Journal articles: 'Mechanical reasoning'

1

Hegarty, Mary. "Mechanical reasoning by mental simulation." Trends in Cognitive Sciences 8, no. 6 (June 2004): 280–85. http://dx.doi.org/10.1016/j.tics.2004.04.001.

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Wilson, Randall H., and Jean-Claude Latombe. "Geometric reasoning about mechanical assembly." Artificial Intelligence 71, no. 2 (December 1994): 371–96. http://dx.doi.org/10.1016/0004-3702(94)90048-5.

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Injoque-Ricle, Irene, Jésica Formoso, Alejandra Calero, and Guido Caruso. "Razonamiento mecánico, memoria de trabajo y velocidad de procesamiento." Liberabit: Revista Peruana de Psicología 25, no. 1 (June 27, 2019): 71–84. http://dx.doi.org/10.24265/liberabit.2019.v25n1.06.

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Bardasz, Theodore, and Ibrahim Zeid. "DEJAVU: Case-based reasoning for mechanical design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 7, no. 2 (May 1993): 111–24. http://dx.doi.org/10.1017/s0890060400000809.

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The architecture and implementation of a mechanical designer's assistant shell called DEJAVU is presented. The architecture is based on an integration of design and CAD with some of the more well known concepts in case-based reasoning (CBR). DEJAVU provides a flexible and cognitively intuitive approach for acquiring and utilizing design knowledge. It is a domain independent mechanical design shell that can incrementally acquire design knowledge in the domain of the user. DEJAVU provides a design environment that can learn from the designer(s) until it can begin to perform design tasks autonomously or semi-autonomously. The main components of DEJAVU are a knowledge base of design plans, an evaluation module in the form of a design plan system, and a blackboard-based adaptation module. The existance of these components are derived from the utilization of a CBR architecture. DEJAVU is the first step in developing a robust designer's assistant shell for mechanical design problems. One of the major contributions of DEJAVU is the development of a clean architecture for the utilization of case-based reasoning in a mechanical designer's assistant shell. In addition, the components of the architecture have been developed, tailored or modified from a general CBR context into a more synergistic relationship with mechanical design.

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Zeyda, Frank, and Ana Cavalcanti. "Mechanical reasoning about families of UTP theories." Science of Computer Programming 77, no. 4 (April 2012): 444–79. http://dx.doi.org/10.1016/j.scico.2010.02.010.

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Zeyda, Frank, and Ana Cavalcanti. "Mechanical Reasoning about Families of UTP Theories." Electronic Notes in Theoretical Computer Science 240 (July 2009): 239–57. http://dx.doi.org/10.1016/j.entcs.2009.05.055.

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Fusaoka, Akira, and Kazuko Takahashi. "On a mechanical reasoning about causal relations." Artificial Intelligence in Engineering 1, no. 1 (July 1986): 15–22. http://dx.doi.org/10.1016/0954-1810(86)90030-0.

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Joskowicz, Leo. "Reasoning about the kinematics of mechanical devices." Artificial Intelligence in Engineering 4, no. 1 (January 1989): 22–31. http://dx.doi.org/10.1016/0954-1810(89)90022-8.

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Hall, D. L., R. J. Hansen, and D. C. Lang. "The Negative Information Problem in Mechanical Diagnostics." Journal of Engineering for Gas Turbines and Power 119, no. 2 (April 1, 1997): 370–77. http://dx.doi.org/10.1115/1.2815584.

