Relevant bibliographies by topics / Extended Fuzzy Rules

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Extended Fuzzy Rules'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Extended Fuzzy Rules"

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Intan, Rolly, Oviliani Yenty Yuliana, and Dwi Kristanto. "Bayesian Belief Network untuk Menghasilkan Fuzzy Association Rules." Jurnal Teknik Industri 12, no. 1 (2010): 55–60. http://dx.doi.org/10.9744/jti.12.1.55-60.

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Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels of secondary disease and complication disease are defined for a disease classification. The implement
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Chen, Guoqing, and Qiang Wei. "Fuzzy association rules and the extended mining algorithms." Information Sciences 147, no. 1-4 (2002): 201–28. http://dx.doi.org/10.1016/s0020-0255(02)00264-5.

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Shen, Qiang, and Longzhi Yang. "Generalisation of Scale and Move Transformation-Based Fuzzy Interpolation." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 3 (2011): 288–98. http://dx.doi.org/10.20965/jaciii.2011.p0288.

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Fuzzy interpolative reasoning has been extensively studied due to its ability to enhance the robustness of fuzzy systems and reduce system complexity. In particular, the scale and move transformation-based approach is able to handle interpolation with multiple antecedent rules involving triangular, complex polygon, Gaussian and bell-shaped fuzzy membership functions [1]. Also, this approach has been extended to deal with interpolation and extrapolation with multiple multi-antecedent rules [2]. However, the generalised extrapolation approach based on multiple rules may not degenerate back to th
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Wang, Yingxu. "Formal Rules for Fuzzy Causal Analyses and Fuzzy Inferences." International Journal of Software Science and Computational Intelligence 4, no. 4 (2012): 70–86. http://dx.doi.org/10.4018/jssci.2012100105.

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Causal inference is one of the central capabilities of the natural intelligence that plays a crucial role in thinking, perception, and problem solving. Fuzzy inferences are an extended form of formal inferences that provide a denotational mathematical means for rigorously dealing with degrees of matters, uncertainties, and vague semantics of linguistic variables, as well as for rational reasoning the semantics of fuzzy causalities. This paper presents a set of formal rules for causal analyses and fuzzy inferences such as those of deductive, inductive, abductive, and analogical inferences. Rule
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Chien, Been-Chian, Ming-Huang Zhong, and Jeng-Jung Wang. "Mining Fuzzy Association Rules on Has-A and Is-A Hierarchical Structures." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 4 (2007): 423–32. http://dx.doi.org/10.20965/jaciii.2007.p0423.

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Preliminary studies on data mining focus on finding association rules from transaction databases containing items without relationships among them. However, relationships among items often exist in real applications. Most of the previous works only concern about Is-A hierarchy. In this paper, hierarchical relationships include a Has-A hierarchy and multiple Is-A hierarchies are discussed. The proposed method first reduces a Has-A & Is-A hierarchy into an extended Has-A hierarchy using the IsA-Reduce algorithm. The quantitative data is transformed into fuzzy items. The RPFApriori algorithm
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CHEN, Yi-fei. "Extended fuzzy grouping queries in relational databases and derivation rules." Journal of Computer Applications 28, no. 5 (2008): 1177–79. http://dx.doi.org/10.3724/sp.j.1087.2008.01177.

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Chen, Shyi-Ming, Jyh-Sheng Ke, and Jin-Fu Chang. "AN INEXACT REASONING TECHNIQUE BASED ON EXTENDED FUZZY PRODUCTION RULES." Cybernetics and Systems 22, no. 2 (1991): 151–71. http://dx.doi.org/10.1080/01969729108902276.

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Salman, Nur, Mustikasari Mustikasari, and Muhammad Nur Akbar. "Penambangan Pengklasifiksi Fuzzy dengan Multiobjective Evolutionary Fuzzy Classifier." Journal Software, Hardware and Information Technology 2, no. 1 (2022): 59–65. http://dx.doi.org/10.24252/shift.v2i1.24.

