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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Liang Wang Dongxu Gao Hongwei Gao Guozhang Jiang Zhaojie Ju School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China School of Computing England University of Portsmouth Britain Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Optical coherence tomography angiography (OCTA) is an advanced, non-invasive imaging technique. Automatic segmentation of vascular networks in retinal OCTA images is essential for the early diagnosis and progression assessment of various vision-related diseases. However, most existing methods for OCTA image vessel segmentation rely on encoder-decoder architectures, which typically involve a high number of parameters and result in slower inference speeds. In this paper, we introduce FRNet V2, an accurate and efficient neural network specifically designed for retinal vessel segmentation in OCTA images. This is achieved by integrating a modified recursive ConvNeXt V2 block and a recursive HAAM attention mechanism into a full-resolution convolutional network framework. We evaluate our proposed method on two large public datasets: ROSSA and OCTA-500. Experimental results demonstrate that our network achieves performance on par with other methods, while boasting significantly fewer parameters and faster inference speeds. This makes it highly suitable for practical industrial applications.

关键词： Image segmentation Accuracy Three-dimensional displays Computational modeling Angiography Optical coherence tomography Neural networks Object segmentation Retinal vessels Convolutional neural networks

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Dissolution Behavior and Dissolution Kinetics of the Laves Phase for Directed Energy Deposited Inconel 718 Alloy During δ Aging and Solution Treatment

SSRN

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SSRN 2024年

作者： Zhang, Yiting Lan, Liangyun Shi, Quanqiang Shi, Enyu School of Mechanical Engineering and Automation Northeastern University Liaoning Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education of China Northeastern University Shenyang110819 China Nuclear Professional School China

In this study, δ aging and solution treatment were employed to eliminate Laves phase in Inconel 718 alloy fabricated by directed energy deposition. The effect of δ phase precipitated during the δ aging treatment on the dissolution behavior of the Laves phase was revealed. Compared to the direct solution treatment, the Laves phase characteristic parameters (i.e., size and volume fraction) and Nb segregation degree are significantly deceased when δ aging treatment is performed before solution treatment. The composition and morphology of the pre-precipitated δ phase are crucial for the Laves phase dissolution. On one hand, the formation of δ (Ni3Nb) phase consumes numerous Nb atoms in the γ matrix, which increases the Nb element concentration gradient at the Laves/γ interface. On the other hand, a large amount of needle-like δ phase precipitated around the Laves phase decreases the area of the Laves/γ interface. As a result, the number of Nb atoms diffusing through the unit interfacial area is increased, which significantly enhances the rate of mass transfer at the Laves/γ interface and accelerates the dissolution of the Laves phase. The dissolution kinetics of the Laves phase during δ aging and solution treatment was investigated using the Johnson-Mehl-Avrami-Kolmogorov model, Singh-Flemings model, H.B. Aaron and M.J. Whelan model, and interfacial reaction model. Singh-Flemings model and H.B. Aaron and M.J. Whelan model demonstrated that the diffusion of Nb atoms is the dominant mechanism when Laves phase morphology transforms from the long-striped shape to the blocky shape. However, the interfacial reaction model clarified that the interfacial reaction plays a dominant role during the dissolution of the blocky Laves phase, which may be due to a decrease in Nb segregation. © 2024, The Authors. All rights reserved.

关键词： Dissolution

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WindowMixer: Intra-Window and Inter-Window Modeling for Time Series Forecasting

