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arXiv 2024年

作者： Asahara, Keisuke Funakawa, Daiju Seki, Motoki Suzuki, Akito Data Science and AI Innovation Research Center Shiga University 1-1-1 Banba hikone Shiga522-8522 Japan Department of Electronics and Information Engineering Hokkai-Gakuen University Hokkaido Sapporo062-8605 Japan Department of Mathematics Faculty of Science Hokkaido University Sapporo060-0810 Japan Department of Production Systems Engineering and Sciences Komatsu University Awazu Campus Nu 1-3 Shicho-machi Ishikawa Komatsu923-8511 Japan

Quantum walks, mathematical models referred to as the quantum counterparts of random walks, have garnered significant attention in recent years with the advancement of quantum computing. The weak limit theorem for quantum walks, analogous to the central limit theorem for random walks, is one of the most important theorems in this field. In this study, we investigated the weak limit theorem for a two-state discrete-time quantum walk on a two-dimensional square lattice. As a result, we derived a two-dimensional probability distribution whose support is the intersection of two ellipses. The probability distribution we obtained resembles the distribution of one-dimensional quantum walks while possessing a unique form specific to two dimensions. © 2024, CC BY-NC-ND.

关键词： Random processes

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Development of a Life Prediction Model of Ti-6Al-4V obtained by Additive Manufacturing

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Procedia Structural Integrity 2024年 53卷 236-245页

作者： João Alves Teresa Morgado Ivan Galvão António Pereira Manuel Pereira Lisbon School of Engineering of Polytechnic Institute of Lisbon Portugal UNIDEMI - Research & Development Unit in Mechanical and Industrial Engineering Department of Mechanical and Industrial Engineering FCT NOVA - Faculty of Science and Technology Universidade Nova de Lisboa Almada Portugal Intelligent Systems Associate Laboratory Guimarães Portugal Navy Research Center Almada Portugal CEMMPRE – Centre for Mechanical Engineering Materials and Processes Department of Mechanical Engineering University of Coimbra Coimbra Portugal CDRsp & ESTG - Centre for Rapid and Sustainable Product Development & School of Technology and Management of Institute Polytechnic of Leiria Leiria Portugal CERENA- Center of Natural Resources and Environment Univerdade Lisboa Lisboa Portugal

The present study aimed to develop a new life prediction model of a Ti-6Al-4V alloy obtained by additive manufacturing (AM). The Selective Laser Melting (SLM) method was used to obtain specimens. This study contemplates four phases. The first phase encompasses an analysis of the manufacturing process, including the raw materials cycle, specimen design, and the cutting process. In the second phase, tensile and fatigue tests are conducted to obtain the elastic and plastic proprieties and fatigue parameters (S-N Curve). The third phase involves an analytical and quantitative study of the manufacturing intrinsic defects of the SLM process, using optical microscopy, micro and nanotomography, and image treatment. The last phase focused on developing a life prediction model for Ti-6Al-4V based on experimental findings from the preceding steps. In conclusion, Ti-6Al-4V alloy obtained by additive manufacturing showed better overall mechanical properties when compared to the same alloy produced by a traditional method, exhibiting robust plastic behaviour and good ductility. Concerning life prediction models, two new models were developed to predict the fatigue limit of a Ti-6Al-4V obtained by additive manufacturing based on two different defect quantification methods.

关键词： Ti-6Al-4V Additive Manufacturing Selective Laser Melting Life Prediction Modelos Tomography

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AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fall Risk and Treatment Efficacy in Balance Disorders 24

AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fal...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Karapintzou, Efterpi Tsakanikas, Vassilios Kikidis, Dimitrios Nikitas, Christos Nairn, Brooke Pavlou, Marousa Bamiou, Doris-Eva Exarchos, Themis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece Dept. of Otorhinolaryngology Head and Neck Surgery National and Kapodistrian University of Athens Hippocrateion General Hospital Athens Greece Ear Institute Faculty of Brain Sciences University College London London United Kingdom University College London Hospitals Dept. of Neuro-Otology London United Kingdom Centre for Human and Applied Physiological Sciences Faculty of Life Sciences and Medicine School of Basic and Medical Biosciences King's College London London United Kingdom Ionian University Dept. of Informatics Corfu Greece Biomedical Research Institute Foundation for Research and Technology Hellas FORTH Ioannina Greece

ISBN: (纸本)9798331518622

This study focuses on the development of artificial intelligence models to enhance telerehabilitation practices. We utilized diverse datasets to create clinically relevant models for predicting two critical outcomes: fall risk and treatment effectiveness. By applying various machine learning techniques, including K-Nearest Neighbors, Random Forest, Decision Tree, Support Vector Machine, and XGBoost, our models demonstrated high accuracy, sensitivity, and specificity. Notably, the Random Forest model achieved an accuracy of 0.97 in predicting fall risk and 0.96 in assessing treatment effectiveness. These models equip clinicians with powerful tools for data-driven decision-making, ultimately improving patient outcomes in rehabilitation settings. © 2024 IEEE.

