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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Jing Yang, Jin Wu, Chenwei Jiang, Gaozhe Lv, Yaning Liu, Bingyan Xu, Feng Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis United States Evident Battery Inc. Massachusetts United States Institute of Operations Research and Analytics National University of Singapore Singapore School of Mathematics and Statistics Xi'an University of Electronic Science and Technology Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering and Design Technical University of Munich Munich Germany

ISBN: (纸本)9798350368741

Multiple Sclerosis (MS) disrupts nerve communication, potentially leading to permanent damage. Convolutional Neural Networks (CNNs) are commonly recommended to accelerate magnetic resonance imaging (MRI) analysis for MS. Traditional CNN-based methods often face challenges with feature selection, imbalanced classification, and hyperparameter optimization, which can reduce their effectiveness. To address these challenges, this paper introduces a reinforcement learning (RL) strategy for tackling feature selection and class imbalance, complemented by an advanced Differential Evolution (DE) algorithm for hyperparameter tuning. The RL algorithm dynamically adapts, enhancing feature selection and class balance, and consistently outperforms traditional static methods by continually optimizing its response to the complex patterns in MS detection. Furthermore, the DE is enhanced with a novel mutation mechanism that utilizes k-means clustering to identify significant clusters. Tested on the SNAC-MS and ISBI datasets, our model demonstrates robust accuracy with average F-measures of 92.873% and 93.783%, respectively, highlighting its effectiveness and adaptability in diagnosing MS. © 2025 IEEE.

关键词： feature selection hyperparameter optimization imbalanced classification Multiple sclerosis reinforcement learning

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design of Shocks & Vibrations Monitoring system for Long Distance Transportation in Confined Spaces

Design of Shocks & Vibrations Monitoring System for Long Dis...

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Chinese Automation Congress (CAC)

作者： Shiyong Huang Yuru Geng Yong Han Mingfei Gu Chengdu China Nuclear Haichuan Nuclear Technology Co. Ltd Nuclear Power Institute of China Chengdu China School of Network and Communication Engineering Chengdu Technological University Chengdu China Chengdu Vocational & Technical College of Industry Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China

In real life, many valuable products and highvalue important products are transported in long distances. Shocks and vibrations are caused during transportation, loading and unloading for various reasons. Any shock and vibration exceeding the regulations may cause damage to the transported products. So, it is necessary to monitor and analyze these data of shocks and vibrations scientifically, while considering that some transportation conditions have special requirements for installation space and transportation time. Therefore, an ultra-thin and long-lasting monitoring system has been developed based on MEMS accelerometer technology to measure shocks and vibrations. After series of independent tests and calibration tests conducted by a third party on the product, the results show that it can record and store the threeaxis acceleration, temperature, humidity, pressure, time and other parameters of the transportation process in real time, and complete the preliminary assessment of the transportation status of high-value products. Then engineers can conduct professional analysis of the data to obtain scientific transportation monitoring reports, and provide scientific basis for taking safety prevention and treatment measures for subsequent transportation.

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Corrosion Behavior of Micro-Arc Oxidation Coating on Zirconium Alloy in Superheated Steam Condition

SSRN

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SSRN 2022年

作者： Li, Zheng-Yang Zheng, Mei-Yin Yang, Zhong-Bo Ren, Quan-Yao Cai, Zhen-Bing Jiao, Yong-Jun Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China Tribology Research Institute Key Lab of Advanced Technologies of Materials Southwest Jiaotong University Chengdu610031 China

The corrosion behavior of MAO coating on Zr alloy was investigated in 400 ºC/10.3 MPa condition. The surface morphology, phase composition, and element distribution of MAO coating were analyzed. The results indicate that the oxide layer on MAO coating presents fewer cracks and pores than Zr alloy, because the high compressive stress can stabilize the t-ZrO2 and restrain the phase transformation, thus reducing the volume expansion and future decreasing defect. Based on the XRD, Raman mapping, SEM, XPS, and TEM results, the formation mechanism of t-ZrO2 and phase transformation process of MAO coating in superheated steam condition was summarized. © 2022, The Authors. All rights reserved.

关键词： Phase transitions

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A Position Probability Prediction Method of Marine Moving Targets for Optimization Search by Satellite

A Position Probability Prediction Method of Marine Moving Ta...

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IEEE International Conference on Control science and systems Engineering (CCSSE)

作者： Xiao Zheng Xiaodong Peng Zhiyuan Guo Huimin Qiang Junbao Zhao Laboratory of System Simulation and Concurrent Design Technology (National Space Science Center Chinese Academy of Sciences) University of Chinese Academy of Science Beijing China Beijing Remote Sensing Information Institute Beijing China

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

Marine moving ship target search has great application demand and development prospects in marine surveillance, marine search and rescue, customs anti-smuggling, and other aspects. Satellites are a vital means of searching for marine moving targets. Ground coverage for imaging satellites is relatively limited; the vast marine area makes searching for marine moving ships challenging. Target position prediction, used as the guidance information for satellite detection to reduce the uncertainty of the target position and improve search efficiency, is an essential approach to achieving target optimization search. This paper proposes a target position prediction method based on joint learning of multi-source factors for optimal satellite search of marine moving targets. Firstly, it analyzes the characteristics of marine moving targets and the factors affecting motion. Then, it puts forward the motion prediction model based on a deep neural network, integrating multifactor, like the state of the moving target, environmental attributes such as nautical charts, meteorology and hydrology, and kinematic constraints, to predict the target positional distribution probability. Finally, the target search strategy based on the maximum sum of the distribution probabilities of the satellite coverage area is adopted to improve target detection probability. The experiment results show that the prediction method can realize the target positional distribution probability prediction, reduce the uncertainty search when making the satellite search plan, and improve efficiency.

关键词： Satellites Uncertainty Object detection Kinematics Predictive models Search problems Satellite navigation systems Marine vehicles Modeling Optimization

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Robust Output Synchronization of Discrete-Time Linear-Time-Invariant Multi-Agent systems Using Phase Tool

Robust Output Synchronization of Discrete-Time Linear-Time-I...

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European Control Conference (ECC)

作者： Xin Mao Dan Wang Wei Chen Li Qiu School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Southern University of Science and Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong S.A.R. China

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, the output synchronization in large-scale discrete-time networks is examined by utilizing the novel phase tool, where the agent dynamics are allowed to be significantly heterogeneous. The synchronization synthesis problem is formulated and thoroughly investigated, with the goal of characterizing the allowable heterogeneity among the agents to ensure synchronization under a uniform controller. The solvability condition is provided in terms of the phases of the residue matrices of the agents at the persistent modes. When the condition is satisfied, a design procedure is given, producing a low-gain synchronizing controller. Numerical examples are given to illustrate the results.

关键词： Sufficient conditions Europe Heterogeneous networks Synchronization Multi-agent systems

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A data-enabled physics-informed neural network with comprehensive numerical study on solving neutron diffusion eigenvalue problems

arXiv

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arXiv 2022年

作者： Yang, Yu Gong, Helin Zhang, Shiquan Yang, Qihong Chen, Zhang He, Qiaolin Li, Qing ParisTech Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China School of Mathematics Sichuan University Chengdu610065 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China

We put forward a data-enabled physics-informed neural network (DEPINN) with comprehensive numerical study for solving industrial scale neutron diffusion eigenvalue problems (NDEPs). In order to achieve an engineering acceptable accuracy for complex engineering problems, a very small amount of prior data from physical experiments are suggested to be used, to improve the accuracy and efficiency of training. We design an adaptive optimization procedure with Adam and LBFGS to accelerate the convergence in the training stage. We discuss the effect of different physical parameters, sampling techniques, loss function allocation and the generalization performance of the proposed DEPINN model for solving complex eigenvalue problems. The feasibility of proposed DEPINN model is verified on three typical benchmark problems, from simple geometry to complex geometry, and from mono-energetic equation to two-group equations. Numerous numerical results show that DEPINN can efficiently solve NDEPs with an appropriate optimization procedure. The proposed DEPINN can be generalized for other input parameter settings once its structure been trained. This work confirms the possibility of DEPINN for practical engineering applications in nuclear reactor physics. Copyright © 2022, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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A Data-Enabled Physics-Informed Neural Network with Comprehensive Numerical Study on Solving Neutron Diffusion Eigenvalue Problems

SSRN

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SSRN 2022年

We present a data-enabled physics-informed neural network (DEPINN) with comprehensive numerical study for solving industrial scale neutron diffusion eigenvalue problems (NDEPs). In order to achieve an engineering acceptable accuracy for complex engineering problems, a small amount of prior data from physical experiments are suggested to be used, to improve the accuracy and efficiency. We design an adaptive optimization procedure with Adam and LBFGS to accelerate the convergence. We discuss the effect of different physical parameters, sampling techniques, loss function allocation and the generalization performance of the proposed DEPINN model for solving complex problem. The feasibility of proposed DEPINN model is tested on three typical benchmark problems, from simple geometry to complex geometry, and from mono-energetic equation to two-group equations. The proposed DEPINN can be generalized for other input parameter settings once its structure been trained. This work confirms the possibility of DEPINN for practical engineering applications in nuclear reactor physics. © 2022, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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RGO/MXENE INKS FOR EMBEDDED SENSING IN FRP COMPOSITES 23

