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EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer

Computers in Biology and Medicine 2024年 179卷 108771-108771页

作者： Wang, Wenqing He, Ji Liu, Han 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 University of Technology Xi'an710048 China

Multimodal medical image fusion fuses images with different modalities and provides more comprehensive and integrated diagnostic information. However, current multimodal image fusion methods cannot effectively model non-local contextual feature relationships, and due to direct aggregation of the extracted features, they introduce unnecessary implicit noise into the fused images. To solve the above problems, this paper proposes a novel dual-branch hybrid fusion network called EMOST for medical image fusion that combines a convolutional neural network (CNN) and a transformer. First, to extract more comprehensive feature information, an effective feature extraction module is proposed, which consists of an efficient dense block (EDB), an attention module (AM), a multiscale convolution block (MCB), and three sparse transformer blocks (STB). Meanwhile, a lightweight efficient model (EMO) is used in the feature extraction module to exploit the efficiency of the CNN with the dynamic modeling capability of the transformer. Additionally, the STB is incorporated to adaptively maintain the most useful self-attention values and remove as much redundant noise as possible by developing the top-k selection operator. Moreover, a novel feature fusion rule is designed to efficiently integrate the features. Experiments are conducted on four types of multimodal medical images. The proposed method shows higher performance than the art-of-the-state methods in terms of quantitative and qualitative evaluations. The code of the proposed method is available at https://***/XUTauto/EMOST. © 2024 Elsevier Ltd

关键词： Feature extraction

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Distributed Fault-Tolerant Consensus control for Linear Multi-Agent system with K-Step Hierarchical Protocol

Distributed Fault-Tolerant Consensus Control for Linear Mult...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Wang, Yuanwen Dong, Jiuxiang College of Information Science and Engineering Northeastern University Shenyang110819 China Liaoning Province Key Laboratory of Safe Operation Technique for Autonomous Unmanned Systems Northeastern University Shenyang110819 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education Northeastern University Shenyang110819 China

ISBN: (纸本)9789811966125

The distributed fault-tolerant consensus control using k-step hierarchical protocol for linear multi-agent system (MAS) is studied in this note. Unlike most existing fault-tolerant methods for compensating actuator fault, a hierarchical protocol consisting of a virtual layer and a actual layer that considers both fault compensation and introducing fault information into the trajectory planning process which means that the motion of the MAS does not exceed the fault-tolerant compensation range of the faulty agent is proposed. Trajectory planning is responsible for the virtual layer consisting of virtual agent system and a co-controller that introduces fault information. The actual layer, consisting of the actual agent system, actuator fault observer, and tracking trajectory controller, traces the virtual layer for consensus. It is shown that the integrity of the MAS is guaranteed. Finally, the validity of the developed protocols is illustrated by simulation examples. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation

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Results in Physics 2025年 73卷

作者： Hou, Tiantian Jiao, Shangbin Wang, Yi Song, Nianlong Li, Jianghua Cui, Wenchuan Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China

The theory of Stochastic Resonance (SR) has drawn significant attention due to its exceptional ability to detect faint signals. Despite this, research to date indicates that for SR systems, whether they are monostable, bistable, or multi-stable, modifications to the system parameters lead to concurrent alterations in the depth and breadth of the potential wells when analyzing engineering signals, which results in suboptimal detection outcomes. To address these issues, a two-dimensional Gaussian bistable coupled SR (GBCSR) system has been proposed that can individually adjust the potential well characteristics. This innovative system facilitates the separate adjustment of shape characteristics of potential, allowing for more precise manipulation of the system's dynamic response. The system's non-linear dynamic traits are explicated through an analysis of the steady-state probability density (SPD) function and the mean first passage time (MFPT), substantiating the effectiveness of the new model. In practical scenarios, a variety of bearing defect signals serve as the objects of detection. The structural parameters of the GBCSR system are co-optimized using the Brain Storm Optimization (BSO) algorithm. This optimization approach leverages the algorithm's ability to enhance population diversity and improve convergence accuracy, thereby optimizing the system's performance. The experimental outcome results show that the proposed system can accurately detect the frequency of bearing fault signals. When compared with traditional SR systems such as the traditional bistable stochastic SR (TBSR), the traditional Gaussian SR system (TGSR), and the cascade stochastic resonance system, the proposed coupled system demonstrates superior performance. This is achieved through the transfer of energy or information between subsystems, which enables more efficient utilization of noise energy. The system can trigger the resonance effect over a broader range of noise intensities and signi

关键词： Bearing fault detection Coupled stochastic resonance Independent parameter regulation MFPT SPD

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Streaming-Data-Driven Multi-Objective Optimal Operation for Ammonia-based Integrated Energy Chemical systems:An Event-Triggered Scheme

Streaming-Data-Driven Multi-Objective Optimal Operation for ...

