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检索条件"任意字段=International Conference on Data and Knowledge Systems for Manufacturing and Engineering"

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Concept Maps as a Support for Learning: A Proposal from the Consent for the Processing of Personal data in Private Organizations 3rd

Concept Maps as a Support for Learning: A Proposal from the ...

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3rd EAI international conference on data and Information in Online Environments (DIONE)

作者： Ferreira, Herbert Alcantara de Sales, Rodrigo Univ Estadual Montes Claros Montes Claros Brazil Univ Fed Santa Catarina Florianopolis Brazil

ISBN: (纸本)9783031223235;9783031223242

knowledge Organization has knowledge Organization systems (KOS) to represent, organize and retrieve information. Among these systems are the concept maps, which represent ideas from the illustration of concepts that are linked together. Originating from pedagogy, concept maps began to be used in several areas as learning methods, including in the context of private institutions. Therefore, the demand to implement the rules of the Brazilian General Law for Personal data Protection (LGPD in Portuguese) requires learning from agents involved in the treatment of this type of data, especially in corporations. The general objective of this article is to prepare a concept map that represents the main rules of the LGPD regarding personal data processed based on the consent of its data subjects. As specific objectives, it seeks to investigate the main characteristics and typologies of concept maps, in addition to analyzing the aspects and fundamental rules of the LGPD. The purpose of the proposal is to use the map to study the aforementioned law. The methodology of construction of input and output concept maps was used, in addition to data Life Cycle, which interprets the "life" of data in four phases: collection, storage, retrieval and disposal. As a result, a simplified map was arrived at, but with a broad view of personal datamanagement from collection to disposal. The constructed map serves as a representative model of the treatment addressed and can be adjusted according to the needs of each institution.

关键词： knowledge organization Concept maps General law for personal data protection

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Seamless Machine Learning Models to Detect Faulty Solar Panels 3

Seamless Machine Learning Models to Detect Faulty Solar Pane...

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3rd IEEE international conference on Electronic engineering, ICEEM 2023

作者： Elgamal, Muhammad Abdelmaksoud, Ahmed Ismail, Yehea The American University in Cairo Center of Nanoelectronics and Devices Cairo Egypt The American University in Cairo Department of Electronics and Communication Engineering Cairo Egypt

ISBN: (纸本)9798350323511

Photovoltaic energy is a renewable source that provides good opportunities for investments by manufacturing and deployment companies. Several management paradigms exist to monitor solar stations, most importantly industry 4.0 technology, which incorporates the internet of things (IoT) and machine learning (ML), among others, to provide commercial systems for fault detection and localization. We compare various ML models based on their capability of classifying faulty and functional solar panels from their measured current-voltage (IV) curves. Regularly, one needs to compare the measurements taken in the field to the values projected from the datasheets of the solar panel. This method is sensitive to the accuracy of measuring devices (i.e., characterizers) attached to each panel in our photovoltaic 4.0 monitoring technology. However, we depend solely on the historical measurements of each panel when it is faulty or functional (possibly by imitating operational faults), and we do not care about the quality of characterizers (an issue raised when we depend on datasheet performance), and then we can successfully detect operation faults. By depending on many features extracted from IV curves, we can use several ML algorithms. However, we can depend on lesser features and an even smaller training dataset. Among our tried models, we found random forest to be the most successful ML model for classification. Additionally, it is insensitive to the reduction of the training dataset and suitable for data augmentation techniques like SMOTE (Synthetic Minority Oversampling Technique). © 2023 IEEE.

关键词： Support vector machines

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A Comparative Analysis of Energy Consumption Between Visual Scripting models and C plus plus in Unreal Engine: Raising Awareness on the importance of Green MDD 24

A Comparative Analysis of Energy Consumption Between Visual ...

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27th international conference on Model Driven engineering Languages and systems (MODELS)

作者： Veron, Javier Perez, Carlos Calero, Coral Moraga, Ma Angeles Perez, Francisca Cetina, Carlos Univ San Jorge SVIT Res Grp Villanueva De Gallego Spain Univ Castilla La Mancha Alarcos Res Grp Ciudad Real Spain

ISBN: (纸本)9798400705045

Video game engines are used in most modern video games because they simplify and speed up development. In addition, some of the most popular engines, such as Unreal Engine 5 (UE5), also integrate visual scripting tools. Visual scripting in UE5, through Blueprints, is a model-driven development approach that replaces text code, like C++, with a visual language of interconnected nodes representing functions and data flows, forming a flowchart-like logic diagram. This approach simplifies game development by abstracting complex code into intuitive, visual models, enabling creators to construct and iterate game components without extensive programming knowledge. Although Blueprint models usually decrease the complexity of implementing components, thus accelerating the development, they might lead to less energy-efficient runtime performance than C++. In this work, we evaluate the energy consumption of three relevant video game components (health system management, inputs processing, and collections operations for an inventory), each implemented with Blueprint models and C++. The results show that the energy consumption per frame when using C++ is up to 48% lower than when using Blueprint models. The combination of artistic and technical profiles in video game developments has favoured the adoption of Blueprint models. However, there is a lack of works analyzing the energy consumption. Until this work, there was no evidence that the success of models for developing video games, like the one under study in this work, was accompanied by a cost in energy consumption for certain situations. Given the huge popularity of video games, this cost in energy might reach up to the equivalent of the energy consumption of 28 million European households.