关键词： Prediction models

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Drive System Integrated Magnetic Multiple Spur Gear and High-Speed Motors for Low Floor Light Rail Vehicles

Drive System Integrated Magnetic Multiple Spur Gear and High...

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International Conference on Electrical Machines and systems (ICEMS)

作者： Yoshiki Nishioka Kan Akatsu Department of Mathematics Physics Electrical Engineering and Computer Science Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama Japan Department of Faculty of Engineering Division of Intelligent Systems Engineering Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama Japan

ISBN: (数字)9781665493024

ISBN: (纸本)9781665493031

The purpose of this research is to realize a small and lightweight drive system for low floor Light Rail Vehicles (LRVs) by high-speed motor driven. High-speed motor input is limited due to friction in conventional drive systems using mechanical gears. Therefore, we focused on magnetic gears to solve the problem of friction in mechanical gears. However, conventional magnetic gears have problems in terms of efficiency and mechanical strength at high-speed drive. Thus, we propose a small and lightweight drive system integrated Magnetic Multiple Spur Gear and High-speed Motors for low floor LRVs which is suitable for high-speed motor drive. In this paper, the performances of the designed drive system and the proposed method for torque ripple reduction were verified by finite element analysis. From verification, results show that the proposed system and ripple reduction method is highly effective for reducing volume and weight and suppressing ripple.

关键词： Motor drives Torque Friction Magnetic gears Light rail systems Inverters Magnetic analysis

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Towards Feature Distribution Alignment and Diversity Enhancement for data-Free Quantization

Towards Feature Distribution Alignment and Diversity Enhance...

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IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Richang Hong Haijun Zhang Jicong Fan Shuicheng Yan School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Department of Computer Science Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Singapore

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or calibration of quantized model, which makes them inapplicable to the cases that original data are not accessed due to privacy or security. This gives birth to the data-free quantization method with synthetic data generation. While current data-free quantization methods still suffer from severe performance degradation when quantizing a model into lower bit, caused by the low inter-class separability of semantic features. To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the feature distribution alignment for synthetic data generation. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics to imitate the distribution of real data, so that the performance degradation is alleviated. Moreover, we incorporate the diversity enhancement to solve class-wise mode collapse. We also employ the exponential moving average to update the centroid of each cluster for further feature distribution improvement. Extensive experiments based on different deep models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate that our proposed ClusterQ model obtains state-of-the-art performance.

关键词： Degradation Training Quantization (signal) Semantics Neural networks Memory management data models

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FineFormer: Fine-Grained Adaptive Object Transformer for Image Captioning

FineFormer: Fine-Grained Adaptive Object Transformer for Ima...

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IEEE International Conference on data Mining (ICDM)

作者： Bo Wang Zhao Zhang Jicong Fan Mingbo Zhao Choujun Zhan Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Information Science and Technology Donghua University Shanghai China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou China School of Information Engineering Zhengzhou University Zhengzhou China

Image captioning is still a challenging task aiming at describing the contents of image by words. Current image caption methods usually assume the object relation to be important if the semantic and spatial geometric relationships between objects are close and large, but the relations meeting this assumption are not necessarily important to describe the contents of image in a fine-grained way. That is, the importance of fine-grained object relations is not properly taken into account. Besides, current Transformer based image caption models also fail to consider the importance of fine-grained objects, since they generate all the words of a sentence at one time, which cannot Figure out which objects are more important and vice versa. In this paper, we propose a novel Fine-grained Adaptive Object Transformer (FineFormer) network, which can jointly discover the importance of fine-grained objects and object relations for image captioning. Specifically, a new concept of adaptive soft-foreground attention is proposed to highlight the fine-grained objects dominating the descriptive contents. To characterize and calculate the important relations between fine-grained objects, we also propose an adaptive object relation attention to refine the object relation from the generation process of relation. As such, FineFormer can describe the contents of image more accurately, by reducing the interference of unimportant objects in the background. Extensive experiments on the highly-competitive MS-COCO dataset demonstrated the superiority of our FineFormer.

关键词： Adaptation models Adaptive systems Current transformers Semantics Interference data mining Task analysis

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Cognitive-inspired Post-processing of optical character recognition for Swedish addresses

Cognitive-inspired Post-processing of optical character reco...

