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General Assembly and Scientific Symposium, UrSI

作者： N. Yudis d. Kanyas A. Gamliel M. Adgo J. Gavan M. Haridim Esmaizadeh Lab Merchavim Institute of R&D in Negev Merchavim Regional Council Israel Investigations and Intelligence Dept. Israel Police Jerusalem Israel The Department of Applied Physics The Hebrew University of Jerusalem Jerusalem Israel Faculty of Electrical Engineering Holon Institute of Technology Holon Israel

Theoretical analysis of the current distribution along thin film transparent antennas is presented and analyzed. It is shown that as the conductivity of the radiating element decreases the current distribution along the antenna shifts from cosinusoidal distribution to exponential *** critical conductivity at which the current distribution law changes is determined for different values of the antenna’s width and thickness. The theoretical results are validated by numerical simulations.

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Investigation on cutting fluid use in finish milling of polylactide (PLA) 3d-printed parts 20

Investigation on cutting fluid use in finish milling of poly...

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20th CIrP Conference on Modeling of Machining Operations in Mons, CIrP CMMO 2025

作者： Lorenzoni, Margaux Spitaels, Laurent rivière-Lorphèvre, Edouard Odent, Jérémy M'Saoubi, rachid Cloëz, Liam Fontaine, Michaël ducobu, François Machine Design and Production Engineering Lab Research Institute for Materials Science and Engineering University of Mons Place du Parc 20 Mons7000 Belgium University of Mons Place du Parc 20 Mons7000 Belgium R&D Material and Technology Development Seco Tools AB Björnbacksvägen 2 FagerstaSE-737 82 Sweden Department of Mechanical Engineering Sciences Division of Production and Materials Engineering Lund University Ole Römers väg 1 LundSE-221 00 Sweden FEMTO-ST Institute-Applied Mechanics Dept. Université de Franche-Comté-SUPMICROTECH-CNRS Rue de l'épitaphe 24 BesançonF-25000 France

Fused Filament Fabrication (FFF) is an additive manufacturing process based on Material Extrusion (MEX) of polymeric filament. This manufacturing technology is commonly used for personalized production applications and prototyping, allowing to obtain customized parts with complex geometries at a low cost. However, this production technique has its limitations regarding dimensional accuracy and surface roughness of the final parts. To overcome these limitations, finish milling is considered to be a promising technique as it is widely used for metallic parts. Yet, the specific thermal properties of polymers such as low thermal conductivity and low melting temperature are adding a challenge in the determination of optimal cutting conditions. In this context, the use of cutting fluid could be the key to keep the material from melting during milling. Therefore, this paper proposes to compare the impact of using a compressed air flow to dry conditions on the cutting forces and the surface quality obtained on 3d-printed polylactide (PLA) parts. The qualification test of the tool-material couple standard (NF E 66-520-6) will be used as a guideline to determine the relevance of cutting fluid use for cutting conditions varying around an operating point. Moreover, simulated cutting forces using a mechanistic model will be compared to experimental data to evaluate the applicability of the model. © 2025 The Authors.

关键词： Compressed air

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Instance segmentation based recognition system tracking tomatoes by ripeness in natural light conditions

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Journal of Institute of Control, robotics and Systems 2020年第11期26卷 940-948页

作者： Lee, Woo-Young Ko, Kwang Eun Kang, Jaehyeon Park, Hyun Ji Jang, Inhoon Applied Robot R&D Department Korea Institute of Industrial Technology Korea Republic of Department of computer Science & Engineering Hanyang University Korea Republic of

This paper describes a tomato recognition system to be applied to a harvesting robot used in farms under natural light conditions. The tomato recognition system is composed of a stereo camera, light, deep learning based instance segmentation model and Kalman filter based object tracking algorithm. In order to train the instance segmentation model, a large-scale dataset of the actual smartfarm background is made, and performance verification is performed on the test dataset. The average precision of the deep learning based instance segmentation model achieved 96.8% of performance when the threshold of the prediction score for classifying ripeness is over 0.75 (Table. 3), and the multiple object tracking accuracy of the Kalman filter based tracking method is approximately 74.3%. © ICrOS 2020.

