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共 568 条记录，以下是11-20 订阅

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Didactic FPGA-in-the-Loop Scalar Fuzzy Control Setup for Motor Drive Education 8

Didactic FPGA-in-the-Loop Scalar Fuzzy Control Setup for Mot...

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8th Southern Power Electronics conference and the 17th Brazilian Power Electronics conference, SPEC / COBEP 2023

作者： Barbosa, Rhuan Pelzl, Matheus Cordero, Raymundo Caramalac, Matheus Suemitsu, Walter Infi Federal Univ. Mato Grosso Do Sul MS Campo Grande Brazil Faeng Federal Univ. Mato Grosso Do Sul MS Campo Grande Brazil PPGEE-FAENG Federal Univ. Mato Grosso Do Sul MS Campo Grande Brazil Coppe Federal Univ. Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350321128

Electric motor is a nonlinear plant whose dynamics depends on the operation point. Non-linear controllers based on artificial intelligence, such as Fuzzy logic, usually have better performance to control power electronics devices than linear controllers (e.g., PID regulators). Fuzzy controllers are applied in many power electronics applications and motor drives. On the other hand, many FPGAs are being used to implement motor drives control algorithms as a FPGA allows creating a customized control architecture and has fast processing speed. However, learning and implement Fuzzy controllers in FPGA for undergraduation students is a difficult task. this paper presents a didactic implementation of a Fuzzy controller in FPGA applied to scalar speed control of an induction motor. Scalar control was selected due to it has a less theoretical complexity that other control techniques such as direct torque control (DTC) and field oriented control (FOC). FPGA-in-the-loop (FIL) methodology was applied to test the Fuzzy controller: the controller was implemented in FPGA, and the inverter and motor was designed in a simulation software. Tests show that the methodology used to create the Fuzzy controller allows students to learn about Fuzzy logic and FPGA programming. © 2023 IEEE.

关键词： Education FPGA fuzzy logic motor drives

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Construction of basic programming ability course group based on new engineering 23

Construction of basic programming ability course group based...

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8th International conference on Distance Education and Learning, ICDEL 2023

作者： Ma, Jiying Wang, Yana Yang, Shaozu Wu, Jinya Wang, Jun College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China

ISBN: (纸本)9798400700422

In order to cultivate students' basic programming ability of engineering majors in colleges and universities, this paper analyzes the problems existing in the construction of engineering courses with large curriculum group as the main teaching strategy, such as lagging practice teaching, difficulty in connecting curriculum groups, lack of measurement standards and inspection methods of professional ability, which greatly restrict the teaching needs of talent training construction under the background of new engineering. therefore, based on the requirements of new engineering construction, combined with the teaching concept of CDIO mode, this paper puts forward the teaching idea of small course group construction to cultivate students' basic logic programming ability. MOOC and flipped classroom online and offline hybrid teaching mode are used to promote students' autonomous learning ability, cultivate social talents that meet the requirements of engineering education certification, and set up practical teaching with goal orientation. Based on the assessment mechanism of PAT certification + comprehensive practice project ability, it can effectively improve the development of students' professional skills, strengthen the connection of basic courses and curriculum organization innovation. © 2023 ACM.

关键词： Curricula

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Combining logical and Probabilistic Reasoning to Improve a home care platform 8

Combining Logical and Probabilistic Reasoning to Improve a h...

