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12th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2022

作者： Cui, Hao Dong, Xiaorui Shang, Kai Shandong Institute of Petroleum and Chemical Technology Key Laboratory of Intelligent Information Processing Shandong Province Dongying China College of Computer Science and Technology China University of Petroleum Shandong Province Qingdao China

ISBN: (数字)9781665407540

ISBN: (纸本)9781665407540

Sensor long-time drift compensation has always been a huge and important research challenge. To address this problem, this paper proposes a simple and efficient long-time drift compensation method based on a short-time memory-forgetting mechanism. The proposed improved method controls the update frequency of memory and forgetting by introducing a batch control coefficient to achieve a balance between the update frequency of memory queue and model iteration. In this paper, we use the gas sensor array drift dataset as the research object and design two experimental strategies to verify the processing time compression degree and the accuracy change of the classification model by adjusting the batch control coefficient. The experiments prove that the proposed method in this paper has an excellent effect on compressing data processing time and relieving hardware pressure, which has a certain reference value for related research. © 2022 IEEE.

关键词： Support vector machines

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Electroactive Polypyrrole Bacterial Cellulose with Platelet Rich Plasma Composite Gel as Immunomodulatory Diabetes Wound Dressing

SSRN

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SSRN 2024年

作者： Wang, Hao He, Pengyu Tang, Guoliang Qi, Fuyu Lu, Xu Zhang, Maoxu Zheng, Ruizhu Li, Xiaohong Shi, Zhijun Yang, Guang Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile University Wuhan430200 China

Diabetic ulcer is a common and severe chronic wound condition that presents significant challenges for healthcare. In this regard, Numerous therapeutic interventions have been investigated, with platelet-rich plasma (PRP) emerging as a promising option. However, PRP has inherent limitations, such as its susceptibility to rupture, and uncontrolled release of growth factors, necessitate alternative approaches. This study proposes an innovative design that combines polypyrrole-wrapped bacterial cellulose (PPy-BC) hydrogel with PRP to obtain the PPy-BC/ PRP (PBP) composite hydrogel with both electrical and biological activity. The PBP composite hydrogel exhibits favorable mechanical properties and conductivity, enabling precise regulation of PRP release and the immune microenvironment of diabetic wounds via electrical stimulation. The PBP composite hydrogel effectively promotes macrophage anti-inflammatory (M2) polarization, inhibits pro-inflammatory (M1) polarization, and reduces the expression of inflammatory cytokines, thereby reducing the wound hyperinflammation. In addition, diabetic wounds treated with PBP composite hydrogel show accelerated revascularization, collagen deposition, and overall acceleration of wound healing. Collectively, these demonstrate the promising potential of PBP composite hydrogel for treating diabetic wounds in clinic. © 2024, The Authors. All rights reserved.

关键词： Platelets

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A Tolerance Classes Partition-Based Re-Definition of the Rough Approximations for Incomplete Information System

A Tolerance Classes Partition-Based Re-Definition of the Rou...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2021

作者： Wang, Lei Liu, Bin Cai, Xiangxiang Wang, Chong Jiangxi Provincial Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang330099 China College of Information Engineering Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9789811669620

The information with missing attribute value is known as the incomplete information system. In an incomplete information system, there exist tolerance relations satisfying reflexivity and symmetry among objects in the universe of discourse and the tolerance classes for each object can be derived from the tolerance relation directly. Generally speaking, the tolerance classes of all objects can form some coverages of the universe. The research of this paper is based on the tolerance classes of each object in the incomplete information system. Firstly, a method is proposed in which a partition of the universe is constructed by using tolerance classes of objects. Then, the upper and lower approximations of concept, i.e., a subset of universe, is re-defined on the basis of acquisition of the partition of the universe using proposed method and this new definition can improve the approximation accuracy of concept effectively compared to the traditional definition. Finally, an illustrated example is exhibited for demonstrating the method to obtain a partition to universe by tolerance class as well as the calculation of the rough approximations of concept based on the partition of universe by tolerance class. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information use

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Imagine Before Go: Self-Supervised Generative Map for Object Goal Navigation

Imagine Before Go: Self-Supervised Generative Map for Object...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Sixian Zhang Xinyao Yu Xinhang Song Xiaohan Wang Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing University of Chinese Academy of Sciences Beijing Institute of Intelligent Computing Technology Suzhou CAS

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The Object Goal navigation (ObjectNav) task requires the agent to navigate to a specified target in an unseen environment. Since the environment layout is unknown, the agent needs to infer the unknown contextual objects from partially observations, thereby deducing the likely location of the target. Previous end-to-end RL methods capture contextual relationships through implicit representations while they lack notion of geometry. Alternatively, modular methods construct local maps for recording the observed geometric structure of unseen environment, however, lacking the reasoning of contextual relation limits the exploration efficiency. In this work, we propose the self-supervised generative map (SGM), a modular method that learns the explicit context relation via self-supervised learning. The SGM is trained to leverage both episodic observations and general knowledge to reconstruct the masked pixels of a cropped global map. During navigation, the agent maintains an incomplete local semantic map, meanwhile, the unknown regions of the local map are generated by the pretrained SGM. Based on the generated map, the agent sets the predicted location of the target as the goal and moves towards it. Experiments on Gibson, MP3D and HM3D show the effectiveness of our method. The code is available at https://***/sx-zhang/SGM.

关键词： Training Geometry Navigation Large language models Semantics Layout Self-supervised learning

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A new model based on differential evolutionary algorithm and Markov clustering for identifying protein complexes

A new model based on differential evolutionary algorithm and...

