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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Tang, Yiming Xi, Lei Wu, Wenbin Wu, Xi Li, Shujie Chen, Rui Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789819923847

Granular clustering is an emerging branch in the field of clustering. However, the existing granular clustering algorithms are still immature in terms of weight setting of granular data and noise resistance. In this study, a weighting possibilistic fuzzy c-means algorithm for interval granularity (WPFCM-IG) is proposed. To begin with, a new weight setting method for interval granular data is given. The principle of justifiable granularity is used as the evaluation criterion of granular data, and a weight is assigned to each granular data from two perspectives of coverage and specificity to measure the quality of the granular data. In addition, the idea of possibilistic clustering is introduced, which is helpful to improve the noise resistance. And, with the proposed weights of interval granular data, the influence of data with smaller weights on the clustering results can be reduced during the clustering process. Based upon these ideas, the WPFCM-IG algorithm is put forward, and its core idea, formula derivation and implementation process are described. Finally, the performance of the proposed algorithm is verified by comparison experiments on the artificial and UCI datasets. The experimental results show that the WPFCM-IG algorithm is better than other advanced algorithms in this field in terms of reconstruction error. Next, the WPFCM-IG algorithm is smoother than other algorithms on the collaborative relationship curve between the fuzzy coefficient and the reconstruction error, so WPFCM-IG can better optimize the fuzzy coefficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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Controllable selective anchoring of subnanometric Ru clusters or single-atoms/clusters on tungsten nitride for the hydrogen oxidation

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Science China Chemistry 2024年第11期67卷 3749-3756页

作者： Qijiao Sun Chuanqiang Wu Yiqiang Sun Yuan Shen Lei Zhao Xiaodong Yang Li Yang Yunxiang Lin Keke Mao Kun Xu School of Chemistry and Chemical Engineering Key Laboratory of Functional Inorganic Material Chemistry of Anhui ProvinceAnhui Province Key Laboratory of Chemistry for Inorganic/Organic Hybrid Functionalized MaterialsKey Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationAnhui UniversityHefei 230601China Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China School of Chemistry and Chemical Engineering University of JinanJinan 250022China School of Energy and Environment Anhui University of TechnologyMa’anshan 243032China

The impregnation method in the preparation of metal cluster catalysts typically inadvertently introduces single atoms(SAs) into the substrate. However, the question of whether the introduction of SAs will further improve the catalytic activity of cluster systems for specific reactions such as the hydrogen oxidation reaction(HOR) remains unraveled. Herein, we demonstrate Ru clusters anchored on WN nanowires(RuC/WN) show a higher alkaline HOR catalytic activity in comparison with Ru SAs and nanoclusters(NCs)-coupled catalyst anchored on WN nanowires system(RuC,S/WN). Notably, the RuC/WN exhibits superb intrinsic catalytic activity with a mass-normalized exchange current density of 890 m A mg^(-1)PGM, which is among the top level of well developed Ru-based HOR catalysts. Both theoretical simulation and experimental investigation suggest that RuC/WN owns an optimized H^(*)and OH^(*) reaction intermediates for the alkaline HOR, therefore resulting in the excellent intrinsic HOR catalytic performance.

关键词： hydrogen oxidation reaction Ru cluster catalyst Ru single atoms and nanoclusters coupled catalyst tungsten nitride substrate

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A Feature Reduction-Induced Subspace Multiple Kernel Fuzzy Clustering Algorithm 17th

A Feature Reduction-Induced Subspace Multiple Kernel Fuzzy C...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Tang, Yiming Li, Bing Pan, Zhifu Sun, Xiao Chen, Renhao Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Anhui Hefei230601 China Engineering Research Center of Safety Critical Industry Measure and Control Technology Ministry of Education Hefei University of Technology Anhui Hefei230601 China

