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Predictive Models for the Early Diagnosis and Prognosis of Knee Osteoarthritis Using Deep Learning Techniques 1st

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Predictive Models for the Early Diagnosis and Prognosis of K...

1st International Conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Malathi, S.Y. Bharamagoudar, Geeta Shiragudikar, Sharada K. G. Totad, Shashikumar CSE Department KLEIT Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Department of Automation and Robotics KLE Technological University Karnataka Hubballi India

ISBN: (纸本)9783031756047

Uncovering hidden patterns and relationships in knee OA data using machine learning algorithms can be incredibly useful for predicting illness progression and developing more effective treatments. By analyzing large amounts of data, machine learning algorithms can identify patterns and relationships that might not be immediately apparent to human researchers. This can help us better understand the mechanisms behind knee OA and identify potential targets for treatment. By developing more potent treatments, we may be able to slow the progression of the disease and improve patient outcomes. The knee osteoarthritis dataset information was initially acquired from Kaggle and the information on knee osteoarthritis is processed before model training. The study aims to develop and apply advanced machine learning methods, such as deep learning techniques like CNN-SVM, Mobilenet v2, Resnet152v2, Resnet 50, and EfficientnetB5, to analyze knee OA data and develop accurate prediction models for early diagnosis and prognosis. The study evaluates the performance of these models on a dataset of individuals with knee OA to determine their efficacy and accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CNN EfficientNetB5 Knee osteoarthritis mobilenetV2 ResNet50

Verifying Nonlinear Neural Feedback Systems using Polyhedral Enclosures

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arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

Grasping by Spiraling: Reproducing Elephant Movements with Rigid-Soft Robot Synergy

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arXiv 2025年

作者： Huang, Huishi Wang, Haozhe Fang, Chongyu Yan, Mingge Xu, Ruochen Zhang, Yiyuan Wang, Zhanchi Ying, Fengkang Liu, Jun Laschi, Cecilia Ang, Marcelo H. Advanced Robotics Centre National University of Singapore 117608 Singapore Integrative Sciences and Engineering Programme National University of Singapore Graduate School 119077 Singapore School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China 138632 Singapore

—The logarithmic spiral is observed as a common pattern in several living beings across kingdoms and species. Some examples include fern shoots, prehensile tails, and soft limbs like octopus arms and elephant trunks. In the latter cases, spiraling is also used for grasping. Motivated by how this strategy simplifies behavior into kinematic primitives and combines them to develop smart grasping movements, this work focuses on the elephant trunk, which is more deeply investigated in the literature. We present a soft arm combined with a rigid robotic system to replicate elephant grasping capabilities based on the combination of a soft trunk with a solid body. In our system, the rigid arm ensures positioning and orientation, mimicking the role of the elephant’s head, while the soft manipulator reproduces trunk motion primitives of bending and twisting under proper actuation patterns. This synergy replicates 9 distinct elephant grasping strategies reported in the literature, accommodating objects of varying shapes and sizes. The synergistic interaction between the rigid and soft components of the system minimizes the control complexity while maintaining a high degree of adaptability. Copyright © 2025, The Authors. All rights reserved.

关键词： Robotic arms

Privacy Preserving Charge Location Prediction for Electric Vehicles

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arXiv 2025年

作者： Marlin, Robert Jurdak, Raja Abuadbba, Alsharif Miller, Dimity School of Computer Science Australia School of Electrical Engineering and Robotics Queensland University of Technology Australia CSIRO’s Data61 Cyber Security Cooperative Research Centre Australia CSIRO’s Data61 Australia

By 2050, electric vehicles (EVs) are projected to account for 70% of global vehicle sales. While EVs provide environmental benefits, they also pose challenges for energy generation, grid infrastructure, and data privacy. Current research on EV routing and charge management often overlooks privacy when predicting energy demands, leaving sensitive mobility data vulnerable. To address this, we developed a Federated Learning Transformer Network (FLTN) to predict EVs’ next charge location with enhanced privacy measures. Each EV operates as a client, training an onboard FLTN model that shares only model weights, not raw data with a community based Distributed Energy Resource Management System (DERMS), which aggregates them into a community global model. To further enhance privacy, non-transitory EVs use peer-to-peer weight sharing and augmentation within their community, obfuscating individual contributions and improving model accuracy. Community DERMS global model weights are then redistributed to EVs for continuous training. Our FLTN approach achieved up to 92% accuracy while preserving data privacy, compared to our baseline centralised model, which achieved 98% accuracy with no data privacy. Simulations conducted across diverse charge levels confirm the FLTN’s ability to forecast energy demands over extended periods. We present a privacy focused solution for forecasting EV charge location prediction, effectively mitigating data leakage risks. © 2025, CC BY-NC-ND.

关键词： Differential privacy

Testing Facility Location with Constrained Queue Time Problem: A Case Study in Florida, USA 24

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Testing Facility Location with Constrained Queue Time Proble...

Proceedings of the Winter Simulation Conference

作者： Almir Antonio Monteiro Junior Rafael Schneider Vanessa de Almeida Guimarães Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro BRAZIL COPPEAD Graduate School of Business Federal University of Rio de Janeiro BRAZIL

ISBN: (纸本)9798331534202

This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectious diseases, while constraining the maximum time in the queue with a given probability. An integer programming model is introduced and applied to the three biggest counties, in terms of population, of Florida, United States. Moreover, the Monte Carlo method is used to evaluate the model's output, aiming to check if the queueing time constraint is being satisfied. Through the experiments, a testing facility deployment plan can be determined for each county and further validated by the simulation. The results show that the solutions returned by the model behaved successfully when submitted to the Monte Carlo method, not exceeding the time in the queue in more than the predefined probability.

