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Neural-Network-Based Constrained Output-Feedback Control for MEMS Gyroscopes Considering Scarce Transmission Bandwidth

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 12351-12363页

作者： Shao, Xingling Shi, Yi North Univ China Minist Educ Key Lab Instrumentat Sci & Dynam Measurement Taiyuan 030051 Peoples R China North Univ China Sch Instrument & Elect Taiyuan 030051 Peoples R China

In this article, a neural-network-based constrained output-feedback control is considered for microelectromechanical system (MEMS) gyroscopes subject to scarce transmission bandwidth and lumped disturbances resulting from model uncertainties, dynamic coupling, and environmental disturbances. First, a hybrid quantizer capable of achieving an adjustable communication rate and quantization density is proposed to convert continuous control signals into discrete values, allowing for reduced chattering behavior even when control actions vary within large regions and enhanced tracking accuracy can be ensured. Subsequently, by applying two types of nonlinear mapping, all state variables of MEMS gyroscopes are restrained within the predefined time-varying asymmetric functions without imposing stringent feasibility conditions on virtual control laws. Furthermore, an echo-state network-based minimal learning parameter neural observer is developed to simultaneously recover the unmeasurable velocity-state variables, matched as well as unmatched disturbances in constrained MEMS gyroscopes dynamics, enabling an output-feedback control solution with a decreased online learning complexity. It is shown via the Lyapunov stability and nonsmooth analysis that all signals in the closed-loop system remain ultimately uniformly bounded even with discontinuous control actions. Comparison simulations are produced to certify the effectiveness of the presented controller.

关键词： Micromechanical devices Gyroscopes Quantization (signal) Bandwidth Artificial neural networks Velocity measurement Nonlinear systems Hybrid quantizer (HQ) learning complexity microelectromechanical system (MEMS) gyroscopes neural adaptive output feedback

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Gamification tools in the learning of shipbuilding in the undergraduate marine engineering education

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2022年第2期30卷 458-471页

作者： Leon, Alejandro Pena, Marta Univ Politecn Cataluna Naut Sci & Engn Dept Barcelona Spain Univ Politecn Cataluna Dept Math Av Diagonal 647 Barcelona 08028 Spain

This study assesses the implementation of different gamification tools and learning complexity in the of the subject Shipbuilding Principles of the Universitat Politecnica de Catalunya center dot BarcelonaTech (UPC). This experience has been carried out under a triple new scenario: the virtuality due to the Covid-19 pandemic, added to the gamified training, and the assessment carried out remotely. Both the content and the assessment of the subject have been presented using three gamification web tools (Kahoot!, Mentimeter, and Socrative) and three learning complexity (Lexicon, Comprehension, and Visual & Relationship). The content of the subject has been taught in a dynamic, entertaining, and active way. An important factor that has been taken into account was student diversity when responding to gamified elements, using different gamification tools and different learning complexity. This experience of gamification had a positive impact on student motivation, class attendance, participation, collaborative learning, and classroom climate, which is key in a scenario like the current one, with an adaptation of classes to online learning due to the pandemic. From the results obtained, it can be stated that the gamified experience, even offering a good result compared to previous academic years, did not provide a significant improvement in the overall academic performance of the students. Regarding the gamification tools, the one that presented a slight advantage over the rest was Socrative, which is based on a particular challenge without competition between peers or groups. Regarding the learning complexity, all presented a similar level of success.

关键词： assessment gamification learning complexity motivation online learning

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learning of monotone functions with single error correction

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DISCRETE MATHEMATICS AND APPLICATIONS 2021年第3期31卷 193-205页

作者： Selezneva, Svetlana N. Liu, Yu Lomonosov Moscow State Univ Moscow Russia

learning of monotone functions is a well-known problem. Results obtained by V. K. Korobkov and G. Hansel imply that the complexity phi(M)(n) of learning of monotone Boolean functions equals C-n((sic)n/2(sic))+C-n((sic)n/2(sic)+1) (phi(M)(n) denotes the least number of queries on the value of an unknown monotone function on a given input sufficient to identify an arbitrary..-ary monotone function). In our paper we consider learning of monotone functions in the case when the teacher is allowed to return an incorrect response to at most one query on the value of an unknown function so that it is still possible to correctly identify the function. We show that learning complexity in case of the possibility of a single error is equal to the complexity in the situation when all responses are correct.