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第42届中国控制会议

作者： Longyan Li Chao Ning Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology

As an important component in the industry sector,the integrated energy chemical system is inescapably responsible for carbon reduction in addition to economic and reliability *** propose a novel streaming-data-driven multi-objective optimal operation for ammonia-based integrated energy chemical systems（AIECS）.In the day ahead scheduling,a detailed mathematical model for AIECS is presented,upon which a multi-objective optimization considering cost,CO emission and reliability is *** the intra-day operation,an event-triggered mechanism is firstly developed in the real-time operation of AIECS,and it enables the AIECS to be risk-aware just in *** monitoring the difference between the predicted state of charge and the capacity limitations of battery,the AIECS identifies whether it is in hazardous ***,the intelligent switch between certainty equivalence model predictive control and online-learning based distributionally robust model predictive control is proposed,such that their respective advantages are *** studies demonstrate that the proposed approach guarantees the system safety,and brings good revenue.

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Fast 3D Room Layout Estimation Based on Compact High-Level Representation

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IEEE transactions on image processing : a publication of the IEEE Signal processing Society 2025年 34卷 3930-3943页

作者： Weidong Zhang Yulei Qiao Ying Liu Ran Song Wei Zhang School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi’an China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

3D room layout estimation aims to reconstruct the holistic 3D structure from an indoor RGB image. For most of the deep learning-based methods, layout inference is guided by a kind of learned 2D mid-level representation such as pixel-wise surface labels. However, learning such high-resolution 2D representation might suffer from information redundancy and memory consumption, and will increase the runtime of estimation and deployment cost for practical applications. In this paper, we attempt to learn a compact high-level representation with only 29 real numbers for estimating the 3D layout using general regression networks. The learned compact high-level representation contains three components: instance-wise plane parameters, camera intrinsic parameters, and plane location indicators. With the learned representation, the inverse depth map of each plane can be calculated to reconstruct the 3D layout. We further design a set of order-agnostic loss functions to restrict the produced inverse depth maps, with which the model can be trained with either weak 2D layout labels or full 3D layout supervision. Moreover, by jointly learning the plane parameters and locations, the model is benefited from 3D reasoning. Experimental results show that our method is much faster than the existing layout estimation methods and obtains competitive performance on benchmark datasets, showing its potential for real-time applications.

关键词： Layout Three-dimensional displays Estimation Image reconstruction Cameras Solid modeling Training Cognition Surface reconstruction Floors

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Data-Driven Distributionally Robust Planning of Energy Hub Coupled with Carbon Capture Utilization and Storage Facilities

Data-Driven Distributionally Robust Planning of Energy Hub C...

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第42届中国控制会议

作者： Xutao Ma Chao Ning Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology

Toward carbon peaking and carbon neutrality goals in China, a transition from traditional fossil power to renewable energy becomes an imperative need. This paper aims to address the planning problem of a highly renewable-energy penetrated energy hub system, coupled with carbon capture facilities and downstream production processes of hydrogen and methanol,under the uncertainty of renewable power output. To this end, we propose a two-stage distributionally robust optimal planning model, where investment decisions are the first-stage variables and operation variables are determined at the second stage after the realization of uncertainty. In the proposed model, we utilize historical data samples to construct a strengthened ambiguity set that leverages both Wasserstein distance and moment information for a better characterization of renewable generation uncertainty. Moreover, to efficiently solve the proposed planning problem, we develop a simplified affine decision rule method in a general setting with both moment and Wasserstein ambiguity, and then apply it to reformulate the distributionally robust planning problem. The derived reformulation possesses the favorable property that its scale is free from the sample size, thus greatly reducing the computational complexity. Computational results validate the effectiveness of the proposed planning model and the computational efficiency of the solution methodology.

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Grain size control in quasi-two-dimensional perovskite thin film via intermediate phase engineering for efficient bound exciton generation

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Science China Materials 2024年第12期67卷 3925-3931页

作者： Guohui Li Wenhui Zhao Kai Lin Kefan Zhao Yujing Wang Aohua Niu Rong Weng Kaibo Zheng Yanxia Cui College of Electronic Information and Optical Engineering Key Laboratory of Interface Science and Engineering in Advanced MaterialsKey Lab of Advanced Transducers and Intelligent Control System of Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China Chemical Physics Division and NanoLund Lund UniversityBox 12422100LundSweden