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第42届中国控制会议

作者： Zengyu Feng Shouheng Tuo Tianrui Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing

As the material basis for all life activities, proteins play a crucial role in performing life activities. Most real-life proteins perform various functions in the form of protein complexes, so it is essential for understanding of life activities to accurately identify protein complexes. Most of the existing approaches learn protein interactions directly from protein topology,ignoring the biological characteristics of the actual proteins. Nevertheless, actual protein complexes are usually composed of protein cores and protein attachments. In this study, a new MP-DE algorithm is designed from the core-attachment structure to generate protein complex cores using Markov clustering, while searching for attached proteins in the second-order neighborhood of core protein complexes based on differential evolution(DE) algorithm. The experimental results show that the proposed method has high detection accuracy and efficiency on the current mainstream protein-protein interaction(PPI) database.

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Removing Scattered Light in Biomedical Images

Removing Scattered Light in Biomedical Images

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IEEE International Symposium on Biomedical Imaging

作者： Yuanhao Gong Wanlin Huang Wenhui Wu Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China Reexen Technology Ltd.

Scattered light is a common issue in various biomedical images such as fluorescence and X-ray images, leading to a hazy view of the imaging objects and thus degrading the image quality. To tackle this issue, this paper proposes a novel mathematical model to remove such scattered light. Our model is guaranteed to produce an image with better image contrast than the input. We also present a Local Guided Total Variation filter to solve this model. The proposed filter has linear computation complexity with respect to the total number of pixels, resulting in a high computational performance. Extensive experiments have been conducted to confirm the effectiveness of the proposed model and efficiency of the presented filter. Our method can be applied to a wide range of images to remove their scattered light. The open-source code is publicly available at http://***.

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Inner Co Synergizing Outer Ru Supported on Carbon Nanotubes for Efficient pH-Universal Hydrogen Evolution Catalysis

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Nano-Micro Letters 2022年第11期14卷 275-289页

作者： Jian Chen Yuan Ha Ruirui Wang Yanxia Liu Hongbin Xu Bin Shang Renbing Wu Hongge Pan Institute of Science and Technology for New Energy Xi’an Technological UniversityXi’an 710021People’s Republic of China School of Advanced Materials and Nanotechnology Xidian UniversityXi’an 710126People’s Republic of China Department of Materials Science Fudan UniversityShanghai 200433People’s Republic of China State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhan 430073People’s Republic of China State Key Laboratory of Silicon Materials and School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027People’s Republic of China

Exploring highly active but inexpensive electrocatalysts for the hydrogen evolution reaction(HER)is of critical importance for hydrogen production from electrochemical water ***,we report a multicomponent catalyst with exceptional activity and durability for HER,in which cobalt nanoparticles were in-situ confined inside bamboo-like carbon nanotubes(CNTs)while ultralow ruthenium loading(~2.6μg per electrode area~cm^(−2))is uniformly deposited on their exterior walls(Co@CNTsǀRu).The atomic-scale structural investigations and theoretical calculations indicate that the confined inner Co and loaded outer Ru would induce charge redistribution and a synergistic electron coupling,not only optimizing the adsorption energy of H intermediates(ΔGH*)but also facilitating the electron/mass *** as-developed Co@CNTsǀRu composite catalyst requires overpotentials of only 10,32,and 63 mV to afford a current density of 10 mA cm^(−2) in alkaline,acidic and neutral media,respectively,representing top-level catalytic activity among all reported HER *** current work may open a new insight into the rational design of carbon-supported metal catalysts for practical applications.

关键词： Cobalt nanoparticles Carbon nanotubes Ru nanoclusters Hydrogen evolution reaction

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TMMM: Transformer in Multimodal Sentiment Analysis under Missing Modalities

TMMM: Transformer in Multimodal Sentiment Analysis under Mis...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huanyu Wu Siyang Li Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The cross-task gap presents a significant challenge for multimodal models because of the differences in input-output workflows. For instance, multimodal pre-trained transformers may encounter uni-modal data during testing. To mitigate the gap, this paper introduces a Transformer in Multimodal Sentiment Analysis under Missing Modalities (TMMM) aims to perform well using missing-modal data during testing. TMMM uses a missing multimodal training approach to prevent accuracy degradation in testing. At the same time, a new network architecture allows the model to reconstruct missing modalities during testing. Classification token fusion and Mixture-of-Experts structures further enhance the model’s performance. A pre-training method utilizing contrastive learning, which can construct negative samples with positive samples, is proposed to overcome insufficient labeled data. Our experiments demonstrated the effectiveness of TMMM on two datasets with no modalities missing, i.e., it consistently achieved the highest classification accuracy and Macro-F1, which outperformed the best state-of-the-art baseline on each dataset by about 2% and 2.5%. Additionally, TMMM usually performs better than other baselines on datasets with missing modalities during testing.

关键词： Training Sentiment analysis Accuracy Contrastive learning Network architecture Transformers Brain modeling

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Kalman Filter Adaptive Speed Tracking Control for High-speed Trains with Noise Interference

Kalman Filter Adaptive Speed Tracking Control for High-speed...

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IEEE International Conference of intelligent Robotic and Control Engineering (IRCE)

作者： Chunli Zhang Yuwei Xie Lei Yan Fucai Qian College of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively.

关键词： Resistance Filtering Noise Process control Interference Predictive models Mathematical models Kalman filters Adaptive control MATLAB

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Rockburst prediction using artificial intelligence techniques:A review

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

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