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Joint high-resolution feature learning and vessel-shape aware convolutions for efficient vessel segmentation

Computers in Biology and Medicine 2025年 191卷 109982-109982页

作者： Zhang, Xiang Zhu, Qiang Hu, Tao Guo, Song Bian, Genqing Dong, Wei Hong, Rao Lin, Xia Ling Wu, Peng Zhou, Meili Yan, Qingsen Mohi-ud-din, Ghulam Ai, Chen Li, Zhou College of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China Shaanxi Provincial Key Lab of Bigdata of Energy and Intelligence Processing School of Physics and Electronic Information Yanan University Yanan China School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China School of Software Nanchang University Nanchang China School of Cyber Security Zhejiang University Hangzhou China Department of Basic Education and Research Jiangxi Police College Nanchang China Northwestern Polytechnical University China

Clear imagery of retinal vessels is one of the critical shreds of evidence in specific disease diagnosis and evaluation, including sophisticated hierarchical topology and plentiful-and-intensive capillaries. In this work, we propose a new topology- and shape-aware model named Multi-branch Vessel-shaped Convolution Network (MVCN) to adaptively learn high-resolution representations from retinal vessel imagery and thereby capture high-quality topology and shape information thereon. Two steps are involved in our pipeline. The former step is proposed as Multiple High-resolution Ensemble Module (MHEM) to enhance high-resolution characteristics of retinal vessel imagery via fusing scale-invariant hierarchical topology thereof. The latter is a novel vessel-shaped convolution that captures the retinal vessel topology to emerge from unrelated fundus structures. Moreover, our MVCN of separating such topology from the fundus is a dynamical multiple sub-label generation via using epistemic uncertainty, instead of manually separating raw labels to distinguish definitive and uncertain vessels. Compared to other existing methods, our method achieves the most advanced AUC values of 98.31%, 98.80%, 98.83%, and 98.65%, and the most advanced ACC of 95.83%, 96.82%, 97.09%,and 96.66% in DRIVE, CHASE_DB1, STARE, and HRF datasets. We also employ correctness, completeness, and quality metrics to evaluate skeletal similarity. Our method's evaluation metrics have doubled compared to previous methods, thereby demonstrating the effectiveness thereof. © 2025 Elsevier Ltd

关键词： Ophthalmology

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Optimized fractional-order PID controller for temperature control of CSTH: a comparative study with PID cascade controller

Optimized fractional-order PID controller for temperature co...

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International Conference on Energy, Power, Environment, control and Computing (ICEPECC 2025)

作者： Abdulaziz F. Alaswadi Nezar M. Alyazidi Fatima N. Al-Aswadi Amjed Abbas Ahmed Department of Control and Instrumentation Engineering King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Institute of Computer Science & Digital Innovation (ICSDI) UCSI University Kuala Lumpur 56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (数字)9781837243150

controlling the temperature in chemical reactors, heating furnaces, distillation columns, and reboilers for circulating heatin g fluids is crucial across all industries. This project is a simulation-based study where both PID-cascade and fractional order PID (FO-PID) controllers were used to control the temperature of water in the continuously stirred tank heater system. The simulation was designed by using SIMULINK. Initially, the work reproduces results from a previous study that evaluated the performance of PID cascade controller applied for controlling the temperature of continuous stirred tank heater. Building on this foundat ion, a novel fractional-order PID (FO-PID) controller was designed and its parameters were optimized using the Whale Optimizer Algorithm (WOA). Results showed that the FO-PID outperforms the PID-cascade controller in terms of settling time and percentage overshoot. This study demonstrates significant advancements in process innovation and optimization, supporting sustainable industrial systems. The FO-PID controller in comparison to the Cascade PID controller reduced the percentage overshoot by approximately 97%, reduced settling time by 87% and maintained comparable steady-state error.

