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Intelligent Computing and Control (IC&C), International Conference on

作者： Xijuan Ye Kai Mao Jian Ma Lixin Song Yuxin Liu Qiuming Zhu The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing Mechanical and Electrical Hydraulic Engineering Research Center of Aviation Industry Aeronautical Key Laboratory of Aeronautical Science and Technology Nanjing China

Communication inside aircraft cabin has gained more and more attentions to provide in-flight connectivity for high-quality user experience and enable the wireless communication of electromechanical equipment. In this paper, a power prediction method considering the coordinates of transceiver, propagation distance and carrier frequency based on the parameterized channel model and machine learning (ML) framework is proposed for wireless communication inside the aircraft cabin. In the proposed channel model, a random forest (RF) network is trained for channel characteristics based on ray tracing (RT) simulation data and applied to estimate the power parameters. The proposed method is verified inside a typical aircraft cabin at 1GHz and compared with traditional model and background propagation neural network (BPNN). The simulation results show that RF can accurately predict the power parameter and reproduce the channel environment. The prediction results of power distribution can provide valuable reference for the layout and optimization of communication nodes inside the aircraft cabin.

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CT-UIO: Continuous-Time UWB-Inertial-Odometer Localization Using Non-Uniform B-spline with Fewer Anchors

arXiv

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arXiv 2025年

作者： Sun, Jian Sun, Wei Zhang, Genwei Yang, Kailun Li, Song Meng, Xiangqi Deng, Na Tan, Chongbin National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410012 China College of Electrical and Information Engineering Hunan University China School of Robotics Hunan University Changsha410012 China State Key Laboratory of NBC Protection for Civilian Beijing102205 China

Ultra-wideband (UWB) based positioning with fewer anchors has attracted significant research interest in recent years, especially under energy-constrained conditions. However, most existing methods rely on discrete-time representations and smoothness priors to infer a robot’s motion states, which often struggle with ensuring multi-sensor data synchronization. In this paper, we present an efficient UWB-Inertial-odometer localization system, utilizing a non-uniform B-spline framework with fewer anchors. Unlike traditional uniform B-spline-based continuous-time methods, we introduce an adaptive knot-span adjustment strategy for non-uniform continuous-time trajectory representation. This is accomplished by adjusting control points dynamically based on movement speed. To enable efficient fusion of IMU and odometer data, we propose an improved Extended Kalman Filter (EKF) with innovation-based adaptive estimation to provide short-term accurate motion prior. Furthermore, to address the challenge of achieving a fully observable UWB localization system under few-anchor conditions, the Virtual Anchor (VA) generation method based on multiple hypotheses is proposed. At the backend, we propose a CT-UIO factor graph with an adaptive sliding window for global trajectory estimation. Comprehensive experiments conducted on corridor and exhibition hall datasets validate the proposed system’s high precision and robust performance. The codebase and datasets of this work will be open-sourced at https://***/JasonSun623/CT-UIO. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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TS-CGNet: Temporal-Spatial Fusion Meets centerline-Guided Diffusion for BEV Mapping

arXiv

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arXiv 2025年

作者： Hong, Xinying Li, Siyu Zeng, Kang Shi, Hao Peng, Bomin Yang, Kailun Li, Zhiyong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China

Bird’s Eye View (BEV) perception technology is crucial for autonomous driving, as it generates top-down 2D maps for environment perception, navigation, and decision-making. Nevertheless, the majority of current BEV map generation studies focusing on visual map generation lack depth-aware reasoning capabilities. They exhibit limited efficacy in managing occlusions and handling complex environments, with a notable decline in perceptual performance under adverse weather conditions or low-light scenarios. Therefore, this paper proposes TS-CGNet, which leverages Temporal-Spatial fusion with centerline-Guided diffusion. This visual framework, grounded in prior knowledge, is designed for integration into any existing network for building BEV maps. Specifically, this framework is decoupled into three parts: Local mapping system involves the initial generation of semantic maps using purely visual information;The Temporal-Spatial Aligner Module (TSAM) integrates historical information into mapping generation by applying transformation matrices;The centerline-Guided Diffusion Model (CGDM) is a prediction module based on the diffusion model. CGDM incorporates centerline information through spatial-attention mechanisms to enhance semantic segmentation reconstruction. We construct BEV semantic segmentation maps by our methods on the public nuScenes and the robustness benchmarks under various corruptions. Our method improves 1.90%, 1.73%, and 2.87% for perceived ranges of 60×30m, 120×60m, and 240×60m in the task of BEV HD mapping. TS-CGNet attains an improvement of 1.92% for perceived ranges of 100×100m in the task of BEV semantic mapping. Moreover, TS-CGNet achieves an average improvement of 2.92% in detection accuracy under varying weather conditions and sensor interferences in the perception range of 240×60m. The source code will be publicly available at https://***/krabs-H/TS-CGNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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Deep Natural Image Reconstruction from Human Brain Activity Based on Conditional Progressively Growing Generative Adversarial Networks

