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arXiv 2025年

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C.L. Philip The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of New Network Pengcheng Laboratory Shenzhen518000 China The China Tobacco Guangdong Industrial Corporation Guangzhou510385 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed for semi-supervised semantic segmentation with Vision Transformers. TokenMix aligns well with the global attention mechanism by mixing images at the token level, enhancing learning capability for contextual information among image patches. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. Copyright © 2025, The Authors. All rights reserved.

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A segmentation method for oral CBCT image based on Segment Anything Model and semi-supervised teacher-student model

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Medical Physics 2025年 2025 May 7页

作者： Gan, Jianhong Kang, Runqing Deng, Xun He, Tongli Yu, Nie Gan, Yuling Wei, Peiyang Chen, Xiangyi Peng, Xiaoli Li, Zhibin College of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service Chengdu University of Information Technology Chengdu China Key Laboratory of Meteorological Software China Meteorological Administration Chengdu China Dazhou Key Laboratory of Government Data Security Sichuan University of Arts and Science Dazhou China College of Applied Mathematics Chengdu University of Information Technology Chengdu China The Fifth Research Institute of Telecommunications Technology Chengdu China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Xinjiang Technical Institute of Physics &Chemistry Chinese Academy of Sciences Urumqi China College of Instrumentation and Electrical Engineering Jilin University Changchun China

Background: Accurate segmentation of oral cone beam computed tomography (CBCT) images is essential for research and clinical diagnosis. However, irregular and blurred tooth boundaries in CBCT images complicate the labeling of oral tissues, and insufficient labeled samples further limit the generalization ability of segmentation models. The Segment Anything Model (SAM) demonstrates strong generalization and segmentation accuracy across diverse tasks as a vision foundation model. The Teacher-Student (TS) model has proven effective in semi-supervised learning approaches. Purpose: To accurately segment various parts of oral CBCT, such as enamel, pulp, bone, blood vessels, air, etc., an improved segmentation method named SAM-TS is proposed, which combines SAM with the TS model. SAM-TS leverages Low-Rank Adaptation (LoRA) to fine-tune the SAM model on oral CBCT images with fewer parameters. Methods: To efficiently utilize numerous unlabeled images for training models, the LoRA strategy is improved to fine-tune the SAM. The fine-tuned SAM and teacher models collaboratively generate pseudo-labels on unlabeled images, which are filtered and utilized to train the student model. Then, a data augmentation-based Mean Intersection over Union (MIoU) method is proposed to filter out unreliable or spurious pseudo-labels. Finally, the Exponential Moving Average (EMA) method is used to transfer the student model's parameters to the teacher model. After repeating this process, the final optimized student model for segmentation is obtained. The experimental results demonstrate that incorporating unlabeled data into model training through SAM-TS significantly enhances the model's generalization ability and segmentation accuracy. Results: Compared to the baseline algorithm, the proposed method achieves an overall improvement of over 6.48% in MIoU. In the tooth segmentation task, the minimum MIoU and maximum MIoU increased by at least 10% and 27.32%, respectively. In the bone segmentation

关键词： Semi-supervised learning

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Optimization of DCM boost chopper performance controlled by optimal PIDF regulator for stand-alone PV system

Solar Compass

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Solar Compass 2025年 14卷

作者： Nkouna Paul Lionnel, Ella Obono Biyobo, Arnaud Owoundi Etouke, Paul Dangwe Sounsoumou, Yves Paulin Jean Jacques Molu, Reagan Dzonde Naoussi, Serge Raoul Computer and Automatic Engineering Research Laboratory ENSET University of Douala Douala Cameroun PB: 1872 Douala Cameroon Faculty of Science Renewable Energies Department University of Garoua Garoua Cameroon PB 346 Garoua Cameroon Technologies and Applied Sciences Laboratory University Institute of Technology of Douala University of Douala P.O. Box: 8689 Douala Cameroon

