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SIEVL-Track: Exploring Semantic information Enhancement for Visual-Language Object Tracking

IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5872-5884页

作者： Li, Ning Zhong, Bineng Liang, Qihua Mo, Zhiyi Nong, Jian Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China

With the assistance of language descriptions, Visual-Language (VL) object tracking can obtain more accurate semantic information compared to traditional Visual-Only object tracking. However, the ability of current VL trackers to obtain target semantic information has not been fully developed due to limitations such as wasted modeling capabilities and insufficient utilization of historical temporal information. On the one hand, the modeling output from Transformer shallow encoders often does not directly participate in the prediction of tracking results, resulting in a certain degree of model capability waste. On the other hand, the semantic information of historical tracking results has also not been fully utilized in the tracking process, resulting in a certain degree of lack of semantic assistance capability. Therefore, we propose a novel hierarchical multi-stage VL tracker called SIEVL-Track to enhance target semantic information. Specifically, we first design a multi-stage visual language tracking framework for modeling multi-scale semantic information in Visual-Language tracking pipeline. Secondly, we propose a selective deep and shallow semantic information fusion module (S-DSFM) that explicitly integrates shallow output features into deep output features, so to reduce the waste of modeling capabilities and obtain more high-frequency semantic information related to the target. Finally, we design a temporal cue modeling module based on linguistic classification and multi-frame historical information(MHLS-TCM), with the aim of more comprehensive utilization of historical temporal semantic information. Benefit from the above designs, our VL tracker can obtain stronger target semantic information. Competitive performance from extensive experimental results on five popular vision-language tracking benchmarks, including LaSOT, OTB99-Lang, WebUAV-3M, LaSOText and TNL2K, have demonstrated the superiority and effectiveness of our SIEVL-Track. © 1991-2012 IEEE.

关键词： Semantics

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Identifying important nodes of hypergraph:An improved PageRank algorithm

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Chinese Physics B 2025年第4期34卷 162-171页

作者： Yu-Hao Piao Jun-Yi Wang Ke-Zan Li School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin 541004China Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin University of Electronic TechnologyGuilin 541004China

Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important *** this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important nodes in a directed *** algorithm introduces the Jaccard similarity of directed *** comparing the numbers of common neighbors between nodes with the total number of their neighbors,the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected,and can reflect the potential correlation between *** improved susceptible–infected(SI)model in directed hypergraph is proposed,which considers nonlinear propagation mode and more realistic propagation *** addition,some important node evaluation methods are transferred from undirected hypergraphs and applied to directed ***,the ImPageRank algorithm is used to evaluate the performance of the SI model,network robustness and *** of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs.

关键词： hypergraph important node PageRank susceptible-infected(SI)model centrality index

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Ranking the Spreading Influence of Nodes in Complex Networks by Combining Local Average Weight and Average Normalized Link Entropy 4

Ranking the Spreading Influence of Nodes in Complex Networks...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Fan, Kaiyu Yang, Fan Tu, Dexin Tang, Xinlai Mao, Xiuyong Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Guangxi University of Science and Technology Key Laboratory of Intelligent Information Processing and Graph Processing Liuzhou545006 China

ISBN: (纸本)9798350343618

Ranking the spreading influence of nodes is of great importance for controlling the spreading process on networks. Although a few measures are proposed for ranking nodes in weighted networks, it is still a challenge in accurately and efficiently ranking the spreading influence of nodes in weighted networks. In this paper, by utilizing both of the local and global topological structure information, a novel method is proposed. First, we define a local average weight (LAW) to describe the local topological structure information of nodes. Second, partly based on the link diversity, we define an average normalized link entropy (ANLE) to describe the global topological structure information of nodes. Further, by combining the LAW and ANLE, we propose a novel ranking measure termed as LAW-ANLE (LA) centrality. The proposed LA is verified by extensive experiments on a series of networks, and the results show that the LA is feasible and effective in accurately ranking the spreading influence of nodes. © 2023 IEEE.