关键词： Boolean function monotone function learning of functions learning complexity n-dimensional Boolean cube chain chain partition Hansel chains error error correction

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'A Little Knowledge is a Dangerous Thing': A method to automatically detect knowledge compartmentalization and oversimplification 20

'A Little Knowledge is a Dangerous Thing': A method to autom...

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20th IEEE International Conference on Advanced learning Technologies (ICALT)

作者： Pereira, Crystiam Kelle Nunes, Bernardo Pereira Siqueira, Sean W. M. Manrique, Ruben Medeiros, Jerry Fernandes Fed Univ State Rio de Janeiro Rio De Janeiro Brazil Australian Natl Univ Canberra ACT Australia Univ Los Andes Bogota Colombia

ISBN: (纸本)9781728160900

Simplification is a common practice to allow students to understand complex concepts. However, such practice may lead to oversimplification and knowledge compartmentalization problems. This paper proposes a method to minimize the effects of oversimplification and knowledge compartmentalization through a five-step processing chain, following instructional design principles, to foster advanced knowledge acquisition. The proposed method was applied to online courses and a discussion for an example of a Data Science course is provided to show its applicability.

关键词： learning complexity concepts sequencing oversimplification knowledge compartmentalization

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How Complex is the complexity of a Concept in Exploratory Search 19

How Complex is the Complexity of a Concept in Exploratory Se...

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19th IEEE International Conference on Advanced learning Technologies (ICALT)

作者： Pereira, Crystiam Kelle Medeiros, Jerry Fernandes Siqueira, Sean W. M. Nunes, Bernardo Pereira Fed Univ State Rio de Janeiro Dept Appl Informat Rio De Janeiro Brazil Pontifical Catholic Univ Rio de Janeiro Rio De Janeiro Brazil

ISBN: (纸本)9781728134857

Exploratory search is a specialized form of information retrieval and discovery. The main aim of the exploratory search is to investigate given information domain to acquire knowledge about it - that is, to learn something about that domain. The level of difficulty of information can affect the learning process and its success. Information with low complexity and relative ease may be uninteresting to learners, who might find it tedious and unrewarding. On the other hand, information with high complexity and difficulty can demoralize learners who perceive it as being impossible, triggering feelings of anxiety. Whether too complex or not complex enough, the seek for information can result in little or no learning. This paper aims to (i) identify features that indicate the complexity of the information represented by a concept;(ii) analyze the features in order to provide a coherent sequence of concepts that balances complexity and learners interest;and, (iii) discuss the challenges, difficulties, and validity of sequencing concepts based on features that indicate complexity. The results show that diversity and variety of concepts as well as detailed semantic descriptions and relations act as indicators for the ideal order of presentation of the information.

关键词： learning complexity concepts sequencing exploratory search

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Differentially Private Semi-Supervised learning With Known Class Priors

Differentially Private Semi-Supervised Learning With Known C...

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IEEE International Conference on Big Data (Big Data)

作者： Pham, Anh T. Xi, Jing Expedia Bellevue WA 98004 USA

ISBN: (纸本)9781538650356

Machine learning algorithms benefit greatly from a large amount of data in the big data regime, however, the labeling cost for all training samples is prohibitively high. Semi-supervised learning is a designed framework to overcome the labeling challenge. In particular, in semi-supervised learning, there are only a small number of labeled samples and unlabeled samples are used as side information to leverage the overall performance. Additionally, preserving the privacy of the data is another challenge in big data analysis. This paper addresses challenges by designing a privacy-preserving method for semi-supervised learning. Assuming that class priors are available, the proposed method can achieve the optimal classifier. Moreover, the privacy and utility of the proposed method are theoretically guaranteed. Experiments on real UCI datasets illustrate the effectiveness of our proposed method.