arXiv

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arXiv 2024年

作者： Liu, Quangao Li, Ruiqi Jiang, Maowei Yang, Wei Liang, Chen Pang, LongLong Zou, Zhuozhang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shengyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Time series forecasting (TSF) is widely applied in various fields such as economic forecasting, weather prediction, traffic flow analysis, and public health surveillance. The core of TSF is to capture the underlying temporal patterns in the data. However, real-world time series data exhibit complex temporal patterns and may include additional noise, outliers, missing values, etc., making accurate forecasting highly challenging. Current mainstream methods typically naively treat each time point as an isolated entity, attempting to model point-to-point relationships and uncover temporal patterns within the data, which has limitations when dealing with complex time series data. Specifically, point-by-point modeling fails to take advantage of the abundant contextual information in time series data. This results in a reduced ability to capture temporal patterns and an increased susceptibility to noise. To address these issues, we examine temporal variations from a window-based perspective and present the WindowMixer model, which is built on an all-MLP framework. WindowMixer stems from a simple observation: Time series are recorded in a continuous manner, and the information at any given time point relies on the preceding and succeeding time points for a complete representation. WindowMixer decomposes time series into trend and seasonal components via a sequence decomposition and fusion architecture, handling them individually. For the trend component, we employ a straightforward fully connected (FC) layer to make predictions. For the seasonal component, we first introduce a time window at each time point and project each time window to produce window tokens. Then, we apply the Intra-Window-Mixer and Inter-Window-Mixer to further process these tokens. The Intra-Window-Mixer models relationships within each window token, while the Inter-Window-Mixer models relationships between window tokens. These dual modules allows us to capture more intricate patterns and long-range de

关键词： Time series

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Cooperative Prediction Approach for the Small-Signal Stability Margin of Renewable Power System Based on Vertical Federated Learning

Cooperative Prediction Approach for the Small-Signal Stabili...

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Power Systems and Electrical Technology (PSET), IEEE International Conference on

作者： Xiang Lai Zhihao Chen Yun Liu Tianchen Dai Haiqing Cai Haohan Gu Wei Chen School of Electric Power Engineering South China University of Technology Guangzhou China State Key Laboratory of HVDC CSG Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Since sub-synchronous oscillations have been frequently witnessed in large-scale wind farms under weak grid conditions, it is significant to predict the critical stability situations in short term so that timely preventative actions can be taken to avoid potential small-signal stability (SS) risks. To this end, this paper proposes a SS prediction approach based on the grid strength metric of generalized short-circuit ratio (gSCR) under the mechanism of vertical federated learning. Specifically, the improved ridge regression prediction model is employed and its corresponding distributed training and the distributed predicting are derived by utilizing the separable characteristic of gradient vector calculation in the context of federated learning. On this basis, the original largescale predicting problem is decomposed into a large number of small-scale subproblems, and each subproblem is calculated locally by the corresponding wind farm. The feature data information of adjacent wind farms can be effectively used to improve the accuracy of the prediction of the stability margin while ensuring the data privacy and security of all participants. Finally, simulation results illustrate the effectiveness and efficiency of the proposed approach under various scenarios.

关键词： Training Accuracy Federated learning Simulation Power system stability Wind farms Predictive models Stability analysis Vectors Security

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Prediction model of effluent quality based on BiLSTM & GRU combined algorithm

Prediction model of effluent quality based on BiLSTM & GRU c...

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Chinese control and Decision Conference, CCDC

作者： Qilan Huang Qi Liu Lijin Guo School of Control Science and Engineering Tiangong University Tianjin Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tiangong University Tianjin

ISBN: (纸本)9781665478977

Aiming at the low efficiency of sewage treatment caused by the difficulty of timely measurement of effluent ammonia nitrogen concentration in the process of sewage treatment, a prediction model of effluent ammonia nitrogen concentration based on BiLSTM & GRU combined algorithm is proposed. Firstly, VMD algorithm is introduced to obtain the local characteristics of six water quality factors affecting effluent ammonia nitrogen concentration, such as BOD, COD and SS, on different time scales, and then PCA algorithm is used to reduce the dimension to eliminate the redundancy and correlation between data; Then, based on BiLSTM prediction model, combined with the GRU model, the effluent ammonia nitrogen concentration is predicted, and the Sparrow Search Algorithm is introduced to determine the weight of each prediction model to make up for the limitations of a single prediction model. Finally, it is verified by the measured data of a sewage treatment plant for one month, the simulation results show that the accuracy of the proposed prediction model is higher than that of the traditional water quality prediction model.