关键词： Complex networks

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Distributed robust iterative learning control for AC microgrids with external disturbances

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Science China(information Sciences) 2025年

作者： Hongyu SU Jingang LAI School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent ControlMinistry of Education Huazhong University of Science and Technology

This study proposes a distributed secondary control scheme based on distributed robust iterative learning control(DRILC) for islanded alternating current(AC) microgrids subjected to external disturbances. By employing the decoupled sliding mode consensus approach, voltage regulation, frequency restoration, and accurate active power sharing can be achieved within a finite time on the proposed novel integral terminal sliding mode(ITSM) manifold. Furthermore, the appropriate iterative update law in the ITSM-based controller is utilized to learn and eliminate external disturbances as effectively as possible. In the proposed control scheme, the iterative learning control and sliding mode control are designed to function in a complementary manner, which enhances the performance of the secondary control scheme for multi-objective regulations. The stability criteria and robustness to external disturbances of the closed-loop microgrid system in the iteration and time domains are also rigorously derived with the help of the Lyapunov direct method. Finally, the effectiveness of the proposed secondary control scheme is validated by case studies of an islanded AC microgrid test system in the MATLAB/SimPowerSystems software environment.

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Improved Kalman Filtering Algorithm Based Multi-Sensor Integration for Silicon Single Crystal Growth Process 7

Improved Kalman Filtering Algorithm Based Multi-Sensor Integ...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Xie, Ruidong Zhu, Pei Ma, Wenle Zhang, Zheqi Yi, Yingmin Guo, Zheng Xi'an University of Technology Key Lab of Manufacturing Equipment of Shaanxi Province Xi'an710048 China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China

ISBN: (纸本)9798350363173

The study primarily delves into addressing the data filtering challenge inherent in the silicon single crystal growth process by proposing a multi-sensor integrated detection approach. This approach is designed to alleviate the diminished filtering accuracy resulting from the reliance on a single sensor and the conventional Kalman filtering algorithm. By utilizing an enhanced adaptive Kalman filtering algorithm and the maximum likelihood estimation criterion to incorporate the innovation variance directly into the gain calculation of the Kalman filter, dynamic adjustment of the estimation model is achieved while interference from system noise and measurement noise is reduced. Finally, simulation-based comparative experiments between the adaptive and conventional Kalman filtering algorithms are performed. The results show that the performance of the adaptive Kalman filtering algorithm is superior to that of the conventional Kalman filtering algorithm in terms of accuracy and stability when detecting signals from multiple sensors. The research findings furnish dependable data support for refining the decision-making process in crystal growth technology, augmenting the precision and scientific integrity of crystal safety production scheduling and decision-making endeavors. © 2024 IEEE.

关键词： Kalman filters

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A Novel Approach for State of Health Estimation and Remaining Useful Life Prediction of Supercapacitors Using an Improved Honey Badger Algorithm Assisted Hybrid Neural Network

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Protection and Control of Modern Power Systems 2024年第6期9卷 1-18页

作者： Zhenxiao Yi Shi Wang Zhaoting Li Licheng Wang Kai Wang the School of Electrical Engineering Qingdao UniversityQingdao 266071Chinaand the State Key Laboratory of Reliability and Intelligence of Electrical EquipmentHebei University of TechnologyTianjin 300130China the Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China the School of Engineering Brown UniversityProvidenceRhode Island 02912USA the School of Information Engineering Zhejiang University of TechnologyHangzhou 310014China the School of Electrical Engineering Qingdao UniversityQingdao 266071China

Supercapacitors(SCs)are widely recognized as excellent clean energy storage *** state of health(SOH)estimation and remaining useful life(RUL)prediction are essential for ensuring their safe and reliable *** paper introduces a novel method for SOH estimation and RUL prediction,based on a hybrid neural network optimized by an improved honey badger algorithm(HBA).The method combines the advantages of convolutional neural network(CNN)and bidirectional long-short-term memory(BiLSTM)neural *** HBA optimizes the hyperparameters of the hybrid neural *** CNN automatically extracts deep features from time series data and reduces dimensionality,which are then used as input for the ***,recurrent dropout is introduced in the recurrent layer to reduce overfitting and facilitate the learning *** approach not only improves the accuracy of estimates and forecasts but also significantly reduces data processing *** under different working conditions are used to validate the proposed *** results show that the proposed hybrid model effectively extracts features,enriches local details,and enhances global perception *** proposed hybrid model outperforms single models,reducing the root mean square error to below 1%,and offers higher prediction accuracy and robustness compared to other methods.