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Neuroscience Bulletin 2021年第3期37卷 369-379页

作者： Wei Huang Hongmei Yan Chong Wang Xiaoqing Yang Jiyi Li Zhentao Zuo Jiang Zhang Huafu Chen The MOE Key Lab for Neuroinformation University of Electronic Science and Technology of ChinaChengdu 610054China State Key Laboratory of Brain and Cognitive Science Beijing MR Center for Brain ResearchInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China Department of Medical Information Engineering Sichuan UniversityChengdu 610065China

Brain decoding based on functional magnetic resonance imaging has recently enabled the identification of visual perception and mental ***,due to the limitations of sample size and the lack of an effective reconstruction model,accurate reconstruction of natural images is still a major *** current,rapid development of deep learning models provides the possibility of overcoming these ***,we propose a deep learning-based framework that includes a latent feature extractor,a latent feature decoder,and a natural image generator,to achieve the accurate reconstruction of natural images from brain *** latent feature extractor is used to extract the latent features of natural *** latent feature decoder predicts the latent features of natural images based on the response signals from the higher visual *** natural image generatoris applied to generate reconstructed images from the predicted latent features of natural images and the response signals from the visual *** and qualitative evaluations were conducted with test *** results showed that the reconstructed image achieved comparable,accurate reproduction of the presented image in both highlevel semantic category information and low-level pixel *** framework we propose shows promise for decoding the brain activity.

关键词： Brain decoding fMRI Deep learning

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Short-Term Load Forecasting with TDCI: Temporal Decomposition and Channel Independence 7

Short-Term Load Forecasting with TDCI: Temporal Decompositio...

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7th International Conference on Electrical, Mechanical and Computer engineering, ICEMCE 2023

作者： Li, Wenxue Cheng, Chi Xu, Peihua Liu, Renfeng Zhang, Yun Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Prov. Wuhan430205 China Wuhan Institute of Technology Hubei Provincial Key Laboratory of Intelligent Robot Hubei Prov. Wuhan430205 China Hubei Meteorological Service Center Wuhan430079 China School of Educational Information Technology Central China Normal University Faculty of Artificial Intelligence Education Wuhan430079 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China

ISBN: (纸本)9798350382877

Given the substantial load fluctuations, pronounced stochasticity, and non-linearity influenced by factors like weather and temperature in power load forecasting, we present a short-term load forecasting model based on TDCI. This model introduces several enhancements to boost forecasting accuracy. Firstly, TDCI employs temporal decomposition, separating time-series data into seasonal and trend components, enabling the capture of inherent seasonal patterns and long-term trends that significantly impact load forecasting accuracy. Additionally, the incorporation of locality-sensitive hashing attention efficiently processes relevant temporal patterns, enhancing model performance while reducing computational complexity. Lastly, a channel independence approach is applied to each variable, allowing tailored processing based on individual feature characteristics. This adaptive feature handling ensures effective capture of distinct properties, thereby enhancing forecasting accuracy. Experimental evaluations on a real-world power load dataset demonstrate the superior forecasting precision of our proposed model compared to existing models. © 2023 IEEE.

关键词： Electric power plant loads

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Regulation of nerve cells using conductive nanofibrous scaffolds for controlled release of Lycium barbarum polysaccharides and nerve growth factor

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Regenerative Biomaterials 2023年第1期10卷 919-932页

