检索结果-内蒙古大学图书馆

KAC-Unet: A Medical Image Segmentation With the Adaptive Group Strategy and Kolmogorov-Arnold Network

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Lin, Shiying Hu, Rong Li, Zuoyong Lin, Qinghua Zeng, Kun Wu, Xiang Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Fuzhou University Affiliated Provincial Hospital Provincial Clinical Medical College Fujian Medical University Department of Urology Fuzhou350001 China

In the field of deep learning-based medical image segmentation, convolutional neural networks (CNNs) extract image features by combining linear convolutional layers with nonlinear activation functions. However, excessive stacking of linear layers in the network limits the model’s ability to capture fine-grained details. In addition, the feature distribution imbalance caused by the traditional fixed grouping strategy (FGS) can affect the deep model’s capacity to perceive the overall structure of the image. To address these challenges, we propose a medical image segmentation framework, called Kolmogorov-Arnold Network with the adaptive group strategy and contextual Transformer based on Unet (KAC-Unet). First, we propose the adaptive group strategy (AGS) to balance the grouping of different input channels, alleviating the performance degradation caused by differences in group information. Then, we propose the Shift Tokenized Kolmogorov-Arnold Network (KAN) Block to capture complex features in medical images through flexible nonlinear transformations and shift operations. Extensive experiments are conducted on three medical image segmentation datasets. The results demonstrate the effectiveness and superiority of our proposed method compared with state-of-the-art algorithms. © 1963-2012 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Deducing Complete Selection Rule Set for Driver Nodes to Guarantee Network’s Structural controllability

引用

IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1152-1165页

作者： Xichen Wang Yugeng Xi Wenzhen Huang Shuai Jia the Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240

Structural controllability is critical for operating and controlling large-scale complex networks. In real applications, for a given network, it is always desirable to have more selections for driver nodes which make the network structurally controllable. Different from the works in complex network field where structural controllability is often used to explore the emergence properties of complex networks at a macro level,in this paper, we investigate it for control design purpose at the application level and focus on describing and obtaining the solution space for all selections of driver nodes to guarantee structural controllability. In accord with practical applications,we define the complete selection rule set as the solution space which is composed of a series of selection rules expressed by intuitive algebraic forms. It explicitly indicates which nodes must be controlled and how many nodes need to be controlled in a node set and thus is particularly helpful for freely selecting driver nodes. Based on two algebraic criteria of structural controllability, we separately develop an input-connectivity algorithm and a relevancy algorithm to deduce selection rules for driver nodes. In order to reduce the computational complexity,we propose a pretreatment algorithm to reduce the scale of network's structural matrix efficiently, and a rearrangement algorithm to partition the matrix into several smaller ones. A general procedure is proposed to get the complete selection rule set for driver nodes which guarantee network's structural controllability. Simulation tests with efficiency analysis of the proposed algorithms are given and the result of applying the proposed procedure to some real networks is also shown, and these all indicate the validity of the proposed procedure.

关键词： Algebraic criteria complete selection rule set structural controllability driver nodes

来源：评论

学校读者我要写书评

暂无评论

Incipient fault diagnosis method for silicon single crystal growth assisted by deep learning

引用

CrystEngComm 2025年

作者： Liu, Yu-Yu Liu, Ding Ren, Jun Chao Wu, Shi Hai School of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

In this study, an accurate diagnostic classification algorithm based on a deep belief network and entropy value (C-DBN-E) incorporating signal decomposition, entropy theory and deep belief network (DBN) network is proposed to address the problem of tiny faults that may occur during the growth of silicon single crystals (SSC). The study is divided into three stages: feature selection, fault feature extraction and fault pattern classification. In the feature selection stage, the recursive feature elimination (RFE) method based on support vector machine (SVM) is employed to rank features according to their importance and eliminate redundant features. In the subsequent feature extraction stage, the adaptive decomposition theory is introduced to select components that contain a greater number of fault features for signal reconstruction. The deep features of the reconstructed signal are then mined in depth using a DBN and supplemented with energy entropy and multi-scale arrangement entropy as supplementary features to further refine the capture of tiny fault information. Finally, all the features are integrated and inputted into the SVM classifier to enable accurate classification of micro-fault modes in SSC growth. The effectiveness of the method in distinguishing micro-faults in the SSC growth process is verified by simulation results. This study is of great significance in solving the problem of fault diagnosis in the SSC growth system, which is expected to contribute to the improvement of work performance and the reduction of accident risk and economic loss. © 2025 The Royal Society of Chemistry.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Model Predictive control for Vapor Compression Cycle of Refrigeration Process