关键词： Supercapacitors state of health remaining useful life honey badger algorithm recurrent dropout.

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Study of multilevel feature weighted fusion based image classification method for silicon single crystal growth process

Study of multilevel feature weighted fusion based image clas...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Xie, Ruidong Zhang, Zheqi Zhu, Pei Ma, Wenle Yi, Yingmin Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China

ISBN: (纸本)9781510681514

A multi-level feature weighted fusion based image classification method for silicon single crystal growth process has been proposed to address the issue of the long silicon single crystal pulling times at industrial sites, which prevents timely understanding of the crystal growth process. A multi-level feature weighted fusion method was employed to identify and classify the growth process of silicon single crystals. A Czochralski single crystal silicon growth process recognition system was then designed. A comparison of the experimental results obtained from this design with the traditional single image classification method clearly showed that using different levels of silicon single crystal image information simultaneously effectively improved the accuracy of silicon single crystal growth image classification. © 2024 SPIE.

关键词： Image classification

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A zeroing neurodynamics‐based location method for nodes in underwater acoustic sensor network

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CAAI Transactions on Intelligence technology 2023年第3期8卷 661-669页

作者： Shuqiao Wang Xiujuan Du Tiantai Deng College of Computer Qinghai Provincial Key Laboratory of loTQinghai Normal UniversityXiningChina The State Key Laboratory of Tibetan Intelligent Information Processing and Application XiningChina The Department of Electronics and Electrical Engineering The University of SheffieldSheffieldEnglandUnited Kingdom

Zeroing neurodynamics methodology,which dedicates to finding equilibrium points of equations,has been proven to be a powerful tool in the online solving of problems with considerable *** this paper,a method for underwater acoustic sensor network(UASN)localisation is proposed based on zeroing neurodynamics methodology to preferably locate moving underwater nodes.A zeroing neurodynamics model specifically designed for UASN localisation is constructed with rigorous theoretical analyses of its *** proposed zeroing neurodynamics model is compatible with some localisation algorithms,which can be utilised to eliminate error in non‐ideal situations,thus further improving its ***,the effectiveness and compatibility of the proposed zeroing neurodynamics model are substantiated by examples and computer simulations.

关键词： artificial neural network internet of things underwater acoustic sensor network

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Evidential Open Set Recognition for Imbalanced Medical Images via Multi-level Data Augmentation

Evidential Open Set Recognition for Imbalanced Medical Image...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Rui-Wei Xu, Yiqian Zhang, Ying Feng, Rui Shanghai Maritime University College of Information Engineering China Fudan University Academy for Engineering and Technology China Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9798350386226

Due to the exstence of common and rare diseases, the complex clinical scenario often poses the challenging class imbalanced open set recognition problem. Unfortunately, most existing approaches are ill-suited for such situations with limited and imbalanced data during training and the possibility of encountering unseen classes during test. In this work, we propose a novel Multi-level mixup-based Evidential Open Set Recognition (ME-OSR) approach to more explicitly and effectively address the open set recognition for class-imbalanced medical images. Briefly, we first extract disentangled discriminative and background image features. Then, based on the original images and extracted features, we propose to sample and conduct Multi-level Open Mixup (MOM) for a more balanced open set data augmentation. It includes extensive intra- and interclass mixup operations in both image and feature spaces, which can augment rare classes with different feature combinations and generate potential pseudo-unknown class examples in the open set to boost the model training. Based on the augmented data and their extracted discriminative features, we propose a Regularized Evidential Deep Learning (REDL) classifier to work with the augmented data to achieve open set recognition with prediction uncertainty estimation and unknown example rejection. Through comparative experiments and ablation studies on several representative medical datasets, we showed that our proposed method outperforms other state-of-the-arts on four popular medical OSR datasets. © 2024 IEEE.

关键词： Medical imaging

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