关键词： Kalman filters

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Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches

arXiv

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arXiv 2024年

作者： Asimopoulos, dimitris Siniosoglou, Ilias Argyriou, Vasileios Karamitsou, Thomai Fountoukidis, Eleftherios Goudos, Sotirios K. Moscholios, Ioannis d. Psannis, Konstantinos E. Sarigiannidis, Panagiotis R&D Department MetaMind Innovations P.C. Kozani Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom Sidroco Holdings Ltd. Nicosia Cyprus Physics Department Aristotle University of Thessaloniki Thessaloniki Greece Department of Informatics and Telecommunications Department University of Peloponnese Tripoli Greece Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece

In the realm of data privacy, the ability to effectively anonymise text is paramount. With the proliferation of deep learning and, in particular, transformer architectures, there is a burgeoning interest in leveraging these advanced models for text anonymisation tasks. This paper presents a comprehensive benchmarking study comparing the performance of transformer-based models and Large Language Models(LLM) against traditional architectures for text anonymisation. Utilising the CoNLL-2003 dataset, known for its robustness and diversity, we evaluate several models. Our results showcase the strengths and weaknesses of each approach, offering a clear perspective on the efficacy of modern versus traditional methods. Notably, while modern models exhibit advanced capabilities in capturing contextual nuances, certain traditional architectures still keep high performance. This work aims to guide researchers in selecting the most suitable model for their anonymisation needs, while also shedding light on potential paths for future advancements in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches

Benchmarking Advanced Text Anonymisation Methods: A Comparat...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： dimitris Asimopoulos Ilias Siniosoglou Vasileios Argyriou Thomai Karamitsou Eleftherios Fountoukidis Sotirios K. Goudos Ioannis d. Moscholios Konstantinos E. Psannis Panagiotis Sarigiannidis R&D Department MetaMind Innovations P.C. Kozani Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Sidroco Holdings Ltd. Nicosia Cyprus Department of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom Physics Department Aristotle University of Thessaloniki Thessaloniki Greece Department of Informatics and Telecommunications Department University of Peloponnese Tripoli Greece Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece

ISBN: (数字)9798350385427

ISBN: (纸本)9798350385434

关键词： deep learning data privacy Circuits and systems Benchmark testing Transformers robustness Task analysis

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Self-attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts

arXiv

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arXiv 2024年

作者： Van Poecke, Aaron Finn, Tobias Sebastian Meng, ruoke Van den Bergh, Joris Smet, Geert demaeyer, Jonathan Termonia, Piet Tabari, Hossein Hellinckx, Peter M4S Faculty of Applied Engineering University of Antwerp Belgium CEREA École des Ponts EDF R&D Île-de-France France Royal Meteorological Institute of Belgium Brussels Belgium Department of Physics and Astronomy Ghent University Ghent Belgium United Nations University Institute for Water Environment and Health HamiltonON Canada

Current postprocessing techniques often require separate models for each lead time and disregard possible inter-ensemble relationships by either correcting each member separately or by employing distributional approaches. In this work, we tackle these shortcomings with an innovative, fast and accurate Transformer which postprocesses each ensemble member individually while allowing information exchange across variables, spatial dimensions and lead times by means of multi-headed self-attention. Weather foreacasts are postprocessed over 20 lead times simultaneously while including up to twelve meteorological predictors. We use the EUPPBench dataset for training which contains ensemble predictions from the European Center for Medium-range Weather Forecasts' integrated forecasting system alongside corresponding observations. The work presented here is the first to postprocess the ten and one hundred-meter wind speed forecasts within this benchmark dataset, while also correcting the two-meter temperature. Our approach significantly improves the original forecasts, as measured by the CrPS, with 17.5 % for two-meter temperature, nearly 5% for ten-meter wind speed and 5.3 % for one hundred-meter wind speed, outperforming a classical member-by-member approach employed as competitive benchmark. Furthermore, being up to 75 times faster, it fulfills the demand for rapid operational weather forecasts in various downstream applications, including renewable energy forecasting. © 2024, CC BY.