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8th International conference on Control, Decision and Information Technologies (CoDIT)

作者： Gueddes, Abdelweheb Mahjoub, Mohamed Ali Sousse Univ Natl Sch Engineers Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia Sousse Univ LATIS Lab Adv Technol & Intelligent Syst IsitCom Sousse 4023 Tunisia

ISBN: (纸本)9781665496070

Several areas have evolved with the evolution of technology. Telemedicine and home care are one of them. While there are many studies on home care, the overall problem of decision-making is not sufficiently addressed. In previous research, we proposed an ontological-based intervention system. Despite its abilities, reasoning on an ontology is currently limited to logic. However, in the case of inconsistent knowledge or unreliable and incomplete information, it is difficult for a system to make a decision or assess the degree of truth of a hypothesis. Several approaches have been made and focused mainly on how to represent probabilistic information in ontologies and then use them for reasoning. Although, this requires a great effort for systems already designed and based on ontological or Bayesian knowledge bases. In this article, we propose two approaches to combining probabilistic and logical reasoning within a homebased medical intervention system. the first, using probabilistic ontology PR-OWL. the second, providing a Jena API (Application programming Interface).

关键词： Telemedicine Knowledge based systems Decision making Ontologies Probabilistic logic Cognition Bayes methods

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the Effect of Gamification-Based programming E-Learning Platform on Students' Achievement 23

The Effect of Gamification-Based Programming E-Learning Plat...

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8th International conference on Sustainable Information Engineering and Technology, SIET 2023

作者： Pradana, Fajar Setyosari, Punaji Ulfa, Saida Hirashima, Tsukasa Amalia, Faizatul Universitas Brawijaya Malang Indonesia State University of Malang Malang Indonesia Hiroshima University Higashihiroshima Japan

ISBN: (纸本)9798400708503

Gamification is gaining popularity because it has been shown to positively influence behaviour and cognitive processes by increasing user motivation and engagement through a game-like experience. Gamification supports making the teaching and learning process more exciting and enjoyable by applying various game elements such as levels, progression, points and leaderboards. In using the concept of gamification in learning, three principles need to be considered: increasing engagement, giving autonomy to learners, and providing learning progress. However, there are still gaps in research on gamification because most of the research uses generic platforms applied to non-specific learning contexts. programming is one of the main competencies possessed by students in computer science. Learning computer programming requires critical thinking, problem-solving, computational logic, and system design skills. One of the problems that is often encountered is the low engagement of students, which causes student learning achievements to be not optimal. this study aims to investigate whether gamification-based learning platforms affect student achievement. this study is expected to offer researchers and practitioners empirical data regarding the effectiveness of gamification-based learning platforms in developing student achievement in the context of programming learning in computer science. this study involved 109 students in programming lessons divided into experimental and control groups. Based on the calculation results, it is found that the learning achievements based on the learning platform get a significance value of 0.000, which means that there is a significant difference in learning achievements between groups of students who apply gamification-based platforms and groups of students who use non-gamification platforms. © 2023 ACM.

关键词： Students

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INDUSTRIAL MODELING AND programming LANGUAGE (IMPL) FOR COMPLEX DATA ANALYTICS AND DECISION-MAKING PROBLEMS 16

INDUSTRIAL MODELING AND PROGRAMMING LANGUAGE (IMPL) FOR COMP...

引用

16th International conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International conference on Connected Smart Cities, CSC 2022, 7th International conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International conference on theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

作者： Menezes, Brenno Kelly, Jeffrey Elsherif, Munier Franzoi, Robert Division of Engineering Management College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Education City LAS Building Doha Qatar Industrial Algorithms Limited. Toronto Canada Department of Chemical Engineering Faculty of Engineering University of Tripoli Tripoli Libya