RGO/MXENE INKS FOR EMBEDDED SENSING IN FRP COMPOSITES

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23rd International Conference on Composite Materials, ICCM 2023

作者： Anchalee, D. Hanmo, Z. Qingqing, W. Limin, Z. School of System Design and Intelligent Manufacturing Southern University of Science and Technology Nanshan District Shenzhen China Department of Applied Physics The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Department of Mechanical Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

This study presents the development and characterization of nonintrusive reduced graphene oxide/MXene (GM) sensors embedded within Fiber Reinforced Polymer (FRP) laminates. The GM sensors, fabricated using non-polymer GM ink with optimized formulation, demonstrate exceptional sensing capabilities and performance. The ink's sensitivity is carefully tuned to ensure its successful integration into FRP laminates without compromising their interlaminar shear stress (ILSS) properties. The integrated GM sensors exhibit an extraordinarily high gauge factor, along with remarkable reversibility and repeatability in responding to dynamic strains. The hybrid functionality achieved by combining GM sensors with GFRP composites offers a unique combination of superior mechanical properties and strain-sensing capabilities. This breakthrough opens up exciting possibilities for a wide range of applications, as FRPs can now provide enhanced structural integrity while simultaneously monitoring and measuring strains with precision. © 2023 International Committee on Composite Materials. All rights reserved.

关键词： Graphene

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Facility Layout in Horizontal Cylindrical Space Based on Adaptive Gradient Descent Algorithm

Facility Layout in Horizontal Cylindrical Space Based on Ada...

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第40届中国控制会议

作者： Peng Yuan Yiran Miao Deqing Huang Junkai Hao Na Qin School of Electrical Engineering Southwest Jiaotong University Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China

FLP（Facility layout problem） is a common problem in engineering field. The past research tend to focus on the twodimensional space and a small amount of three-dimensional box space as well as vertical cylinder space. The FLP in horizontal cylinder space is less researched. Lots of FLP of box space and vertical cylinder space can transform to FLP of two-dimensional space easily, however, the FLP of horizontal cylinder space need design special calculation method of overlap constraint and boundary constraint. This paper designed the calculation method of overlap constraint and boundary constraint, used adaptive gradient descent algorithm with heuristic strategy to optimize the facility layout in horizontal cylinder space. On this basis,added symmetry constraint, absolute height constraint and relative distance constraint to close to the real engineering *** experiment results confirm the feasible of proposed scheme.

关键词： facility layout problem horizontal cylinder space gradient descent algorithm

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Experimental study of heat transfer characteristics of bent high-temperature heat pipe

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International Journal of Thermal sciences 2025年 217卷

作者： Zhipeng Zhang Meng Xue Yugao Ma Kailun Guo Shuhua Ding Chenglong Wang Xiaoming Chai Wenxi Tian Suizheng Qiu Guanghui Su Department of Nuclear Science and Technology Xi'an Jiaotong University Xi'an China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China

The heat pipe, as a highly efficient heat transfer element, is well adapted to industrial applications with high heat flux. For compact system designs and compatibility with energy converters, bent high-temperature heat pipes are essential. However, the current research on the bending effect of heat pipes mainly focuses on the medium and low-temperature range, and there is a lack of systematic experiments on the heat transfer characteristics of bent high-temperature heat pipes. In this study, high-temperature sodium heat pipes were used as experimental objects, and the bend radius, wick structure type, bend position and bend number were set as variables to examine the changes in heat transfer characteristics of high-temperature heat pipes before and after bending by utilizing the axial temperature distribution, thermal resistance and heat transfer limit. High-temperature heat pipes exhibit performance degradation after bending, with increased thermal resistance and reduced heat transfer limits, significantly influenced by bend radius and wick structure. A bend radius of 250 mm raises temperatures by 40 °C and reduces the heat transfer limit by 10.48 %. Screen wick types show better resistance to bending with less than 10 % performance loss, while artery types deteriorate by 35.21 %. Bend position also affects performance; a lower bend position has better results. The performance of the heat pipe is not significantly degraded by the double bends and the bends near the evaporation section are the main cause of the degradation.

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