作者： Jing Wang Yuan Liu Minmin Lv Xiaoli Zhao Kwok Fai So Hui Li Mohamed EL-Newehy Hany EL-Hamshary Yosry Morsi Xiumei Mo Research Center for Human Tissues and Organs Degeneration Institute of Biomedicine and BiotechnologyShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055P.R.China Department of Orthopedics Shanghai Sixth People’s HospitalShanghai201306P.R.China University of Hong Kong-Shenzhen Hospital Shenzhen518053P.R.China State Key Laboratory of Brain and Cognitive Sciences The University of Hong KongPokfulamHong KongP.R.China Department of Ophthalmology The University of Hong KongPokfulamHong KongP.R.China Guangdong-Hong Kong-Macao Institute of CNS Regeneration Jinan UniversityGuangzhouP.R.China Department of Chemistry College of ScienceKing Saud UniversityRiyadh 11451P.O.Box 2455Saudi Arabia Faculty of Engineering and Industrial Sciences Swinburne University of TechnologyBoroondaraVIC 3122Australia State Key Laboratory for Modification of Chemical Fibers and Polymer Materials Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative MedicineCollege of Biological Science and Medical EngineeringDonghua UniversitySongjiangShanghai201600P.R.China

Currently,more and more patients suffer from peripheral nerve injury due to trauma,tumor and other causes ***-based nerve conduits are increasingly recognized as a potential alternative to nerve autografts for the treatment of peripheral nerve ***,an ideal nerve conduit must offer topological guidance and biochemical and electrical signal transduction *** this work,aligned conductive nanofibrous scaffolds comprising polylactic-co-glycolic acid and multiwalled carbon nanotubes(MWCNTs)were fabricated via coaxial electrospinning,and nerve growth factor(NGF)and Lycium barbarum polysaccharides(LBP)purified from the wolfberry were loaded on the core and shell layers of the nanofibers,*** were confirmed to accelerate long-distance axon regeneration after severe peripheral nerve *** addition,the synergistic promotion of LBP and NGF on nerve cell proliferation and neurite outgrowth was *** were introduced into the aligned fibers to further increase the electrical conductivity,which promoted the directional growth and neurite extension of neurons in ***,the combination of conductive fibrous scaffolds with electrical stimulation that mimics endogenous electric fields significantly promoted the differentiation of PC12 cells and the axon outgrowth of *** on robust cell-induced behaviors,conductive composite fibers with optimized fiber alignment may be used for the promotion of nerve recovery.

关键词： conductive nanofibrous scaffold LBP NGF nerve cells electrical stimulation

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Optimal Minimum Energy Path Planning of Mobile robot Based on Improved WOA-GA with Hybrid Strategy for Community-Centric Application

Optimal Minimum Energy Path Planning of Mobile Robot Based o...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Du, Jiang Da-Lin, Zhou Gong-Fa, Li Bao-Li, Lu Jun-Tong, Yun Ying, Liu Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan430081 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom China Three Gorges University Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance Yichang443002 China Wuhan University of Science and Technology Key Laboratory of Mechanical Transmission and Manufacturing Engineering of Hubei Province Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9798350303780

The path planning of mobile robots is investigated to minimize energy loss in intelligent manufacturing. To achieve optimal energy consumption planning, this paper proposes a hybrid strategy that enhances the whale optimization algorithm (WOA) and the genetic algorithm (GA). The WOA-GA algorithm combines their respective advantages and effectively mitigates premature convergence exhibited by the standard traditional algorithm. To enhance the global and local search capabilities of the standard WOA, we employ adaptive weighting and a Cauchy mutation strategy. Additionally, we introduce an adaptive crossover mutation probability and an elite strategy to mitigate the detrimental effects of the genetic algorithm's evolutionary operation in the late iterations, thereby improving its performance. This paper compares the performance of the standard GA, the standard WOA-GA algorithm, and the enhanced WOA-GA algorithm. The experimental results demonstrate that the enhanced WOA-GA algorithm can achieve optimal energy-efficient path planning using the hybrid strategy. The average energy consumption of the enhanced WOA-GA algorithm can be reduced by 30%, while the maximum energy consumption can be reduced by 47%. The results confirm the effectiveness and feasibility of the proposed method in enhancing the level of green manufacturing. © 2023 IEEE.

关键词： Genetic algorithms

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A Fusion Tuning Method for Named Entity Recognition 12th

A Fusion Tuning Method for Named Entity Recognition

引用

12th CCF Conference on BigData, BigData 2024

作者： Wang, Jitian Chen, Yanping Zou, Anqi Qin, Yongbin Huang, Ruizhang Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819610235