ISBN: (纸本)9789898704429

the Industrial Modeling and programming Language (IMPL) is a sophisticated computational system for tackling large-scale and complex-scope data analytics and decision-making problems in the engineering and operations research fields. Although the software provides both scalar-based (e.g., MATLAB) and set-based modeling (GAMS, AIMMS, AMPL, MOSEL, OPL) as found in most of the modeling programming languages (MPL), IMPL is mainly built as ad hoc and specialized proprietary industrial software platform (closed-source). this is made by built-in structured- and semantic-based modeling that rely on the Unit-Operation-Port-State Superstructure (UOPSS) constructs (for the structure) and Quantity-logic-Quality Phenomena (QLQP) concepts (for the semantics). Besides the data analytics package included in the IMPL-DATA version, this mathematical programming language enables the modeling and solving of industrial-scale discrete, nonlinear, and dynamic optimization, estimation, and simulation problems found in both the batch and continuous process industries. IMPL's data analytics (DA) includes several functions to concatenate, substitute, sort, cluster, regress, etc., datasets in order to categorize, reduce, track, etc., the given data. IMPL's decision-making (DM) is suitable to support the modeling and solving of design, planning, scheduling, coordinating production, process, operations, optimization, and control problems as well as parameter identification, estimation, and data reconciliation problems. IMPL includes links to various community and commercial LP, QP, MILP and NLP solvers. To summarize, IMPL may be considered as a confluence with the scientific disciplines of applied engineering, management, information and computer science, statistical and data science. © MCCSIS *** rights reserved.

关键词： Semantics

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FTSCS 2022 - Proceedings of the 8th ACM SIGPLAN International Workshop on Formal Techniques for Safety-Critical Systems, co-located with SPLASH 2022

FTSCS 2022 - Proceedings of the 8th ACM SIGPLAN Internationa...

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8th ACM SIGPLAN International Workshop on Formal Techniques for Safety-Critical Systems, FTSCS 2022, co-located with the ACM SIGPLAN conference on

ISBN: (纸本)9781450399074

the proceedings contain 10 papers. the topics discussed include: cooperative verification: towards reliable safety-critical systems;rewriting logic semantics and symbolic analysis for parametric timed automata;q: a sound verification framework for statecharts and their implementations;strategFTO: untimed control for timed opacity;symbolic reachability analysis of distributed systems using narrowing and heuristic search;proving memory access violations in Isabelle/HOL;bounded model checking of PLC ST programs using rewriting Modulo SMT;synchronous programming and refinement types in robotics: from verification to implementation;formal probabilistic risk assessment of a nuclear power plant;and modelling a blockchain for smart contract verification using DeepSEA.

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A Jena API for combining ontologies and Bayesian object-oriented networks 8

A Jena API for combining ontologies and Bayesian object-orie...

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8th International conference on Control, Decision and Information Technologies (CoDIT)

作者： Gueddes, Abdelweheb Mahjoub, Mohamed Ali Sousse Univ LATIS Lab Adv Technol & Intelligent Syst Natl Sch Engineers Sousse Sousse 4023 Tunisia Sousse Univ LATIS Lab Adv Technol & Intelligent Syst IsitCom Sousse 4023 Tunisia

ISBN: (纸本)9781665496070

Reasoning on an ontology is presently limited to the logical one. However, in the case of inconsistent knowledge or unreliable and incomplete information, it is difficult for a system to make a decision or measure the degree of truth of a hypothesis. Several approaches have been made and focused mainly on how to represent probabilistic information in ontologies and then use them for reasoning. Although, this requires a great effort for systems already designed and based on ontological or Bayesian knowledge bases. In these cases the users must integrate the probabilistic information of Bayesian networks (BN) in ontology manually. We propose a new approach to integrate the BN (more particularly Object-Oriented Bayesian Networks (OOBn)) with OWL by providing a Jena API (Application programming Interface). OOBNs is an extension of the standard BN using the paradigm object. Our approach (i) uses the BN knowledge base for ontological enrichment, (ii) allows the adjustment of BN structures through structural and parametric learning based on ontological knowledge bases, (iii) integrate probabilistic information into SPARQL queries. In addition, we propose a solution in which we use a selection of instances according to the needs of the user described by a set of SPARQL requests.

关键词： Knowledge based systems Semantics OWL Prototypes Ontologies Probabilistic logic Cognition

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Assessing the Impact of Psychosocial Factors on the Behavior of Diabetic Patients with Neuropathy 8th

Assessing the Impact of Psychosocial Factors on the Behavior...