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Adaptive ray tracing of subdivision surfaces

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COMPUTER GRAPHICS FORUM 2003年第3期22卷 553-562页

作者： Miller, K Techmann, T Fellner, D Department of Control Engineering and Information Technology Budapest University of Technology Hungary

Subdivision Surfaces as well as (interactive) ray tracing have become an important issue in computer graphics. But ray tracing of subdivision surfaces has received only little attention. We present a new approach for ray tracing of subdivision surfaces. The algorithm uses a projection of the ray onto the surface and works mainly in two dimensions along this projection. While proceeding from patch to patch, we examine the bounding volume of their borders: the lower the distance between ray and subdivision surface, the more refinement steps are adaptively applied to the surface but only along the projection of the rays The adaptive refinement of a patch is controlled by curvature, size, its membership to the silhouette, and its potential contribution to the light transport. The algorithm is simple and mainly consists of elementary geometric computations. Hence it is fast and easy to implement without the need for elaborate preprocessing. The algorithm is robust in the sense that it deals with all features of subdivision surfaces like creases and corners.

关键词： Algorithms Computer graphics Curvature Silhouettes

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Exploring Lightweight Structures for Tiny Object Detection in Remote Sensing Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Dongyang Liu Junping Zhang Yunxiao Qi Yunqiao Xi Jing Jin School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of Control Science and Engineering Harbin Institute of Technology Harbin China

Detecting tiny objects in remote sensing images has been an intriguing yet challenging topic in remote sensing image processing. While significant progress has been made in many studies, most existing methods focus on improving the accuracy of tiny object detection without particular consideration for computational complexity, which restricts their applicability in resource-limited conditions. Therefore, this article aims to design a lightweight detection algorithm tailored for tiny objects in remote sensing images. First, we investigate the impact of the complexity of different components in deep learning-based object detection models on the accuracy of tiny object detection, including the backbone and detection head. Then, a dedicated backbone for tiny object detection is proposed, achieving competitive detection accuracy while remaining lightweight. Moreover, we propose a lightweight detection head that incorporates deformable convolution and optimize the channel dimension. Finally, we combine the above methods to introduce a lightweight network, lightweight tiny object detecection network (LTDNet), for tiny object detection in remote sensing images. Benefiting from the dedicated designs for the backbone and detection head specifically for tiny objects, the proposed method can achieve competitive detection accuracy with very low parameters and computational complexity. Extensive experiments are conducted on the artificial intelligence (AI)-TODv2 and learning vision and remote sensing (LEVIR)-Ship datasets, and the results demonstrate the effectiveness of our proposed method. Specifically, the proposed method achieves 54.6% AP50 on the AI-TODv2 dataset with only 4.85 M parameters and 38.19 G floating-point operations (FLOPs). The code will be released soon on the GitHub repository: https://***/dyl96/LTDNet

关键词： Object detection Feature extraction Remote sensing Accuracy Training Head Deep learning Convergence Computational complexity Transformers

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Joint Data Compression and Task Scheduling for LEO Satellite Networks

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IEEE Transactions on Vehicular technology 2025年

作者： He, Lijun Li, Shiyin Jia, Ziye Wang, Juncheng Han, Zhu China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Shaanxi Xi'an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China Hong Kong Baptist University Department of Computer Science China 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

We investigate a joint data compression and task scheduling problem for Low Earth Orbit Satellite Networks (LEOSNs), to maximize the sum weights of tasks while simultaneously minimizing the total data loss. First, we propose a novel Multi-Resource Conflict Graph (MRCG) model to characterize the intertwined communication and computation allocation conflicts inherent in typical data offloading processes of LEOSNs. Leveraging the proposed MRCG model, we then formulate the studied problem for LEOSNs as a linear integer program, to maximize the normalized weighted sum of both the sum task weight and the total data loss. We further explore the intrinsic structure of the linear integer program to propose an efficient solution using the Semi-Definite Relaxation (SDR) technique. Finally, the simulation results underscore that the synergistic optimization of data compression and task scheduling significantly facilitates the data offloading efficiency of LEOSNs, and the performances surpass existing benchmarks across a broad range of system parameters. © 1967-2012 IEEE.