This paper proposes a Maximum Power Point Tracking (MPPT) strategy for a photovoltaic (PV) panel, combining the Perturb and Observe (P&O) technique, Particle Swarm Optimization (PSO), and an optimal Duty Cycle Modulation (DCM) circuit. The Boost chopper, regulated by a Proportional-Integral-Derivative controller with a first-order low-pass filter (PIDF), serves as an interface between the photovoltaic source (PV) and the load, maximizing the extraction of energy produced by the solar panel. The results obtained in virtual simulation in the MATLAB/Simulink environment are satisfactory: we observe in standard conditions, namely temperature T = 25 °C and solar irradiation of 1000W/m², a response without overshoot, a zero static error, a system stability time of 0.32 s, 0.13 s and 0.15 s respectively for three respective load values (R₀ = 50 Ω, R₀₁ = 37.5 Ω and R₀₂ = 25 Ω) in steady state. In transient state, taking into account the variation in solar irradiation, it can be seen that despite the fluctuations, the system manages to remain stable, attesting to the robustness of the controller used. To control the DCM Boost chopper. The comparative study carried out with other MPPT techniques such as (VSS-P&O, MIC, FOCV, FSS-RBFA, VSS-RBFA, AFLC, PSO and CS) applied to conventional boost converters attests that the proposed new prototype offers quite satisfactory performances compared to the latter. © 2025 The Author(s)

关键词： DCM (duty cycle modulation) LQR (linear quadratic regulator) P&O (Perturb and Observe) PIDF (Proportional, Integral, Derivative with low-pass Filter) PSO (Particle Swarm Optimization)

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Adaptive ELM-Based Time-Constrained control of Uncertain Robotic Manipulators With Minimum Learning Computation

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IEEE Transactions on Emerging Topics in Computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

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State Feedback Tracking control for Indirect Field-oriented Induction Motor Using Fuzzy Approach

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International Journal of Automation and computing 2013年第2期10卷 99-110页

作者： M. Allouche M. Chaabane M. Souissi D. Mehdi F. Tadeo Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering (Lab-STA) National School of Engineering of SfaxUniversity of Sfax Laboratory of Computer and Automatic for System (LIAS) Higher School of Engineering of Poitiers (ESIP) University of Poitiers Department of Systems Engineering University of Valladolid

This paper deals with the synthesis of fuzzy controller applied to the induction motor with a guaranteed model reference tracking performance. First, the Takagi-Sugeno （T-S） fuzzy model is used to approximate the nonlinear system in the synchronous d-q frame rotating with field-oriented control strategy. Then, a fuzzy state feedback controller is designed to reduce the tracking error by minimizing the disturbance level. The proposed controller is based on a T-S reference model in which the desired trajectory has been specified. The inaccessible rotor flux is estimated by a T-S fuzzy observer. The developed approach for the controller design is based on the synthesis of an augmented fuzzy model which regroups the model of induction machine, fuzzy observer, and reference model. The gains of the observer and controller are obtained by solving a set of linear matrix inequalities （LMIs）. Finally, simulation and experimental results are given to show the performance of the observer-based tracking controller.

关键词： Fuzzy tracking control H∞ performance feedback controller linear matrix inequality （LMI） fuzzy observer

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Self-Powered Broadband Photodetectors Based on WS2-Anchored MoS2 with Enhanced Responsivity and Detectivity

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Small 2025年第20期21卷 e2502900页

作者： Kumar, Ashok Chakkar, Atul G. Das, Chayan Kumar, Pradeep Sahu, Satyajit Saliba, Michael Kumar, Mahesh Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland School of Physical Sciences Indian Institute of Technology Mandi Himachal Pradesh Mandi175005 India Department of Physics Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India University of Stuttgart Stuttgart70569 Germany Helmholtz Young Investigator Group FRONTRUNNERIEK5-Photovoltaik Forschungszentrum Jülich Jülich52425 Germany Department of Electrical Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India

