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Data-driven Optimization of Inductive Generalization
[article]
2021
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In this paper, we introduce a data-driven method for inductive generalization, whose performance can be automatically improved through historical runs over similar instances. ...
Inductive generalization (IG) is the key to the efficiency of modern Symbolic Model Checkers (SMCs). ...
In this paper, we turn this observation into a practical inductive generalization method with the help of data-driven approach.
III. ...
doi:10.34727/2021/isbn.978-3-85448-046-4_17
fatcat:22fq5ladezaufjftykytdegw7m
Mathematical modeling of an inductive link for optimizing efficiency
2009
2009 IEEE Symposium on Industrial Electronics & Applications
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This paper presents a generalized model which encompasses all possible voltage driven circuit realizations of an inductive link and presents a comparison on the bases of link efficiency and voltage gain ...
Design of an optimized RF transcutaneous link through inductive coils is an arduous design process which involves complex mathematical modeling to search for optimized design parameters. ...
Figure 1 : 1 Generalized Circuit Models of inductive link (a) Voltage driven model; (b) current driven model. ...
doi:10.1109/isiea.2009.5356338
fatcat:yexw6dfrsrd4zjb27jxkjywwru
The AQ17-DCI system for data-driven constructive induction and its application to the analysis of world economics
[chapter]
1996
Lecture Notes in Computer Science
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In data-driven constructive induction (DCI), a learning system searches for a better representation space by analyzing the input examples (data). ...
Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: one-for the "best" representation space, and two-for the "best" hypothesis in that space. ...
One component performs a data-driven search for an improved representation space (hence DCI-data-driven constructive induction). ...
doi:10.1007/3-540-61286-6_136
fatcat:rngrxtizozcvdmbegzcb4czy2y
Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor
2021
International Journal of Power Electronics and Drive Systems (IJPEDS)
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Analysis and impact of faults on the performance of the induction motor is presented. A real time monitoring platform is proposed to detect and classify the fault accurately using machine learning. ...
Induction motors are most commonly used in industries. Faults in the power electronic circuits may occur periodically. ...
Simulink model of VSI driven Induction motor
Figure 4 . 4 Figure 4. ...
doi:10.11591/ijpeds.v12.i2.pp1205-1215
fatcat:35jr4cifcbgqjnlcvp2w4t3g5q
Failure Diagnosis and Prognosis of Induction Machines
2022
Energies
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Induction motors have numerous advantages due to their robustness and their power–weight ratio [...] ...
Conflicts of Interest: The author declares no conflict of interest. ...
Acknowledgments: I am grateful to the MDPI Publisher for the invitation to act as guest editor of this special issue. ...
doi:10.3390/en15041483
fatcat:ane5eya7drcvzfrs7nqqhjsiyy
Page 294 of American Society of Civil Engineers. Collected Journals Vol. 8, Issue 3
[page]
1994
American Society of Civil Engineers. Collected Journals
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In stage five, constructive induction of decision rules from examples, two knowledge induction processes were conducted: (1) Generation of decision rules from examples using data-driven constructive induction ...
Constructive induction of decision rules from examples: In this stage, two experimental learning systems, based on data-driven and hypothesis- driven constructive induction, were used to produce decision ...
Development of a knowledge-driven constructive induction mechanism
[chapter]
1997
Lecture Notes in Computer Science
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The objectives of developing this system were to demonstrate the usefulness of the approach and to provide knowledge-driven constructive induction support in our data analysis research. ...
This paper discusses the advantages of knowledge-driven constructive induction (KDCI). ...
The preliminary design of our constructive induction mechanism was done by Serge Oliveira. ...
doi:10.1007/bfb0052839
fatcat:72qrsod4irchrf6wgbnq6arqsm
Throughput-driven IC communication fabric synthesis
2002
Computer-Aided Design (ICCAD), IEEE International Conference on
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In this paper we propose a throughput-driven synthesis methodology for on-chip communication fabrics based on optimized bus models. ...
The characterized models facilitate a flexible interconnect fabric optimization engine that can be embedded into a system planner for throughput-driven synthesis. ...
p * , S gate * , L seg * , N * ) is the optimal solution of the throughput driven optimization in Section 3. ...
doi:10.1145/774572.774613
dblp:conf/iccad/LinP02
fatcat:iid7al755fgznn6ksymf43cfii
Residual Life Prediction for Induction Furnace by Sequential Encoder with s-Convolutional LSTM
2021
Processes
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Based on our experimental results, our method outperforms general neural network models and enhances the safe use of induction furnaces. ...