ISBN: (纸本)9789819923847

High-dimensional data poses a great challenge to clustering, and subspace clustering algorithms have unique advantages when working with high-dimensional data. However, there are still some difficulties in adapting the nonlinear feature dimension, the selection and collaborative settings of kernel functions. Therefore, in this study, we come up with a feature reduction-induced subspace multiple kernel fuzzy clustering. Firstly, in order to solve the information loss caused by the nonlinear characteristics of the data, the multiple kernel clustering method is introduced, and some nonlinear features of the data are collaboratively mapped to the high-dimensional linear space, so as to better understand the characteristic information of the data. Secondly, due to the complexity of subspace and multiple kernel learning, we introduce the idea of feature reduction, and reduce the data dimension according to the importance of information, which can reduce the complexity of the algorithm on the one hand, and improve the role of important feature attributes in clustering on the other hand. Finally, the proposed RS-MKFC algorithm and the related 6 algorithms are compared on 6 ordinary datasets and 4 high-dimensional datasets, and it is found that the proposed algorithms are superior over the other 6 algorithms. At the same time, we discover the ability of RS-MKFC algorithm to screen important features as well as the function of feature reduction, and good results are achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networks

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 893-913页

作者： Lei Zhang Yutong Liu Haipeng Yang Fan Cheng Qi Liu Xingyi Zhang Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing of Ministry of EducationSchool of Computer Science and TechnologyAnhui UniversityHefeiChina School of Computer Science and Technology University of Science and Technology of ChinaHefeiChina

Influence maximization,whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network,has many applications such as goods advertising and rumour *** the existing influence maximization methods,the community‐based ones can achieve a good balance between effectiveness and ***,this kind of algorithm usually utilise the network community structures by viewing each node as a non‐overlapping *** fact,many nodes in social networks are overlapping ones,which play more important role in influence *** this end,an overlapping community‐based particle swarm opti-mization algorithm named OCPSO for influence maximization in social networks,which can make full use of overlapping nodes,non‐overlapping nodes,and their interactive information is ***,an overlapping community detection algorithm is used to obtain the information of overlapping community structures,based on which three novel evolutionary strategies,such as initialisation,mutation,and local search are designed in OCPSO for better finding influential *** results in terms of influence spread and running time on nine real‐world social networks demonstrate that the proposed OCPSO is competitive and promising comparing to several state‐of‐the‐arts(***,CMA‐IM,CIM,CDH‐SHRINK,CNCG,and CFIN).

关键词： computational intelligence data mining social network

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Utilizing machine learning models to grasp water quality dynamic changes in lake eutrophication through phytoplankton parameters

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Frontiers of Environmental Science & Engineering 2025年第2期19卷 1-16页

作者： Yong Fang Ruting Huang Yeyin Zhang Jun Zhang Wenni Xi Xianyang Shi Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration School of Resources and Environmental EngineeringAnhui UniversityHefei 230601China

Phytoplankton serve as vital indicators of eutrophication ***,relying solely on phytoplankton parameters,such as chlorophyll-a,limits our comprehensive understanding of the intricate eutrophication conditions in natural lakes,particularly in terms of timely analysis of changes in limiting nutrients and their *** study presents machine learning(ML)models for predicting and identifying lake *** tree-based ML models were developed using the latest data on hydrological,water quality,and meteorological parameters obtained from 34 sites in the Huating Lake basin over 5 *** extreme gradient boosting model exhibited high accuracy in predicting the total nitrogen/total phosphorus ratio(TN/TP)(R^(2)=0.88;RMSE=24.60;MAPE=26.14%).Analysis of the TN/TP ratio and output eigenvalue weight revealed that phosphorus plays a crucial role in eutrophication,probably because of the low-flow and deep-water characteristics of the ***,the light gradient boosting machine model exhibited outstanding performance and high accuracy in predicting phytoplankton parameters,especially the Shannon index(H′)(R^(2)=0.92;RMSE=0.11;MAPE=4.95%).The mesotrophic classification of the Huating Lake determined using the H′threshold,coincided with the findings from the H′*** research should cover a wider range of pollution sources and spatiotemporal dimensions to further validate our ***,this study highlights the potential of incorporating the TN/TP ratio and phytoplankton parameters into ML techniques for effective monitoring and management of environmental conditions.

关键词： machine learning Lake Phytoplankton Water quality

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A Portrait Segmentation Algorithm Combining MobileNeXt and Attention Mechanism 24

A Portrait Segmentation Algorithm Combining MobileNeXt and A...

