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Cerebral ischemia detection using deep learning techniques

Health Information Science and Systems 2025年第1期13卷 1-19页

作者： Pastor-Vargas, Rafael Antón-Munárriz, Cristina Haut, Juan M. Robles-Gómez, Antonio Paoletti, Mercedes E. Benítez-Andrades, José Alberto Communications and Control Systems Computer Engineering Science Faculty UNED Calle Juan del Rosal 16 Madrid 28040 Spain Radiology area Hospital Universitario de Navarra Navarra Spain School of Technology University of Extremadura Extremadura Cáceres Spain ALBA Research Group Department of Electric Systems and Automatics Engineering University of Leon Campus of Vegazana s/n Castilla y León León 24071 Spain

Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT) remains the primary diagnostic tool in stroke emergencies due to its speed, accessibility, and cost-effectiveness. NCCT enables the exclusion of hemorrhage and directs attention to ischemic causes resulting from arterial flow obstruction. Quantification of NCCT findings employs the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which evaluates affected brain structures. This study seeks to identify early alterations in NCCT density in patients with stroke symptoms using a binary classifier distinguishing NCCT scans with and without stroke. To achieve this, various well-known deep learning architectures, namely VGG3D, ResNet3D, and DenseNet3D, validated in the ImageNet challenges, are implemented with 3D images covering the entire brain volume. The training results of these networks are presented, wherein diverse parameters are examined for optimal performance. The DenseNet3D network emerges as the most effective model, attaining a training set accuracy of 98% and a test set accuracy of 95%. The aim is to alert medical professionals to potential stroke cases in their early stages based on NCCT findings displaying altered density patterns. © The Author(s) 2025.

关键词： Cerebral ischemia Computed tomography Deep learning Ictus dataset Transfer learning

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Discrete control algorithms for systems with big dead time for control101 MATLAB toolbox

Discrete control algorithms for systems with big dead time f...

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Cybernetics & Informatics (K&I)

作者： Ruth Bars Gyula Max J. Anthony Rossiter László Keviczky Department of Automation and Applied Informatics Budapest University of Technology and Economics Budapest Hungary School of Electrical and Electronic Engineering University of Sheffield Sheffield UK Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331541811

ISBN: (纸本)9798331541828

The first course of control is under a critical review. Both the teaching material covered and the teaching methods require new considerations. Introducing interactivity in the education process makes the learning more successful and enjoyable. MATLAB provides an effective environment for learning and applying different disciplines. control101 is a new MATLAB toolbox under development which provides tools for interactive learning of control disciplines. This paper presents the framework for teaching discrete control algorithms applied for processes containing large dead times.

关键词： Visualization Systematics PI control Reviews Education Laboratories Process control Prediction algorithms PD control MATLAB

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Energy-Based Trajectory Tracking for Underactuated Mechanical Systems: Velocity-Free and Disturbance Rejection Methods

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International Journal of Robust and Nonlinear control 2025年

作者： Javanmardi, N. Borja, P. van der Schaft, A.J. Yazdanpanah, M.J. Scherpen, J.M.A. Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom Jan C. Willems Center for Systems and Control Bernoulli Institute Faculty of Science and Engineering University of Groningen Groningen Netherlands

This work investigates three energy-shaping control approaches to address the trajectory-tracking problem for specific classes of underactuated mechanical systems. In particular, the notions of contractive systems and dynamic extensions are utilized to solve the trajectory-tracking problem while addressing implementation issues such as the lack of velocity sensors and the presence of constant disturbances. To this end, a first tracking controller is developed without the need for velocity measurements. A second controller is introduced, solving the trajectory-tracking problem while rejecting matched constant disturbances. Then, a third approach establishes conditions to combine both mentioned controllers. It is shown that the proposed design methods guarantee exponential convergence of closed-loop systems for specific classes of underactuated mechanical systems. The third method is illustrated with an example. © 2025 The Author(s). International Journal of Robust and Nonlinear control published by John Wiley & Sons Ltd.

