 1 and type 2 fuzzy inference systems can be obtained from the projection of Fuzzy eIX granules.  ...  We perform the principle of the balanced information granularity within Fuzzy eIX classifiers to achieve a higher level of model understandability.  ...  Acc(%) = T P + T N T P + F P + T N + F N . 100%. (21) Several possible extensions of the Fuzzy eIX framework can be mentioned: • Granules' initial dimensions as well as minimum acceptable dimensions can  ... 
arXiv:2003.12381v1 fatcat:yb7tatjbujhyhhuly5dcrgcbaq

Robust and Automated Space System Design [chapter]

Martin Fuchs, Daniela Girimonte, Dario Izzo, Arnold Neumaier
2008 Robust Intelligent Systems  
This chapter proposes a novel, simple approach based on the clouds formalism to elicit and process the uncertainty information provided by expert designers and to incorporate this information into the  ...  However, the problem of taking into account the uncertainties of variables and models defining an optimal and robust spacecraft design have not been tackled effectively yet.  ...  Section 1.2.2) is the key to a good uncertainty representation with clouds. In higher dimensions the weight computation is very expensive.  ... 
doi:10.1007/978-1-84800-261-6_12 fatcat:mi5ih2uhmvfzjk2hbcxfvuw4nm

Design of Project Cost Risk Management System Based on Improved Fuzzy Rule Weight Algorithm

Chen Huang, Yanfeng Zhu, Xiaoming Shao
2021 Security and Communication Networks  
fuzzy rule weight algorithm.  ...  In the software part, an improved fuzzy rule weight algorithm is used to calculate the weight of project cost risk indicators, thereby improving the effectiveness of risk management.  ...  Weight calculation of fuzzy rules is an important application of fuzzy set theory. It is a good representation of weight calculation knowledge and has readability and interpretability.  ... 
doi:10.1155/2021/4688846 doaj:d9aeea1d3ccd46a7a55281e033b5d01f fatcat:ef2sonidpjesrcnpfrymlkq6qa

Simulated polyhedral clouds in robust optimisation

Martin Fuchs
2012 International Journal of Reliability and Safety  
Past studies of uncertainty handling with polyhedral clouds have already shown strength in dealing with higher dimensional uncertainties in robust optimisation, even in case of partial ignorance of statistical  ...  However, the number of function evaluations necessary to quantify and propagate the uncertainties has been too high to be useful in many real-life applications with respect to limitations of computational  ...  Acknowledgements The author would like to thank Vladik Kreinovich for his helpful comments on CD for arbitrary convex sets.  ... 
doi:10.1504/ijrs.2012.044298 fatcat:ktkxmdemxnhtpayekpsfeb5fua

Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

Muhammad Sualeh, Gon-Woo Kim
2019 Sensors  
Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios.  ...  In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for a non-stationary base is presented, using multiple 3D LiDARs for perception.  ...  These data sets also yield higher number of FP and FN that contribute in loss of track and higher deviation in P.  ... 
doi:10.3390/s19061474 fatcat:di4rn4oiurh5vlt3ibrdgtfn4q

Visual-LiDAR based 3D Object Detection and Tracking for Embedded Systems

Muhammad Sualeh, Gon-Woo Kim
2020 IEEE Access  
Other tracks fall under the class of (PT). A higher number of (MT) and few (ML) is desirable.  ...  The clustering module developed in the former work used a rectangular grid-based representation of point cloud, requiring relatively higher resolution.  ... 
doi:10.1109/access.2020.3019187 fatcat:2macumn6fzhkjmylsvxs5tk7p4

Contextual genetic algorithms: Evolving developmental rules [chapter]

Luis Mateus Rocha
1995 Lecture Notes in Computer Science  
structures based on fuzzy set and evidence theories.  ...  In biological systems, RNA editing represents a significant and potentially regulatory step in gene expression.  ...  To allow for a better representation of uncertainty, that is, if we desire the physical characteristics to observe in addition to fuzziness the two other recognized forms of uncertainty -nonspecificity  ... 
doi:10.1007/3-540-59496-5_312 fatcat:h3i2tu42vzdszj2fadoju5453u

