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3rd Conference on Fully Actuated system Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping

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Concerning Analysis and control of Deviations of Economic Indicators of Countries Depending on Development of Hydrogen Power engineering 14

Concerning Analysis and Control of Deviations of Economic In...

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14th International Conference Management of Large-Scale system Development, MLSD 2021

作者： Bogomolov, Aleksey Kushnikov, Vadim Ivaschenko, Vladimir Tolobaev, Igor Laboratory of System Problems of Control and Automation in Mechanical Engineering Institute of Precision Mechanics and Control of RAS Saratov Russia Saratov State University Saratov Russia

ISBN: (纸本)9781665412308

We develop mathematical models and methods to determine the appropriate path for developing hydrogen energy. The urgency of choosing this path proceeds from the fact that the development of hydrogen energy has a complex and not always a positive impact on other industries, the environment, and the leading economic indicators of the countries in the world. In this article, we propose to use system dynamics models to describe such relationships. The equations of system dynamics play the role of a control model in the considered problem of the variations calculus. This task determines control actions that minimize the weighted total deviation of the leading economic indicators from the required or indicative values. In determining these values, governments take into account the financial situation and the experience of previous periods. In addition, the problem under consideration includes restrictions on the resources and goals of the system and boundary conditions. Therefore, solving a problem means choosing a finite set of solutions and corresponding controls that satisfy the given requirements. Then we select control actions that minimize the objective function. These impacts correspond to scenarios and action plans to reduce the deviation of the leading economic indicators from the desired values under the influence of the hydrogen energy development in the country. © 2021 IEEE.

关键词： Calculations

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Multiple kernel subspace clustering with dual tensors learning

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Applied Intelligence 2025年第11期55卷 1-11页

作者： Liao, Yinsong Ren, Zhenwen Wu, Bin Zhao, Chunyu School of Mianyang City College Mianyang City College Mianyang China School of Information Engineering Southwest University of Science and Technology Mianyang China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

Multiple kernel clustering (MKC) excels at integrating information from multiple kernels for effective data clustering. Multiple kernel subspace clustering (MKSC) further enhances this by incorporating subspace learning, achieving significant performance improvements. However, most of existing MKSC methods often learn the affinity graphs directly and overlook the rich high-order correlations between different kernels, leading to suboptimal clustering results. This paper introduces MKSC-DT, a novel approach that addresses this limitation by utilizing dual tensors to capture high-order correlations in both feature space and semantic space. MKSC-DT learns multiple new kernels in original feature spaces and projects them onto a clean subspace to generate candidate affinity graphs. These new kernels and candidate graphs are then stacked into two third-order tensors, enabling the exploration of high-order relationships. An efficient alternating optimization algorithm is proposed to solve the resulting objective function. Extensive experiments on benchmark datasets demonstrate the superiority of MKSC-DT compared to state-of-the-art MKC methods, showcasing its effectiveness in leveraging high-order correlations for improved clustering performance.

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Natural Gas Load Forecasting Based on Multi-Stage Model Identification 14

Natural Gas Load Forecasting Based on Multi-Stage Model Iden...

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14th IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems, CYBER 2024

作者： Zhang, Pengbin Wang, Hongrui Xing, Kunpeng Yuan, Mingzhe Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China China Nuclear Power Engineering Co. Ltd. Shenzhen518124 China

ISBN: (纸本)9798331506056

Natural gas load forecasting is crucial for the safe operation of urban natural gas. Based on the system identification method, the short-term natural gas load forecasting is studied in the paper. Employing a multi-stage identification framework, the initial values of the model parameters using the least squares and the instrumental variables method, while the model parameters are further optimized by the prediction error method to improve the accuracy of the model identification. The proposed method is validated by the simulation based on the public natural gas data in the U.S., demonstrating the algorithm's effectiveness in forecasting the daily natural gas load effectively. © 2024 IEEE.