关键词： Ammonia Effluents Water quality Sewage treatment Predictive models Prediction algorithms Feature extraction

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Environment Modeling for Service Robots From a Task Execution Perspective

arXiv

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arXiv 2025年

作者： Zhang, Ying Tian, Guohui Zhang, Cui-Hua Hua, Changchun Ding, Weili Ahn, Choon Ki School of Electrical Engineering Hebei Key Laboratory of Intelligent Rehabilitation and Neuromodulation Yanshan University Qinhuangdao066004 China School of Control Science and Engineering Shandong University Jinan250061 China School of Electrical Engineering Korea University Seoul136-701 Korea Republic of

Service robots are increasingly entering the home to provide domestic tasks for residents. However, when working in an open, dynamic, and unstructured home environment, service robots still face challenges such as low intelligence for task execution and poor long-term autonomy (LTA), which has limited their deployment. As the basis of robotic task execution, environment modeling has attracted significant attention. This integrates core technologies such as environment perception, understanding, and representation to accurately recognize environmental information. This paper presents a comprehensive survey of environmental modeling from a new task-execution-oriented perspective. In particular, guided by the requirements of robots in performing domestic service tasks in the home environment, we systematically review the progress that has been made in task-execution-oriented environmental modeling in four respects: 1) localization, 2) navigation, 3) manipulation, and 4) LTA. Current challenges are discussed, and potential research opportunities are also highlighted. Copyright © 2025, The Authors. All rights reserved.

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Deep Reinforcement Learning-Based Cooperative Guidance Law with Multi-Constraint

Deep Reinforcement Learning-Based Cooperative Guidance Law w...

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Chinese automation Congress (CAC)

作者： Zhihao Gao Junfang Fan Denghui Dou Yi Ji Yafeng Li Jinmin Wu Beijing Key Laboratory of High Dynamic Navigation Technology School of Automation Beijing Information Science and Technology University Beijing China Beijing Key Laboratory of UAV Autonomous Control School of Aerospace Engineering Beijing Institute of Technology Beijing Institute of Technology Beijing China School of Automation Beijing Information Science and Technology University Beijing China

To enhance the destructive capabilities of small air-to-surface missiles, it is necessary to address the challenges related to terminal attack angle constraints and multi-missile coordinated attacks, we propose a cooperative guidance law with multi-constraint based on deep reinforcement learning in this paper. Firstly, an intelligent control term is introduced into the biased proportional navigation law with angle constraints. Secondly, a reward function is designed to reduce terminal attack angle error and flight time error, ensuring that the missile successfully reaches the target within the expected flight time, while simultaneously converging the attack angle to the desired value. Finally, the deep deterministic policy gradient algorithm is employed for the purpose of training and generating intelligent guidance instructions. Simulation results substantiate the efficacy of the devised guidance method in achieving accurate target strikes, both for individual missiles and in scenarios involving multiple missiles. And exhibiting better stability and guidance accuracy compared to the traditional biased proportional navigation law.

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Difference in Uptake Pattern Heterogeneity and Reproducibility of Radiomics Features Between FAPI and FDG Pet/Ct Imaging of Tumors

Difference in Uptake Pattern Heterogeneity and Reproducibili...