关键词： Integer programming

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Co-regulated Protein Functional Modules with Varying Activities in Dynamic PPI Networks

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Tsinghua Science and technology 2013年第5期18卷 530-540页

作者： Yuan Zhang Nan Du Kang L Kebin Jia Aidong Zhang the Department of Electronic Information and Control Engineering Beijing University of Technology the Department of Computer Science and Engineering State University of New York at Buffalo the Department of Electronic Information and Control Engineering Beijing University of Technology Beijing 100124 China

Current methods for the detection of differential gene expression focus on finding individual genes that may be responsible for certain diseases or external irritants. However, for common genetic diseases, multiple genes and their interactions should be understood and treated together during the exploration of disease causes and possible drug design. The present study focuses on analyzing the dynamic patterns of co-regulated modules during biological progression and determining those having remarkably varying activities, using the yeast cell cycle as a case study. We first constructed dynamic active protein-protein interaction networks by modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. The dynamic active modules were detected using a method based on the Bayesian graphical model and then the modules with the most varied dispersion of clustering coefficients, which could be responsible for the dynamic mechanism of the cell cycle, were identified. Comparison of results from our functional module detection with the state-of-art functional module detection methods and validation of the ranking of activities of functional modules using GO annotations demonstrate the efficacy of our method for narrowing the scope of possible essential responding modules that could provide multiple targets for biologists to further experimentally validate.

关键词： dynamic protein-protein interaction networks dynamic active modules varying activities Bayesian graphical mode

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Evaluation of insulin sensitivity temporal prediction by using quantile regression combined with neural network model

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International Journal of Medical Informatics 2025年 202卷 105964-105964页

作者： Omer S. Alkhafaf J.Geoffrey Chase Balázs Benyó Budapest University of Technology and Economics Faculty of Electrical Engineering and Information Technology Department of Control Engineering and Information Technology Budapest Hungary College of Dentistry University of Mosul Mosul Iraq Department of Mechanical Engineering University of Canterbury Christchurch New Zealand

Background Stress-induced hyperglycemia, a pathologically high blood glucose level, is a frequent complication in intensive care units. Blood glucose (BG) level control is crucial but challenging due to patient variability. The Stochastic TARgeted (STAR) protocol is clinically used for blood glucose control, which uses the current and predicted future patient insulin sensitivity (SI) parameter to assess BG outcomes of alternative treatment options. Objective Neural network (NN) models using quantile regression (QR) have enhanced SI prediction performance. However, remains a challenge in determining the optimal NN configuration to best predict SI. This study aims to find the NN configuration yielding the highest prediction accuracy to improve the STAR protocol and explores the behaviour of the QR method in predicting the percentiles of a non-Gaussian multi-mode distribution of a physiological parameter. Method Alternative NN architectures combined with QR were implemented and trained on a large dataset comprising 1,897 patients collected between 2011 and 2023 using five-fold cross-validation ensuring model robustness. Prediction performance was evaluated among NN configurations and compared using case-specific metrics across the global SI domain as well as within subdomains to analyse the models’ local performance. Results Outcomes indicate QR applied to simpler NN, consisting of one-hidden layer with four neurons, achieves a best prediction performance at a minimum network size. Using more complex NN did not improve the prediction performance significantly. However, at long prediction horizons, no compact network demonstrated improved outcomes. A more general methodological outcome of the study is that QR-based prediction does not need to be combined with complex NN to achieve the best prediction performance. Conclusion The QR-based method was found to be appropriate for the SI prediction problem in short-term predictions which may improve the STAR protocol’s clinic

关键词： Insulin sensitivity Quantile regression Network configuration Multi-mode distribution Confidence interval

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A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

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BUDGET CONSTRAINTS IN HOMOGENOUS MULTI-UNIT AUCTIONS

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Journal of Systems Science & Complexity 2012年第3期25卷 549-555页

作者： Yaqiong WU Yong ZHAO Chaoyuan YUE Xianglin WU College of Information Science and Technology Beijing University of Chemical Technology Institute of Systems Engineering Department of Control Science and EngineeringHuazhong University of Science and Technology

A homogenous multi-unit auction model is studied in two conditions： One is that the bidders face budget constraints and another is that the bidders do not face budget constraints. Two equilibrium bidding strategies of each bidder are presented in these two conditions. By comparing them, the authors show that the equilibrium unit price bid in the presence of budget constraints will be less than that in the absence of budget constraints. The difference between the two equilibrium bids leads to the reduced revenue of the seller. And this difference will decrease as the number of the bidders increases.

关键词： Auctions/bidding budget constraints equilibrium bidding strategy homogenous multi-unit.

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