Self-powered broadband photodetectors utilizing 2D transition metal dichalcogenides (TMDs) are highly promising due to their remarkable light absorption capabilities and high sensitivity, making them suitable for applications such as military surveillance and wireless light detection systems. However, their performance is constrained by inadequate absorption, suboptimal charge carrier separation, and slow response times. In response to these limitations, the study fabricates a self-powered photodetector employing a heterostructure composed of WS2 nanoparticles anchored to CVD-synthesized MoS2, operating within the visible to near-infrared spectrum. The device demonstrates a responsivity of 283 mA W−1 and a detectivity 6.44 × 1012 Jones, alongside an external quantum efficiency of 61% under exposure of 580 nm. In comparison to pristine MoS2, the MoS2-WS2 photodetector exhibited approximately 12-fold and 11-fold enhancements in responsivity and detectivity, respectively, in addition to fast response time of ≈375 µs and 6 ms. Additionally, density functional theory (DFT) calculations are used to analyze the increase in dark current that is observed following WS₂ nanoparticle anchored on MoS₂. This investigation highlights the potential of 2D heterostructures in the development of high-performance broadband photodetectors, which offer improved responsivity, stability, and self-powered operation for advanced optoelectronic applications. © 2025 Wiley-VCH GmbH.

关键词： Layered semiconductors

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Tumor Cellularity Assessment Using Artificial Intelligence Trained on Immunohistochemistry-Restained Slides Improves Selection of Lung Adenocarcinoma Samples for Molecular Testing

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American Journal of Pathology 2025年第5期195卷 907-922页

作者： Gertych, Arkadiusz Zurek, Natalia Piaseczna, Natalia Szkaradnik, Kamil Cui, Yujie Zhang, Yi Nurzynska, Karolina Pyciński, Bartłomiej Paul, Piotr Bartczak, Artur Chmielik, Ewa Walts, Ann E. Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Department of Pathology and Laboratory Medicine Cedars-Sinai Medical Center Los Angeles California United States Department of Surgery Cedars-Sinai Medical Center Los Angeles California United States F. Widjaja Inflammatory Bowel Disease Institute Cedars-Sinai Medical Center Los Angeles California United States Biostatistics Shared Resource Cedars-Sinai Medical Center Los Angeles California United States Biobank and Research Pathology Resource Cedars-Sinai Medical Center Los Angeles California United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Tumor Pathology Department Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Gliwice Poland Hospital for Lung Diseases “Rebirth ” Zakopane Poland

Tumor cellularity (TC) in lung adenocarcinoma slides submitted for molecular testing is important in identifying actionable mutations, but lack of best practice guidelines results in high interobserver variability in TC assessments. An artificial intelligence (AI)–based pipeline developed to assess TC in hematoxylin and eosin (H&E) whole slide images (WSIs) and in tumor areas (TAs) within WSIs includes a new model (CaBeSt-Net) trained to mask cancer cells, benign epithelial cells, stroma in H&E WSIs using immunohistochemistry-restained slides, and a model to detect all cell nuclei. High masking accuracy (>91%) by CaBeSt-Net computed using 1024 H&E regions of interest and intraclass correlation coefficient >0.97 assessing TC assessments reliability by one pathologist and AI in 20 test regions of interest supported the pipeline's applicability to TC assessment in 50 study H&E WSIs. Using the pipeline, TCs assessed in TAs and WSIs were compared with those by three pathologists. Reliabilities of these ratings by the pathologists supported by the pipeline were good (intraclass correlation coefficient >0.82, P < 0.0001). The consistency of sample categorizations as inadequate or adequate (TC ≤ 20% cut point) for molecular testing among the pathologists assessing TCs without AI support was moderate in TAs (κ = 0.410, P < 0.0001) and slight in WSIs (κ = 0.132, nonsignificant). With AI support, the consistency was substantial in both WSIs (κ = 0.602, P < 0.0001) and TAs (κ = 0.704, P < 0.0001). By visualizing cancer and measuring TC in the sample, this novel AI-based pipeline assists pathologists in selecting samples for molecular testing. © 2025 American Society for Investigative Pathology

