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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Meta-Learning for Time Series Analysis and/or Forecasting: Concept Review and Comprehensive Critical Comparative Survey 1

引用

15th International Conference on Autonomous systems

作者： Kambale, Witesyavwirwa Vianney D'Ambrosi, Denis Fasouli, Paraskevi Kyamakya, Kyandoghere Univ Klagenfurt Inst Smart Syst Technol Univ Str 65-67 A-9020 Klagenfurt Austria

ISBN: (数字)9783031614187

ISBN: (纸本)9783031614170;9783031614187

Meta-Learning has emerged as a solution to address the limitations of data unavailability and the lack of extensive computing resources. The aim of this work is to consolidate a discussion on the application of Meta-Learning in Time Series forecasting, specifically by comprehensively contrasting Zero-Shot Learning (ZSL), One-Shot Learning (OSL), and Few-Shot Learning (FSL). In our implementation setup, the comparative analysis of results identifies Few-Shot Learning as the best performer and One-shot learning as the worst. The performance improvement registered for FSL is credited to the additional meta-knowledge learned through the MAML-based method. Implemented as a Siamese network, OSL had to learn the strong periodic components within the time series. However, the selected datasets did not display such strong periodicity;instead, they were dominated by trend and noise components. Future work intends to analyze the asymptotic performance of increasingly complex predictors in tandem with increasingly longer training regimes.

关键词： Zero-shot learning

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Fast Tunnel Traversal for Ground Vehicles by Bearing Estimation with Neural networks 6th

Fast Tunnel Traversal for Ground Vehicles by Bearing Estimat...

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6th Iberian Robotics Conference (Robot)

作者： Cano, Lorenzo Tardioli, Danilo Mosteo, Alejandro R. Univ Zaragoza Zaragoza Spain Inst Invest Ingn Aragon Zaragoza Spain Ctr Univ Def Zaragoza Spain

ISBN: (纸本)9783031586750;9783031586767

Underground environments are challenging scenarios for autonomous robotics, as they have a set of properties that make most common self-localization and navigation methods struggle. We propose a system to allow wheeled autonomous vehicles to traverse tunnels at high speeds. Our proposal is built around a Convolutional Neural Network (CNN) trained to predict the bearing that centers the robot into the tunnel as it traverses it. This exploits the generalization capabilities of CNNs, so that the method works in environments outside of the training data, achieving speeds of up to 10m/s.

关键词： Navigation Underground Robotics Deep Learning

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Cost Optimisation in Offshore Wind Through Procurement Data Analytics 1

引用

Computing Conference

作者： Shittu, Quadri Ezenkwu, Chinedu Pascal Robert Gordon Univ Aberdeen AB10 7AQ Scotland

ISBN: (数字)9783031622731

ISBN: (纸本)9783031622755;9783031622731;9783031622724

Governments have implemented a variety of national and international efforts to reduce carbon emissions to prevent the damaging effects of climate change on the environment and the global economy through the execution of several policies, including the Paris Agreement. Achieving the objectives of the Paris Agreement seeks to keep the increase in average global temperature to well below 2 degrees C and, preferably, below 1.5 degrees C above pre-industrial levels, will need a shift towards renewable energy sources like solar and wind power. As a result of these efforts, renewable energy sources' capacity is projected to expand in the upcoming years. Offshore wind is the UK's leading renewable energy source for power generation. Recently, cost optimisation efforts in the offshore wind industry have been through engineering design, especially with increasing turbine capacities. This research demonstrates how data analytics can achieve cost optimisation in procurement activities of offshore wind projects, which presents opportunities to reduce the Levelized Cost of Energy (LCOE). Ten (10) offshore wind projects have been selected in the UK offshore wind industry by building a workflow and designing an analytic app using Alteryx Designer. The workflow is built on procurement quotations of wind turbines, marine vessels, and export cables to optimise procurement costs and delivery timelines while fulfilling the established constraints for the respective projects. The optimisation results identified three areas of cost-optimisation opportunities: Contracting, Collaboration, and Reusing Components.