关键词： Prediction models

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DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Pan, Qingtao Qiao, Wenhao Lou, Jingjiao Ji, Bing Li, Shuo School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

Semi-supervised medical image segmentation (SSMIS) uses consistency learning to regularize model training, which alleviates the burden of pixel-wise manual annotations. However, it often suffers from error supervision from low-quality pseudo labels. Vision-Language Model (VLM) has great potential to enhance pseudo labels by introducing text prompt guided multimodal supervision information. It nevertheless faces the cross-modal problem: the obtained messages tend to correspond to multiple targets. To address aforementioned problems, we propose a Dual Semantic Similarity-Supervised VLM (DuSSS) for SSMIS. Specifically, 1) a Dual Contrastive Learning (DCL) is designed to improve cross-modal semantic consistency by capturing intrinsic representations within each modality and semantic correlations across modalities. 2) To encourage the learning of multiple semantic correspondences, a Semantic Similarity-Supervision strategy (SSS) is proposed and injected into each contrastive learning process in DCL, supervising semantic similarity via the distribution-based uncertainty levels. Furthermore, a novel VLM-based SSMIS network is designed to compensate for the quality deficiencies of pseudo-labels. It utilizes the pretrained VLM to generate text prompt guided supervision information, refining the pseudo label for better consistency regularization. Experimental results demonstrate that our DuSSS achieves outstanding performance with Dice of 82.52%, 74.61% and 78.03% on three public datasets (QaTa-COV19, BM-Seg and MoNuSeg). Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Multiple-Scale Vision-Based Object Detection Algorithm for UAVs Based on the Local Feature Matching with Transformer Model 3rd

A Multiple-Scale Vision-Based Object Detection Algorithm for...

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3rd International Conference on Autonomous Unmanned systems, ICAUS 2023

作者： Yang, Xiaolong Deng, Hui Duan, Xuting Xia, Haiying School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing China China Telecom Digital City Technology Co. Ltd. Hebei China Key Laboratory of Operation Safety Technology on Transport Vehicles Ministry of Transport Beijing100088 China

ISBN: (纸本)9789819711024

With the addition of UAVs, the perception and recognition of ground targets have also advanced significantly. UAV photography has gradually become an integral part of this field. Due to the flexibility and lightness of UAVs, they can freely adjust their height and angle. Moreover, multiple UAVs can perceive and recognize the same target from different directions, which has led to increased attention toward collaborative perception using multiple viewpoints of UAVs. Given the diverse image information captured by UAVs from various angles, heights, and environments, i.e., multi-scale conditions, this paper addresses the challenge of target detection by leveraging the Local Feature Matching with Transformer (LoFTR) approach. The paper also explains the process of coordinating transformations for different images captured by multiple UAVs to ensure data consistency and synergy. Finally, experimental validation using the VisDrone dataset demonstrates that the developed detection method can reliably and efficiently operate on targets at different angles and altitudes. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Object detection

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Autonomous Traffic Prediction for LEO Satellite-Based IoT Based on Satellite Spatiotemporal Features Mapping

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IEEE Internet of Things Journal 2025年

作者： Gong, Lizeng Chen, Quan Yang, Lei Yin, Zhenglong Wang, Yi National University of Defense Technology State Key Laboratory of Space System Operation and Control College of Aerospace Science and Engineering Changsha410073 China Nanjing Research Institute of Electronics Technology Nanjing210013 China

The traffic prediction method for Low Earth Orbit (LEO) satellite-based Internet of Things (IoT) provides prior conditions for addressing challenges in resource allocation and management of LEO satellite-based IoT. However, traditional methods rely on the pre-generation of terrestrial centers, leading to substantial computational complexity and delays when applying prediction results to LEO satellite-based IoT. Therefore, achieving autonomous LEO satellite-based IoT traffic prediction is essential. Unlike the spatiotemporal features of terrestrial IoT, LEO satellite-based IoT traffic is influenced by both satellite dynamics and terrestrial user behavior. Fortunately, real IoT traffic information can be shared between adjacent satellites through Inter-satellite Links (ISL). Inspired by the above, this paper proposes, for the first time, an autonomous LEO satellite-based IoT traffic prediction method. A Spatiotemporal Fusion Neural Network (SFNN) employing a four-layer hybrid neural network architecture is designed to accommodate the unique spatiotemporal features of LEO satellite-based IoT traffic. The simulation results demonstrate that the proposed method has low computational complexity and achieves a better prediction performance than the five baseline methods in LEO constellations. Further simulations demonstrate that the proposed method achieves higher accuracy in large-scale LEO constellations, and the algorithm remains applicable under ISL instability and satellite failure conditions. © 2014 IEEE.