A fuzzy computational model of emotion for cloud based sentiment analysis

Charalampos Karyotis, Faiyaz Doctor, Rahat Iqbal, Anne James, Victor Chang
2018 Information Sciences  
The fuzzy technique is evaluated in terms of its ability to model user affective states in comparison to other existing machine learning approaches.  ...  Finally, discussions on research contributions to cloud intelligence on sentiment analysis, emotion modeling, big data science, and comparisons with other approaches are presented in detail.  ...  Let 𝐼 𝑖𝑛 𝑞 be the corresponding fuzzy set for the input 𝑖𝑛 = 1, . . ,3 and 𝐺 𝑜𝑢𝑡 𝑝 be the corresponding fuzzy set for output 𝑜𝑢𝑡 = 1, . . ,8 𝑤ℎ𝑒𝑟𝑒 𝑞 𝑎𝑛𝑑 𝑝 = 1, … ,5.  ... 
doi:10.1016/j.ins.2017.02.004 fatcat:cwuoijmopreilghnx2y4xfsn6m

Fuzzy uncertainty modeling for grid based localization of mobile robots

D. Herrero-Pérez, H. Martínez-Barberá, K. LeBlanc, A. Saffiotti
2010 International Journal of Approximate Reasoning  
This paper presents a localization method using fuzzy logic to represent the different facets of uncertainty present in sensor data.  ...  The main advantages of this fuzzy logic method compared to most current ones are: (i) only an approximate sensor model is required, (ii) several facets of location uncertainty can be represented, and (  ...  Acknowledgments This work has been supported by DPI2004-07993-C03-02 and DPI-2007-66556-C03-02 CICYT projects, from the Spanish Ministry of Science and Innovation, by the Swedish graduate school in computer  ... 
doi:10.1016/j.ijar.2010.06.001 fatcat:xtnpszp4wzgxlamyvb5asgw56u

Fuzzy Methods on the Web: A Critical Discussion [chapter]

Steven Schockaert, Nataliya Makarytska, Martine De Cock
2010 Studies in Fuzziness and Soft Computing  
In each case, we contrast fuzzy methods with other approaches, analyzing why and how the ideas of fuzzy set theory may be beneficial.  ...  In this chapter, we give an overview of applications of fuzzy set theory in this area, focusing in particular on information retrieval, the semantic web, and recommender systems.  ...  Tag clouds [94] , for instance, are little more than a fuzzy set of keywords.  ... 
doi:10.1007/978-3-642-16629-7_12 fatcat:hujd23mzc5e7bocbvggjdtfize

LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance System [article]

Pan Wei, Lucas Cagle, Tasmia Reza, John Ball, James Gafford
2018 arXiv   pre-print
In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets.  ...  Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics.  ...  Set P LT = ∅. for each point P j in the modified point cloud do if Point P j has intensity ≥ T H then Add P j to P HT . if Point P j has intensity ≥ T L then Add P j to P LT .  ... 
arXiv:1807.10573v1 fatcat:4enqszky7bcopmeslyfjknvzmq

LiDAR and Camera Detection Fusion in a Real-Time Industrial Multi-Sensor Collision Avoidance System

Pan Wei, Lucas Cagle, Tasmia Reza, John Ball, James Gafford
2018 Electronics  
In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets.  ...  Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics.  ...  This statement basically means a fuzzy set is the mathematical representation of linguistic variables. An example of a fuzzy set is "a class of tall students".  ... 
doi:10.3390/electronics7060084 fatcat:55glbvbddrdxbjfxvl5cbejisa

A Review on Evolving Interval and Fuzzy Granular Systems

Daniel Leite, Pyramo Costa Jr., Fernando Gomide
2016 Learning and Nonlinear Models  
Essential notions of interval analysis and fuzzy sets are addressed from the granular computing point of view.  ...  Evolving granular systems extend evolving intelligent systems allowing data, variables and parameters to be granules (intervals and fuzzy sets).  ...  In these cases, uncertainty in data representation may be useful to improve the quality of the results.  ... 
doi:10.21528/lnlm-vol14-no2-art3 fatcat:mw4xwirbsbg3ln2fciuovvlg6u
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