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8th International conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Vicente, Henrique Portela, Manuel Ávidos, Liliana Neves, João Marreiros, Goreti Neves, José Departamento de Química e Bioquímica Escola de Ciências e Tecnologia REQUIMTE/LAQV Universidade de Évora Évora Portugal Centro Algoritmi/LASI Universidade do Minho Braga Portugal Instituto Politécnico de Saúde do Norte CESPU Famalicão Portugal Mediclinic Arabian Ranches Dubai United Arab Emirates Departamento de Engenharia Informática Instituto Superior de Engenharia do Porto Porto Portugal

ISBN: (纸本)9789819707430

Diabetic neuropathy is a prevalent consequence of diabetes, impacting as many as half of those diagnosed with the condition. this ailment results from nerve damage, manifesting as sensory and motor symptoms such as tingling, numbness, and discomfort in the extremities, especially the hands and feet. Diabetic neuropathy considerably diminishes the patient’s quality of life, prompting both physical and emotional upheaval and consequently altering their behaviors and daily routines. this study aimed to achieve two main goals: first, to understand the role played by psychosocial factors in influencing the actions of those with diabetes and neuropathy, and second, to determine the relationship between a patient’s psychosocial factors and their responses under these circumstances. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： logic programming

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Projection as a way of thinking to find factors in factor space 8

Projection as a way of thinking to find factors in factor sp...

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8th International conference on Information Technology and Quantitative Management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Kong, Qi-wei He, Jing Zhang, Zhi-wang Zheng, Hui Wang, Pei-zhuang Nanjing Univ Finance & Econ Inst Informat Engn Nanjing Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Hawthorn Vic Australia Liaoning Tech Univ Inst Intelligence Engn & Math Fuxin Liaoning Peoples R China

With the development of artificial intelligence, statistical learning methods are widely used. Among all artificial intelligence problems, obtaining excellent training models through massive data mining and huge computing power is currently a hot topic. Although such statistical learning methods have shown great potential in academic and industrial applications, the causal logic of machine learning still has poor interpretability. In the factor space theory proposed in 1982, Professor Peizhuang Wang first proposed causal reasoning to find the relationship between causation and effect and created factor space to illustrate causal relationships. Factors play a vital role in the factor space, which can be applied in various scenarios. the projection method provides a way of thinking and perspective for finding factors by projecting the vectors in the n-dimensional real vector space R-n into subspace, and this method has great value in knowledge representation and causal reasoning. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： Factor space artificial intelligence linear programming Hat projection Causal reasoning

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Reinforcement-based Display Selection for Frugal Learning 26

Reinforcement-based Display Selection for Frugal Learning

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26th International conference on Pattern Recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Deschamps, Sebastien Sahbi, Hichem Sorbonne Univ UPMC CNRS LIP6 Paris France Theresis Thales Paris France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Most of the existing learning models, particularly deep neural networks, are reliant on large datasets whose hand-labeling is expensive and time demanding. A current trend is to make the learning of these models frugal and less dependent on large collections of labeled data. Among the existing solutions, deep active learning is currently witnessing a major interest and its purpose is to train deep networks using as few labeled samples as possible. However, the success of active learning is highly dependent on how critical are these samples when training models. In this paper, we devise a novel active learning approach for label-efficient training. the proposed method is iterative and aims at minimizing a constrained objective function that mixes diversity, representativity and uncertainty criteria. the proposed approach is probabilistic and unifies all these criteria in a single objective function whose solution models the probability of relevance of samples (i.e., how critical) when learning a decision function. We also introduce a novel weighting mechanism based on reinforcement learning, which adaptively balances these criteria at each training iteration, using a particular stateless Q-learning model. Extensive experiments conducted on staple image classification data, including Object-DOTA, show the effectiveness of our proposed model w.r.t. several baselines including random, uncertainty and flat as well as other work.

关键词： Training Adaptation models Uncertainty Self-supervised learning Linear programming Probabilistic logic Data models

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