关键词： Energy consumption Video Games Green software Green Video Games Software sustainability Game Engines Unreal Engine Software Models Visual Scripting Blueprints C plus Game Software engineering

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Structural Causal Models as Boundary Objects in AI System Development 22

Structural Causal Models as Boundary Objects in AI System De...

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1st international conference on AI engineering - Software engineering for AI, CAIN 2022

作者： Heyn, Hans-Martin Knauss, Eric University of Gothenburg Department of Computer Science and Engineering Chalmers Sweden

ISBN: (纸本)9781450392754

Artificial Intelligence (AI), and especially machine learning can be used to find statistical patterns in datasets with thousands of variables with ease. But an understanding of causality is difficult to learn for a machine. For humans however, realising causal relations is often not a difficult process, as we can refer to experience or scientific knowledge. Here we propose the use of structural causal models, represented through direct acyclic graphs, to design, determine, and communicate causal relations hidden beyond the statistical models of an AI. The idea is to make human insight in causal relations explicit and use this knowledge during AI system development. In a joint-industry project we discovered that structural causal models can serve as living boundary objects that facilitate coordination of domain experts, data scientists, systems engineers, and AI experts in AI system development. © 2022 ACM.

关键词： Machine learning

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Artificial Neural Network Guided Compensation of Nonlinear Payload and Wear Effects for Industrial Robots 19

Artificial Neural Network Guided Compensation of Nonlinear P...

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19th IEEE international conference on Automation Science and engineering, CASE 2023

作者： Raible, Julian Rettig, Oliver Alt, Benjamin Yaman, Alper Gauger, Isabelle Biasi, Lorenzo Müller, Silvan Katic, Darko Strand, Marcus Huber, Marco F. Institute of Industrial Manufacturing and Management Iff University of Stuttgart Stuttgart70569 Germany Baden-Württemberg Cooperative State University Karlsruhe76133 Germany ArtiMinds Robotics GmbH Karlsruhe76131 Germany Stuttgart70569 Germany

ISBN: (纸本)9798350320695

The absolute accuracy of industrial robots is influ-enced by numerous geometric and non-geometric errors. Most state-of-the-art calibration and compensation methods consider only the geometric errors and neglect the non-geometric ones. In this paper, a hybrid compensation approach is proposed that combines well-known kinematic models with a model-free data-driven component using a neural network. This allows the use of established calibration methods for the geometric influences captured in the kinematic model to improve the non-geometric error compensation by the neural network. The proposed approach is applied in two use cases: payload and wear compensation. Simulations and real experiments show the improved absolute accuracy of the hybrid compensation approach compared to classical calibration methods. © 2023 IEEE.

关键词： Error compensation

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OASIS: An Ontology-Driven Autonomic Management System for Intelligent Substations 24

OASIS: An Ontology-Driven Autonomic Management System for In...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on data Science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xie, Xiaodong Yang, Jianxu Hua, Xiong Zhou, Qianru State Grid Corporation of China Beijing100031 China Nari Group Corporation State Grid Electric Power Research Institute Hefei230088 China School of Compute Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350319934

With the soar of the demand for electricity all over the so-ciety, an intelligent autonomic management system for substations is in urgent need. Ontology driven knowledge based system plays a vital role in achieving a truly autonomic, zero-touch substation management system. This paper proposes OASIS, an ontology driven autonomic management system for intelligent substations, which is able to organize first-nary devices and verify human operations to prevent mis-operation in real-time. The ontology used and develop procedures are presented in detail. OASIS has been tested in a real-life substation, and the test results are presented in detail. It is evident that OASIS is competent for autonomic substation management, including device power status calculation, main device running status retrieval, interval organization, general faulty check and single operation mis-operation verification, in a efficient way, with the responding time less than two seconds in general. At last, several lessons learnt during the developing procedure are discussed as a conclusion. © 2022 IEEE.

关键词： Ontology

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A Study of Human Resource Management Optimization Strategies Based on Artificial Intelligence

A Study of Human Resource Management Optimization Strategies...