关键词： Differential privacy Semi-supervised learning learning complexity Risk bound

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Measuring learning complexity with Criteria Epitomizers

Measuring Learning Complexity with Criteria Epitomizers

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28th International Symposium on Theoretical Aspects of Computer Science (SATCS)

作者： Case, John Koetzing, Timo Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Max Planck Inst Informat D-66123 Saarbrucken Germany

ISBN: (纸本)9783939897255

In prior papers, beginning with the seminal work by Freivalds et al. 1995, the notion of intrinsic complexity is used to analyze the learning complexity of sets of functions in a Gold-style learning setting. Herein are pointed out some weaknesses of this notion. Offered is an alternative based on epitomizing sets of functions - sets, which are learnable under a given learning criterion, but not under other criteria which are not at least as powerful. To capture the idea of epitomizing sets, new reducibility notions are given based on robust learning (closure of learning under certain classes of operators). Various degrees of epitomizing sets are characterized as the sets complete with respect to corresponding reducibility notions! These characterizations also provide an easy method for showing sets to be epitomizers, and they are, then, employed to prove several sets to be epitomizing. Furthermore, a scheme is provided to generate easily very strong epitomizers for a multitude of learning criteria. These strong epitomizers are so-called self-learning sets, previously applied by Case & Kotzing, 2010. These strong epitomizers can be generated and employed in a myriad of settings to witness the strict separation in learning power between the criteria so epitomized and other not as powerful criteria!

关键词： Algorithmic learning Theory learning complexity Robustness in learning

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On teaching sets of k-threshold functions

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INFORMATION AND COMPUTATION 2016年 251卷 301-313页

作者： Zamaraeva, Elena Nizhnii Novgorod State Univ Gagarin Ave 23 Nizhnii Novgorod 603600 Russia

In this paper we study teaching sets of k-threshold functions, i.e. functions that can be represented as a conjunction of k threshold functions. We reveal a connection between essential points of k threshold functions and essential points of the corresponding k-threshold function. We show that in two-dimensional case the number of minimal teaching sets of a 2-threshold function can grow as Omega(n(2)). We also consider the class of polytopes with vertices in the d-dimensional cube. Each polytope from this class can be defined by a k-threshold function for some k. We prove that such a polytope has a unique minimal teaching set which is equal to the set of its essential points. For d = 2 we describe structure of the minimal teaching set of a polytope and show that its cardinality is either Theta(n(2)) or O(n) and depends on the perimeter and the minimum angle of the polytope. it) (C) 2016 Elsevier Inc. All rights reserved.

关键词： Machine learning Threshold function Essential point Teaching set learning complexity k-threshold function

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The complexity of learning concept classes with polynomial general dimension

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THEORETICAL COMPUTER SCIENCE 2006年第1期350卷 49-62页

作者： Köbler, J Lindner, W Humboldt Univ Inst Informat D-10099 Berlin Germany Univ Ulm Theoret Informat Abt D-89069 Ulm Germany

The general dimension is a combinatorial measure that characterizes the number of queries needed to learn a concept class. We use this notion to show that any p-evaluatable concept class with polynomial query complexity can be learned in polynomial time with the help of an oracle in the polynomial hierarchy, where the complexity of the required oracle depends on the query-types used by the learning algorithm. In particular, we show that for subset and superset queries an oracle in Sigma(P)(3) suffices. Since the concept class of DNF formulas has polynomial query complexity with respect to subset and superset queries with DNF formulas as hypotheses, it follows that DNF formulas are properly learnable in polynomial time with subset and superset queries and the help of an oracle in Sigma(P)(3). We also show that the required oracle in our main theorem cannot be replaced by an oracle in a lower level of the polynomial-time hierarchy, unless the hierarchy collapses. (c) 2005 Elsevier B.V. All rights reserved.

关键词： query learning learning complexity learning DNF formulas polynomial-time hierarchy

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The complexity of learning concept classes with polynomial general dimension

The complexity of learning concept classes with polynomial g...

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13th Annual International Conference on Algorithmic learning Theory

作者： Köbler, J Lindner, W Humboldt Univ Inst Informat D-10099 Berlin Germany Univ Ulm Theoret Informat Abt D-89069 Ulm Germany

ISBN: (纸本)3540001700

关键词： query learning learning complexity learning DNF formulas polynomial-time hierarchy

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