关键词： Resource allocation

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Application of Fe(Ⅵ)in abating contaminants in water:State of art and knowledge gaps

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Frontiers of Environmental Science & engineering 2021年第5期15卷 7-27页

作者： Shuchang Wang Binbin Shao Junlian Qiao Xiaohong Guan State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science and EngineeringTongji UniversityShanghai200092China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092China International Joint Research Center for Sustainable Urban Water System Tongji UniversityShanghai200092China

The past two decades have witnessed the rapid development and wide application of Fe(Ⅵ)in the field of water de-contamination because of its environmentally benign ***(Ⅵ)has been mainly applied as a highly efficient oxidant/disinfectant for the selective elimination of *** in situ generated iron(III)(hydr)oxides with the function of adsorption/coagulation can further increase the removal of contaminants by Fe(Ⅵ)in some *** of the limitations of Fe(Ⅵ)per se,various modified methods have been developed to improve the performance of Fe(Ⅵ)oxidation *** on the published literature,this paper summarized the current views on the intrinsic properties of Fe(Ⅵ)with the emphasis on the self-decay mechanism of Fe(Ⅵ).The applications of Fe(Ⅵ)as a sole oxidant for decomposing organic contaminants rich in electron-donating moieties,as a bi-functional reagent(both oxidant and coagulant)for eliminating some special contaminants,and as a disinfectant for inactivating microorganisms were systematically ***,the difficulties in synthesizing and preserving Fe(Ⅵ),which limits the large-scale application of Fe(Ⅵ),and the potential formation of toxic byproducts during Fe(Ⅵ)application were *** paper also systematically reviewed the important nodes in developing methods to improve the performance of Fe(Ⅵ)as oxidant or disinfectant in the past two decades,and proposed the future research needs for the development of Fe(Ⅵ)technologies.

关键词： Ferrate Oxidation Disinfection Coagulation Enhancement

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Design of Pelton Turbine Test Stand in Plateau Environment 4

Design of Pelton Turbine Test Stand in Plateau Environment

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4th IAHR Asian Working Group Symposium on Hydraulic Machinery and systems

作者： Cai, Jinhan Zhou, Xuejun Luo, Hongying Wang, Zhengwei Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China College of Mechanical Engineering Tianjin Sino-German University of Applied Science No. 2 Yashen Road Tianjin China College of Hydraulic and Civil Engineering Tibet Agricultural and Animal Husbandry University China Department of Energy and Power Engineering Tsinghua University China

In recent years, with the continuous development of China's hydropower projects, Pelton turbines are gradually being valued, and the focus of development has shifted from plain areas to plateau areas. Environmental impacts such as high altitude, water temperature, and high sediment content in the plateau area have caused serious interference to equipment that originally worked in a stable environment, for example, the failure of the cooperation between the parts of the turbine and the mutual seizure, the increased wear of the nozzle needle, the decrease of the flow rate and the water outlet speed of the nozzle, and the decrease of the air pressure in the plateau environment lead to the change of the flow state of the nozzle. In view of the above problems, this paper puts forward the design scheme of impulse turbine test-bed, using the method of changing specific speed to determine the basic parameters of impulse turbine unit, so as to eliminate the influence of plateau environment on the normal operation of turbine, obtain the efficiency condition of model experiment under low ambient pressure, and improve the operation efficiency and reliability of turbine unit. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Nozzles

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Concept-Level Semantic Transfer and Context-Level Distribution Modeling for Few-Shot Segmentation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Luo, Yuxuan Chen, Jinpeng Cong, Runmin Ip, Horace Ho Shing Kwong, Sam City University of Hong Kong Department of Computer Science Hong Kong Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Lingnan University Hong Kong

Few-shot segmentation (FSS) methods aim to segment objects using only a few pixel-level annotated samples. Current approaches either derive a generalized class representation from support samples to guide the segmentation of query samples, which often discards crucial spatial contextual information, or rely heavily on spatial affinity between support and query samples, without adequately summarizing and utilizing the core information of the target class. Consequently, the former struggles with fine detail accuracy, while the latter tends to produce errors in overall localization. To address these issues, we propose a novel FSS framework, CCFormer, which balances the transmission of core semantic concepts with the modeling of spatial context, improving both macro and micro-level segmentation accuracy. Our approach introduces three key modules: (1) the Concept Perception Generation (CPG) module, which leverages pre-trained category perception capabilities to capture high-quality core representations of the target class;(2) the Concept-Feature Integration (CFI) module, which injects the core class information into both support and query features during feature extraction;and (3) the Contextual Distribution Mining (CDM) module, which utilizes a Brownian Distance Covariance matrix to model the spatial-channel distribution between support and query samples, preserving the fine-grained integrity of the target. Experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that CCFormer achieves state-of-the-art performance, with visualizations further validating its effectiveness. Our code is available at ***/lourise/ccformer. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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