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Condition-based maintenance (CBM) is an emerging technology, which seeks to develop sensors and processing systems aimed at monitoring the operation of complex machinery such as turbine engines, rotor craft drivetrains, and industrial equipment. The goal of CBM systems is to determine the state of the equipment (i.e., the mechanical health and status), and to predict the remaining useful life for the system being monitored. The success of such systems depends upon a number of factors, including: (1) the ability to design or use robust sensors for measuring relevant phenomena such as vibration, acoustic spectra, infrared emissions, oil debris, etc.; (2) real-time processing of the sensor data to extract useful information (such as features or data characteristics) in a noisy environment and to detect parametric changes that might be indicative of impending failure conditions; (3) fusion of multi-sensor data to obtain improved information beyond that available to a single sensor; (4) micro and macro level models, which predict the temporal evolution of failure phenomena; and finally, (5) the capability to perform automated approximate reasoning to interpret the results of the sensor measurements, processed data, and model predictions in the context of an operational environment. The latter capability is the focus of this paper. Although numerous techniques have emerged from the discipline of artificial intelligence for automated reasoning (e.g., rule-based expert systems, blackboard systems, case-based reasoning, neural networks, etc.), none of these techniques are able to satisfy all of the requirements for reasoning about condition-based maintenance. This paper provides an assessment of automated reasoning techniques for CBM and identifies a particular problem for CBM, namely, the ability to reason with negative information (viz., data which by their absence are indicative of mechanical status and health). A general architecture is introduced for CBM automated reasoning, which hierarchically combines implicit and explicit reasoning techniques. Initial experiments with fuzzy logic are also described.

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Kannapan, Srikanth M., and Kurt M. Marshek. "Design synthetic reasoning." Mechanism and Machine Theory 26, no. 7 (January 1991): 711–39. http://dx.doi.org/10.1016/0094-114x(91)90033-z.

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Hegarty, Mary, and Valerie K. Sims. "Individual differences in mental animation during mechanical reasoning." Memory & Cognition 22, no. 4 (July 1994): 411–30. http://dx.doi.org/10.3758/bf03200867.

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Lindsay, Peter A. "A survey of mechanical support for formal reasoning." Software Engineering Journal 3, no. 1 (1988): 3. http://dx.doi.org/10.1049/sej.1988.0002.

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Kannapan, Srikanth M., and Kurt M. Marshek. "Design synthetic reasoning: A methodology for mechanical design." Research in Engineering Design 2, no. 4 (December 1991): 221–38. http://dx.doi.org/10.1007/bf01579219.

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Soo, Von-Wun, and Tse-Ching Wang. "Integration of qualitative and quantitative reasoning in iterative parametric mechanical design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 6, no. 2 (May 1992): 95–109. http://dx.doi.org/10.1017/s0890060400002985.

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A framework IPD (Iterative Parametric Design) is proposed to assist the iterative parametric mechanical design process. To effectively find a set of satisfiable values for the design parameters the key is to find good heuristics to adjust or tune the parametric values resulting from previous design iterations. We propose that heuristics can come from two aspects by both qualitative and quantitative reasoning. Qualitative reasoning, based on confluences, provides global control over the feasible directions of variable adjustments, while quantitative reasoning, based on the dependency network and perturbation analysis, can be used to propose actual quantity of local variable adjustments. We used the design of a helical compression spring as an example to illustrate the performance of IPD system. We show that IPD can often find a solution faster than those without guidance of qualitative and quantitative reasoning.

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OHKI, Masaru. "Qualitative reasoning and its application." Journal of the Japan Society for Precision Engineering 54, no. 8 (1988): 1418–22. http://dx.doi.org/10.2493/jjspe.54.1418.

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Maurines, L. "Spontaneous reasoning on the propagation of visible mechanical signals." International Journal of Science Education 14, no. 3 (July 1992): 279–93. http://dx.doi.org/10.1080/0950069920140305.

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McKenna, Ann F., and Alice M. Agogino. "Supporting Mechanical Reasoning with a Representationally-Rich Learning Environment." Journal of Engineering Education 93, no. 2 (April 2004): 97–104. http://dx.doi.org/10.1002/j.2168-9830.2004.tb00794.x.

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Wu, Huizhong, Fang Wang, Xia Zhang, and Nian He. "A model of inexact reasoning in mechanical design evaluation." Artificial Intelligence in Engineering 10, no. 4 (November 1996): 357–62. http://dx.doi.org/10.1016/0954-1810(96)00005-2.