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IEEE International Conference on Cognitive Informatics

作者： Moa Andersson Summrina Kanwal Faiza Khan School of Electrical Engineering and Computer Science Kth Royal Institute of Technology in Stockholm Sweden School of Electrical Engineering and Computer Science Kth Royal Institute of Technology in Stockholm Centre for Applied Intelligent Systems Research Halmstad University Sweden Faculty of Computing Riphah International University Islamabad Pakistan

Optical character recognition (OCR) has many ap-plications, such as digitizing historical documents, automating processes, and helping visually impaired people read. However, extracting text from images into a digital format is not an easy problem to solve, and the outputs from the OCR frameworks often include errors. The complexity comes from the many variations in (digital) fonts, handwriting, lighting, etc. To tackle this problem, this thesis investigates two different methods for correcting the errors in OCR output. The used dataset consists of Swedish addresses. The methods are therefore applied to postal automation to investigate the usage of these methods for further automating postal work by automatically reading addresses on parcels using OCR. The first method, the lexical implementation, uses a dataset of Swedish addresses so that any valid address should be in this dataset (hence there is a known and limited vocabulary), and misspelled addresses are corrected to the address in the lexicon with the smallest Levenshtein distance. The second approach uses the same dataset, but with artificial errors, or artificial noise, added. The addresses with this artificial noise are then used together with their correct spelling to train a machine learning model based on Neural machine translation (NMT) to automatically correct errors in OCR read addresses. The results from this study could contribute by defining in what direction future work connected to OCR and postal addresses should go. The results were that the lexical implementation outperformed the NMT model. However, more experiments including real data would be required to draw definitive conclusions as to how the methods would work in real-life applications.

关键词： Vocabulary Automation Computational modeling Optical character recognition Lighting Machine learning Complexity theory

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Generalized Contrastive Optimization of Siamese Networks for Place Recognition

arXiv

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arXiv 2021年

作者： Leyva-Vallina, María Strisciuglio, Nicola Petkov, Nicolai The Intelligent Systems Group The Bernoulli Institute The University of Groningen Netherlands The Faculty of Electrical Engineering Mathematics and Computer Science The University of Twente Netherlands

Visual place recognition is a challenging task in computer vision and a key component of camera-based localization and navigation systems. Recently, Convolutional Neural Networks (CNNs) achieved high results and good generalization capabilities. They are usually trained using pairs or triplets of images labeled as either similar or dissimilar, in a binary fashion. In practice, the similarity between two images is not binary, but continuous. Furthermore, training these CNNs is computationally complex and involves costly pair and triplet mining strategies. We propose a Generalized Contrastive loss (GCL) function that relies on image similarity as a continuous measure, and use it to train a siamese CNN. Furthermore, we present three techniques for automatic annotation of image pairs with labels indicating their degree of similarity, and deploy them to re-annotate the MSLS, TB-Places, and 7Scenes datasets. We demonstrate that siamese CNNs trained using the GCL function and the improved annotations consistently outperform their binary counterparts. Our models trained on MSLS outperform the state-of-the-art methods, including NetVLAD, NetVLAD-SARE, AP-GeM and Patch-NetVLAD, and generalize well on the Pittsburgh30k, Tokyo 24/7, RobotCar Seasons v2 and Extended CMU Seasons datasets. Furthermore, training a siamese network using the GCL function does not require complex pair mining. We release the source code at https://***/marialeyvallina/generalized_contrastive_loss. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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GoSafe: Globally Optimal Safe Robot Learning

GoSafe: Globally Optimal Safe Robot Learning

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Dominik Baumann Alonso Marco Matteo Turchetta Sebastian Trimpe Institute for Data Science in Mechanical Engineering RWTH Aachen University Germany Max Planck Institute for Intelligent Systems Stuttgart Germany ETH Zürich Switzerland

When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage. SafeOpt is an efficient Bayesian optimization (BO) algorithm that can learn policies while guaranteeing safety with high probability. However, its search space is limited to an initially given safe region. We extend this method by exploring outside the initial safe area while still guaranteeing safety with high probability. This is achieved by learning a set of initial conditions from which we can recover safely using a learned backup controller in case of a potential failure. We derive conditions for guaranteed convergence to the global optimum and validate GoSafe in hardware experiments.

关键词： Automation Conferences Switches Hardware Robot learning Safety Bayes methods

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Reinforcement Learning Infused MAC for Adaptive Connectivity

Reinforcement Learning Infused MAC for Adaptive Connectivity

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IEEE Conference on Wireless Communications and Networking

作者： Dinesh Kumar Sah Ali Nauman Muhammad Ali Jamshed Korhan Cengiz Nikola Ivković Vedran Uroš Division of Networked and Embedded Systems Research Group Data Communication Mälardalens University Västerås Sweden Department of Information and Communication Engineering Yeungnam University Gyeongbuk Republic of Korea College of Science and Engineering University of Glasgow United Kingdom Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic “Marko Marulić” in Knin Knin Croatia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

关键词： 6G mobile communication Adaptive systems Redundancy Reinforcement learning Media Access Protocol Throughput Resource management

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