关键词： Wind forecasting

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Flow curve prediction of cold forging steel by artificial neural network model 24

Flow curve prediction of cold forging steel by artificial ne...

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24th International ESAFOrM Conference on Material Forming, ESAFOrM 2021

作者： Kocatürk, Fatih Toparli, M. Burak Tanrıkulu, Barış Yurtdaş, Sezgin Zeren, doğuş Kılıçaslan, Cenk R&D Center Norm Cıvata San. ve Tic. A.Ş. AOSB İzmir Turkey Graduate School Applied Mathematics and Statistics İzmir University of Economics İzmir Turkey The Graduate School of Natural and Applied Sciences Dokuz Eylül University İzmir Turkey Mechanical Engineering Department Katip Çelebi University İzmir Turkey

ISBN: (纸本)9782870193020

A limited number of material models or flow curves are available in commercial finite element softwares at varying temperature and strain rate ranges for plasticity analysis. To obtain more realistic finite element results, flow curves at wide temperature and strain rate ranges are required. For this purpose, a material model for a medium carbon alloy steel material which is used for fastener production was prepared. Firstly, flow curves of the material were obtained at 4 temperatures (20, 100, 200, 400 °C) and 3 strain rates (1, 10, 50 s-1). Then, experimental data was used to construct an artificial neural networks model (ANN) for the material. 75% of the experimental data was used to train the model and the rest was employed for validation and verification. ANN model used in flow curve prediction was developed using the scikit-learn library on Python. Temperature, strain rate and strain were employed as input parameters and flow stress as output parameter in ANN model. In order to increase the accuracy of the ANN model, the number of hidden layers and the number of neurons were also optimized by mean squared error approach. As a result of studies, an ANN-based material model that can be used for wide range of temperature and strain rate values were developed based on the experimental data. © ESAFOrM 2021 - 24th Inter. Conf. on Mat. Forming. All rights reserved.

关键词： Python

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AirCode: A robust Object Encoding Method

arXiv

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arXiv 2021年

作者： Xu, Kuan Wang, Chen Chen, Chao Wu, Wei Scherer, Sebastian The Robot R&D Department Geekplus Corp. Beijing100107 China The Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

Object encoding and identification are crucial for many robotic tasks such as autonomous exploration and semantic relocalization. Existing works heavily rely on the tracking of detected objects but have difficulty recalling revisited objects precisely. In this paper, we propose a novel object encoding method, which is named as AirCode, based on a graph of keypoints. To be robust to the number of key-points detected, we propose a feature sparse encoding and object dense encoding method to ensure that each key-point can only affect a small part of the object descriptors, leading it to be robust to viewpoint changes, scaling, occlusion, and even object deformation. In the experiments, we show that it achieves superior performance for object identification than the state-of-the-art algorithms and is able to provide reliable semantic relocalization. It is a plug-and-play module and we expect that it will play an important role in various applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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Privacy-Preserving Human Activity recognition in Smart Homes using deep Learning and Edge Computing for real-Time Processing

Privacy-Preserving Human Activity Recognition in Smart Homes...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： K. Mariappan Baskar Gopal J. ParthaSarathy G. r. Sreekanth r Nanmaran B. Jegajothi Department of Computer Science and Applications SRM Institute of Science and Technology Chennai College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri Information Technology Nandha Engineering College Erode Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai SRS Tech Solutions Chennai

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAr) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAr, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAr dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAr systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MrIs, to improve healthcare outcomes.

关键词： deep learning Measurement Privacy differential privacy Accuracy Federated learning Smart homes real-time systems Human activity recognition Edge computing

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Correction to: Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm

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International Journal of Computational Intelligence Systems 2024年第1期17卷 1-2页

作者： Pandya, Sundaram B. Kalita, Kanak Jangir, Pradeep Ghadai, ranjan Kumar Abualigah, Laith Department of Electrical Engineering Shri K.J. Polytechnic Bharuch India Department of Mechanical Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi India University Centre for Research and Development Chandigarh University Mohali India Department of Biosciences Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences Chennai India Department of Mechanical and Industrial Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Gelugor Malaysia

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