关键词： Disturbance rejection

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Toward Using Fuzzy Grey Cognitive Maps in Manned and Autonomous Collision Avoidance at Sea

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IEEE Journal of Oceanic engineering 2025年第2期50卷 1210-1230页

作者： Gil, Mateusz Poczęta, Katarzyna Wróbel, Krzysztof Yang, Zaili Chen, Pengfei Gdynia Maritime University Research Group on Maritime Transportation Risk and Safety Faculty of Navigation Gdynia81-345 Poland Faculty of Electrical Engineering Automatics Control and Computer Science Kielce University of Technology Kielce25-314 Poland Research Institute LiverpoolL3 3AF United Kingdom Wuhan University of Technology School of Navigation Wuhan430063 China Hubei Key Laboratory of Inland Shipping Technology Wuhan430063 China

With Maritime Autonomous Surface Ships (MASS) slowly but steadily nearing full-scale implementation, the question of their safety persists. Regardless of being a disruptive technology, they will likely be subject to the same factors shaping their safety performance as manned ships nowadays are. Yet, the impact of these factors may be different in each case. The current study presents an application of Fuzzy Grey Cognitive Maps (FGCMs) to the comparative evaluation of factors affecting collision avoidance at sea. To this end, subject matter experts have been elicited, and the data obtained from them have been analyzed, concerning how changes in the intensity of given factors would affect safety performance. The obtained results showed that with the use of FGCM, it was possible to model the relative impact of selected factors both on a specific phase of the maritime collision avoidance process as well as on its entirety. The conducted analysis shows noticeable variability of the influence of some factors, depending on the timing of their activation during the process (time dependence), and using FGCM, it was possible to assess its quantification. Furthermore, the results indicate that greater differences can be found between the factors’ impact on phases of an encounter than between manned and autonomous ships. The outcome of this study may be found interesting for all parties involved in maritime safety modeling as well as working on the forthcoming introduction of autonomous ships. © 1976-2012 IEEE.

关键词： Fuzzy Cognitive Maps

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Post-Processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological Images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Image segmentation

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Causal Discovery from Shifted Multiple Environments 25

Causal Discovery from Shifted Multiple Environments

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Dezhi Yang Guoxian Yu Jun Wang Jinglin Zhang Carlotta Domeniconi School of Software Shandong University Jinan Shandong China School of Control Science and Engineering Shandong University Jinan Shandong China Department of Computer Science George Mason University Fairfax VA USA

ISBN: (纸本)9798400712456

A fundamental problem in many science domains is learning the causal structure of a system from observed data. The observed data canonically come from multiple environments (i.e. different times, locations, and measurements), and causal models may have unobserved shifts. Although the causal graphs can be identified by modeling the distribution changes among different environments, existing solutions can only learn causal structures when given environmental information. In contrast, we propose a causal discovery approach (CausalSME) which automatically identifies pseudo environments and unobserved distribution shifts. Specifically, CausalSME learns a causal model containing unobserved variables, which can correct the distribution shifts with mixed environments. The heart of CausalSME is a variational autoencoder that infers shifted causal effects of unobserved variables and guides the identification of environment information. It further divides the shifted samples by the identified environments to jointly learn an invariant causal model. We prove the structure identifiability of CausalSME with the causal additive model. In our extensive experiments we show that CausalSME achieves state-of-the-art performance.