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Tselishchev, Vitaly V. "Mathematical Reasoning." Epistemology & Philosophy of Science 57, no. 4 (2020): 74–86. http://dx.doi.org/10.5840/eps202057459.

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The article is devoted to the comparison of two types of proofs in mathematical practice, the methodological differences of which go back to the difference in the understanding of the nature of mathematics by Descartes and Leibniz. In modern philosophy of mathematics, we talk about conceptual and formal proofs in connection with the so-called Hilbert Thesis, according to which every proof can be transformed into a logical conclusion in a suitable formal system. The analysis of the arguments of the proponents and opponents of the Thesis, “conceptualists” and “formalists”, is presented respectively by the two main antagonists – Y. Rav and J. Azzouni. The focus is on the possibility of reproducing the proof of “interesting” mathematical theorems in the form of a strict logical conclusion, in principle feasible by a mechanical procedure. The argument of conceptualists is based on pointing out the importance of other aspects of the proof besides the logical conclusion, namely, in introducing new concepts, methods, and establishing connections between different sections of meaningful mathematics, which is often illustrated by the case of proving Fermat’s Last Theorem (Y. Rav). Formalists say that a conceptual proof “points” to the formal logical structure of the proof (J. Azzouni). The article shows that the disagreement is based on the assumption of asymmetry of mutual translation of syntactic and semantic structures of the language, as a result of which the formal proof loses important semantic factors of proof. In favor of a formal proof, the program of univalent foundations of mathematics In. Vojevodski, according to which the future of mathematical proofs is associated with the availability of computer verification programs. In favor of conceptual proofs, it is stated (A. Pelc) that the number of steps in the supposed formal logical conclusion when proving an “interesting” theorem exceeds the cognitive abilities of a person. The latter circumstance leads the controversy beyond the actual topic of mathematical proof into the epistemological sphere of discussions of “mentalists” and “mechanists” on the question of the supposed superiority of human intelligence over the machine, initiated by R. Penrose in his interpretation of the Second Theorem of Goedel, among whose supporters, as it turned out, was Goedel himself.

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Sii, H. S., J. Wang, A. G. Eleye-Datubo, J. B. Yang, and J. Liu. "Safety Assessment of FPSO Turret-Mooring System Using Approximate Reasoning and Evidential Reasoning." Marine Technology and SNAME News 42, no. 02 (April 1, 2005): 88–102. http://dx.doi.org/10.5957/mt1.2005.42.2.88.

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Numerous acts of Parliament and statutory instruments that apply to floating production, storage, and offloading (FPSO) developments in the United Kingdom Continental Shelf, covering a wide range of issues, including health, technical safety, work place safety, lifting operations, environmental protection, and pollution prevention and control, are described. A comprehensive study of system safety evaluation of a typical turret-mooring system used on FPSOs is described in this paper. A safety assessment method suggested using approximate reasoning and evidential reasoning approaches is proposed in this study. Subjective safety modeling at the bottom level in a hierarchical framework is carried out using an approximate reasoning approach. The evidential reasoning method is used to combine or aggregate safety estimates at lower levels to produce the safety estimate at the system level. The four main subsystems(turret, fluid transfer system, turret transfer system, and interfacing system) are thoroughly examined in order to perform a subjective safety assessment of the turret-mooring system.

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Jehiel, Philippe, and Larry Samuelson. "Reputation with Analogical Reasoning*." Quarterly Journal of Economics 127, no. 4 (November 1, 2012): 1927–69. http://dx.doi.org/10.1093/qje/qjs031.

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Abstract We consider a repeated interaction between a long-run player and a sequence of short-run players, in which the long-run player may either be rational or may be a mechanical type who plays the same (possibly mixed) action in every stage game. We depart from the classical model in assuming that the short-run players make inferences by analogical reasoning, meaning that they correctly identify the average strategy of each type of long-run player, but do not recognize how this play varies across histories. Concentrating on 2 × 2 games, we provide a characterization of equilibrium payoffs, establishing a payoff bound for the rational long-run player that can be strictly larger than the familiar “Stackelberg” bound. We also provide a characterization of equilibrium behavior, showing that play begins with either a reputation-building or a reputation-spending stage (depending on parameters), followed by a reputation-manipulation stage.

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Julià, Carme, and Juan Òscar Antolì. "Enhancing spatial ability and mechanical reasoning through a STEM course." International Journal of Technology and Design Education 28, no. 4 (September 27, 2017): 957–83. http://dx.doi.org/10.1007/s10798-017-9428-x.

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Rosen, David W., and Thomas J. Peters. "Special issue: Advances in representation and reasoning for mechanical CAD." Research in Engineering Design 5, no. 3-4 (September 1993): 123–24. http://dx.doi.org/10.1007/bf01608358.

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Šlekienė, Violeta, and Loreta Ragulienė. "REALIZATION OF VISUAL PRINCIPLE USING MECHANICS DEMONSTRATIONS OF GRADE 11 IN PHYSICS EDUCATION." GAMTAMOKSLINIS UGDYMAS / NATURAL SCIENCE EDUCATION 6, no. 3 (December 5, 2009): 29–37. http://dx.doi.org/10.48127/gu-nse/09.6.29b.

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This article reveals the importance of the visual principle in physics education process and its im-plementation using mechanics demonstrations. Visual principle is a guiding principle of educational activi-ties in selecting the contents and the methods of teaching. Physics is an experimental science. Students can successfully master the basics of Physics provided the source of knowledge is a physical experiment based on the visual didactical principle. To optimize the teaching process it is necessary to visualize phenomena, processes or objects. Visualization is significant for problem solving in research and teaching. Physics demonstrations inure to this aim very well. Physics demonstration experiments illustrating mechanical phe-nomena for grade XI are presented and analyzed. The most of mechanical phenomena are characterized by different kinds of forces and energy. The relationships between them are showed by demonstration experi-ments. Demonstration experiments for determining the coefficient of friction, the body weight change of accelerating falling and the potential energy minimum principle are discussed. The place of the demonstra-tions and the possibilities of applying them during physics teaching are analyzed. Reasoning sequences for giving a logical sense to these physics demonstrations are introduced. These sequences have been based on the system of specially thinking schemes and prepared as a guide determining the steady movement toward a correct result. The demonstration and its reasoning sequence enable the pupils to understand essence of new subject, to colligate, to make conclusions. Reasoning sequences, prepared for demonstration experi-ments are efficient in training pupils’ way of thinking. Key words: visual principle, mechanical phenomena, force, energy, physics demonstration experiment, reasoning sequence, logical sense.

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Li, Jing, Xiang Jun Zou, Hong Jun Wang, and Yan Chen. "A Method for Knowledge Reasoning of Mechanical Product Intelligent Design Using Information Entropy." Key Engineering Materials 522 (August 2012): 313–18. http://dx.doi.org/10.4028/www.scientific.net/kem.522.313.

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Because of the universality, complexity and uncertainty of mechanical design knowledge, knowledge reasoning and integration of intelligent design system was one of the difficult problems. The valuation of weight by human was used in many methods for knowledge reasoning at present. In order to make the intelligent product design become more scientific and rational, and eliminate the subjective human factors, a method for reasoning using information entropy was proposed. First, by means of the advantage of the relational database include the redundancy, consistency and integrity, the mechanical product design knowledge base was established. Second, based on the information entropy theory, the weight of each attribute was calculated directly through the objective information. Third, based on the objective data in the original sample, the method for calculating the similarity between accurate attributes and uncertainty attributes and design schemes was given. Finally, this method was verified by fruit picking robot intelligent design system, and the result showed that it is objective and effective.

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Dixon, John R., and Clive L. Dym. "Artificial Intelligence and Geometric Reasoning in Manufacturing Technology." Applied Mechanics Reviews 39, no. 9 (September 1, 1986): 1325–30. http://dx.doi.org/10.1115/1.3149521.

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This article presents a brief review of the current literature on the applications of artificial intelligence (AI) technologies, and especially expert (knowledge-based) systems, to manufacturing. Emphasis is placed on geometric representation and reasoning in design as an aid to manufacturing. Also discussed are applications of AI to process planning and design, process control, assembly, and other phases of manufacturing.

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Tang, L. "Many-Degree-Of-Freedom Mechanism Combination in Automated Conceptual Design of Mechanism Systems." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 220, no. 5 (May 1, 2006): 727–40. http://dx.doi.org/10.1243/09544062jmes222.

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This article presents the mechanism combination's modes, fundamentals, reasoning rules, and realization methods in the automated conceptual design of mechanism systems. According to the combination form between two function units, with the aid of morphology matrix, applying a recursive mode of mechanism combination and the reasoning system based on set theory, the function units' solutions in the form of combination mechanisms can conveniently be generated. The theory and method presented in this article can simplify the process of mechanism combination and the reasoning rules and are suitable for the combination between not only single-degree-of-freedom mechanisms, but also many-degree-of-freedom mechanisms. They have successfully been applied to the software development of automated conceptual design of mechanism systems.

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Zeinali, Meysar, and Leila Notash. "SYSTEMATIC ADAPTIVE FUZZY LOGIC MODELLING OF COMPLEX SYSTEMS FROM INPUT-OUTPUT DATA." Transactions of the Canadian Society for Mechanical Engineering 29, no. 4 (December 2005): 569–80. http://dx.doi.org/10.1139/tcsme-2005-0036.

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The complex nonlinear systems, which are difficult to be mathematically modelled, can be described by a fuzzy model. This article attempts to improve and to address the problems concerning the systematic fuzzy-logic modelling of multi-input-muiti-output (MIMO) systems, by introducing the following three concepts. 1) A generalized and parameterized reasoning mechanism constructed based on the weighted sum of the normalized defuzzified output value of each individual rule. Then the crisp outputs of the fuzzy model can be directly calculated from the crisp inputs using the parameterized reasoning mechanism. This reasoning mechanism is suitable for online learning and real-time control applications. 2) A gradient-descent based parameter adjustment to tune the parameters of reasoning mechanism (which are equal to the number of rules) instead of the existing heuristic complex parameter identification in the literature. 3) An improved method to select the main system input from all input candidates in the presence of singularity. The proposed systematic method of fuzzy modelling has the advantages of simplicity, flexibility, and high accuracy. The two example data, which have been widely used in the literature as benchmark, are used to evaluate the performance of the proposed method.

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Dear, Peter. "Divine Illumination, Mechanical Calculators, and the Roots of Modern Reason." Science in Context 23, no. 3 (July 30, 2010): 351–66. http://dx.doi.org/10.1017/s0269889710000116.

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ArgumentTalk of “reason” and “rationality” has been perennial in the philosophy and sciences of the European, Latin tradition since antiquity. But the use of these terms in the early-modern period has left especial marks on the specialties and disciplines that emerged as components of “science” in the modern world. By examining discussions by seventeenth-century philosophers, including natural philosophers such as Descartes, Pascal, and Hobbes, the practical meanings of, specifically, inferential reasoning can be seen as reducing, for most, to intellectual processes deriving from foundations that required intuitional insight that was owing to God. Mechanical reasoning, or artificial intelligence, was a contradiction in terms for such as Pascal, whose views of his own arithmetical machine illustrate the issue well. Hobbes’ analysis of reason, however, replaced the ineffable authority of God with the authority of the civil power, to reveal the social reality of “reason” as nothing other than authorized judgment.

