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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Liu, Xin Wen, Shuhuan Yuan, Mingxing Li, Pengjiang Zhao, Yongjie Manfredi, Luigi Yanshan University Eng. Res. Ctr. of the Ministry of Education for Intelligent Control System and Intelligent Equipment Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China Nankai University School of Artificial Intelligence China University of Dundee United Kingdom

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the development of simultaneous localization and mapping (SLAM) technology, Dynamic SLAM has been a challenging research topic. This paper presents a dynamic probability fusion SLAM (DPF-SLAM) algorithm, which adds a semantic segmentation thread and a dense reconstruction thread to ORB-SLAM2. We integrate dynamic prior probability obtained by semantic segmentation with dynamic probability obtained by dynamic point detection, which can decrease the effect of dynamic objects on the localization accuracy and meanwhile achieve the dense reconstruction of a static background. We evaluate DPF-SLAM system on the public TUM RGBD dataset. The experimental results show that the proposed algorithm has better localization accuracy than DS-SLAM and ORB-SLAM2, which are two relatively new algorithms in this area, and can obtain a good dense reconstruction effect. Moreover, through the performance comparison with our previous work, it is found that the algorithm speed and positioning accuracy are improved. © 2022 IEEE.

关键词： Semantics

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Securing Mobile Robotic Networks Against Replacement Attack

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IEEE Transactions on Automatic control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

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MPC Implementation for Optimal Active Power control of Large-Scale Wind-PV-Battery Hybrid system

MPC Implementation for Optimal Active Power Control of Large...

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第42届中国控制会议

作者： Zihan Zhang Zhongwei Lin Ze Zhao Jiawei Zhou Chenzhi Qu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University

This paper investigates the operational strategy for real-time optimal active power control of large-scale wind-PV-battery hybrid system. The MPC strategy is established with objective, forecast models and constraints for active power control of wind-PV-battery hybrid system, which simultaneously maximizes tracking accuracy and minimizes settling time. To guarantee the industrial reliability, the corresponding optimization solver and control architecture have been specifically developed and designed. Hardware-in-loop(HIL) experiments are conducted using the real-time digital simulator(RTDS) and programmable logic controllers(PLCs). Under different operating scenarios, HIL-RTDS testing results demonstrate that the MPC strategy significantly improves the tracking speed and accuracy compared to the conventional strategy in both normal and abnormal operating scenarios, which also sustain the feasibility of industrial implementation.

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A LiDAR-assist Yaw Misalignment Compensation Method based on Machine Learning Algorithms

A LiDAR-assist Yaw Misalignment Compensation Method based on...

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第42届中国控制会议

作者： Pei Chen Zhongwei Lin Zihan Zhang Zhen Xie State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power University

Yaw misalignment is common in the yaw system control of wind turbines,resulting in reduced power generation efficiency and increased *** advantage of LiDAR is that the accuracy of wind measurement is greatly improved,while its disadvantage is that the cost remains *** this paper,an efficient machine learning method for estimating LiDAR measurement,XGBoost,is used to establish the data-driven wind turbine yaw misalignment compensation *** choose a suitable algorithm,four machine learning algorithms are studied,namely Linear Regression,Random Forest,Gradient Boosting and ***,to verify the effectiveness of the proposed method,a 2 MW WT was taken as the research object for filed testing,and the power generation was quantitatively evaluated under the three strategies of wind vane control,LiDAR control and ML algorithm *** results show that the proposed yaw misalignment compensation method is feasible for field *** this way,the yaw-to-wind error can be mitigated even after the LiDAR is removed.

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A Copyright Authentication Method Balancing Watermark Robustness and Data Distortion

A Copyright Authentication Method Balancing Watermark Robust...

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International Conference on computer Supported Cooperative Work in Design

作者： Chundong Wang Yue Li Tianjin University of Technology Tianjin China National Engineering Laboratory for Virus Prevention and Control Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China

Database watermarking plays an irreplaceable role in copyright authentication and data integrity protection, but the robustness of the watermark and the resulting data distortion are a pair of contradictory objects that cannot be ignored. To solve this problem, a reversible database watermarking method, named IGADEW, is proposed to balance the relationship between them. The biggest difference from previous research is that IGADEW synthesizes the optimization objects and obtain various parameters through genetic algorithm (GA). Second, the fitness function considers the weights of robustness and distortion, aiming to find the optimal balance between the two. IGADEW uses the Hash-based Message Authentication Code (HMAC) algorithm to encrypt the experimental parameters and uses the primary key hash algorithm for data grouping, both to ensure robustness. And the data distortion is limited with the help of threshold constraints. Finally, experiments using the UCI dataset demonstrate the effectiveness of IGADEW. Experimental results show that, compared with existing methods, IGADEW is more robust against common attacks, with lower data distortion.

