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Recognition of Residual Cores in Aero-Engine Blade Neutron Images Using Improved Patch SVDD

IEEE Transactions on Nuclear science 2025年第5期72卷 1663-1671页

作者： Wu, Yang Yang, Zhikai Yang, Hongchao Sun, Yong Tang, Bin Tuo, Xianguo Yin, Wei Wang, Qibiao China Academy of Engineering Physics Institute of Nuclear Physics and Chemistry Mianyang621000 China Sichuan University of Science & Engineering School of Computer Science and Engineering Zigong643000 China Sichuan University of Science & Engineering School of Physics and Electronic Engineering Zigong643000 China

The recognition of residual cores in aero-engine blades is a crucial task in ensuring the safety and reliability of aircraft. Compared to techniques such as borescope and X-ray radiography, neutron radiography, with its strong penetration ability and high sensitivity to light elements, can detect residual cores as thin as 2 mm within complex cavities. This significantly enhances the detection rate of residual cores in aero-engine blades. However, the recognition of residual cores in neutron images currently relies heavily on manual inspection by professionals, which is subjective and inefficient. To address this issue, an improved residual core recognition method based on a Patch-level Support Vector Data Description (Patch SVDD) algorithm is proposed for neutron images. This study employs an improved gamma transformation to enhance the quality of neutron images and highlight the features of aero-engine blades. A fusion of Dilated Residual Network (DRN) and Efficient Channel Attention (ECA) serves as the feature extraction network in Patch SVDD, improving the capability of feature extraction. Additionally, a residual core grading module is designed to improve core leaching efficiency in production. Neutron images of aero-engine blades were acquired through the reactor-based cold neutron radiography facility to construct a dataset. The results demonstrate that this improved method achieves Areas Under the receiver operating characteristic Curves (AUC) of 94.8% at the image level and 95.6% at the pixel level, indicating its favorable recognition efficacy. This study provides an intelligent method for quality monitoring in aero-engine blades. © 2025 IEEE.

关键词： Neutron radiography

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Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

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science China(physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

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More than just a name?From magnetic to optical skyrmions and the topology of light

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Light(science & Applications) 2025年第1期14卷 26-29页

作者： Jian Chen Andrew Forbes Cheng-Wei Qiu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore School of Physics University of the WitwatersrandJohannesburgSouth Africa

Topology is usually perceived intrinsically immutable for a given *** argue that optical topologies do not immediately enjoy such ***'optical skyrmions'as an example,we show that they will exhibit varying textures and topological invariants(skyrmion numbers),depending on how to construct the skyrmion vector when projecting from real to parameter *** demonstrate the fragility of optical skyrmions under a ubiquitous scenario-simple reflection off an optical *** topology is not without benefit,but it must not be assumed.

关键词： numbers space. topology

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AI-based energy management strategies for electric vehicles: Challenges and future directions

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Energy Reports 2025年 13卷 5535-5550页

作者： Kermansaravi, Azadeh Refaat, Shady S. Trabelsi, Mohamed Vahedi, Hani Delft University of Technology Delft Netherlands School of Physics Engineering and Computer Science University of Hertfordshire United Kingdom Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait

Electric vehicles (EVs) offer a promising solution for mitigating greenhouse gas emissions and minimizing the transportation sector's dependency on non-renewable energy sources. However, efficient energy management poses a significant challenge for their broader adoption, particularly optimizing battery usage, maximizing driving range, and improving overall vehicle performance. This paper presents the state-of-the-art Artificial Intelligence (AI) techniques used in electric vehicle energy management systems (EV-EMS), discussing a variety of deep learning algorithms of AI methodologies, such as, neural networks, and fuzzy logic. Additionally, This paper discusses the role of auxiliary techniques like transfer learning, which enhances model adaptability and reduces training time in AI-driven EMS applications. Through a systematic analysis of each method, this review identifies key trends, highlights the challenges and limitations of each technique, and offers perspectives on potential solutions and future research directions. The paper aims to support researchers, industry professionals, and policymakers in developing advanced, sustainable, and adaptable EV-EMS solutions that maximize battery life, improve vehicle performance, and facilitate real-time adaptive control. Finally, this review highlights the importance of AI-driven strategies in making EV technology more efficient, reliable, and scalable. © 2025

关键词： Battery management systems

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SAM-MedUS: a foundational model for universal ultrasound image segmentation

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Journal of Medical Imaging 2025年第2期12卷 027001页

作者： Tian, Feng Zhai, Jintao Gong, Jinru Lei, Weirui Chang, Shuai Ju, Fangfang Qian, Shengyou Zou, Xiao Hunan Normal University The School of Physics and Electronics Changsha China Hunan University College of Computer Science and Electronic Engineering Changsha China

Purpose: Segmentation of ultrasound images for medical diagnosis, monitoring, and research is crucial, and although existing methods perform well, they are limited by specific organs, tumors, and image devices. Applications of the Segment Anything Model (SAM), such as SAM-med2d, use a large number of medical datasets that contain only a small fraction of the ultrasound medical images. Approach: In this work, we proposed a SAM-MedUS model for generic ultrasound image segmentation that utilizes the latest publicly available ultrasound image dataset to create a diverse dataset containing eight site categories for training and testing. We integrated ConvNext V2 and CM blocks in the encoder for better global context extraction. In addition, a boundary loss function is used to improve the segmentation of fuzzy boundaries and low-contrast ultrasound images. Results: Experimental results show that SAM-MedUS outperforms recent methods on multiple ultrasound datasets. For the more easily datasets such as the adult kidney, it achieves 87.93% IoU and 93.58% dice, whereas for more complex ones such as the infant vein, IoU and dice reach 62.31% and 78.93%, respectively. Conclusions: We collected and collated an ultrasound dataset of multiple different site types to achieve uniform segmentation of ultrasound images. In addition, the use of additional auxiliary branches ConvNext V2 and CM block enhances the ability of the model to extract global information and the use of boundary loss allows the model to exhibit robust performance and excellent generalization ability. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Image segmentation

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Autonomous embodied navigation task generation from natural language dialogues

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science China(Information sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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Feature selection for determining input parameters in antenna modeling

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Journal of Systems engineering and Electronics 2025年第1期36卷 15-23页

作者： LIU Zhixian SHAO Wei CHENG Xi OU Haiyan DING Xiao School of Physics University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer and Information Engineering Xinjiang Agricultural UniversityUrumqi 830052China

In this paper,a feature selection method for determining input parameters in antenna modeling is *** antenna modeling,the input feature of artificial neural network(ANN)is geometric *** selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)*** information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear *** EM response range is utilized to evaluate the *** wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is *** the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly *** modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed *** number of input parameters decreases from8 to *** testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.

关键词： antenna modeling artificial neural network(ANN) feature selection maximal information coefficient(MIC)

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Multi-View Few-Shot Reasoning for Emerging Entities in Knowledge Graphs

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IEEE Transactions on Big Data 2025年第3期11卷 1321-1333页

作者： Yan, Cheng Zhao, Feng Tao, Xiaohui Zhu, Xiaofeng Huazhong University of Science and Technology Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Wuhan430074 China University of Southern Queensland School of Mathematics Physics & Computing Toowoomba4350 Australia University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China

A knowledge graph (KG) is a form of representing knowledge of the objective world. With the expansion of knowledge, KGs frequently incorporate new entities, which often possess limited associated data, known as few-shot features. Addressing the missing knowledge for these emerging entities is crucial practically, but there are significant challenges due to data scarcity. Previously developed methods based on knowledge graph embedding (KGE) and graph neural networks (GNNs) focusing on instance-level KGs are confronted with challenges of data scarcity and model simplicity, rendering them inapplicable to reasoning tasks in few-shot scenarios. To tackle these issues, we propose a multi-view few-shot KG reasoning method for emerging entities. The primary focus of our method lies in resolving the problem of link prediction for emerging entities with limited associated triples from multiple perspectives. Distinct from previous methods, our approach initially abstracts a concept-view KG from the conventional instance-view KG, enabling the formulation of commonsense rules. Additionally, we employ the aggregation of multi-hop subgraph features to enhance the representation of emerging entities. Furthermore, we introduce a more efficient cross-domain negative sampling strategy and a multi-view triple scoring function based on commonsense rules. Our experimental evaluations highlight the effectiveness of our method in few-shot contexts, demonstrating its robustness and adaptability in both cross-shot and zero-shot scenarios, significantly outperforming existing models in these challenging settings. © 2015 IEEE.

关键词： Knowledge graph

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Generating Synthetic EEG Data Using Generative AI for Mental States Prediction in Human-Machine Interaction 16th

Generating Synthetic EEG Data Using Generative AI for Menta...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Venugopal, Archana Faria, Diego Resende School of Physics Engineering and Computer Science University of Hertfordshire Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9789819635245

This work presents a novel approach using Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic EEG waves corresponding to concentration and relaxation mental states. By addressing the challenge of limited biophysical data, our model produces realistic synthetic brainwaves learned from real data, which is vital for studies restricted by volunteer availability and privacy concerns. Combining real and synthetic data improved classification accuracy from 92% to 98.45%, highlighting the benefit of expanding the dataset for better machine learning model performance. The WGAN-GP model generated synthetic EEG data with 96.84% accuracy for relaxation and optimum accuracy for concentration, as classified by a Convolutional Neural Network (CNN). Additionally, 50% synthetic data combined with the original dataset achieved the highest accuracy (98.48%). A comparison was made between a basic GAN and the WGAN-GP model, demonstrated that the WGAN-GP outperformed the basic GAN in this study. Techniques like discrete wavelet transform, downsampling, and upsampling were employed. This method shows potential in addressing EEG data scarcity and enhancing human-robot interaction in assistive technologies and mental health monitoring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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An Effective Intrusion Detection System Based on the FSA-BGRU Hybrid Model

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China Communications 2025年第2期22卷 188-198页

作者： Deng Zaihui Li Zihang Guo Jianzhong Gan Guangming Kong Dejin School of Electronic and Electrical Engineering Wuhan Textile UniversityWuhan 430200China School of Computer Science and Artificial Intelligence Wuhan Textile UniversityWuhan 430200China School of Physics and Electronic Information Engineering Hubei Engineering UniversityXiaogan 432000China

Intrusion detection systems play a vital role in cyberspace *** this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature ***,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior ***,a support vector machine(SVM)classifier is used to classify intrusion *** proposed model is verified by experiments on the NSL-KDD *** results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion *** proposed method can provide new insight into network intrusion detection.

关键词： bidirectional GRU feature selection intrusion detection system multilayer perceptron recursive feature elimination support vector machine

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