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Classification is one of the key issues in the field of data mining and knowledge discovery. This paper implements a method of constructing a fuzzy rule mining classifier, which is extended in the context of classification. There are three stages of this approach: fuzzy rule set extraction, second; a linguistic labeling process that assigns a linguistic label to each fuzzy set. Owing to many attributes in the database, the feature selection process is also carried out, reducing the complexity to build the final classifier. Third: incorporate strategies to avoid rule redundancy and conflict int
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Alzubi, Maen, Mohammad Almseidin, Szilveszter Kovacs, Jamil Al-Sawwa, and Mouhammd Alkasassbeh. "EI-FRI: Extended Incircle Fuzzy Rule Interpolation for Multidimensional Antecedents, Multiple Fuzzy Rules, and Extrapolation Using Total Weight Measurement and Shift Ratio." Journal of Robotics and Control (JRC) 5, no. 1 (2024): 217–27. http://dx.doi.org/10.18196/jrc.v5i1.20515.

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Traditional fuzzy reasoning techniques demand a condensed fuzzy rule base to conclude a result. Still, due to incomplete data or a deficiency of expertise and knowledge, dense rule bases are not always available. Fuzzy interpolation methods have been widely explored to reasonably allow the interpolation of a fuzzy result using the closest current rules. Fuzzy rule interpolation is a type of fuzzy inference system in which conclusions can be obtained even with a few fuzzy rules. This benefit could be used to adapt the FRI to different application areas that suffer from a lack of knowledge. Alzu
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Di Martino, Ferdinando, and Salvatore Sessa. "A Method Based on Extended Fuzzy Transforms to Approximate Fuzzy Numbers in Mamdani Fuzzy Rule-Based System." Advances in Fuzzy Systems 2018 (September 12, 2018): 1–16. http://dx.doi.org/10.1155/2018/8458916.

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We propose a new Mamdani fuzzy rule-based system in which the fuzzy sets in the antecedents and consequents are assigned in a discrete set of points and approximated by using the extended inverse fuzzy transforms, whose components are calculated by verifying that the dataset is sufficiently dense with respect to the uniform fuzzy partition. We test our system in the problem of spatial analysis consisting in the evaluation of the livability of residential housings in all the municipalities of the district of Naples (Italy). Comparisons are done with the results obtained by using trapezoidal fuz
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Dissertations / Theses on the topic "Extended Fuzzy Rules"

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Wang, Chien-Hsin, and 王健行. "Extended Kalman Filter and Fuzzy Heuristic Rules in the application of GPS." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/88874412860004835596.

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碩士<br>龍華科技大學<br>電機工程研究所<br>99<br>How to provide a continuous vehicle location service without interruption, is an important issue. In order to meet the vehicle navigation system must be in at any time and place, able to provide positioning information. The paper by reading the information of GPS and OBDII, and use the Extended Kalman Filter with the Fuzzy Heuristic Rules to achieve DR.
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Tsai, Si-Ja, and 蔡思杰. "Efficiency Improvement of Extended Kalman Filter via Fuzzy Rule Base of Initial Conditions." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/18321074144929575900.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>91<br>In this thesis, we propose an algorithm for adaptive control of nonlinear static systems. As well known the Kalman filter are sensitive to initial conditions, and may not converge for all initial condition. In order to overcome this problem, the algorithm incorporates the Kalman filter with fuzzy rule base. And the fuzzy rule base is introduced to solve the problem that is caused by initial conditions. The rule base is automatically updated by some criteria of “good”initial conditions and using an unsupervised learning algorithm. Because the criteria of the ini
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Book chapters on the topic "Extended Fuzzy Rules"

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Dubois, Didier, and Henri Prade. "Bipolarity in Possibilistic Logic and Fuzzy Rules (Extended Abstract)." In SOFSEM 2002: Theory and Practice of Informatics. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36137-5_10.

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Alias, Muhammad A’rif Shah, Habibollah Haron, and Teresa Riesgo. "Fuzzy Rules Enhancement of CHEF to Extend Network Lifetime." In Proceedings of the Second International Conference on the Future of ASEAN (ICoFA) 2017 – Volume 2. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8471-3_39.

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Afsharinafar, Reza, Fudziah Ismail, Mohamed Suleiman, and Ali Ahmadian Hosseini. "Extended Simpson Rule for Solving First-Order Fuzzy Differential Equations Using Generalized Differentiability." In International Conference on Mathematical Sciences and Statistics 2013. Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-4585-33-0_10.

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Porebski, Sebastian, and Ewa Straszecka. "Diagnostic Rule Extraction Using the Dempster-Shafer Theory Extended for Fuzzy Focal Elements." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59162-9_7.

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Datta, Niladri Sekhar, Himadri Sekhar Dutta, and Koushik Majumder. "Application of Fuzzy Logic and Fuzzy Optimization Techniques in Medical Image Processing." In Biometrics. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch036.