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KARA, LEVENT BURAK, and THOMAS F. STAHOVICH. "Causal reasoning using geometric analysis." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 5 (November 2002): 363–84. http://dx.doi.org/10.1017/s0890060402165036.

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We describe an approach that uses causal and geometric reasoning to construct explanations for the purposes of the geometric features on the parts of a mechanical device. To identify the purpose of a feature, the device is simulated with and without the feature. The simulations are then translated into a “causal-process” representation, which allows qualitatively important differences to be identified. These differences reveal the behaviors caused and prevented by the feature and thus provide useful cues about the feature's purpose. A clear understanding of the feature's purpose, however, requires a detailed analysis of the causal connections between the caused and prevented behaviors. This presents a significant challenge because one has to understand how a behavior that normally takes place affects (or is affected by) another behavior that is normally absent. This article describes techniques for identifying such elusive relationships. These techniques employ a set of rules that can determine if one behavior enables or disables another that is spatially and temporally far away. They do so by geometrically examining the traces of the causal processes in the device's configuration space. Using the results of this analysis, our program can automatically generate text output describing how the feature performs its function.

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Callan, Mitchell J., Joshua Moreton, and Gethin Hughes. "Immanent Justice Reasoning by Spatial Proximity." Social Psychological and Personality Science 12, no. 1 (February 19, 2020): 25–33. http://dx.doi.org/10.1177/1948550619893969.

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Immanent justice reasoning involves causally attributing someone’s bad outcome to their prior immoral actions. Building on the idea that causality is mentally linked with spatial proximity, we investigated whether such reasoning might lead participants to spatially bind together immoral actions and bad outcomes. Across four experiments ( N = 553, Mechanical Turk workers), participants positioned sentences describing other people’s bad (vs. good) outcomes closer in space to previous immoral behaviors. This effect was observed both when the position of the initial action remained in a fixed location and when it “chased” the outcome across the screen. Importantly, we also found that this spatial positioning of immoral actions and bad outcomes is mediated by perceived deservingness of the outcome and is not merely due to perceived similarity of events. These findings suggest that perceived deservingness biases the spatial proximity of representations of others’ random bad outcomes and their prior immoral actions.

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Long, Xinjiani, Haitao Li, Wen Ren, Yuefeng Du, Enrong Mao, and Ning Ding. "A parameter-extended case-based reasoning method based on a functional basis for automated experiential reasoning in mechanical product designs." Advanced Engineering Informatics 50 (October 2021): 101409. http://dx.doi.org/10.1016/j.aei.2021.101409.

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Sivard, Gunilla, Lars Lindberg, and Erik Agerman. "Customer-Based Design with Constraint Reasoning." CIRP Annals - Manufacturing Technology 42, no. 1 (January 1993): 139–42. http://dx.doi.org/10.1016/s0007-8506(07)62411-1.

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SHIMOMURA, Yoshiki, Yasushi UMEDA, Tetsuo TOMIYAMA, and Hiroyuki YOSHIKAWA. "An Application of Case Based Reasoning to Maintenance Strategy." Journal of the Japan Society for Precision Engineering 59, no. 1 (1993): 77–82. http://dx.doi.org/10.2493/jjspe.59.77.

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An, M., W. Lin, and A. Stirling. "Fuzzy-Reasoning-Based Approach to Qualitative Railway Risk Assessment." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 220, no. 2 (March 2006): 153–67. http://dx.doi.org/10.1243/09544097jrrt34.

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Li, Kai, Zhen-Zhen Yi, Wei Xu, Ke Zhao, and Lin Wang. "Research on SDG-Based Qualitative Reasoning in Conceptual Design." Advances in Mechanical Engineering 5 (January 2013): 816438. http://dx.doi.org/10.1155/2013/816438.