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2024 International Conference on computing, machine Learning and Data Science, CMLDS 2024

作者： Cheng, Yan Huang, Zou Wang, Guixi Lai, Jianming Xiong, Yuqi School of Software Jiangxi Normal University Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing China School of Digital Industry Jiangxi Normal University Jiangxi Provincial Key Laboratory of Intelligent Information Processing and Affective Computing China

ISBN: (纸本)9798400716393

Existing portrait segmentation methods are easily affected by the background. To address this challenge, we propose a simple convolution-based portrait segmentation algorithm to solve the problems of complex background, low accuracy and poor efficiency in real environments. Combined with multi-scale convolution, we design a multi-scale sandglass module structure (MultiScale-Sandglass-CA, MSC), which expands the depth and width of the network without increasing the number of parameters, and enhances the feature learning ability of the model for complex backgrounds;and the MSC module is used for feature extraction, and the introduction of coordinate attention mechanism not only improves the learning ability of the portrait features, but also enhances the network's ability to learn the key information. The introduction of the coordinate attention mechanism not only improves the learning ability of the portrait features, but also enhances the focusing of the network on the key information;at the same time, the introduction of the hybrid loss function, which helps to obtain clear portrait edges. Experiments are conducted on Matting and EG1800 public datasets, and the results show that the algorithm can significantly improve the mean intersection and merger ratio (mIoU) and pixel accuracy (Acc), etc. The mIoU can reach up to 97%, and the pixel accuracy can reach up to 10%. Among them, the mIoU index can reach 97.91% (Matting) and 94.72% (EG1800). Compared with the mIoU predicted by PortraitNet, it is 1.29% and 1.19% higher, respectively. In addition, experiments are conducted for the MSC structure proposed in this paper, which proves the superiority of the model in this paper. © 2024 ACM.

关键词： Convolution

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Uncertainty-aware coarse-to-fine alignment for text-image person retrieval

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Visual Intelligence 2025年第1期3卷 1-14页

作者： Deng, Yifei Chen, Zhengyu Li, Chenglong Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei 230601 China

Text-to-image person retrieval, a fine-grained cross-modal retrieval problem, aims to search for person images from an image library that match a given textual caption. Existing text-to-image person retrieval methods usually use fixed-point embedding to express the semantics of the two modalities and perform multi-granularity alignment between modalities in the embedding space. However, owing to the inherent mutual one-to-many correspondence between images and texts, it is often difficult for fixed-point embedding methods to adequately capture this relationship, leading to erroneous retrieval results. To address this problem, we propose a novel uncertainty-aware coarse-to-fine alignment method, which first maps fixed-point embedding to probability distributions and then aligns two modalities in terms of distributions and sampling points at a coarse-to-fine granularity, for accurate text-to-image person retrieval. Specifically, we first introduce two contrastive learning tasks of distribution contrast learning and point contrast learning, to achieve coarse-grained inter-modal alignment with uncertainty-aware. The distribution contrast learning task ensures that distributions with the same identity are as similar as possible across modalities through distribution-based contrastive learning. The point contrast learning task performs the contrastive learning of inter-modal and intra-modal sampling points, which not only models rich and diverse cross-modal associations, but also optimizes the learning of distributions. For the fine-grained association requirements of text-to-image person retrieval, we design the task of uncertainty-aware attribute masking language reconstruction, which achieves fine-grained alignment by randomly masking attribute words in the text and reconstructing them via inter-modal sample point interactions. Extensive experiments on two public datasets demonstrate the superior performance of our method. © The Author(s) 2025.

关键词： Coarse-to-fine alignment Contrastive learning Cross-modal retrieval Probalility distribution Uncertainty-aware

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Design of Novel and Low Cost Triple-node Upset Self-recoverable Latch

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原子能科学技术 2023年第12期57卷 2326-2336页

作者： BAI Na MING Tianbo XU Yaohua WANG Yi LI Yunfei LI Li Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui UniversityHefei 230601China Shanxi Key Laboratory of Advanced Semiconductor Optoelectronic Devices and Integrated Systems Jincheng 048000China Jincheng Research Institute of Opo-mechatronics Industry Jincheng 048000China