Quasi-two dimensional(2D)perovskites have emerged as a promising class of materials due to their remarkable photoluminescence efficiency,which stems from their exceptionally high exciton binding *** spatial confinement of excitons within smaller grain sizes could enhance the formation of biexcitons leading to higher radiative recombination ***,the synthesis of high-quality quasi-2D perovskite thin films with controllable grain sizes remains a challenging *** this study,we present a facile method for achieving quasi-2D perovskite thin films with controllable grain sizes ranging from 500 to 900 *** is accomplished by intermediate phase engineering during the film fabrication *** results demonstrate that quasi-2D perovskite films with smaller grain sizes exhibit more efficient bound exciton generation and a reduced stimulated emission threshold down to 15.89µJ cm^(−2).Furthermore,femtosecond transient absorption measurements reveal that the decay time of bound excitons is shorter in quasi-2D perovskites with smaller grain sizes compared to that of those with larger grains at the same pump density,which is 230.5 *** observation suggests a more efficient exciton recombination process in the smaller grain size *** findings would offer a promising approach for the development of efficient bound exciton lasers.

关键词： quasi-2D perovskite intermediate phase engineering bound exciton generation grain size control

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Classification of Dunhuang Mural Image Based on Small-sample and Semi-supervised Learning 6

Classification of Dunhuang Mural Image Based on Small-sample...

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6th IEEE information Technology, Networking, Electronic and Automation control Conference, ITNEC 2023

作者： Cui, Hui Su, Zhibin Zhang, Luyue Bai, Liyang Communication University of China School of Information and Engineering China Key Laboratory of Acoustic Visual Technology and Intelligent Control System China Ministry of Culture and Tourism P.R.C. Center for Ethnic and Folk Literature and Art Development China

ISBN: (纸本)9781665460033

Dunhuang mural images classification belongs to the research task in the field of image recognition. In this paper, the semi-supervised model is established with multidimensional features extracted by transfer learning. A small number of labeled samples were used to obtain a large number of unlabeled data, combined with Active Learning and iterative strategy for multiple rounds of label transfer of selected samples. After several rounds of iterations, we can get a more powerful classification learner. Experiments on the self-built Dunhuang mural dataset have shown that the results can approach or even exceed some supervised learning methods when the number of known label samples is less than 4%. Our research implements the cultural resource image classification algorithm based on small samples, which is conductive to improving the accuracy when label samples are scarce. © 2023 IEEE.

关键词： Image recognition

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Carrier Frequency Offset Estimation for MIMO OCDM Underwater Acoustic Communications Based on Null Symbols

Carrier Frequency Offset Estimation for MIMO OCDM Underwater...

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第43届中国控制会议

作者： Buyiyi Wang Peishuo Huang Yichen Li Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of information and Communication Engineering Hainan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Orthogonal chirp division multiplexing(OCDM) has emerged as a promising approach for underwater acoustic(UWA)*** the constraints of limited bandwidth resources in the underwater environment,enhancing the data transmission rate necessitates leveraging spatial resources ***,we should integrate multiple-input multipleoutput(MIMO) technology with OCDM UWA communication ***,existing MIMO OCDM systems ignore the interference caused by carrier frequency offset(CFO),which is a primary challenge faced by co-located MIMO systems in underwater *** this paper,we propose a CFO estimator based on the null symbols of the Fresnel domain tailored for MIMO OCDM *** estimator regards the energy leakage on null symbols as the objective function to identify the optimal CFO ***,we design pilot allocation strategies for different transmitters to mitigate co-channel interference in MIMO *** enables the estimation of the channel transfer matrix and simultaneous transmission of information,thereby enhancing adaptability to dynamic UWA ***,we derive a low-complexity equalizer *** simulations,the proposed algorithm demonstrates superior communication performance compared to existing algorithms with and without CFO interference.

关键词： Orthogonal chirp division multiplexing(OCDM) carrier frequency offset(CFO) multiple-input multiple-output(MIMO) underwater communications

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Data-driven technology of fault diagnosis in railway point machines:review and challenges

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Transportation Safety and Environment 2022年第4期4卷 19-32页

作者： Xiaoxi Hu Yuan Cao Tao Tang Yongkui Sun State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation Control System Beijing Jiaotong UniversityBeijing 100044China.

Safety and reliability are absolutely vital for sophisticated Railway Point Machines(RPMs).Hence,various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven *** paper firstly analyses and summarizes six RPMs’characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past *** provides not only the process and evaluation metrics but also the pros and cons of these different ***,regarding the characteristics of RPMs and the existing studies,eight challenging problems and promising research directions are pointed out.

关键词： Railway point machine(RPM) fault diagnosis data-driven technology condition monitoring

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