关键词： artificial intelligence computational pathology immunohistochemical staining lung adenocarcinoma restained slides tumor burden tumor cellularity assessment

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OBSERVER-BASED H_∞ control FOR TIME-DELAY-CONFORMABLE FRACTIONAL-ORDER TAKAGI–SUGENO FUZZY SYSTEMS

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Fractals 2025年

作者： Rhaima, Mohamed Gassara, Hamdi McHiri, Lassaad Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh 11451 Saudi Arabia Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax PB 1173 Sfax 3038 Tunisia Department of Mathematics Panthéon-Assas Université Paris II 92 Rue d’Assas Paris 75006 France

This work focuses on implementing H∞ Observer-Based control (OBC) for a class of Nonlinear Conformable fractional-order Delayed Systems (NCDSs) represented by the Takagi–Sugeno fuzzy model (TSFM). The aim is to deve... 详细信息

This work focuses on implementing H_∞ Observer-Based control (OBC) for a class of Nonlinear Conformable fractional-order Delayed Systems (NCDSs) represented by the Takagi–Sugeno fuzzy model (TSFM). The aim is to develop conditions, utilizing the Lyapunov approach, that guarantee the existence of both a controller and an observer. These conditions are formulated as linear matrix inequalities (LMIs). Additionally, an H_∞ optimization technique is employed to mitigate the effects of disturbances on the controlled output. An example is provided to demonstrate the validity of the proposed scheme. © The Author(s)

关键词： Conformable Fractional-Order Derivative Lyapunov Method Observer Design Stability Analysis Takagi–Sugeno Fuzzy Model

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Konjac glucomannan (KGM) aerogel immobilized microalgae: A new way for marine oil spills remediation

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Marine Pollution Bulletin 2025年 218卷 118158页

作者： Wang, Xudong Du, Zhuorong Song, Zhao Liu, Mei He, Pengfei Feng, Lijuan Chen, Zhi Chen, Qingguo Zhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry Zhejiang Ocean University Zhoushan316022 China National & local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology Zhejiang Ocean University Zhoushan316022 China School of Marine Science & Technology Zhejiang Ocean University Zhoushan316022 China Zhejiang Marine Fisheries Research Institute Zhoushan316021 China Department of Building Civil and Environmental Engineering Faculty of Engineering & Computer Sciences Concordia University MontrealQCH3G1M8 Canada

This study investigated the oil pollution remediation ability of konjac glucomannan (KGM) aerogel immobilized Chlorella vulgaris LH-1 (C. vulgaris). The KGM aerogel exhibited a high porosity of 86.83 %, and with the change of crude oil concentration (1–10 g/L), the crude oil adsorption efficiency of KGM aerogel was up to 18.75 %. The KGM aerogel carrier enhanced the crude oil biodegradation efficiency of C. vulgaris by 13.86 %. The concentration of residual n-alkanes of the immobilized C. vulgaris group was only 36.83 % of that of the free C. vulgaris group after 14 days of cultivation. In the crude oil metabolites of immobilized C. vulgaris, the concentrations of benzoic acid, phthalic acid, and benzophenone were 1.35, 21.18 and 3.43 times higher than those of the free C. vulgaris, respectively. Compared with free C. vulgaris group, 9 monooxygenase genes, 11 dioxygenase genes and 2 cytochrome P450 genes of immobilized C. vulgaris group were up-regulation and the "hydrocarbon metabolism process" was significantly enriched (enrichment factor is 0.85) in the gene ontology (GO) analysis of the transcriptome of C. vulgaris. These results showed that the ability of C. vulgaris to degrade crude oil was improved due to the existence of the KGM aerogel immobilized carrier. © 2025 Elsevier Ltd

关键词： Marine pollution

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