引用

International Journal of Automation and computing 2018年第6期15卷 707-715页

作者： Xiao-Hong Yin Shao-Yuan Li Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China

A model predictive controller based on a novel structure selection criterion for the vapor compression cycle （VCC） of refrigeration process is proposed in this paper. Firstly, those system variables are analyzed which exert significant influences on the system performance. Then the structure selection criterion, a trade-off between computation complexity and model performance, is applied to different model structures, and the results are utilized to determine the optimized model structure for controller design. The controller based on multivariable model predictive control （MPC） strategy is designed, and the optimization problem for the reduced order models is formulated as a constrained minimization problem. The effectiveness of the proposed MPC controller is verified on the experimental rig.

关键词： Vapor compression refrigeration cycle model structure structure selection criterion model reduction model predictive control.

来源：评论

学校读者我要写书评

暂无评论

Mdct-Unet: A Dual-Encoder Network Combining Multi-Scale Dilated Convolutions with Transformer for Medical Image Segmentation

SSRN

引用

SSRN 2025年

作者： Chen, Zhengshi He, Juanjuan Liu, Xiaoming Tang, Jinshan College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Health Informatics Department of Health Administration Policy George Mason University FairfaxVA22030 United States

Precise medical image segmentation is crucial in clinical diagnosis and pathological analysis. Most segmentation methods are based on U-shaped convolutional neural networks (U-Net). Although U-Net performs well in medical image segmentation, as a method based on CNN, its main drawback lies in the difficulty of establishing long-range pixels dependencies and has a constrained receptive field, which restricts segmentation accuracy. Many models address this issue by incorporating Transformer models into U-Net architectures to better capture long-range dependencies. However, these methods often suffer from simple feature fusion techniques and limited receptive fields for local features. To address these challenges, we propose a dual-encoder framework, named MDCT-Unet, which combines Swin-Transformer and CNN for enhanced medical image segmentation. This framework introduces a novel dynamic feature fusion module to better integrate of local and global features. By combining channel and spatial attention mechanisms and inducing competition between them, we enhance the coupling of these two types of features, ensuring richer information representation. Moreover, we supplement the fused features into the decoder via skip connections to recover details lost during encoding. In addition, to better extract multi-scale local features from images, we design a dilated convolution encoder (DCE) as the CNN branch of our model. We conducted extensive experiments on four datasets: Synapse, ISIC2018, CHASEDB1, and MMWHS. The experimental results show that our method outperforms most current medical image segmentation methods © 2025, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Unknown Task Selection and Worker Recruitment Using Two-Stage Multi-Armed Bandit in Crowdsensing

引用

IEEE Internet of Things Journal 2025年

作者： Song, Haoyuan Li, Peng Du, Rui Yu, Hai Huang, Weiyi Nie, Lei Bao, Haizhou Liu, Qin Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hubei Polytechnic University School of Computer Huangshi435003 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Mobile Crowdsensing (MCS) faces significant challenges in selecting tasks with expected high revenue and recruiting workers with expected high qualities to maximize overall utility. Existing approaches often assume the revenue of posting tasks or the quality of workers is determined, which limits their practical applicability. This paper tackles these challenges by modeling the long-term multi-task, multi-worker selection problem as a Two-Stage Multi-Armed Bandit (TS-MAB) problem under uncertainty. In each round, the MCS platform selects a long-term task from a pool and recruits the necessary workers, determining payments accordingly. We model the task selection phase as a MAB problem, and the worker recruitment phase, influenced by the task selection, as a Combinatorial MAB (CMAB) problem. We propose an extended Upper Confidence Bound (UCB)-based strategy and develop an incentive mechanism based on Auction theory, combined with TS-MAB (i.e., ATS-MAB), for unknown task selection and worker recruitment. Our mechanism theoretically guarantees truthfulness and individual rationality, with a theoretical analysis of its regret bounds. Furthermore, we introduce an adaptive incentive mechanism called AATS-MAB, which improves worker recruitment and quality updates, achieving higher total sensing quality and lower regret. Extensive simulations demonstrate the effectiveness and scalability of the proposed methods. © 2014 IEEE.