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IEEE International Symposium on Biomedical Imaging

作者： Tianmeng Xu Yanchao Huang Tingting Ang Shuilin Chu Peiyin Luo Shunfang Wang Yufeng Zhang Lijun Lu Ganghua Tang Wenbing Lv School of Information and Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China Department of Nuclear Medicine Laboratory for Quality Control and Evaluation of Radiopharmaceuticals Nanfang Hospital Southern Medical University Guangzhou China School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

To investigate the difference in uptake pattern heterogeneity between fluorodeoxyglucose (FDG) and fibroblast activation protein inhibitor (FAPI) PET/CT and the reproducibility of radiomics features with respect to different calculation methods of standardized uptake value (SUV). 210 patients with various tumors were involved. Among the extracted 497 radiomics features, there are only 20 features that exhibited an ICC difference of more than 0.25 between the two tracers when using different SUV calculation methods, revealing the similar influence of SUV calculation methods. In contrast, the ICC values for most texture features were generally lower (0.22-0.39) between FDG and FAPI PET images, indicating a large difference in uptake pattern heterogeneity between the two tracers. Our results demonstrated that the influence of SUV calculation methods on the reproducibility of FDG and FAPI PET features is similar. The uptake pattern heterogeneity characterized by radiomics features between FDG and FAPI PET images is significantly different.

关键词： Proteins Protein engineering Inhibitors Fibroblasts Feature extraction Reproducibility of results Radiomics

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A Remote Sensing Object Classification and Extraction Method Based on Improved U-Net 1

A Remote Sensing Object Classification and Extraction Method...

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Wen, Lili Qu, Hui Wu, Man Lai, Junxiang Zhang, Wei Research Center for Carbon Sink and Low-Carbon Engineering Beibu Gulf of Guangxi Guangxi Academy of Marine Sciences line Nanning China National Marine Data and Information Service Tianjin China School of Electrical Engineering Guangxi University Nanning China Guangxi Academy of Sciences Guangxi Key Laboratory of Marine Environmental Science Nanning China Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Guangxi Key Laboratory of Intelligent Control and Operation and Maintenance of Power Equipment Nanning China

ISBN: (纸本)9798331504434

This paper addresses the problems of low accuracy and poor adaptability to complex scenes in traditional remote sensing object classification methods, and proposes a remote sensing object classification extraction method based on an improved U-Net. We retained the U-shaped symmetrical structure of U-Net and introduced the Swin Transformer block with self-attention mechanism to replace the original convolutional operation, enhancing the model's ability to extract global features. We also constructed a Bottleneck structure between the encoder and decoder to learn deep feature representations and form a new U-shaped network based on Transformer, improving the network's image semantic segmentation ability in scenarios with small targets and complexity. Experimental results show that the method proposed in this paper has an improvement of more than 9% in evaluation indicators such as recognition accuracy and recall rate compared with existing U-Net and Res-UNet methods, has high semantic segmentation accuracy and robustness, and provides new ideas and technical support for remote sensing image object classification extraction. © 2024 IEEE.

关键词： Semantic Segmentation

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controllable four-wave mixing response in a dual-cavity hybrid optomechanical system

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Chinese Physics B 2021年第5期30卷 406-411页

作者： Lei Shang Bin Chen Li-Li Xing Jian-Bin Chen Hai-Bin Xue Kang-Xian Guo Department of Physics College of Physics and OptoelectronicsTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi ProvinceTaiyuan 030024China Department of Physics School of Physics and Material ScienceGuangzhou UniversityGuangzhou 510006China

We systematically investigate the four-wave mixing(FWM)spectrum in a dual-cavity hybrid optomechanical system,which is made up of one optical cavity with an ensemble of two-level atoms and another with a mechanical *** this work,we propose that the hybrid dual-cavity optomechanical system can be employed as a highly sensitive mass sensor due to the fact that the FWM spectrum generated in this system has a narrow spectral width and the intensity of the FWM can be easily tuned by controlling the coupling strength(cavity-cavity,atom-cavity).More fascinatingly,the dual-cavity hybrid optomechanical system can also be used as an all-optical switch in view of the easy on/off control of FWM signals by adjusting the atom-pump detuning to be positive or *** proposed schemes have great potential applications in quantum information processing and highly sensitive detection.

关键词： four-wave mixing dual-cavity optomechanical system atomic ensemble

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