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Wei, Yaobing, Xin Wang, Yuanyuan Kong, and Changfeng Yan. "A probability uncertainty method of fault classification for steam turbine generator set based on Bayes and Holospectrum." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 20 (August 9, 2016): 3767–76. http://dx.doi.org/10.1177/0954406215616146.

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With the rapid development of the machinery and the increasing complexity of the steam turbine generator set, it is a great challenge for the safe and reliable operation of the steam turbine generator set. The uncertainties of fault classification and complicated working conditions become important research fields of steam turbine generator. A probability method on the uncertainty reasoning of fault classification for steam turbine generator is proposed in this paper based on the 2D-holospectrum and Bayesian decision theory. Firstly, Bayesian decision theory is adopted for the preliminary fault estimation on actual risk loss by calculating the loss expectation of each decision. Then, the area ratio of overlap region in 2D-holospectrum and the evidence theory can give the probability of the fault. Framework and model of the uncertainty reasoning are also described in this paper. Finally, the model is verified by the experiment of the rotor vibration on test rig. The results show that the method proposed is feasible for reasoning under imperfect information condition.

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Zhang, Yi, Zongbin Li, Jianmin Gao, Jun Hong, Francesco Villecco, and Yunlong Li. "A Method for Designing Assembly Tolerance Networks of Mechanical Assemblies." Mathematical Problems in Engineering 2012 (2012): 1–26. http://dx.doi.org/10.1155/2012/513958.

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When designing mechanical assemblies, assembly tolerance design is an important issue which must be seriously considered by designers. Assembly tolerances reflect functional requirements of assembling, which can be used to control assembling qualities and production costs. This paper proposes a new method for designing assembly tolerance networks of mechanical assemblies. The method establishes the assembly structure tree model of an assembly based on its product structure tree model. On this basis, assembly information model and assembly relation model are set up based on polychromatic sets (PS) theory. According to the two models, the systems of location relation equations and interference relation equations are established. Then, using methods of topologically related surfaces (TTRS) theory and variational geometric constraints (VGC) theory, three VGC reasoning matrices are constructed. According to corresponding relations between VGCs and assembly tolerance types, the reasoning matrices of tolerance types are also established by using contour matrices of PS. Finally, an exemplary product is used to construct its assembly tolerance networks and meanwhile to verify the feasibility and effectiveness of the proposed method.

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Hegarty, Mary, and Kathryn Steinhoff. "Individual differences in use of diagrams as external memory in mechanical reasoning." Learning and Individual Differences 9, no. 1 (January 1997): 19–42. http://dx.doi.org/10.1016/s1041-6080(97)90018-2.

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Tor, S. B., G. A. Britton, W. Y. Zhang, and Y. M. Deng. "Guiding functional design of mechanical products through rule-based causal behavioural reasoning." International Journal of Production Research 40, no. 3 (January 2002): 667–82. http://dx.doi.org/10.1080/00207540110090957.

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Guo, Chen, Ming Huang, and Xu Liang. "Uncertainy Reasoning Research Based on the Expert System of Mechanical Product Design." Journal of Applied Sciences 13, no. 21 (October 15, 2013): 4902–6. http://dx.doi.org/10.3923/jas.2013.4902.4906.

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Hegarty, Mary, Marcel Adam Just, and Ian R. Morrison. "Mental models of mechanical systems: Individual differences in qualitative and quantitative reasoning." Cognitive Psychology 20, no. 2 (April 1988): 191–236. http://dx.doi.org/10.1016/0010-0285(88)90019-9.

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Liu, De Fang, Bin Wang, and Hong Pan Wu. "Mechanical-Design-Oriented Knowledge-Driven Model." Applied Mechanics and Materials 44-47 (December 2010): 1987–90. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1987.