关键词： Personnel selection

来源：评论

学校读者我要写书评

暂无评论

A Short-Term Traffic Flow Forecasting Method and Its Applications

引用

Journal of Shanghai Jiaotong university(Science) 2015年第2期20卷 156-163页

作者：刘思妍李德伟席裕庚汤奇峰 Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiaotong University

Short-term forecast of urban traffic flow is very important to intelligent transportation. Although the conventional methods have some advantages, to some extent, in improving the traffic forecast's precision, it is still hard to achieve high accuracy. In this paper, we propose a short-term traffic flow forecasting method, which is based on the hybrid particle swarm optimization-neural network(HPSO-NN) with error compensation *** HPSO-NN, the hybrid PSO algorithm is employed to train the structures and parameters of the feed-forward advanced neural network, while the error compensation mechanism is employed to improve the accuracy. HPSONN is used to forecast the vehicle velocity in Shanghai North-South Viaduct. Experimental results show that the HPSO-NN, compared with the auto-regressive and moving average(ARMA) model, can forecast traffic flow with a higher accuracy. What's more, we have also found that HPSO-NN with error compensation mechanism has better performance than that of HPSO-NN alone.

关键词： short-term velocity forecast intelligent transportation systems(ITS) neural network(NN) hybrid particle swarm optimization-neura

来源：评论

学校读者我要写书评

暂无评论

Improved independent component analysis based on epanechnikov kernel function

International Journal of Control and Automation

引用

International Journal of control and Automation 2016年第7期9卷 147-158页

作者： Yu, Liang-Ju Yang, Gen-Ke Chen, Yue Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University China

Traditionally, the key idea of estimating independent component analysis (ICA) model is to maximize the non-Gaussianity, however, often with the assumption that density of data is near the standardized Gaussian density. To avoid the unsuitable assumption, this article uses the nonparametric density estimating method. A nonparametric independent component analysis algorithm based on Epanechnikov kernel function is proposed in this paper. This algorithm uses the Epanechnikov kernel estimator to estimate random variable distribution, meanwhile, employs the hypothesis test to derive the nonparametric likelihood ratio (NLR) objective function. For optimizing the nonparametric density estimation, the selection of kernel function and bandwidth is crucial. From the perspective of minimizing the mean integrated square error (MISE), this paper discusses the optimal selection and conducts experiments for further study. To increase the algorithmic convergence rate and reduce the running time, the quasi-newton method has been used to optimize the objective function. Compared with previous nonparametric ICA algorithm, the simulation results demonstrate that the proposed method offers better performance both on speech separation and computing capability. © 2016 SERSC.

关键词： Independent component analysis

来源：评论

学校读者我要写书评

暂无评论

Implementation of Dynamic Matrix control on Field Programmable Gate Array

引用

Journal of Shanghai Jiaotong university(Science) 2011年第4期16卷 441-446页

作者：兰建李德伟杨楠席裕庚 Department of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information ProcessingMinistry of Education

High performance computer is often required by model predictive control(MPC) systems due to the heavy online computation *** extend MPC to more application cases with low-cost computation facilities, the implementation of MPC controller on field programmable gate array(FPGA) system is *** the dynamic matrix control(DMC) algorithm,the main design idea and the implemental strategy of DMC controller are introduced based on a FPGA’s embedded *** performance tests show that both the computation efficiency and the accuracy of the proposed controller can be satisfied due to the parallel computing capability of FPGA.

关键词： model predictive control(MPC) dynamic matrix control(DMC) quadratic programming(QP) active set programmable logic device field programmable gate array(FPGA)

来源：评论

学校读者我要写书评

暂无评论

Soft-Sensing Method with Online Correction Based on Semi-Supervised Learning

引用

Journal of Shanghai Jiaotong university(Science) 2015年第2期20卷 171-176页

作者：汤奇峰李德伟席裕庚 Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiaotong University

Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning(SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.

关键词： soft-sensing semi-supervised learning(SSL) online correction neural network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：