关键词： Data analytics Offshore wind Cost optimisation Climate change Renewable energy

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Intelligent Rising Sprawl Tuned Autonomous Robot 27th

Intelligent Rising Sprawl Tuned Autonomous Robot

引用

27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Siboni, Tomer Coronel, Matan Berman, Sigal Zarrouk, David Ben Gurion Univ Negev Dept Mech Engn Beer Sheva Israel Ben Gurion Univ Negev Dept Ind Engn Beer Sheva Israel

ISBN: (纸本)9783031713002;9783031713019

Miniature crawling or driving robots have been developed in recent years for off-road tasks such as search and rescue, excavation, and reconnaissance. Their small dimensions, low weight, and high navigability enable their deployment in large numbers to quickly inspect large areas. There are some examples of palm-sized robots that we designed and created that can crawl or drive, including instances from a particular family of Sprawl-Tuned Autonomous Robots (STAR), i.e., STAR [1] and Rising-STAR [2]. These robots can actively adjust their sprawl angle to change the geometry between wheels and "whegs" in different planes. Using their unique configuration, these robots can move on varying terrain surfaces and traverse obstacles. The Rising-STAR has been simulated and examined with different types of machine learning algorithms [3, 4], to improve its abilities in different aspects.

关键词： Reconfigurable robot Bio-Inspiration Reinforcement learning

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Decision Support System Based on Machine Learning Techniques to Diagnosis Heart Disease Using Four-Lead ECG Recordings 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Hosni, Mohamed Medarhri, Ibtissam Touiti, Soufiane Tazi, Amal Mezalek Ngote, Nabil Moulay Ismail Univ Meknes M253 Lab MOSI Team ENSAM Meknes Morocco MINES Rabat LMAID Lab MMCS Team Rabat Morocco Abulcasis Int Univ Hlth Sci Rabat Morocco Cheikh Zaid Int Univ Hosp Rabat Morocco

ISBN: (数字)9783031456459

ISBN: (纸本)9783031456442;9783031456459

Cardiovascular disease is one of the leading causes of death in the world. Accurately and rapidly diagnosis of this disease remains an important challenge in research. Different decision support systems (DSS) implementing Machine Learning (ML) techniques have been proposed in the literature. In this study, we attempt to build a DSS that implements ML and try to classify patients according to their clinical characteristics and four-lead Electrocardiogram (ECG) recorded by mean of a smart watch. The principal goal of this study is to check whether the proposed DSS can play the same role as a standard ECG. Indeed, the obtained results suggest that this DSS can accurately screen and diagnose heart conditions and may be used as an alternative to a standard ECG. Moreover, among the four optimized ML techniques by grid search optimization technique, the Support Vector Machines achieved the highest accuracy score.

关键词： ECG Machine Learning Cardiovascular Disease Smart Watch Withings ScanWatch

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Method for Intelligent Optimization of Adaptive Terminal Control of Product Output of an Enterprise

Method for Intelligent Optimization of Adaptive Terminal Con...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Shorikov, Andrey F. Russian Acad Sci NN Krasovskii Inst Math & Mech Ural Branch Ekaterinburg Russia Russian Acad Sci Inst Econ Ural Branch Ekaterinburg Russia

ISBN: (纸本)9783031671944;9783031671951

The article discusses the problem of intelligent optimization of adaptive control of product output of a manufacturing enterprise. To simulate the dynamics of the process under consideration, a mathematical model has been developed in the form of a system of linear discrete-time recurrent equations. This model contains the phase vector of the controlled object (an enterprise), the vector of demand for the enterprise's products and the vector of the control influence (control) of the control subject. During the control process, at each time period the control subject knows (measures) the phase vector of the controlled object. The quality of the control process is assessed by a convex objective function, the values of which are determined on the final phase states of the controlled object. This article proposes a mathematical formalization of the problem of intelligent optimization of adaptive terminal control of product output of an enterprise in the presence of a convex objective function. For the problem under study, a constructive method for solving it is given, which allows us to develop numerical algorithms for creating intelligent software systems and automating support for management decision-making by managers of manufacturing enterprises.

关键词： manufacturing enterprise system of a linear discrete-time recurrent equations intelligent optimization of adaptive control convex objective function

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Improvement of Small Object Detection Effectiveness Based on Swin Transformer 7th

Improvement of Small Object Detection Effectiveness Based on...