In named entity recognition, the main methods for constructing deep neural networks are fine-tuning and prompt tuning. Fine-tuning is a commonly used paradigm to optimize neural networks by using task-specific objective functions to make them more adapted to solve downstream tasks. In which pre-trained language models are used as external resources to provide contextual representations. Prompt tuning is a recent paradigm in which the downstream tasks are reformulated to look more like those solved during the original PLM training with the help of a textual prompt. It is effective to utilize potential knowledge of PLMs. In this paper, we propose the fusion approach, which sufficiently fuses the semantics obtained by extracting fine-tuning and prompt-tuning. In our approach, the model makes decisions based on the information obtained from the reconstructive fusion of the fine-tuned semantics and the semantics of the pre-trained language model. The representation obtained by fine-tuning and the representation obtained by prompt tuning are reconstructed into a multidimensional semantics using a gate fusion module (GFM). In particularly, our model has two gate components, one gate to extract the semantics of fine-tuning and the other gate to extract the semantics of prompt tuning, and finally the reconstructed semantic representation is obtained. This takes full advantage of the potential knowledge of PLM. Our model is evaluated on the CONLL2003, ACE2005 and GENIA corpora. It achieves performance close to the state-of-the-art in F1 scores. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Mechanism of Local Electric Oxidation on Two-Dimensional MoS2 for Resistive Memory Application

SSRN

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SSRN 2024年

作者： Dong, Hui Mu, Junzheng Peng, Jinfeng Zheng, Xuejun Chu, Liang School of Electro-mechanical Engineering Guangdong University of Technology Guangzhou510006 China School of Electronics and Information Institute of Carbon Neutrality and New Energy Hangzhou Dianzi University Hangzhou310018 China School of Mechanical Engineering & Engineering Research Center of Complex Tracks Processing Technology and Equipment of MoE Key Laboratory of Welding Robot and Application Technology of Hunan Province Xiangtan University Xiangtan411105 China

The manipulation and mechanism of two-dimensional (2D) transition metal dichalcogenides (TMDs) by external electric field are extremely significant to the photoelectric properties for guiding their applications. Herein, the 2D MoS2 nanosheets were locally oxidated to form MoS2-MoO3 local heterojunctions by electric field from atomic force microscopy tip, which were further applied in mutilstate memristors for the proposal of NanoQR code. A modified thermal oxidation model has been derived through introducing Ohmic dissipation to reveal the mechanism of local electric oxidation on 2D MoS2. From current-voltage curves, the barrier height of MoS2 device showed an increase of 0.39 eV due to local oxidation after applying voltage for 480 s. Based on density-functional theory, the increase of barrier height was theoretical calculated as 0.38 eV between MoS2-MoS2 and MoS2-MoO3 supercells, supporting the formation of MoO3 under the local electric field. The manipulated 2D MoS2-MoO3 local heterojunctions were further applied as unprecedented mutilstate memory storage at the nanoscale, which induced the proposal of NanoQR code as strong-encryption information. The findings suggest a novel strategy on precisely controlling local electric oxidation on 2D TMDs to manipulate the properties for application of photoelectric memory nanodevices. © 2024, The Authors. All rights reserved.

关键词： Heterojunctions

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FR2Seg: Continual Segmentation Across Multiple Sites via Fourier Style Replay and Adaptive Consistency Regularization 39

FR2Seg: Continual Segmentation Across Multiple Sites via Fou...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Cheng Zhang, Weiwen Zhang, Hongrui Xu, Xuemiao Zhang, Huaidong Zou, Jing Qin, Jing South China University of Technology China The Hong Kong Polytechnic University Hong Kong Guangdong Engineering Center for Large Model and GenAI Technology China State Key Laboratory of Subtropical Building Science China Ministry of Education Key Laboratory of Big Data and Intelligent Robot China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China

ISBN: (纸本)157735897X

In clinical imaging, medical segmentation networks typically require continually adapting to new data from multiple sites over time, as aggregating all data for learning at once can be impractical due to storage limitations and privacy concerns. However, existing methods basically overlook domain-specific characteristics and fall short of adequately capturing domain-invariant knowledge during continual learning, leading to undesired catastrophic forgetting of previous sites and inferior generalization to new sites. To tackle this issue, this paper introduces FR2Seg, to sufficiently exploit both domain-specific and domain-invariant knowledge for efficient continual learning with the aid of low-frequency cues. For the former aspect, we propose a Fourier style replay module to synthesize pseudo images with old-site styles for data augmentation during new-site training, effectively preventing catastrophic forgetting without sacrificing data privacy. For the latter, we present a Fourier adaptive consistency regularization to identify and constrain the optimization of domain-invariant parameters with explicit awareness of knowledge transferability across sites, ensuring excellent generalizability to new sites. Experimental results on two public datasets confirm our method's superiority over existing state-of-the-art continual learning methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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