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According to the characters of mechanical product design, product design knowledge is classified into explicit knowledge and indefinite knowledge. A knowledge-driven product design system model was proposed based on the knowledge management. To meet the normal product design process, the design system structure was built on four layers. A mixed knowledge reasoning strategy was proposed, which is combined by design cases, models, and rules. The system provides a public integration interface, so different design tools such as UG NX, Catia and Pro-E can be applied. To resolve the design conflict in product design process, a collaborative design technique was put forward that the CAD, CAPP, CAM engineers worked togeth

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Zou, Jiehui, and Qungui Du. "A Functional Reasoning Cube Model for Conceptual Design of Mechatronic Systems." Strojniški vestnik – Journal of Mechanical Engineering 59, no. 5 (May 15, 2013): 323–32. http://dx.doi.org/10.5545/sv-jme.2012.757.

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Shpitalni, M., and H. Lipson. "Automatic Reasoning for Design under Geometrical Constraints." CIRP Annals 46, no. 1 (1997): 85–88. http://dx.doi.org/10.1016/s0007-8506(07)60781-1.

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Huang, Xiao Ci. "Research on Reasoning Method of Diagnosis for the Electronic Engine in Fault Knowledge." Advanced Materials Research 1023 (August 2014): 154–59. http://dx.doi.org/10.4028/www.scientific.net/amr.1023.154.

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Vehicle is a complicated mechanical system. Its fault often caused by the interactions between the hidden deep reason and multiple systems. Our work is form the electronic engine diagnosis reasoning knowledge to solve the problems of such faults. In order to make fault process and fault concept clearly, constraint relation and causal chain between fault and symptom are introduced. Problems solved including the binding interaction between ontologies, transfer process from the failure to of the final symptom, and inference rules of reasoning rules.

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Zhang, Guoquan. "RESEARCH ON CONCEPTUAL DESIGN OF COMPLEX MECHANICAL PRODUCT BASED ON FUNCTIONAL REASONING MECHANISM." Chinese Journal of Mechanical Engineering 39, no. 04 (2003): 19. http://dx.doi.org/10.3901/jme.2003.04.019.

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He, Yong Bo, and Sheng Liang Dou. "Mechanical Failure Analysis of Aircraft Alternator Based on Fault Tree and Case Reasoning." Advanced Materials Research 548 (July 2012): 516–20. http://dx.doi.org/10.4028/www.scientific.net/amr.548.516.

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Aircraft alternator is in the central position of the aircraft AC power system. The existing troubleshooting process is complex and tedious. In this paper, the aircraft alternator fault tree is established, and a fault example of under-voltage condition is analyzed in-depth qualitative and quantitative. According to the importance degree, the order of troubleshooting is obtained, making the fault diagnosis fast and effective. In order to overcome the limitation of the fault tree analysis, case-based reasoning is combined, making the fault diagnosis more comprehensive and accurate.

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QIN, XIAOLI, and WILLIAM C. REGLI. "A study in applying case-based reasoning to engineering design: Mechanical bearing design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, no. 3 (August 2003): 235–52. http://dx.doi.org/10.1017/s0890060403173064.

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Case-based reasoning (CBR) is a promising methodology for solving many complex engineering design problems. CBR employs past problem-solving experiences when solving new problems. This paper presents a case study of how to apply CBR to a specific engineering problem: mechanical bearing design. A system is developed that retrieves previous design cases from a case repository and uses adaptation techniques to modify them to satisfy the current problem requirements. The approach combines both parametric and constraint satisfaction adaptations. Parametric adaptation considers not only parameter substitution but also the interrelationships between the problem definition and its solution. Constraint satisfaction provides a method to globally check the design requirements to assess case adaptability. Currently, our system has been implemented and tested in the domain of rolling bearings. This work serves as a template for application of CBR techniques to realistic engineering problems.

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Feng, H., C. Shao, and Y. Xu. "Using qualitative spatial reasoning in the conceptual design stage of a mechanical system." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 223, no. 2 (December 2008): 175–85. http://dx.doi.org/10.1243/09596518jsce624.

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Journal articles on the topic 'Mechanical reasoning'

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