Herein, we propose a data-driven method for induction furnaces by proposing a novel 2D matrix called a sequential feature matrix(s-encoder) and multi-channel convolutional long short-term memory (s-ConLSTM ...
It is necessary to provide data-driven approaches for the safe use of induction furnaces. ...
doi:10.3390/pr9071121
fatcat:zxa5ngszdrbn3ivfgpc4nowqhy
Study and Optimal Design of a Direct-Driven Stator Coreless Axial Flux Permanent Magnet Synchronous Generator with Improved Dynamic Performance
2018
Energies
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In this paper, the study and optimization design of stator coreless axial flux permanent magnet synchronous generators is presented for direct driven variable speed renewable energy generation system applications ...
A 3_kW AFPMSG is optimally designed to minimize the output voltage overshooting—the index of dynamic performance for direct driven variable speed generation application. ...
of P.R.C. ...
doi:10.3390/en11113162
fatcat:74n3jjf2w5gaje5tauk2fqvioi
Ryszard S. Michalski: The Vision and Evolution of Machine Learning
[chapter]
2010
Studies in Computational Intelligence
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model, inductive databases, methods of plausible reasoning, and the inferential theory of learning. ...
The most important topics mentioned in this chapter are: natural induction, knowledge mining, AQ learning, conceptual clustering, VL1 and attributional calculus, constructive induction, the learnable evolution ...
A general classification of constructive induction methods include data-driven constrictive induction (DCI) in which modifications of the representation space are based on the analysis of input data, hypothesis-driven ...
doi:10.1007/978-3-642-05177-7_1
fatcat:cfnpbpjwsjc7lfnpbnfa4q3r7a
Data-Driven Virtual Inertia Control Method of Doubly Fed Wind Turbine
2021
Energies
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This paper presents a data-driven virtual inertia control method for doubly fed induction generator (DFIG)-based wind turbine to provide inertia support in the presence of frequency events. ...
Then, a data-driven state observer is developed to evaluate the state vector of the optimal controller. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/en14175572
fatcat:h2igxywn7ff43hjypq7dkypciq
Evolutionary Computing as a Tool for Grammar Development
[chapter]
2003
Lecture Notes in Computer Science
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In this paper, an agent-based evolutionary computing technique is introduced, that is geared towards the automatic induction and optimization of grammars for natural language (grael). ...
Finally, by employing a separate grammar induction module at the onset of the society, grael-3 can be used as an unsupervised grammar induction technique. ...
Yet so far, little or no progress has been achieved in evaluating evolutionary computing as a tool for the induction or optimization of data-driven parsing techniques. ...
doi:10.1007/3-540-45105-6_67
fatcat:r3d4mxzgcvgmnkmo7lm2whor4m
Formation and Development of Self-Organizing Intelligent Technologies of Inductive Modeling
2018
Kibernetika i vyčislitelʹnaâ tehnika
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The data-driven methods are basic for solving typical tacks of data mining; they implement an inductive process of transition from particular data to models generalizing the data. ...
To construct adequate predictive models, many modern methods and tools are available which may be generally based on two principal approaches: theory-driven (deductive) and data-driven (inductive) ones ...
The data-driven methods are basic for solving tacks of data mining; they implement an inductive process of transition from particular data to models generalizing the data. ...
doi:10.15407/kvt194.04.041
fatcat:7yzpnsegn5cwdfwca5xsdzdjyq
2020 Index IEEE Open Access Journal of Power and Energy Vol. 7
2020
IEEE Open Access Journal of Power and Energy
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., +,
OAJPE 2020 41-50
Data-Driven Risk Analysis of Joint Electric Vehicle and Solar Operation in
Distribution Networks. ...
., +,
OAJPE 2020 403-413
Data-Driven Risk Analysis of Joint Electric Vehicle and Solar Operation in
Distribution Networks. ...
doi:10.1109/oajpe.2021.3051814
fatcat:b6jtoa3s5nhq5ke4eqnkrm23xy
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