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2024 international conference on Innovative Design and Intelligent manufacturing, IDIM 2024

作者： Wu, Xin Lai, Yan School of Economics and Management Shaanxi Fashion Engineering University Shaanxi 712046 China

ISBN: (纸本)9781643685427

In order to solve the problem of system calculation errors in traditional human resources information management systems, research on human resource management optimization strategies based on artificial intelligence is proposed. This paper first builds a behavior record matrix based on the Hadoop distributed architecture platform using employees' satisfaction with the company's projects, and then analyzes and designs a system suitable for human resources information management through the collaborative filtering algorithm to achieve parameter setting and partitioning. Comprehensive management of five major modules: management, staff management, user management and production management. Finally, a specific experimental analysis was conducted on the human resources information management system. The experimental results show that when 1571 employees generate 28325 employee rating records, the system response times of 2 nodes and 3 nodes are 7.4s and 4.8s respectively, which are significantly shorter than the non-distributed algorithm. At the same time, the collaborative filtering algorithm is faster than the non-distributed algorithm. The MSE and MPE of the formula algorithm are 0.39% and 0.52% smaller respectively. Conclusion: The collaborative filtering algorithm in this study can be competent for the data distributed computing tasks of human resource management systems. © 2024 The Authors.

关键词： Collaborative filtering

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Towards Bridging the Gap Between knowledge Graphs and Chatbots 22nd

Towards Bridging the Gap Between Knowledge Graphs and Chatbo...

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22nd international conference on Web engineering (ICWE)

作者： Wittig, Annemarie Perevalov, Aleksandr Both, Andreas Anhalt Univ Appl Sci Kothen Germany Leipzig Univ Appl Sci Leipzig Germany DATEV eG Nurnberg Germany

ISBN: (纸本)9783031099175;9783031099168

Chatbots are nowadays being applied widely in different life domains. One major reason for this trend is the mature development process that is supported by large companies and sophisticated conversational platforms. However, the required development steps are mostly done manually while transforming existing knowledge bases into interaction configurations, s.t., algorithms integrated into the conversational platforms are enabled to learn the intended interaction patterns. However, already existing domain knowledge may get vanished while transforming a structured knowledge base into a "flat" text representation without references backwards. In this paper, we aim for an automatic process dedicated to generating interaction configurations for a conversational platform (Google Dialogflow) from an existing domain-specific knowledge base. Our ultimate goal is to generate chatbot configurations automatically, s.t., the quality and efficiency are increased.

关键词： Dialog systems Chatbots knowledge graphs Synthetic data generation Natural-language interfaces Software generator Human-computer interactions

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Efficient close-range photogrammetry reconstruction through autonomous image background removal 22

Efficient close-range photogrammetry reconstruction through ...

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22nd international conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2022

作者： Eastwood, Joe Leach, Richard Piano, Samanta Manufacturing Metrology Team University of Nottingham United Kingdom Taraz Metrology Ltd United Kingdom

ISBN: (纸本)9781998999118

Optical coordinate measurement techniques are growing in popularity due to their high surface coverage and fast data acquisition time, and photogrammetry in particular has the further advantage of inexpensive component requirements. Although data acquisition is fast, data processing speeds can be relatively slow, particularly when high point cloud densities are required, such as for metrology applications. Reconstructed point clouds often also contain superfluous points due to feature matching in the background of images. In this paper, we present a method for exploiting the a-priori knowledge of a measurement system to efficiently and autonomously segment the object of interest from the background within a photogrammetric reconstruction pipeline. We show that using this technique leads to reduced reconstruction times and reduced background feature matching, while maintaining the quality and point density of the point cloud. We show a time reduction of up to 71% and a reduction in background points of up to 77%. The time required to segment the images is also shown to be lower than the time saved during feature matching leading to an overall more efficient measurement pipeline, with potential further gains with a more optimised implementation of the techniques presented. © European Society for Precision engineering and Nanotechnology, conference Proceedings - 22nd international conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： data acquisition

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Heart Disease Diagnosis Detection Based on Chest Pain Using Machine Learning Stacking Classifier 8th

Heart Disease Diagnosis Detection Based on Chest Pain Using ...

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8th international conference on ICT for Sustainable Development, ICT4SD 2023

作者： Shah, Jalpa Shah, Chintan Patel, Premal Department of Computer Engineering College of Technology Silver Oak University Gujarat Ahmedabad India

ISBN: (纸本)9789819965670

One of the most common illnesses today is heart disease, and therefore, it is essential for those who work in healthcare to work with their patients to find ways to protect their health and prolong their lives. In this study, the performance of various classifiers was examined in order to correctly identify the guts disease dataset and/or predict the cases of heart condition using minimal criteria. Healthcare professionals have accumulated a lot of knowledge, including some private data. Taking wise decisions is made easier by this data collection. In this scenario, a heart disease prediction system (HDPS) is built to predict the risk level of gastroenteritis applying K-nearest neighbor, logistic regression, decision tree, support vector machine, and random forest classifier methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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