With the development of semiconductor technology,the size of transistors continues to *** complex radiation environments in aerospace and other fields,small-sized circuits are more prone to soft error(SE).Currently,single-node upset(SNU),double-node upset(DNU)and triple-node upset(TNU)caused by SE are relatively ***’s solution is not yet fully mature.A novel and low-cost TNU self-recoverable latch(named NLCTNURL)was designed which is resistant to harsh radiation *** analyzing circuit resiliency,a double-exponential current source is used to simulate the flipping behavior of a node’s stored value when an error *** results show that the latch has full TNU self-recovery.A comparative analysis was conducted on seven latches related to ***,a comprehensive index combining delay,power,area and self-recovery—DPAN index was proposed,and all eight types of latches from the perspectives of delay,power,area,and DPAN index were analyzed and *** simulation results show that compared with the latches LCTNURL and TNURL which can also achieve TNU self-recoverable,NLCTNURL is reduced by 68.23%and 57.46%respectively from the perspective of *** the perspective of power,NLCTNURL is reduced by 72.84%and 74.19%,*** the area perspective,NLCTNURL is reduced by about 28.57%and 53.13%,*** the DPAN index perspective,NLCTNURL is reduced by about 93.12%and 97.31%.The simulation results show that the delay and power stability of the circuit are very high no matter in different temperatures or operating voltages.

关键词： circuit reliability latch design self-recoverability soft error radiation hardening triple-node upset

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Lightweight fire detection algorithm based on improved YOLOv8

Lightweight fire detection algorithm based on improved YOLOv...

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2024 International Workshop on Automation, Control, and Communication Engineering, IWACCE 2024

作者： Wu, Di Ning, Jinzheng Wang, Yuanzhi School of Computer and Information Anqing Normal University Anqing Anhui246133 China University Key Laboratory of Intelligent Perception and Computing of Anhui Province China

ISBN: (数字)9781510685420

ISBN: (纸本)9781510685413

Aiming at the limitations of traditional fire detection algorithms in terms of accuracy and real-time detection, a lightweight fire detection algorithm based on improved YOLOv8 is proposed. The Slim-neck is used to improve the Neck network, reduce the number of parameters and computation of the model, and improve the detection performance of the model. The C2f-Star module is designed, and the Star block is introduced to replace the bottleneck structure of the C2f module in the Backbone network, to better capture the information of the image, and further reduce the complexity of the model. The Focaler WIoU boundary loss function is used instead of the original loss function, which reduces the influence of low-quality samples and increases the regressivity of the network bounding box. The experimental results show that the number of parameters and the computational volume of the improved model are reduced by 14.0% and 16.0%, while the precision and the mean average precision are improved by 3.0% and 1.2%, compared with the original model, which can help real-time monitoring and early warning of fire. © 2024 SPIE. All rights reserved.

关键词： Fire Detection and tracking algorithms Performance modeling Education and training Target detection Feature extraction Convolution Mathematical optimization Stars Statistical modeling

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An Optimal Adaptive Kalman Filter with Gain Correction Based on Innovation Outlier Detection for Human Motion Tracking 5

An Optimal Adaptive Kalman Filter with Gain Correction Based...

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5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Wang, Xuhui Zhao, Xuxing School of Physics and Electronic Information Huaibei Normal University Anhui Province Key Laboratory of Intelligent Computing and Applications Huaibei China

ISBN: (纸本)9798331529192

In this article, an optimal adaptive Kalman filter algorithm is proposed to mitigate the impact of measurement outliers on human motion tracking. The algorithm utilizes an inertial measurement unit (IMU) equipped with a three-axis gyroscope, accelerometer, and magnetometer for tracking human motion. Due to the presence of modeling issues, an adaptive factor is introduced to adjust the process noise covariance matrix, for compensating modeling errors. When the innovation exceeds the specified threshold range, the Kalman gain K is adjusted to compensate for the impact of measurement outliers. Segmented weight functions are constructed to apply appropriate weights to the Kalman gain to suppress the impact of outliers. When the innovation exceeds the threshold range, the gain K is used to correct the posterior covariance and suppress abnormal values. This approach effectively prevents filter divergence that can occur when the continuous innovation sequence is set to zero without adjusting the posterior covariance. The estimated gravity acceleration and geomagnetic field are then used to calculate the Euler angles via a triaxial attitude determination algorithm. Finally, The effectiveness of the proposed method is demonstrated through various action experiments in different motion scenarios, with experimental results showing a significant reduction in errors. © 2024 IEEE.

关键词： Adaptive filters

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