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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Xu, Jingjing Du, Zhen Wang, Jie Xu, Yao School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350384185

This paper aims to provide a performance comparison of integrated sensing and communications (ISAC) between matched filtering (MF) and data-division filtering (DDF), utilizing probabilistic constellation shaping (PCS) under orthogonal frequency division multiplexing (OFDM) *** this and, we first investigate the complete OFDM matrix models for both MF and DDF to facilitate subsequent data *** then exploit the PCS by devising the fourth-moment of constellation amplitudes, imposed on both sensing approaches, and observe their *** demonstrate that PCS can effectively promote the sidelobe-suppression, albeit at the cost of a certain communication ***, an initial experimental system is established to validate the effectiveness of PCS in balancing sensing and communication performance. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Spin-dependent electrocatalysis

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National science Review 2024年第9期11卷 88-105页

作者： Zhengjie Chen Xiaoning Li Hao Ma Yuwei Zhang Jing Peng Tianyi Ma Zhenxiang Cheng Jose Gracia Yuanmiao Sun Zhichuan J.Xu Faculty of Materials Science and Energy Engineering Shenzhen University of Advanced Technology School of Materials Science and Engineering Nanyang Technological University School of Science RMIT University Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Institute for Superconducting and Electronic Materials ISEM Faculty of Engineering and Information Sciences Innovation Campus University of Wollongong Magneto Cat SL Center for Advanced Catalysis Science and Technology Nanyang Technological University

The shift towards sustainable energy requires efficient electrochemical conversion technologies,emphasizing the crucial need for robust electrocatalyst design. Recent findings reveal that the efficiency of some electrocatalytic reactions is spin-dependent, with spin configuration dictating ***, understanding the spin's role and controlling it in electrocatalysts is important. This review succinctly outlines recent investigations into spin-dependent electrocatalysis, stressing its importance in energy conversion. It begins with an introduction to spin-related features, discusses characterization techniques for identifying spin configurations, and explores strategies for fine-tuning them. At the end, the article provides insights into future research directions, aiming to reveal more unknown fundamentals of spin-dependent electrocatalysis and encourage further exploration in spin-related research and applications.

关键词： spin-dependent electrocatalysis spin configuration spin-related features characterization techniques manipulation strategies

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Attention Enhanced Feature Fusion for Mental Stress Level Classification 21

Attention Enhanced Feature Fusion for Mental Stress Level Cl...

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21st International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2024

作者： Sahlu, Ameneshewa Abush Yohannes, Gobena Mulugeta Dawit, Bekalu Nigus Teshome, Molla Woretaw School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331519254

Mental stress poses significant health risks, manifesting in various psychological and physical issues such as depression, anxiety, and cardiovascular complications. Establishing a reliable method for swiftly and accurately classifying mental stress levels is crucial due to its widespread impact. Existing classification approaches predominantly rely on either data-intensive traditional machine learning algorithms or opaque deep learning models, hindering real-world use and interpretability. This study addresses these limitations by proposing a novel approach for stress level classification using the WESAD dataset. Our method combines an attention-based ResN et with CBAM. This innovative approach synergistically integrates shallow and deep features, harnessing both low-level details and high-level semantic information to effectively classify stress levels into four distinct states: baseline, stress, amusement, and meditation, or three states excluding the meditation state. The experimental result shows 92.74% accuracy and 91.19% F1 score for the 4-class setup, and 97.58% accuracy and 97.51 % F1 score for the 3-class setup. Our approach offers a more robust and interpretable framework for mental stress classification, with potential applications in health care and stress management. © 2024 IEEE.

关键词： Deep learning

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Research on ARINC653 Two level Scheduling Algorithm and Simulation Method Based on Few Partition Splitting 4

Research on ARINC653 Two level Scheduling Algorithm and Simu...

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4th International Conference on electronic information engineering and Computer Communication, EIECC 2024

作者： Lin, Zhongping Guo, Wensheng Yang, Xia Sun, Haiyong University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu Lianan Tech Chengdu China

ISBN: (纸本)9798331534622

ARINC653 is an important standard for IMA integrated modular avionics systems. In today's rapidly developing world, single core ARINC653 can no longer meet the increasingly diverse tasks and complex systems. Therefore, research on multi-core ARINC653 systems is crucial. This article abstracts the task scheduling of ARINC653 partition system, analyzes the scheduling of ARINC653 multi-core tasks from two levels: inter partition scheduling and intra partition scheduling, and proposes an improved two-level scheduling method. Compared with the traditional ARINC653 scheduling algorithm, this method has fewer partition splitting times. And use AADL modeling language to simulate the system. © 2024 IEEE.

关键词： Scheduling algorithms

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Weakly supervised point cloud segmentation via deep morphological semantic information embedding

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CAAI Transactions on Intelligence technology 2024年第3期9卷 695-708页

作者： Wenhao Xue Yang Yang Lei Li Zhongling Huang Xinggang Wang Junwei Han Dingwen Zhang School of Automation Northwestern Polytechnical UniversityXi'anChina Institute of Mechanical and Electrical Engineering BeijingChina School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Segmenting the semantic regions of point clouds is a crucial step for intelligent agents to understand 3D *** supervised point cloud segmentation is highly desirable because entirely labelling point clouds is highly time-consuming and *** the low-costing labelling of 3D point clouds,the scene-level label is one of the most effortless label ***,due to the limitation of classifier discriminative capability and the orderless and structurless nature of the point cloud data,existing scene-level method is hard to transfer the semantic information,which usually leads to the under-activated or over-activated *** this end,a local semantic embedding network is introduced to learn local structural patterns and semantic ***,the proposed network contains graph convolution-based dilation and erosion embedding modules to implement‘inside-out’and‘outside-in’semantic information dissemination ***,the proposed weakly supervised learning framework could achieve the mutual propagation of semantic information in the foreground and *** experiments on the widely used ScanNet benchmark demonstrate the superior capacity of the proposed approach when compared to the current alternatives and baseline models.

关键词： artificial intelligence computer vision deep learning

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A Combination Model of Periodic and Non-periodic Events for Temporal Knowledge Graph Event Prediction

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Chinese Journal of electronics 2025年第3期34卷 952-961页

作者： School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China School of Data Science and Software Engineering Wuzhou University Wuzhou 543002 China

Temporal knowledge graph (TKG) reasoning aims to predict missing facts or future events at given timestamps and has attracted more and more attention in recent years. Existing TKG reasoning methods mainly focus on the interactions between entities and ignore the associations between events where the entities involve. In addition, the characteristics of different types of events have not been studied and exploited, which reduces the performance of event prediction. To address these problems, this paper proposes a combination model of periodic and non-periodic events (CM-PNP). Specifically, there are two basic components designed to process different types of events. The periodic component of CM-PNP learns the recurrent pattern of periodic events and encodes the temporal information in the manner of timespan to prevent the unseen timestamp issue. The non-periodic component of CM-PNP introduces extra information (e.g., entity attributes) to represent non-periodic events, and predicts this type of events according to the related historical events. A combination model of multiple sub-models that focus on encoding different parts of the event is used to improve the performance of single model. The periodic and non-periodic components are combined by a gate block. The experimental results on three real-world datasets demonstrate that CM-PNP outperforms the existing baselines.

关键词： Temporal knowledge graph Temporal knowledge graph reasoning Event prediction Embedding learning Data mining

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SCL-Fall: Reliable Fall Detection Using mmWave Radar With Supervised Contrastive Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 237-248页

作者： Li, Wenxuan Zhang, Dongheng Li, Yadong Song, Ruiyuan Hu, Yang Sun, Qibin Chen, Yan University of Science and Technology of China School of Cyber Science and Technology Hefei230026 China University of Science and Technology of China Department of Electronic Engineering and Information Science Hefei230026 China

Fall is a severe health threat for elders' health care. While existing systems could achieve promising performance under specific scenarios, the required computing resources are usually not affordable, which is not applicable for real-time detection. In this article, we propose SCL-Fall, a real-time fall detection system using millimeter wave signal with supervised contrastive learning, which can achieve impressive accuracy with low computation complexity. Specifically, we first extract the signal variation corresponding to human activity with spatial-temporal processing. We incorporate reweighting and denoising techniques in the signal processing process. To enhance the system performance and robustness, we perform data augmentation by shifting, flipping, extracting, and interpolating the signal. Finally, we design a lightweight convolutional neural network to achieve real-time fall detection. Extensive experimental results demonstrate that the proposed system could achieve state-of-the-art performance with limited computation complexity. © 2024 IEEE.

关键词： Radar Feature extraction Fall detection Sensors Contrastive learning Real-time systems Accuracy Neural networks Millimeter wave radar Wireless sensor networks

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A personalized glucose prediction model based on deep Long Short-Term Memory networks 4

A personalized glucose prediction model based on deep Long S...

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4th International Computational Imaging Conference, CITA 2024

作者： Zou, Yuanyuan Wang, Yixuan Yan, Jiasheng Dai, Bin Guo, Jinhong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China School of Information and Communication Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9781510688834

Diabetes mellitus has emerged as one of the most prevalent chronic diseases leading to death due to its high occurrence and rising incidence rates, resulting in numerous life-threatening complications. Consequently, the effective treatment and management of diabetes mellitus are of paramount importance. Blood glucose prediction technology plays a crucial role in identifying the risk of hypoglycemia or hyperglycemia in patients promptly, aiding physicians in adjusting treatment plans, and guiding patients in modifying their lifestyles to effectively reduce the incidence of complications. Additionally, blood glucose prediction can leverage machine learning algorithms to analyze historical data and offer personalized health recommendations to patients. Addressing the issues of low prediction accuracy, complex features, and poor generalization, this paper proposes a blood glucose prediction model based on a deep Long Short-Term Memory (LSTM) network. By incorporating steps such as feature selection, model optimization, data preprocessing, and hyperparameter tuning, a blood glucose prediction model using only historical blood glucose data as input was constructed. The algorithm's performance was evaluated on the Ohio T1DM dataset. The results demonstrated that the model exhibited strong accuracy in predicting both short-term and long-term blood glucose levels, with a root mean square error (RMSE) of 10.61 mg/dL at a prediction horizon (PH) of 15 minutes and 32.19 mg/dL at a PH of 60 minutes. Furthermore, Clark's Error Grid Analysis (CEGA) was employed to assess the model's clinical performance. At a PH of 15 minutes, over 99% of the data were within the zone of optimal clinical effect (zone A). At a PH of 60 minutes, over 95% of the data were in clinically acceptable zones A and B. © 2025 SPIE.

关键词： Prediction models

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Superior multiphase interfaces in AgCuTe-based composite with significantly enhanced thermoelectric properties

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Journal of Advanced Ceramics 2023年第8期12卷 1511-1520页

作者： Wenpei Li Zhonghai Yu Chengyan Liu Ying Peng Baoquan Feng Jie Gao Guojing Wu Xiaobo Bai Junliang Chen Xiaoyang Wang Lei Miao Guangxi Key Laboratory of Information Materials Electronical Information Materials and Devices Engineering Research Center of Ministry of EducationSchool of Materials Science and EngineeringGuilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Precision Navigation Technology and Application School of Information and CommunicationGuilin University of Electronic TechnologyGuilin 541004China

It is common sense that a phase interface(or grain boundary)could be used to scatter phonons in thermoelectric(TE)materials,resulting in low thermal conductivity(k).However,a large number of impurity phases are always so harmful to the transport of carriers that poor TE performance is ***,we demonstrate that numerous superior multiphase(AgCuTe,Ag_(−2)Te,copper telluride(Cu_(2)Te and Cu_(2−x)Te),and nickel telluride(NiTe))interfaces with simultaneous strong phonon scattering and weak electron scattering could be realized in AgCuTe-based TE *** to the similar chemical bonds in these phases,the depletion region at phase interfaces,which acts as carrier scattering centers,could be ***,the power factor(PF)is obviously enhanced from~609 to~832μW·m^(−1)·K^(−2),and k is simultaneously decreased from~0.52 to~0.43 W·m^(−1)·K^(−1) at 636 ***,a peak figure of merit(zT)of~1.23 at 636 K and an average zT(zTavg)of~1.12 in the temperature range of 523–623 K are achieved,which are one of the best values among the AgCuTe-based TE *** study could provide new guidance to enhance the performance by designing superior multiphase interfaces in the TE materials.

关键词： thermoelectric(TE)materials AgCuTe phase interface carrier scattering phonon scattering

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Knowledge and Task-Driven Multimodal Adaptive Transfer Through LLMs with Limited Data

Knowledge and Task-Driven Multimodal Adaptive Transfer Throu...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xu Chen, Zhikui Ren, Hao Tian, Yang The Dalian University of Technology School of Software Technology International School of Information Science and Engineering Dalian China

ISBN: (纸本)9798350386226

The scarcity of annotated data and the challenge of generative models that meet the authenticity requirements collectively constrain the modeling of highly specialized deep medical models, making data remain a central issue in model training. The emergence of large-scale models has facilitated access to knowledge and data references for various tasks, even those with high specialization. Therefore, in the absence of data sources, latent knowledge and model structure can be considered for reuse instead of directly utilizing samples, and generalizing specific medical models based on large-scale models is expected to be competitive in the future. However, due to significant differences between fine-grained medical features and tasks, generalization based on large language models (LLMs) is not entirely satisfactory, it requires a more targeted inheritance of knowledge and important structures to achieve effective generalization. This paper presents a novel multi-modal medical model training approach designed to circumvent the need for extensive real data preparation, leveraging the capabilities of LLMs to generalize downstream tasks. Initially, knowledge threads are extracted to facilitate multi-modal diagnostic knowledge retrieval from LLMs without relying on massive datasets. These threads are then utilized to generate multimodal task instructions, guiding LLMs in providing feedback and further fine-tuning them for direct adaptation to downstream tasks by encoding samples into a unified feature representation, thereby simplifying the search and traversal process. Finally, an adaptive knowledge transfer strategy is proposed, implementing collaborative modeling with key patterns anchored under the joint influence of knowledge threads and task instructions, leading to the generation of an equivalent and concise initialization model guided by LLMs. The experimental design validates the efficacy of the approach in rapid generalizing specific models, demonstrating high accu

关键词： Deep learning

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