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Multi-type energy storage expansion planning: A review for high-penetration renewable energy integration

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Renewable and Sustainable Energy Reviews 2025年 219卷

作者： Zhang, Zhi Chen, Yanbo Ma, Tianyang Tian, Haoxin Liu, Jingyu Zhou, Ming Wang, Wei State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Energy storage has emerged as a pivotal enabling technology for the integration of renewable energy and the decarbonization of energy systems. The growing penetration of renewable energy sources (RES) has brought about a significant transformation in the operational characteristics of power systems, posing substantial challenges in maintaining power balance and ensuring stable operation across multiple time scales. Multi-type energy storage, with their distinct regulation characteristics, can meet the multi-time scale regulation requirements of power systems. As a result, scientific and efficient storage expansion planning (SEP) has become a critical task in promoting the energy transition. Although numerous studies have thoroughly explored the advancements of energy storage technologies, a comprehensive and systematic review of SEP is still remains underexplored. To fill this research gap, this study first delves into the operational challenges faced by high-penetration RES power systems and synthesizes current research on multifaceted energy storage applications. Subsequently, it offers a systematic review of planning methodologies for multi-type energy storage, covering traditional application scenarios such as source-side, grid-side, and load-side. This research further extends to SEP methods tailored to address the challenges of high-RES integration, including medium- and long-term energy balance, power balance considering uncertainties, real-time security support, and a detailed analysis of the corresponding models, algorithms, and storage technologies is also provided. Finally, the study explores emerging challenges related to power system decarbonization, the impacts of climate change, and the evolving energy storage market landscape. Furthermore, it proposes future research directions and development trends in SEP. © 2025 Elsevier Ltd

关键词： Energy transition

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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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Application of Genetic Algorithm for Calibration of an X-band Vector-sum Phase Shifter with Integrated Auxiliary DAC

Application of Genetic Algorithm for Calibration of an X-ban...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Evgeniy A. Fedorov Feodor I. Sheyerman Alexey V. Pomazanov Andrey A. Kokolov Dmitriy E. Minenko Leonid I. Babak Laboratory of Intelligent Computer System (LICS) Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

This paper presents a genetic algorithm for the discrete optimization problem, namely, the selection of optimal states of the auxiliary DACs of the X-band vector-sum phase shifter. The auxiliary DAC allows to fine-tune the VGA module and change accordingly the phase and amplitude of a signal in a small range. Genetic algorithms are able to select the optimal configuration of the auxiliary DAC of the vector-sum phase shifter in a few minutes (800-1000 iterations) in order to reduce RMS phase and amplitude errors at a defined frequency. In addition, this configuration of the vector-sum phase-shifter and optimization algorithm allows to achieve significant improvements in the characteristics in a narrow frequency band, and in this manner, it is possible to compensate for process variations. Vector-sum phase shifter manufactured using 0.25 m SiGe BiCMOS technology. The chip size is 1.51x1.05 mm 2 Minimum RMS phase and amplitude error values for 10 GHz center frequency were from 0.61 degrees and 0.26 dB respectively.

关键词： Heuristic algorithms Phase shifters Optimization methods BiCMOS integrated circuits Calibration Electron devices Genetic algorithms

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Multi-Resolution Planar Region Extraction for Uneven Terrains

Multi-Resolution Planar Region Extraction for Uneven Terrain...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yinghan Sun Linfang Zheng Hua Chen Wei Zhang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Computer Science University of Birmingham UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper studies the problem of extracting planar regions in uneven terrains from unordered point cloud measurements. Such a problem is critical in various robotic applications such as robotic perceptive locomotion. While existing approaches have shown promising results in effectively extracting planar regions from the environment, they often suffer from issues such as low computational efficiency or loss of resolution. To address these issues, we propose a multi-resolution planar region extraction strategy in this paper that balances the accuracy in boundaries and computational efficiency. Our method begins with a pointwise classification preprocessing module, which categorizes all sampled points according to their local geometric properties to facilitate multi-resolution segmentation. Subsequently, we arrange the categorized points using an octree, followed by an in-depth analysis of nodes to finish multi-resolution plane segmentation. The efficiency and robustness of the proposed approach are verified via synthetic and real-world experiments, demonstrating our method’s ability to generalize effectively across various uneven terrains while maintaining real-time performance, achieving frame rates exceeding 35 FPS.

关键词： Point cloud compression Deep learning Accuracy Octrees Noise Robustness Real-time systems

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A Node Confidence Determination Based Moving Target Defense Strategy for SDN

A Node Confidence Determination Based Moving Target Defense ...

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International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Shimin Sun Guowei Xu Li Han Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

With the rapid development of Internet technology, network assets have become an indispensable part of modern society. Especially in the face of high-value network assets, the attacker is faced with paying a greater cost to study the network structure. Due to the unknown attacker and attack means, the attacker has an advantage in the attack and defense situation. The moving target defense technique can flexibly change the static characteristics of the network attack surface and increase the difficulty for the attacker to detect the network, thus achieving the purpose of enhancing network security. Based on this technique, this paper proposes a defense strategy based on node confidence. It puts forward stricter constraints on the differential selection of address/port hopping paths, dynamically selects the hopping objects according to the node’s confidence, and implements the defense strategy of differential complexity. This further increases the network’s uncertainty and the difficulty for the attacker to detect the network structure. The experimental results show that in the path differentiation comparison, the maximum improvement is about 18% compared to the Dynamic Random Route Hopping strategy. In the CPU load experiment, the CPU load rate is reduced by about 4.625% on average compared to the Double Address Hopping strategy. Moreover, for larger hopping period, the proposed strategy also performs well in terms of CPU load rate. The proposed strategy also exhibits good performance in terms of CPU load ratio for large hopping cycles.

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