关键词： adversarial learning

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Multiphase LLC Resonant Converter with Natural Current Sharing and Phase-Shedding

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IEEE Transactions on Industry Applications 2025年

作者： Ahmad, Ubaid Giral, Roberto Olalla, Carlos Sangwongwanich, Ariya Davari, Pooya Blaabjerg, Frede Universitat Rovira i Virgili Països Catalans Department of Electrical Electronics and Automatic Control Engineering School of Electrical and Computer Engineering Catalonia Tarragona26 43007 Spain Aalborg University Department of Energy Aalborg Ost9220 Denmark

The multiphase LLC converter is commonly used for high-power and high step-down applications. However, the tolerances in the tank circuit parameters cause uneven current sharing (CS), reducing system reliability. Additionally, phase-shedding (PS) can improve converter efficiency at partial load by turning-OFF phases. This paper proposes a multiphase input-parallel output-parallel LLC resonant converter with a common inductor-capacitor branch, with the objective to achieve excellent CS and phase-shedding at the same time. Thanks to the shared branch, current-sharing is natural, and phase-shedding is realized without affecting the operating point. With fewer passive comp- onents and a simple phase connection, the design offers low-cost, high-power density, and improved light-load efficiency compared to existing solutions. Detailed analysis shows improved CS on both the primary and secondary sides, when compared to previous approaches. Simulation and experimental results for the proposed two-phase LLC resonant converter validate the analysis and illustrate the effectiveness of the CS and PS capabilities. At the rated load of 800 W, CS error is 2.48% on the primary side and 2.0% on the secondary side. Phase-shedding improves efficiency by 3.60% at 50% load and 6.82% at 25% load. © 1972-2012 IEEE.

关键词： Resonant converters

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Concept-Level Semantic Transfer and Context-Level Distribution Modeling for Few-Shot Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Luo, Yuxuan Chen, Jinpeng Cong, Runmin Ip, Horace Ho Shing Kwong, Sam City University of Hong Kong Department of Computer Science Hong Kong Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Lingnan University Hong Kong

Few-shot segmentation (FSS) methods aim to segment objects using only a few pixel-level annotated samples. Current approaches either derive a generalized class representation from support samples to guide the segmentation of query samples, which often discards crucial spatial contextual information, or rely heavily on spatial affinity between support and query samples, without adequately summarizing and utilizing the core information of the target class. Consequently, the former struggles with fine detail accuracy, while the latter tends to produce errors in overall localization. To address these issues, we propose a novel FSS framework, CCFormer, which balances the transmission of core semantic concepts with the modeling of spatial context, improving both macro and micro-level segmentation accuracy. Our approach introduces three key modules: (1) the Concept Perception Generation (CPG) module, which leverages pre-trained category perception capabilities to capture high-quality core representations of the target class;(2) the Concept-Feature Integration (CFI) module, which injects the core class information into both support and query features during feature extraction;and (3) the Contextual Distribution Mining (CDM) module, which utilizes a Brownian Distance Covariance matrix to model the spatial-channel distribution between support and query samples, preserving the fine-grained integrity of the target. Experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that CCFormer achieves state-of-the-art performance, with visualizations further validating its effectiveness. Our code is available at ***/lourise/ccformer. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Wu, Qihui Zhu, Qiuming Niyato, Dusit Han, Zhu Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Xidian University State Key Laboratory of ISN Xian710071 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the sixth generation communication technologies, the space-air-ground integrated network (SAGIN) attracts increasing attentions in recent years. However, due to the mobility and heterogeneity of the components such as satellites and unmanned aerial vehiclesin multi-layer SAGIN, the challenges of inefficient resource allocation and management complexity are aggregated. To this end, the network function virtualization technology is introduced and can be implemented via service function chains (SFCs) deployment. However, urgent unexpected tasks may bring conflicts and resource competition during SFC deployment, and how to schedule the SFCs of multiple tasks in SAGIN is a key issue. In this paper, we address the dynamic and complexity of SAGIN by presenting a reconfigurable time extension graph and further propose the dynamic SFC scheduling model. Then, we formulate the SFC scheduling problem to maximize the number of successful deployed SFCs within limited resources and time horizons. Since the problem is in the form of integer linear programming and intractable to solve, we propose the algorithm by incorporating deep reinforcement learning. Finally, simulation results show that the proposed algorithm has better convergence and performance compared to other benchmark algorithms. © 2025 IEEE.

关键词： Network function virtualization

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

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