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7th International Conference on Green Technology and Sustainable Development

作者： Dung Nguyen Van-Dung Hoang Van-Tuong-Lan Le Ngoc-Thuy Nguyen Hue Univ Univ Sci Hue City Vietnam HCMC Univ Technol & Educ Ho Chi Minh City Vietnam Hue Univ Hue City Vietnam

ISBN: (纸本)9783031761966;9783031761973

This paper proposes a novel approach to improve performance and effectiveness of the detection transformer (DETR) model for small object detection. The proposed approach utilizes the Swin transformer as the backbone for the DETR model and applies a local attention mechanism. The Swin transformer possesses better multi-level representation capability than the conventional CNN backbones, which helps improve training efficiency and object detection capability. The Focal loss function supports the model converge quickly when dealing with imbalanced training datasets. Experimental results demonstrate that the DETR model using Swin transformer as the backbone and Focal Loss function achieves higher training performance and better small object detection capability than the DETR model using conventional CNN backbones. The model almost converged at Epoch 50th, much lower than the convergence rate of the original DETR model.

关键词： DETR Swin-Transformer focal loss object detection small object training performance

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Impact of Feature Normalization Techniques for Recognition of Speech for Mathematical Expression 1

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Kherdekar, Vaishali A. Naik, Sachin MIT Arts Commerce & Sci Coll Pune Maharashtra India Symbiosis Int Deemed Univ SIU SICSR Pune Maharashtra India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

Speech recognition is an important aspect of Human-Computer Interaction (HCI). Speech recognition systems can be implemented with the help of speech signal patterns. In pattern recognition, feature selection and feature extraction are important phases. The performance of the model depends not only on the feature selection and feature extraction but also on feature preprocessing. In our existing work, we have proposed a hybrid feature extraction algorithm for the recognition of speech for audio mathematical expression. In this paper, we have studied five feature normalization techniques used for audio processing. We compared five feature normalization functions with time domain, frequency domain, and cepstral domain features using Audio Mathematical Expression Recognition (AMER) dataset. Six types of audio mathematical expression are classified using five feature normalization functions. The observations indicate that the standard scaler and robust scaler give more accuracy and quantile transformer scaler gives less accuracy. The combination of feature normalization, feature extraction, and classification complies with higher accuracies.

关键词： Speech recognition Feature normalization Pattern recognition Audio mathematical expression recognition

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Detection of Racism on Multilingual Social Media: An NLP Approach 1

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： El Miqdadi, Ikram Kharroubi, Jamal Nikolov, Nikola S. Univ Sidi Mohamed Ben Abdellah Lab Intelligent Syst & Applicat Fes Morocco Univ Limerick Dept CSIS Limerick Ireland

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

This paper presents a comparison between various text vectorization andmachine learning algorithms for solving the problem of detection of racism on multi-lingual social media. We train classification models on Facebook comments and tweets in three different languages: English, French and Arabic. Our findings suggest that for the English-language comments, the combination of KNN with TF-IDF works best with an accuracy of 78.34%, while for French, the use of the SVM classifier with BOWprovides an accuracy of 82.56%. For Arabic we obtain an accuracy of 91.13% when KNN is coupled with BOW. Overall, our results suggest that the combination of SVM and TF-IDF is the best choice for detection of racism on social media that contains content in English, French and Arabic at the same time. As part of this work, we also present a new annotated dataset of social media comments in three languages.

关键词： NLP machine learning detection of racism

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Planning and Scheduling of Robotic Welding Processes in the Rolling Stock Industry: A Literature Review 7th

Planning and Scheduling of Robotic Welding Processes in the ...

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7th International Conference on Logistics Operations Management (GOL)

作者： Payami, Kimia Lehoux, Nadia Cloutier, Caroline Morin, Michael Univ Laval Dept Mech Engn Quebec City PQ Canada Univ Laval Dept Operat & Decis Syst Quebec City PQ Canada

ISBN: (纸本)9783031686337;9783031686344

Robotized welding processes in the manufacturing industry play a crucial role in enhancing competitiveness through automation, adaptability, and increased productivity. To optimize welding parameters, modeling approaches have gained significance, enabling users to simulate welding experiments and determine appropriate settings. With the growing need for reduced development phases and costs while maintaining quality standards and production volumes, flexible and robust manufacturing technologies are essential. In this paper, we present a literature review highlighting best practices for welding processes. We address five research questions related to welding techniques, planning models, factors affecting welding processes, and performance indicators. Our findings reveal various models and techniques for planning robot operations, focusing on welding robots. Bythis review, we contribute to the development of effective strategies for optimizing robotized welding processes, leading to improved efficiency in manufacturing systems.

关键词： Optimization models Robot welding process Welding techniques

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