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Fuzzy logic deals with approximate rather than fixed and exact reasoning. Fuzzy variables may have a truth value that ranges in degree between 0 and 1; extended to handle the concept of partial truth where the truth value may range between completely true or completely false. This computational logic uses truth degrees as a mathematical model of the vagueness phenomenon while probability is a mathematical model of ignorance. A huge number of complex problems may be solve using Fuzzy logic specifically Fuzzy modeling and optimization method. Fuzzy modeling is the understanding of the problem an
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Datta, Niladri Sekhar, Himadri Sekhar Dutta, and Koushik Majumder. "Application of Fuzzy Logic and Fuzzy Optimization Techniques in Medical Image Processing." In Handbook of Research on Natural Computing for Optimization Problems. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0058-2.ch033.

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Fuzzy logic deals with approximate rather than fixed and exact reasoning. Fuzzy variables may have a truth value that ranges in degree between 0 and 1; extended to handle the concept of partial truth where the truth value may range between completely true or completely false. This computational logic uses truth degrees as a mathematical model of the vagueness phenomenon while probability is a mathematical model of ignorance. A huge number of complex problems may be solve using Fuzzy logic specifically Fuzzy modeling and optimization method. Fuzzy modeling is the understanding of the problem an
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Hsairi, Lobna, Khaled Ghédira, Adel M. Alim, and Abdellatif BenAbdelhafid. "R2-IBN." In Open Information Management. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-246-6.ch006.

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In the age of information proliferation, openness, open information management, interconnectivity, collaboration and communication advances, extended enterprises must be up to date to the new strategic, economic and organizational structures. Consequently, intelligent software based on agent technology emerges to improve system design, and to increase enterprise competitive position as well. The competitiveness is based on the information management, cooperation, collaboration and interconnectivity. Thus, within these interconnectivity and cooperation, conflicts may arise. The automated negoti
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Fred Ana, Filipe Joaquim, Partinen Markku, and Paiva Teresa. "Chapter 3.3 - PSG-EXPERT An Expert System for the Diagnosis of Sleep Disorders." In Studies in Health Technology and Informatics. IOS Press, 2000. https://doi.org/10.3233/978-1-60750-922-6-127.

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This paper describes PSG-EXPERT, an expert system in the domain of sleep disorders exploring polysomnographic data. The developed software tool is addressed from two points of view: (1)- as an integrated environment for the development of diagnosis-oriented expert systems; (2)- as an auxiliary diagnosis tool in the particular domain of sleep disorders. Developed over a Windows platform, this software tool extends one of the most popular shells &amp;mdash; CLIPS (C Language Integrated Production System) with the following features: backward chaining engine; graph-based explanation facilities; k
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Sutton, Rebecca. "What is the distinction?" In The Humanitarian Civilian. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198863816.003.0002.

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Chapter 2 introduces, but does not resolve, the conundrum of what the idea of distinction is that is circulating in international practices and how it relates to the IHL targeting rule. The main point conveyed is that distinction in the conduct of hostilities sense is in play, but that it is being deployed in ways we might not expect. Actors routinely move the line to assert intra-civilian distinctions, and collapse IHL’s principle of distinction together with other values, ideas and principles to compose the ‘distinction vernacular’. The muddling of distinction is easier to observe in the tra
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Conference papers on the topic "Extended Fuzzy Rules"

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Pazos-Revilla, Marbin, Terry Guo, and Motoya Machida. "Extended Kalman Filter combined with fuzzy rules for localization using wireless transceivers." In 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS). IEEE, 2016. http://dx.doi.org/10.1109/nafips.2016.7851578.

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Otsuka, Junji, and Tomoharu Nagao. "Flexible design of image classification rules using extended Fuzzy Oriented Classifier Evolution." In 2012 Joint 6th Intl. Conference on Soft Computing and Intelligent Systems (SCIS) and 13th Intl. Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2012. http://dx.doi.org/10.1109/scis-isis.2012.6505082.

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Hüsener, Dominik, and Jürgen Roßmann. "Integrating Rule Based Expert Systems into a Simulation framework for digital twins." In 10th International Conference on Human Interaction and Emerging Technologies (IHIET 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004066.