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High-performance UV polarization sensitive photodetector for a graphene(2D)/GaN(3D) junction with a non-centrosymmetric electric field

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Photonics Research 2025年第6期13卷 1544-1553页

作者： Can Zou Qing Liu Lu Zhang Xiao Tang Xiaohang Li Shuti Li Fangliang Gao Guangdong Engineering Research Center of Optoelectronic Functional Materials and Devices School of Electronic Science and Engineering (School of Microelectronics) South China Normal University Guangzhou 510631 China Advanced Semiconductor Laboratory Electrical and Computer Engineering Program CEMSE Division King Abdullah University of Science and Technology Thuwal 239556900 Saudi Arabia e-mail: lishuti@*** e-mail: gaofl@m.***

This study pioneers a high-performance UV polarization-sensitive photodetector by ingeniously integrating non-centrosymmetric metal nanostructures into a graphene ( Gr ) / Al 2 O 3 / GaN heterojunction. Unlike conventional approaches constrained by graphene’s intrinsic isotropy or complex nanoscale patterning, our design introduces asymmetric metal architectures (E-/T-type) to artificially create directional anisotropy. These structures generate plasmon-enhanced localized electric fields that selectively amplify photogenerated carrier momentum under polarized UV light (325 nm), synergized with Fowler-Nordheim tunneling (FNT) across an atomically thin Al 2 O 3 barrier. The result is a breakthrough in performance: a record anisotropy ratio of 115.5 (E-type, − 2 V ) and exceptional responsivity (97.7 A/W), surpassing existing graphene-based detectors by over an order of magnitude. Crucially, by systematically modulating metal geometry and density, we demonstrate a universal platform adaptable to diverse 2D/3D systems. This study provides a valuable reference for developing and practically applying photodetectors with higher anisotropy than ultraviolet polarization sensitivity.

关键词： Chemical vapor deposition Metals Nanostructures Quantum efficiency Surface plasmon resonance X-ray diffraction

A Novel Tactile Sensing Skin for Surface Perception

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IEEE Sensors Journal 2025年

作者： Das, Subham Thiyagarajan, Karthick Kodagoda, Sarath Krishnan, Athul Bhattacharjee, Mitradip Indian Institute of Science Education and Research Bhopal i-Labs Electrical Engineering and Computer Science Department Madhya Pradesh 462066 India Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment PenrithNSW2751 Australia UTS Robotics Institute University of Technology Sydney Australia SydneyNSW2007 Australia

Humans employ a fusion of pressure and temperature signals to perceive tactile stimuli. While replicating the complexity of human tactile sensing poses challenges, advancements in artificial tactile sensing skins play a crucial role in the progress of robotics and prosthetics. Here, we present a novel tactile sensing skin designed for the perception of diverse surfaces. It utilizes a microstructured porous tactile sensor, exhibiting notable sensitivity at 21.5 kPa-1 under low-pressure conditions and 2.5 kPa-1 in high-pressure scenarios. The sensor also boasts an impressively low detection limit of 2.5 Pa, allowing for discernment of subtle pressure variations. Simultaneously, we utilize a flexible temperature sensor exhibiting a sensitivity of 0.58 % ∘C-1 and swift response attributes to facilitate the detection of radiated heat from diverse surfaces. The incorporation of a multilayer perceptron algorithm into the developed glove-like system, equipped with tactile and temperature sensors, achieves 87% accuracy in classifying eleven distinct surfaces. © 2001-2012 IEEE.

关键词： Tactile sensors

Analysis of Partial Discharges in Oil-Impregnated Transformer Paper Insulation and PET-G Insulation

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IEEE Open Journal of Instrumentation and Measurement

IEEE Open Journal of Instrumentation and Measurement 2025年 4卷

作者： Kameli, Sayed Mohammad Abuelrub, Abdelaziz AlShaikh Saleh, Mohammad Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Olesz, Marek Texas A&M University at Qatar Electrical and Computer Engineering Department Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States University of Hertfordshire School of Physics Engineering and Computer Science Department of Engineering HatfieldAL10 9EU United Kingdom Hamad Bin Khalifa University Qatar Foundation Electrical and Computer Engineering Program in College of Science and Engineering Doha Qatar Gdańsk University of Technology Department of Electrical Power Engineering Gdańsk80-233 Poland

Partial discharge (PD) is a widespread phenomenon instigated in power transformer (PT) insulation systems. PDs are triggered by voids that vary in size and position within the PT insulation. The electrical characteristics of those damaging, PD-causing cavities must be well understood, to accelerate the development of advanced PD detection techniques. Thus, the impact of varying the radius and position of spherical air voids on the characteristics of PDs in PTs is examined using a 3-D finite element analysis (FEA) model designed in COMSOL Multiphysics. The spherical voids are positioned between two windings of a 512 kV, three-phase (3φ ) PT. The peak electric field (EF) and aggregate energy in the FEA model are used in conjunction with laboratory measurements of the apparent discharge magnitude, for detailed analysis of Polyethylene Terephthalate Glycol (PET-G)-based cylindrical voids with different heights. Simulations demonstrate that the inception of PD activity in the PT model occurs for spherical voids with a radius exceeding 1 mm. Furthermore, the most severe PDs occur within the press-board insulation, adjacent to the uppermost part of the innermost windings. Experiments demonstrate that a significant increase in PD activity was observed for PET-G-based cylindrical voids with heights exceeding 1 mm. © 2022 IEEE.

关键词： Partial discharges

Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

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arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks