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7th International Conference on intelligent Computing and Signal Processing, ICSP 2022

作者： Yan, Jing Zhou, Shihua Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian China

ISBN: (纸本)9781665478571

Extraction summarization and abstraction summarization have advantages and disadvantages, so how to better combine these two ways has become a difficult problem. To address this challenge, this paper proposed a new fusion method. The major novelty lies in the design of the new method. Wherein our approach, first, use the text structure information and the idea of the K-means algorithm to divide the text into regions, and then the main part and the non-main parts are determined according to the distribution of the subject words. Next, apply information extraction on the main part and text generation on the non-main parts. Finally, the two parts of the summarization are merged according to the sequence of the text. Experimental results show that the quality of the summarization is better than that of extraction summarization and abstraction summarization. In addition, to make the method more targeted in Chinese text processing, the Cw2vec model based on Chinese stroke information is used in the encoding process, and the experiment proves that the quality of summarization can be further improved. © 2022 IEEE.

关键词： Text processing

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Advancements in graphene-based composites: A review of the emerging applications in healthcare

Smart Materials in Medicine

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Smart Materials in Medicine 2025年第1期6卷 120-138页

作者： Batool, Farwa Muhammad, Saz Muazzam, Rabia Waqas, Muhammad Ullah, Zia Roy, Shubham Zhang, Yinghe Wang, Ke Guo, Bing Department of Chemistry Lahore Garrison University DHA Phase VI Lahore Pakistan School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China School of Science Shenzhen Key Laboratory of Flexible Printed Electronics Technology Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application Harbin Institute of Technology Shenzhen518055 China Government Queen Mary Graduate College Lahore Pakistan

Graphene and related materials are emerging revolutionary materials due to fascinating stimuli-responsive physiochemical properties having widespread applications in developing advanced nano-systems for detection, diagnostics, and therapeutics in biomedical fields. Various materials like metal nanoparticles, polymers, dyes, drugs, and proteins could be combined yielding graphene-based composite materials (GBCMs) to synergistically fulfill different biomedical requirements. The 2D conjugated structure, easy surface functionalization via covalent or van der Waal interactions, and combination with different components for versatile functionalities altogether contribute to the development of GBCM for healthcare applications. This review specifically focuses on GBCMs and discusses specific material properties relevant to their material applications in healthcare. Furthermore, the synthesis, toxicity, and biocompatibility of these GBCMs, and their interactions with biological materials are thoroughly discussed. Importantly, the recent cutting-edge applications in the field of healthcare, including its role in the development of antibacterial, anti-viral, and photothermal therapies, wound healing, drug delivery, neurodegenerative disease therapy, and biosensing based on GBCMs are well-updated. Additionally, the applications of GBCMs in point-of-care devices, wearable electronics, and tissue engineering are well summarized. We hope this review opens up more possibilities to design more innovative healthcare materials derived from graphene. © 2025 The Authors

关键词： Biocompatibility

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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5208-5222页

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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Robust keyword Spotting Based on Long-Term Feature Fusion and Band-Weighted Normalization

Robust Keyword Spotting Based on Long-Term Feature Fusion an...

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Computer Information Science and Application Technology (CISAT), International Conference on

作者： Zepeng Qiu Lasheng Zhao Ling Wang Tingting Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Currently, convolution-based models for keyword spotting focus predominantly on research conducted in clean speech environments. However, their recognition accuracy is lower in low signal-to-noise ratio conditions. To enhance model robustness, we propose the long-term feature fusion module. By extracting and fusing long-term features in both the time and frequency domains, the model’s perception of long-term features is strengthened, improving its recognition performance in noisy environments. At the same time, we propose a band-weighted normalization method, allowing the model to adjust the importance of different frequency bands during the normalization process, further enhancing the robustness of the model. Experimental results indicate that, on the Google Speech Command V2 dataset, the proposed model achieves higher recognition accuracy under various signal-to-noise ratios compared to the comparative models, with a lower parameter count. Moreover, the proposed model exhibits superior generalization to signal-to-noise ratios not covered during the training phase.

关键词： Training Time-frequency analysis Accuracy Convolution Speech recognition Feature extraction Robustness Internet Noise measurement Signal to noise ratio

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Cross-Stage Mutual Distillation for Replay Attack Detection

Cross-Stage Mutual Distillation for Replay Attack Detection

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6th International Conference on intelligent Computing and Signal Processing (ICSP)

作者： Yinqing Cheng Lasheng Zhao Ling Wang Han Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Automated speaker verification systems face a higher risk of replay attacks in practice. However, the existing studies face the problem of limited detection capabilities and insufficient use of shallow fine-grained information. To address these issues, we propose the cross-stage mutual distillation(CS-MD) framework, which involves two models learning from a deep network output of each other in different stages of training. This mutual learning approach enhances the ability of shallow networks to capture fine-grained speech information. Additionally, we use an attentional feature fusion module to integrate shallow information more effectively. The multi-scale attention mechanisms in this module can combine local and global speech features while preserving detailed information. Experimental results on the ASVspoof 2019 physical access dataset demonstrate that our proposed method outperforms state-of-the-art methods in terms of EER and min t-DCF metrics, validating the effectiveness of our CS-MD framework.

关键词： Training Voice activity detection Measurement Knowledge engineering Attention mechanisms Speech enhancement Signal processing Feature extraction Faces

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Hierarchical Cooperative Control of Connected Vehicles:From Heterogeneous Parameters to Heterogeneous Structures

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1590-1602页

作者： Manjiang Hu Lingkun Bu Yougang Bian Hongmao Qin Ning Sun Dongpu Cao Zhihua Zhong the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsha 410082China Wuxi Intelligent Control Research Institute Hunan UniversityWuxi 214115China the School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China

As one of the typical applications of connected vehicles(CVs),the vehicle platoon control technique has been proven to have the advantages of reducing emissions,improving traffic throughout and driving *** this paper,a unified hierarchical framework is designed for cooperative control of CVs with both heterogeneous model parameters and *** separating neighboring information interaction from local dynamics control,the proposed framework is designed to contain an upper-level observing layer and a lower-level tracking control layer,which helps address the heterogeneity in vehicle parameters and *** the proposed framework,an observer is designed for following vehicles to observe the leading vehicle's states using neighboring communication,while a tracking controller is designed to track the observed leading vehicle using local feedback ***-loop stability in the absence and presence of communication time delay is analyzed,and the observer is further extended to a finite time convergent one to address string stability under general communication *** simulation and field experiment verify the effectiveness of the proposed method.

关键词： Communication delay connected vehicle(CV) heterogeneity string stability vehicle platoon

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Semi-Supervised Semantic Segmentation Based on Class Balanced Framework

Semi-Supervised Semantic Segmentation Based on Class Balance...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Jiacheng Yang Jing Dong Pengfei Yi Rui Liu Ling Wang The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Semi-supervised semantic segmentation is a task that utilizes a small amount of labeled data and a large amount of unlabeled data to achieve pixel-level annotation. Due to the common long tail distribution phenomenon in semi-supervised semantic segmentation datasets and the fixed threshold strategy commonly used in filtering pseudo labels, the model tends to favor the head classes when generating pseudo labels, resulting in poor segmentation performance on tail data. This article proposes a semi-supervised semantic segmentation method based on a class- balanced framework, which improves the model's recognition ability for minority classes and the quality of pseudo labels through tail classes oversampling and dynamic threshold adjustment. This method first constructs a Class Prediction Sampling-based Self-balancing Module (CPS 2 M). It identifies tail classes through Intersection-over-Union (IoU) analysis and constrains the random oversampling process based on the tail classes data distribution degree. Subsequently, the sampled labeled data is mixed with unlabeled samples to transfer prior knowledge, increase training frequency for tail classes, and solve the experience mismatch problem. Finally, we design a Dynamic Threshold Updating module (DTU) based on a non-linear function. The confidence threshold can dynamically change according to the model's learning ability to avoid discarding correct tail classes with low confidence, which alleviates the class imbalance problem of generating pseudo-labels. Based on the DeepLabV3+ network framework and ResNet-101 pre-training model, this method achieves 82.1 mIoU under 1464 labeled partition protocols on the Classic Pascal VOC 2012 dataset, demonstrating the most advanced performance compared to existing methods.

关键词： Training Protocols Filtering Annotations Semantic segmentation Semantics Tail

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Single-gait crawling robot:A solution for improving mobility and payload of soft locomotor via simple structure and easy control

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Science China(Technological Sciences) 2024年第11期67卷 3626-3628页

作者： MA Hao HAO TianZe MA JiChao SUN ZhenHao LI Xiang LIU YiBo XIAO HuaPing LIU ShuHai College of Mechanical and Transportation Engineering China University of Petroleum-BeijingBeijing 102249China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical EngineeringTianjin University of TechnologyTianjin 300384China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education(Tianjin University of Technology) Tianjin 300384China

Motion performance,including movement speed,adaptability to various environments,and load-carrying capability,is crucial for soft crawling robots,tasked with field exploration and the transporting material in often complex and unstructured environments.A wide variety of soft crawling robots,which are driven by electricity[1],chemical power[2],and pressurized fluid[3]to move forward via cyclic deformation,have thus far been inspired by the mobile patterns of soft-bodied animals,such as worms[4],snake[5],octopus[6],*** performance of soft robots in terms of speed and load has developed impressively in recent years.

关键词： robot payload environments

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keyword mask pre-training model for the review of standard declaration 4

Keyword mask pre-training model for the review of standard d...

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4th International Conference on Computer Engineering and Application, ICCEA 2023

作者： Feng, Chuang Che, Chao Zhang, Qiang Ministry of Education Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian116622 China School of Computer Science and Technology Dalian University of Technology Dalian116081 China

ISBN: (纸本)9798350347548

The review of standard declaration of import and export commodities refers to the evaluation of whether the declaration information of the commodities filled in by enterprises conforms to the customs filling standards according to the standardized declaration catalogue The manual review method cannot meet the actual work needs. Therefore, using text classification algorithm to conduct this task is necessary. The classification method based on the pre-training model would rely on the keywords. However, for the review of standard declaration task, excessive reliance on keywords will reduce the classification effect because of ignoring the overall information of the text. keyword mask pre-training model is proposed. In the fine-tuning stage, the model designs a keyword masking strategy and a keyword blinding strategy. The keyword masking strategy regularizes the BERT model with a mask mechanism, that is, the keywords are reconstructed with other text information except keywords, so that the model focuses on non-keywords information;keyword blinding strategy makes the model unable to distinguish the categories of text information without contextual text information, which can eliminate the influence of keywords on model decision-making. The experimental results on the real customs declaration data set showing that the method in this thesis exceeds the baseline method in terms of precision and true negative rate. © 2023 IEEE.

关键词： Classification (of information)

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Reliability prediction model of bolted connection based on uncertainty 10

Reliability prediction model of bolted connection based on u...

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2024 10th International Conference on Applied Materials and Manufacturing Technology, ICAMMT 2024

作者： Niu, Nana Zhao, Yongsheng Zhang, Caixia Machinery Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Technology Beijing University of Technology Beijing100124 China Institute of Advanced Manufacturing and Intelligent Technology Beijing University of Technology Beijing100124 China

During the service of bolts, their preload exhibits a random decreasing trend due to external loads, affecting contact stiffness and connection reliability. System uncertainties can also lead to preload variations within the same group of bolts. In this paper, we use the Wiener process to model this preload decrease, incorporating drift and wave coefficients to capture temporal variability and system uncertainty. This approach accounts for monotonic preload reduction and random fluctuations, enabling accurate calculations. By integrating the time-varying preload uncertainty into the bolted structure's contact stiffness model, we establish a limit state function for structural failure and assess the reliability of the bolted connection over time. © Published under licence by IOP Publishing Ltd.

关键词： Bolted joints

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