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The loss of expert knowledge due to experienced workers retiring in the coming years poses a significant challenge. To address this, we proposed to digitize and preserve this knowledge using the digital twin concept. Traditionally, expert systems have been used to emulate human experts. Rule-based expert systems, such as Prolog and CLIPS (C Language Integrated Production Systems), are one class of expert systems. Fuzzy CLIPS is an extension to CLIPS that can handle Fuzzy logic. Compared to machine learning techniques such as neural networks, rule-based expert systems are more reliable and can
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Zomorodi-Moghadam, Hesam, Mahdi Haghshenas-Jaryani, and Farzam Farahmand. "A Supervisory Fuzzy-PID Controller for a MIMO Biped Robot Balance in Frontal Plane." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67118.

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In this paper we propose to control a bipedal robot in an unstable position by means of a PID controller that gains are turned by a fuzzy logic system. For that, a model of planar 3 linked segment consisting of limb, trunk and extended arms with fixed base is used. Fuzzy if-then rules are constructed based on human expert knowledge and biomechanics studies for tuning of PID’s gain. For construction of tuning rules, we have developed an optical measuring system to record experimental data of balance keeping of a human in an unstable position. The control model is based on three sets of differen
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Pitsikalis, Manolis, Thanh-Toan Do, Alexei Lisitsa, and Shan Luo. "Logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification (Extended Abstract)." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/744.

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The shipping industry is an important component of the global trade and economy. In order to ensure law compliance and safety, it needs to be monitored. In this paper, we present a novel ship type classification model that combines vessel transmitted data from the Automatic Identification System, with vessel imagery. The main components of our approach are the Faster R-CNN Deep Neural Network and a Neuro-Fuzzy system with IF-THEN rules. We evaluate our model using real world data and showcase the advantages of this combination while also compare it with other methods. Results show that our mod
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Salar, Amin, Ali Khaki Sedigh, SeyedMehrdad Hosseini, and Hiwa Khaledi. "A Hybrid EKF-Fuzzy Approach to Fault Detection and Isolation of Industrial Gas Turbines." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-45878.

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Based on the Gas Path Analysis (GPA) method, nonlinear estimation and fuzzy classification theories, a comprehensive fault diagnosis system has been developed for an industrial Gas Turbine (GT). The hybrid method consists of two parts, in the first part noisy sensor output changes are translated to changes in the health parameters using an Extended Kalman Filter (EKF). In the second part the outputs of the EKF are used as the inputs of a fuzzy system. This system can isolate and evaluate the physical faults based on the predetermined rules obtained mostly from experimental data and aerothermod
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Rusa´k, Zolta´n, Imre Horva´th, Gyo¨rgy Kuczogi, and Evren Akar. "Shape Instance Generation From Domain Distributed Vague Models." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/cie-34496.

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Representing the possible shapes of a cluster of objects by a variation domain, vague discrete modeling is a powerful method for describing non-nominal shapes. A vague discrete model (VDM) is vague in the sense that multiple objects are represented by one domain model, and that multiple shape instances can be generated based on certain instantiation rules. The vaguely defined particles contained in a VDM provide a discrete representation that supports multi-resolution manipulation of shapes. This paper presents the latest results related to deriving instance shapes from a given VDM. The proble
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Intan, Rolly, and Noboru Takagi. "Extended concept of logic minimization for rule reduction." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584521.

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Sebaaly, Milad Fares, and Hideo Fujimoto. "Automatic Assembly Sequence Planning With Fuzzy Graph Representation and Genetic Search." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/cie-1322.

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Abstract The Assembly Sequence Planning (ASP) problem is a complicated task that is still performed manually in most advanced industries. It consists of finding the best or optimal sequence to assemble a certain product, given its CAD design. Although it seems simple at first, its complexity drastically increases with increasing numbers of product parts, so that complexity is very high for most actual industrial products. Many ASP planners have been developed by researchers to automate this problem, but most of these are not practical and general enough to deal with actual industrial products.
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Shahidi, Parham, Steve C. Southward, and Mehdi Ahmadian. "Fuzzy Analysis of Speech Metrics to Estimate Conductor Alertness." In ASME 2009 Rail Transportation Division Fall Technical Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/rtdf2009-18031.

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A Fuzzy Logic-based algorithm has been developed for processing a series of speech metrics with the ultimate goal of estimating train conductor alertness. The output is a single metric, which directly quantifies the alertness level of the conductor. The metrics were selected based on their correlation to alertness through processed speech, but without any interpretation of the spoken words or phrases. Metrics that are used include: speech duration, silence duration, word production rate and word intensity. The assessment of these metrics is an experience and human knowledge based task, which g
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