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3D medical image segmentation using the serial-parallel convolutional neural network and transformer based on crosswindow self-attention

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CAAI Transactions on Intelligence technology 2025年第2期10卷 337-348页

作者： Bin Yu Quan Zhou Li Yuan Huageng Liang Pavel Shcherbakov Xuming Zhang Department of Biomedical Engineering College of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanChina Department of Ultrasound Imaging Wuhan Children's Hospital(Wuhan Maternal and Child Healthcare Hospital)Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina Department of Urology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina Institute for Control Science Russian Academy of SciencesMoscowRussia

Convolutional neural network(CNN)with the encoder-decoder structure is popular in medical image segmentation due to its excellent local feature extraction ability but it faces limitations in capturing the global *** transformer can extract the global information well but adapting it to small medical datasets is challenging and its computational complexity can be *** this work,a serial and parallel network is proposed for the accurate 3D medical image segmentation by combining CNN and transformer and promoting feature interactions across various semantic *** core components of the proposed method include the cross window self-attention based transformer(CWST)and multi-scale local enhanced(MLE)*** CWST module enhances the global context understanding by partitioning 3D images into non-overlapping windows and calculating sparse global attention between *** MLE module selectively fuses features by computing the voxel attention between different branch features,and uses convolution to strengthen the dense local *** experiments on the prostate,atrium,and pancreas MR/CT image datasets consistently demonstrate the advantage of the proposed method over six popular segmentation models in both qualitative evaluation and quantitative indexes such as dice similarity coefficient,Intersection over Union,95%Hausdorff distance and average symmetric surface distance.

关键词： convolution neural network cross window self‐attention medical image segmentation transformer

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EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture

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IEEE Transactions on Affective Computing 2024年第2期16卷 1-18页

作者： PETRESCU, Alexandru TRUICA, Ciprian-Octavian APOSTOL, Elena-Simona PASCHKE, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Bucharest Romania Fraunhofer Institute for Open Communication Systems Berlin Germany

As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSAEnsemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models: raw frequency, TFIDF, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and Deep Learning models. Authors

关键词： Sentiment analysis

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Combination of Zero Common Mode and Spread Spectrum for Four-Module Motor Drive Systems 7

Combination of Zero Common Mode and Spread Spectrum for Four...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Gong, Jiwen Liu, Hui Ma, Yafei Jiang, Dong Liu, Hongyang Yang, Guang School of Electrical and Electronics Engineering Huazhong University of Science and Technology Wuhan China Flight Automatic Control Research Institute Xi'an China

ISBN: (纸本)9798350359558

In parallel three-phase inverters, zero common-mode voltage can theoretically be achieved through zero common-mode modulation techniques. The advantage of spread spectrum PWM over constant switching frequency PWM is its ability to eliminate peaks of EMI. Therefore, this paper proposes a modulation method that combines zero common-mode and variable switching frequency to simultaneously reduce common-mode EMI and differential-mode EMI. Under the premise of using the zero common-mode vector, this method uniformly changes the switching period within a certain range, employing a four-module motor as the load for modulation. Simulation results show that this strategy not only achieves zero common-mode voltage but also reduces conducted EMI by more than 20 dB, confirming the effectiveness of the proposed method. © 2024 IEEE.

关键词： Pulse width modulation

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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Torque Ripple Suppression of Permanent Magnet Synchronous Motor Based on Improved Duty Ratio Regulation Considering Speed Region 26

Torque Ripple Suppression of Permanent Magnet Synchronous Mo...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Wang, Xuming Yang, Kai Luo, Yixiao Huang, Yuhao Li, Wei Huazhong University of Science and Technology Department of Electrical and Control Engineering Wuhan China

ISBN: (纸本)9798350317589

The model predictive control (MPC) with duty ratio regulation has been widely studied. However, most of the existing MPC regulates the duty ratio without considering the speed region. In this paper, the generation of torque ripple is analyzed quantitatively. Based on the cause of torque ripple, this paper performs a four quadrants correction on the duty ratio regulation. The equation for the torque variation during one inverter cycle is rewritten. The torque variation is calculated in a new perspective. The proposed method can further reduce torque ripple in the whole speed region. In specific, the effect of the proposed method in the low-speed zone is better than that in the high-speed zone. Finally, simulation results are provided to demonstrate the effectiveness of this method. © 2023 IEEE.

关键词： Model predictive control

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Hexagon Trajectory Flux Weakening control for Permanent magnet assisted SynRM 26

Hexagon Trajectory Flux Weakening Control for Permanent magn...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Li, Wei Yang, Kai Luo, Cheng Wang, Xuming Qiu, Lingfeng Huazhong University of Science and Technology Department of Electrical and Control Engineering Wuhan China

ISBN: (纸本)9798350317589

The synchronous reluctance motor has a simple structure and smaller torque ripple, making it more robust and reliable. However, it does not have a wide speed range and the output torque is not fully utilized. The flux weakening (FW) control is helpful to improve voltage utilization of synchronous reluctance motor (SynRM) drives in high-speed region. This paper is based on the d-q axis equivalent circuit of SynRM and derives the voltage and current constrain for FW process. Deriving the modulation algorithm and applying it to the weak magnetic operation of permanent magnet assisted SynRM to achieve higher utilization of DC bus voltage. The effectiveness of the proposed method will be verified through simulation. © 2023 IEEE.

关键词： Equivalent circuits

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An Extended State Observer-Based Model Predictive Current control Strategy for IPMSM Drive System

An Extended State Observer-Based Model Predictive Current Co...

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2023 IEEE International Conference on Predictive control of Electrical Drives and Power Electronics, PRECEDE 2023

作者： Wang, Xuming Yang, Kai Luo, Yixiao Huazhong University of Science and Technology Department of Electrical and Control Engineering Wuhan China

ISBN: (纸本)9798350396867

The current loop is significant for disturbance rejection performance in interior permanent magnet synchronous motor (IPMSM) drive system. This article presents a robust current model predictive control method which significantly expands the bandwidth of the current loop. The proposed method is based on an incremental model which can avoid the disturbance of inaccurate flux linkage. However, disturbance from inaccurate inductors and resistors in the motor model can also affect predictive control. To cope with this problem, the proposed method combines with an extended state observer (ESO) which is used to estimate disturbance and compensate for it. Finally, the effectiveness of the proposed method is verified in Simulink. © 2023 IEEE.

关键词： Model predictive control

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Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1524-1537页

作者： Mingwei Wen Quan Zhou Bo Tao Pavel Shcherbakov Yang Xu Xuming Zhang Department of Biomedical Engineering College of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanChina State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhanChina Institute for Control Science Russian Academy of SciencesMoscowRussia Hubei Medical Devices Quality Supervision and Test Institute WuhanChina

Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and *** popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and *** former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume *** existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure *** address these issues,a short‐term and long‐term memory self‐attention network is ***,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the ***,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple ***,the multi‐layer reconstruction blocks will predict the tumour *** results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative *** with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.

关键词： 3D medical images convolutional neural network self‐attention network transformer tumor segmentation

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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica university of Bucharest. All rights reserved.

关键词： Radiation effects

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CDQKL: Consensus-Based Distributed Quantum Kernel Learning 1

CDQKL: Consensus-Based Distributed Quantum Kernel Learning

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： Ma, Wenxuan Liu, Mengxiang Deng, Ruilong State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350366778

The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL. © 2024 IEEE.

关键词： Quantum computers

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